From 6fa863b653b5c177103f8db159f83941d32e331a Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Sun, 2 May 2021 19:56:53 -0400
Subject: [PATCH 001/267] Code formatting

---
 benchmarks/hash_table/dynamic_map_bench.cu  | 100 ++++---
 benchmarks/hash_table/static_map_bench.cu   | 105 ++++---
 benchmarks/reduce_by_key/reduce_by_key.cu   |  12 +-
 include/cuco/allocator.hpp                  |   3 +-
 include/cuco/detail/dynamic_map_kernels.cuh | 301 ++++++++++---------
 include/cuco/detail/hash_functions.cuh      |   1 -
 include/cuco/detail/pair.cuh                |   4 +-
 include/cuco/detail/static_map.inl          |  16 +-
 include/cuco/detail/static_map_kernels.cuh  | 308 +++++++++-----------
 include/cuco/static_map.cuh                 |  38 +--
 tests/dynamic_map/dynamic_map_test.cu       |  72 ++---
 tests/static_map/static_map_test.cu         |  19 +-
 12 files changed, 474 insertions(+), 505 deletions(-)

diff --git a/benchmarks/hash_table/dynamic_map_bench.cu b/benchmarks/hash_table/dynamic_map_bench.cu
index cd6eadb47..995e53903 100644
--- a/benchmarks/hash_table/dynamic_map_bench.cu
+++ b/benchmarks/hash_table/dynamic_map_bench.cu
@@ -15,120 +15,118 @@
  */
 
 #include <benchmark/benchmark.h>
-#include <synchronization.hpp>
 #include <cuco/dynamic_map.cuh>
 #include <iostream>
 #include <random>
+#include <synchronization.hpp>
 
-enum class dist_type {
-  UNIQUE,
-  UNIFORM,
-  GAUSSIAN
-};
+enum class dist_type { UNIQUE, UNIFORM, GAUSSIAN };
 
-template<dist_type Dist, typename Key, typename OutputIt>
-static void generate_keys(OutputIt output_begin, OutputIt output_end) {
+template <dist_type Dist, typename Key, typename OutputIt>
+static void generate_keys(OutputIt output_begin, OutputIt output_end)
+{
   auto num_keys = std::distance(output_begin, output_end);
-  
+
   std::random_device rd;
   std::mt19937 gen{rd()};
 
-  switch(Dist) {
+  switch (Dist) {
     case dist_type::UNIQUE:
-      for(auto i = 0; i < num_keys; ++i) {
+      for (auto i = 0; i < num_keys; ++i) {
         output_begin[i] = i;
       }
       break;
     case dist_type::UNIFORM:
-      for(auto i = 0; i < num_keys; ++i) {
+      for (auto i = 0; i < num_keys; ++i) {
         output_begin[i] = std::abs(static_cast<Key>(gen()));
       }
       break;
     case dist_type::GAUSSIAN:
       std::normal_distribution<> dg{1e9, 1e7};
-      for(auto i = 0; i < num_keys; ++i) {
+      for (auto i = 0; i < num_keys; ++i) {
         output_begin[i] = std::abs(static_cast<Key>(dg(gen)));
       }
       break;
   }
 }
 
-static void gen_final_size(benchmark::internal::Benchmark* b) {
-  for(auto size = 10'000'000; size <= 150'000'000; size += 20'000'000) {
+static void gen_final_size(benchmark::internal::Benchmark* b)
+{
+  for (auto size = 10'000'000; size <= 150'000'000; size += 20'000'000) {
     b->Args({size});
   }
 }
 
 template <typename Key, typename Value, dist_type Dist>
-static void BM_dynamic_insert(::benchmark::State& state) {
+static void BM_dynamic_insert(::benchmark::State& state)
+{
   using map_type = cuco::dynamic_map<Key, Value>;
-  
-  std::size_t num_keys = state.range(0);
-  std::size_t initial_size = 1<<27;
-  
-  std::vector<Key> h_keys( num_keys );
-  std::vector<cuco::pair_type<Key, Value>> h_pairs ( num_keys );
-  
+
+  std::size_t num_keys     = state.range(0);
+  std::size_t initial_size = 1 << 27;
+
+  std::vector<Key> h_keys(num_keys);
+  std::vector<cuco::pair_type<Key, Value>> h_pairs(num_keys);
+
   generate_keys<Dist, Key>(h_keys.begin(), h_keys.end());
 
-  for(auto i = 0; i < num_keys; ++i) {
-    Key key = h_keys[i];
-    Value val = h_keys[i];
-    h_pairs[i].first = key;
+  for (auto i = 0; i < num_keys; ++i) {
+    Key key           = h_keys[i];
+    Value val         = h_keys[i];
+    h_pairs[i].first  = key;
     h_pairs[i].second = val;
   }
 
-  thrust::device_vector<cuco::pair_type<Key, Value>> d_pairs( h_pairs );
+  thrust::device_vector<cuco::pair_type<Key, Value>> d_pairs(h_pairs);
 
   std::size_t batch_size = 1E6;
-  for(auto _ : state) {
+  for (auto _ : state) {
     map_type map{initial_size, -1, -1};
     {
-      cuda_event_timer raii{state}; 
-      for(auto i = 0; i < num_keys; i += batch_size) {
+      cuda_event_timer raii{state};
+      for (auto i = 0; i < num_keys; i += batch_size) {
         map.insert(d_pairs.begin() + i, d_pairs.begin() + i + batch_size);
       }
     }
   }
 
-  state.SetBytesProcessed((sizeof(Key) + sizeof(Value)) *
-                          int64_t(state.iterations()) *
+  state.SetBytesProcessed((sizeof(Key) + sizeof(Value)) * int64_t(state.iterations()) *
                           int64_t(state.range(0)));
 }
 
 template <typename Key, typename Value, dist_type Dist>
-static void BM_dynamic_search_all(::benchmark::State& state) {
+static void BM_dynamic_search_all(::benchmark::State& state)
+{
   using map_type = cuco::dynamic_map<Key, Value>;
-  
-  std::size_t num_keys = state.range(0);
-  std::size_t initial_size = 1<<27;
 
-  std::vector<Key> h_keys( num_keys );
-  std::vector<cuco::pair_type<Key, Value>> h_pairs ( num_keys );
+  std::size_t num_keys     = state.range(0);
+  std::size_t initial_size = 1 << 27;
+
+  std::vector<Key> h_keys(num_keys);
+  std::vector<cuco::pair_type<Key, Value>> h_pairs(num_keys);
 
   generate_keys<Dist, Key>(h_keys.begin(), h_keys.end());
-  
-  for(auto i = 0; i < num_keys; ++i) {
-    Key key = h_keys[i];
-    Value val = h_keys[i];
-    h_pairs[i].first = key;
+
+  for (auto i = 0; i < num_keys; ++i) {
+    Key key           = h_keys[i];
+    Value val         = h_keys[i];
+    h_pairs[i].first  = key;
     h_pairs[i].second = val;
   }
 
-  thrust::device_vector<Key> d_keys( h_keys );
-  thrust::device_vector<cuco::pair_type<Key, Value>> d_pairs( h_pairs );
-  thrust::device_vector<Value> d_results( num_keys );
+  thrust::device_vector<Key> d_keys(h_keys);
+  thrust::device_vector<cuco::pair_type<Key, Value>> d_pairs(h_pairs);
+  thrust::device_vector<Value> d_results(num_keys);
 
   map_type map{initial_size, -1, -1};
   map.insert(d_pairs.begin(), d_pairs.end());
 
-  for(auto _ : state) {
+  for (auto _ : state) {
     cuda_event_timer raii{state};
     map.find(d_keys.begin(), d_keys.end(), d_results.begin());
   }
 
-  state.SetBytesProcessed((sizeof(Key) + sizeof(Value)) *
-                          int64_t(state.iterations()) *
+  state.SetBytesProcessed((sizeof(Key) + sizeof(Value)) * int64_t(state.iterations()) *
                           int64_t(state.range(0)));
 }
 
@@ -161,7 +159,7 @@ BENCHMARK_TEMPLATE(BM_dynamic_search_all, int32_t, int32_t, dist_type::GAUSSIAN)
   ->Unit(benchmark::kMillisecond)
   ->Apply(gen_final_size)
   ->UseManualTime();
-  
+
 BENCHMARK_TEMPLATE(BM_dynamic_insert, int64_t, int64_t, dist_type::UNIQUE)
   ->Unit(benchmark::kMillisecond)
   ->Apply(gen_final_size)
diff --git a/benchmarks/hash_table/static_map_bench.cu b/benchmarks/hash_table/static_map_bench.cu
index 165465518..af8e09de1 100644
--- a/benchmarks/hash_table/static_map_bench.cu
+++ b/benchmarks/hash_table/static_map_bench.cu
@@ -15,40 +15,37 @@
  */
 
 #include <benchmark/benchmark.h>
-#include "cuco/static_map.cuh"
-#include <thrust/for_each.h>
 #include <thrust/device_vector.h>
-#include <iostream>
+#include <thrust/for_each.h>
 #include <fstream>
+#include <iostream>
 #include <random>
+#include "cuco/static_map.cuh"
 
-enum class dist_type {
-  UNIQUE,
-  UNIFORM,
-  GAUSSIAN
-};
+enum class dist_type { UNIQUE, UNIFORM, GAUSSIAN };
 
-template<dist_type Dist, typename Key, typename OutputIt>
-static void generate_keys(OutputIt output_begin, OutputIt output_end) {
+template <dist_type Dist, typename Key, typename OutputIt>
+static void generate_keys(OutputIt output_begin, OutputIt output_end)
+{
   auto num_keys = std::distance(output_begin, output_end);
-  
+
   std::random_device rd;
   std::mt19937 gen{rd()};
 
-  switch(Dist) {
+  switch (Dist) {
     case dist_type::UNIQUE:
-      for(auto i = 0; i < num_keys; ++i) {
+      for (auto i = 0; i < num_keys; ++i) {
         output_begin[i] = i;
       }
       break;
     case dist_type::UNIFORM:
-      for(auto i = 0; i < num_keys; ++i) {
+      for (auto i = 0; i < num_keys; ++i) {
         output_begin[i] = std::abs(static_cast<Key>(gen()));
       }
       break;
     case dist_type::GAUSSIAN:
       std::normal_distribution<> dg{1e9, 1e7};
-      for(auto i = 0; i < num_keys; ++i) {
+      for (auto i = 0; i < num_keys; ++i) {
         output_begin[i] = std::abs(static_cast<Key>(dg(gen)));
       }
       break;
@@ -59,7 +56,8 @@ static void generate_keys(OutputIt output_begin, OutputIt output_end) {
  * @brief Generates input sizes and hash table occupancies
  *
  */
-static void generate_size_and_occupancy(benchmark::internal::Benchmark* b) {
+static void generate_size_and_occupancy(benchmark::internal::Benchmark* b)
+{
   for (auto size = 100'000'000; size <= 100'000'000; size *= 10) {
     for (auto occupancy = 10; occupancy <= 90; occupancy += 10) {
       b->Args({size, occupancy});
@@ -67,31 +65,30 @@ static void generate_size_and_occupancy(benchmark::internal::Benchmark* b) {
   }
 }
 
-
-
 template <typename Key, typename Value, dist_type Dist>
-static void BM_static_map_insert(::benchmark::State& state) {
+static void BM_static_map_insert(::benchmark::State& state)
+{
   using map_type = cuco::static_map<Key, Value>;
-  
+
   std::size_t num_keys = state.range(0);
-  float occupancy = state.range(1) / float{100};
-  std::size_t size = num_keys / occupancy;
+  float occupancy      = state.range(1) / float{100};
+  std::size_t size     = num_keys / occupancy;
+
+  std::vector<Key> h_keys(num_keys);
+  std::vector<cuco::pair_type<Key, Value>> h_pairs(num_keys);
 
-  std::vector<Key> h_keys( num_keys );
-  std::vector<cuco::pair_type<Key, Value>> h_pairs( num_keys );
-  
   generate_keys<Dist, Key>(h_keys.begin(), h_keys.end());
-  
-  for(auto i = 0; i < num_keys; ++i) {
-    Key key = h_keys[i];
-    Value val = h_keys[i];
-    h_pairs[i].first = key;
+
+  for (auto i = 0; i < num_keys; ++i) {
+    Key key           = h_keys[i];
+    Value val         = h_keys[i];
+    h_pairs[i].first  = key;
     h_pairs[i].second = val;
   }
 
-  thrust::device_vector<cuco::pair_type<Key, Value>> d_pairs( h_pairs );
+  thrust::device_vector<cuco::pair_type<Key, Value>> d_pairs(h_pairs);
 
-  for(auto _ : state) {
+  for (auto _ : state) {
     state.ResumeTiming();
     state.PauseTiming();
     map_type map{size, -1, -1};
@@ -102,45 +99,43 @@ static void BM_static_map_insert(::benchmark::State& state) {
     state.PauseTiming();
   }
 
-  state.SetBytesProcessed((sizeof(Key) + sizeof(Value)) *
-                          int64_t(state.iterations()) *
+  state.SetBytesProcessed((sizeof(Key) + sizeof(Value)) * int64_t(state.iterations()) *
                           int64_t(state.range(0)));
 }
 
-
-
 template <typename Key, typename Value, dist_type Dist>
-static void BM_static_map_search_all(::benchmark::State& state) {
+static void BM_static_map_search_all(::benchmark::State& state)
+{
   using map_type = cuco::static_map<Key, Value>;
-  
+
   std::size_t num_keys = state.range(0);
-  float occupancy = state.range(1) / float{100};
-  std::size_t size = num_keys / occupancy;
+  float occupancy      = state.range(1) / float{100};
+  std::size_t size     = num_keys / occupancy;
 
   map_type map{size, -1, -1};
   auto view = map.get_device_mutable_view();
 
-  std::vector<Key> h_keys( num_keys );
-  std::vector<Value> h_values( num_keys );
-  std::vector<cuco::pair_type<Key, Value>> h_pairs ( num_keys );
-  std::vector<Value> h_results (num_keys);
+  std::vector<Key> h_keys(num_keys);
+  std::vector<Value> h_values(num_keys);
+  std::vector<cuco::pair_type<Key, Value>> h_pairs(num_keys);
+  std::vector<Value> h_results(num_keys);
 
   generate_keys<Dist, Key>(h_keys.begin(), h_keys.end());
-  
-  for(auto i = 0; i < num_keys; ++i) {
-    Key key = h_keys[i];
-    Value val = h_keys[i];
-    h_pairs[i].first = key;
+
+  for (auto i = 0; i < num_keys; ++i) {
+    Key key           = h_keys[i];
+    Value val         = h_keys[i];
+    h_pairs[i].first  = key;
     h_pairs[i].second = val;
   }
 
-  thrust::device_vector<Key> d_keys( h_keys ); 
-  thrust::device_vector<Value> d_results( num_keys);
-  thrust::device_vector<cuco::pair_type<Key, Value>> d_pairs( h_pairs );
+  thrust::device_vector<Key> d_keys(h_keys);
+  thrust::device_vector<Value> d_results(num_keys);
+  thrust::device_vector<cuco::pair_type<Key, Value>> d_pairs(h_pairs);
 
   map.insert(d_pairs.begin(), d_pairs.end());
-  
-  for(auto _ : state) {
+
+  for (auto _ : state) {
     map.find(d_keys.begin(), d_keys.end(), d_results.begin());
   }
 
@@ -148,8 +143,6 @@ static void BM_static_map_search_all(::benchmark::State& state) {
                           int64_t(state.range(0)));
 }
 
-
-
 BENCHMARK_TEMPLATE(BM_static_map_insert, int32_t, int32_t, dist_type::UNIQUE)
   ->Unit(benchmark::kMillisecond)
   ->Apply(generate_size_and_occupancy);
diff --git a/benchmarks/reduce_by_key/reduce_by_key.cu b/benchmarks/reduce_by_key/reduce_by_key.cu
index 0ca08144f..3e4776c6b 100644
--- a/benchmarks/reduce_by_key/reduce_by_key.cu
+++ b/benchmarks/reduce_by_key/reduce_by_key.cu
@@ -16,13 +16,13 @@
 
 #include <benchmark/benchmark.h>
 
+#include <thrust/device_vector.h>
 #include <thrust/iterator/iterator_traits.h>
 #include <thrust/iterator/zip_iterator.h>
 #include <thrust/pair.h>
 #include <thrust/reduce.h>
 #include <thrust/sort.h>
 #include <thrust/transform.h>
-#include <thrust/device_vector.h>
 
 /**
  * @brief Generates input sizes and number of unique keys
@@ -76,13 +76,11 @@ static void BM_thrust(::benchmark::State& state)
   }
 }
 BENCHMARK_TEMPLATE(BM_thrust, int32_t, int32_t)
-    ->Unit(benchmark::kMillisecond)
-    ->Apply(generate_size_and_num_unique);
+  ->Unit(benchmark::kMillisecond)
+  ->Apply(generate_size_and_num_unique);
 
 BENCHMARK_TEMPLATE(BM_thrust, int64_t, int64_t)
-    ->Unit(benchmark::kMillisecond)
-    ->Apply(generate_size_and_num_unique);
+  ->Unit(benchmark::kMillisecond)
+  ->Apply(generate_size_and_num_unique);
 
 // TODO: Hash based reduce by key benchmark
-
-
diff --git a/include/cuco/allocator.hpp b/include/cuco/allocator.hpp
index 21295c905..71d590384 100644
--- a/include/cuco/allocator.hpp
+++ b/include/cuco/allocator.hpp
@@ -29,7 +29,8 @@ class cuda_allocator {
 
   template <class U>
   cuda_allocator(cuda_allocator<U> const&) noexcept
-  { }
+  {
+  }
 
   value_type* allocate(std::size_t n)
   {
diff --git a/include/cuco/detail/dynamic_map_kernels.cuh b/include/cuco/detail/dynamic_map_kernels.cuh
index 28690b0dc..c1e21e863 100644
--- a/include/cuco/detail/dynamic_map_kernels.cuh
+++ b/include/cuco/detail/dynamic_map_kernels.cuh
@@ -19,17 +19,17 @@ namespace detail {
 namespace cg = cooperative_groups;
 
 /**
- * @brief Inserts all key/value pairs in the range `[first, last)`. 
- *  
+ * @brief Inserts all key/value pairs in the range `[first, last)`.
+ *
  * If multiple keys in `[first, last)` compare equal, it is unspecified which
  * element is inserted.
- * 
- * @tparam block_size 
+ *
+ * @tparam block_size
  * @tparam pair_type Type of the pairs contained in the map
  * @tparam InputIt Device accessible input iterator whose `value_type` is
  * convertible to the map's `value_type`
  * @tparam viewT Type of the `static_map` device views
- * @tparam mutableViewT Type of the `static_map` device mutable views 
+ * @tparam mutableViewT Type of the `static_map` device mutable views
  * @tparam atomicT Type of atomic storage
  * @tparam viewT Type of device view allowing access of hash map storage
  * @tparam Hash Unary callable type
@@ -38,7 +38,7 @@ namespace cg = cooperative_groups;
  * @param last End of the sequence of key/value pairs
  * @param submap_views Array of `static_map::device_view` objects used to
  * perform `contains` operations on each underlying `static_map`
- * @param submap_mutable_views Array of `static_map::device_mutable_view` objects 
+ * @param submap_mutable_views Array of `static_map::device_mutable_view` objects
  * used to perform an `insert` into the target `static_map` submap
  * @param num_successes The number of successfully inserted key/value pairs
  * @param insert_idx The index of the submap we are inserting into
@@ -46,14 +46,14 @@ namespace cg = cooperative_groups;
  * @param hash The unary function to apply to hash each key
  * @param key_equal The binary function used to compare two keys for equality
  */
-template<uint32_t block_size,
-         typename pair_type,
-         typename InputIt,
-         typename viewT,
-         typename mutableViewT,
-         typename atomicT,
-         typename Hash, 
-         typename KeyEqual>
+template <uint32_t block_size,
+          typename pair_type,
+          typename InputIt,
+          typename viewT,
+          typename mutableViewT,
+          typename atomicT,
+          typename Hash,
+          typename KeyEqual>
 __global__ void insert(InputIt first,
                        InputIt last,
                        viewT* submap_views,
@@ -62,58 +62,55 @@ __global__ void insert(InputIt first,
                        uint32_t insert_idx,
                        uint32_t num_submaps,
                        Hash hash,
-                       KeyEqual key_equal) {
+                       KeyEqual key_equal)
+{
   typedef cub::BlockReduce<std::size_t, block_size> BlockReduce;
   __shared__ typename BlockReduce::TempStorage temp_storage;
   std::size_t thread_num_successes = 0;
-  
+
   auto tid = blockDim.x * blockIdx.x + threadIdx.x;
 
-  while(first + tid < last) {
+  while (first + tid < last) {
     pair_type insert_pair = *(first + tid);
-    auto exists = false;
-    
+    auto exists           = false;
+
     // manually check for duplicates in those submaps we are not inserting into
-    for(auto i = 0; i < num_submaps; ++i) {
-      if(i != insert_idx) {
+    for (auto i = 0; i < num_submaps; ++i) {
+      if (i != insert_idx) {
         exists = submap_views[i].contains(insert_pair.first, hash, key_equal);
-        if(exists) {
-          break;
-        }
+        if (exists) { break; }
       }
     }
-    if(!exists) {
-      if(submap_mutable_views[insert_idx].insert(insert_pair, hash, key_equal)) {
+    if (!exists) {
+      if (submap_mutable_views[insert_idx].insert(insert_pair, hash, key_equal)) {
         thread_num_successes++;
       }
     }
 
     tid += gridDim.x * blockDim.x;
   }
-  
+
   std::size_t block_num_successes = BlockReduce(temp_storage).Sum(thread_num_successes);
-  if(threadIdx.x == 0) {
-    *num_successes += block_num_successes;
-  }
+  if (threadIdx.x == 0) { *num_successes += block_num_successes; }
 }
 
 /**
- * @brief Inserts all key/value pairs in the range `[first, last)`. 
- *  
+ * @brief Inserts all key/value pairs in the range `[first, last)`.
+ *
  * If multiple keys in `[first, last)` compare equal, it is unspecified which
  * element is inserted. Uses the CUDA Cooperative Groups API to leverage groups
- * of multiple threads to perform each key/value insertion. This provides a 
+ * of multiple threads to perform each key/value insertion. This provides a
  * significant boost in throughput compared to the non Cooperative Group
  * `insert` at moderate to high load factors.
- * 
- * @tparam block_size 
+ *
+ * @tparam block_size
  * @tparam tile_size The number of threads in the Cooperative Groups used to perform
  * inserts
  * @tparam pair_type Type of the pairs contained in the map
  * @tparam InputIt Device accessible input iterator whose `value_type` is
  * convertible to the map's `value_type`
  * @tparam viewT Type of the `static_map` device views
- * @tparam mutableViewT Type of the `static_map` device mutable views 
+ * @tparam mutableViewT Type of the `static_map` device mutable views
  * @tparam atomicT Type of atomic storage
  * @tparam viewT Type of device view allowing access of hash map storage
  * @tparam Hash Unary callable type
@@ -122,7 +119,7 @@ __global__ void insert(InputIt first,
  * @param last End of the sequence of key/value pairs
  * @param submap_views Array of `static_map::device_view` objects used to
  * perform `contains` operations on each underlying `static_map`
- * @param submap_mutable_views Array of `static_map::device_mutable_view` objects 
+ * @param submap_mutable_views Array of `static_map::device_mutable_view` objects
  * used to perform an `insert` into the target `static_map` submap
  * @param num_successes The number of successfully inserted key/value pairs
  * @param insert_idx The index of the submap we are inserting into
@@ -130,14 +127,15 @@ __global__ void insert(InputIt first,
  * @param hash The unary function to apply to hash each key
  * @param key_equal The binary function used to compare two keys for equality
  */
-template<uint32_t block_size, uint32_t tile_size,
-         typename pair_type,
-         typename InputIt,
-         typename viewT,
-         typename mutableViewT,
-         typename atomicT,
-         typename Hash, 
-         typename KeyEqual>
+template <uint32_t block_size,
+          uint32_t tile_size,
+          typename pair_type,
+          typename InputIt,
+          typename viewT,
+          typename mutableViewT,
+          typename atomicT,
+          typename Hash,
+          typename KeyEqual>
 __global__ void insert(InputIt first,
                        InputIt last,
                        viewT* submap_views,
@@ -146,54 +144,51 @@ __global__ void insert(InputIt first,
                        uint32_t insert_idx,
                        uint32_t num_submaps,
                        Hash hash,
-                       KeyEqual key_equal) {
+                       KeyEqual key_equal)
+{
   typedef cub::BlockReduce<std::size_t, block_size> BlockReduce;
   __shared__ typename BlockReduce::TempStorage temp_storage;
   std::size_t thread_num_successes = 0;
-  
+
   auto tile = cg::tiled_partition<tile_size>(cg::this_thread_block());
-  auto tid = blockDim.x * blockIdx.x + threadIdx.x;
-  auto it = first + tid / tile_size;
+  auto tid  = blockDim.x * blockIdx.x + threadIdx.x;
+  auto it   = first + tid / tile_size;
 
-  while(it < last) {
+  while (it < last) {
     pair_type insert_pair = *it;
-    auto exists = false;
-    
+    auto exists           = false;
+
     // manually check for duplicates in those submaps we are not inserting into
-    for(auto i = 0; i < num_submaps; ++i) {
-      if(i != insert_idx) {
+    for (auto i = 0; i < num_submaps; ++i) {
+      if (i != insert_idx) {
         exists = submap_views[i].contains(tile, insert_pair.first, hash, key_equal);
-        if(exists) {
-          break;
-        }
+        if (exists) { break; }
       }
     }
-    if(!exists) {
-      if(submap_mutable_views[insert_idx].insert(tile, insert_pair, hash, key_equal) && 
-         tile.thread_rank() == 0) {
+    if (!exists) {
+      if (submap_mutable_views[insert_idx].insert(tile, insert_pair, hash, key_equal) &&
+          tile.thread_rank() == 0) {
         thread_num_successes++;
       }
     }
 
     it += (gridDim.x * blockDim.x) / tile_size;
   }
-  
+
   std::size_t block_num_successes = BlockReduce(temp_storage).Sum(thread_num_successes);
-  if(threadIdx.x == 0) {
-    *num_successes += block_num_successes;
-  }
+  if (threadIdx.x == 0) { *num_successes += block_num_successes; }
 }
 
 /**
  * @brief Finds the values corresponding to all keys in the range `[first, last)`.
- * 
- * If the key `*(first + i)` exists in the map, copies its associated value to `(output_begin + i)`. 
- * Else, copies the empty value sentinel. 
+ *
+ * If the key `*(first + i)` exists in the map, copies its associated value to `(output_begin + i)`.
+ * Else, copies the empty value sentinel.
  * @tparam block_size The number of threads in the thread block
- * @tparam Value The mapped value type for the map 
+ * @tparam Value The mapped value type for the map
  * @tparam InputIt Device accessible input iterator whose `value_type` is
  * convertible to the map's `key_type`
- * @tparam OutputIt Device accessible output iterator whose `value_type` is 
+ * @tparam OutputIt Device accessible output iterator whose `value_type` is
  * convertible to the map's `mapped_type`
  * @tparam viewT Type of `static_map` device view
  * @tparam Hash Unary callable type
@@ -207,37 +202,39 @@ __global__ void insert(InputIt first,
  * @param hash The unary function to apply to hash each key
  * @param key_equal The binary function to compare two keys for equality
  */
-template<uint32_t block_size,
-         typename Value,
-         typename InputIt, typename OutputIt, 
-         typename viewT,
-         typename Hash, 
-         typename KeyEqual>
+template <uint32_t block_size,
+          typename Value,
+          typename InputIt,
+          typename OutputIt,
+          typename viewT,
+          typename Hash,
+          typename KeyEqual>
 __global__ void find(InputIt first,
                      InputIt last,
                      OutputIt output_begin,
                      viewT* submap_views,
                      uint32_t num_submaps,
                      Hash hash,
-                     KeyEqual key_equal) {
-  auto tid = blockDim.x * blockIdx.x + threadIdx.x;
+                     KeyEqual key_equal)
+{
+  auto tid                  = blockDim.x * blockIdx.x + threadIdx.x;
   auto empty_value_sentinel = submap_views[0].get_empty_value_sentinel();
   __shared__ Value writeBuffer[block_size];
 
-  while(first + tid < last) {
-    auto key = *(first + tid);
+  while (first + tid < last) {
+    auto key         = *(first + tid);
     auto found_value = empty_value_sentinel;
-    for(auto i = 0; i < num_submaps; ++i) {
+    for (auto i = 0; i < num_submaps; ++i) {
       auto submap_view = submap_views[i];
-      auto found = submap_view.find(key, hash, key_equal);
-      if(found != submap_view.end()) {
+      auto found       = submap_view.find(key, hash, key_equal);
+      if (found != submap_view.end()) {
         found_value = found->second;
         break;
       }
     }
 
-    /* 
-     * The ld.relaxed.gpu instruction used in view.find causes L1 to 
+    /*
+     * The ld.relaxed.gpu instruction used in view.find causes L1 to
      * flush more frequently, causing increased sector stores from L2 to global memory.
      * By writing results to shared memory and then synchronizing before writing back
      * to global, we no longer rely on L1, preventing the increase in sector stores from
@@ -252,10 +249,10 @@ __global__ void find(InputIt first,
 
 /**
  * @brief Finds the values corresponding to all keys in the range `[first, last)`.
- * 
- * If the key `*(first + i)` exists in the map, copies its associated value to `(output_begin + i)`. 
+ *
+ * If the key `*(first + i)` exists in the map, copies its associated value to `(output_begin + i)`.
  * Else, copies the empty value sentinel. Uses the CUDA Cooperative Groups API to leverage groups
- * of multiple threads to find each key. This provides a significant boost in throughput compared 
+ * of multiple threads to find each key. This provides a significant boost in throughput compared
  * to the non Cooperative Group `find` at moderate to high load factors.
  *
  * @tparam block_size The number of threads in the thread block
@@ -264,7 +261,7 @@ __global__ void find(InputIt first,
  * @tparam Value The mapped value type for the map
  * @tparam InputIt Device accessible input iterator whose `value_type` is
  * convertible to the map's `key_type`
- * @tparam OutputIt Device accessible output iterator whose `value_type` is 
+ * @tparam OutputIt Device accessible output iterator whose `value_type` is
  * convertible to the map's `mapped_type`
  * @tparam viewT Type of `static_map` device view
  * @tparam Hash Unary callable type
@@ -278,49 +275,50 @@ __global__ void find(InputIt first,
  * @param hash The unary function to apply to hash each key
  * @param key_equal The binary function to compare two keys for equality
  */
-template<uint32_t block_size, uint32_t tile_size,
-         typename Value,
-         typename InputIt, typename OutputIt, 
-         typename viewT,
-         typename Hash, 
-         typename KeyEqual>
+template <uint32_t block_size,
+          uint32_t tile_size,
+          typename Value,
+          typename InputIt,
+          typename OutputIt,
+          typename viewT,
+          typename Hash,
+          typename KeyEqual>
 __global__ void find(InputIt first,
                      InputIt last,
                      OutputIt output_begin,
                      viewT* submap_views,
                      uint32_t num_submaps,
                      Hash hash,
-                     KeyEqual key_equal) {
-  auto tile = cg::tiled_partition<tile_size>(cg::this_thread_block());
-  auto tid = blockDim.x * blockIdx.x + threadIdx.x;
-  auto key_idx = tid / tile_size;
+                     KeyEqual key_equal)
+{
+  auto tile                 = cg::tiled_partition<tile_size>(cg::this_thread_block());
+  auto tid                  = blockDim.x * blockIdx.x + threadIdx.x;
+  auto key_idx              = tid / tile_size;
   auto empty_value_sentinel = submap_views[0].get_empty_value_sentinel();
   __shared__ Value writeBuffer[block_size];
 
-  while(first + key_idx < last) {
-    auto key = *(first + key_idx);
+  while (first + key_idx < last) {
+    auto key         = *(first + key_idx);
     auto found_value = empty_value_sentinel;
-    for(auto i = 0; i < num_submaps; ++i) {
+    for (auto i = 0; i < num_submaps; ++i) {
       auto submap_view = submap_views[i];
-      auto found = submap_view.find(tile, key, hash, key_equal);
-      if(found != submap_view.end()) {
+      auto found       = submap_view.find(tile, key, hash, key_equal);
+      if (found != submap_view.end()) {
         found_value = found->second;
         break;
       }
     }
 
-    /* 
-     * The ld.relaxed.gpu instruction used in view.find causes L1 to 
+    /*
+     * The ld.relaxed.gpu instruction used in view.find causes L1 to
      * flush more frequently, causing increased sector stores from L2 to global memory.
      * By writing results to shared memory and then synchronizing before writing back
      * to global, we no longer rely on L1, preventing the increase in sector stores from
      * L2 to global and improving performance.
      */
-    if(tile.thread_rank() == 0) {
-      writeBuffer[threadIdx.x / tile_size] = found_value;
-    }
+    if (tile.thread_rank() == 0) { writeBuffer[threadIdx.x / tile_size] = found_value; }
     __syncthreads();
-    if(tile.thread_rank() == 0) {
+    if (tile.thread_rank() == 0) {
       *(output_begin + key_idx) = writeBuffer[threadIdx.x / tile_size];
     }
     key_idx += (gridDim.x * blockDim.x) / tile_size;
@@ -329,13 +327,13 @@ __global__ void find(InputIt first,
 
 /**
  * @brief Indicates whether the keys in the range `[first, last)` are contained in the map.
- * 
+ *
  * Writes a `bool` to `(output + i)` indicating if the key `*(first + i)` exists in the map.
  *
  * @tparam block_size The number of threads in the thread block
  * @tparam InputIt Device accessible input iterator whose `value_type` is
  * convertible to the map's `key_type`
- * @tparam OutputIt Device accessible output iterator whose `value_type` is 
+ * @tparam OutputIt Device accessible output iterator whose `value_type` is
  * convertible to the map's `mapped_type`
  * @tparam viewT Type of `static_map` device view
  * @tparam Hash Unary callable type
@@ -349,33 +347,33 @@ __global__ void find(InputIt first,
  * @param hash The unary function to apply to hash each key
  * @param key_equal The binary function to compare two keys for equality
  */
-template<uint32_t block_size,
-         typename InputIt, typename OutputIt, 
-         typename viewT,
-         typename Hash, 
-         typename KeyEqual>
+template <uint32_t block_size,
+          typename InputIt,
+          typename OutputIt,
+          typename viewT,
+          typename Hash,
+          typename KeyEqual>
 __global__ void contains(InputIt first,
                          InputIt last,
                          OutputIt output_begin,
                          viewT* submap_views,
                          uint32_t num_submaps,
                          Hash hash,
-                         KeyEqual key_equal) {
+                         KeyEqual key_equal)
+{
   auto tid = blockDim.x * blockIdx.x + threadIdx.x;
   __shared__ bool writeBuffer[block_size];
 
-  while(first + tid < last) {
-    auto key = *(first + tid);
+  while (first + tid < last) {
+    auto key   = *(first + tid);
     auto found = false;
-    for(auto i = 0; i < num_submaps; ++i) {
+    for (auto i = 0; i < num_submaps; ++i) {
       found = submap_views[i].contains(key, hash, key_equal);
-      if(found) {
-        break;
-      }
+      if (found) { break; }
     }
-    
-    /* 
-     * The ld.relaxed.gpu instruction used in view.find causes L1 to 
+
+    /*
+     * The ld.relaxed.gpu instruction used in view.find causes L1 to
      * flush more frequently, causing increased sector stores from L2 to global memory.
      * By writing results to shared memory and then synchronizing before writing back
      * to global, we no longer rely on L1, preventing the increase in sector stores from
@@ -390,10 +388,10 @@ __global__ void contains(InputIt first,
 
 /**
  * @brief Indicates whether the keys in the range `[first, last)` are contained in the map.
- * 
+ *
  * Writes a `bool` to `(output + i)` indicating if the key `*(first + i)` exists in the map.
- * Uses the CUDA Cooperative Groups API to leverage groups of multiple threads to perform the 
- * contains operation for each key. This provides a significant boost in throughput compared 
+ * Uses the CUDA Cooperative Groups API to leverage groups of multiple threads to perform the
+ * contains operation for each key. This provides a significant boost in throughput compared
  * to the non Cooperative Group `contains` at moderate to high load factors.
  *
  * @tparam block_size The number of threads in the thread block
@@ -401,7 +399,7 @@ __global__ void contains(InputIt first,
  * perform find operations
  * @tparam InputIt Device accessible input iterator whose `value_type` is
  * convertible to the map's `key_type`
- * @tparam OutputIt Device accessible output iterator whose `value_type` is 
+ * @tparam OutputIt Device accessible output iterator whose `value_type` is
  * convertible to the map's `mapped_type`
  * @tparam viewT Type of `static_map` device view
  * @tparam Hash Unary callable type
@@ -415,49 +413,48 @@ __global__ void contains(InputIt first,
  * @param hash The unary function to apply to hash each key
  * @param key_equal The binary function to compare two keys for equality
  */
-template<uint32_t block_size, uint32_t tile_size,
-         typename InputIt, typename OutputIt, 
-         typename viewT,
-         typename Hash, 
-         typename KeyEqual>
+template <uint32_t block_size,
+          uint32_t tile_size,
+          typename InputIt,
+          typename OutputIt,
+          typename viewT,
+          typename Hash,
+          typename KeyEqual>
 __global__ void contains(InputIt first,
                          InputIt last,
                          OutputIt output_begin,
                          viewT* submap_views,
                          uint32_t num_submaps,
                          Hash hash,
-                         KeyEqual key_equal) {
-  auto tile = cg::tiled_partition<tile_size>(cg::this_thread_block());
-  auto tid = blockDim.x * blockIdx.x + threadIdx.x;
+                         KeyEqual key_equal)
+{
+  auto tile    = cg::tiled_partition<tile_size>(cg::this_thread_block());
+  auto tid     = blockDim.x * blockIdx.x + threadIdx.x;
   auto key_idx = tid / tile_size;
   __shared__ bool writeBuffer[block_size];
 
-  while(first + key_idx < last) {
-    auto key = *(first + key_idx);
+  while (first + key_idx < last) {
+    auto key   = *(first + key_idx);
     auto found = false;
-    for(auto i = 0; i < num_submaps; ++i) {
+    for (auto i = 0; i < num_submaps; ++i) {
       found = submap_views[i].contains(tile, key, hash, key_equal);
-      if(found) {
-        break;
-      }
+      if (found) { break; }
     }
 
-    /* 
-     * The ld.relaxed.gpu instruction used in view.find causes L1 to 
+    /*
+     * The ld.relaxed.gpu instruction used in view.find causes L1 to
      * flush more frequently, causing increased sector stores from L2 to global memory.
      * By writing results to shared memory and then synchronizing before writing back
      * to global, we no longer rely on L1, preventing the increase in sector stores from
      * L2 to global and improving performance.
      */
-    if(tile.thread_rank() == 0) {
-      writeBuffer[threadIdx.x / tile_size] = found;
-    }
+    if (tile.thread_rank() == 0) { writeBuffer[threadIdx.x / tile_size] = found; }
     __syncthreads();
-    if(tile.thread_rank() == 0) {
+    if (tile.thread_rank() == 0) {
       *(output_begin + key_idx) = writeBuffer[threadIdx.x / tile_size];
     }
     key_idx += (gridDim.x * blockDim.x) / tile_size;
   }
 }
-} // namespace detail
-} // namespace cuco
\ No newline at end of file
+}  // namespace detail
+}  // namespace cuco
\ No newline at end of file
diff --git a/include/cuco/detail/hash_functions.cuh b/include/cuco/detail/hash_functions.cuh
index de7074f04..aec550aba 100644
--- a/include/cuco/detail/hash_functions.cuh
+++ b/include/cuco/detail/hash_functions.cuh
@@ -20,7 +20,6 @@ namespace cuco {
 
 namespace detail {
 
-
 // MurmurHash3_32 implementation from
 // https://github.com/aappleby/smhasher/blob/master/src/MurmurHash3.cpp
 //-----------------------------------------------------------------------------
diff --git a/include/cuco/detail/pair.cuh b/include/cuco/detail/pair.cuh
index da50f7258..dfdf7632e 100644
--- a/include/cuco/detail/pair.cuh
+++ b/include/cuco/detail/pair.cuh
@@ -68,8 +68,8 @@ struct is_thrust_pair_like_impl : std::false_type {
 
 template <typename T>
 struct is_thrust_pair_like_impl<T,
-                           std::void_t<decltype(thrust::get<0>(std::declval<T>())),
-                                       decltype(thrust::get<1>(std::declval<T>()))>>
+                                std::void_t<decltype(thrust::get<0>(std::declval<T>())),
+                                            decltype(thrust::get<1>(std::declval<T>()))>>
   : std::conditional_t<thrust::tuple_size<T>::value == 2, std::true_type, std::false_type> {
 };
 
diff --git a/include/cuco/detail/static_map.inl b/include/cuco/detail/static_map.inl
index b8f7ccdba..2b72cfa32 100644
--- a/include/cuco/detail/static_map.inl
+++ b/include/cuco/detail/static_map.inl
@@ -63,7 +63,7 @@ void static_map<Key, Value, Scope, Allocator>::insert(InputIt first,
                                                       Hash hash,
                                                       KeyEqual key_equal)
 {
-  auto num_keys         = std::distance(first, last);
+  auto num_keys = std::distance(first, last);
   if (num_keys == 0) { return; }
 
   auto const block_size = 128;
@@ -87,9 +87,9 @@ void static_map<Key, Value, Scope, Allocator>::insert(InputIt first,
 template <typename Key, typename Value, cuda::thread_scope Scope, typename Allocator>
 template <typename InputIt, typename OutputIt, typename Hash, typename KeyEqual>
 void static_map<Key, Value, Scope, Allocator>::find(
-  InputIt first, InputIt last, OutputIt output_begin, Hash hash, KeyEqual key_equal) 
+  InputIt first, InputIt last, OutputIt output_begin, Hash hash, KeyEqual key_equal)
 {
-  auto num_keys         = std::distance(first, last);
+  auto num_keys = std::distance(first, last);
   if (num_keys == 0) { return; }
 
   auto const block_size = 128;
@@ -108,7 +108,7 @@ template <typename InputIt, typename OutputIt, typename Hash, typename KeyEqual>
 void static_map<Key, Value, Scope, Allocator>::contains(
   InputIt first, InputIt last, OutputIt output_begin, Hash hash, KeyEqual key_equal)
 {
-  auto num_keys         = std::distance(first, last);
+  auto num_keys = std::distance(first, last);
   if (num_keys == 0) { return; }
 
   auto const block_size = 128;
@@ -175,7 +175,8 @@ __device__ bool static_map<Key, Value, Scope, Allocator>::device_mutable_view::i
 
     // The user provide `key_equal` can never be used to compare against `empty_key_sentinel` as the
     // sentinel is not a valid key value. Therefore, first check for the sentinel
-    auto const slot_is_empty = detail::bitwise_compare(existing_key,this->get_empty_key_sentinel());
+    auto const slot_is_empty =
+      detail::bitwise_compare(existing_key, this->get_empty_key_sentinel());
 
     // the key we are trying to insert is already in the map, so we return with failure to insert
     if (g.ballot(not slot_is_empty and key_equal(existing_key, insert_pair.first))) {
@@ -374,7 +375,7 @@ __device__ bool static_map<Key, Value, Scope, Allocator>::device_view::contains(
   while (true) {
     auto const existing_key = current_slot->first.load(cuda::std::memory_order_relaxed);
 
-    if (detail::bitwise_compare(existing_key,empty_key_sentinel_)) { return false; }
+    if (detail::bitwise_compare(existing_key, empty_key_sentinel_)) { return false; }
 
     if (key_equal(existing_key, k)) { return true; }
 
@@ -394,7 +395,8 @@ __device__ bool static_map<Key, Value, Scope, Allocator>::device_view::contains(
 
     // The user provide `key_equal` can never be used to compare against `empty_key_sentinel` as the
     // sentinel is not a valid key value. Therefore, first check for the sentinel
-    auto const slot_is_empty = detail::bitwise_compare(existing_key,this->get_empty_key_sentinel());
+    auto const slot_is_empty =
+      detail::bitwise_compare(existing_key, this->get_empty_key_sentinel());
 
     // the key we were searching for was found by one of the threads, so we return an iterator to
     // the entry
diff --git a/include/cuco/detail/static_map_kernels.cuh b/include/cuco/detail/static_map_kernels.cuh
index aa4606aa6..e166de3c6 100644
--- a/include/cuco/detail/static_map_kernels.cuh
+++ b/include/cuco/detail/static_map_kernels.cuh
@@ -12,9 +12,9 @@
  * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  * See the License for the specific language governing permissions and
  * limitations under the License.
- */ 
+ */
 
-namespace cuco{
+namespace cuco {
 namespace detail {
 namespace cg = cooperative_groups;
 
@@ -23,7 +23,7 @@ namespace cg = cooperative_groups;
  *
  * Each space in `slots` that can hold a key value pair is initialized to a
  * `pair_atomic_type` containing the key `k` and the value `v`.
- * 
+ *
  * @tparam atomic_key_type Type of the `Key` atomic container
  * @tparam atomic_mapped_type Type of the `Value` atomic container
  * @tparam Key key type
@@ -34,16 +34,14 @@ namespace cg = cooperative_groups;
  * @param v Value to which all values in `slots` are initialized
  * @param size Size of the storage pointed to by `slots`
  */
-template<std::size_t block_size,
-         typename atomic_key_type,
-         typename atomic_mapped_type,
-         typename Key,
-         typename Value,
-         typename pair_atomic_type>
-__global__ void initialize(
-  pair_atomic_type* const slots, Key k,
-  Value v, std::size_t size) {
-  
+template <std::size_t block_size,
+          typename atomic_key_type,
+          typename atomic_mapped_type,
+          typename Key,
+          typename Value,
+          typename pair_atomic_type>
+__global__ void initialize(pair_atomic_type* const slots, Key k, Value v, std::size_t size)
+{
   auto tid = block_size * blockIdx.x + threadIdx.x;
   while (tid < size) {
     new (&slots[tid].first) atomic_key_type{k};
@@ -53,14 +51,14 @@ __global__ void initialize(
 }
 
 /**
- * @brief Inserts all key/value pairs in the range `[first, last)`. 
- *  
+ * @brief Inserts all key/value pairs in the range `[first, last)`.
+ *
  * If multiple keys in `[first, last)` compare equal, it is unspecified which
  * element is inserted.
- * 
- * @tparam block_size 
+ *
+ * @tparam block_size
  * @tparam InputIt Device accessible input iterator whose `value_type` is
-* convertible to the map's `value_type`
+ * convertible to the map's `value_type`
  * @tparam atomicT Type of atomic storage
  * @tparam viewT Type of device view allowing access of hash map storage
  * @tparam Hash Unary callable type
@@ -72,25 +70,22 @@ __global__ void initialize(
  * @param hash The unary function to apply to hash each key
  * @param key_equal The binary function used to compare two keys for equality
  */
-template<std::size_t block_size,
-         typename InputIt,
-         typename atomicT,
-         typename viewT,
-         typename Hash, 
-         typename KeyEqual>
-__global__ void insert(InputIt first,
-                       InputIt last,
-                       atomicT* num_successes,
-                       viewT view,
-                       Hash hash,
-                       KeyEqual key_equal) {
+template <std::size_t block_size,
+          typename InputIt,
+          typename atomicT,
+          typename viewT,
+          typename Hash,
+          typename KeyEqual>
+__global__ void insert(
+  InputIt first, InputIt last, atomicT* num_successes, viewT view, Hash hash, KeyEqual key_equal)
+{
   typedef cub::BlockReduce<std::size_t, block_size> BlockReduce;
   __shared__ typename BlockReduce::TempStorage temp_storage;
   std::size_t thread_num_successes = 0;
-  
+
   auto tid = block_size * blockIdx.x + threadIdx.x;
-  auto it = first + tid;
-  
+  auto it  = first + tid;
+
   while (it < last) {
     typename viewT::value_type const insert_pair{*it};
     if (view.insert(insert_pair, hash, key_equal)) { thread_num_successes++; }
@@ -100,25 +95,23 @@ __global__ void insert(InputIt first,
   // compute number of successfully inserted elements for each block
   // and atomically add to the grand total
   std::size_t block_num_successes = BlockReduce(temp_storage).Sum(thread_num_successes);
-  if(threadIdx.x == 0) {
-    *num_successes += block_num_successes;
-  }
+  if (threadIdx.x == 0) { *num_successes += block_num_successes; }
 }
 
 /**
- * @brief Inserts all key/value pairs in the range `[first, last)`. 
- *  
+ * @brief Inserts all key/value pairs in the range `[first, last)`.
+ *
  * If multiple keys in `[first, last)` compare equal, it is unspecified which
  * element is inserted. Uses the CUDA Cooperative Groups API to leverage groups
- * of multiple threads to perform each key/value insertion. This provides a 
+ * of multiple threads to perform each key/value insertion. This provides a
  * significant boost in throughput compared to the non Cooperative Group
  * `insert` at moderate to high load factors.
- * 
- * @tparam block_size 
+ *
+ * @tparam block_size
  * @tparam tile_size The number of threads in the Cooperative Groups used to perform
  * inserts
  * @tparam InputIt Device accessible input iterator whose `value_type` is
-* convertible to the map's `value_type`
+ * convertible to the map's `value_type`
  * @tparam atomicT Type of atomic storage
  * @tparam viewT Type of device view allowing access of hash map storage
  * @tparam Hash Unary callable type
@@ -130,27 +123,24 @@ __global__ void insert(InputIt first,
  * @param hash The unary function to apply to hash each key
  * @param key_equal The binary function used to compare two keys for equality
  */
-template<std::size_t block_size,
-         uint32_t tile_size,
-         typename InputIt,
-         typename atomicT,
-         typename viewT,
-         typename Hash, 
-         typename KeyEqual>
-__global__ void insert(InputIt first,
-                       InputIt last,
-                       atomicT* num_successes,
-                       viewT view,
-                       Hash hash,
-                       KeyEqual key_equal) {
+template <std::size_t block_size,
+          uint32_t tile_size,
+          typename InputIt,
+          typename atomicT,
+          typename viewT,
+          typename Hash,
+          typename KeyEqual>
+__global__ void insert(
+  InputIt first, InputIt last, atomicT* num_successes, viewT view, Hash hash, KeyEqual key_equal)
+{
   typedef cub::BlockReduce<std::size_t, block_size> BlockReduce;
   __shared__ typename BlockReduce::TempStorage temp_storage;
   std::size_t thread_num_successes = 0;
 
   auto tile = cg::tiled_partition<tile_size>(cg::this_thread_block());
-  auto tid = block_size * blockIdx.x + threadIdx.x;
-  auto it = first + tid / tile_size;
-  
+  auto tid  = block_size * blockIdx.x + threadIdx.x;
+  auto it   = first + tid / tile_size;
+
   while (it < last) {
     // force conversion to value_type
     typename viewT::value_type const insert_pair{*it};
@@ -159,25 +149,23 @@ __global__ void insert(InputIt first,
     }
     it += (gridDim.x * block_size) / tile_size;
   }
-  
+
   // compute number of successfully inserted elements for each block
   // and atomically add to the grand total
   std::size_t block_num_successes = BlockReduce(temp_storage).Sum(thread_num_successes);
-  if(threadIdx.x == 0) {
-    *num_successes += block_num_successes;
-  }
+  if (threadIdx.x == 0) { *num_successes += block_num_successes; }
 }
 
 /**
  * @brief Finds the values corresponding to all keys in the range `[first, last)`.
- * 
- * If the key `*(first + i)` exists in the map, copies its associated value to `(output_begin + i)`. 
- * Else, copies the empty value sentinel. 
+ *
+ * If the key `*(first + i)` exists in the map, copies its associated value to `(output_begin + i)`.
+ * Else, copies the empty value sentinel.
  * @tparam block_size The size of the thread block
  * @tparam Value The type of the mapped value for the map
  * @tparam InputIt Device accessible input iterator whose `value_type` is
  * convertible to the map's `key_type`
- * @tparam OutputIt Device accessible output iterator whose `value_type` is 
+ * @tparam OutputIt Device accessible output iterator whose `value_type` is
  * convertible to the map's `mapped_type`
  * @tparam viewT Type of device view allowing access of hash map storage
  * @tparam Hash Unary callable type
@@ -189,37 +177,34 @@ __global__ void insert(InputIt first,
  * @param hash The unary function to apply to hash each key
  * @param key_equal The binary function to compare two keys for equality
  */
-template<std::size_t block_size,
-         typename Value,
-         typename InputIt, typename OutputIt, 
-         typename viewT,
-         typename Hash, 
-         typename KeyEqual>
-__global__ void find(InputIt first,
-                     InputIt last,
-                     OutputIt output_begin,
-                     viewT view,
-                     Hash hash,
-                     KeyEqual key_equal) {
-  auto tid = block_size * blockIdx.x + threadIdx.x;
+template <std::size_t block_size,
+          typename Value,
+          typename InputIt,
+          typename OutputIt,
+          typename viewT,
+          typename Hash,
+          typename KeyEqual>
+__global__ void find(
+  InputIt first, InputIt last, OutputIt output_begin, viewT view, Hash hash, KeyEqual key_equal)
+{
+  auto tid     = block_size * blockIdx.x + threadIdx.x;
   auto key_idx = tid;
   __shared__ Value writeBuffer[block_size];
-  
-  while(first + key_idx < last) {
-    auto key = *(first + key_idx);
+
+  while (first + key_idx < last) {
+    auto key   = *(first + key_idx);
     auto found = view.find(key, hash, key_equal);
 
-    /* 
-     * The ld.relaxed.gpu instruction used in view.find causes L1 to 
+    /*
+     * The ld.relaxed.gpu instruction used in view.find causes L1 to
      * flush more frequently, causing increased sector stores from L2 to global memory.
      * By writing results to shared memory and then synchronizing before writing back
      * to global, we no longer rely on L1, preventing the increase in sector stores from
      * L2 to global and improving performance.
      */
-    writeBuffer[threadIdx.x] =
-      found == view.end()
-        ? view.get_empty_value_sentinel()
-        : found->second.load(cuda::std::memory_order_relaxed);
+    writeBuffer[threadIdx.x] = found == view.end()
+                                 ? view.get_empty_value_sentinel()
+                                 : found->second.load(cuda::std::memory_order_relaxed);
     __syncthreads();
     *(output_begin + key_idx) = writeBuffer[threadIdx.x];
     key_idx += gridDim.x * block_size;
@@ -228,10 +213,10 @@ __global__ void find(InputIt first,
 
 /**
  * @brief Finds the values corresponding to all keys in the range `[first, last)`.
- * 
- * If the key `*(first + i)` exists in the map, copies its associated value to `(output_begin + i)`. 
+ *
+ * If the key `*(first + i)` exists in the map, copies its associated value to `(output_begin + i)`.
  * Else, copies the empty value sentinel. Uses the CUDA Cooperative Groups API to leverage groups
- * of multiple threads to find each key. This provides a significant boost in throughput compared 
+ * of multiple threads to find each key. This provides a significant boost in throughput compared
  * to the non Cooperative Group `find` at moderate to high load factors.
  *
  * @tparam block_size The size of the thread block
@@ -240,7 +225,7 @@ __global__ void find(InputIt first,
  * @tparam Value The type of the mapped value for the map
  * @tparam InputIt Device accessible input iterator whose `value_type` is
  * convertible to the map's `key_type`
- * @tparam OutputIt Device accessible output iterator whose `value_type` is 
+ * @tparam OutputIt Device accessible output iterator whose `value_type` is
  * convertible to the map's `mapped_type`
  * @tparam viewT Type of device view allowing access of hash map storage
  * @tparam Hash Unary callable type
@@ -252,42 +237,40 @@ __global__ void find(InputIt first,
  * @param hash The unary function to apply to hash each key
  * @param key_equal The binary function to compare two keys for equality
  */
-template<std::size_t block_size, uint32_t tile_size,
-         typename Value,
-         typename InputIt, typename OutputIt, 
-         typename viewT,
-         typename Hash, 
-         typename KeyEqual>
-__global__ void find(InputIt first,
-                     InputIt last,
-                     OutputIt output_begin,
-                     viewT view,
-                     Hash hash,
-                     KeyEqual key_equal) {
-  auto tile = cg::tiled_partition<tile_size>(cg::this_thread_block());
-  auto tid = block_size * blockIdx.x + threadIdx.x;
+template <std::size_t block_size,
+          uint32_t tile_size,
+          typename Value,
+          typename InputIt,
+          typename OutputIt,
+          typename viewT,
+          typename Hash,
+          typename KeyEqual>
+__global__ void find(
+  InputIt first, InputIt last, OutputIt output_begin, viewT view, Hash hash, KeyEqual key_equal)
+{
+  auto tile    = cg::tiled_partition<tile_size>(cg::this_thread_block());
+  auto tid     = block_size * blockIdx.x + threadIdx.x;
   auto key_idx = tid / tile_size;
   __shared__ Value writeBuffer[block_size];
-  
-  while(first + key_idx < last) {
-    auto key = *(first + key_idx);
+
+  while (first + key_idx < last) {
+    auto key   = *(first + key_idx);
     auto found = view.find(tile, key, hash, key_equal);
-    
-    /* 
-     * The ld.relaxed.gpu instruction used in view.find causes L1 to 
+
+    /*
+     * The ld.relaxed.gpu instruction used in view.find causes L1 to
      * flush more frequently, causing increased sector stores from L2 to global memory.
      * By writing results to shared memory and then synchronizing before writing back
      * to global, we no longer rely on L1, preventing the increase in sector stores from
      * L2 to global and improving performance.
      */
-    if(tile.thread_rank() == 0) {
+    if (tile.thread_rank() == 0) {
       writeBuffer[threadIdx.x / tile_size] =
-        found == view.end()
-          ? view.get_empty_value_sentinel()
-          : found->second.load(cuda::std::memory_order_relaxed);
+        found == view.end() ? view.get_empty_value_sentinel()
+                            : found->second.load(cuda::std::memory_order_relaxed);
     }
     __syncthreads();
-    if(tile.thread_rank() == 0) {
+    if (tile.thread_rank() == 0) {
       *(output_begin + key_idx) = writeBuffer[threadIdx.x / tile_size];
     }
     key_idx += (gridDim.x * block_size) / tile_size;
@@ -296,7 +279,7 @@ __global__ void find(InputIt first,
 
 /**
  * @brief Indicates whether the keys in the range `[first, last)` are contained in the map.
- * 
+ *
  * Writes a `bool` to `(output + i)` indicating if the key `*(first + i)` exists in the map.
  *
  * @tparam block_size The size of the thread block
@@ -306,7 +289,7 @@ __global__ void find(InputIt first,
  * convertible to the map's `mapped_type`
  * @tparam viewT Type of device view allowing access of hash map storage
  * @tparam Hash Unary callable type
- * @tparam KeyEqual Binary callable type 
+ * @tparam KeyEqual Binary callable type
  * @param first Beginning of the sequence of keys
  * @param last End of the sequence of keys
  * @param output_begin Beginning of the sequence of booleans for the presence of each key
@@ -314,26 +297,24 @@ __global__ void find(InputIt first,
  * @param hash The unary function to apply to hash each key
  * @param key_equal The binary function to compare two keys for equality
  */
-template<std::size_t block_size,
-         typename InputIt, typename OutputIt, 
-         typename viewT,
-         typename Hash, 
-         typename KeyEqual>
-__global__ void contains(InputIt first,
-                         InputIt last,
-                         OutputIt output_begin,
-                         viewT view,
-                         Hash hash,
-                         KeyEqual key_equal) {
-  auto tid = block_size * blockIdx.x + threadIdx.x;
+template <std::size_t block_size,
+          typename InputIt,
+          typename OutputIt,
+          typename viewT,
+          typename Hash,
+          typename KeyEqual>
+__global__ void contains(
+  InputIt first, InputIt last, OutputIt output_begin, viewT view, Hash hash, KeyEqual key_equal)
+{
+  auto tid     = block_size * blockIdx.x + threadIdx.x;
   auto key_idx = tid;
   __shared__ bool writeBuffer[block_size];
-  
-  while(first + key_idx < last) {
+
+  while (first + key_idx < last) {
     auto key = *(first + key_idx);
-    
-    /* 
-     * The ld.relaxed.gpu instruction used in view.find causes L1 to 
+
+    /*
+     * The ld.relaxed.gpu instruction used in view.find causes L1 to
      * flush more frequently, causing increased sector stores from L2 to global memory.
      * By writing results to shared memory and then synchronizing before writing back
      * to global, we no longer rely on L1, preventing the increase in sector stores from
@@ -348,10 +329,10 @@ __global__ void contains(InputIt first,
 
 /**
  * @brief Indicates whether the keys in the range `[first, last)` are contained in the map.
- * 
+ *
  * Writes a `bool` to `(output + i)` indicating if the key `*(first + i)` exists in the map.
- * Uses the CUDA Cooperative Groups API to leverage groups of multiple threads to perform the 
- * contains operation for each key. This provides a significant boost in throughput compared 
+ * Uses the CUDA Cooperative Groups API to leverage groups of multiple threads to perform the
+ * contains operation for each key. This provides a significant boost in throughput compared
  * to the non Cooperative Group `contains` at moderate to high load factors.
  *
  * @tparam block_size The size of the thread block
@@ -363,7 +344,7 @@ __global__ void contains(InputIt first,
  * convertible to the map's `mapped_type`
  * @tparam viewT Type of device view allowing access of hash map storage
  * @tparam Hash Unary callable type
- * @tparam KeyEqual Binary callable type 
+ * @tparam KeyEqual Binary callable type
  * @param first Beginning of the sequence of keys
  * @param last End of the sequence of keys
  * @param output_begin Beginning of the sequence of booleans for the presence of each key
@@ -371,43 +352,40 @@ __global__ void contains(InputIt first,
  * @param hash The unary function to apply to hash each key
  * @param key_equal The binary function to compare two keys for equality
  */
-template<std::size_t block_size, uint32_t tile_size,
-         typename InputIt, typename OutputIt, 
-         typename viewT,
-         typename Hash, 
-         typename KeyEqual>
-__global__ void contains(InputIt first,
-                         InputIt last,
-                         OutputIt output_begin,
-                         viewT view,
-                         Hash hash,
-                         KeyEqual key_equal) {
-  auto tile = cg::tiled_partition<tile_size>(cg::this_thread_block());
-  auto tid = block_size * blockIdx.x + threadIdx.x;
+template <std::size_t block_size,
+          uint32_t tile_size,
+          typename InputIt,
+          typename OutputIt,
+          typename viewT,
+          typename Hash,
+          typename KeyEqual>
+__global__ void contains(
+  InputIt first, InputIt last, OutputIt output_begin, viewT view, Hash hash, KeyEqual key_equal)
+{
+  auto tile    = cg::tiled_partition<tile_size>(cg::this_thread_block());
+  auto tid     = block_size * blockIdx.x + threadIdx.x;
   auto key_idx = tid / tile_size;
   __shared__ bool writeBuffer[block_size];
-  
-  while(first + key_idx < last) {
-    auto key = *(first + key_idx);
+
+  while (first + key_idx < last) {
+    auto key   = *(first + key_idx);
     auto found = view.contains(tile, key, hash, key_equal);
-    
-    /* 
-     * The ld.relaxed.gpu instruction used in view.find causes L1 to 
+
+    /*
+     * The ld.relaxed.gpu instruction used in view.find causes L1 to
      * flush more frequently, causing increased sector stores from L2 to global memory.
      * By writing results to shared memory and then synchronizing before writing back
      * to global, we no longer rely on L1, preventing the increase in sector stores from
      * L2 to global and improving performance.
      */
-    if(tile.thread_rank() == 0) {
-      writeBuffer[threadIdx.x / tile_size] = found;
-    }
+    if (tile.thread_rank() == 0) { writeBuffer[threadIdx.x / tile_size] = found; }
     __syncthreads();
-    if(tile.thread_rank() == 0) {
+    if (tile.thread_rank() == 0) {
       *(output_begin + key_idx) = writeBuffer[threadIdx.x / tile_size];
     }
     key_idx += (gridDim.x * block_size) / tile_size;
   }
 }
 
-} // namespace detail
-} // namespace cuco
+}  // namespace detail
+}  // namespace cuco
diff --git a/include/cuco/static_map.cuh b/include/cuco/static_map.cuh
index 900197e5a..1b41a16cd 100644
--- a/include/cuco/static_map.cuh
+++ b/include/cuco/static_map.cuh
@@ -25,7 +25,8 @@
 
 #include <cuco/allocator.hpp>
 
-#if defined(CUDART_VERSION) && (CUDART_VERSION >= 11000) && defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 700)
+#if defined(CUDART_VERSION) && (CUDART_VERSION >= 11000) && defined(__CUDA_ARCH__) && \
+  (__CUDA_ARCH__ >= 700)
 #define CUCO_HAS_CUDA_BARRIER
 #endif
 
@@ -548,10 +549,10 @@ class static_map {
      * @param empty_value_sentinel The reserved value for mapped values to
      * represent empty slots
      */
-     __host__ __device__ device_mutable_view(pair_atomic_type* slots,
-                                             std::size_t capacity,
-                                             Key empty_key_sentinel,
-                                             Value empty_value_sentinel) noexcept
+    __host__ __device__ device_mutable_view(pair_atomic_type* slots,
+                                            std::size_t capacity,
+                                            Key empty_key_sentinel,
+                                            Value empty_value_sentinel) noexcept
       : device_view_base{slots, capacity, empty_key_sentinel, empty_value_sentinel}
     {
     }
@@ -633,10 +634,10 @@ class static_map {
      * @param empty_value_sentinel The reserved value for mapped values to
      * represent empty slots
      */
-     __host__ __device__ device_view(pair_atomic_type* slots,
-                                     std::size_t capacity,
-                                     Key empty_key_sentinel,
-                                     Value empty_value_sentinel) noexcept
+    __host__ __device__ device_view(pair_atomic_type* slots,
+                                    std::size_t capacity,
+                                    Key empty_key_sentinel,
+                                    Value empty_value_sentinel) noexcept
       : device_view_base{slots, capacity, empty_key_sentinel, empty_value_sentinel}
     {
     }
@@ -644,7 +645,8 @@ class static_map {
     /**
      * @brief Makes a copy of given `device_view` using non-owned memory.
      *
-     * This function is intended to be used to create shared memory copies of small static maps, although global memory can be used as well.
+     * This function is intended to be used to create shared memory copies of small static maps,
+     * although global memory can be used as well.
      *
      * Example:
      * @code{.cpp}
@@ -673,7 +675,8 @@ class static_map {
      * @tparam CG The type of the cooperative thread group
      * @param g The ooperative thread group used to copy the slots
      * @param source_device_view `device_view` to copy from
-     * @param memory_to_use Array large enough to support `capacity` elements. Object does not take the ownership of the memory
+     * @param memory_to_use Array large enough to support `capacity` elements. Object does not take
+     * the ownership of the memory
      * @return Copy of passed `device_view`
      */
     template <typename CG>
@@ -683,9 +686,7 @@ class static_map {
     {
 #if defined(CUDA_HAS_CUDA_BARRIER)
       __shared__ cuda::barrier<cuda::thread_scope::thread_scope_block> barrier;
-      if (g.thread_rank() == 0) {
-        init(&barrier, g.size());
-      }
+      if (g.thread_rank() == 0) { init(&barrier, g.size()); }
       g.sync();
 
       cuda::memcpy_async(g,
@@ -697,10 +698,11 @@ class static_map {
       barrier.arrive_and_wait();
 #else
       pair_atomic_type const* const slots_ptr = source_device_view.get_slots();
-      for (std::size_t i = g.thread_rank(); i < source_device_view.get_capacity(); i += g.size())
-      {
-        new (&memory_to_use[i].first) atomic_key_type{slots_ptr[i].first.load(cuda::memory_order_relaxed)};
-        new (&memory_to_use[i].second) atomic_mapped_type{slots_ptr[i].second.load(cuda::memory_order_relaxed)};
+      for (std::size_t i = g.thread_rank(); i < source_device_view.get_capacity(); i += g.size()) {
+        new (&memory_to_use[i].first)
+          atomic_key_type{slots_ptr[i].first.load(cuda::memory_order_relaxed)};
+        new (&memory_to_use[i].second)
+          atomic_mapped_type{slots_ptr[i].second.load(cuda::memory_order_relaxed)};
       }
       g.sync();
 #endif
diff --git a/tests/dynamic_map/dynamic_map_test.cu b/tests/dynamic_map/dynamic_map_test.cu
index 3e4b94f02..fd38ea642 100644
--- a/tests/dynamic_map/dynamic_map_test.cu
+++ b/tests/dynamic_map/dynamic_map_test.cu
@@ -22,33 +22,30 @@
 #include <cuco/dynamic_map.cuh>
 #include <random>
 
-enum class dist_type {
-  UNIQUE,
-  UNIFORM,
-  GAUSSIAN
-};
-
-template<dist_type Dist, typename Key, typename OutputIt>
-static void generate_keys(OutputIt output_begin, OutputIt output_end) {
+enum class dist_type { UNIQUE, UNIFORM, GAUSSIAN };
+
+template <dist_type Dist, typename Key, typename OutputIt>
+static void generate_keys(OutputIt output_begin, OutputIt output_end)
+{
   auto num_keys = std::distance(output_begin, output_end);
 
   std::random_device rd;
   std::mt19937 gen{rd()};
 
-  switch(Dist) {
+  switch (Dist) {
     case dist_type::UNIQUE:
-      for(auto i = 0; i < num_keys; ++i) {
+      for (auto i = 0; i < num_keys; ++i) {
         output_begin[i] = i;
       }
       break;
     case dist_type::UNIFORM:
-      for(auto i = 0; i < num_keys; ++i) {
+      for (auto i = 0; i < num_keys; ++i) {
         output_begin[i] = std::abs(static_cast<Key>(gen()));
       }
       break;
     case dist_type::GAUSSIAN:
       std::normal_distribution<> dg{1e9, 1e7};
-      for(auto i = 0; i < num_keys; ++i) {
+      for (auto i = 0; i < num_keys; ++i) {
         output_begin[i] = std::abs(static_cast<Key>(dg(gen)));
       }
       break;
@@ -78,12 +75,15 @@ bool none_of(Iterator begin, Iterator end, Predicate p)
 }
 }  // namespace
 
-
-TEMPLATE_TEST_CASE_SIG("Unique sequence of keys", "", 
+TEMPLATE_TEST_CASE_SIG("Unique sequence of keys",
+                       "",
                        ((typename T, dist_type Dist), T, Dist),
-   (int32_t, dist_type::UNIQUE), (int64_t, dist_type::UNIQUE),
-   (int32_t, dist_type::UNIFORM), (int64_t, dist_type::UNIFORM),
-   (int32_t, dist_type::GAUSSIAN), (int64_t, dist_type::GAUSSIAN))
+                       (int32_t, dist_type::UNIQUE),
+                       (int64_t, dist_type::UNIQUE),
+                       (int32_t, dist_type::UNIFORM),
+                       (int64_t, dist_type::UNIFORM),
+                       (int32_t, dist_type::GAUSSIAN),
+                       (int64_t, dist_type::GAUSSIAN))
 {
   using Key   = T;
   using Value = T;
@@ -91,25 +91,25 @@ TEMPLATE_TEST_CASE_SIG("Unique sequence of keys", "",
   constexpr std::size_t num_keys{50'000'000};
   cuco::dynamic_map<Key, Value> map{30'000'000, -1, -1};
 
-  std::vector<Key> h_keys( num_keys );
-  std::vector<Value> h_values( num_keys );
-  std::vector<cuco::pair_type<Key, Value>> h_pairs ( num_keys );
+  std::vector<Key> h_keys(num_keys);
+  std::vector<Value> h_values(num_keys);
+  std::vector<cuco::pair_type<Key, Value>> h_pairs(num_keys);
 
   generate_keys<Dist, Key>(h_keys.begin(), h_keys.end());
 
-  for(auto i = 0; i < num_keys; ++i) {
-    Key key = h_keys[i];
-    Value val = h_keys[i];
-    h_values[i] = val;
-    h_pairs[i].first = key;
+  for (auto i = 0; i < num_keys; ++i) {
+    Key key           = h_keys[i];
+    Value val         = h_keys[i];
+    h_values[i]       = val;
+    h_pairs[i].first  = key;
     h_pairs[i].second = val;
   }
 
-  thrust::device_vector<Key> d_keys( h_keys );
-  thrust::device_vector<Value> d_values( h_values );
-  thrust::device_vector<cuco::pair_type<Key, Value>> d_pairs( h_pairs );
-  thrust::device_vector<Value> d_results( num_keys );
-  thrust::device_vector<bool> d_contained( num_keys );
+  thrust::device_vector<Key> d_keys(h_keys);
+  thrust::device_vector<Value> d_values(h_values);
+  thrust::device_vector<cuco::pair_type<Key, Value>> d_pairs(h_pairs);
+  thrust::device_vector<Value> d_results(num_keys);
+  thrust::device_vector<bool> d_contained(num_keys);
 
   // bulk function test cases
   SECTION("All inserted keys-value pairs should be correctly recovered during find")
@@ -118,8 +118,7 @@ TEMPLATE_TEST_CASE_SIG("Unique sequence of keys", "",
     map.find(d_keys.begin(), d_keys.end(), d_results.begin());
     auto zip = thrust::make_zip_iterator(thrust::make_tuple(d_results.begin(), d_values.begin()));
 
-    REQUIRE(all_of(zip, zip + num_keys, 
-      [] __device__(auto const& p) {
+    REQUIRE(all_of(zip, zip + num_keys, [] __device__(auto const& p) {
       return thrust::get<0>(p) == thrust::get<1>(p);
     }));
   }
@@ -128,7 +127,8 @@ TEMPLATE_TEST_CASE_SIG("Unique sequence of keys", "",
   {
     map.find(d_keys.begin(), d_keys.end(), d_results.begin());
 
-    REQUIRE(all_of(d_results.begin(), d_results.end(), [] __device__(auto const& p) { return p == -1; }));
+    REQUIRE(
+      all_of(d_results.begin(), d_results.end(), [] __device__(auto const& p) { return p == -1; }));
   }
 
   SECTION("All inserted keys-value pairs should be contained")
@@ -136,13 +136,15 @@ TEMPLATE_TEST_CASE_SIG("Unique sequence of keys", "",
     map.insert(d_pairs.begin(), d_pairs.end());
     map.contains(d_keys.begin(), d_keys.end(), d_contained.begin());
 
-    REQUIRE(all_of(d_contained.begin(), d_contained.end(), [] __device__(bool const& b) { return b; }));
+    REQUIRE(
+      all_of(d_contained.begin(), d_contained.end(), [] __device__(bool const& b) { return b; }));
   }
 
   SECTION("Non-inserted keys-value pairs should not be contained")
   {
     map.contains(d_keys.begin(), d_keys.end(), d_contained.begin());
 
-    REQUIRE(none_of(d_contained.begin(), d_contained.end(), [] __device__(bool const& b) { return b; }));
+    REQUIRE(
+      none_of(d_contained.begin(), d_contained.end(), [] __device__(bool const& b) { return b; }));
   }
 }
\ No newline at end of file
diff --git a/tests/static_map/static_map_test.cu b/tests/static_map/static_map_test.cu
index f27ee77c9..f382e5409 100644
--- a/tests/static_map/static_map_test.cu
+++ b/tests/static_map/static_map_test.cu
@@ -217,7 +217,6 @@ TEST_CASE("User defined key and value type", "")
   }
 }
 
-
 TEMPLATE_TEST_CASE_SIG("Unique sequence of keys",
                        "",
                        ((typename T, dist_type Dist), T, Dist),
@@ -283,8 +282,7 @@ TEMPLATE_TEST_CASE_SIG("Unique sequence of keys",
     map.contains(d_keys.begin(), d_keys.end(), d_contained.begin());
 
     REQUIRE(
-      none_of(d_contained.begin(), d_contained.end(), [] __device__(bool const& b) { return b;
-}));
+      none_of(d_contained.begin(), d_contained.end(), [] __device__(bool const& b) { return b; }));
   }
 
   SECTION("Inserting unique keys should return insert success.")
@@ -311,8 +309,8 @@ TEMPLATE_TEST_CASE_SIG("Unique sequence of keys",
     SECTION("const view")
     {
       REQUIRE(all_of(
-        d_pairs.begin(), d_pairs.end(), [view] __device__(cuco::pair_type<Key, Value> const& pair)
-{ return view.find(pair.first) == view.end();
+        d_pairs.begin(), d_pairs.end(), [view] __device__(cuco::pair_type<Key, Value> const& pair) {
+          return view.find(pair.first) == view.end();
         }));
     }
   }
@@ -342,9 +340,10 @@ TEMPLATE_TEST_CASE_SIG("Unique sequence of keys",
     {
       // All keys should be found
       REQUIRE(all_of(
-        d_pairs.begin(), d_pairs.end(), [view] __device__(cuco::pair_type<Key, Value> const& pair)
-{ auto const found = view.find(pair.first); return (found != view.end()) and (found->first.load()
-== pair.first and found->second.load() == pair.second);
+        d_pairs.begin(), d_pairs.end(), [view] __device__(cuco::pair_type<Key, Value> const& pair) {
+          auto const found = view.find(pair.first);
+          return (found != view.end()) and
+                 (found->first.load() == pair.first and found->second.load() == pair.second);
         }));
     }
   }
@@ -489,8 +488,8 @@ TEMPLATE_TEST_CASE_SIG("Shared memory static map",
                                d_keys_exist.data().get(),
                                d_keys_and_values_correct.data().get());
 
-    REQUIRE(none_of(d_keys_exist.begin(), d_keys_exist.end(), [] __device__(const bool key_found)
-{ return key_found;
+    REQUIRE(none_of(d_keys_exist.begin(), d_keys_exist.end(), [] __device__(const bool key_found) {
+      return key_found;
     }));
   }
 }

From a7398dd2577d56f13e663a55a0b7dc338086e8d1 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Wed, 24 Feb 2021 17:33:13 -0800
Subject: [PATCH 002/267] Fix several typos in comments + code formatting

---
 include/cuco/detail/dynamic_map.inl | 4 ++--
 include/cuco/dynamic_map.cuh        | 4 ++--
 include/cuco/static_map.cuh         | 2 +-
 3 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/include/cuco/detail/dynamic_map.inl b/include/cuco/detail/dynamic_map.inl
index 57950ea45..e511489a4 100644
--- a/include/cuco/detail/dynamic_map.inl
+++ b/include/cuco/detail/dynamic_map.inl
@@ -35,7 +35,7 @@ dynamic_map<Key, Value, Scope, Allocator>::dynamic_map(std::size_t initial_capac
   submap_mutable_views_.push_back(submaps_[0]->get_device_mutable_view());
 
   CUCO_CUDA_TRY(cudaMallocManaged(&num_successes_, sizeof(atomic_ctr_type)));
-}  // namespace cuco
+}
 
 template <typename Key, typename Value, cuda::thread_scope Scope, typename Allocator>
 dynamic_map<Key, Value, Scope, Allocator>::~dynamic_map()
@@ -153,4 +153,4 @@ void dynamic_map<Key, Value, Scope, Allocator>::contains(
   CUCO_CUDA_TRY(cudaDeviceSynchronize());
 }
 
-}  // namespace cuco
\ No newline at end of file
+}  // namespace cuco
diff --git a/include/cuco/dynamic_map.cuh b/include/cuco/dynamic_map.cuh
index c234a3579..957d8dc33 100644
--- a/include/cuco/dynamic_map.cuh
+++ b/include/cuco/dynamic_map.cuh
@@ -51,7 +51,7 @@ namespace cuco {
  * Example:
  * \code{.cpp}
  * int empty_key_sentinel = -1;
- * int empty_value_sentine = -1;
+ * int empty_value_sentinel = -1;
  *
  * // Constructs a map with 100,000 initial slots using -1 and -1 as the empty key/value
  * // sentinels. Performs one bulk insert of 50,000 keys and a second bulk insert of
@@ -257,4 +257,4 @@ class dynamic_map {
 };
 }  // namespace cuco
 
-#include <cuco/detail/dynamic_map.inl>
\ No newline at end of file
+#include <cuco/detail/dynamic_map.inl>
diff --git a/include/cuco/static_map.cuh b/include/cuco/static_map.cuh
index 1b41a16cd..cf7de0a7e 100644
--- a/include/cuco/static_map.cuh
+++ b/include/cuco/static_map.cuh
@@ -83,7 +83,7 @@ class dynamic_map;
  * Example:
  * \code{.cpp}
  * int empty_key_sentinel = -1;
- * int empty_value_sentine = -1;
+ * int empty_value_sentinel = -1;
  *
  * // Constructs a map with 100,000 slots using -1 and -1 as the empty key/value
  * // sentinels. Note the capacity is chosen knowing we will insert 50,000 keys,

From a1e2dfd92fcd63ff1a6422b7b92ca0bcc8b62577 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Wed, 24 Feb 2021 19:08:38 -0800
Subject: [PATCH 003/267] Add static_multi_map class

---
 include/cuco/static_multi_map.cuh | 42 +++++++++++++++++++++++++++++++
 1 file changed, 42 insertions(+)
 create mode 100644 include/cuco/static_multi_map.cuh

diff --git a/include/cuco/static_multi_map.cuh b/include/cuco/static_multi_map.cuh
new file mode 100644
index 000000000..95525b40c
--- /dev/null
+++ b/include/cuco/static_multi_map.cuh
@@ -0,0 +1,42 @@
+/*
+ * Copyright (c) 2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cooperative_groups.h>
+#include <cub/cub.cuh>
+
+#include <cuco/detail/error.hpp>
+
+namespace cuco {
+
+template <typename Key,
+          typename Value,
+          cuda::thread_scope Scope = cuda::thread_scope_device,
+          typename Allocator       = cuco::cuda_allocator<char>>
+class static_multi_map : public static_map<Key, Value, Scope, Allocator> {
+ public:
+  static_multi_map(std::size_t capacity,
+                   Key empty_key_sentinel,
+                   Value empty_value_sentinel,
+                   Allocator const& alloc = Allocator{});
+
+  ~static_multi_map();
+
+};  // class static_multi_map
+}  // namespace cuco
+
+#include <cuco/detail/static_map.inl>

From 67a4dcdacced469bfa22ca9a367555bd88490794 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Thu, 25 Feb 2021 11:59:52 -0800
Subject: [PATCH 004/267] Add STATIC_MULTI_MAP_EXAMPLE

---
 examples/CMakeLists.txt                       |  1 +
 .../static_map/static_multi_map_example.cu    | 56 +++++++++++++++++++
 2 files changed, 57 insertions(+)
 create mode 100644 examples/static_map/static_multi_map_example.cu

diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt
index a70b53da8..faf60ef0b 100644
--- a/examples/CMakeLists.txt
+++ b/examples/CMakeLists.txt
@@ -28,3 +28,4 @@ endfunction(ConfigureExample)
 ###################################################################################################
 
 ConfigureExample(STATIC_MAP_EXAMPLE "${CMAKE_CURRENT_SOURCE_DIR}/static_map/static_map_example.cu")
+ConfigureExample(STATIC_MULTI_MAP_EXAMPLE "${CMAKE_CURRENT_SOURCE_DIR}/static_map/static_multi_map_example.cu")
diff --git a/examples/static_map/static_multi_map_example.cu b/examples/static_map/static_multi_map_example.cu
new file mode 100644
index 000000000..ce176e3ff
--- /dev/null
+++ b/examples/static_map/static_multi_map_example.cu
@@ -0,0 +1,56 @@
+/*
+ * Copyright (c) 2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <limits>
+
+#include <thrust/device_vector.h>
+#include <thrust/sequence.h>
+#include <thrust/transform.h>
+
+#include <cuco/static_multi_map.cuh>
+
+int main(void)
+{
+  int empty_key_sentinel   = -1;
+  int empty_value_sentinel = -1;
+
+  // Constructs a map with 100,000 slots using -1 and -1 as the empty key/value
+  // sentinels. Note the capacity is chosen knowing we will insert 50,000 keys,
+  // for an load factor of 50%.
+  cuco::static_multi_map<int, int> map{100'000, empty_key_sentinel, empty_value_sentinel};
+
+  thrust::device_vector<thrust::pair<int, int>> pairs(50'000);
+
+  // Create a sequence of pairs {{0,0}, {1,1}, ... {i,i}}
+  thrust::transform(thrust::make_counting_iterator<int>(0),
+                    thrust::make_counting_iterator<int>(pairs.size()),
+                    pairs.begin(),
+                    [] __device__(auto i) { return thrust::make_pair(i, i); });
+
+  // Inserts all pairs into the map
+  // map.insert(pairs.begin(), pairs.end());
+
+  // Sequence of keys {0, 1, 2, ...}
+  // thrust::device_vector<int> keys_to_find(50'000);
+  // thrust::sequence(keys_to_find.begin(), keys_to_find.end(), 0);
+  // thrust::device_vector<int> found_values(50'000);
+
+  // Finds all keys {0, 1, 2, ...} and stores associated values into `found_values`
+  // If a key `keys_to_find[i]` doesn't exist, `found_values[i] == empty_value_sentinel`
+  // map.find(keys_to_find.begin(), keys_to_find.end(), found_values.begin());
+
+  return 0;
+}

From 74193905798bc476b85a6c2e606ea582f56b380d Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Thu, 25 Feb 2021 15:27:38 -0800
Subject: [PATCH 005/267] Restructure the code to accommodate static_multimap

---
 examples/CMakeLists.txt                       |   2 +-
 .../static_multimap_example.cu}               |  11 +-
 include/cuco/detail/pair.cuh                  |   2 +-
 include/cuco/detail/static_kernels.cuh        |  52 +++
 include/cuco/detail/static_map.inl            |   6 +-
 include/cuco/detail/static_map_kernels.cuh    |  12 +-
 include/cuco/detail/static_multimap.inl       | 376 ++++++++++++++++++
 .../cuco/detail/static_multimap_kernels.cuh   | 118 ++++++
 include/cuco/static_map.cuh                   |   4 +-
 include/cuco/static_multi_map.cuh             |  42 --
 include/cuco/static_multimap.cuh              |  82 ++++
 11 files changed, 648 insertions(+), 59 deletions(-)
 rename examples/{static_map/static_multi_map_example.cu => static_multimap/static_multimap_example.cu} (84%)
 create mode 100644 include/cuco/detail/static_kernels.cuh
 create mode 100644 include/cuco/detail/static_multimap.inl
 create mode 100644 include/cuco/detail/static_multimap_kernels.cuh
 delete mode 100644 include/cuco/static_multi_map.cuh
 create mode 100644 include/cuco/static_multimap.cuh

diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt
index faf60ef0b..2e67e0aed 100644
--- a/examples/CMakeLists.txt
+++ b/examples/CMakeLists.txt
@@ -28,4 +28,4 @@ endfunction(ConfigureExample)
 ###################################################################################################
 
 ConfigureExample(STATIC_MAP_EXAMPLE "${CMAKE_CURRENT_SOURCE_DIR}/static_map/static_map_example.cu")
-ConfigureExample(STATIC_MULTI_MAP_EXAMPLE "${CMAKE_CURRENT_SOURCE_DIR}/static_map/static_multi_map_example.cu")
+ConfigureExample(STATIC_MULTIMAP_EXAMPLE "${CMAKE_CURRENT_SOURCE_DIR}/static_multimap/static_multimap_example.cu")
diff --git a/examples/static_map/static_multi_map_example.cu b/examples/static_multimap/static_multimap_example.cu
similarity index 84%
rename from examples/static_map/static_multi_map_example.cu
rename to examples/static_multimap/static_multimap_example.cu
index ce176e3ff..2a7a86500 100644
--- a/examples/static_map/static_multi_map_example.cu
+++ b/examples/static_multimap/static_multimap_example.cu
@@ -20,7 +20,7 @@
 #include <thrust/sequence.h>
 #include <thrust/transform.h>
 
-#include <cuco/static_multi_map.cuh>
+#include <cuco/static_multimap.cuh>
 
 int main(void)
 {
@@ -30,18 +30,19 @@ int main(void)
   // Constructs a map with 100,000 slots using -1 and -1 as the empty key/value
   // sentinels. Note the capacity is chosen knowing we will insert 50,000 keys,
   // for an load factor of 50%.
-  cuco::static_multi_map<int, int> map{100'000, empty_key_sentinel, empty_value_sentinel};
+  cuco::static_multimap<int, int> map{100'000, empty_key_sentinel, empty_value_sentinel};
 
   thrust::device_vector<thrust::pair<int, int>> pairs(50'000);
 
-  // Create a sequence of pairs {{0,0}, {1,1}, ... {i,i}}
+  // Create a sequence of pairs. Eeach key corresponds to two pairs.
+  // E.g., {{0,0}, {1,1}, ... {0,25'000}, {1, 25'001}, ...}
   thrust::transform(thrust::make_counting_iterator<int>(0),
                     thrust::make_counting_iterator<int>(pairs.size()),
                     pairs.begin(),
-                    [] __device__(auto i) { return thrust::make_pair(i, i); });
+                    [] __device__(auto i) { return thrust::make_pair(i % (50'000 / 2), i); });
 
   // Inserts all pairs into the map
-  // map.insert(pairs.begin(), pairs.end());
+  map.insert(pairs.begin(), pairs.end());
 
   // Sequence of keys {0, 1, 2, ...}
   // thrust::device_vector<int> keys_to_find(50'000);
diff --git a/include/cuco/detail/pair.cuh b/include/cuco/detail/pair.cuh
index dfdf7632e..eec3f3b50 100644
--- a/include/cuco/detail/pair.cuh
+++ b/include/cuco/detail/pair.cuh
@@ -135,4 +135,4 @@ __host__ __device__ pair_type<F, S> make_pair(F&& f, S&& s) noexcept
 {
   return pair_type<F, S>{std::forward<F>(f), std::forward<S>(s)};
 }
-}  // namespace cuco
\ No newline at end of file
+}  // namespace cuco
diff --git a/include/cuco/detail/static_kernels.cuh b/include/cuco/detail/static_kernels.cuh
new file mode 100644
index 000000000..671c0ec8e
--- /dev/null
+++ b/include/cuco/detail/static_kernels.cuh
@@ -0,0 +1,52 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+namespace cuco {
+namespace detail {
+
+/**
+ * @brief Initializes each slot in the flat `slots` storage to contain `k` and `v`.
+ *
+ * Each space in `slots` that can hold a key value pair is initialized to a
+ * `pair_atomic_type` containing the key `k` and the value `v`.
+ *
+ * @tparam atomic_key_type Type of the `Key` atomic container
+ * @tparam atomic_mapped_type Type of the `Value` atomic container
+ * @tparam Key key type
+ * @tparam Value value type
+ * @tparam pair_atomic_type key/value pair type
+ * @param slots Pointer to flat storage for the map's key/value pairs
+ * @param k Key to which all keys in `slots` are initialized
+ * @param v Value to which all values in `slots` are initialized
+ * @param size Size of the storage pointed to by `slots`
+ */
+template <typename atomic_key_type,
+          typename atomic_mapped_type,
+          typename Key,
+          typename Value,
+          typename pair_atomic_type>
+__global__ void initialize(pair_atomic_type* const slots, Key k, Value v, std::size_t size)
+{
+  auto tid = threadIdx.x + blockIdx.x * blockDim.x;
+  while (tid < size) {
+    new (&slots[tid].first) atomic_key_type{k};
+    new (&slots[tid].second) atomic_mapped_type{v};
+    tid += gridDim.x * blockDim.x;
+  }
+}
+
+}  // namespace detail
+}  // namespace cuco
diff --git a/include/cuco/detail/static_map.inl b/include/cuco/detail/static_map.inl
index 2b72cfa32..2c50b58d1 100644
--- a/include/cuco/detail/static_map.inl
+++ b/include/cuco/detail/static_map.inl
@@ -77,7 +77,7 @@ void static_map<Key, Value, Scope, Allocator>::insert(InputIt first,
   CUCO_CUDA_TRY(cudaGetDevice(&device_id));
   CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_successes_, sizeof(atomic_ctr_type), device_id));
 
-  detail::insert<block_size, tile_size>
+  detail::map::insert<block_size, tile_size>
     <<<grid_size, block_size>>>(first, first + num_keys, num_successes_, view, hash, key_equal);
   CUCO_CUDA_TRY(cudaDeviceSynchronize());
 
@@ -98,7 +98,7 @@ void static_map<Key, Value, Scope, Allocator>::find(
   auto const grid_size  = (tile_size * num_keys + stride * block_size - 1) / (stride * block_size);
   auto view             = get_device_view();
 
-  detail::find<block_size, tile_size, Value>
+  detail::map::find<block_size, tile_size, Value>
     <<<grid_size, block_size>>>(first, last, output_begin, view, hash, key_equal);
   CUCO_CUDA_TRY(cudaDeviceSynchronize());
 }
@@ -117,7 +117,7 @@ void static_map<Key, Value, Scope, Allocator>::contains(
   auto const grid_size  = (tile_size * num_keys + stride * block_size - 1) / (stride * block_size);
   auto view             = get_device_view();
 
-  detail::contains<block_size, tile_size>
+  detail::map::contains<block_size, tile_size>
     <<<grid_size, block_size>>>(first, last, output_begin, view, hash, key_equal);
   CUCO_CUDA_TRY(cudaDeviceSynchronize());
 }
diff --git a/include/cuco/detail/static_map_kernels.cuh b/include/cuco/detail/static_map_kernels.cuh
index e166de3c6..aaf95b40c 100644
--- a/include/cuco/detail/static_map_kernels.cuh
+++ b/include/cuco/detail/static_map_kernels.cuh
@@ -70,7 +70,7 @@ __global__ void initialize(pair_atomic_type* const slots, Key k, Value v, std::s
  * @param hash The unary function to apply to hash each key
  * @param key_equal The binary function used to compare two keys for equality
  */
-template <std::size_t block_size,
+template <uint32_t block_size,
           typename InputIt,
           typename atomicT,
           typename viewT,
@@ -123,7 +123,7 @@ __global__ void insert(
  * @param hash The unary function to apply to hash each key
  * @param key_equal The binary function used to compare two keys for equality
  */
-template <std::size_t block_size,
+template <uint32_t block_size,
           uint32_t tile_size,
           typename InputIt,
           typename atomicT,
@@ -177,7 +177,7 @@ __global__ void insert(
  * @param hash The unary function to apply to hash each key
  * @param key_equal The binary function to compare two keys for equality
  */
-template <std::size_t block_size,
+template <uint32_t block_size,
           typename Value,
           typename InputIt,
           typename OutputIt,
@@ -237,7 +237,7 @@ __global__ void find(
  * @param hash The unary function to apply to hash each key
  * @param key_equal The binary function to compare two keys for equality
  */
-template <std::size_t block_size,
+template <uint32_t block_size,
           uint32_t tile_size,
           typename Value,
           typename InputIt,
@@ -297,7 +297,7 @@ __global__ void find(
  * @param hash The unary function to apply to hash each key
  * @param key_equal The binary function to compare two keys for equality
  */
-template <std::size_t block_size,
+template <uint32_t block_size,
           typename InputIt,
           typename OutputIt,
           typename viewT,
@@ -352,7 +352,7 @@ __global__ void contains(
  * @param hash The unary function to apply to hash each key
  * @param key_equal The binary function to compare two keys for equality
  */
-template <std::size_t block_size,
+template <uint32_t block_size,
           uint32_t tile_size,
           typename InputIt,
           typename OutputIt,
diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
new file mode 100644
index 000000000..9109aa9bf
--- /dev/null
+++ b/include/cuco/detail/static_multimap.inl
@@ -0,0 +1,376 @@
+/*
+ * Copyright (c) 2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+namespace cuco {
+
+template <typename Key, typename Value, cuda::thread_scope Scope, typename Allocator>
+static_multimap<Key, Value, Scope, Allocator>::static_multimap(std::size_t capacity,
+                                                               Key empty_key_sentinel,
+                                                               Value empty_value_sentinel,
+                                                               Allocator const& alloc)
+  : static_map<Key, Value, Scope, Allocator>{
+      capacity, empty_key_sentinel, empty_value_sentinel, alloc}
+{
+}
+
+template <typename Key, typename Value, cuda::thread_scope Scope, typename Allocator>
+static_multimap<Key, Value, Scope, Allocator>::~static_multimap()
+{
+}
+
+template <typename Key, typename Value, cuda::thread_scope Scope, typename Allocator>
+template <typename InputIt, typename Hash, typename KeyEqual>
+void static_multimap<Key, Value, Scope, Allocator>::insert(InputIt first,
+                                                           InputIt last,
+                                                           Hash hash,
+                                                           KeyEqual key_equal)
+{
+  auto num_keys         = std::distance(first, last);
+  auto const block_size = 128;
+  auto const stride     = 1;
+  auto const tile_size  = 4;
+  auto const grid_size  = (tile_size * num_keys + stride * block_size - 1) / (stride * block_size);
+  auto view             = get_device_mutable_view();
+
+  *num_successes_ = 0;
+  int device_id;
+  CUCO_CUDA_TRY(cudaGetDevice(&device_id));
+  CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_successes_, sizeof(atomic_ctr_type), device_id));
+
+  detail::multimap::insert<block_size, tile_size>
+    <<<grid_size, block_size>>>(first, first + num_keys, num_successes_, view, hash, key_equal);
+  CUCO_CUDA_TRY(cudaDeviceSynchronize());
+
+  size_ += num_successes_->load(cuda::std::memory_order_relaxed);
+}
+
+/*
+template <typename Key, typename Value, cuda::thread_scope Scope, typename Allocator>
+template <typename InputIt, typename OutputIt, typename Hash, typename KeyEqual>
+void static_map<Key, Value, Scope, Allocator>::find(
+  InputIt first, InputIt last, OutputIt output_begin, Hash hash, KeyEqual key_equal) noexcept
+{
+  auto num_keys         = std::distance(first, last);
+  auto const block_size = 128;
+  auto const stride     = 1;
+  auto const tile_size  = 4;
+  auto const grid_size  = (tile_size * num_keys + stride * block_size - 1) / (stride * block_size);
+  auto view             = get_device_view();
+
+  detail::find<block_size, tile_size, Value>
+    <<<grid_size, block_size>>>(first, last, output_begin, view, hash, key_equal);
+  CUCO_CUDA_TRY(cudaDeviceSynchronize());
+}
+
+template <typename Key, typename Value, cuda::thread_scope Scope, typename Allocator>
+template <typename InputIt, typename OutputIt, typename Hash, typename KeyEqual>
+void static_map<Key, Value, Scope, Allocator>::contains(
+  InputIt first, InputIt last, OutputIt output_begin, Hash hash, KeyEqual key_equal) noexcept
+{
+  auto num_keys         = std::distance(first, last);
+  auto const block_size = 128;
+  auto const stride     = 1;
+  auto const tile_size  = 4;
+  auto const grid_size  = (tile_size * num_keys + stride * block_size - 1) / (stride * block_size);
+  auto view             = get_device_view();
+
+  detail::contains<block_size, tile_size>
+    <<<grid_size, block_size>>>(first, last, output_begin, view, hash, key_equal);
+  CUCO_CUDA_TRY(cudaDeviceSynchronize());
+}
+
+template <typename Key, typename Value, cuda::thread_scope Scope, typename Allocator>
+template <typename Hash, typename KeyEqual>
+__device__ bool static_map<Key, Value, Scope, Allocator>::device_mutable_view::insert(
+  value_type const& insert_pair, Hash hash, KeyEqual key_equal) noexcept
+{
+  auto current_slot{initial_slot(insert_pair.first, hash)};
+
+  while (true) {
+    using cuda::std::memory_order_relaxed;
+    auto expected_key   = this->get_empty_key_sentinel();
+    auto expected_value = this->get_empty_value_sentinel();
+    auto& slot_key      = current_slot->first;
+    auto& slot_value    = current_slot->second;
+
+    bool key_success =
+      slot_key.compare_exchange_strong(expected_key, insert_pair.first, memory_order_relaxed);
+    bool value_success =
+      slot_value.compare_exchange_strong(expected_value, insert_pair.second, memory_order_relaxed);
+
+    if (key_success) {
+      while (not value_success) {
+        value_success =
+          slot_value.compare_exchange_strong(expected_value = this->get_empty_value_sentinel(),
+                                             insert_pair.second,
+                                             memory_order_relaxed);
+      }
+      return true;
+    } else if (value_success) {
+      slot_value.store(this->get_empty_value_sentinel(), memory_order_relaxed);
+    }
+
+    // if the key was already inserted by another thread, than this instance is a
+    // duplicate, so the insert fails
+    if (key_equal(insert_pair.first, expected_key)) { return false; }
+
+    // if we couldn't insert the key, but it wasn't a duplicate, then there must
+    // have been some other key there, so we keep looking for a slot
+    current_slot = next_slot(current_slot);
+  }
+}
+
+template <typename Key, typename Value, cuda::thread_scope Scope, typename Allocator>
+template <typename CG, typename Hash, typename KeyEqual>
+__device__ bool static_map<Key, Value, Scope, Allocator>::device_mutable_view::insert(
+  CG g, value_type const& insert_pair, Hash hash, KeyEqual key_equal) noexcept
+{
+  auto current_slot = initial_slot(g, insert_pair.first, hash);
+
+  while (true) {
+    key_type const existing_key = current_slot->first.load(cuda::memory_order_relaxed);
+
+    // The user provide `key_equal` can never be used to compare against `empty_key_sentinel` as the
+    // sentinel is not a valid key value. Therefore, first check for the sentinel
+    auto const slot_is_empty = (existing_key == this->get_empty_key_sentinel());
+
+    // the key we are trying to insert is already in the map, so we return with failure to insert
+    if (g.ballot(not slot_is_empty and key_equal(existing_key, insert_pair.first))) {
+      return false;
+    }
+
+    auto const window_contains_empty = g.ballot(slot_is_empty);
+
+    // we found an empty slot, but not the key we are inserting, so this must
+    // be an empty slot into which we can insert the key
+    if (window_contains_empty) {
+      // the first lane in the group with an empty slot will attempt the insert
+      insert_result status{insert_result::CONTINUE};
+      uint32_t src_lane = __ffs(window_contains_empty) - 1;
+
+      if (g.thread_rank() == src_lane) {
+        using cuda::std::memory_order_relaxed;
+        auto expected_key   = this->get_empty_key_sentinel();
+        auto expected_value = this->get_empty_value_sentinel();
+        auto& slot_key      = current_slot->first;
+        auto& slot_value    = current_slot->second;
+
+        bool key_success =
+          slot_key.compare_exchange_strong(expected_key, insert_pair.first, memory_order_relaxed);
+        bool value_success = slot_value.compare_exchange_strong(
+          expected_value, insert_pair.second, memory_order_relaxed);
+
+        if (key_success) {
+          while (not value_success) {
+            value_success =
+              slot_value.compare_exchange_strong(expected_value = this->get_empty_value_sentinel(),
+                                                 insert_pair.second,
+                                                 memory_order_relaxed);
+          }
+          status = insert_result::SUCCESS;
+        } else if (value_success) {
+          slot_value.store(this->get_empty_value_sentinel(), memory_order_relaxed);
+        }
+
+        // our key was already present in the slot, so our key is a duplicate
+        if (key_equal(insert_pair.first, expected_key)) { status = insert_result::DUPLICATE; }
+        // another key was inserted in the slot we wanted to try
+        // so we need to try the next empty slot in the window
+      }
+
+      uint32_t res_status = g.shfl(static_cast<uint32_t>(status), src_lane);
+      status              = static_cast<insert_result>(res_status);
+
+      // successful insert
+      if (status == insert_result::SUCCESS) { return true; }
+      // duplicate present during insert
+      if (status == insert_result::DUPLICATE) { return false; }
+      // if we've gotten this far, a different key took our spot
+      // before we could insert. We need to retry the insert on the
+      // same window
+    }
+    // if there are no empty slots in the current window,
+    // we move onto the next window
+    else {
+      current_slot = next_slot(g, current_slot);
+    }
+  }
+}
+
+template <typename Key, typename Value, cuda::thread_scope Scope, typename Allocator>
+template <typename Hash, typename KeyEqual>
+__device__ typename static_map<Key, Value, Scope, Allocator>::device_view::iterator
+static_map<Key, Value, Scope, Allocator>::device_view::find(Key const& k,
+                                                            Hash hash,
+                                                            KeyEqual key_equal) noexcept
+{
+  auto current_slot = initial_slot(k, hash);
+
+  while (true) {
+    auto const existing_key = current_slot->first.load(cuda::std::memory_order_relaxed);
+    // Key doesn't exist, return end()
+    if (existing_key == this->get_empty_key_sentinel()) { return this->end(); }
+
+    // Key exists, return iterator to location
+    if (key_equal(existing_key, k)) { return current_slot; }
+
+    current_slot = next_slot(current_slot);
+  }
+}
+
+template <typename Key, typename Value, cuda::thread_scope Scope, typename Allocator>
+template <typename Hash, typename KeyEqual>
+__device__ typename static_map<Key, Value, Scope, Allocator>::device_view::const_iterator
+static_map<Key, Value, Scope, Allocator>::device_view::find(Key const& k,
+                                                            Hash hash,
+                                                            KeyEqual key_equal) const noexcept
+{
+  auto current_slot = initial_slot(k, hash);
+
+  while (true) {
+    auto const existing_key = current_slot->first.load(cuda::std::memory_order_relaxed);
+    // Key doesn't exist, return end()
+    if (existing_key == this->get_empty_key_sentinel()) { return this->end(); }
+
+    // Key exists, return iterator to location
+    if (key_equal(existing_key, k)) { return current_slot; }
+
+    current_slot = next_slot(current_slot);
+  }
+}
+
+template <typename Key, typename Value, cuda::thread_scope Scope, typename Allocator>
+template <typename CG, typename Hash, typename KeyEqual>
+__device__ typename static_map<Key, Value, Scope, Allocator>::device_view::iterator
+static_map<Key, Value, Scope, Allocator>::device_view::find(CG g,
+                                                            Key const& k,
+                                                            Hash hash,
+                                                            KeyEqual key_equal) noexcept
+{
+  auto current_slot = initial_slot(g, k, hash);
+
+  while (true) {
+    auto const existing_key = current_slot->first.load(cuda::std::memory_order_relaxed);
+
+    // The user provide `key_equal` can never be used to compare against `empty_key_sentinel` as the
+    // sentinel is not a valid key value. Therefore, first check for the sentinel
+    auto const slot_is_empty = (existing_key == this->get_empty_key_sentinel());
+
+    // the key we were searching for was found by one of the threads,
+    // so we return an iterator to the entry
+    auto const exists = g.ballot(not slot_is_empty and key_equal(existing_key, k));
+    if (exists) {
+      uint32_t src_lane = __ffs(exists) - 1;
+      // TODO: This shouldn't cast an iterator to an int to shuffle. Instead, get the index of the
+      // current_slot and shuffle that instead.
+      intptr_t res_slot = g.shfl(reinterpret_cast<intptr_t>(current_slot), src_lane);
+      return reinterpret_cast<iterator>(res_slot);
+    }
+
+    // we found an empty slot, meaning that the key we're searching for isn't present
+    if (g.ballot(slot_is_empty)) { return this->end(); }
+
+    // otherwise, all slots in the current window are full with other keys, so we move onto the
+    // next window
+    current_slot = next_slot(g, current_slot);
+  }
+}
+
+template <typename Key, typename Value, cuda::thread_scope Scope, typename Allocator>
+template <typename CG, typename Hash, typename KeyEqual>
+__device__ typename static_map<Key, Value, Scope, Allocator>::device_view::const_iterator
+static_map<Key, Value, Scope, Allocator>::device_view::find(CG g,
+                                                            Key const& k,
+                                                            Hash hash,
+                                                            KeyEqual key_equal) const noexcept
+{
+  auto current_slot = initial_slot(g, k, hash);
+
+  while (true) {
+    auto const existing_key = current_slot->first.load(cuda::std::memory_order_relaxed);
+
+    // The user provide `key_equal` can never be used to compare against `empty_key_sentinel` as the
+    // sentinel is not a valid key value. Therefore, first check for the sentinel
+    auto const slot_is_empty = (existing_key == this->get_empty_key_sentinel());
+
+    // the key we were searching for was found by one of the threads, so we return an iterator to
+    // the entry
+    auto const exists = g.ballot(not slot_is_empty and key_equal(existing_key, k));
+    if (exists) {
+      uint32_t src_lane = __ffs(exists) - 1;
+      // TODO: This shouldn't cast an iterator to an int to shuffle. Instead, get the index of the
+      // current_slot and shuffle that instead.
+      intptr_t res_slot = g.shfl(reinterpret_cast<intptr_t>(current_slot), src_lane);
+      return reinterpret_cast<const_iterator>(res_slot);
+    }
+
+    // we found an empty slot, meaning that the key we're searching
+    // for isn't in this submap, so we should move onto the next one
+    if (g.ballot(slot_is_empty)) { return this->end(); }
+
+    // otherwise, all slots in the current window are full with other keys,
+    // so we move onto the next window in the current submap
+
+    current_slot = next_slot(g, current_slot);
+  }
+}
+
+template <typename Key, typename Value, cuda::thread_scope Scope, typename Allocator>
+template <typename Hash, typename KeyEqual>
+__device__ bool static_map<Key, Value, Scope, Allocator>::device_view::contains(
+  Key const& k, Hash hash, KeyEqual key_equal) noexcept
+{
+  auto current_slot = initial_slot(k, hash);
+
+  while (true) {
+    auto const existing_key = current_slot->first.load(cuda::std::memory_order_relaxed);
+
+    if (existing_key == empty_key_sentinel_) { return false; }
+
+    if (key_equal(existing_key, k)) { return true; }
+
+    current_slot = next_slot(current_slot);
+  }
+}
+
+template <typename Key, typename Value, cuda::thread_scope Scope, typename Allocator>
+template <typename CG, typename Hash, typename KeyEqual>
+__device__ bool static_map<Key, Value, Scope, Allocator>::device_view::contains(
+  CG g, Key const& k, Hash hash, KeyEqual key_equal) noexcept
+{
+  auto current_slot = initial_slot(g, k, hash);
+
+  while (true) {
+    key_type const existing_key = current_slot->first.load(cuda::std::memory_order_relaxed);
+
+    // The user provide `key_equal` can never be used to compare against `empty_key_sentinel` as the
+    // sentinel is not a valid key value. Therefore, first check for the sentinel
+    auto const slot_is_empty = (existing_key == this->get_empty_key_sentinel());
+
+    // the key we were searching for was found by one of the threads, so we return an iterator to
+    // the entry
+    if (g.ballot(not slot_is_empty and key_equal(existing_key, k))) { return true; }
+
+    // we found an empty slot, meaning that the key we're searching for isn't present
+    if (g.ballot(slot_is_empty)) { return false; }
+
+    // otherwise, all slots in the current window are full with other keys, so we move onto the next
+    // window
+    current_slot = next_slot(g, current_slot);
+  }
+}
+*/
+}  // namespace cuco
diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap_kernels.cuh
new file mode 100644
index 000000000..f6480a65f
--- /dev/null
+++ b/include/cuco/detail/static_multimap_kernels.cuh
@@ -0,0 +1,118 @@
+/*
+ * Copyright (c) 2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+namespace cuco {
+namespace detail {
+namespace cg = cooperative_groups;
+
+namespace multimap {
+
+/**
+ * @brief Inserts all key/value pairs in the range `[first, last)`.
+ *
+ * If multiple keys in `[first, last)` compare equal, it is unspecified which
+ * element is inserted.
+ *
+ * @tparam block_size
+ * @tparam InputIt Device accessible input iterator whose `value_type` is
+ * convertible to the map's `value_type`
+ * @tparam atomicT Type of atomic storage
+ * @tparam viewT Type of device view allowing access of hash map storage
+ * @tparam Hash Unary callable type
+ * @tparam KeyEqual Binary callable type
+ * @param first Beginning of the sequence of key/value pairs
+ * @param last End of the sequence of key/value pairs
+ * @param num_successes The number of successfully inserted key/value pairs
+ * @param view Mutable device view used to access the hash map's slot storage
+ * @param hash The unary function to apply to hash each key
+ * @param key_equal The binary function used to compare two keys for equality
+ */
+template <uint32_t block_size,
+          typename InputIt,
+          typename atomicT,
+          typename viewT,
+          typename Hash,
+          typename KeyEqual>
+__global__ void insert(
+  InputIt first, InputIt last, atomicT* num_successes, viewT view, Hash hash, KeyEqual key_equal)
+{
+}
+
+/**
+ * @brief Inserts all key/value pairs in the range `[first, last)`.
+ *
+ * If multiple keys in `[first, last)` compare equal, it is unspecified which
+ * element is inserted. Uses the CUDA Cooperative Groups API to leverage groups
+ * of multiple threads to perform each key/value insertion. This provides a
+ * significant boost in throughput compared to the non Cooperative Group
+ * `insert` at moderate to high load factors.
+ *
+ * @tparam block_size
+ * @tparam tile_size The number of threads in the Cooperative Groups used to perform
+ * inserts
+ * @tparam InputIt Device accessible input iterator whose `value_type` is
+ * convertible to the map's `value_type`
+ * @tparam atomicT Type of atomic storage
+ * @tparam viewT Type of device view allowing access of hash map storage
+ * @tparam Hash Unary callable type
+ * @tparam KeyEqual Binary callable type
+ * @param first Beginning of the sequence of key/value pairs
+ * @param last End of the sequence of key/value pairs
+ * @param num_successes The number of successfully inserted key/value pairs
+ * @param view Mutable device view used to access the hash map's slot storage
+ * @param hash The unary function to apply to hash each key
+ * @param key_equal The binary function used to compare two keys for equality
+ */
+template <uint32_t block_size,
+          uint32_t tile_size,
+          typename InputIt,
+          typename atomicT,
+          typename viewT,
+          typename Hash,
+          typename KeyEqual>
+__global__ void insert(
+  InputIt first, InputIt last, atomicT* num_successes, viewT view, Hash hash, KeyEqual key_equal)
+{
+  /*
+  typedef cub::BlockReduce<std::size_t, block_size> BlockReduce;
+  __shared__ typename BlockReduce::TempStorage temp_storage;
+  std::size_t thread_num_successes = 0;
+
+  auto tile = cg::tiled_partition<tile_size>(cg::this_thread_block());
+  auto tid = blockDim.x * blockIdx.x + threadIdx.x;
+  auto it = first + tid / tile_size;
+
+  while (it < last) {
+    // force conversion to value_type
+    typename viewT::value_type const insert_pair{*it};
+    if (view.insert(tile, insert_pair, hash, key_equal) && tile.thread_rank() == 0) {
+      thread_num_successes++;
+    }
+    it += (gridDim.x * blockDim.x) / tile_size;
+  }
+
+  // compute number of successfully inserted elements for each block
+  // and atomically add to the grand total
+  std::size_t block_num_successes = BlockReduce(temp_storage).Sum(thread_num_successes);
+  if(threadIdx.x == 0) {
+    *num_successes += block_num_successes;
+  }
+  */
+}
+}  // namespace multimap
+
+}  // namespace detail
+}  // namespace cuco
diff --git a/include/cuco/static_map.cuh b/include/cuco/static_map.cuh
index cf7de0a7e..533273c30 100644
--- a/include/cuco/static_map.cuh
+++ b/include/cuco/static_map.cuh
@@ -37,7 +37,9 @@
 #include <cuco/detail/error.hpp>
 #include <cuco/detail/hash_functions.cuh>
 #include <cuco/detail/pair.cuh>
+#include <cuco/detail/static_kernels.cuh>
 #include <cuco/detail/static_map_kernels.cuh>
+#include <cuco/detail/static_multimap_kernels.cuh>
 
 namespace cuco {
 
@@ -911,7 +913,7 @@ class static_map {
     return device_mutable_view(slots_, capacity_, empty_key_sentinel_, empty_value_sentinel_);
   }
 
- private:
+ protected:
   pair_atomic_type* slots_{nullptr};      ///< Pointer to flat slots storage
   std::size_t capacity_{};                ///< Total number of slots
   std::size_t size_{};                    ///< Number of keys in map
diff --git a/include/cuco/static_multi_map.cuh b/include/cuco/static_multi_map.cuh
deleted file mode 100644
index 95525b40c..000000000
--- a/include/cuco/static_multi_map.cuh
+++ /dev/null
@@ -1,42 +0,0 @@
-/*
- * Copyright (c) 2021, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#pragma once
-
-#include <cooperative_groups.h>
-#include <cub/cub.cuh>
-
-#include <cuco/detail/error.hpp>
-
-namespace cuco {
-
-template <typename Key,
-          typename Value,
-          cuda::thread_scope Scope = cuda::thread_scope_device,
-          typename Allocator       = cuco::cuda_allocator<char>>
-class static_multi_map : public static_map<Key, Value, Scope, Allocator> {
- public:
-  static_multi_map(std::size_t capacity,
-                   Key empty_key_sentinel,
-                   Value empty_value_sentinel,
-                   Allocator const& alloc = Allocator{});
-
-  ~static_multi_map();
-
-};  // class static_multi_map
-}  // namespace cuco
-
-#include <cuco/detail/static_map.inl>
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
new file mode 100644
index 000000000..31db9a472
--- /dev/null
+++ b/include/cuco/static_multimap.cuh
@@ -0,0 +1,82 @@
+/*
+ * Copyright (c) 2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cooperative_groups.h>
+#include <cub/cub.cuh>
+#include <cuda/std/atomic>
+
+#include <cuco/allocator.hpp>
+#include <cuco/detail/error.hpp>
+
+#include <cuco/static_map.cuh>
+
+namespace cuco {
+
+template <typename Key,
+          typename Value,
+          cuda::thread_scope Scope = cuda::thread_scope_device,
+          typename Allocator       = cuco::cuda_allocator<char>>
+class static_multimap : public static_map<Key, Value, Scope, Allocator> {
+ public:
+  using typename static_map<Key, Value, Scope, Allocator>::value_type;
+  using typename static_map<Key, Value, Scope, Allocator>::key_type;
+  using typename static_map<Key, Value, Scope, Allocator>::mapped_type;
+  using typename static_map<Key, Value, Scope, Allocator>::atomic_key_type;
+  using typename static_map<Key, Value, Scope, Allocator>::atomic_mapped_type;
+  using typename static_map<Key, Value, Scope, Allocator>::pair_atomic_type;
+  using typename static_map<Key, Value, Scope, Allocator>::atomic_ctr_type;
+  using typename static_map<Key, Value, Scope, Allocator>::allocator_type;
+  using typename static_map<Key, Value, Scope, Allocator>::slot_allocator_type;
+
+  using static_map<Key, Value, Scope, Allocator>::get_device_mutable_view;
+
+  static_multimap(std::size_t capacity,
+                  Key empty_key_sentinel,
+                  Value empty_value_sentinel,
+                  Allocator const& alloc = Allocator{});
+
+  ~static_multimap();
+
+  /**
+   * @brief Inserts all key/value pairs in the range `[first, last)`.
+   *
+   * @tparam InputIt Device accessible input iterator whose `value_type` is
+   * convertible to the map's `value_type`
+   * @tparam Hash Unary callable type
+   * @tparam KeyEqual Binary callable type
+   * @param first Beginning of the sequence of key/value pairs
+   * @param last End of the sequence of key/value pairs
+   * @param hash The unary function to apply to hash each key
+   * @param key_equal The binary function to compare two keys for equality
+   */
+  template <typename InputIt,
+            typename Hash     = cuco::detail::MurmurHash3_32<key_type>,
+            typename KeyEqual = thrust::equal_to<key_type>>
+  void insert(InputIt first,
+              InputIt last,
+              Hash hash          = Hash{},
+              KeyEqual key_equal = KeyEqual{}) override;
+
+ private:
+  using static_map<Key, Value, Scope, Allocator>::num_successes_;
+  using static_map<Key, Value, Scope, Allocator>::size_;
+
+};  // class static_multi_map
+}  // namespace cuco
+
+#include <cuco/detail/static_multimap.inl>

From c6a7917bcda07b5764374de277b597febaa58721 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Fri, 26 Feb 2021 13:55:06 -0800
Subject: [PATCH 006/267] Add static_multimap tests

---
 tests/CMakeLists.txt                          |   7 +-
 tests/static_multimap/static_multimap_test.cu | 130 ++++++++++++++++++
 2 files changed, 136 insertions(+), 1 deletion(-)
 create mode 100644 tests/static_multimap/static_multimap_test.cu

diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt
index 9c142ef58..ee52d648e 100644
--- a/tests/CMakeLists.txt
+++ b/tests/CMakeLists.txt
@@ -57,4 +57,9 @@ set(DYNAMIC_MAP_TEST_SRC
     "${CMAKE_CURRENT_SOURCE_DIR}/dynamic_map/dynamic_map_test.cu")
 
 ConfigureTest(DYNAMIC_MAP_TEST "${DYNAMIC_MAP_TEST_SRC}")
-####################################################################################################
\ No newline at end of file
+####################################################################################################
+set(STATIC_MULTIMAP_TEST_SRC 
+    "${CMAKE_CURRENT_SOURCE_DIR}/static_multimap/static_multimap_test.cu")
+
+  ConfigureTest(STATIC_MULTIMAP_TEST "${STATIC_MULTIMAP_TEST_SRC}")
+####################################################################################################
diff --git a/tests/static_multimap/static_multimap_test.cu b/tests/static_multimap/static_multimap_test.cu
new file mode 100644
index 000000000..8aa68d937
--- /dev/null
+++ b/tests/static_multimap/static_multimap_test.cu
@@ -0,0 +1,130 @@
+/*
+ * Copyright (c) 2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <thrust/count.h>
+#include <thrust/device_vector.h>
+#include <thrust/for_each.h>
+#include <algorithm>
+#include <catch2/catch.hpp>
+#include <cuco/static_multimap.cuh>
+#include <limits>
+
+namespace {
+namespace cg = cooperative_groups;
+
+// Thrust logical algorithms (any_of/all_of/none_of) don't work with device
+// lambdas: See https://github.com/thrust/thrust/issues/1062
+template <typename Iterator, typename Predicate>
+bool all_of(Iterator begin, Iterator end, Predicate p)
+{
+  auto size = thrust::distance(begin, end);
+  return size == thrust::count_if(begin, end, p);
+}
+
+template <typename Iterator, typename Predicate>
+bool any_of(Iterator begin, Iterator end, Predicate p)
+{
+  return thrust::count_if(begin, end, p) > 0;
+}
+
+template <typename Iterator, typename Predicate>
+bool none_of(Iterator begin, Iterator end, Predicate p)
+{
+  return not all_of(begin, end, p);
+}
+}  // namespace
+
+enum class dist_type { UNIQUE, UNIFORM, GAUSSIAN };
+
+template <dist_type Dist, typename Key, typename OutputIt>
+static void generate_keys(OutputIt output_begin, OutputIt output_end)
+{
+  auto num_keys = std::distance(output_begin, output_end);
+
+  std::random_device rd;
+  std::mt19937 gen{rd()};
+
+  switch (Dist) {
+    case dist_type::UNIQUE:
+      for (auto i = 0; i < num_keys; ++i) {
+        output_begin[i] = i;
+      }
+      break;
+    case dist_type::UNIFORM:
+      for (auto i = 0; i < num_keys; ++i) {
+        output_begin[i] = std::abs(static_cast<Key>(gen()));
+      }
+      break;
+    case dist_type::GAUSSIAN:
+      std::normal_distribution<> dg{1e9, 1e7};
+      for (auto i = 0; i < num_keys; ++i) {
+        output_begin[i] = std::abs(static_cast<Key>(dg(gen)));
+      }
+      break;
+  }
+}
+
+TEMPLATE_TEST_CASE_SIG("Unique sequence of keys",
+                       "",
+                       ((typename T, dist_type Dist), T, Dist),
+                       (int32_t, dist_type::UNIQUE),
+                       (int64_t, dist_type::UNIQUE),
+                       (int32_t, dist_type::UNIFORM),
+                       (int64_t, dist_type::UNIFORM),
+                       (int32_t, dist_type::GAUSSIAN),
+                       (int64_t, dist_type::GAUSSIAN))
+{
+  using Key   = T;
+  using Value = T;
+
+  constexpr std::size_t num_keys{50'000'000};
+  cuco::static_multimap<Key, Value> map{100'000'000, -1, -1};
+
+  auto m_view = map.get_device_mutable_view();
+  auto view   = map.get_device_view();
+
+  std::vector<Key> h_keys(num_keys);
+  std::vector<Value> h_values(num_keys);
+  std::vector<cuco::pair_type<Key, Value>> h_pairs(num_keys);
+
+  generate_keys<Dist, Key>(h_keys.begin(), h_keys.end());
+
+  for (auto i = 0; i < num_keys; ++i) {
+    Key key           = h_keys[i];
+    Value val         = h_keys[i];
+    h_pairs[i].first  = key;
+    h_pairs[i].second = val;
+    h_values[i]       = val;
+  }
+
+  thrust::device_vector<Key> d_keys(h_keys);
+  thrust::device_vector<Value> d_values(h_values);
+  thrust::device_vector<cuco::pair_type<Key, Value>> d_pairs(h_pairs);
+  thrust::device_vector<Value> d_results(num_keys);
+  thrust::device_vector<bool> d_contained(num_keys);
+
+  // bulk function test cases
+  SECTION("All inserted keys-value pairs should be correctly recovered during find")
+  {
+    map.insert(d_pairs.begin(), d_pairs.end());
+    map.find(d_keys.begin(), d_keys.end(), d_results.begin());
+    auto zip = thrust::make_zip_iterator(thrust::make_tuple(d_results.begin(), d_values.begin()));
+
+    REQUIRE(all_of(zip, zip + num_keys, [] __device__(auto const& p) {
+      return thrust::get<0>(p) == thrust::get<1>(p);
+    }));
+  }
+}

From f805577aba1e0fa7663042b4649c88fee38f4845 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Fri, 26 Feb 2021 13:57:15 -0800
Subject: [PATCH 007/267] Add static_multimap::insert() & code restructuring

---
 include/cuco/detail/dynamic_map_kernels.cuh   | 301 +++---
 include/cuco/detail/hash_functions.cuh        |   1 +
 include/cuco/detail/pair.cuh                  |  29 +-
 include/cuco/detail/static_kernels.cuh        |  52 --
 include/cuco/detail/static_map.inl            |   6 +-
 include/cuco/detail/static_map_kernels.cuh    | 191 ++--
 include/cuco/detail/static_multimap.inl       |  85 +-
 .../cuco/detail/static_multimap_kernels.cuh   | 366 +++++++-
 include/cuco/static_map.cuh                   |   4 +-
 include/cuco/static_multimap.cuh              | 875 +++++++++++++++++-
 10 files changed, 1514 insertions(+), 396 deletions(-)
 delete mode 100644 include/cuco/detail/static_kernels.cuh

diff --git a/include/cuco/detail/dynamic_map_kernels.cuh b/include/cuco/detail/dynamic_map_kernels.cuh
index c1e21e863..28690b0dc 100644
--- a/include/cuco/detail/dynamic_map_kernels.cuh
+++ b/include/cuco/detail/dynamic_map_kernels.cuh
@@ -19,17 +19,17 @@ namespace detail {
 namespace cg = cooperative_groups;
 
 /**
- * @brief Inserts all key/value pairs in the range `[first, last)`.
- *
+ * @brief Inserts all key/value pairs in the range `[first, last)`. 
+ *  
  * If multiple keys in `[first, last)` compare equal, it is unspecified which
  * element is inserted.
- *
- * @tparam block_size
+ * 
+ * @tparam block_size 
  * @tparam pair_type Type of the pairs contained in the map
  * @tparam InputIt Device accessible input iterator whose `value_type` is
  * convertible to the map's `value_type`
  * @tparam viewT Type of the `static_map` device views
- * @tparam mutableViewT Type of the `static_map` device mutable views
+ * @tparam mutableViewT Type of the `static_map` device mutable views 
  * @tparam atomicT Type of atomic storage
  * @tparam viewT Type of device view allowing access of hash map storage
  * @tparam Hash Unary callable type
@@ -38,7 +38,7 @@ namespace cg = cooperative_groups;
  * @param last End of the sequence of key/value pairs
  * @param submap_views Array of `static_map::device_view` objects used to
  * perform `contains` operations on each underlying `static_map`
- * @param submap_mutable_views Array of `static_map::device_mutable_view` objects
+ * @param submap_mutable_views Array of `static_map::device_mutable_view` objects 
  * used to perform an `insert` into the target `static_map` submap
  * @param num_successes The number of successfully inserted key/value pairs
  * @param insert_idx The index of the submap we are inserting into
@@ -46,14 +46,14 @@ namespace cg = cooperative_groups;
  * @param hash The unary function to apply to hash each key
  * @param key_equal The binary function used to compare two keys for equality
  */
-template <uint32_t block_size,
-          typename pair_type,
-          typename InputIt,
-          typename viewT,
-          typename mutableViewT,
-          typename atomicT,
-          typename Hash,
-          typename KeyEqual>
+template<uint32_t block_size,
+         typename pair_type,
+         typename InputIt,
+         typename viewT,
+         typename mutableViewT,
+         typename atomicT,
+         typename Hash, 
+         typename KeyEqual>
 __global__ void insert(InputIt first,
                        InputIt last,
                        viewT* submap_views,
@@ -62,55 +62,58 @@ __global__ void insert(InputIt first,
                        uint32_t insert_idx,
                        uint32_t num_submaps,
                        Hash hash,
-                       KeyEqual key_equal)
-{
+                       KeyEqual key_equal) {
   typedef cub::BlockReduce<std::size_t, block_size> BlockReduce;
   __shared__ typename BlockReduce::TempStorage temp_storage;
   std::size_t thread_num_successes = 0;
-
+  
   auto tid = blockDim.x * blockIdx.x + threadIdx.x;
 
-  while (first + tid < last) {
+  while(first + tid < last) {
     pair_type insert_pair = *(first + tid);
-    auto exists           = false;
-
+    auto exists = false;
+    
     // manually check for duplicates in those submaps we are not inserting into
-    for (auto i = 0; i < num_submaps; ++i) {
-      if (i != insert_idx) {
+    for(auto i = 0; i < num_submaps; ++i) {
+      if(i != insert_idx) {
         exists = submap_views[i].contains(insert_pair.first, hash, key_equal);
-        if (exists) { break; }
+        if(exists) {
+          break;
+        }
       }
     }
-    if (!exists) {
-      if (submap_mutable_views[insert_idx].insert(insert_pair, hash, key_equal)) {
+    if(!exists) {
+      if(submap_mutable_views[insert_idx].insert(insert_pair, hash, key_equal)) {
         thread_num_successes++;
       }
     }
 
     tid += gridDim.x * blockDim.x;
   }
-
+  
   std::size_t block_num_successes = BlockReduce(temp_storage).Sum(thread_num_successes);
-  if (threadIdx.x == 0) { *num_successes += block_num_successes; }
+  if(threadIdx.x == 0) {
+    *num_successes += block_num_successes;
+  }
 }
 
 /**
- * @brief Inserts all key/value pairs in the range `[first, last)`.
- *
+ * @brief Inserts all key/value pairs in the range `[first, last)`. 
+ *  
  * If multiple keys in `[first, last)` compare equal, it is unspecified which
  * element is inserted. Uses the CUDA Cooperative Groups API to leverage groups
- * of multiple threads to perform each key/value insertion. This provides a
+ * of multiple threads to perform each key/value insertion. This provides a 
  * significant boost in throughput compared to the non Cooperative Group
  * `insert` at moderate to high load factors.
- *
- * @tparam block_size
+ * 
+ * @tparam block_size 
  * @tparam tile_size The number of threads in the Cooperative Groups used to perform
  * inserts
  * @tparam pair_type Type of the pairs contained in the map
  * @tparam InputIt Device accessible input iterator whose `value_type` is
  * convertible to the map's `value_type`
  * @tparam viewT Type of the `static_map` device views
- * @tparam mutableViewT Type of the `static_map` device mutable views
+ * @tparam mutableViewT Type of the `static_map` device mutable views 
  * @tparam atomicT Type of atomic storage
  * @tparam viewT Type of device view allowing access of hash map storage
  * @tparam Hash Unary callable type
@@ -119,7 +122,7 @@ __global__ void insert(InputIt first,
  * @param last End of the sequence of key/value pairs
  * @param submap_views Array of `static_map::device_view` objects used to
  * perform `contains` operations on each underlying `static_map`
- * @param submap_mutable_views Array of `static_map::device_mutable_view` objects
+ * @param submap_mutable_views Array of `static_map::device_mutable_view` objects 
  * used to perform an `insert` into the target `static_map` submap
  * @param num_successes The number of successfully inserted key/value pairs
  * @param insert_idx The index of the submap we are inserting into
@@ -127,15 +130,14 @@ __global__ void insert(InputIt first,
  * @param hash The unary function to apply to hash each key
  * @param key_equal The binary function used to compare two keys for equality
  */
-template <uint32_t block_size,
-          uint32_t tile_size,
-          typename pair_type,
-          typename InputIt,
-          typename viewT,
-          typename mutableViewT,
-          typename atomicT,
-          typename Hash,
-          typename KeyEqual>
+template<uint32_t block_size, uint32_t tile_size,
+         typename pair_type,
+         typename InputIt,
+         typename viewT,
+         typename mutableViewT,
+         typename atomicT,
+         typename Hash, 
+         typename KeyEqual>
 __global__ void insert(InputIt first,
                        InputIt last,
                        viewT* submap_views,
@@ -144,51 +146,54 @@ __global__ void insert(InputIt first,
                        uint32_t insert_idx,
                        uint32_t num_submaps,
                        Hash hash,
-                       KeyEqual key_equal)
-{
+                       KeyEqual key_equal) {
   typedef cub::BlockReduce<std::size_t, block_size> BlockReduce;
   __shared__ typename BlockReduce::TempStorage temp_storage;
   std::size_t thread_num_successes = 0;
-
+  
   auto tile = cg::tiled_partition<tile_size>(cg::this_thread_block());
-  auto tid  = blockDim.x * blockIdx.x + threadIdx.x;
-  auto it   = first + tid / tile_size;
+  auto tid = blockDim.x * blockIdx.x + threadIdx.x;
+  auto it = first + tid / tile_size;
 
-  while (it < last) {
+  while(it < last) {
     pair_type insert_pair = *it;
-    auto exists           = false;
-
+    auto exists = false;
+    
     // manually check for duplicates in those submaps we are not inserting into
-    for (auto i = 0; i < num_submaps; ++i) {
-      if (i != insert_idx) {
+    for(auto i = 0; i < num_submaps; ++i) {
+      if(i != insert_idx) {
         exists = submap_views[i].contains(tile, insert_pair.first, hash, key_equal);
-        if (exists) { break; }
+        if(exists) {
+          break;
+        }
       }
     }
-    if (!exists) {
-      if (submap_mutable_views[insert_idx].insert(tile, insert_pair, hash, key_equal) &&
-          tile.thread_rank() == 0) {
+    if(!exists) {
+      if(submap_mutable_views[insert_idx].insert(tile, insert_pair, hash, key_equal) && 
+         tile.thread_rank() == 0) {
         thread_num_successes++;
       }
     }
 
     it += (gridDim.x * blockDim.x) / tile_size;
   }
-
+  
   std::size_t block_num_successes = BlockReduce(temp_storage).Sum(thread_num_successes);
-  if (threadIdx.x == 0) { *num_successes += block_num_successes; }
+  if(threadIdx.x == 0) {
+    *num_successes += block_num_successes;
+  }
 }
 
 /**
  * @brief Finds the values corresponding to all keys in the range `[first, last)`.
- *
- * If the key `*(first + i)` exists in the map, copies its associated value to `(output_begin + i)`.
- * Else, copies the empty value sentinel.
+ * 
+ * If the key `*(first + i)` exists in the map, copies its associated value to `(output_begin + i)`. 
+ * Else, copies the empty value sentinel. 
  * @tparam block_size The number of threads in the thread block
- * @tparam Value The mapped value type for the map
+ * @tparam Value The mapped value type for the map 
  * @tparam InputIt Device accessible input iterator whose `value_type` is
  * convertible to the map's `key_type`
- * @tparam OutputIt Device accessible output iterator whose `value_type` is
+ * @tparam OutputIt Device accessible output iterator whose `value_type` is 
  * convertible to the map's `mapped_type`
  * @tparam viewT Type of `static_map` device view
  * @tparam Hash Unary callable type
@@ -202,39 +207,37 @@ __global__ void insert(InputIt first,
  * @param hash The unary function to apply to hash each key
  * @param key_equal The binary function to compare two keys for equality
  */
-template <uint32_t block_size,
-          typename Value,
-          typename InputIt,
-          typename OutputIt,
-          typename viewT,
-          typename Hash,
-          typename KeyEqual>
+template<uint32_t block_size,
+         typename Value,
+         typename InputIt, typename OutputIt, 
+         typename viewT,
+         typename Hash, 
+         typename KeyEqual>
 __global__ void find(InputIt first,
                      InputIt last,
                      OutputIt output_begin,
                      viewT* submap_views,
                      uint32_t num_submaps,
                      Hash hash,
-                     KeyEqual key_equal)
-{
-  auto tid                  = blockDim.x * blockIdx.x + threadIdx.x;
+                     KeyEqual key_equal) {
+  auto tid = blockDim.x * blockIdx.x + threadIdx.x;
   auto empty_value_sentinel = submap_views[0].get_empty_value_sentinel();
   __shared__ Value writeBuffer[block_size];
 
-  while (first + tid < last) {
-    auto key         = *(first + tid);
+  while(first + tid < last) {
+    auto key = *(first + tid);
     auto found_value = empty_value_sentinel;
-    for (auto i = 0; i < num_submaps; ++i) {
+    for(auto i = 0; i < num_submaps; ++i) {
       auto submap_view = submap_views[i];
-      auto found       = submap_view.find(key, hash, key_equal);
-      if (found != submap_view.end()) {
+      auto found = submap_view.find(key, hash, key_equal);
+      if(found != submap_view.end()) {
         found_value = found->second;
         break;
       }
     }
 
-    /*
-     * The ld.relaxed.gpu instruction used in view.find causes L1 to
+    /* 
+     * The ld.relaxed.gpu instruction used in view.find causes L1 to 
      * flush more frequently, causing increased sector stores from L2 to global memory.
      * By writing results to shared memory and then synchronizing before writing back
      * to global, we no longer rely on L1, preventing the increase in sector stores from
@@ -249,10 +252,10 @@ __global__ void find(InputIt first,
 
 /**
  * @brief Finds the values corresponding to all keys in the range `[first, last)`.
- *
- * If the key `*(first + i)` exists in the map, copies its associated value to `(output_begin + i)`.
+ * 
+ * If the key `*(first + i)` exists in the map, copies its associated value to `(output_begin + i)`. 
  * Else, copies the empty value sentinel. Uses the CUDA Cooperative Groups API to leverage groups
- * of multiple threads to find each key. This provides a significant boost in throughput compared
+ * of multiple threads to find each key. This provides a significant boost in throughput compared 
  * to the non Cooperative Group `find` at moderate to high load factors.
  *
  * @tparam block_size The number of threads in the thread block
@@ -261,7 +264,7 @@ __global__ void find(InputIt first,
  * @tparam Value The mapped value type for the map
  * @tparam InputIt Device accessible input iterator whose `value_type` is
  * convertible to the map's `key_type`
- * @tparam OutputIt Device accessible output iterator whose `value_type` is
+ * @tparam OutputIt Device accessible output iterator whose `value_type` is 
  * convertible to the map's `mapped_type`
  * @tparam viewT Type of `static_map` device view
  * @tparam Hash Unary callable type
@@ -275,50 +278,49 @@ __global__ void find(InputIt first,
  * @param hash The unary function to apply to hash each key
  * @param key_equal The binary function to compare two keys for equality
  */
-template <uint32_t block_size,
-          uint32_t tile_size,
-          typename Value,
-          typename InputIt,
-          typename OutputIt,
-          typename viewT,
-          typename Hash,
-          typename KeyEqual>
+template<uint32_t block_size, uint32_t tile_size,
+         typename Value,
+         typename InputIt, typename OutputIt, 
+         typename viewT,
+         typename Hash, 
+         typename KeyEqual>
 __global__ void find(InputIt first,
                      InputIt last,
                      OutputIt output_begin,
                      viewT* submap_views,
                      uint32_t num_submaps,
                      Hash hash,
-                     KeyEqual key_equal)
-{
-  auto tile                 = cg::tiled_partition<tile_size>(cg::this_thread_block());
-  auto tid                  = blockDim.x * blockIdx.x + threadIdx.x;
-  auto key_idx              = tid / tile_size;
+                     KeyEqual key_equal) {
+  auto tile = cg::tiled_partition<tile_size>(cg::this_thread_block());
+  auto tid = blockDim.x * blockIdx.x + threadIdx.x;
+  auto key_idx = tid / tile_size;
   auto empty_value_sentinel = submap_views[0].get_empty_value_sentinel();
   __shared__ Value writeBuffer[block_size];
 
-  while (first + key_idx < last) {
-    auto key         = *(first + key_idx);
+  while(first + key_idx < last) {
+    auto key = *(first + key_idx);
     auto found_value = empty_value_sentinel;
-    for (auto i = 0; i < num_submaps; ++i) {
+    for(auto i = 0; i < num_submaps; ++i) {
       auto submap_view = submap_views[i];
-      auto found       = submap_view.find(tile, key, hash, key_equal);
-      if (found != submap_view.end()) {
+      auto found = submap_view.find(tile, key, hash, key_equal);
+      if(found != submap_view.end()) {
         found_value = found->second;
         break;
       }
     }
 
-    /*
-     * The ld.relaxed.gpu instruction used in view.find causes L1 to
+    /* 
+     * The ld.relaxed.gpu instruction used in view.find causes L1 to 
      * flush more frequently, causing increased sector stores from L2 to global memory.
      * By writing results to shared memory and then synchronizing before writing back
      * to global, we no longer rely on L1, preventing the increase in sector stores from
      * L2 to global and improving performance.
      */
-    if (tile.thread_rank() == 0) { writeBuffer[threadIdx.x / tile_size] = found_value; }
+    if(tile.thread_rank() == 0) {
+      writeBuffer[threadIdx.x / tile_size] = found_value;
+    }
     __syncthreads();
-    if (tile.thread_rank() == 0) {
+    if(tile.thread_rank() == 0) {
       *(output_begin + key_idx) = writeBuffer[threadIdx.x / tile_size];
     }
     key_idx += (gridDim.x * blockDim.x) / tile_size;
@@ -327,13 +329,13 @@ __global__ void find(InputIt first,
 
 /**
  * @brief Indicates whether the keys in the range `[first, last)` are contained in the map.
- *
+ * 
  * Writes a `bool` to `(output + i)` indicating if the key `*(first + i)` exists in the map.
  *
  * @tparam block_size The number of threads in the thread block
  * @tparam InputIt Device accessible input iterator whose `value_type` is
  * convertible to the map's `key_type`
- * @tparam OutputIt Device accessible output iterator whose `value_type` is
+ * @tparam OutputIt Device accessible output iterator whose `value_type` is 
  * convertible to the map's `mapped_type`
  * @tparam viewT Type of `static_map` device view
  * @tparam Hash Unary callable type
@@ -347,33 +349,33 @@ __global__ void find(InputIt first,
  * @param hash The unary function to apply to hash each key
  * @param key_equal The binary function to compare two keys for equality
  */
-template <uint32_t block_size,
-          typename InputIt,
-          typename OutputIt,
-          typename viewT,
-          typename Hash,
-          typename KeyEqual>
+template<uint32_t block_size,
+         typename InputIt, typename OutputIt, 
+         typename viewT,
+         typename Hash, 
+         typename KeyEqual>
 __global__ void contains(InputIt first,
                          InputIt last,
                          OutputIt output_begin,
                          viewT* submap_views,
                          uint32_t num_submaps,
                          Hash hash,
-                         KeyEqual key_equal)
-{
+                         KeyEqual key_equal) {
   auto tid = blockDim.x * blockIdx.x + threadIdx.x;
   __shared__ bool writeBuffer[block_size];
 
-  while (first + tid < last) {
-    auto key   = *(first + tid);
+  while(first + tid < last) {
+    auto key = *(first + tid);
     auto found = false;
-    for (auto i = 0; i < num_submaps; ++i) {
+    for(auto i = 0; i < num_submaps; ++i) {
       found = submap_views[i].contains(key, hash, key_equal);
-      if (found) { break; }
+      if(found) {
+        break;
+      }
     }
-
-    /*
-     * The ld.relaxed.gpu instruction used in view.find causes L1 to
+    
+    /* 
+     * The ld.relaxed.gpu instruction used in view.find causes L1 to 
      * flush more frequently, causing increased sector stores from L2 to global memory.
      * By writing results to shared memory and then synchronizing before writing back
      * to global, we no longer rely on L1, preventing the increase in sector stores from
@@ -388,10 +390,10 @@ __global__ void contains(InputIt first,
 
 /**
  * @brief Indicates whether the keys in the range `[first, last)` are contained in the map.
- *
+ * 
  * Writes a `bool` to `(output + i)` indicating if the key `*(first + i)` exists in the map.
- * Uses the CUDA Cooperative Groups API to leverage groups of multiple threads to perform the
- * contains operation for each key. This provides a significant boost in throughput compared
+ * Uses the CUDA Cooperative Groups API to leverage groups of multiple threads to perform the 
+ * contains operation for each key. This provides a significant boost in throughput compared 
  * to the non Cooperative Group `contains` at moderate to high load factors.
  *
  * @tparam block_size The number of threads in the thread block
@@ -399,7 +401,7 @@ __global__ void contains(InputIt first,
  * perform find operations
  * @tparam InputIt Device accessible input iterator whose `value_type` is
  * convertible to the map's `key_type`
- * @tparam OutputIt Device accessible output iterator whose `value_type` is
+ * @tparam OutputIt Device accessible output iterator whose `value_type` is 
  * convertible to the map's `mapped_type`
  * @tparam viewT Type of `static_map` device view
  * @tparam Hash Unary callable type
@@ -413,48 +415,49 @@ __global__ void contains(InputIt first,
  * @param hash The unary function to apply to hash each key
  * @param key_equal The binary function to compare two keys for equality
  */
-template <uint32_t block_size,
-          uint32_t tile_size,
-          typename InputIt,
-          typename OutputIt,
-          typename viewT,
-          typename Hash,
-          typename KeyEqual>
+template<uint32_t block_size, uint32_t tile_size,
+         typename InputIt, typename OutputIt, 
+         typename viewT,
+         typename Hash, 
+         typename KeyEqual>
 __global__ void contains(InputIt first,
                          InputIt last,
                          OutputIt output_begin,
                          viewT* submap_views,
                          uint32_t num_submaps,
                          Hash hash,
-                         KeyEqual key_equal)
-{
-  auto tile    = cg::tiled_partition<tile_size>(cg::this_thread_block());
-  auto tid     = blockDim.x * blockIdx.x + threadIdx.x;
+                         KeyEqual key_equal) {
+  auto tile = cg::tiled_partition<tile_size>(cg::this_thread_block());
+  auto tid = blockDim.x * blockIdx.x + threadIdx.x;
   auto key_idx = tid / tile_size;
   __shared__ bool writeBuffer[block_size];
 
-  while (first + key_idx < last) {
-    auto key   = *(first + key_idx);
+  while(first + key_idx < last) {
+    auto key = *(first + key_idx);
     auto found = false;
-    for (auto i = 0; i < num_submaps; ++i) {
+    for(auto i = 0; i < num_submaps; ++i) {
       found = submap_views[i].contains(tile, key, hash, key_equal);
-      if (found) { break; }
+      if(found) {
+        break;
+      }
     }
 
-    /*
-     * The ld.relaxed.gpu instruction used in view.find causes L1 to
+    /* 
+     * The ld.relaxed.gpu instruction used in view.find causes L1 to 
      * flush more frequently, causing increased sector stores from L2 to global memory.
      * By writing results to shared memory and then synchronizing before writing back
      * to global, we no longer rely on L1, preventing the increase in sector stores from
      * L2 to global and improving performance.
      */
-    if (tile.thread_rank() == 0) { writeBuffer[threadIdx.x / tile_size] = found; }
+    if(tile.thread_rank() == 0) {
+      writeBuffer[threadIdx.x / tile_size] = found;
+    }
     __syncthreads();
-    if (tile.thread_rank() == 0) {
+    if(tile.thread_rank() == 0) {
       *(output_begin + key_idx) = writeBuffer[threadIdx.x / tile_size];
     }
     key_idx += (gridDim.x * blockDim.x) / tile_size;
   }
 }
-}  // namespace detail
-}  // namespace cuco
\ No newline at end of file
+} // namespace detail
+} // namespace cuco
\ No newline at end of file
diff --git a/include/cuco/detail/hash_functions.cuh b/include/cuco/detail/hash_functions.cuh
index aec550aba..de7074f04 100644
--- a/include/cuco/detail/hash_functions.cuh
+++ b/include/cuco/detail/hash_functions.cuh
@@ -20,6 +20,7 @@ namespace cuco {
 
 namespace detail {
 
+
 // MurmurHash3_32 implementation from
 // https://github.com/aappleby/smhasher/blob/master/src/MurmurHash3.cpp
 //-----------------------------------------------------------------------------
diff --git a/include/cuco/detail/pair.cuh b/include/cuco/detail/pair.cuh
index eec3f3b50..e52a23387 100644
--- a/include/cuco/detail/pair.cuh
+++ b/include/cuco/detail/pair.cuh
@@ -12,7 +12,7 @@
  * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  * See the License for the specific language governing permissions and
  * limitations under the License.
- */
+ */ 
 
 #include <thrust/device_reference.h>
 #include <thrust/pair.h>
@@ -24,13 +24,12 @@ namespace cuco {
 namespace detail {
 
 /**
- * @brief Rounds `v` to the nearest power of 2 greater than or equal to `v`.
- *
- * @param v
+ * @brief Rounds `v` to the nearest power of 2 greater than or equal to `v`. 
+ * 
+ * @param v 
  * @return The nearest power of 2 greater than or equal to `v`.
  */
-constexpr std::size_t next_pow2(std::size_t v) noexcept
-{
+constexpr std::size_t next_pow2(std::size_t v) noexcept {
   --v;
   v |= v >> 1;
   v |= v >> 2;
@@ -46,8 +45,7 @@ constexpr std::size_t next_pow2(std::size_t v) noexcept
  * whichever is smaller.
  */
 template <typename First, typename Second>
-constexpr std::size_t pair_alignment()
-{
+constexpr std::size_t pair_alignment() {
   return std::min(std::size_t{16}, next_pow2(sizeof(First) + sizeof(Second)));
 }
 
@@ -91,7 +89,7 @@ struct is_thrust_pair_like
  */
 template <typename First, typename Second>
 struct alignas(detail::pair_alignment<First, Second>()) pair {
-  using first_type  = First;
+  using first_type = First;
   using second_type = Second;
   First first;
   Second second;
@@ -123,16 +121,15 @@ using pair_type = cuco::pair<K, V>;
 
 /**
  * @brief Creates a pair of type `pair_type`
- *
- * @tparam F
- * @tparam S
- * @param f
- * @param s
+ * 
+ * @tparam F 
+ * @tparam S 
+ * @param f 
+ * @param s 
  * @return pair_type with first element `f` and second element `s`.
  */
 template <typename F, typename S>
-__host__ __device__ pair_type<F, S> make_pair(F&& f, S&& s) noexcept
-{
+__host__ __device__ pair_type<F, S> make_pair(F&& f, S&& s) noexcept {
   return pair_type<F, S>{std::forward<F>(f), std::forward<S>(s)};
 }
 }  // namespace cuco
diff --git a/include/cuco/detail/static_kernels.cuh b/include/cuco/detail/static_kernels.cuh
deleted file mode 100644
index 671c0ec8e..000000000
--- a/include/cuco/detail/static_kernels.cuh
+++ /dev/null
@@ -1,52 +0,0 @@
-/*
- * Copyright (c) 2020, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-namespace cuco {
-namespace detail {
-
-/**
- * @brief Initializes each slot in the flat `slots` storage to contain `k` and `v`.
- *
- * Each space in `slots` that can hold a key value pair is initialized to a
- * `pair_atomic_type` containing the key `k` and the value `v`.
- *
- * @tparam atomic_key_type Type of the `Key` atomic container
- * @tparam atomic_mapped_type Type of the `Value` atomic container
- * @tparam Key key type
- * @tparam Value value type
- * @tparam pair_atomic_type key/value pair type
- * @param slots Pointer to flat storage for the map's key/value pairs
- * @param k Key to which all keys in `slots` are initialized
- * @param v Value to which all values in `slots` are initialized
- * @param size Size of the storage pointed to by `slots`
- */
-template <typename atomic_key_type,
-          typename atomic_mapped_type,
-          typename Key,
-          typename Value,
-          typename pair_atomic_type>
-__global__ void initialize(pair_atomic_type* const slots, Key k, Value v, std::size_t size)
-{
-  auto tid = threadIdx.x + blockIdx.x * blockDim.x;
-  while (tid < size) {
-    new (&slots[tid].first) atomic_key_type{k};
-    new (&slots[tid].second) atomic_mapped_type{v};
-    tid += gridDim.x * blockDim.x;
-  }
-}
-
-}  // namespace detail
-}  // namespace cuco
diff --git a/include/cuco/detail/static_map.inl b/include/cuco/detail/static_map.inl
index 2c50b58d1..2b72cfa32 100644
--- a/include/cuco/detail/static_map.inl
+++ b/include/cuco/detail/static_map.inl
@@ -77,7 +77,7 @@ void static_map<Key, Value, Scope, Allocator>::insert(InputIt first,
   CUCO_CUDA_TRY(cudaGetDevice(&device_id));
   CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_successes_, sizeof(atomic_ctr_type), device_id));
 
-  detail::map::insert<block_size, tile_size>
+  detail::insert<block_size, tile_size>
     <<<grid_size, block_size>>>(first, first + num_keys, num_successes_, view, hash, key_equal);
   CUCO_CUDA_TRY(cudaDeviceSynchronize());
 
@@ -98,7 +98,7 @@ void static_map<Key, Value, Scope, Allocator>::find(
   auto const grid_size  = (tile_size * num_keys + stride * block_size - 1) / (stride * block_size);
   auto view             = get_device_view();
 
-  detail::map::find<block_size, tile_size, Value>
+  detail::find<block_size, tile_size, Value>
     <<<grid_size, block_size>>>(first, last, output_begin, view, hash, key_equal);
   CUCO_CUDA_TRY(cudaDeviceSynchronize());
 }
@@ -117,7 +117,7 @@ void static_map<Key, Value, Scope, Allocator>::contains(
   auto const grid_size  = (tile_size * num_keys + stride * block_size - 1) / (stride * block_size);
   auto view             = get_device_view();
 
-  detail::map::contains<block_size, tile_size>
+  detail::contains<block_size, tile_size>
     <<<grid_size, block_size>>>(first, last, output_begin, view, hash, key_equal);
   CUCO_CUDA_TRY(cudaDeviceSynchronize());
 }
diff --git a/include/cuco/detail/static_map_kernels.cuh b/include/cuco/detail/static_map_kernels.cuh
index aaf95b40c..d28464e16 100644
--- a/include/cuco/detail/static_map_kernels.cuh
+++ b/include/cuco/detail/static_map_kernels.cuh
@@ -12,9 +12,9 @@
  * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  * See the License for the specific language governing permissions and
  * limitations under the License.
- */
+ */ 
 
-namespace cuco {
+namespace cuco{
 namespace detail {
 namespace cg = cooperative_groups;
 
@@ -51,14 +51,43 @@ __global__ void initialize(pair_atomic_type* const slots, Key k, Value v, std::s
 }
 
 /**
- * @brief Inserts all key/value pairs in the range `[first, last)`.
+ * @brief Initializes each slot in the flat `slots` storage to contain `k` and `v`.
  *
+ * Each space in `slots` that can hold a key value pair is initialized to a
+ * `pair_atomic_type` containing the key `k` and the value `v`.
+ * 
+ * @tparam atomic_key_type Type of the `Key` atomic container
+ * @tparam atomic_mapped_type Type of the `Value` atomic container
+ * @tparam Key key type
+ * @tparam Value value type
+ * @tparam pair_atomic_type key/value pair type
+ * @param slots Pointer to flat storage for the map's key/value pairs
+ * @param k Key to which all keys in `slots` are initialized
+ * @param v Value to which all values in `slots` are initialized
+ * @param size Size of the storage pointed to by `slots`
+ */
+template<typename atomic_key_type, typename atomic_mapped_type, typename Key, typename Value, typename pair_atomic_type>
+__global__ void initialize(
+  pair_atomic_type* const slots, Key k,
+  Value v, std::size_t size) {
+  
+  auto tid = threadIdx.x + blockIdx.x * blockDim.x;
+  while (tid < size) {
+    new (&slots[tid].first) atomic_key_type{k};
+    new (&slots[tid].second) atomic_mapped_type{v};
+    tid += gridDim.x * blockDim.x;
+  }
+}
+
+/**
+ * @brief Inserts all key/value pairs in the range `[first, last)`. 
+ *  
  * If multiple keys in `[first, last)` compare equal, it is unspecified which
  * element is inserted.
- *
- * @tparam block_size
+ * 
+ * @tparam block_size 
  * @tparam InputIt Device accessible input iterator whose `value_type` is
- * convertible to the map's `value_type`
+* convertible to the map's `value_type`
  * @tparam atomicT Type of atomic storage
  * @tparam viewT Type of device view allowing access of hash map storage
  * @tparam Hash Unary callable type
@@ -70,15 +99,18 @@ __global__ void initialize(pair_atomic_type* const slots, Key k, Value v, std::s
  * @param hash The unary function to apply to hash each key
  * @param key_equal The binary function used to compare two keys for equality
  */
-template <uint32_t block_size,
-          typename InputIt,
-          typename atomicT,
-          typename viewT,
-          typename Hash,
-          typename KeyEqual>
-__global__ void insert(
-  InputIt first, InputIt last, atomicT* num_successes, viewT view, Hash hash, KeyEqual key_equal)
-{
+template<uint32_t block_size,
+         typename InputIt,
+         typename atomicT,
+         typename viewT,
+         typename Hash, 
+         typename KeyEqual>
+__global__ void insert(InputIt first,
+                       InputIt last,
+                       atomicT* num_successes,
+                       viewT view,
+                       Hash hash,
+                       KeyEqual key_equal) {
   typedef cub::BlockReduce<std::size_t, block_size> BlockReduce;
   __shared__ typename BlockReduce::TempStorage temp_storage;
   std::size_t thread_num_successes = 0;
@@ -95,23 +127,25 @@ __global__ void insert(
   // compute number of successfully inserted elements for each block
   // and atomically add to the grand total
   std::size_t block_num_successes = BlockReduce(temp_storage).Sum(thread_num_successes);
-  if (threadIdx.x == 0) { *num_successes += block_num_successes; }
+  if(threadIdx.x == 0) {
+    *num_successes += block_num_successes;
+  }
 }
 
 /**
- * @brief Inserts all key/value pairs in the range `[first, last)`.
- *
+ * @brief Inserts all key/value pairs in the range `[first, last)`. 
+ *  
  * If multiple keys in `[first, last)` compare equal, it is unspecified which
  * element is inserted. Uses the CUDA Cooperative Groups API to leverage groups
- * of multiple threads to perform each key/value insertion. This provides a
+ * of multiple threads to perform each key/value insertion. This provides a 
  * significant boost in throughput compared to the non Cooperative Group
  * `insert` at moderate to high load factors.
- *
- * @tparam block_size
+ * 
+ * @tparam block_size 
  * @tparam tile_size The number of threads in the Cooperative Groups used to perform
  * inserts
  * @tparam InputIt Device accessible input iterator whose `value_type` is
- * convertible to the map's `value_type`
+* convertible to the map's `value_type`
  * @tparam atomicT Type of atomic storage
  * @tparam viewT Type of device view allowing access of hash map storage
  * @tparam Hash Unary callable type
@@ -123,16 +157,19 @@ __global__ void insert(
  * @param hash The unary function to apply to hash each key
  * @param key_equal The binary function used to compare two keys for equality
  */
-template <uint32_t block_size,
-          uint32_t tile_size,
-          typename InputIt,
-          typename atomicT,
-          typename viewT,
-          typename Hash,
-          typename KeyEqual>
-__global__ void insert(
-  InputIt first, InputIt last, atomicT* num_successes, viewT view, Hash hash, KeyEqual key_equal)
-{
+template<uint32_t block_size,
+         uint32_t tile_size,
+         typename InputIt,
+         typename atomicT,
+         typename viewT,
+         typename Hash, 
+         typename KeyEqual>
+__global__ void insert(InputIt first,
+                       InputIt last,
+                       atomicT* num_successes,
+                       viewT view,
+                       Hash hash,
+                       KeyEqual key_equal) {
   typedef cub::BlockReduce<std::size_t, block_size> BlockReduce;
   __shared__ typename BlockReduce::TempStorage temp_storage;
   std::size_t thread_num_successes = 0;
@@ -149,23 +186,25 @@ __global__ void insert(
     }
     it += (gridDim.x * block_size) / tile_size;
   }
-
+  
   // compute number of successfully inserted elements for each block
   // and atomically add to the grand total
   std::size_t block_num_successes = BlockReduce(temp_storage).Sum(thread_num_successes);
-  if (threadIdx.x == 0) { *num_successes += block_num_successes; }
+  if(threadIdx.x == 0) {
+    *num_successes += block_num_successes;
+  }
 }
 
 /**
  * @brief Finds the values corresponding to all keys in the range `[first, last)`.
- *
- * If the key `*(first + i)` exists in the map, copies its associated value to `(output_begin + i)`.
- * Else, copies the empty value sentinel.
+ * 
+ * If the key `*(first + i)` exists in the map, copies its associated value to `(output_begin + i)`. 
+ * Else, copies the empty value sentinel. 
  * @tparam block_size The size of the thread block
  * @tparam Value The type of the mapped value for the map
  * @tparam InputIt Device accessible input iterator whose `value_type` is
  * convertible to the map's `key_type`
- * @tparam OutputIt Device accessible output iterator whose `value_type` is
+ * @tparam OutputIt Device accessible output iterator whose `value_type` is 
  * convertible to the map's `mapped_type`
  * @tparam viewT Type of device view allowing access of hash map storage
  * @tparam Hash Unary callable type
@@ -190,13 +229,13 @@ __global__ void find(
   auto tid     = block_size * blockIdx.x + threadIdx.x;
   auto key_idx = tid;
   __shared__ Value writeBuffer[block_size];
-
-  while (first + key_idx < last) {
-    auto key   = *(first + key_idx);
+  
+  while(first + key_idx < last) {
+    auto key = *(first + key_idx);
     auto found = view.find(key, hash, key_equal);
 
-    /*
-     * The ld.relaxed.gpu instruction used in view.find causes L1 to
+    /* 
+     * The ld.relaxed.gpu instruction used in view.find causes L1 to 
      * flush more frequently, causing increased sector stores from L2 to global memory.
      * By writing results to shared memory and then synchronizing before writing back
      * to global, we no longer rely on L1, preventing the increase in sector stores from
@@ -213,10 +252,10 @@ __global__ void find(
 
 /**
  * @brief Finds the values corresponding to all keys in the range `[first, last)`.
- *
- * If the key `*(first + i)` exists in the map, copies its associated value to `(output_begin + i)`.
+ * 
+ * If the key `*(first + i)` exists in the map, copies its associated value to `(output_begin + i)`. 
  * Else, copies the empty value sentinel. Uses the CUDA Cooperative Groups API to leverage groups
- * of multiple threads to find each key. This provides a significant boost in throughput compared
+ * of multiple threads to find each key. This provides a significant boost in throughput compared 
  * to the non Cooperative Group `find` at moderate to high load factors.
  *
  * @tparam block_size The size of the thread block
@@ -225,7 +264,7 @@ __global__ void find(
  * @tparam Value The type of the mapped value for the map
  * @tparam InputIt Device accessible input iterator whose `value_type` is
  * convertible to the map's `key_type`
- * @tparam OutputIt Device accessible output iterator whose `value_type` is
+ * @tparam OutputIt Device accessible output iterator whose `value_type` is 
  * convertible to the map's `mapped_type`
  * @tparam viewT Type of device view allowing access of hash map storage
  * @tparam Hash Unary callable type
@@ -252,13 +291,13 @@ __global__ void find(
   auto tid     = block_size * blockIdx.x + threadIdx.x;
   auto key_idx = tid / tile_size;
   __shared__ Value writeBuffer[block_size];
-
-  while (first + key_idx < last) {
-    auto key   = *(first + key_idx);
+  
+  while(first + key_idx < last) {
+    auto key = *(first + key_idx);
     auto found = view.find(tile, key, hash, key_equal);
-
-    /*
-     * The ld.relaxed.gpu instruction used in view.find causes L1 to
+    
+    /* 
+     * The ld.relaxed.gpu instruction used in view.find causes L1 to 
      * flush more frequently, causing increased sector stores from L2 to global memory.
      * By writing results to shared memory and then synchronizing before writing back
      * to global, we no longer rely on L1, preventing the increase in sector stores from
@@ -270,7 +309,7 @@ __global__ void find(
                             : found->second.load(cuda::std::memory_order_relaxed);
     }
     __syncthreads();
-    if (tile.thread_rank() == 0) {
+    if(tile.thread_rank() == 0) {
       *(output_begin + key_idx) = writeBuffer[threadIdx.x / tile_size];
     }
     key_idx += (gridDim.x * block_size) / tile_size;
@@ -279,7 +318,7 @@ __global__ void find(
 
 /**
  * @brief Indicates whether the keys in the range `[first, last)` are contained in the map.
- *
+ * 
  * Writes a `bool` to `(output + i)` indicating if the key `*(first + i)` exists in the map.
  *
  * @tparam block_size The size of the thread block
@@ -289,7 +328,7 @@ __global__ void find(
  * convertible to the map's `mapped_type`
  * @tparam viewT Type of device view allowing access of hash map storage
  * @tparam Hash Unary callable type
- * @tparam KeyEqual Binary callable type
+ * @tparam KeyEqual Binary callable type 
  * @param first Beginning of the sequence of keys
  * @param last End of the sequence of keys
  * @param output_begin Beginning of the sequence of booleans for the presence of each key
@@ -309,12 +348,12 @@ __global__ void contains(
   auto tid     = block_size * blockIdx.x + threadIdx.x;
   auto key_idx = tid;
   __shared__ bool writeBuffer[block_size];
-
-  while (first + key_idx < last) {
+  
+  while(first + key_idx < last) {
     auto key = *(first + key_idx);
-
-    /*
-     * The ld.relaxed.gpu instruction used in view.find causes L1 to
+    
+    /* 
+     * The ld.relaxed.gpu instruction used in view.find causes L1 to 
      * flush more frequently, causing increased sector stores from L2 to global memory.
      * By writing results to shared memory and then synchronizing before writing back
      * to global, we no longer rely on L1, preventing the increase in sector stores from
@@ -329,10 +368,10 @@ __global__ void contains(
 
 /**
  * @brief Indicates whether the keys in the range `[first, last)` are contained in the map.
- *
+ * 
  * Writes a `bool` to `(output + i)` indicating if the key `*(first + i)` exists in the map.
- * Uses the CUDA Cooperative Groups API to leverage groups of multiple threads to perform the
- * contains operation for each key. This provides a significant boost in throughput compared
+ * Uses the CUDA Cooperative Groups API to leverage groups of multiple threads to perform the 
+ * contains operation for each key. This provides a significant boost in throughput compared 
  * to the non Cooperative Group `contains` at moderate to high load factors.
  *
  * @tparam block_size The size of the thread block
@@ -344,7 +383,7 @@ __global__ void contains(
  * convertible to the map's `mapped_type`
  * @tparam viewT Type of device view allowing access of hash map storage
  * @tparam Hash Unary callable type
- * @tparam KeyEqual Binary callable type
+ * @tparam KeyEqual Binary callable type 
  * @param first Beginning of the sequence of keys
  * @param last End of the sequence of keys
  * @param output_begin Beginning of the sequence of booleans for the presence of each key
@@ -366,26 +405,28 @@ __global__ void contains(
   auto tid     = block_size * blockIdx.x + threadIdx.x;
   auto key_idx = tid / tile_size;
   __shared__ bool writeBuffer[block_size];
-
-  while (first + key_idx < last) {
-    auto key   = *(first + key_idx);
+  
+  while(first + key_idx < last) {
+    auto key = *(first + key_idx);
     auto found = view.contains(tile, key, hash, key_equal);
-
-    /*
-     * The ld.relaxed.gpu instruction used in view.find causes L1 to
+    
+    /* 
+     * The ld.relaxed.gpu instruction used in view.find causes L1 to 
      * flush more frequently, causing increased sector stores from L2 to global memory.
      * By writing results to shared memory and then synchronizing before writing back
      * to global, we no longer rely on L1, preventing the increase in sector stores from
      * L2 to global and improving performance.
      */
-    if (tile.thread_rank() == 0) { writeBuffer[threadIdx.x / tile_size] = found; }
+    if(tile.thread_rank() == 0) {
+      writeBuffer[threadIdx.x / tile_size] = found;
+    }
     __syncthreads();
-    if (tile.thread_rank() == 0) {
+    if(tile.thread_rank() == 0) {
       *(output_begin + key_idx) = writeBuffer[threadIdx.x / tile_size];
     }
     key_idx += (gridDim.x * block_size) / tile_size;
   }
 }
 
-}  // namespace detail
-}  // namespace cuco
+} // namespace detail
+} // namespace cuco
diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index 9109aa9bf..a90cb9bb9 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2021, NVIDIA CORPORATION.
+ * Copyright (c) 2020, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -16,27 +16,50 @@
 
 namespace cuco {
 
+/**---------------------------------------------------------------------------*
+ * @brief Enumeration of the possible results of attempting to insert into
+ *a hash bucket
+ *---------------------------------------------------------------------------**/
+enum class insert_result {
+  CONTINUE,  ///< Insert did not succeed, continue trying to insert
+  SUCCESS,   ///< New pair inserted successfully
+  DUPLICATE  ///< Insert did not succeed, key is already present
+};
+
 template <typename Key, typename Value, cuda::thread_scope Scope, typename Allocator>
 static_multimap<Key, Value, Scope, Allocator>::static_multimap(std::size_t capacity,
-                                                               Key empty_key_sentinel,
-                                                               Value empty_value_sentinel,
-                                                               Allocator const& alloc)
-  : static_map<Key, Value, Scope, Allocator>{
-      capacity, empty_key_sentinel, empty_value_sentinel, alloc}
+                                                     Key empty_key_sentinel,
+                                                     Value empty_value_sentinel,
+                                                     Allocator const& alloc)
+  : capacity_{capacity},
+    empty_key_sentinel_{empty_key_sentinel},
+    empty_value_sentinel_{empty_value_sentinel},
+    slot_allocator_{alloc}
 {
+  slots_ = std::allocator_traits<slot_allocator_type>::allocate(slot_allocator_, capacity);
+
+  auto constexpr block_size = 256;
+  auto constexpr stride     = 4;
+  auto const grid_size      = (capacity + stride * block_size - 1) / (stride * block_size);
+  detail::initialize<atomic_key_type, atomic_mapped_type>
+    <<<grid_size, block_size>>>(slots_, empty_key_sentinel, empty_value_sentinel, capacity);
+
+  CUCO_CUDA_TRY(cudaMallocManaged(&num_successes_, sizeof(atomic_ctr_type)));
 }
 
 template <typename Key, typename Value, cuda::thread_scope Scope, typename Allocator>
 static_multimap<Key, Value, Scope, Allocator>::~static_multimap()
 {
+  std::allocator_traits<slot_allocator_type>::deallocate(slot_allocator_, slots_, capacity_);
+  CUCO_CUDA_TRY(cudaFree(num_successes_));
 }
 
 template <typename Key, typename Value, cuda::thread_scope Scope, typename Allocator>
 template <typename InputIt, typename Hash, typename KeyEqual>
 void static_multimap<Key, Value, Scope, Allocator>::insert(InputIt first,
-                                                           InputIt last,
-                                                           Hash hash,
-                                                           KeyEqual key_equal)
+                                                      InputIt last,
+                                                      Hash hash,
+                                                      KeyEqual key_equal)
 {
   auto num_keys         = std::distance(first, last);
   auto const block_size = 128;
@@ -50,17 +73,16 @@ void static_multimap<Key, Value, Scope, Allocator>::insert(InputIt first,
   CUCO_CUDA_TRY(cudaGetDevice(&device_id));
   CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_successes_, sizeof(atomic_ctr_type), device_id));
 
-  detail::multimap::insert<block_size, tile_size>
+  detail::insert<block_size, tile_size>
     <<<grid_size, block_size>>>(first, first + num_keys, num_successes_, view, hash, key_equal);
   CUCO_CUDA_TRY(cudaDeviceSynchronize());
 
   size_ += num_successes_->load(cuda::std::memory_order_relaxed);
 }
 
-/*
 template <typename Key, typename Value, cuda::thread_scope Scope, typename Allocator>
 template <typename InputIt, typename OutputIt, typename Hash, typename KeyEqual>
-void static_map<Key, Value, Scope, Allocator>::find(
+void static_multimap<Key, Value, Scope, Allocator>::find(
   InputIt first, InputIt last, OutputIt output_begin, Hash hash, KeyEqual key_equal) noexcept
 {
   auto num_keys         = std::distance(first, last);
@@ -77,7 +99,7 @@ void static_map<Key, Value, Scope, Allocator>::find(
 
 template <typename Key, typename Value, cuda::thread_scope Scope, typename Allocator>
 template <typename InputIt, typename OutputIt, typename Hash, typename KeyEqual>
-void static_map<Key, Value, Scope, Allocator>::contains(
+void static_multimap<Key, Value, Scope, Allocator>::contains(
   InputIt first, InputIt last, OutputIt output_begin, Hash hash, KeyEqual key_equal) noexcept
 {
   auto num_keys         = std::distance(first, last);
@@ -94,7 +116,7 @@ void static_map<Key, Value, Scope, Allocator>::contains(
 
 template <typename Key, typename Value, cuda::thread_scope Scope, typename Allocator>
 template <typename Hash, typename KeyEqual>
-__device__ bool static_map<Key, Value, Scope, Allocator>::device_mutable_view::insert(
+__device__ bool static_multimap<Key, Value, Scope, Allocator>::device_mutable_view::insert(
   value_type const& insert_pair, Hash hash, KeyEqual key_equal) noexcept
 {
   auto current_slot{initial_slot(insert_pair.first, hash)};
@@ -125,7 +147,7 @@ __device__ bool static_map<Key, Value, Scope, Allocator>::device_mutable_view::i
 
     // if the key was already inserted by another thread, than this instance is a
     // duplicate, so the insert fails
-    if (key_equal(insert_pair.first, expected_key)) { return false; }
+    //if (key_equal(insert_pair.first, expected_key)) { return false; }
 
     // if we couldn't insert the key, but it wasn't a duplicate, then there must
     // have been some other key there, so we keep looking for a slot
@@ -135,7 +157,7 @@ __device__ bool static_map<Key, Value, Scope, Allocator>::device_mutable_view::i
 
 template <typename Key, typename Value, cuda::thread_scope Scope, typename Allocator>
 template <typename CG, typename Hash, typename KeyEqual>
-__device__ bool static_map<Key, Value, Scope, Allocator>::device_mutable_view::insert(
+__device__ bool static_multimap<Key, Value, Scope, Allocator>::device_mutable_view::insert(
   CG g, value_type const& insert_pair, Hash hash, KeyEqual key_equal) noexcept
 {
   auto current_slot = initial_slot(g, insert_pair.first, hash);
@@ -148,9 +170,9 @@ __device__ bool static_map<Key, Value, Scope, Allocator>::device_mutable_view::i
     auto const slot_is_empty = (existing_key == this->get_empty_key_sentinel());
 
     // the key we are trying to insert is already in the map, so we return with failure to insert
-    if (g.ballot(not slot_is_empty and key_equal(existing_key, insert_pair.first))) {
-      return false;
-    }
+    //if (g.ballot(not slot_is_empty and key_equal(existing_key, insert_pair.first))) {
+    //  return false;
+    //}
 
     auto const window_contains_empty = g.ballot(slot_is_empty);
 
@@ -186,7 +208,7 @@ __device__ bool static_map<Key, Value, Scope, Allocator>::device_mutable_view::i
         }
 
         // our key was already present in the slot, so our key is a duplicate
-        if (key_equal(insert_pair.first, expected_key)) { status = insert_result::DUPLICATE; }
+        // if (key_equal(insert_pair.first, expected_key)) { status = insert_result::DUPLICATE; }
         // another key was inserted in the slot we wanted to try
         // so we need to try the next empty slot in the window
       }
@@ -196,8 +218,6 @@ __device__ bool static_map<Key, Value, Scope, Allocator>::device_mutable_view::i
 
       // successful insert
       if (status == insert_result::SUCCESS) { return true; }
-      // duplicate present during insert
-      if (status == insert_result::DUPLICATE) { return false; }
       // if we've gotten this far, a different key took our spot
       // before we could insert. We need to retry the insert on the
       // same window
@@ -212,8 +232,8 @@ __device__ bool static_map<Key, Value, Scope, Allocator>::device_mutable_view::i
 
 template <typename Key, typename Value, cuda::thread_scope Scope, typename Allocator>
 template <typename Hash, typename KeyEqual>
-__device__ typename static_map<Key, Value, Scope, Allocator>::device_view::iterator
-static_map<Key, Value, Scope, Allocator>::device_view::find(Key const& k,
+__device__ typename static_multimap<Key, Value, Scope, Allocator>::device_view::iterator
+static_multimap<Key, Value, Scope, Allocator>::device_view::find(Key const& k,
                                                             Hash hash,
                                                             KeyEqual key_equal) noexcept
 {
@@ -233,8 +253,8 @@ static_map<Key, Value, Scope, Allocator>::device_view::find(Key const& k,
 
 template <typename Key, typename Value, cuda::thread_scope Scope, typename Allocator>
 template <typename Hash, typename KeyEqual>
-__device__ typename static_map<Key, Value, Scope, Allocator>::device_view::const_iterator
-static_map<Key, Value, Scope, Allocator>::device_view::find(Key const& k,
+__device__ typename static_multimap<Key, Value, Scope, Allocator>::device_view::const_iterator
+static_multimap<Key, Value, Scope, Allocator>::device_view::find(Key const& k,
                                                             Hash hash,
                                                             KeyEqual key_equal) const noexcept
 {
@@ -254,8 +274,8 @@ static_map<Key, Value, Scope, Allocator>::device_view::find(Key const& k,
 
 template <typename Key, typename Value, cuda::thread_scope Scope, typename Allocator>
 template <typename CG, typename Hash, typename KeyEqual>
-__device__ typename static_map<Key, Value, Scope, Allocator>::device_view::iterator
-static_map<Key, Value, Scope, Allocator>::device_view::find(CG g,
+__device__ typename static_multimap<Key, Value, Scope, Allocator>::device_view::iterator
+static_multimap<Key, Value, Scope, Allocator>::device_view::find(CG g,
                                                             Key const& k,
                                                             Hash hash,
                                                             KeyEqual key_equal) noexcept
@@ -291,8 +311,8 @@ static_map<Key, Value, Scope, Allocator>::device_view::find(CG g,
 
 template <typename Key, typename Value, cuda::thread_scope Scope, typename Allocator>
 template <typename CG, typename Hash, typename KeyEqual>
-__device__ typename static_map<Key, Value, Scope, Allocator>::device_view::const_iterator
-static_map<Key, Value, Scope, Allocator>::device_view::find(CG g,
+__device__ typename static_multimap<Key, Value, Scope, Allocator>::device_view::const_iterator
+static_multimap<Key, Value, Scope, Allocator>::device_view::find(CG g,
                                                             Key const& k,
                                                             Hash hash,
                                                             KeyEqual key_equal) const noexcept
@@ -330,7 +350,7 @@ static_map<Key, Value, Scope, Allocator>::device_view::find(CG g,
 
 template <typename Key, typename Value, cuda::thread_scope Scope, typename Allocator>
 template <typename Hash, typename KeyEqual>
-__device__ bool static_map<Key, Value, Scope, Allocator>::device_view::contains(
+__device__ bool static_multimap<Key, Value, Scope, Allocator>::device_view::contains(
   Key const& k, Hash hash, KeyEqual key_equal) noexcept
 {
   auto current_slot = initial_slot(k, hash);
@@ -348,7 +368,7 @@ __device__ bool static_map<Key, Value, Scope, Allocator>::device_view::contains(
 
 template <typename Key, typename Value, cuda::thread_scope Scope, typename Allocator>
 template <typename CG, typename Hash, typename KeyEqual>
-__device__ bool static_map<Key, Value, Scope, Allocator>::device_view::contains(
+__device__ bool static_multimap<Key, Value, Scope, Allocator>::device_view::contains(
   CG g, Key const& k, Hash hash, KeyEqual key_equal) noexcept
 {
   auto current_slot = initial_slot(g, k, hash);
@@ -372,5 +392,4 @@ __device__ bool static_map<Key, Value, Scope, Allocator>::device_view::contains(
     current_slot = next_slot(g, current_slot);
   }
 }
-*/
 }  // namespace cuco
diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap_kernels.cuh
index f6480a65f..5132c8ef5 100644
--- a/include/cuco/detail/static_multimap_kernels.cuh
+++ b/include/cuco/detail/static_multimap_kernels.cuh
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2021, NVIDIA CORPORATION.
+ * Copyright (c) 2020, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -12,23 +12,50 @@
  * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  * See the License for the specific language governing permissions and
  * limitations under the License.
- */
+ */ 
 
-namespace cuco {
+namespace cuco{
 namespace detail {
 namespace cg = cooperative_groups;
 
-namespace multimap {
-
 /**
- * @brief Inserts all key/value pairs in the range `[first, last)`.
+ * @brief Initializes each slot in the flat `slots` storage to contain `k` and `v`.
  *
+ * Each space in `slots` that can hold a key value pair is initialized to a
+ * `pair_atomic_type` containing the key `k` and the value `v`.
+ * 
+ * @tparam atomic_key_type Type of the `Key` atomic container
+ * @tparam atomic_mapped_type Type of the `Value` atomic container
+ * @tparam Key key type
+ * @tparam Value value type
+ * @tparam pair_atomic_type key/value pair type
+ * @param slots Pointer to flat storage for the map's key/value pairs
+ * @param k Key to which all keys in `slots` are initialized
+ * @param v Value to which all values in `slots` are initialized
+ * @param size Size of the storage pointed to by `slots`
+ */
+template<typename atomic_key_type, typename atomic_mapped_type, typename Key, typename Value, typename pair_atomic_type>
+__global__ void initialize(
+  pair_atomic_type* const slots, Key k,
+  Value v, std::size_t size) {
+  
+  auto tid = threadIdx.x + blockIdx.x * blockDim.x;
+  while (tid < size) {
+    new (&slots[tid].first) atomic_key_type{k};
+    new (&slots[tid].second) atomic_mapped_type{v};
+    tid += gridDim.x * blockDim.x;
+  }
+}
+
+/**
+ * @brief Inserts all key/value pairs in the range `[first, last)`. 
+ *  
  * If multiple keys in `[first, last)` compare equal, it is unspecified which
  * element is inserted.
- *
- * @tparam block_size
+ * 
+ * @tparam block_size 
  * @tparam InputIt Device accessible input iterator whose `value_type` is
- * convertible to the map's `value_type`
+* convertible to the map's `value_type`
  * @tparam atomicT Type of atomic storage
  * @tparam viewT Type of device view allowing access of hash map storage
  * @tparam Hash Unary callable type
@@ -40,31 +67,53 @@ namespace multimap {
  * @param hash The unary function to apply to hash each key
  * @param key_equal The binary function used to compare two keys for equality
  */
-template <uint32_t block_size,
-          typename InputIt,
-          typename atomicT,
-          typename viewT,
-          typename Hash,
-          typename KeyEqual>
-__global__ void insert(
-  InputIt first, InputIt last, atomicT* num_successes, viewT view, Hash hash, KeyEqual key_equal)
-{
+template<uint32_t block_size,
+         typename InputIt,
+         typename atomicT,
+         typename viewT,
+         typename Hash, 
+         typename KeyEqual>
+__global__ void insert(InputIt first,
+                       InputIt last,
+                       atomicT* num_successes,
+                       viewT view,
+                       Hash hash,
+                       KeyEqual key_equal) {
+  typedef cub::BlockReduce<std::size_t, block_size> BlockReduce;
+  __shared__ typename BlockReduce::TempStorage temp_storage;
+  std::size_t thread_num_successes = 0;
+  
+  auto tid = blockDim.x * blockIdx.x + threadIdx.x;
+  auto it = first + tid;
+  
+  while (it < last) {
+    typename viewT::value_type const insert_pair{*it};
+    if (view.insert(insert_pair, hash, key_equal)) { thread_num_successes++; }
+    it += gridDim.x * blockDim.x;
+  }
+
+  // compute number of successfully inserted elements for each block
+  // and atomically add to the grand total
+  std::size_t block_num_successes = BlockReduce(temp_storage).Sum(thread_num_successes);
+  if(threadIdx.x == 0) {
+    *num_successes += block_num_successes;
+  }
 }
 
 /**
- * @brief Inserts all key/value pairs in the range `[first, last)`.
- *
+ * @brief Inserts all key/value pairs in the range `[first, last)`. 
+ *  
  * If multiple keys in `[first, last)` compare equal, it is unspecified which
  * element is inserted. Uses the CUDA Cooperative Groups API to leverage groups
- * of multiple threads to perform each key/value insertion. This provides a
+ * of multiple threads to perform each key/value insertion. This provides a 
  * significant boost in throughput compared to the non Cooperative Group
  * `insert` at moderate to high load factors.
- *
- * @tparam block_size
+ * 
+ * @tparam block_size 
  * @tparam tile_size The number of threads in the Cooperative Groups used to perform
  * inserts
  * @tparam InputIt Device accessible input iterator whose `value_type` is
- * convertible to the map's `value_type`
+* convertible to the map's `value_type`
  * @tparam atomicT Type of atomic storage
  * @tparam viewT Type of device view allowing access of hash map storage
  * @tparam Hash Unary callable type
@@ -76,17 +125,19 @@ __global__ void insert(
  * @param hash The unary function to apply to hash each key
  * @param key_equal The binary function used to compare two keys for equality
  */
-template <uint32_t block_size,
-          uint32_t tile_size,
-          typename InputIt,
-          typename atomicT,
-          typename viewT,
-          typename Hash,
-          typename KeyEqual>
-__global__ void insert(
-  InputIt first, InputIt last, atomicT* num_successes, viewT view, Hash hash, KeyEqual key_equal)
-{
-  /*
+template<uint32_t block_size,
+         uint32_t tile_size,
+         typename InputIt,
+         typename atomicT,
+         typename viewT,
+         typename Hash, 
+         typename KeyEqual>
+__global__ void insert(InputIt first,
+                       InputIt last,
+                       atomicT* num_successes,
+                       viewT view,
+                       Hash hash,
+                       KeyEqual key_equal) {
   typedef cub::BlockReduce<std::size_t, block_size> BlockReduce;
   __shared__ typename BlockReduce::TempStorage temp_storage;
   std::size_t thread_num_successes = 0;
@@ -94,7 +145,7 @@ __global__ void insert(
   auto tile = cg::tiled_partition<tile_size>(cg::this_thread_block());
   auto tid = blockDim.x * blockIdx.x + threadIdx.x;
   auto it = first + tid / tile_size;
-
+  
   while (it < last) {
     // force conversion to value_type
     typename viewT::value_type const insert_pair{*it};
@@ -103,16 +154,249 @@ __global__ void insert(
     }
     it += (gridDim.x * blockDim.x) / tile_size;
   }
-
+  
   // compute number of successfully inserted elements for each block
   // and atomically add to the grand total
   std::size_t block_num_successes = BlockReduce(temp_storage).Sum(thread_num_successes);
   if(threadIdx.x == 0) {
     *num_successes += block_num_successes;
   }
-  */
 }
-}  // namespace multimap
 
-}  // namespace detail
-}  // namespace cuco
+/**
+ * @brief Finds the values corresponding to all keys in the range `[first, last)`.
+ * 
+ * If the key `*(first + i)` exists in the map, copies its associated value to `(output_begin + i)`. 
+ * Else, copies the empty value sentinel. 
+ * @tparam block_size The size of the thread block
+ * @tparam Value The type of the mapped value for the map
+ * @tparam InputIt Device accessible input iterator whose `value_type` is
+ * convertible to the map's `key_type`
+ * @tparam OutputIt Device accessible output iterator whose `value_type` is 
+ * convertible to the map's `mapped_type`
+ * @tparam viewT Type of device view allowing access of hash map storage
+ * @tparam Hash Unary callable type
+ * @tparam KeyEqual Binary callable type
+ * @param first Beginning of the sequence of keys
+ * @param last End of the sequence of keys
+ * @param output_begin Beginning of the sequence of values retrieved for each key
+ * @param view Device view used to access the hash map's slot storage
+ * @param hash The unary function to apply to hash each key
+ * @param key_equal The binary function to compare two keys for equality
+ */
+template<uint32_t block_size,
+         typename Value,
+         typename InputIt, typename OutputIt, 
+         typename viewT,
+         typename Hash, 
+         typename KeyEqual>
+__global__ void find(InputIt first,
+                     InputIt last,
+                     OutputIt output_begin,
+                     viewT view,
+                     Hash hash,
+                     KeyEqual key_equal) {
+  auto tid = blockDim.x * blockIdx.x + threadIdx.x;
+  auto key_idx = tid;
+  __shared__ Value writeBuffer[block_size];
+  
+  while(first + key_idx < last) {
+    auto key = *(first + key_idx);
+    auto found = view.find(key, hash, key_equal);
+
+    /* 
+     * The ld.relaxed.gpu instruction used in view.find causes L1 to 
+     * flush more frequently, causing increased sector stores from L2 to global memory.
+     * By writing results to shared memory and then synchronizing before writing back
+     * to global, we no longer rely on L1, preventing the increase in sector stores from
+     * L2 to global and improving performance.
+     */
+    writeBuffer[threadIdx.x] = found->second.load(cuda::std::memory_order_relaxed);
+    __syncthreads();
+    *(output_begin + key_idx) = writeBuffer[threadIdx.x];
+    key_idx += gridDim.x * blockDim.x;
+  }
+}
+
+/**
+ * @brief Finds the values corresponding to all keys in the range `[first, last)`.
+ * 
+ * If the key `*(first + i)` exists in the map, copies its associated value to `(output_begin + i)`. 
+ * Else, copies the empty value sentinel. Uses the CUDA Cooperative Groups API to leverage groups
+ * of multiple threads to find each key. This provides a significant boost in throughput compared 
+ * to the non Cooperative Group `find` at moderate to high load factors.
+ *
+ * @tparam block_size The size of the thread block
+ * @tparam tile_size The number of threads in the Cooperative Groups used to perform
+ * inserts
+ * @tparam Value The type of the mapped value for the map
+ * @tparam InputIt Device accessible input iterator whose `value_type` is
+ * convertible to the map's `key_type`
+ * @tparam OutputIt Device accessible output iterator whose `value_type` is 
+ * convertible to the map's `mapped_type`
+ * @tparam viewT Type of device view allowing access of hash map storage
+ * @tparam Hash Unary callable type
+ * @tparam KeyEqual Binary callable type
+ * @param first Beginning of the sequence of keys
+ * @param last End of the sequence of keys
+ * @param output_begin Beginning of the sequence of values retrieved for each key
+ * @param view Device view used to access the hash map's slot storage
+ * @param hash The unary function to apply to hash each key
+ * @param key_equal The binary function to compare two keys for equality
+ */
+template<uint32_t block_size, uint32_t tile_size,
+         typename Value,
+         typename InputIt, typename OutputIt, 
+         typename viewT,
+         typename Hash, 
+         typename KeyEqual>
+__global__ void find(InputIt first,
+                     InputIt last,
+                     OutputIt output_begin,
+                     viewT view,
+                     Hash hash,
+                     KeyEqual key_equal) {
+  auto tile = cg::tiled_partition<tile_size>(cg::this_thread_block());
+  auto tid = blockDim.x * blockIdx.x + threadIdx.x;
+  auto key_idx = tid / tile_size;
+  __shared__ Value writeBuffer[block_size];
+  
+  while(first + key_idx < last) {
+    auto key = *(first + key_idx);
+    auto found = view.find(tile, key, hash, key_equal);
+    
+    /* 
+     * The ld.relaxed.gpu instruction used in view.find causes L1 to 
+     * flush more frequently, causing increased sector stores from L2 to global memory.
+     * By writing results to shared memory and then synchronizing before writing back
+     * to global, we no longer rely on L1, preventing the increase in sector stores from
+     * L2 to global and improving performance.
+     */
+    if(tile.thread_rank() == 0) {
+      writeBuffer[threadIdx.x / tile_size] = found->second.load(cuda::std::memory_order_relaxed);
+    }
+    __syncthreads();
+    if(tile.thread_rank() == 0) {
+      *(output_begin + key_idx) = writeBuffer[threadIdx.x / tile_size];
+    }
+    key_idx += (gridDim.x * blockDim.x) / tile_size;
+  }
+}
+
+/**
+ * @brief Indicates whether the keys in the range `[first, last)` are contained in the map.
+ * 
+ * Writes a `bool` to `(output + i)` indicating if the key `*(first + i)` exists in the map.
+ *
+ * @tparam block_size The size of the thread block
+ * @tparam InputIt Device accessible input iterator whose `value_type` is
+ * convertible to the map's `key_type`
+ * @tparam OutputIt Device accessible output iterator whose `value_type` is
+ * convertible to the map's `mapped_type`
+ * @tparam viewT Type of device view allowing access of hash map storage
+ * @tparam Hash Unary callable type
+ * @tparam KeyEqual Binary callable type 
+ * @param first Beginning of the sequence of keys
+ * @param last End of the sequence of keys
+ * @param output_begin Beginning of the sequence of booleans for the presence of each key
+ * @param view Device view used to access the hash map's slot storage
+ * @param hash The unary function to apply to hash each key
+ * @param key_equal The binary function to compare two keys for equality
+ */
+template<uint32_t block_size,
+         typename InputIt, typename OutputIt, 
+         typename viewT,
+         typename Hash, 
+         typename KeyEqual>
+__global__ void contains(InputIt first,
+                         InputIt last,
+                         OutputIt output_begin,
+                         viewT view,
+                         Hash hash,
+                         KeyEqual key_equal) {
+  auto tid = blockDim.x * blockIdx.x + threadIdx.x;
+  auto key_idx = tid;
+  __shared__ bool writeBuffer[block_size];
+  
+  while(first + key_idx < last) {
+    auto key = *(first + key_idx);
+    
+    /* 
+     * The ld.relaxed.gpu instruction used in view.find causes L1 to 
+     * flush more frequently, causing increased sector stores from L2 to global memory.
+     * By writing results to shared memory and then synchronizing before writing back
+     * to global, we no longer rely on L1, preventing the increase in sector stores from
+     * L2 to global and improving performance.
+     */
+    writeBuffer[threadIdx.x] = view.contains(key, hash, key_equal);
+    __syncthreads();
+    *(output_begin + key_idx) = writeBuffer[threadIdx.x];
+    key_idx += gridDim.x * blockDim.x;
+  }
+}
+
+/**
+ * @brief Indicates whether the keys in the range `[first, last)` are contained in the map.
+ * 
+ * Writes a `bool` to `(output + i)` indicating if the key `*(first + i)` exists in the map.
+ * Uses the CUDA Cooperative Groups API to leverage groups of multiple threads to perform the 
+ * contains operation for each key. This provides a significant boost in throughput compared 
+ * to the non Cooperative Group `contains` at moderate to high load factors.
+ *
+ * @tparam block_size The size of the thread block
+ * @tparam tile_size The number of threads in the Cooperative Groups used to perform
+ * inserts
+ * @tparam InputIt Device accessible input iterator whose `value_type` is
+ * convertible to the map's `key_type`
+ * @tparam OutputIt Device accessible output iterator whose `value_type` is
+ * convertible to the map's `mapped_type`
+ * @tparam viewT Type of device view allowing access of hash map storage
+ * @tparam Hash Unary callable type
+ * @tparam KeyEqual Binary callable type 
+ * @param first Beginning of the sequence of keys
+ * @param last End of the sequence of keys
+ * @param output_begin Beginning of the sequence of booleans for the presence of each key
+ * @param view Device view used to access the hash map's slot storage
+ * @param hash The unary function to apply to hash each key
+ * @param key_equal The binary function to compare two keys for equality
+ */
+template<uint32_t block_size, uint32_t tile_size,
+         typename InputIt, typename OutputIt, 
+         typename viewT,
+         typename Hash, 
+         typename KeyEqual>
+__global__ void contains(InputIt first,
+                         InputIt last,
+                         OutputIt output_begin,
+                         viewT view,
+                         Hash hash,
+                         KeyEqual key_equal) {
+  auto tile = cg::tiled_partition<tile_size>(cg::this_thread_block());
+  auto tid = blockDim.x * blockIdx.x + threadIdx.x;
+  auto key_idx = tid / tile_size;
+  __shared__ bool writeBuffer[block_size];
+  
+  while(first + key_idx < last) {
+    auto key = *(first + key_idx);
+    auto found = view.contains(tile, key, hash, key_equal);
+    
+    /* 
+     * The ld.relaxed.gpu instruction used in view.find causes L1 to 
+     * flush more frequently, causing increased sector stores from L2 to global memory.
+     * By writing results to shared memory and then synchronizing before writing back
+     * to global, we no longer rely on L1, preventing the increase in sector stores from
+     * L2 to global and improving performance.
+     */
+    if(tile.thread_rank() == 0) {
+      writeBuffer[threadIdx.x / tile_size] = found;
+    }
+    __syncthreads();
+    if(tile.thread_rank() == 0) {
+      *(output_begin + key_idx) = writeBuffer[threadIdx.x / tile_size];
+    }
+    key_idx += (gridDim.x * blockDim.x) / tile_size;
+  }
+}
+
+} // namespace detail
+} // namespace cuco
\ No newline at end of file
diff --git a/include/cuco/static_map.cuh b/include/cuco/static_map.cuh
index 533273c30..cf7de0a7e 100644
--- a/include/cuco/static_map.cuh
+++ b/include/cuco/static_map.cuh
@@ -37,9 +37,7 @@
 #include <cuco/detail/error.hpp>
 #include <cuco/detail/hash_functions.cuh>
 #include <cuco/detail/pair.cuh>
-#include <cuco/detail/static_kernels.cuh>
 #include <cuco/detail/static_map_kernels.cuh>
-#include <cuco/detail/static_multimap_kernels.cuh>
 
 namespace cuco {
 
@@ -913,7 +911,7 @@ class static_map {
     return device_mutable_view(slots_, capacity_, empty_key_sentinel_, empty_value_sentinel_);
   }
 
- protected:
+ private:
   pair_atomic_type* slots_{nullptr};      ///< Pointer to flat slots storage
   std::size_t capacity_{};                ///< Total number of slots
   std::size_t size_{};                    ///< Number of keys in map
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index 31db9a472..b765a6bfd 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -17,44 +17,157 @@
 #pragma once
 
 #include <cooperative_groups.h>
+#include <thrust/distance.h>
+#include <thrust/functional.h>
 #include <cub/cub.cuh>
 #include <cuda/std/atomic>
+#include <memory>
 
 #include <cuco/allocator.hpp>
-#include <cuco/detail/error.hpp>
+#include <cuco/detail/cuda_memcmp.cuh>
+#ifndef CUDART_VERSION
+#error CUDART_VERSION Undefined!
+#elif (CUDART_VERSION >= 11000)  // including with CUDA 10.2 leads to compilation errors
+#include <cuda/barrier>
+#endif
 
-#include <cuco/static_map.cuh>
+#include <cuco/detail/error.hpp>
+#include <cuco/detail/hash_functions.cuh>
+#include <cuco/detail/pair.cuh>
+#include <cuco/detail/static_multimap_kernels.cuh>
 
 namespace cuco {
 
+/**
+ * @brief A GPU-accelerated, unordered, associative container of key-value
+ * pairs with unique keys.
+ *
+ * Allows constant time concurrent inserts or concurrent find operations (not
+ * concurrent insert and find) from threads in device code.
+ *
+ * Current limitations:
+ * - Requires keys that are Arithmetic
+ * - Does not support erasing keys
+ * - Capacity is fixed and will not grow automatically
+ * - Requires the user to specify sentinel values for both key and mapped value
+ * to indicate empty slots
+ * - Does not support concurrent insert and find operations
+ *
+ * The `static_multimap` supports two types of operations:
+ * - Host-side "bulk" operations
+ * - Device-side "singular" operations
+ *
+ * The host-side bulk operations include `insert`, `find`, and `contains`. These
+ * APIs should be used when there are a large number of keys to insert or lookup
+ * in the map. For example, given a range of keys specified by device-accessible
+ * iterators, the bulk `insert` function will insert all keys into the map.
+ *
+ * The singular device-side operations allow individual threads to perform
+ * independent insert or find/contains operations from device code. These
+ * operations are accessed through non-owning, trivially copyable "view" types:
+ * `device_view` and `mutable_device_view`. The `device_view` class is an
+ * immutable view that allows only non-modifying operations such as `find` or
+ * `contains`. The `mutable_device_view` class only allows `insert` operations.
+ * The two types are separate to prevent erroneous concurrent insert/find
+ * operations.
+ *
+ * Example:
+ * \code{.cpp}
+ * int empty_key_sentinel = -1;
+ * int empty_value_sentinel = -1;
+ *
+ * // Constructs a map with 100,000 slots using -1 and -1 as the empty key/value
+ * // sentinels. Note the capacity is chosen knowing we will insert 50,000 keys,
+ * // for an load factor of 50%.
+ * static_multimap<int, int> m{100'000, empty_key_sentinel, empty_value_sentinel};
+ *
+ * // Create a sequence of pairs {{0,0}, {1,1}, ... {i,i}}
+ * thrust::device_vector<thrust::pair<int,int>> pairs(50,000);
+ * thrust::transform(thrust::make_counting_iterator(0),
+ *                   thrust::make_counting_iterator(pairs.size()),
+ *                   pairs.begin(),
+ *                   []__device__(auto i){ return thrust::make_pair(i,i); };
+ *
+ *
+ * // Inserts all pairs into the map
+ * m.insert(pairs.begin(), pairs.end());
+ *
+ * // Get a `device_view` and passes it to a kernel where threads may perform
+ * // `find/contains` lookups
+ * kernel<<<...>>>(m.get_device_view());
+ * \endcode
+ *
+ *
+ * @tparam Key Arithmetic type used for key
+ * @tparam Value Type of the mapped values
+ * @tparam Scope The scope in which insert/find operations will be performed by
+ * individual threads.
+ * @tparam Allocator Type of allocator used for device storage
+ */
 template <typename Key,
           typename Value,
           cuda::thread_scope Scope = cuda::thread_scope_device,
           typename Allocator       = cuco::cuda_allocator<char>>
-class static_multimap : public static_map<Key, Value, Scope, Allocator> {
+class static_multimap {
+  static_assert(std::is_arithmetic<Key>::value, "Unsupported, non-arithmetic key type.");
+
  public:
-  using typename static_map<Key, Value, Scope, Allocator>::value_type;
-  using typename static_map<Key, Value, Scope, Allocator>::key_type;
-  using typename static_map<Key, Value, Scope, Allocator>::mapped_type;
-  using typename static_map<Key, Value, Scope, Allocator>::atomic_key_type;
-  using typename static_map<Key, Value, Scope, Allocator>::atomic_mapped_type;
-  using typename static_map<Key, Value, Scope, Allocator>::pair_atomic_type;
-  using typename static_map<Key, Value, Scope, Allocator>::atomic_ctr_type;
-  using typename static_map<Key, Value, Scope, Allocator>::allocator_type;
-  using typename static_map<Key, Value, Scope, Allocator>::slot_allocator_type;
-
-  using static_map<Key, Value, Scope, Allocator>::get_device_mutable_view;
+  using value_type         = cuco::pair_type<Key, Value>;
+  using key_type           = Key;
+  using mapped_type        = Value;
+  using atomic_key_type    = cuda::atomic<key_type, Scope>;
+  using atomic_mapped_type = cuda::atomic<mapped_type, Scope>;
+  using pair_atomic_type   = cuco::pair_type<atomic_key_type, atomic_mapped_type>;
+  using atomic_ctr_type    = cuda::atomic<std::size_t, Scope>;
+  using allocator_type     = Allocator;
+  using slot_allocator_type =
+    typename std::allocator_traits<Allocator>::rebind_alloc<pair_atomic_type>;
+
+  static_multimap(static_multimap const&) = delete;
+  static_multimap(static_multimap&&)      = delete;
+  static_multimap& operator=(static_multimap const&) = delete;
+  static_multimap& operator=(static_multimap&&) = delete;
 
+  /**
+   * @brief Construct a fixed-size map with the specified capacity and sentinel values.
+   * @brief Construct a statically sized map with the specified number of slots
+   * and sentinel values.
+   *
+   * The capacity of the map is fixed. Insert operations will not automatically
+   * grow the map. Attempting to insert more unique keys than the capacity of
+   * the map results in undefined behavior.
+   *
+   * Performance begins to degrade significantly beyond a load factor of ~70%.
+   * For best performance, choose a capacity that will keep the load factor
+   * below 70%. E.g., if inserting `N` unique keys, choose a capacity of
+   * `N * (1/0.7)`.
+   *
+   * The `empty_key_sentinel` and `empty_value_sentinel` values are reserved and
+   * undefined behavior results from attempting to insert any key/value pair
+   * that contains either.
+   *
+   * @param capacity The total number of slots in the map
+   * @param empty_key_sentinel The reserved key value for empty slots
+   * @param empty_value_sentinel The reserved mapped value for empty slots
+   * @param alloc Allocator used for allocating device storage
+   */
   static_multimap(std::size_t capacity,
-                  Key empty_key_sentinel,
-                  Value empty_value_sentinel,
-                  Allocator const& alloc = Allocator{});
+             Key empty_key_sentinel,
+             Value empty_value_sentinel,
+             Allocator const& alloc = Allocator{});
 
+  /**
+   * @brief Destroys the map and frees its contents.
+   *
+   */
   ~static_multimap();
 
   /**
    * @brief Inserts all key/value pairs in the range `[first, last)`.
    *
+   * If multiple keys in `[first, last)` compare equal, it is unspecified which
+   * element is inserted.
+   *
    * @tparam InputIt Device accessible input iterator whose `value_type` is
    * convertible to the map's `value_type`
    * @tparam Hash Unary callable type
@@ -67,16 +180,730 @@ class static_multimap : public static_map<Key, Value, Scope, Allocator> {
   template <typename InputIt,
             typename Hash     = cuco::detail::MurmurHash3_32<key_type>,
             typename KeyEqual = thrust::equal_to<key_type>>
-  void insert(InputIt first,
-              InputIt last,
-              Hash hash          = Hash{},
-              KeyEqual key_equal = KeyEqual{}) override;
+  void insert(InputIt first, InputIt last, Hash hash = Hash{}, KeyEqual key_equal = KeyEqual{});
+
+  /**
+   * @brief Finds the values corresponding to all keys in the range `[first, last)`.
+   *
+   * If the key `*(first + i)` exists in the map, copies its associated value to `(output_begin +
+   * i)`. Else, copies the empty value sentinel.
+   *
+   * @tparam InputIt Device accessible input iterator whose `value_type` is
+   * convertible to the map's `key_type`
+   * @tparam OutputIt Device accessible output iterator whose `value_type` is
+   * convertible to the map's `mapped_type`
+   * @tparam Hash Unary callable type
+   * @tparam KeyEqual Binary callable type
+   * @param first Beginning of the sequence of keys
+   * @param last End of the sequence of keys
+   * @param output_begin Beginning of the sequence of values retrieved for each key
+   * @param hash The unary function to apply to hash each key
+   * @param key_equal The binary function to compare two keys for equality
+   */
+  template <typename InputIt,
+            typename OutputIt,
+            typename Hash     = cuco::detail::MurmurHash3_32<key_type>,
+            typename KeyEqual = thrust::equal_to<key_type>>
+  void find(InputIt first,
+            InputIt last,
+            OutputIt output_begin,
+            Hash hash          = Hash{},
+            KeyEqual key_equal = KeyEqual{}) noexcept;
+
+  /**
+   * @brief Indicates whether the keys in the range `[first, last)` are contained in the map.
+   *
+   * Writes a `bool` to `(output + i)` indicating if the key `*(first + i)` exists in the map.
+   *
+   * @tparam InputIt Device accessible input iterator whose `value_type` is
+   * convertible to the map's `key_type`
+   * @tparam OutputIt Device accessible output iterator whose `value_type` is
+   * convertible to the map's `mapped_type`
+   * @tparam Hash Unary callable type
+   * @tparam KeyEqual Binary callable type
+   * @param first Beginning of the sequence of keys
+   * @param last End of the sequence of keys
+   * @param output_begin Beginning of the sequence of booleans for the presence of each key
+   * @param hash The unary function to apply to hash each key
+   * @param key_equal The binary function to compare two keys for equality
+   */
+  template <typename InputIt,
+            typename OutputIt,
+            typename Hash     = cuco::detail::MurmurHash3_32<key_type>,
+            typename KeyEqual = thrust::equal_to<key_type>>
+  void contains(InputIt first,
+                InputIt last,
+                OutputIt output_begin,
+                Hash hash          = Hash{},
+                KeyEqual key_equal = KeyEqual{}) noexcept;
 
  private:
-  using static_map<Key, Value, Scope, Allocator>::num_successes_;
-  using static_map<Key, Value, Scope, Allocator>::size_;
+  class device_view_base {
+   protected:
+    // Import member type definitions from `static_multimap`
+    using value_type     = value_type;
+    using key_type       = Key;
+    using mapped_type    = Value;
+    using iterator       = pair_atomic_type*;
+    using const_iterator = pair_atomic_type const*;
+
+   private:
+    pair_atomic_type* slots_{};     ///< Pointer to flat slots storage
+    std::size_t capacity_{};        ///< Total number of slots
+    Key empty_key_sentinel_{};      ///< Key value that represents an empty slot
+    Value empty_value_sentinel_{};  ///< Initial Value of empty slot
+
+   protected:
+    __host__ __device__ device_view_base(pair_atomic_type* slots,
+                                         std::size_t capacity,
+                                         Key empty_key_sentinel,
+                                         Value empty_value_sentinel) noexcept
+      : slots_{slots},
+        capacity_{capacity},
+        empty_key_sentinel_{empty_key_sentinel},
+        empty_value_sentinel_{empty_value_sentinel}
+    {
+    }
+
+    /**
+     * @brief Gets slots array.
+     *
+     * @return Slots array
+     */
+    __device__ pair_atomic_type* get_slots() noexcept { return slots_; }
+
+    /**
+     * @brief Gets slots array.
+     *
+     * @return Slots array
+     */
+    __device__ pair_atomic_type const* get_slots() const noexcept { return slots_; }
+
+    /**
+     * @brief Returns the initial slot for a given key `k`
+     *
+     * @tparam Hash Unary callable type
+     * @param k The key to get the slot for
+     * @param hash The unary callable used to hash the key
+     * @return Pointer to the initial slot for `k`
+     */
+    template <typename Hash>
+    __device__ iterator initial_slot(Key const& k, Hash hash) noexcept
+    {
+      return &slots_[hash(k) % capacity_];
+    }
+
+    /**
+     * @brief Returns the initial slot for a given key `k`
+     *
+     * @tparam Hash Unary callable type
+     * @param k The key to get the slot for
+     * @param hash The unary callable used to hash the key
+     * @return Pointer to the initial slot for `k`
+     */
+    template <typename Hash>
+    __device__ const_iterator initial_slot(Key const& k, Hash hash) const noexcept
+    {
+      return &slots_[hash(k) % capacity_];
+    }
+
+    /**
+     * @brief Returns the initial slot for a given key `k`
+     *
+     * To be used for Cooperative Group based probing.
+     *
+     * @tparam CG Cooperative Group type
+     * @tparam Hash Unary callable type
+     * @param g the Cooperative Group for which the initial slot is needed
+     * @param k The key to get the slot for
+     * @param hash The unary callable used to hash the key
+     * @return Pointer to the initial slot for `k`
+     */
+    template <typename CG, typename Hash>
+    __device__ iterator initial_slot(CG g, Key const& k, Hash hash) noexcept
+    {
+      return &slots_[(hash(k) + g.thread_rank()) % capacity_];
+    }
+
+    /**
+     * @brief Returns the initial slot for a given key `k`
+     *
+     * To be used for Cooperative Group based probing.
+     *
+     * @tparam CG Cooperative Group type
+     * @tparam Hash Unary callable type
+     * @param g the Cooperative Group for which the initial slot is needed
+     * @param k The key to get the slot for
+     * @param hash The unary callable used to hash the key
+     * @return Pointer to the initial slot for `k`
+     */
+    template <typename CG, typename Hash>
+    __device__ const_iterator initial_slot(CG g, Key const& k, Hash hash) const noexcept
+    {
+      return &slots_[(hash(k) + g.thread_rank()) % capacity_];
+    }
+
+    /**
+     * @brief Given a slot `s`, returns the next slot.
+     *
+     * If `s` is the last slot, wraps back around to the first slot.
+     *
+     * @param s The slot to advance
+     * @return The next slot after `s`
+     */
+    __device__ iterator next_slot(iterator s) noexcept { return (++s < end()) ? s : begin_slot(); }
+
+    /**
+     * @brief Given a slot `s`, returns the next slot.
+     *
+     * If `s` is the last slot, wraps back around to the first slot.
+     *
+     * @param s The slot to advance
+     * @return The next slot after `s`
+     */
+    __device__ const_iterator next_slot(const_iterator s) const noexcept
+    {
+      return (++s < end()) ? s : begin_slot();
+    }
 
-};  // class static_multi_map
+    /**
+     * @brief Given a slot `s`, returns the next slot.
+     *
+     * If `s` is the last slot, wraps back around to the first slot. To
+     * be used for Cooperative Group based probing.
+     *
+     * @tparam CG The Cooperative Group type
+     * @param g The Cooperative Group for which the next slot is needed
+     * @param s The slot to advance
+     * @return The next slot after `s`
+     */
+    template <typename CG>
+    __device__ iterator next_slot(CG g, iterator s) noexcept
+    {
+      uint32_t index = s - slots_;
+      return &slots_[(index + g.size()) % capacity_];
+    }
+
+    /**
+     * @brief Given a slot `s`, returns the next slot.
+     *
+     * If `s` is the last slot, wraps back around to the first slot. To
+     * be used for Cooperative Group based probing.
+     *
+     * @tparam CG The Cooperative Group type
+     * @param g The Cooperative Group for which the next slot is needed
+     * @param s The slot to advance
+     * @return The next slot after `s`
+     */
+    template <typename CG>
+    __device__ const_iterator next_slot(CG g, const_iterator s) const noexcept
+    {
+      uint32_t index = s - slots_;
+      return &slots_[(index + g.size()) % capacity_];
+    }
+
+   public:
+    /**
+     * @brief Gets the maximum number of elements the hash map can hold.
+     *
+     * @return The maximum number of elements the hash map can hold
+     */
+    __host__ __device__ std::size_t get_capacity() const noexcept { return capacity_; }
+
+    /**
+     * @brief Gets the sentinel value used to represent an empty key slot.
+     *
+     * @return The sentinel value used to represent an empty key slot
+     */
+    __host__ __device__ Key get_empty_key_sentinel() const noexcept { return empty_key_sentinel_; }
+
+    /**
+     * @brief Gets the sentinel value used to represent an empty value slot.
+     *
+     * @return The sentinel value used to represent an empty value slot
+     */
+    __host__ __device__ Value get_empty_value_sentinel() const noexcept
+    {
+      return empty_value_sentinel_;
+    }
+
+    /**
+     * @brief Returns iterator to the first slot.
+     *
+     * @note Unlike `std::map::begin()`, the `begin_slot()` iterator does _not_ point to the first
+     * occupied slot. Instead, it refers to the first slot in the array of contiguous slot storage.
+     * Iterating from `begin_slot()` to `end_slot()` will iterate over all slots, including those
+     * both empty and filled.
+     *
+     * There is no `begin()` iterator to avoid confusion as it is not possible to provide an
+     * iterator over only the filled slots.
+     *
+     * @return Iterator to the first slot
+     */
+    __device__ iterator begin_slot() noexcept { return slots_; }
+
+    /**
+     * @brief Returns iterator to the first slot.
+     *
+     * @note Unlike `std::map::begin()`, the `begin_slot()` iterator does _not_ point to the first
+     * occupied slot. Instead, it refers to the first slot in the array of contiguous slot storage.
+     * Iterating from `begin_slot()` to `end_slot()` will iterate over all slots, including those
+     * both empty and filled.
+     *
+     * There is no `begin()` iterator to avoid confusion as it is not possible to provide an
+     * iterator over only the filled slots.
+     *
+     * @return Iterator to the first slot
+     */
+    __device__ const_iterator begin_slot() const noexcept { return slots_; }
+
+    /**
+     * @brief Returns a const_iterator to one past the last slot.
+     *
+     * @return A const_iterator to one past the last slot
+     */
+    __host__ __device__ const_iterator end_slot() const noexcept { return slots_ + capacity_; }
+
+    /**
+     * @brief Returns an iterator to one past the last slot.
+     *
+     * @return An iterator to one past the last slot
+     */
+    __host__ __device__ iterator end_slot() noexcept { return slots_ + capacity_; }
+
+    /**
+     * @brief Returns a const_iterator to one past the last slot.
+     *
+     * `end()` calls `end_slot()` and is provided for convenience for those familiar with checking
+     * an iterator returned from `find()` against the `end()` iterator.
+     *
+     * @return A const_iterator to one past the last slot
+     */
+    __host__ __device__ const_iterator end() const noexcept { return end_slot(); }
+
+    /**
+     * @brief Returns an iterator to one past the last slot.
+     *
+     * `end()` calls `end_slot()` and is provided for convenience for those familiar with checking
+     * an iterator returned from `find()` against the `end()` iterator.
+     *
+     * @return An iterator to one past the last slot
+     */
+    __host__ __device__ iterator end() noexcept { return end_slot(); }
+  };
+
+ public:
+  /**
+   * @brief Mutable, non-owning view-type that may be used in device code to
+   * perform singular inserts into the map.
+   *
+   * `device_mutable_view` is trivially-copyable and is intended to be passed by
+   * value.
+   *
+   * Example:
+   * \code{.cpp}
+   * cuco::static_multimap<int,int> m{100'000, -1, -1};
+   *
+   * // Inserts a sequence of pairs {{0,0}, {1,1}, ... {i,i}}
+   * thrust::for_each(thrust::make_counting_iterator(0),
+   *                  thrust::make_counting_iterator(50'000),
+   *                  [map = m.get_mutable_device_view()]
+   *                  __device__ (auto i) mutable {
+   *                     map.insert(thrust::make_pair(i,i));
+   *                  });
+   * \endcode
+   */
+  class device_mutable_view : public device_view_base {
+   public:
+    using value_type     = typename device_view_base::value_type;
+    using key_type       = typename device_view_base::key_type;
+    using mapped_type    = typename device_view_base::mapped_type;
+    using iterator       = typename device_view_base::iterator;
+    using const_iterator = typename device_view_base::const_iterator;
+    /**
+     * @brief Construct a mutable view of the first `capacity` slots of the
+     * slots array pointed to by `slots`.
+     *
+     * @param slots Pointer to beginning of initialized slots array
+     * @param capacity The number of slots viewed by this object
+     * @param empty_key_sentinel The reserved value for keys to represent empty
+     * slots
+     * @param empty_value_sentinel The reserved value for mapped values to
+     * represent empty slots
+     */
+    __host__ __device__ device_mutable_view(pair_atomic_type* slots,
+                                            std::size_t capacity,
+                                            Key empty_key_sentinel,
+                                            Value empty_value_sentinel) noexcept
+      : device_view_base{slots, capacity, empty_key_sentinel, empty_value_sentinel}
+    {
+    }
+
+    /**
+     * @brief Inserts the specified key/value pair into the map.
+     *
+     * Returns a pair consisting of an iterator to the inserted element (or to
+     * the element that prevented the insertion) and a `bool` denoting whether
+     * the insertion took place.
+     *
+     * @tparam Hash Unary callable type
+     * @tparam KeyEqual Binary callable type
+     * @param insert_pair The pair to insert
+     * @param hash The unary callable used to hash the key
+     * @param key_equal The binary callable used to compare two keys for
+     * equality
+     * @return `true` if the insert was successful, `false` otherwise.
+     */
+    template <typename Hash     = cuco::detail::MurmurHash3_32<key_type>,
+              typename KeyEqual = thrust::equal_to<key_type>>
+    __device__ bool insert(value_type const& insert_pair,
+                           Hash hash          = Hash{},
+                           KeyEqual key_equal = KeyEqual{}) noexcept;
+    /**
+     * @brief Inserts the specified key/value pair into the map.
+     *
+     * Returns a pair consisting of an iterator to the inserted element (or to
+     * the element that prevented the insertion) and a `bool` denoting whether
+     * the insertion took place. Uses the CUDA Cooperative Groups API to
+     * to leverage multiple threads to perform a single insert. This provides a
+     * significant boost in throughput compared to the non Cooperative Group
+     * `insert` at moderate to high load factors.
+     *
+     * @tparam Cooperative Group type
+     * @tparam Hash Unary callable type
+     * @tparam KeyEqual Binary callable type
+     *
+     * @param g The Cooperative Group that performs the insert
+     * @param insert_pair The pair to insert
+     * @param hash The unary callable used to hash the key
+     * @param key_equal The binary callable used to compare two keys for
+     * equality
+     * @return `true` if the insert was successful, `false` otherwise.
+     */
+    template <typename CG,
+              typename Hash     = cuco::detail::MurmurHash3_32<key_type>,
+              typename KeyEqual = thrust::equal_to<key_type>>
+    __device__ bool insert(CG g,
+                           value_type const& insert_pair,
+                           Hash hash          = Hash{},
+                           KeyEqual key_equal = KeyEqual{}) noexcept;
+
+  };  // class device mutable view
+
+  /**
+   * @brief Non-owning view-type that may be used in device code to
+   * perform singular find and contains operations for the map.
+   *
+   * `device_view` is trivially-copyable and is intended to be passed by
+   * value.
+   *
+   */
+  class device_view : public device_view_base {
+   public:
+    using value_type     = typename device_view_base::value_type;
+    using key_type       = typename device_view_base::key_type;
+    using mapped_type    = typename device_view_base::mapped_type;
+    using iterator       = typename device_view_base::iterator;
+    using const_iterator = typename device_view_base::const_iterator;
+    /**
+     * @brief Construct a view of the first `capacity` slots of the
+     * slots array pointed to by `slots`.
+     *
+     * @param slots Pointer to beginning of initialized slots array
+     * @param capacity The number of slots viewed by this object
+     * @param empty_key_sentinel The reserved value for keys to represent empty
+     * slots
+     * @param empty_value_sentinel The reserved value for mapped values to
+     * represent empty slots
+     */
+    __host__ __device__ device_view(pair_atomic_type* slots,
+                                    std::size_t capacity,
+                                    Key empty_key_sentinel,
+                                    Value empty_value_sentinel) noexcept
+      : device_view_base{slots, capacity, empty_key_sentinel, empty_value_sentinel}
+    {
+    }
+
+    /**
+     * @brief Makes a copy of given `device_view` using non-owned memory.
+     *
+     * This function is intended to be used to create shared memory copies of small static maps,
+     * although global memory can be used as well.
+     *
+     * Example:
+     * @code{.cpp}
+     * template <typename MapType, int CAPACITY>
+     * __global__ void use_device_view(const typename MapType::device_view device_view,
+     *                                 map_key_t const* const keys_to_search,
+     *                                 map_value_t* const values_found,
+     *                                 const size_t number_of_elements)
+     * {
+     *     const size_t index = blockIdx.x * blockDim.x + threadIdx.x;
+     *
+     *     __shared__ typename MapType::pair_atomic_type sm_buffer[CAPACITY];
+     *
+     *     auto g = cg::this_thread_block();
+     *
+     *     const map_t::device_view sm_static_multimap = device_view.make_copy(g,
+     *                                                                    sm_buffer);
+     *
+     *     for (size_t i = g.thread_rank(); i < number_of_elements; i += g.size())
+     *     {
+     *         values_found[i] = sm_static_multimap.find(keys_to_search[i])->second;
+     *     }
+     * }
+     * @endcode
+     *
+     * @tparam CG The type of the cooperative thread group
+     * @param g The ooperative thread group used to copy the slots
+     * @param source_device_view `device_view` to copy from
+     * @param memory_to_use Array large enough to support `capacity` elements. Object does not take
+     * the ownership of the memory
+     * @return Copy of passed `device_view`
+     */
+    template <typename CG>
+    __device__ static device_view make_copy(CG g,
+                                            pair_atomic_type* const memory_to_use,
+                                            device_view source_device_view) noexcept
+    {
+#ifndef CUDART_VERSION
+#error CUDART_VERSION Undefined!
+#elif (CUDART_VERSION >= 11000)
+      __shared__ cuda::barrier<cuda::thread_scope::thread_scope_block> barrier;
+      if (g.thread_rank() == 0) { init(&barrier, g.size()); }
+      g.sync();
+
+      cuda::memcpy_async(g,
+                         memory_to_use,
+                         source_device_view.get_slots(),
+                         sizeof(pair_atomic_type) * source_device_view.get_capacity(),
+                         barrier);
+
+      barrier.arrive_and_wait();
+#else
+      pair_atomic_type const* const slots_ptr = source_device_view.get_slots();
+      for (std::size_t i = g.thread_rank(); i < source_device_view.get_capacity(); i += g.size()) {
+        new (&memory_to_use[i].first)
+          atomic_key_type{slots_ptr[i].first.load(cuda::memory_order_relaxed)};
+        new (&memory_to_use[i].second)
+          atomic_mapped_type{slots_ptr[i].second.load(cuda::memory_order_relaxed)};
+      }
+      g.sync();
+#endif
+
+      return device_view(memory_to_use,
+                         source_device_view.get_capacity(),
+                         source_device_view.get_empty_key_sentinel(),
+                         source_device_view.get_empty_value_sentinel());
+    }
+
+    /**
+     * @brief Finds the value corresponding to the key `k`.
+     *
+     * Returns an iterator to the pair whose key is equivalent to `k`.
+     * If no such pair exists, returns `end()`.
+     *
+     * @tparam Hash Unary callable type
+     * @tparam KeyEqual Binary callable type
+     * @param k The key to search for
+     * @param hash The unary callable used to hash the key
+     * @param key_equal The binary callable used to compare two keys
+     * for equality
+     * @return An iterator to the position at which the key/value pair
+     * containing `k` was inserted
+     */
+    template <typename Hash     = cuco::detail::MurmurHash3_32<key_type>,
+              typename KeyEqual = thrust::equal_to<key_type>>
+    __device__ iterator find(Key const& k,
+                             Hash hash          = Hash{},
+                             KeyEqual key_equal = KeyEqual{}) noexcept;
+
+    /** @brief Finds the value corresponding to the key `k`.
+     *
+     * Returns a const_iterator to the pair whose key is equivalent to `k`.
+     * If no such pair exists, returns `end()`.
+     *
+     * @tparam Hash Unary callable type
+     * @tparam KeyEqual Binary callable type
+     * @param k The key to search for
+     * @param hash The unary callable used to hash the key
+     * @param key_equal The binary callable used to compare two keys
+     * for equality
+     * @return An iterator to the position at which the key/value pair
+     * containing `k` was inserted
+     */
+    template <typename Hash     = cuco::detail::MurmurHash3_32<key_type>,
+              typename KeyEqual = thrust::equal_to<key_type>>
+    __device__ const_iterator find(Key const& k,
+                                   Hash hash          = Hash{},
+                                   KeyEqual key_equal = KeyEqual{}) const noexcept;
+
+    /**
+     * @brief Finds the value corresponding to the key `k`.
+     *
+     * Returns an iterator to the pair whose key is equivalent to `k`.
+     * If no such pair exists, returns `end()`. Uses the CUDA Cooperative Groups API to
+     * to leverage multiple threads to perform a single find. This provides a
+     * significant boost in throughput compared to the non Cooperative Group
+     * `find` at moderate to high load factors.
+     *
+     * @tparam CG Cooperative Group type
+     * @tparam Hash Unary callable type
+     * @tparam KeyEqual Binary callable type
+     * @param g The Cooperative Group used to perform the find
+     * @param k The key to search for
+     * @param hash The unary callable used to hash the key
+     * @param key_equal The binary callable used to compare two keys
+     * for equality
+     * @return An iterator to the position at which the key/value pair
+     * containing `k` was inserted
+     */
+    template <typename CG,
+              typename Hash     = cuco::detail::MurmurHash3_32<key_type>,
+              typename KeyEqual = thrust::equal_to<key_type>>
+    __device__ iterator
+    find(CG g, Key const& k, Hash hash = Hash{}, KeyEqual key_equal = KeyEqual{}) noexcept;
+
+    /**
+     * @brief Finds the value corresponding to the key `k`.
+     *
+     * Returns a const_iterator to the pair whose key is equivalent to `k`.
+     * If no such pair exists, returns `end()`. Uses the CUDA Cooperative Groups API to
+     * to leverage multiple threads to perform a single find. This provides a
+     * significant boost in throughput compared to the non Cooperative Group
+     * `find` at moderate to high load factors.
+     *
+     * @tparam CG Cooperative Group type
+     * @tparam Hash Unary callable type
+     * @tparam KeyEqual Binary callable type
+     * @param g The Cooperative Group used to perform the find
+     * @param k The key to search for
+     * @param hash The unary callable used to hash the key
+     * @param key_equal The binary callable used to compare two keys
+     * for equality
+     * @return An iterator to the position at which the key/value pair
+     * containing `k` was inserted
+     */
+    template <typename CG,
+              typename Hash     = cuco::detail::MurmurHash3_32<key_type>,
+              typename KeyEqual = thrust::equal_to<key_type>>
+    __device__ const_iterator
+    find(CG g, Key const& k, Hash hash = Hash{}, KeyEqual key_equal = KeyEqual{}) const noexcept;
+
+    /**
+     * @brief Indicates whether the key `k` was inserted into the map.
+     *
+     * If the key `k` was inserted into the map, find returns
+     * true. Otherwise, it returns false.
+     *
+     * @tparam Hash Unary callable type
+     * @tparam KeyEqual Binary callable type
+     * @param k The key to search for
+     * @param hash The unary callable used to hash the key
+     * @param key_equal The binary callable used to compare two keys
+     * for equality
+     * @return A boolean indicating whether the key/value pair
+     * containing `k` was inserted
+     */
+    template <typename Hash     = cuco::detail::MurmurHash3_32<key_type>,
+              typename KeyEqual = thrust::equal_to<key_type>>
+    __device__ bool contains(Key const& k,
+                             Hash hash          = Hash{},
+                             KeyEqual key_equal = KeyEqual{}) noexcept;
+
+    /**
+     * @brief Indicates whether the key `k` was inserted into the map.
+     *
+     * If the key `k` was inserted into the map, find returns
+     * true. Otherwise, it returns false. Uses the CUDA Cooperative Groups API to
+     * to leverage multiple threads to perform a single contains operation. This provides a
+     * significant boost in throughput compared to the non Cooperative Group
+     * `contains` at moderate to high load factors.
+     *
+     * @tparam CG Cooperative Group type
+     * @tparam Hash Unary callable type
+     * @tparam KeyEqual Binary callable type
+     * @param g The Cooperative Group used to perform the contains operation
+     * @param k The key to search for
+     * @param hash The unary callable used to hash the key
+     * @param key_equal The binary callable used to compare two keys
+     * for equality
+     * @return A boolean indicating whether the key/value pair
+     * containing `k` was inserted
+     */
+    template <typename CG,
+              typename Hash     = cuco::detail::MurmurHash3_32<key_type>,
+              typename KeyEqual = thrust::equal_to<key_type>>
+    __device__ bool contains(CG g,
+                             Key const& k,
+                             Hash hash          = Hash{},
+                             KeyEqual key_equal = KeyEqual{}) noexcept;
+  };  // class device_view
+
+  /**
+   * @brief Gets the maximum number of elements the hash map can hold.
+   *
+   * @return The maximum number of elements the hash map can hold
+   */
+  std::size_t get_capacity() const noexcept { return capacity_; }
+
+  /**
+   * @brief Gets the number of elements in the hash map.
+   *
+   * @return The number of elements in the map
+   */
+  std::size_t get_size() const noexcept { return size_; }
+
+  /**
+   * @brief Gets the load factor of the hash map.
+   *
+   * @return The load factor of the hash map
+   */
+  float get_load_factor() const noexcept { return static_cast<float>(size_) / capacity_; }
+
+  /**
+   * @brief Gets the sentinel value used to represent an empty key slot.
+   *
+   * @return The sentinel value used to represent an empty key slot
+   */
+  Key get_empty_key_sentinel() const noexcept { return empty_key_sentinel_; }
+
+  /**
+   * @brief Gets the sentinel value used to represent an empty value slot.
+   *
+   * @return The sentinel value used to represent an empty value slot
+   */
+  Value get_empty_value_sentinel() const noexcept { return empty_value_sentinel_; }
+
+  /**
+   * @brief Constructs a device_view object based on the members of the `static_multimap` object.
+   *
+   * @return A device_view object based on the members of the `static_multimap` object
+   */
+  device_view get_device_view() const noexcept
+  {
+    return device_view(slots_, capacity_, empty_key_sentinel_, empty_value_sentinel_);
+  }
+
+  /**
+   * @brief Constructs a device_mutable_view object based on the members of the `static_multimap` object
+   *
+   * @return A device_mutable_view object based on the members of the `static_multimap` object
+   */
+  device_mutable_view get_device_mutable_view() const noexcept
+  {
+    return device_mutable_view(slots_, capacity_, empty_key_sentinel_, empty_value_sentinel_);
+  }
+
+ private:
+  pair_atomic_type* slots_{nullptr};      ///< Pointer to flat slots storage
+  std::size_t capacity_{};                ///< Total number of slots
+  std::size_t size_{};                    ///< Number of keys in map
+  Key empty_key_sentinel_{};              ///< Key value that represents an empty slot
+  Value empty_value_sentinel_{};          ///< Initial value of empty slot
+  atomic_ctr_type* num_successes_{};      ///< Number of successfully inserted keys on insert
+  slot_allocator_type slot_allocator_{};  ///< Allocator used to allocate slots
+};
 }  // namespace cuco
 
 #include <cuco/detail/static_multimap.inl>

From bcaf28632042ed68338e28096c85558111b873e9 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Fri, 26 Feb 2021 14:18:20 -0800
Subject: [PATCH 008/267] Code formatting

---
 include/cuco/detail/dynamic_map_kernels.cuh   | 301 +++++++++---------
 include/cuco/detail/hash_functions.cuh        |   1 -
 include/cuco/detail/pair.cuh                  |  29 +-
 include/cuco/detail/static_map_kernels.cuh    | 172 +++++-----
 include/cuco/detail/static_multimap.inl       |  36 +--
 .../cuco/detail/static_multimap_kernels.cuh   | 290 ++++++++---------
 include/cuco/static_multimap.cuh              |   9 +-
 7 files changed, 406 insertions(+), 432 deletions(-)

diff --git a/include/cuco/detail/dynamic_map_kernels.cuh b/include/cuco/detail/dynamic_map_kernels.cuh
index 28690b0dc..c1e21e863 100644
--- a/include/cuco/detail/dynamic_map_kernels.cuh
+++ b/include/cuco/detail/dynamic_map_kernels.cuh
@@ -19,17 +19,17 @@ namespace detail {
 namespace cg = cooperative_groups;
 
 /**
- * @brief Inserts all key/value pairs in the range `[first, last)`. 
- *  
+ * @brief Inserts all key/value pairs in the range `[first, last)`.
+ *
  * If multiple keys in `[first, last)` compare equal, it is unspecified which
  * element is inserted.
- * 
- * @tparam block_size 
+ *
+ * @tparam block_size
  * @tparam pair_type Type of the pairs contained in the map
  * @tparam InputIt Device accessible input iterator whose `value_type` is
  * convertible to the map's `value_type`
  * @tparam viewT Type of the `static_map` device views
- * @tparam mutableViewT Type of the `static_map` device mutable views 
+ * @tparam mutableViewT Type of the `static_map` device mutable views
  * @tparam atomicT Type of atomic storage
  * @tparam viewT Type of device view allowing access of hash map storage
  * @tparam Hash Unary callable type
@@ -38,7 +38,7 @@ namespace cg = cooperative_groups;
  * @param last End of the sequence of key/value pairs
  * @param submap_views Array of `static_map::device_view` objects used to
  * perform `contains` operations on each underlying `static_map`
- * @param submap_mutable_views Array of `static_map::device_mutable_view` objects 
+ * @param submap_mutable_views Array of `static_map::device_mutable_view` objects
  * used to perform an `insert` into the target `static_map` submap
  * @param num_successes The number of successfully inserted key/value pairs
  * @param insert_idx The index of the submap we are inserting into
@@ -46,14 +46,14 @@ namespace cg = cooperative_groups;
  * @param hash The unary function to apply to hash each key
  * @param key_equal The binary function used to compare two keys for equality
  */
-template<uint32_t block_size,
-         typename pair_type,
-         typename InputIt,
-         typename viewT,
-         typename mutableViewT,
-         typename atomicT,
-         typename Hash, 
-         typename KeyEqual>
+template <uint32_t block_size,
+          typename pair_type,
+          typename InputIt,
+          typename viewT,
+          typename mutableViewT,
+          typename atomicT,
+          typename Hash,
+          typename KeyEqual>
 __global__ void insert(InputIt first,
                        InputIt last,
                        viewT* submap_views,
@@ -62,58 +62,55 @@ __global__ void insert(InputIt first,
                        uint32_t insert_idx,
                        uint32_t num_submaps,
                        Hash hash,
-                       KeyEqual key_equal) {
+                       KeyEqual key_equal)
+{
   typedef cub::BlockReduce<std::size_t, block_size> BlockReduce;
   __shared__ typename BlockReduce::TempStorage temp_storage;
   std::size_t thread_num_successes = 0;
-  
+
   auto tid = blockDim.x * blockIdx.x + threadIdx.x;
 
-  while(first + tid < last) {
+  while (first + tid < last) {
     pair_type insert_pair = *(first + tid);
-    auto exists = false;
-    
+    auto exists           = false;
+
     // manually check for duplicates in those submaps we are not inserting into
-    for(auto i = 0; i < num_submaps; ++i) {
-      if(i != insert_idx) {
+    for (auto i = 0; i < num_submaps; ++i) {
+      if (i != insert_idx) {
         exists = submap_views[i].contains(insert_pair.first, hash, key_equal);
-        if(exists) {
-          break;
-        }
+        if (exists) { break; }
       }
     }
-    if(!exists) {
-      if(submap_mutable_views[insert_idx].insert(insert_pair, hash, key_equal)) {
+    if (!exists) {
+      if (submap_mutable_views[insert_idx].insert(insert_pair, hash, key_equal)) {
         thread_num_successes++;
       }
     }
 
     tid += gridDim.x * blockDim.x;
   }
-  
+
   std::size_t block_num_successes = BlockReduce(temp_storage).Sum(thread_num_successes);
-  if(threadIdx.x == 0) {
-    *num_successes += block_num_successes;
-  }
+  if (threadIdx.x == 0) { *num_successes += block_num_successes; }
 }
 
 /**
- * @brief Inserts all key/value pairs in the range `[first, last)`. 
- *  
+ * @brief Inserts all key/value pairs in the range `[first, last)`.
+ *
  * If multiple keys in `[first, last)` compare equal, it is unspecified which
  * element is inserted. Uses the CUDA Cooperative Groups API to leverage groups
- * of multiple threads to perform each key/value insertion. This provides a 
+ * of multiple threads to perform each key/value insertion. This provides a
  * significant boost in throughput compared to the non Cooperative Group
  * `insert` at moderate to high load factors.
- * 
- * @tparam block_size 
+ *
+ * @tparam block_size
  * @tparam tile_size The number of threads in the Cooperative Groups used to perform
  * inserts
  * @tparam pair_type Type of the pairs contained in the map
  * @tparam InputIt Device accessible input iterator whose `value_type` is
  * convertible to the map's `value_type`
  * @tparam viewT Type of the `static_map` device views
- * @tparam mutableViewT Type of the `static_map` device mutable views 
+ * @tparam mutableViewT Type of the `static_map` device mutable views
  * @tparam atomicT Type of atomic storage
  * @tparam viewT Type of device view allowing access of hash map storage
  * @tparam Hash Unary callable type
@@ -122,7 +119,7 @@ __global__ void insert(InputIt first,
  * @param last End of the sequence of key/value pairs
  * @param submap_views Array of `static_map::device_view` objects used to
  * perform `contains` operations on each underlying `static_map`
- * @param submap_mutable_views Array of `static_map::device_mutable_view` objects 
+ * @param submap_mutable_views Array of `static_map::device_mutable_view` objects
  * used to perform an `insert` into the target `static_map` submap
  * @param num_successes The number of successfully inserted key/value pairs
  * @param insert_idx The index of the submap we are inserting into
@@ -130,14 +127,15 @@ __global__ void insert(InputIt first,
  * @param hash The unary function to apply to hash each key
  * @param key_equal The binary function used to compare two keys for equality
  */
-template<uint32_t block_size, uint32_t tile_size,
-         typename pair_type,
-         typename InputIt,
-         typename viewT,
-         typename mutableViewT,
-         typename atomicT,
-         typename Hash, 
-         typename KeyEqual>
+template <uint32_t block_size,
+          uint32_t tile_size,
+          typename pair_type,
+          typename InputIt,
+          typename viewT,
+          typename mutableViewT,
+          typename atomicT,
+          typename Hash,
+          typename KeyEqual>
 __global__ void insert(InputIt first,
                        InputIt last,
                        viewT* submap_views,
@@ -146,54 +144,51 @@ __global__ void insert(InputIt first,
                        uint32_t insert_idx,
                        uint32_t num_submaps,
                        Hash hash,
-                       KeyEqual key_equal) {
+                       KeyEqual key_equal)
+{
   typedef cub::BlockReduce<std::size_t, block_size> BlockReduce;
   __shared__ typename BlockReduce::TempStorage temp_storage;
   std::size_t thread_num_successes = 0;
-  
+
   auto tile = cg::tiled_partition<tile_size>(cg::this_thread_block());
-  auto tid = blockDim.x * blockIdx.x + threadIdx.x;
-  auto it = first + tid / tile_size;
+  auto tid  = blockDim.x * blockIdx.x + threadIdx.x;
+  auto it   = first + tid / tile_size;
 
-  while(it < last) {
+  while (it < last) {
     pair_type insert_pair = *it;
-    auto exists = false;
-    
+    auto exists           = false;
+
     // manually check for duplicates in those submaps we are not inserting into
-    for(auto i = 0; i < num_submaps; ++i) {
-      if(i != insert_idx) {
+    for (auto i = 0; i < num_submaps; ++i) {
+      if (i != insert_idx) {
         exists = submap_views[i].contains(tile, insert_pair.first, hash, key_equal);
-        if(exists) {
-          break;
-        }
+        if (exists) { break; }
       }
     }
-    if(!exists) {
-      if(submap_mutable_views[insert_idx].insert(tile, insert_pair, hash, key_equal) && 
-         tile.thread_rank() == 0) {
+    if (!exists) {
+      if (submap_mutable_views[insert_idx].insert(tile, insert_pair, hash, key_equal) &&
+          tile.thread_rank() == 0) {
         thread_num_successes++;
       }
     }
 
     it += (gridDim.x * blockDim.x) / tile_size;
   }
-  
+
   std::size_t block_num_successes = BlockReduce(temp_storage).Sum(thread_num_successes);
-  if(threadIdx.x == 0) {
-    *num_successes += block_num_successes;
-  }
+  if (threadIdx.x == 0) { *num_successes += block_num_successes; }
 }
 
 /**
  * @brief Finds the values corresponding to all keys in the range `[first, last)`.
- * 
- * If the key `*(first + i)` exists in the map, copies its associated value to `(output_begin + i)`. 
- * Else, copies the empty value sentinel. 
+ *
+ * If the key `*(first + i)` exists in the map, copies its associated value to `(output_begin + i)`.
+ * Else, copies the empty value sentinel.
  * @tparam block_size The number of threads in the thread block
- * @tparam Value The mapped value type for the map 
+ * @tparam Value The mapped value type for the map
  * @tparam InputIt Device accessible input iterator whose `value_type` is
  * convertible to the map's `key_type`
- * @tparam OutputIt Device accessible output iterator whose `value_type` is 
+ * @tparam OutputIt Device accessible output iterator whose `value_type` is
  * convertible to the map's `mapped_type`
  * @tparam viewT Type of `static_map` device view
  * @tparam Hash Unary callable type
@@ -207,37 +202,39 @@ __global__ void insert(InputIt first,
  * @param hash The unary function to apply to hash each key
  * @param key_equal The binary function to compare two keys for equality
  */
-template<uint32_t block_size,
-         typename Value,
-         typename InputIt, typename OutputIt, 
-         typename viewT,
-         typename Hash, 
-         typename KeyEqual>
+template <uint32_t block_size,
+          typename Value,
+          typename InputIt,
+          typename OutputIt,
+          typename viewT,
+          typename Hash,
+          typename KeyEqual>
 __global__ void find(InputIt first,
                      InputIt last,
                      OutputIt output_begin,
                      viewT* submap_views,
                      uint32_t num_submaps,
                      Hash hash,
-                     KeyEqual key_equal) {
-  auto tid = blockDim.x * blockIdx.x + threadIdx.x;
+                     KeyEqual key_equal)
+{
+  auto tid                  = blockDim.x * blockIdx.x + threadIdx.x;
   auto empty_value_sentinel = submap_views[0].get_empty_value_sentinel();
   __shared__ Value writeBuffer[block_size];
 
-  while(first + tid < last) {
-    auto key = *(first + tid);
+  while (first + tid < last) {
+    auto key         = *(first + tid);
     auto found_value = empty_value_sentinel;
-    for(auto i = 0; i < num_submaps; ++i) {
+    for (auto i = 0; i < num_submaps; ++i) {
       auto submap_view = submap_views[i];
-      auto found = submap_view.find(key, hash, key_equal);
-      if(found != submap_view.end()) {
+      auto found       = submap_view.find(key, hash, key_equal);
+      if (found != submap_view.end()) {
         found_value = found->second;
         break;
       }
     }
 
-    /* 
-     * The ld.relaxed.gpu instruction used in view.find causes L1 to 
+    /*
+     * The ld.relaxed.gpu instruction used in view.find causes L1 to
      * flush more frequently, causing increased sector stores from L2 to global memory.
      * By writing results to shared memory and then synchronizing before writing back
      * to global, we no longer rely on L1, preventing the increase in sector stores from
@@ -252,10 +249,10 @@ __global__ void find(InputIt first,
 
 /**
  * @brief Finds the values corresponding to all keys in the range `[first, last)`.
- * 
- * If the key `*(first + i)` exists in the map, copies its associated value to `(output_begin + i)`. 
+ *
+ * If the key `*(first + i)` exists in the map, copies its associated value to `(output_begin + i)`.
  * Else, copies the empty value sentinel. Uses the CUDA Cooperative Groups API to leverage groups
- * of multiple threads to find each key. This provides a significant boost in throughput compared 
+ * of multiple threads to find each key. This provides a significant boost in throughput compared
  * to the non Cooperative Group `find` at moderate to high load factors.
  *
  * @tparam block_size The number of threads in the thread block
@@ -264,7 +261,7 @@ __global__ void find(InputIt first,
  * @tparam Value The mapped value type for the map
  * @tparam InputIt Device accessible input iterator whose `value_type` is
  * convertible to the map's `key_type`
- * @tparam OutputIt Device accessible output iterator whose `value_type` is 
+ * @tparam OutputIt Device accessible output iterator whose `value_type` is
  * convertible to the map's `mapped_type`
  * @tparam viewT Type of `static_map` device view
  * @tparam Hash Unary callable type
@@ -278,49 +275,50 @@ __global__ void find(InputIt first,
  * @param hash The unary function to apply to hash each key
  * @param key_equal The binary function to compare two keys for equality
  */
-template<uint32_t block_size, uint32_t tile_size,
-         typename Value,
-         typename InputIt, typename OutputIt, 
-         typename viewT,
-         typename Hash, 
-         typename KeyEqual>
+template <uint32_t block_size,
+          uint32_t tile_size,
+          typename Value,
+          typename InputIt,
+          typename OutputIt,
+          typename viewT,
+          typename Hash,
+          typename KeyEqual>
 __global__ void find(InputIt first,
                      InputIt last,
                      OutputIt output_begin,
                      viewT* submap_views,
                      uint32_t num_submaps,
                      Hash hash,
-                     KeyEqual key_equal) {
-  auto tile = cg::tiled_partition<tile_size>(cg::this_thread_block());
-  auto tid = blockDim.x * blockIdx.x + threadIdx.x;
-  auto key_idx = tid / tile_size;
+                     KeyEqual key_equal)
+{
+  auto tile                 = cg::tiled_partition<tile_size>(cg::this_thread_block());
+  auto tid                  = blockDim.x * blockIdx.x + threadIdx.x;
+  auto key_idx              = tid / tile_size;
   auto empty_value_sentinel = submap_views[0].get_empty_value_sentinel();
   __shared__ Value writeBuffer[block_size];
 
-  while(first + key_idx < last) {
-    auto key = *(first + key_idx);
+  while (first + key_idx < last) {
+    auto key         = *(first + key_idx);
     auto found_value = empty_value_sentinel;
-    for(auto i = 0; i < num_submaps; ++i) {
+    for (auto i = 0; i < num_submaps; ++i) {
       auto submap_view = submap_views[i];
-      auto found = submap_view.find(tile, key, hash, key_equal);
-      if(found != submap_view.end()) {
+      auto found       = submap_view.find(tile, key, hash, key_equal);
+      if (found != submap_view.end()) {
         found_value = found->second;
         break;
       }
     }
 
-    /* 
-     * The ld.relaxed.gpu instruction used in view.find causes L1 to 
+    /*
+     * The ld.relaxed.gpu instruction used in view.find causes L1 to
      * flush more frequently, causing increased sector stores from L2 to global memory.
      * By writing results to shared memory and then synchronizing before writing back
      * to global, we no longer rely on L1, preventing the increase in sector stores from
      * L2 to global and improving performance.
      */
-    if(tile.thread_rank() == 0) {
-      writeBuffer[threadIdx.x / tile_size] = found_value;
-    }
+    if (tile.thread_rank() == 0) { writeBuffer[threadIdx.x / tile_size] = found_value; }
     __syncthreads();
-    if(tile.thread_rank() == 0) {
+    if (tile.thread_rank() == 0) {
       *(output_begin + key_idx) = writeBuffer[threadIdx.x / tile_size];
     }
     key_idx += (gridDim.x * blockDim.x) / tile_size;
@@ -329,13 +327,13 @@ __global__ void find(InputIt first,
 
 /**
  * @brief Indicates whether the keys in the range `[first, last)` are contained in the map.
- * 
+ *
  * Writes a `bool` to `(output + i)` indicating if the key `*(first + i)` exists in the map.
  *
  * @tparam block_size The number of threads in the thread block
  * @tparam InputIt Device accessible input iterator whose `value_type` is
  * convertible to the map's `key_type`
- * @tparam OutputIt Device accessible output iterator whose `value_type` is 
+ * @tparam OutputIt Device accessible output iterator whose `value_type` is
  * convertible to the map's `mapped_type`
  * @tparam viewT Type of `static_map` device view
  * @tparam Hash Unary callable type
@@ -349,33 +347,33 @@ __global__ void find(InputIt first,
  * @param hash The unary function to apply to hash each key
  * @param key_equal The binary function to compare two keys for equality
  */
-template<uint32_t block_size,
-         typename InputIt, typename OutputIt, 
-         typename viewT,
-         typename Hash, 
-         typename KeyEqual>
+template <uint32_t block_size,
+          typename InputIt,
+          typename OutputIt,
+          typename viewT,
+          typename Hash,
+          typename KeyEqual>
 __global__ void contains(InputIt first,
                          InputIt last,
                          OutputIt output_begin,
                          viewT* submap_views,
                          uint32_t num_submaps,
                          Hash hash,
-                         KeyEqual key_equal) {
+                         KeyEqual key_equal)
+{
   auto tid = blockDim.x * blockIdx.x + threadIdx.x;
   __shared__ bool writeBuffer[block_size];
 
-  while(first + tid < last) {
-    auto key = *(first + tid);
+  while (first + tid < last) {
+    auto key   = *(first + tid);
     auto found = false;
-    for(auto i = 0; i < num_submaps; ++i) {
+    for (auto i = 0; i < num_submaps; ++i) {
       found = submap_views[i].contains(key, hash, key_equal);
-      if(found) {
-        break;
-      }
+      if (found) { break; }
     }
-    
-    /* 
-     * The ld.relaxed.gpu instruction used in view.find causes L1 to 
+
+    /*
+     * The ld.relaxed.gpu instruction used in view.find causes L1 to
      * flush more frequently, causing increased sector stores from L2 to global memory.
      * By writing results to shared memory and then synchronizing before writing back
      * to global, we no longer rely on L1, preventing the increase in sector stores from
@@ -390,10 +388,10 @@ __global__ void contains(InputIt first,
 
 /**
  * @brief Indicates whether the keys in the range `[first, last)` are contained in the map.
- * 
+ *
  * Writes a `bool` to `(output + i)` indicating if the key `*(first + i)` exists in the map.
- * Uses the CUDA Cooperative Groups API to leverage groups of multiple threads to perform the 
- * contains operation for each key. This provides a significant boost in throughput compared 
+ * Uses the CUDA Cooperative Groups API to leverage groups of multiple threads to perform the
+ * contains operation for each key. This provides a significant boost in throughput compared
  * to the non Cooperative Group `contains` at moderate to high load factors.
  *
  * @tparam block_size The number of threads in the thread block
@@ -401,7 +399,7 @@ __global__ void contains(InputIt first,
  * perform find operations
  * @tparam InputIt Device accessible input iterator whose `value_type` is
  * convertible to the map's `key_type`
- * @tparam OutputIt Device accessible output iterator whose `value_type` is 
+ * @tparam OutputIt Device accessible output iterator whose `value_type` is
  * convertible to the map's `mapped_type`
  * @tparam viewT Type of `static_map` device view
  * @tparam Hash Unary callable type
@@ -415,49 +413,48 @@ __global__ void contains(InputIt first,
  * @param hash The unary function to apply to hash each key
  * @param key_equal The binary function to compare two keys for equality
  */
-template<uint32_t block_size, uint32_t tile_size,
-         typename InputIt, typename OutputIt, 
-         typename viewT,
-         typename Hash, 
-         typename KeyEqual>
+template <uint32_t block_size,
+          uint32_t tile_size,
+          typename InputIt,
+          typename OutputIt,
+          typename viewT,
+          typename Hash,
+          typename KeyEqual>
 __global__ void contains(InputIt first,
                          InputIt last,
                          OutputIt output_begin,
                          viewT* submap_views,
                          uint32_t num_submaps,
                          Hash hash,
-                         KeyEqual key_equal) {
-  auto tile = cg::tiled_partition<tile_size>(cg::this_thread_block());
-  auto tid = blockDim.x * blockIdx.x + threadIdx.x;
+                         KeyEqual key_equal)
+{
+  auto tile    = cg::tiled_partition<tile_size>(cg::this_thread_block());
+  auto tid     = blockDim.x * blockIdx.x + threadIdx.x;
   auto key_idx = tid / tile_size;
   __shared__ bool writeBuffer[block_size];
 
-  while(first + key_idx < last) {
-    auto key = *(first + key_idx);
+  while (first + key_idx < last) {
+    auto key   = *(first + key_idx);
     auto found = false;
-    for(auto i = 0; i < num_submaps; ++i) {
+    for (auto i = 0; i < num_submaps; ++i) {
       found = submap_views[i].contains(tile, key, hash, key_equal);
-      if(found) {
-        break;
-      }
+      if (found) { break; }
     }
 
-    /* 
-     * The ld.relaxed.gpu instruction used in view.find causes L1 to 
+    /*
+     * The ld.relaxed.gpu instruction used in view.find causes L1 to
      * flush more frequently, causing increased sector stores from L2 to global memory.
      * By writing results to shared memory and then synchronizing before writing back
      * to global, we no longer rely on L1, preventing the increase in sector stores from
      * L2 to global and improving performance.
      */
-    if(tile.thread_rank() == 0) {
-      writeBuffer[threadIdx.x / tile_size] = found;
-    }
+    if (tile.thread_rank() == 0) { writeBuffer[threadIdx.x / tile_size] = found; }
     __syncthreads();
-    if(tile.thread_rank() == 0) {
+    if (tile.thread_rank() == 0) {
       *(output_begin + key_idx) = writeBuffer[threadIdx.x / tile_size];
     }
     key_idx += (gridDim.x * blockDim.x) / tile_size;
   }
 }
-} // namespace detail
-} // namespace cuco
\ No newline at end of file
+}  // namespace detail
+}  // namespace cuco
\ No newline at end of file
diff --git a/include/cuco/detail/hash_functions.cuh b/include/cuco/detail/hash_functions.cuh
index de7074f04..aec550aba 100644
--- a/include/cuco/detail/hash_functions.cuh
+++ b/include/cuco/detail/hash_functions.cuh
@@ -20,7 +20,6 @@ namespace cuco {
 
 namespace detail {
 
-
 // MurmurHash3_32 implementation from
 // https://github.com/aappleby/smhasher/blob/master/src/MurmurHash3.cpp
 //-----------------------------------------------------------------------------
diff --git a/include/cuco/detail/pair.cuh b/include/cuco/detail/pair.cuh
index e52a23387..eec3f3b50 100644
--- a/include/cuco/detail/pair.cuh
+++ b/include/cuco/detail/pair.cuh
@@ -12,7 +12,7 @@
  * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  * See the License for the specific language governing permissions and
  * limitations under the License.
- */ 
+ */
 
 #include <thrust/device_reference.h>
 #include <thrust/pair.h>
@@ -24,12 +24,13 @@ namespace cuco {
 namespace detail {
 
 /**
- * @brief Rounds `v` to the nearest power of 2 greater than or equal to `v`. 
- * 
- * @param v 
+ * @brief Rounds `v` to the nearest power of 2 greater than or equal to `v`.
+ *
+ * @param v
  * @return The nearest power of 2 greater than or equal to `v`.
  */
-constexpr std::size_t next_pow2(std::size_t v) noexcept {
+constexpr std::size_t next_pow2(std::size_t v) noexcept
+{
   --v;
   v |= v >> 1;
   v |= v >> 2;
@@ -45,7 +46,8 @@ constexpr std::size_t next_pow2(std::size_t v) noexcept {
  * whichever is smaller.
  */
 template <typename First, typename Second>
-constexpr std::size_t pair_alignment() {
+constexpr std::size_t pair_alignment()
+{
   return std::min(std::size_t{16}, next_pow2(sizeof(First) + sizeof(Second)));
 }
 
@@ -89,7 +91,7 @@ struct is_thrust_pair_like
  */
 template <typename First, typename Second>
 struct alignas(detail::pair_alignment<First, Second>()) pair {
-  using first_type = First;
+  using first_type  = First;
   using second_type = Second;
   First first;
   Second second;
@@ -121,15 +123,16 @@ using pair_type = cuco::pair<K, V>;
 
 /**
  * @brief Creates a pair of type `pair_type`
- * 
- * @tparam F 
- * @tparam S 
- * @param f 
- * @param s 
+ *
+ * @tparam F
+ * @tparam S
+ * @param f
+ * @param s
  * @return pair_type with first element `f` and second element `s`.
  */
 template <typename F, typename S>
-__host__ __device__ pair_type<F, S> make_pair(F&& f, S&& s) noexcept {
+__host__ __device__ pair_type<F, S> make_pair(F&& f, S&& s) noexcept
+{
   return pair_type<F, S>{std::forward<F>(f), std::forward<S>(s)};
 }
 }  // namespace cuco
diff --git a/include/cuco/detail/static_map_kernels.cuh b/include/cuco/detail/static_map_kernels.cuh
index d28464e16..f709071a1 100644
--- a/include/cuco/detail/static_map_kernels.cuh
+++ b/include/cuco/detail/static_map_kernels.cuh
@@ -12,9 +12,9 @@
  * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  * See the License for the specific language governing permissions and
  * limitations under the License.
- */ 
+ */
 
-namespace cuco{
+namespace cuco {
 namespace detail {
 namespace cg = cooperative_groups;
 
@@ -66,11 +66,13 @@ __global__ void initialize(pair_atomic_type* const slots, Key k, Value v, std::s
  * @param v Value to which all values in `slots` are initialized
  * @param size Size of the storage pointed to by `slots`
  */
-template<typename atomic_key_type, typename atomic_mapped_type, typename Key, typename Value, typename pair_atomic_type>
-__global__ void initialize(
-  pair_atomic_type* const slots, Key k,
-  Value v, std::size_t size) {
-  
+template <typename atomic_key_type,
+          typename atomic_mapped_type,
+          typename Key,
+          typename Value,
+          typename pair_atomic_type>
+__global__ void initialize(pair_atomic_type* const slots, Key k, Value v, std::size_t size)
+{
   auto tid = threadIdx.x + blockIdx.x * blockDim.x;
   while (tid < size) {
     new (&slots[tid].first) atomic_key_type{k};
@@ -80,14 +82,14 @@ __global__ void initialize(
 }
 
 /**
- * @brief Inserts all key/value pairs in the range `[first, last)`. 
- *  
+ * @brief Inserts all key/value pairs in the range `[first, last)`.
+ *
  * If multiple keys in `[first, last)` compare equal, it is unspecified which
  * element is inserted.
- * 
- * @tparam block_size 
+ *
+ * @tparam block_size
  * @tparam InputIt Device accessible input iterator whose `value_type` is
-* convertible to the map's `value_type`
+ * convertible to the map's `value_type`
  * @tparam atomicT Type of atomic storage
  * @tparam viewT Type of device view allowing access of hash map storage
  * @tparam Hash Unary callable type
@@ -99,18 +101,15 @@ __global__ void initialize(
  * @param hash The unary function to apply to hash each key
  * @param key_equal The binary function used to compare two keys for equality
  */
-template<uint32_t block_size,
-         typename InputIt,
-         typename atomicT,
-         typename viewT,
-         typename Hash, 
-         typename KeyEqual>
-__global__ void insert(InputIt first,
-                       InputIt last,
-                       atomicT* num_successes,
-                       viewT view,
-                       Hash hash,
-                       KeyEqual key_equal) {
+template <uint32_t block_size,
+          typename InputIt,
+          typename atomicT,
+          typename viewT,
+          typename Hash,
+          typename KeyEqual>
+__global__ void insert(
+  InputIt first, InputIt last, atomicT* num_successes, viewT view, Hash hash, KeyEqual key_equal)
+{
   typedef cub::BlockReduce<std::size_t, block_size> BlockReduce;
   __shared__ typename BlockReduce::TempStorage temp_storage;
   std::size_t thread_num_successes = 0;
@@ -127,25 +126,23 @@ __global__ void insert(InputIt first,
   // compute number of successfully inserted elements for each block
   // and atomically add to the grand total
   std::size_t block_num_successes = BlockReduce(temp_storage).Sum(thread_num_successes);
-  if(threadIdx.x == 0) {
-    *num_successes += block_num_successes;
-  }
+  if (threadIdx.x == 0) { *num_successes += block_num_successes; }
 }
 
 /**
- * @brief Inserts all key/value pairs in the range `[first, last)`. 
- *  
+ * @brief Inserts all key/value pairs in the range `[first, last)`.
+ *
  * If multiple keys in `[first, last)` compare equal, it is unspecified which
  * element is inserted. Uses the CUDA Cooperative Groups API to leverage groups
- * of multiple threads to perform each key/value insertion. This provides a 
+ * of multiple threads to perform each key/value insertion. This provides a
  * significant boost in throughput compared to the non Cooperative Group
  * `insert` at moderate to high load factors.
- * 
- * @tparam block_size 
+ *
+ * @tparam block_size
  * @tparam tile_size The number of threads in the Cooperative Groups used to perform
  * inserts
  * @tparam InputIt Device accessible input iterator whose `value_type` is
-* convertible to the map's `value_type`
+ * convertible to the map's `value_type`
  * @tparam atomicT Type of atomic storage
  * @tparam viewT Type of device view allowing access of hash map storage
  * @tparam Hash Unary callable type
@@ -157,19 +154,16 @@ __global__ void insert(InputIt first,
  * @param hash The unary function to apply to hash each key
  * @param key_equal The binary function used to compare two keys for equality
  */
-template<uint32_t block_size,
-         uint32_t tile_size,
-         typename InputIt,
-         typename atomicT,
-         typename viewT,
-         typename Hash, 
-         typename KeyEqual>
-__global__ void insert(InputIt first,
-                       InputIt last,
-                       atomicT* num_successes,
-                       viewT view,
-                       Hash hash,
-                       KeyEqual key_equal) {
+template <uint32_t block_size,
+          uint32_t tile_size,
+          typename InputIt,
+          typename atomicT,
+          typename viewT,
+          typename Hash,
+          typename KeyEqual>
+__global__ void insert(
+  InputIt first, InputIt last, atomicT* num_successes, viewT view, Hash hash, KeyEqual key_equal)
+{
   typedef cub::BlockReduce<std::size_t, block_size> BlockReduce;
   __shared__ typename BlockReduce::TempStorage temp_storage;
   std::size_t thread_num_successes = 0;
@@ -186,25 +180,23 @@ __global__ void insert(InputIt first,
     }
     it += (gridDim.x * block_size) / tile_size;
   }
-  
+
   // compute number of successfully inserted elements for each block
   // and atomically add to the grand total
   std::size_t block_num_successes = BlockReduce(temp_storage).Sum(thread_num_successes);
-  if(threadIdx.x == 0) {
-    *num_successes += block_num_successes;
-  }
+  if (threadIdx.x == 0) { *num_successes += block_num_successes; }
 }
 
 /**
  * @brief Finds the values corresponding to all keys in the range `[first, last)`.
- * 
- * If the key `*(first + i)` exists in the map, copies its associated value to `(output_begin + i)`. 
- * Else, copies the empty value sentinel. 
+ *
+ * If the key `*(first + i)` exists in the map, copies its associated value to `(output_begin + i)`.
+ * Else, copies the empty value sentinel.
  * @tparam block_size The size of the thread block
  * @tparam Value The type of the mapped value for the map
  * @tparam InputIt Device accessible input iterator whose `value_type` is
  * convertible to the map's `key_type`
- * @tparam OutputIt Device accessible output iterator whose `value_type` is 
+ * @tparam OutputIt Device accessible output iterator whose `value_type` is
  * convertible to the map's `mapped_type`
  * @tparam viewT Type of device view allowing access of hash map storage
  * @tparam Hash Unary callable type
@@ -229,13 +221,13 @@ __global__ void find(
   auto tid     = block_size * blockIdx.x + threadIdx.x;
   auto key_idx = tid;
   __shared__ Value writeBuffer[block_size];
-  
-  while(first + key_idx < last) {
-    auto key = *(first + key_idx);
+
+  while (first + key_idx < last) {
+    auto key   = *(first + key_idx);
     auto found = view.find(key, hash, key_equal);
 
-    /* 
-     * The ld.relaxed.gpu instruction used in view.find causes L1 to 
+    /*
+     * The ld.relaxed.gpu instruction used in view.find causes L1 to
      * flush more frequently, causing increased sector stores from L2 to global memory.
      * By writing results to shared memory and then synchronizing before writing back
      * to global, we no longer rely on L1, preventing the increase in sector stores from
@@ -252,10 +244,10 @@ __global__ void find(
 
 /**
  * @brief Finds the values corresponding to all keys in the range `[first, last)`.
- * 
- * If the key `*(first + i)` exists in the map, copies its associated value to `(output_begin + i)`. 
+ *
+ * If the key `*(first + i)` exists in the map, copies its associated value to `(output_begin + i)`.
  * Else, copies the empty value sentinel. Uses the CUDA Cooperative Groups API to leverage groups
- * of multiple threads to find each key. This provides a significant boost in throughput compared 
+ * of multiple threads to find each key. This provides a significant boost in throughput compared
  * to the non Cooperative Group `find` at moderate to high load factors.
  *
  * @tparam block_size The size of the thread block
@@ -264,7 +256,7 @@ __global__ void find(
  * @tparam Value The type of the mapped value for the map
  * @tparam InputIt Device accessible input iterator whose `value_type` is
  * convertible to the map's `key_type`
- * @tparam OutputIt Device accessible output iterator whose `value_type` is 
+ * @tparam OutputIt Device accessible output iterator whose `value_type` is
  * convertible to the map's `mapped_type`
  * @tparam viewT Type of device view allowing access of hash map storage
  * @tparam Hash Unary callable type
@@ -291,13 +283,13 @@ __global__ void find(
   auto tid     = block_size * blockIdx.x + threadIdx.x;
   auto key_idx = tid / tile_size;
   __shared__ Value writeBuffer[block_size];
-  
-  while(first + key_idx < last) {
-    auto key = *(first + key_idx);
+
+  while (first + key_idx < last) {
+    auto key   = *(first + key_idx);
     auto found = view.find(tile, key, hash, key_equal);
-    
-    /* 
-     * The ld.relaxed.gpu instruction used in view.find causes L1 to 
+
+    /*
+     * The ld.relaxed.gpu instruction used in view.find causes L1 to
      * flush more frequently, causing increased sector stores from L2 to global memory.
      * By writing results to shared memory and then synchronizing before writing back
      * to global, we no longer rely on L1, preventing the increase in sector stores from
@@ -309,7 +301,7 @@ __global__ void find(
                             : found->second.load(cuda::std::memory_order_relaxed);
     }
     __syncthreads();
-    if(tile.thread_rank() == 0) {
+    if (tile.thread_rank() == 0) {
       *(output_begin + key_idx) = writeBuffer[threadIdx.x / tile_size];
     }
     key_idx += (gridDim.x * block_size) / tile_size;
@@ -318,7 +310,7 @@ __global__ void find(
 
 /**
  * @brief Indicates whether the keys in the range `[first, last)` are contained in the map.
- * 
+ *
  * Writes a `bool` to `(output + i)` indicating if the key `*(first + i)` exists in the map.
  *
  * @tparam block_size The size of the thread block
@@ -328,7 +320,7 @@ __global__ void find(
  * convertible to the map's `mapped_type`
  * @tparam viewT Type of device view allowing access of hash map storage
  * @tparam Hash Unary callable type
- * @tparam KeyEqual Binary callable type 
+ * @tparam KeyEqual Binary callable type
  * @param first Beginning of the sequence of keys
  * @param last End of the sequence of keys
  * @param output_begin Beginning of the sequence of booleans for the presence of each key
@@ -348,12 +340,12 @@ __global__ void contains(
   auto tid     = block_size * blockIdx.x + threadIdx.x;
   auto key_idx = tid;
   __shared__ bool writeBuffer[block_size];
-  
-  while(first + key_idx < last) {
+
+  while (first + key_idx < last) {
     auto key = *(first + key_idx);
-    
-    /* 
-     * The ld.relaxed.gpu instruction used in view.find causes L1 to 
+
+    /*
+     * The ld.relaxed.gpu instruction used in view.find causes L1 to
      * flush more frequently, causing increased sector stores from L2 to global memory.
      * By writing results to shared memory and then synchronizing before writing back
      * to global, we no longer rely on L1, preventing the increase in sector stores from
@@ -368,10 +360,10 @@ __global__ void contains(
 
 /**
  * @brief Indicates whether the keys in the range `[first, last)` are contained in the map.
- * 
+ *
  * Writes a `bool` to `(output + i)` indicating if the key `*(first + i)` exists in the map.
- * Uses the CUDA Cooperative Groups API to leverage groups of multiple threads to perform the 
- * contains operation for each key. This provides a significant boost in throughput compared 
+ * Uses the CUDA Cooperative Groups API to leverage groups of multiple threads to perform the
+ * contains operation for each key. This provides a significant boost in throughput compared
  * to the non Cooperative Group `contains` at moderate to high load factors.
  *
  * @tparam block_size The size of the thread block
@@ -383,7 +375,7 @@ __global__ void contains(
  * convertible to the map's `mapped_type`
  * @tparam viewT Type of device view allowing access of hash map storage
  * @tparam Hash Unary callable type
- * @tparam KeyEqual Binary callable type 
+ * @tparam KeyEqual Binary callable type
  * @param first Beginning of the sequence of keys
  * @param last End of the sequence of keys
  * @param output_begin Beginning of the sequence of booleans for the presence of each key
@@ -405,23 +397,21 @@ __global__ void contains(
   auto tid     = block_size * blockIdx.x + threadIdx.x;
   auto key_idx = tid / tile_size;
   __shared__ bool writeBuffer[block_size];
-  
-  while(first + key_idx < last) {
-    auto key = *(first + key_idx);
+
+  while (first + key_idx < last) {
+    auto key   = *(first + key_idx);
     auto found = view.contains(tile, key, hash, key_equal);
-    
-    /* 
-     * The ld.relaxed.gpu instruction used in view.find causes L1 to 
+
+    /*
+     * The ld.relaxed.gpu instruction used in view.find causes L1 to
      * flush more frequently, causing increased sector stores from L2 to global memory.
      * By writing results to shared memory and then synchronizing before writing back
      * to global, we no longer rely on L1, preventing the increase in sector stores from
      * L2 to global and improving performance.
      */
-    if(tile.thread_rank() == 0) {
-      writeBuffer[threadIdx.x / tile_size] = found;
-    }
+    if (tile.thread_rank() == 0) { writeBuffer[threadIdx.x / tile_size] = found; }
     __syncthreads();
-    if(tile.thread_rank() == 0) {
+    if (tile.thread_rank() == 0) {
       *(output_begin + key_idx) = writeBuffer[threadIdx.x / tile_size];
     }
     key_idx += (gridDim.x * block_size) / tile_size;
diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index a90cb9bb9..851213ab1 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -28,9 +28,9 @@ enum class insert_result {
 
 template <typename Key, typename Value, cuda::thread_scope Scope, typename Allocator>
 static_multimap<Key, Value, Scope, Allocator>::static_multimap(std::size_t capacity,
-                                                     Key empty_key_sentinel,
-                                                     Value empty_value_sentinel,
-                                                     Allocator const& alloc)
+                                                               Key empty_key_sentinel,
+                                                               Value empty_value_sentinel,
+                                                               Allocator const& alloc)
   : capacity_{capacity},
     empty_key_sentinel_{empty_key_sentinel},
     empty_value_sentinel_{empty_value_sentinel},
@@ -57,9 +57,9 @@ static_multimap<Key, Value, Scope, Allocator>::~static_multimap()
 template <typename Key, typename Value, cuda::thread_scope Scope, typename Allocator>
 template <typename InputIt, typename Hash, typename KeyEqual>
 void static_multimap<Key, Value, Scope, Allocator>::insert(InputIt first,
-                                                      InputIt last,
-                                                      Hash hash,
-                                                      KeyEqual key_equal)
+                                                           InputIt last,
+                                                           Hash hash,
+                                                           KeyEqual key_equal)
 {
   auto num_keys         = std::distance(first, last);
   auto const block_size = 128;
@@ -147,7 +147,7 @@ __device__ bool static_multimap<Key, Value, Scope, Allocator>::device_mutable_vi
 
     // if the key was already inserted by another thread, than this instance is a
     // duplicate, so the insert fails
-    //if (key_equal(insert_pair.first, expected_key)) { return false; }
+    // if (key_equal(insert_pair.first, expected_key)) { return false; }
 
     // if we couldn't insert the key, but it wasn't a duplicate, then there must
     // have been some other key there, so we keep looking for a slot
@@ -170,7 +170,7 @@ __device__ bool static_multimap<Key, Value, Scope, Allocator>::device_mutable_vi
     auto const slot_is_empty = (existing_key == this->get_empty_key_sentinel());
 
     // the key we are trying to insert is already in the map, so we return with failure to insert
-    //if (g.ballot(not slot_is_empty and key_equal(existing_key, insert_pair.first))) {
+    // if (g.ballot(not slot_is_empty and key_equal(existing_key, insert_pair.first))) {
     //  return false;
     //}
 
@@ -234,8 +234,8 @@ template <typename Key, typename Value, cuda::thread_scope Scope, typename Alloc
 template <typename Hash, typename KeyEqual>
 __device__ typename static_multimap<Key, Value, Scope, Allocator>::device_view::iterator
 static_multimap<Key, Value, Scope, Allocator>::device_view::find(Key const& k,
-                                                            Hash hash,
-                                                            KeyEqual key_equal) noexcept
+                                                                 Hash hash,
+                                                                 KeyEqual key_equal) noexcept
 {
   auto current_slot = initial_slot(k, hash);
 
@@ -255,8 +255,8 @@ template <typename Key, typename Value, cuda::thread_scope Scope, typename Alloc
 template <typename Hash, typename KeyEqual>
 __device__ typename static_multimap<Key, Value, Scope, Allocator>::device_view::const_iterator
 static_multimap<Key, Value, Scope, Allocator>::device_view::find(Key const& k,
-                                                            Hash hash,
-                                                            KeyEqual key_equal) const noexcept
+                                                                 Hash hash,
+                                                                 KeyEqual key_equal) const noexcept
 {
   auto current_slot = initial_slot(k, hash);
 
@@ -276,9 +276,9 @@ template <typename Key, typename Value, cuda::thread_scope Scope, typename Alloc
 template <typename CG, typename Hash, typename KeyEqual>
 __device__ typename static_multimap<Key, Value, Scope, Allocator>::device_view::iterator
 static_multimap<Key, Value, Scope, Allocator>::device_view::find(CG g,
-                                                            Key const& k,
-                                                            Hash hash,
-                                                            KeyEqual key_equal) noexcept
+                                                                 Key const& k,
+                                                                 Hash hash,
+                                                                 KeyEqual key_equal) noexcept
 {
   auto current_slot = initial_slot(g, k, hash);
 
@@ -313,9 +313,9 @@ template <typename Key, typename Value, cuda::thread_scope Scope, typename Alloc
 template <typename CG, typename Hash, typename KeyEqual>
 __device__ typename static_multimap<Key, Value, Scope, Allocator>::device_view::const_iterator
 static_multimap<Key, Value, Scope, Allocator>::device_view::find(CG g,
-                                                            Key const& k,
-                                                            Hash hash,
-                                                            KeyEqual key_equal) const noexcept
+                                                                 Key const& k,
+                                                                 Hash hash,
+                                                                 KeyEqual key_equal) const noexcept
 {
   auto current_slot = initial_slot(g, k, hash);
 
diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap_kernels.cuh
index 5132c8ef5..6ded5e99d 100644
--- a/include/cuco/detail/static_multimap_kernels.cuh
+++ b/include/cuco/detail/static_multimap_kernels.cuh
@@ -12,9 +12,9 @@
  * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  * See the License for the specific language governing permissions and
  * limitations under the License.
- */ 
+ */
 
-namespace cuco{
+namespace cuco {
 namespace detail {
 namespace cg = cooperative_groups;
 
@@ -23,7 +23,7 @@ namespace cg = cooperative_groups;
  *
  * Each space in `slots` that can hold a key value pair is initialized to a
  * `pair_atomic_type` containing the key `k` and the value `v`.
- * 
+ *
  * @tparam atomic_key_type Type of the `Key` atomic container
  * @tparam atomic_mapped_type Type of the `Value` atomic container
  * @tparam Key key type
@@ -34,11 +34,13 @@ namespace cg = cooperative_groups;
  * @param v Value to which all values in `slots` are initialized
  * @param size Size of the storage pointed to by `slots`
  */
-template<typename atomic_key_type, typename atomic_mapped_type, typename Key, typename Value, typename pair_atomic_type>
-__global__ void initialize(
-  pair_atomic_type* const slots, Key k,
-  Value v, std::size_t size) {
-  
+template <typename atomic_key_type,
+          typename atomic_mapped_type,
+          typename Key,
+          typename Value,
+          typename pair_atomic_type>
+__global__ void initialize(pair_atomic_type* const slots, Key k, Value v, std::size_t size)
+{
   auto tid = threadIdx.x + blockIdx.x * blockDim.x;
   while (tid < size) {
     new (&slots[tid].first) atomic_key_type{k};
@@ -48,14 +50,14 @@ __global__ void initialize(
 }
 
 /**
- * @brief Inserts all key/value pairs in the range `[first, last)`. 
- *  
+ * @brief Inserts all key/value pairs in the range `[first, last)`.
+ *
  * If multiple keys in `[first, last)` compare equal, it is unspecified which
  * element is inserted.
- * 
- * @tparam block_size 
+ *
+ * @tparam block_size
  * @tparam InputIt Device accessible input iterator whose `value_type` is
-* convertible to the map's `value_type`
+ * convertible to the map's `value_type`
  * @tparam atomicT Type of atomic storage
  * @tparam viewT Type of device view allowing access of hash map storage
  * @tparam Hash Unary callable type
@@ -67,25 +69,22 @@ __global__ void initialize(
  * @param hash The unary function to apply to hash each key
  * @param key_equal The binary function used to compare two keys for equality
  */
-template<uint32_t block_size,
-         typename InputIt,
-         typename atomicT,
-         typename viewT,
-         typename Hash, 
-         typename KeyEqual>
-__global__ void insert(InputIt first,
-                       InputIt last,
-                       atomicT* num_successes,
-                       viewT view,
-                       Hash hash,
-                       KeyEqual key_equal) {
+template <uint32_t block_size,
+          typename InputIt,
+          typename atomicT,
+          typename viewT,
+          typename Hash,
+          typename KeyEqual>
+__global__ void insert(
+  InputIt first, InputIt last, atomicT* num_successes, viewT view, Hash hash, KeyEqual key_equal)
+{
   typedef cub::BlockReduce<std::size_t, block_size> BlockReduce;
   __shared__ typename BlockReduce::TempStorage temp_storage;
   std::size_t thread_num_successes = 0;
-  
+
   auto tid = blockDim.x * blockIdx.x + threadIdx.x;
-  auto it = first + tid;
-  
+  auto it  = first + tid;
+
   while (it < last) {
     typename viewT::value_type const insert_pair{*it};
     if (view.insert(insert_pair, hash, key_equal)) { thread_num_successes++; }
@@ -95,25 +94,23 @@ __global__ void insert(InputIt first,
   // compute number of successfully inserted elements for each block
   // and atomically add to the grand total
   std::size_t block_num_successes = BlockReduce(temp_storage).Sum(thread_num_successes);
-  if(threadIdx.x == 0) {
-    *num_successes += block_num_successes;
-  }
+  if (threadIdx.x == 0) { *num_successes += block_num_successes; }
 }
 
 /**
- * @brief Inserts all key/value pairs in the range `[first, last)`. 
- *  
+ * @brief Inserts all key/value pairs in the range `[first, last)`.
+ *
  * If multiple keys in `[first, last)` compare equal, it is unspecified which
  * element is inserted. Uses the CUDA Cooperative Groups API to leverage groups
- * of multiple threads to perform each key/value insertion. This provides a 
+ * of multiple threads to perform each key/value insertion. This provides a
  * significant boost in throughput compared to the non Cooperative Group
  * `insert` at moderate to high load factors.
- * 
- * @tparam block_size 
+ *
+ * @tparam block_size
  * @tparam tile_size The number of threads in the Cooperative Groups used to perform
  * inserts
  * @tparam InputIt Device accessible input iterator whose `value_type` is
-* convertible to the map's `value_type`
+ * convertible to the map's `value_type`
  * @tparam atomicT Type of atomic storage
  * @tparam viewT Type of device view allowing access of hash map storage
  * @tparam Hash Unary callable type
@@ -125,27 +122,24 @@ __global__ void insert(InputIt first,
  * @param hash The unary function to apply to hash each key
  * @param key_equal The binary function used to compare two keys for equality
  */
-template<uint32_t block_size,
-         uint32_t tile_size,
-         typename InputIt,
-         typename atomicT,
-         typename viewT,
-         typename Hash, 
-         typename KeyEqual>
-__global__ void insert(InputIt first,
-                       InputIt last,
-                       atomicT* num_successes,
-                       viewT view,
-                       Hash hash,
-                       KeyEqual key_equal) {
+template <uint32_t block_size,
+          uint32_t tile_size,
+          typename InputIt,
+          typename atomicT,
+          typename viewT,
+          typename Hash,
+          typename KeyEqual>
+__global__ void insert(
+  InputIt first, InputIt last, atomicT* num_successes, viewT view, Hash hash, KeyEqual key_equal)
+{
   typedef cub::BlockReduce<std::size_t, block_size> BlockReduce;
   __shared__ typename BlockReduce::TempStorage temp_storage;
   std::size_t thread_num_successes = 0;
 
   auto tile = cg::tiled_partition<tile_size>(cg::this_thread_block());
-  auto tid = blockDim.x * blockIdx.x + threadIdx.x;
-  auto it = first + tid / tile_size;
-  
+  auto tid  = blockDim.x * blockIdx.x + threadIdx.x;
+  auto it   = first + tid / tile_size;
+
   while (it < last) {
     // force conversion to value_type
     typename viewT::value_type const insert_pair{*it};
@@ -154,25 +148,23 @@ __global__ void insert(InputIt first,
     }
     it += (gridDim.x * blockDim.x) / tile_size;
   }
-  
+
   // compute number of successfully inserted elements for each block
   // and atomically add to the grand total
   std::size_t block_num_successes = BlockReduce(temp_storage).Sum(thread_num_successes);
-  if(threadIdx.x == 0) {
-    *num_successes += block_num_successes;
-  }
+  if (threadIdx.x == 0) { *num_successes += block_num_successes; }
 }
 
 /**
  * @brief Finds the values corresponding to all keys in the range `[first, last)`.
- * 
- * If the key `*(first + i)` exists in the map, copies its associated value to `(output_begin + i)`. 
- * Else, copies the empty value sentinel. 
+ *
+ * If the key `*(first + i)` exists in the map, copies its associated value to `(output_begin + i)`.
+ * Else, copies the empty value sentinel.
  * @tparam block_size The size of the thread block
  * @tparam Value The type of the mapped value for the map
  * @tparam InputIt Device accessible input iterator whose `value_type` is
  * convertible to the map's `key_type`
- * @tparam OutputIt Device accessible output iterator whose `value_type` is 
+ * @tparam OutputIt Device accessible output iterator whose `value_type` is
  * convertible to the map's `mapped_type`
  * @tparam viewT Type of device view allowing access of hash map storage
  * @tparam Hash Unary callable type
@@ -184,28 +176,26 @@ __global__ void insert(InputIt first,
  * @param hash The unary function to apply to hash each key
  * @param key_equal The binary function to compare two keys for equality
  */
-template<uint32_t block_size,
-         typename Value,
-         typename InputIt, typename OutputIt, 
-         typename viewT,
-         typename Hash, 
-         typename KeyEqual>
-__global__ void find(InputIt first,
-                     InputIt last,
-                     OutputIt output_begin,
-                     viewT view,
-                     Hash hash,
-                     KeyEqual key_equal) {
-  auto tid = blockDim.x * blockIdx.x + threadIdx.x;
+template <uint32_t block_size,
+          typename Value,
+          typename InputIt,
+          typename OutputIt,
+          typename viewT,
+          typename Hash,
+          typename KeyEqual>
+__global__ void find(
+  InputIt first, InputIt last, OutputIt output_begin, viewT view, Hash hash, KeyEqual key_equal)
+{
+  auto tid     = blockDim.x * blockIdx.x + threadIdx.x;
   auto key_idx = tid;
   __shared__ Value writeBuffer[block_size];
-  
-  while(first + key_idx < last) {
-    auto key = *(first + key_idx);
+
+  while (first + key_idx < last) {
+    auto key   = *(first + key_idx);
     auto found = view.find(key, hash, key_equal);
 
-    /* 
-     * The ld.relaxed.gpu instruction used in view.find causes L1 to 
+    /*
+     * The ld.relaxed.gpu instruction used in view.find causes L1 to
      * flush more frequently, causing increased sector stores from L2 to global memory.
      * By writing results to shared memory and then synchronizing before writing back
      * to global, we no longer rely on L1, preventing the increase in sector stores from
@@ -220,10 +210,10 @@ __global__ void find(InputIt first,
 
 /**
  * @brief Finds the values corresponding to all keys in the range `[first, last)`.
- * 
- * If the key `*(first + i)` exists in the map, copies its associated value to `(output_begin + i)`. 
+ *
+ * If the key `*(first + i)` exists in the map, copies its associated value to `(output_begin + i)`.
  * Else, copies the empty value sentinel. Uses the CUDA Cooperative Groups API to leverage groups
- * of multiple threads to find each key. This provides a significant boost in throughput compared 
+ * of multiple threads to find each key. This provides a significant boost in throughput compared
  * to the non Cooperative Group `find` at moderate to high load factors.
  *
  * @tparam block_size The size of the thread block
@@ -232,7 +222,7 @@ __global__ void find(InputIt first,
  * @tparam Value The type of the mapped value for the map
  * @tparam InputIt Device accessible input iterator whose `value_type` is
  * convertible to the map's `key_type`
- * @tparam OutputIt Device accessible output iterator whose `value_type` is 
+ * @tparam OutputIt Device accessible output iterator whose `value_type` is
  * convertible to the map's `mapped_type`
  * @tparam viewT Type of device view allowing access of hash map storage
  * @tparam Hash Unary callable type
@@ -244,39 +234,38 @@ __global__ void find(InputIt first,
  * @param hash The unary function to apply to hash each key
  * @param key_equal The binary function to compare two keys for equality
  */
-template<uint32_t block_size, uint32_t tile_size,
-         typename Value,
-         typename InputIt, typename OutputIt, 
-         typename viewT,
-         typename Hash, 
-         typename KeyEqual>
-__global__ void find(InputIt first,
-                     InputIt last,
-                     OutputIt output_begin,
-                     viewT view,
-                     Hash hash,
-                     KeyEqual key_equal) {
-  auto tile = cg::tiled_partition<tile_size>(cg::this_thread_block());
-  auto tid = blockDim.x * blockIdx.x + threadIdx.x;
+template <uint32_t block_size,
+          uint32_t tile_size,
+          typename Value,
+          typename InputIt,
+          typename OutputIt,
+          typename viewT,
+          typename Hash,
+          typename KeyEqual>
+__global__ void find(
+  InputIt first, InputIt last, OutputIt output_begin, viewT view, Hash hash, KeyEqual key_equal)
+{
+  auto tile    = cg::tiled_partition<tile_size>(cg::this_thread_block());
+  auto tid     = blockDim.x * blockIdx.x + threadIdx.x;
   auto key_idx = tid / tile_size;
   __shared__ Value writeBuffer[block_size];
-  
-  while(first + key_idx < last) {
-    auto key = *(first + key_idx);
+
+  while (first + key_idx < last) {
+    auto key   = *(first + key_idx);
     auto found = view.find(tile, key, hash, key_equal);
-    
-    /* 
-     * The ld.relaxed.gpu instruction used in view.find causes L1 to 
+
+    /*
+     * The ld.relaxed.gpu instruction used in view.find causes L1 to
      * flush more frequently, causing increased sector stores from L2 to global memory.
      * By writing results to shared memory and then synchronizing before writing back
      * to global, we no longer rely on L1, preventing the increase in sector stores from
      * L2 to global and improving performance.
      */
-    if(tile.thread_rank() == 0) {
+    if (tile.thread_rank() == 0) {
       writeBuffer[threadIdx.x / tile_size] = found->second.load(cuda::std::memory_order_relaxed);
     }
     __syncthreads();
-    if(tile.thread_rank() == 0) {
+    if (tile.thread_rank() == 0) {
       *(output_begin + key_idx) = writeBuffer[threadIdx.x / tile_size];
     }
     key_idx += (gridDim.x * blockDim.x) / tile_size;
@@ -285,7 +274,7 @@ __global__ void find(InputIt first,
 
 /**
  * @brief Indicates whether the keys in the range `[first, last)` are contained in the map.
- * 
+ *
  * Writes a `bool` to `(output + i)` indicating if the key `*(first + i)` exists in the map.
  *
  * @tparam block_size The size of the thread block
@@ -295,7 +284,7 @@ __global__ void find(InputIt first,
  * convertible to the map's `mapped_type`
  * @tparam viewT Type of device view allowing access of hash map storage
  * @tparam Hash Unary callable type
- * @tparam KeyEqual Binary callable type 
+ * @tparam KeyEqual Binary callable type
  * @param first Beginning of the sequence of keys
  * @param last End of the sequence of keys
  * @param output_begin Beginning of the sequence of booleans for the presence of each key
@@ -303,26 +292,24 @@ __global__ void find(InputIt first,
  * @param hash The unary function to apply to hash each key
  * @param key_equal The binary function to compare two keys for equality
  */
-template<uint32_t block_size,
-         typename InputIt, typename OutputIt, 
-         typename viewT,
-         typename Hash, 
-         typename KeyEqual>
-__global__ void contains(InputIt first,
-                         InputIt last,
-                         OutputIt output_begin,
-                         viewT view,
-                         Hash hash,
-                         KeyEqual key_equal) {
-  auto tid = blockDim.x * blockIdx.x + threadIdx.x;
+template <uint32_t block_size,
+          typename InputIt,
+          typename OutputIt,
+          typename viewT,
+          typename Hash,
+          typename KeyEqual>
+__global__ void contains(
+  InputIt first, InputIt last, OutputIt output_begin, viewT view, Hash hash, KeyEqual key_equal)
+{
+  auto tid     = blockDim.x * blockIdx.x + threadIdx.x;
   auto key_idx = tid;
   __shared__ bool writeBuffer[block_size];
-  
-  while(first + key_idx < last) {
+
+  while (first + key_idx < last) {
     auto key = *(first + key_idx);
-    
-    /* 
-     * The ld.relaxed.gpu instruction used in view.find causes L1 to 
+
+    /*
+     * The ld.relaxed.gpu instruction used in view.find causes L1 to
      * flush more frequently, causing increased sector stores from L2 to global memory.
      * By writing results to shared memory and then synchronizing before writing back
      * to global, we no longer rely on L1, preventing the increase in sector stores from
@@ -337,10 +324,10 @@ __global__ void contains(InputIt first,
 
 /**
  * @brief Indicates whether the keys in the range `[first, last)` are contained in the map.
- * 
+ *
  * Writes a `bool` to `(output + i)` indicating if the key `*(first + i)` exists in the map.
- * Uses the CUDA Cooperative Groups API to leverage groups of multiple threads to perform the 
- * contains operation for each key. This provides a significant boost in throughput compared 
+ * Uses the CUDA Cooperative Groups API to leverage groups of multiple threads to perform the
+ * contains operation for each key. This provides a significant boost in throughput compared
  * to the non Cooperative Group `contains` at moderate to high load factors.
  *
  * @tparam block_size The size of the thread block
@@ -352,7 +339,7 @@ __global__ void contains(InputIt first,
  * convertible to the map's `mapped_type`
  * @tparam viewT Type of device view allowing access of hash map storage
  * @tparam Hash Unary callable type
- * @tparam KeyEqual Binary callable type 
+ * @tparam KeyEqual Binary callable type
  * @param first Beginning of the sequence of keys
  * @param last End of the sequence of keys
  * @param output_begin Beginning of the sequence of booleans for the presence of each key
@@ -360,43 +347,40 @@ __global__ void contains(InputIt first,
  * @param hash The unary function to apply to hash each key
  * @param key_equal The binary function to compare two keys for equality
  */
-template<uint32_t block_size, uint32_t tile_size,
-         typename InputIt, typename OutputIt, 
-         typename viewT,
-         typename Hash, 
-         typename KeyEqual>
-__global__ void contains(InputIt first,
-                         InputIt last,
-                         OutputIt output_begin,
-                         viewT view,
-                         Hash hash,
-                         KeyEqual key_equal) {
-  auto tile = cg::tiled_partition<tile_size>(cg::this_thread_block());
-  auto tid = blockDim.x * blockIdx.x + threadIdx.x;
+template <uint32_t block_size,
+          uint32_t tile_size,
+          typename InputIt,
+          typename OutputIt,
+          typename viewT,
+          typename Hash,
+          typename KeyEqual>
+__global__ void contains(
+  InputIt first, InputIt last, OutputIt output_begin, viewT view, Hash hash, KeyEqual key_equal)
+{
+  auto tile    = cg::tiled_partition<tile_size>(cg::this_thread_block());
+  auto tid     = blockDim.x * blockIdx.x + threadIdx.x;
   auto key_idx = tid / tile_size;
   __shared__ bool writeBuffer[block_size];
-  
-  while(first + key_idx < last) {
-    auto key = *(first + key_idx);
+
+  while (first + key_idx < last) {
+    auto key   = *(first + key_idx);
     auto found = view.contains(tile, key, hash, key_equal);
-    
-    /* 
-     * The ld.relaxed.gpu instruction used in view.find causes L1 to 
+
+    /*
+     * The ld.relaxed.gpu instruction used in view.find causes L1 to
      * flush more frequently, causing increased sector stores from L2 to global memory.
      * By writing results to shared memory and then synchronizing before writing back
      * to global, we no longer rely on L1, preventing the increase in sector stores from
      * L2 to global and improving performance.
      */
-    if(tile.thread_rank() == 0) {
-      writeBuffer[threadIdx.x / tile_size] = found;
-    }
+    if (tile.thread_rank() == 0) { writeBuffer[threadIdx.x / tile_size] = found; }
     __syncthreads();
-    if(tile.thread_rank() == 0) {
+    if (tile.thread_rank() == 0) {
       *(output_begin + key_idx) = writeBuffer[threadIdx.x / tile_size];
     }
     key_idx += (gridDim.x * blockDim.x) / tile_size;
   }
 }
 
-} // namespace detail
-} // namespace cuco
\ No newline at end of file
+}  // namespace detail
+}  // namespace cuco
\ No newline at end of file
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index b765a6bfd..31672c447 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -152,9 +152,9 @@ class static_multimap {
    * @param alloc Allocator used for allocating device storage
    */
   static_multimap(std::size_t capacity,
-             Key empty_key_sentinel,
-             Value empty_value_sentinel,
-             Allocator const& alloc = Allocator{});
+                  Key empty_key_sentinel,
+                  Value empty_value_sentinel,
+                  Allocator const& alloc = Allocator{});
 
   /**
    * @brief Destroys the map and frees its contents.
@@ -886,7 +886,8 @@ class static_multimap {
   }
 
   /**
-   * @brief Constructs a device_mutable_view object based on the members of the `static_multimap` object
+   * @brief Constructs a device_mutable_view object based on the members of the `static_multimap`
+   * object
    *
    * @return A device_mutable_view object based on the members of the `static_multimap` object
    */

From 84bbc5afe0b1d6e8dae25a4a8a1c576143c4a599 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Fri, 26 Feb 2021 16:18:30 -0800
Subject: [PATCH 009/267] Complete static multimap example + update comments

---
 .../static_multimap/static_multimap_example.cu |  8 ++++----
 include/cuco/detail/static_multimap.inl        | 18 +++---------------
 2 files changed, 7 insertions(+), 19 deletions(-)

diff --git a/examples/static_multimap/static_multimap_example.cu b/examples/static_multimap/static_multimap_example.cu
index 2a7a86500..00ab44f89 100644
--- a/examples/static_multimap/static_multimap_example.cu
+++ b/examples/static_multimap/static_multimap_example.cu
@@ -45,13 +45,13 @@ int main(void)
   map.insert(pairs.begin(), pairs.end());
 
   // Sequence of keys {0, 1, 2, ...}
-  // thrust::device_vector<int> keys_to_find(50'000);
-  // thrust::sequence(keys_to_find.begin(), keys_to_find.end(), 0);
-  // thrust::device_vector<int> found_values(50'000);
+  thrust::device_vector<int> keys_to_find(50'000);
+  thrust::sequence(keys_to_find.begin(), keys_to_find.end(), 0);
+  thrust::device_vector<int> found_values(50'000);
 
   // Finds all keys {0, 1, 2, ...} and stores associated values into `found_values`
   // If a key `keys_to_find[i]` doesn't exist, `found_values[i] == empty_value_sentinel`
-  // map.find(keys_to_find.begin(), keys_to_find.end(), found_values.begin());
+  map.find(keys_to_find.begin(), keys_to_find.end(), found_values.begin());
 
   return 0;
 }
diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index 851213ab1..0d521dbac 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -145,12 +145,8 @@ __device__ bool static_multimap<Key, Value, Scope, Allocator>::device_mutable_vi
       slot_value.store(this->get_empty_value_sentinel(), memory_order_relaxed);
     }
 
-    // if the key was already inserted by another thread, than this instance is a
-    // duplicate, so the insert fails
-    // if (key_equal(insert_pair.first, expected_key)) { return false; }
-
-    // if we couldn't insert the key, but it wasn't a duplicate, then there must
-    // have been some other key there, so we keep looking for a slot
+    // If we couldn't insert the key, then there must have been some other key there. So we keep
+    // looking for a slot
     current_slot = next_slot(current_slot);
   }
 }
@@ -167,17 +163,9 @@ __device__ bool static_multimap<Key, Value, Scope, Allocator>::device_mutable_vi
 
     // The user provide `key_equal` can never be used to compare against `empty_key_sentinel` as the
     // sentinel is not a valid key value. Therefore, first check for the sentinel
-    auto const slot_is_empty = (existing_key == this->get_empty_key_sentinel());
-
-    // the key we are trying to insert is already in the map, so we return with failure to insert
-    // if (g.ballot(not slot_is_empty and key_equal(existing_key, insert_pair.first))) {
-    //  return false;
-    //}
-
+    auto const slot_is_empty         = (existing_key == this->get_empty_key_sentinel());
     auto const window_contains_empty = g.ballot(slot_is_empty);
 
-    // we found an empty slot, but not the key we are inserting, so this must
-    // be an empty slot into which we can insert the key
     if (window_contains_empty) {
       // the first lane in the group with an empty slot will attempt the insert
       insert_result status{insert_result::CONTINUE};

From 619708d14660919d5efb282d511ecfe0693efa4c Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Mon, 1 Mar 2021 10:11:18 -0800
Subject: [PATCH 010/267] Add fancy iterator & scalar (no cg involved) find_all
 functions on device

---
 include/cuco/detail/static_multimap.inl | 49 ++++++++++++++
 include/cuco/static_multimap.cuh        | 85 +++++++++++++++++++++++++
 2 files changed, 134 insertions(+)

diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index 0d521dbac..365041cdc 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -336,6 +336,55 @@ static_multimap<Key, Value, Scope, Allocator>::device_view::find(CG g,
   }
 }
 
+template <typename Key, typename Value, cuda::thread_scope Scope, typename Allocator>
+template <typename Hash, typename KeyEqual>
+__device__ typename static_multimap<Key, Value, Scope, Allocator>::device_view::fancy_iterator
+static_multimap<Key, Value, Scope, Allocator>::device_view::find_all(Key const& k,
+                                                                     Hash hash,
+                                                                     KeyEqual key_equal) noexcept
+{
+  auto current_slot = initial_slot(k, hash);
+
+  while (true) {
+    auto const existing_key = current_slot->first.load(cuda::std::memory_order_relaxed);
+    // Key doesn't exist, return end()
+    if (existing_key == this->get_empty_key_sentinel()) {
+      return fancy_iterator{this->end(), k, empty_value_sentinel_};
+    }
+
+    // Key exists, return iterator to location
+    if (key_equal(existing_key, k)) {
+      return fancy_iterator{current_slot, k, empty_value_sentinel_};
+    }
+
+    current_slot = next_slot(current_slot);
+  }
+}
+
+template <typename Key, typename Value, cuda::thread_scope Scope, typename Allocator>
+template <typename Hash, typename KeyEqual>
+__device__ typename static_multimap<Key, Value, Scope, Allocator>::device_view::const_fancy_iterator
+static_multimap<Key, Value, Scope, Allocator>::device_view::find_all(
+  Key const& k, Hash hash, KeyEqual key_equal) const noexcept
+{
+  auto current_slot = initial_slot(k, hash);
+
+  while (true) {
+    auto const existing_key = current_slot->first.load(cuda::std::memory_order_relaxed);
+    // Key doesn't exist, return end()
+    if (existing_key == this->get_empty_key_sentinel()) {
+      return fancy_iterator{this->end(), k, empty_value_sentinel_};
+    }
+
+    // Key exists, return iterator to location
+    if (key_equal(existing_key, k)) {
+      return fancy_iterator{current_slot, k, empty_value_sentinel_};
+    }
+
+    current_slot = next_slot(current_slot);
+  }
+}
+
 template <typename Key, typename Value, cuda::thread_scope Scope, typename Allocator>
 template <typename Hash, typename KeyEqual>
 __device__ bool static_multimap<Key, Value, Scope, Allocator>::device_view::contains(
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index 31672c447..8919bc789 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -520,6 +520,7 @@ class static_multimap {
     using mapped_type    = typename device_view_base::mapped_type;
     using iterator       = typename device_view_base::iterator;
     using const_iterator = typename device_view_base::const_iterator;
+
     /**
      * @brief Construct a mutable view of the first `capacity` slots of the
      * slots array pointed to by `slots`.
@@ -697,6 +698,48 @@ class static_multimap {
                          source_device_view.get_empty_value_sentinel());
     }
 
+    struct fancy_iterator {
+      __host__ __device__
+      fancy_iterator(pair_atomic_type* current,
+                     Key key,
+                     Value empty_value_sentinel = get_empty_value_sentinel()) noexcept
+        : current_{current}, key_{key}, empty_value_sentinel_{empty_value_sentinel}
+      {
+      }
+
+      __device__ fancy_iterator operator++()
+      {
+        auto next = ++current_;
+        while (next->first != key_) {
+          if (next->first == empty_value_sentinel_) {
+            return fancy_iterator{device_view::end(), key_, empty_value_sentinel_};
+            ++next;
+          }
+          return fancy_iterator{next, key_, empty_value_sentinel_};
+        }
+      }
+
+      __device__ fancy_iterator operator++(int)
+      {
+        auto next = ++current_;
+        while (next->first != key_) {
+          if (next->first == empty_value_sentinel_) {
+            return fancy_iterator{device_view::end(), key_, empty_value_sentinel_};
+            ++next;
+          }
+          return fancy_iterator{next, key_, empty_value_sentinel_};
+        }
+      }
+
+      __device__ pair_atomic_type operator*() { return *current_; }
+
+     private:
+      pair_atomic_type* current_;
+      Key key_;
+      Value empty_value_sentinel_;
+    };
+    using const_fancy_iterator = fancy_iterator const;
+
     /**
      * @brief Finds the value corresponding to the key `k`.
      *
@@ -790,6 +833,48 @@ class static_multimap {
     __device__ const_iterator
     find(CG g, Key const& k, Hash hash = Hash{}, KeyEqual key_equal = KeyEqual{}) const noexcept;
 
+    /**
+     * @brief Finds the first value corresponding to the key `k`.
+     *
+     * Returns a fancy iterator to the pair whose key is equivalent to `k`.
+     * If no such pair exists, returns `end()`.
+     *
+     * @tparam Hash Unary callable type
+     * @tparam KeyEqual Binary callable type
+     * @param k The key to search for
+     * @param hash The unary callable used to hash the key
+     * @param key_equal The binary callable used to compare two keys
+     * for equality
+     * @return A fancy iterator to the position at which the key/value pair
+     * containing `k` was inserted
+     */
+    template <typename Hash     = cuco::detail::MurmurHash3_32<key_type>,
+              typename KeyEqual = thrust::equal_to<key_type>>
+    __device__ fancy_iterator find_all(Key const& k,
+                                       Hash hash          = Hash{},
+                                       KeyEqual key_equal = KeyEqual{}) noexcept;
+
+    /**
+     * @brief Finds the first value corresponding to the key `k`.
+     *
+     * Returns a const fancy iterator to the pair whose key is equivalent to `k`.
+     * If no such pair exists, returns `end()`.
+     *
+     * @tparam Hash Unary callable type
+     * @tparam KeyEqual Binary callable type
+     * @param k The key to search for
+     * @param hash The unary callable used to hash the key
+     * @param key_equal The binary callable used to compare two keys
+     * for equality
+     * @return A const fancy iterator to the position at which the key/value pair
+     * containing `k` was inserted
+     */
+    template <typename Hash     = cuco::detail::MurmurHash3_32<key_type>,
+              typename KeyEqual = thrust::equal_to<key_type>>
+    __device__ const_fancy_iterator find_all(Key const& k,
+                                             Hash hash          = Hash{},
+                                             KeyEqual key_equal = KeyEqual{}) const noexcept;
+
     /**
      * @brief Indicates whether the key `k` was inserted into the map.
      *

From b92a23a3c07bb2d9a92d90b58c48a22cff20fc8b Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Mon, 1 Mar 2021 13:38:32 -0800
Subject: [PATCH 011/267] Add CUDA Cooperative Group find_all functions

---
 include/cuco/detail/static_multimap.inl | 88 +++++++++++++++++++++++--
 include/cuco/static_multimap.cuh        | 67 +++++++++++++++++--
 2 files changed, 145 insertions(+), 10 deletions(-)

diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index 365041cdc..a7cbf3b86 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -349,12 +349,12 @@ static_multimap<Key, Value, Scope, Allocator>::device_view::find_all(Key const&
     auto const existing_key = current_slot->first.load(cuda::std::memory_order_relaxed);
     // Key doesn't exist, return end()
     if (existing_key == this->get_empty_key_sentinel()) {
-      return fancy_iterator{this->end(), k, empty_value_sentinel_};
+      return fancy_iterator{this->end(), k, this->get_empty_key_sentinel()};
     }
 
     // Key exists, return iterator to location
     if (key_equal(existing_key, k)) {
-      return fancy_iterator{current_slot, k, empty_value_sentinel_};
+      return fancy_iterator{current_slot, k, this->get_empty_key_sentinel()};
     }
 
     current_slot = next_slot(current_slot);
@@ -373,18 +373,98 @@ static_multimap<Key, Value, Scope, Allocator>::device_view::find_all(
     auto const existing_key = current_slot->first.load(cuda::std::memory_order_relaxed);
     // Key doesn't exist, return end()
     if (existing_key == this->get_empty_key_sentinel()) {
-      return fancy_iterator{this->end(), k, empty_value_sentinel_};
+      return const_fancy_iterator{this->end(), k, this->get_empty_key_sentinel()};
     }
 
     // Key exists, return iterator to location
     if (key_equal(existing_key, k)) {
-      return fancy_iterator{current_slot, k, empty_value_sentinel_};
+      return const_fancy_iterator{current_slot, k, this->get_empty_key_sentinel()};
     }
 
     current_slot = next_slot(current_slot);
   }
 }
 
+template <typename Key, typename Value, cuda::thread_scope Scope, typename Allocator>
+template <typename CG, typename Hash, typename KeyEqual>
+__device__ typename static_multimap<Key, Value, Scope, Allocator>::device_view::fancy_iterator
+static_multimap<Key, Value, Scope, Allocator>::device_view::find_all(CG g,
+                                                                     Key const& k,
+                                                                     Hash hash,
+                                                                     KeyEqual key_equal) noexcept
+{
+  auto current_slot = initial_slot(g, k, hash);
+
+  while (true) {
+    auto const existing_key = current_slot->first.load(cuda::std::memory_order_relaxed);
+
+    // The user provide `key_equal` can never be used to compare against `empty_key_sentinel` as the
+    // sentinel is not a valid key value. Therefore, first check for the sentinel
+    auto const slot_is_empty = (existing_key == this->get_empty_key_sentinel());
+
+    // the key we were searching for was found by one of the threads,
+    // so we return an iterator to the entry
+    auto const exists = g.ballot(not slot_is_empty and key_equal(existing_key, k));
+    if (exists) {
+      uint32_t src_lane = __ffs(exists) - 1;
+      // TODO: This shouldn't cast an iterator to an int to shuffle. Instead, get the index of the
+      // current_slot and shuffle that instead.
+      intptr_t res_slot = g.shfl(reinterpret_cast<intptr_t>(current_slot), src_lane);
+      return fancy_iterator{
+        reinterpret_cast<iterator>(res_slot), k, this->get_empty_value_sentinel()};
+    }
+
+    // we found an empty slot, meaning that the key we're searching for isn't present
+    if (g.ballot(slot_is_empty)) {
+      return fancy_iterator{this->end(), k, this->get_empty_value_sentinel()};
+    }
+
+    // otherwise, all slots in the current window are full with other keys, so we move onto the
+    // next window
+    current_slot = next_slot(g, current_slot);
+  }
+}
+
+template <typename Key, typename Value, cuda::thread_scope Scope, typename Allocator>
+template <typename CG, typename Hash, typename KeyEqual>
+__device__ typename static_multimap<Key, Value, Scope, Allocator>::device_view::const_fancy_iterator
+static_multimap<Key, Value, Scope, Allocator>::device_view::find_all(
+  CG g, Key const& k, Hash hash, KeyEqual key_equal) const noexcept
+{
+  auto current_slot = initial_slot(g, k, hash);
+
+  while (true) {
+    auto const existing_key = current_slot->first.load(cuda::std::memory_order_relaxed);
+
+    // The user provide `key_equal` can never be used to compare against `empty_key_sentinel` as the
+    // sentinel is not a valid key value. Therefore, first check for the sentinel
+    auto const slot_is_empty = (existing_key == this->get_empty_key_sentinel());
+
+    // the key we were searching for was found by one of the threads, so we return an iterator to
+    // the entry
+    auto const exists = g.ballot(not slot_is_empty and key_equal(existing_key, k));
+    if (exists) {
+      uint32_t src_lane = __ffs(exists) - 1;
+      // TODO: This shouldn't cast an iterator to an int to shuffle. Instead, get the index of the
+      // current_slot and shuffle that instead.
+      intptr_t res_slot = g.shfl(reinterpret_cast<intptr_t>(current_slot), src_lane);
+      return const_fancy_iterator{
+        reinterpret_cast<iterator>(res_slot), k, this->get_empty_value_sentinel()};
+    }
+
+    // we found an empty slot, meaning that the key we're searching
+    // for isn't in this submap, so we should move onto the next one
+    if (g.ballot(slot_is_empty)) {
+      return const_fancy_iterator{this->end(), k, this->get_empty_value_sentinel()};
+    }
+
+    // otherwise, all slots in the current window are full with other keys,
+    // so we move onto the next window in the current submap
+
+    current_slot = next_slot(g, current_slot);
+  }
+}
+
 template <typename Key, typename Value, cuda::thread_scope Scope, typename Allocator>
 template <typename Hash, typename KeyEqual>
 __device__ bool static_multimap<Key, Value, Scope, Allocator>::device_view::contains(
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index 8919bc789..4464f236c 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -834,9 +834,9 @@ class static_multimap {
     find(CG g, Key const& k, Hash hash = Hash{}, KeyEqual key_equal = KeyEqual{}) const noexcept;
 
     /**
-     * @brief Finds the first value corresponding to the key `k`.
+     * @brief Finds all values corresponding to the key `k`.
      *
-     * Returns a fancy iterator to the pair whose key is equivalent to `k`.
+     * Returns a fancy iterator to the first pair whose key is equivalent to `k`.
      * If no such pair exists, returns `end()`.
      *
      * @tparam Hash Unary callable type
@@ -845,7 +845,7 @@ class static_multimap {
      * @param hash The unary callable used to hash the key
      * @param key_equal The binary callable used to compare two keys
      * for equality
-     * @return A fancy iterator to the position at which the key/value pair
+     * @return A fancy iterator to the position at which the first key/value pair
      * containing `k` was inserted
      */
     template <typename Hash     = cuco::detail::MurmurHash3_32<key_type>,
@@ -855,9 +855,9 @@ class static_multimap {
                                        KeyEqual key_equal = KeyEqual{}) noexcept;
 
     /**
-     * @brief Finds the first value corresponding to the key `k`.
+     * @brief Finds all values corresponding to the key `k`.
      *
-     * Returns a const fancy iterator to the pair whose key is equivalent to `k`.
+     * Returns a const fancy iterator to the first pair whose key is equivalent to `k`.
      * If no such pair exists, returns `end()`.
      *
      * @tparam Hash Unary callable type
@@ -866,7 +866,7 @@ class static_multimap {
      * @param hash The unary callable used to hash the key
      * @param key_equal The binary callable used to compare two keys
      * for equality
-     * @return A const fancy iterator to the position at which the key/value pair
+     * @return A const fancy iterator to the position at which the first key/value pair
      * containing `k` was inserted
      */
     template <typename Hash     = cuco::detail::MurmurHash3_32<key_type>,
@@ -875,6 +875,60 @@ class static_multimap {
                                              Hash hash          = Hash{},
                                              KeyEqual key_equal = KeyEqual{}) const noexcept;
 
+    /**
+     * @brief Finds all values corresponding to the key `k`.
+     *
+     * Returns a fancy iterator to the first pair whose key is equivalent to `k`.
+     * If no such pair exists, returns `end()`. Uses the CUDA Cooperative Groups API to
+     * to leverage multiple threads to perform a single find. This provides a
+     * significant boost in throughput compared to the non Cooperative Group
+     * `find_all` at moderate to high load factors.
+     *
+     * @tparam CG Cooperative Group type
+     * @tparam Hash Unary callable type
+     * @tparam KeyEqual Binary callable type
+     * @param g The Cooperative Group used to perform the find
+     * @param k The key to search for
+     * @param hash The unary callable used to hash the key
+     * @param key_equal The binary callable used to compare two keys
+     * for equality
+     * @return A fancy iterator to the position at which the first key/value pair
+     * containing `k` was inserted
+     */
+    template <typename CG,
+              typename Hash     = cuco::detail::MurmurHash3_32<key_type>,
+              typename KeyEqual = thrust::equal_to<key_type>>
+    __device__ fancy_iterator
+    find_all(CG g, Key const& k, Hash hash = Hash{}, KeyEqual key_equal = KeyEqual{}) noexcept;
+
+    /**
+     * @brief Finds all values corresponding to the key `k`.
+     *
+     * Returns a const_iterator to the first pair whose key is equivalent to `k`.
+     * If no such pair exists, returns `end()`. Uses the CUDA Cooperative Groups API to
+     * to leverage multiple threads to perform a single find. This provides a
+     * significant boost in throughput compared to the non Cooperative Group
+     * `find_all` at moderate to high load factors.
+     *
+     * @tparam CG Cooperative Group type
+     * @tparam Hash Unary callable type
+     * @tparam KeyEqual Binary callable type
+     * @param g The Cooperative Group used to perform the find
+     * @param k The key to search for
+     * @param hash The unary callable used to hash the key
+     * @param key_equal The binary callable used to compare two keys
+     * for equality
+     * @return A const fancy iterator to the position at which the first key/value pair
+     * containing `k` was inserted
+     */
+    template <typename CG,
+              typename Hash     = cuco::detail::MurmurHash3_32<key_type>,
+              typename KeyEqual = thrust::equal_to<key_type>>
+    __device__ const_fancy_iterator find_all(CG g,
+                                             Key const& k,
+                                             Hash hash          = Hash{},
+                                             KeyEqual key_equal = KeyEqual{}) const noexcept;
+
     /**
      * @brief Indicates whether the key `k` was inserted into the map.
      *
@@ -890,6 +944,7 @@ class static_multimap {
      * @return A boolean indicating whether the key/value pair
      * containing `k` was inserted
      */
+
     template <typename Hash     = cuco::detail::MurmurHash3_32<key_type>,
               typename KeyEqual = thrust::equal_to<key_type>>
     __device__ bool contains(Key const& k,

From a6e2d72aefe4f2c6b4c739a11f5d49e31620f114 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Mon, 1 Mar 2021 15:07:58 -0800
Subject: [PATCH 012/267] Add device count functions

---
 include/cuco/detail/static_multimap.inl | 29 ++++++++++++++++++++
 include/cuco/static_multimap.cuh        | 36 +++++++++++++++++++++++++
 2 files changed, 65 insertions(+)

diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index a7cbf3b86..82eb8d61e 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -509,4 +509,33 @@ __device__ bool static_multimap<Key, Value, Scope, Allocator>::device_view::cont
     current_slot = next_slot(g, current_slot);
   }
 }
+
+template <typename Key, typename Value, cuda::thread_scope Scope, typename Allocator>
+template <typename Hash, typename KeyEqual>
+__device__ size_t static_multimap<Key, Value, Scope, Allocator>::device_view::count(
+  Key const& k, Hash hash, KeyEqual key_equal) noexcept
+{
+  auto found   = this->find_all(k, hash, key_equal);
+  size_t count = 0;
+  while (*found != this->end()) {
+    ++found;
+    ++count;
+  }
+  return count;
+}
+
+template <typename Key, typename Value, cuda::thread_scope Scope, typename Allocator>
+template <typename CG, typename Hash, typename KeyEqual>
+__device__ size_t static_multimap<Key, Value, Scope, Allocator>::device_view::count(
+  CG g, Key const& k, Hash hash, KeyEqual key_equal) noexcept
+{
+  auto found   = this->find_all(g, k, hash, key_equal);
+  size_t count = 0;
+  while (*found != this->end()) {
+    ++found;
+    ++count;
+  }
+  return count;
+}
+
 }  // namespace cuco
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index 4464f236c..85b3d35d1 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -978,6 +978,42 @@ class static_multimap {
                              Key const& k,
                              Hash hash          = Hash{},
                              KeyEqual key_equal = KeyEqual{}) noexcept;
+
+    /**
+     * @brief Counts the number of matching key/value pairs contained in the multimap.
+     *
+     * @tparam Hash Unary callable type
+     * @tparam KeyEqual Binary callable type
+     * @param k The key to count for
+     * @param hash The unary callable used to hash the key
+     * @param key_equal The binary callable used to compare two keys
+     * @return The total number of the matching key/value pairs
+     * in the multimap
+     */
+    template <typename Hash     = cuco::detail::MurmurHash3_32<key_type>,
+              typename KeyEqual = thrust::equal_to<key_type>>
+    __device__ size_t count(Key const& k,
+                            Hash hash          = Hash{},
+                            KeyEqual key_equal = KeyEqual{}) noexcept;
+
+    /**
+     * @brief Counts the number of matching key/value pairs contained in the multimap.
+     *
+     * @tparam CG Cooperative Group type
+     * @tparam Hash Unary callable type
+     * @tparam KeyEqual Binary callable type
+     * @param k The key to count for
+     * @param hash The unary callable used to hash the key
+     * @param key_equal The binary callable used to compare two keys
+     * @return The total number of the matching key/value pairs
+     * in the multimap
+     */
+    template <typename CG,
+              typename Hash     = cuco::detail::MurmurHash3_32<key_type>,
+              typename KeyEqual = thrust::equal_to<key_type>>
+    __device__ size_t
+    count(CG g, Key const& k, Hash hash = Hash{}, KeyEqual key_equal = KeyEqual{}) noexcept;
+
   };  // class device_view
 
   /**

From 3742f0256f1e97b9b475f446aba20fbffbf1f5ca Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Tue, 2 Mar 2021 17:15:36 -0800
Subject: [PATCH 013/267] Updates: insert returns void instead of bool

---
 include/cuco/detail/static_multimap.inl       | 19 +++-----
 .../cuco/detail/static_multimap_kernels.cuh   | 44 +++----------------
 include/cuco/static_multimap.cuh              | 20 ++-------
 3 files changed, 15 insertions(+), 68 deletions(-)

diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index 82eb8d61e..87dcd235a 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -43,15 +43,12 @@ static_multimap<Key, Value, Scope, Allocator>::static_multimap(std::size_t capac
   auto const grid_size      = (capacity + stride * block_size - 1) / (stride * block_size);
   detail::initialize<atomic_key_type, atomic_mapped_type>
     <<<grid_size, block_size>>>(slots_, empty_key_sentinel, empty_value_sentinel, capacity);
-
-  CUCO_CUDA_TRY(cudaMallocManaged(&num_successes_, sizeof(atomic_ctr_type)));
 }
 
 template <typename Key, typename Value, cuda::thread_scope Scope, typename Allocator>
 static_multimap<Key, Value, Scope, Allocator>::~static_multimap()
 {
   std::allocator_traits<slot_allocator_type>::deallocate(slot_allocator_, slots_, capacity_);
-  CUCO_CUDA_TRY(cudaFree(num_successes_));
 }
 
 template <typename Key, typename Value, cuda::thread_scope Scope, typename Allocator>
@@ -68,16 +65,12 @@ void static_multimap<Key, Value, Scope, Allocator>::insert(InputIt first,
   auto const grid_size  = (tile_size * num_keys + stride * block_size - 1) / (stride * block_size);
   auto view             = get_device_mutable_view();
 
-  *num_successes_ = 0;
   int device_id;
   CUCO_CUDA_TRY(cudaGetDevice(&device_id));
-  CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_successes_, sizeof(atomic_ctr_type), device_id));
 
   detail::insert<block_size, tile_size>
-    <<<grid_size, block_size>>>(first, first + num_keys, num_successes_, view, hash, key_equal);
+    <<<grid_size, block_size>>>(first, first + num_keys, view, hash, key_equal);
   CUCO_CUDA_TRY(cudaDeviceSynchronize());
-
-  size_ += num_successes_->load(cuda::std::memory_order_relaxed);
 }
 
 template <typename Key, typename Value, cuda::thread_scope Scope, typename Allocator>
@@ -116,7 +109,7 @@ void static_multimap<Key, Value, Scope, Allocator>::contains(
 
 template <typename Key, typename Value, cuda::thread_scope Scope, typename Allocator>
 template <typename Hash, typename KeyEqual>
-__device__ bool static_multimap<Key, Value, Scope, Allocator>::device_mutable_view::insert(
+__device__ void static_multimap<Key, Value, Scope, Allocator>::device_mutable_view::insert(
   value_type const& insert_pair, Hash hash, KeyEqual key_equal) noexcept
 {
   auto current_slot{initial_slot(insert_pair.first, hash)};
@@ -140,7 +133,7 @@ __device__ bool static_multimap<Key, Value, Scope, Allocator>::device_mutable_vi
                                              insert_pair.second,
                                              memory_order_relaxed);
       }
-      return true;
+      return;
     } else if (value_success) {
       slot_value.store(this->get_empty_value_sentinel(), memory_order_relaxed);
     }
@@ -153,7 +146,7 @@ __device__ bool static_multimap<Key, Value, Scope, Allocator>::device_mutable_vi
 
 template <typename Key, typename Value, cuda::thread_scope Scope, typename Allocator>
 template <typename CG, typename Hash, typename KeyEqual>
-__device__ bool static_multimap<Key, Value, Scope, Allocator>::device_mutable_view::insert(
+__device__ void static_multimap<Key, Value, Scope, Allocator>::device_mutable_view::insert(
   CG g, value_type const& insert_pair, Hash hash, KeyEqual key_equal) noexcept
 {
   auto current_slot = initial_slot(g, insert_pair.first, hash);
@@ -195,8 +188,6 @@ __device__ bool static_multimap<Key, Value, Scope, Allocator>::device_mutable_vi
           slot_value.store(this->get_empty_value_sentinel(), memory_order_relaxed);
         }
 
-        // our key was already present in the slot, so our key is a duplicate
-        // if (key_equal(insert_pair.first, expected_key)) { status = insert_result::DUPLICATE; }
         // another key was inserted in the slot we wanted to try
         // so we need to try the next empty slot in the window
       }
@@ -205,7 +196,7 @@ __device__ bool static_multimap<Key, Value, Scope, Allocator>::device_mutable_vi
       status              = static_cast<insert_result>(res_status);
 
       // successful insert
-      if (status == insert_result::SUCCESS) { return true; }
+      if (status == insert_result::SUCCESS) { return; }
       // if we've gotten this far, a different key took our spot
       // before we could insert. We need to retry the insert on the
       // same window
diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap_kernels.cuh
index 6ded5e99d..8c06e0791 100644
--- a/include/cuco/detail/static_multimap_kernels.cuh
+++ b/include/cuco/detail/static_multimap_kernels.cuh
@@ -58,43 +58,26 @@ __global__ void initialize(pair_atomic_type* const slots, Key k, Value v, std::s
  * @tparam block_size
  * @tparam InputIt Device accessible input iterator whose `value_type` is
  * convertible to the map's `value_type`
- * @tparam atomicT Type of atomic storage
  * @tparam viewT Type of device view allowing access of hash map storage
  * @tparam Hash Unary callable type
  * @tparam KeyEqual Binary callable type
  * @param first Beginning of the sequence of key/value pairs
  * @param last End of the sequence of key/value pairs
- * @param num_successes The number of successfully inserted key/value pairs
  * @param view Mutable device view used to access the hash map's slot storage
  * @param hash The unary function to apply to hash each key
  * @param key_equal The binary function used to compare two keys for equality
  */
-template <uint32_t block_size,
-          typename InputIt,
-          typename atomicT,
-          typename viewT,
-          typename Hash,
-          typename KeyEqual>
-__global__ void insert(
-  InputIt first, InputIt last, atomicT* num_successes, viewT view, Hash hash, KeyEqual key_equal)
+template <uint32_t block_size, typename InputIt, typename viewT, typename Hash, typename KeyEqual>
+__global__ void insert(InputIt first, InputIt last, viewT view, Hash hash, KeyEqual key_equal)
 {
-  typedef cub::BlockReduce<std::size_t, block_size> BlockReduce;
-  __shared__ typename BlockReduce::TempStorage temp_storage;
-  std::size_t thread_num_successes = 0;
-
   auto tid = blockDim.x * blockIdx.x + threadIdx.x;
   auto it  = first + tid;
 
   while (it < last) {
     typename viewT::value_type const insert_pair{*it};
-    if (view.insert(insert_pair, hash, key_equal)) { thread_num_successes++; }
+    view.insert(insert_pair, hash, key_equal);
     it += gridDim.x * blockDim.x;
   }
-
-  // compute number of successfully inserted elements for each block
-  // and atomically add to the grand total
-  std::size_t block_num_successes = BlockReduce(temp_storage).Sum(thread_num_successes);
-  if (threadIdx.x == 0) { *num_successes += block_num_successes; }
 }
 
 /**
@@ -111,13 +94,11 @@ __global__ void insert(
  * inserts
  * @tparam InputIt Device accessible input iterator whose `value_type` is
  * convertible to the map's `value_type`
- * @tparam atomicT Type of atomic storage
  * @tparam viewT Type of device view allowing access of hash map storage
  * @tparam Hash Unary callable type
  * @tparam KeyEqual Binary callable type
  * @param first Beginning of the sequence of key/value pairs
  * @param last End of the sequence of key/value pairs
- * @param num_successes The number of successfully inserted key/value pairs
  * @param view Mutable device view used to access the hash map's slot storage
  * @param hash The unary function to apply to hash each key
  * @param key_equal The binary function used to compare two keys for equality
@@ -125,17 +106,11 @@ __global__ void insert(
 template <uint32_t block_size,
           uint32_t tile_size,
           typename InputIt,
-          typename atomicT,
           typename viewT,
           typename Hash,
           typename KeyEqual>
-__global__ void insert(
-  InputIt first, InputIt last, atomicT* num_successes, viewT view, Hash hash, KeyEqual key_equal)
+__global__ void insert(InputIt first, InputIt last, viewT view, Hash hash, KeyEqual key_equal)
 {
-  typedef cub::BlockReduce<std::size_t, block_size> BlockReduce;
-  __shared__ typename BlockReduce::TempStorage temp_storage;
-  std::size_t thread_num_successes = 0;
-
   auto tile = cg::tiled_partition<tile_size>(cg::this_thread_block());
   auto tid  = blockDim.x * blockIdx.x + threadIdx.x;
   auto it   = first + tid / tile_size;
@@ -143,16 +118,9 @@ __global__ void insert(
   while (it < last) {
     // force conversion to value_type
     typename viewT::value_type const insert_pair{*it};
-    if (view.insert(tile, insert_pair, hash, key_equal) && tile.thread_rank() == 0) {
-      thread_num_successes++;
-    }
+    view.insert(tile, insert_pair, hash, key_equal);
     it += (gridDim.x * blockDim.x) / tile_size;
   }
-
-  // compute number of successfully inserted elements for each block
-  // and atomically add to the grand total
-  std::size_t block_num_successes = BlockReduce(temp_storage).Sum(thread_num_successes);
-  if (threadIdx.x == 0) { *num_successes += block_num_successes; }
 }
 
 /**
@@ -383,4 +351,4 @@ __global__ void contains(
 }
 
 }  // namespace detail
-}  // namespace cuco
\ No newline at end of file
+}  // namespace cuco
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index 85b3d35d1..1650e14ae 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -543,33 +543,22 @@ class static_multimap {
     /**
      * @brief Inserts the specified key/value pair into the map.
      *
-     * Returns a pair consisting of an iterator to the inserted element (or to
-     * the element that prevented the insertion) and a `bool` denoting whether
-     * the insertion took place.
-     *
      * @tparam Hash Unary callable type
      * @tparam KeyEqual Binary callable type
      * @param insert_pair The pair to insert
      * @param hash The unary callable used to hash the key
      * @param key_equal The binary callable used to compare two keys for
      * equality
-     * @return `true` if the insert was successful, `false` otherwise.
+     * @return void.
      */
     template <typename Hash     = cuco::detail::MurmurHash3_32<key_type>,
               typename KeyEqual = thrust::equal_to<key_type>>
-    __device__ bool insert(value_type const& insert_pair,
+    __device__ void insert(value_type const& insert_pair,
                            Hash hash          = Hash{},
                            KeyEqual key_equal = KeyEqual{}) noexcept;
     /**
      * @brief Inserts the specified key/value pair into the map.
      *
-     * Returns a pair consisting of an iterator to the inserted element (or to
-     * the element that prevented the insertion) and a `bool` denoting whether
-     * the insertion took place. Uses the CUDA Cooperative Groups API to
-     * to leverage multiple threads to perform a single insert. This provides a
-     * significant boost in throughput compared to the non Cooperative Group
-     * `insert` at moderate to high load factors.
-     *
      * @tparam Cooperative Group type
      * @tparam Hash Unary callable type
      * @tparam KeyEqual Binary callable type
@@ -579,12 +568,12 @@ class static_multimap {
      * @param hash The unary callable used to hash the key
      * @param key_equal The binary callable used to compare two keys for
      * equality
-     * @return `true` if the insert was successful, `false` otherwise.
+     * @return void.
      */
     template <typename CG,
               typename Hash     = cuco::detail::MurmurHash3_32<key_type>,
               typename KeyEqual = thrust::equal_to<key_type>>
-    __device__ bool insert(CG g,
+    __device__ void insert(CG g,
                            value_type const& insert_pair,
                            Hash hash          = Hash{},
                            KeyEqual key_equal = KeyEqual{}) noexcept;
@@ -1078,7 +1067,6 @@ class static_multimap {
   std::size_t size_{};                    ///< Number of keys in map
   Key empty_key_sentinel_{};              ///< Key value that represents an empty slot
   Value empty_value_sentinel_{};          ///< Initial value of empty slot
-  atomic_ctr_type* num_successes_{};      ///< Number of successfully inserted keys on insert
   slot_allocator_type slot_allocator_{};  ///< Allocator used to allocate slots
 };
 }  // namespace cuco

From 0a40db5a65035e6fdb9b61bca7c23828e7296dd5 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Wed, 3 Mar 2021 14:44:50 -0800
Subject: [PATCH 014/267] Update unit tests - to debug

---
 include/cuco/detail/static_multimap.inl       |  26 ++---
 include/cuco/static_multimap.cuh              |  45 ++++----
 tests/static_map/static_map_test.cu           |   1 +
 tests/static_multimap/static_multimap_test.cu | 102 ++++++++++++++++--
 4 files changed, 121 insertions(+), 53 deletions(-)

diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index 87dcd235a..91e968886 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -340,13 +340,11 @@ static_multimap<Key, Value, Scope, Allocator>::device_view::find_all(Key const&
     auto const existing_key = current_slot->first.load(cuda::std::memory_order_relaxed);
     // Key doesn't exist, return end()
     if (existing_key == this->get_empty_key_sentinel()) {
-      return fancy_iterator{this->end(), k, this->get_empty_key_sentinel()};
+      return fancy_iterator{this->end(), k, *this};
     }
 
     // Key exists, return iterator to location
-    if (key_equal(existing_key, k)) {
-      return fancy_iterator{current_slot, k, this->get_empty_key_sentinel()};
-    }
+    if (key_equal(existing_key, k)) { return fancy_iterator{current_slot, k, *this}; }
 
     current_slot = next_slot(current_slot);
   }
@@ -364,13 +362,11 @@ static_multimap<Key, Value, Scope, Allocator>::device_view::find_all(
     auto const existing_key = current_slot->first.load(cuda::std::memory_order_relaxed);
     // Key doesn't exist, return end()
     if (existing_key == this->get_empty_key_sentinel()) {
-      return const_fancy_iterator{this->end(), k, this->get_empty_key_sentinel()};
+      return const_fancy_iterator{this->end(), k, *this};
     }
 
     // Key exists, return iterator to location
-    if (key_equal(existing_key, k)) {
-      return const_fancy_iterator{current_slot, k, this->get_empty_key_sentinel()};
-    }
+    if (key_equal(existing_key, k)) { return const_fancy_iterator{current_slot, k, *this}; }
 
     current_slot = next_slot(current_slot);
   }
@@ -401,14 +397,11 @@ static_multimap<Key, Value, Scope, Allocator>::device_view::find_all(CG g,
       // TODO: This shouldn't cast an iterator to an int to shuffle. Instead, get the index of the
       // current_slot and shuffle that instead.
       intptr_t res_slot = g.shfl(reinterpret_cast<intptr_t>(current_slot), src_lane);
-      return fancy_iterator{
-        reinterpret_cast<iterator>(res_slot), k, this->get_empty_value_sentinel()};
+      return fancy_iterator{reinterpret_cast<iterator>(res_slot), k, *this};
     }
 
     // we found an empty slot, meaning that the key we're searching for isn't present
-    if (g.ballot(slot_is_empty)) {
-      return fancy_iterator{this->end(), k, this->get_empty_value_sentinel()};
-    }
+    if (g.ballot(slot_is_empty)) { return fancy_iterator{this->end(), k, *this}; }
 
     // otherwise, all slots in the current window are full with other keys, so we move onto the
     // next window
@@ -439,15 +432,12 @@ static_multimap<Key, Value, Scope, Allocator>::device_view::find_all(
       // TODO: This shouldn't cast an iterator to an int to shuffle. Instead, get the index of the
       // current_slot and shuffle that instead.
       intptr_t res_slot = g.shfl(reinterpret_cast<intptr_t>(current_slot), src_lane);
-      return const_fancy_iterator{
-        reinterpret_cast<iterator>(res_slot), k, this->get_empty_value_sentinel()};
+      return const_fancy_iterator{reinterpret_cast<iterator>(res_slot), k, *this};
     }
 
     // we found an empty slot, meaning that the key we're searching
     // for isn't in this submap, so we should move onto the next one
-    if (g.ballot(slot_is_empty)) {
-      return const_fancy_iterator{this->end(), k, this->get_empty_value_sentinel()};
-    }
+    if (g.ballot(slot_is_empty)) { return const_fancy_iterator{this->end(), k, *this}; }
 
     // otherwise, all slots in the current window are full with other keys,
     // so we move onto the next window in the current submap
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index 1650e14ae..128557fba 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -688,44 +688,36 @@ class static_multimap {
     }
 
     struct fancy_iterator {
-      __host__ __device__
-      fancy_iterator(pair_atomic_type* current,
-                     Key key,
-                     Value empty_value_sentinel = get_empty_value_sentinel()) noexcept
-        : current_{current}, key_{key}, empty_value_sentinel_{empty_value_sentinel}
+      __host__ __device__ fancy_iterator(pair_atomic_type* current,
+                                         Key key,
+                                         device_view& view) noexcept
+        : current_{current},
+          key_{key},
+          end_{view.end()},
+          empty_key_sentinel_{view.get_empty_key_sentinel()}
       {
       }
 
       __device__ fancy_iterator operator++()
       {
-        auto next = ++current_;
-        while (next->first != key_) {
-          if (next->first == empty_value_sentinel_) {
-            return fancy_iterator{device_view::end(), key_, empty_value_sentinel_};
-            ++next;
+        ++current_;
+        while (current_->first != key_) {
+          if (current_->first == this->empty_key_sentinel_) {
+            current_ = this->end_;
+            return *this;
           }
-          return fancy_iterator{next, key_, empty_value_sentinel_};
+          ++current_;
         }
+        return *this;
       }
 
-      __device__ fancy_iterator operator++(int)
-      {
-        auto next = ++current_;
-        while (next->first != key_) {
-          if (next->first == empty_value_sentinel_) {
-            return fancy_iterator{device_view::end(), key_, empty_value_sentinel_};
-            ++next;
-          }
-          return fancy_iterator{next, key_, empty_value_sentinel_};
-        }
-      }
-
-      __device__ pair_atomic_type operator*() { return *current_; }
+      __device__ pair_atomic_type* operator*() { return current_; }
 
      private:
       pair_atomic_type* current_;
       Key key_;
-      Value empty_value_sentinel_;
+      pair_atomic_type* const end_;
+      Key const empty_key_sentinel_;
     };
     using const_fancy_iterator = fancy_iterator const;
 
@@ -1002,7 +994,6 @@ class static_multimap {
               typename KeyEqual = thrust::equal_to<key_type>>
     __device__ size_t
     count(CG g, Key const& k, Hash hash = Hash{}, KeyEqual key_equal = KeyEqual{}) noexcept;
-
   };  // class device_view
 
   /**
@@ -1068,7 +1059,7 @@ class static_multimap {
   Key empty_key_sentinel_{};              ///< Key value that represents an empty slot
   Value empty_value_sentinel_{};          ///< Initial value of empty slot
   slot_allocator_type slot_allocator_{};  ///< Allocator used to allocate slots
-};
+};                                        // class static_multimap
 }  // namespace cuco
 
 #include <cuco/detail/static_multimap.inl>
diff --git a/tests/static_map/static_map_test.cu b/tests/static_map/static_map_test.cu
index f382e5409..271c435a1 100644
--- a/tests/static_map/static_map_test.cu
+++ b/tests/static_map/static_map_test.cu
@@ -285,6 +285,7 @@ TEMPLATE_TEST_CASE_SIG("Unique sequence of keys",
       none_of(d_contained.begin(), d_contained.end(), [] __device__(bool const& b) { return b; }));
   }
 
+  // device funtion test cases
   SECTION("Inserting unique keys should return insert success.")
   {
     if (Dist == dist_type::UNIQUE) {
diff --git a/tests/static_multimap/static_multimap_test.cu b/tests/static_multimap/static_multimap_test.cu
index 8aa68d937..5efbd5a06 100644
--- a/tests/static_multimap/static_multimap_test.cu
+++ b/tests/static_multimap/static_multimap_test.cu
@@ -47,7 +47,7 @@ bool none_of(Iterator begin, Iterator end, Predicate p)
 }
 }  // namespace
 
-enum class dist_type { UNIQUE, UNIFORM, GAUSSIAN };
+enum class dist_type { UNIQUE, DUAL, UNIFORM, GAUSSIAN };
 
 template <dist_type Dist, typename Key, typename OutputIt>
 static void generate_keys(OutputIt output_begin, OutputIt output_end)
@@ -63,6 +63,11 @@ static void generate_keys(OutputIt output_begin, OutputIt output_end)
         output_begin[i] = i;
       }
       break;
+    case dist_type::DUAL:
+      for (auto i = 0; i < num_keys; ++i) {
+        output_begin[i] = i % (num_keys / 2);
+      }
+      break;
     case dist_type::UNIFORM:
       for (auto i = 0; i < num_keys; ++i) {
         output_begin[i] = std::abs(static_cast<Key>(gen()));
@@ -77,6 +82,15 @@ static void generate_keys(OutputIt output_begin, OutputIt output_end)
   }
 }
 
+template <typename Iterator>
+static void print(Iterator it_begin, Iterator it_end)
+{
+  const int N = std::distance(it_begin, it_end);
+  for (int i = 0; i < N; i++)
+    std::cout << it_begin[i] << "\t";
+  std::cout << "\n### End of print\n\n";
+}
+
 TEMPLATE_TEST_CASE_SIG("Unique sequence of keys",
                        "",
                        ((typename T, dist_type Dist), T, Dist),
@@ -116,15 +130,87 @@ TEMPLATE_TEST_CASE_SIG("Unique sequence of keys",
   thrust::device_vector<Value> d_results(num_keys);
   thrust::device_vector<bool> d_contained(num_keys);
 
-  // bulk function test cases
-  SECTION("All inserted keys-value pairs should be correctly recovered during find")
+  SECTION("Keys are all found.")
+  {
+    // Bulk insert keys
+    map.insert(d_pairs.begin(), d_pairs.end());
+
+    SECTION("non-const view")
+    {
+      REQUIRE(all_of(d_pairs.begin(),
+                     d_pairs.end(),
+                     [view] __device__(cuco::pair_type<Key, Value> const& pair) mutable {
+                       return *(view.find_all(pair.first)) != view.end();
+                     }));
+    }
+  }
+}
+
+TEMPLATE_TEST_CASE_SIG("Each key appears twice",
+                       "",
+                       ((typename T, dist_type Dist), T, Dist),
+                       (int32_t, dist_type::DUAL),
+                       (int64_t, dist_type::DUAL))
+{
+  using Key   = T;
+  using Value = T;
+
+  constexpr std::size_t num_keys{50};
+  cuco::static_multimap<Key, Value> map{100, -1, -1};
+
+  auto m_view = map.get_device_mutable_view();
+  auto view   = map.get_device_view();
+
+  std::vector<Key> h_keys(num_keys);
+  std::vector<Value> h_values(num_keys);
+  std::vector<cuco::pair_type<Key, Value>> h_pairs(num_keys);
+
+  generate_keys<Dist, Key>(h_keys.begin(), h_keys.end());
+
+  for (auto i = 0; i < num_keys; ++i) {
+    Key key           = h_keys[i];
+    Value val         = i;
+    h_pairs[i].first  = key;
+    h_pairs[i].second = val;
+    h_values[i]       = val;
+  }
+
+  thrust::device_vector<Key> d_keys(h_keys);
+  thrust::device_vector<Value> d_values(h_values);
+  thrust::device_vector<cuco::pair_type<Key, Value>> d_pairs(h_pairs);
+  thrust::device_vector<Value> d_results(num_keys);
+  thrust::device_vector<bool> d_contained(num_keys);
+
+  std::cout << "################ begin\n";
+  for (auto it : h_pairs)
+    std::cout << it.first << ":" << it.second << "\t";
+  std::cout << "\n################ end\n\n";
+
+  SECTION("Cannot find any key in an empty hash map with non-const view")
+  {
+    SECTION("non-const view")
+    {
+      REQUIRE(all_of(d_pairs.begin(),
+                     d_pairs.end(),
+                     [view] __device__(cuco::pair_type<Key, Value> const& pair) mutable {
+                       return *(view.find_all(pair.first)) == view.end();
+                     }));
+    }
+  }
+
+
+  SECTION("Counting the number of key/value pairs should return 2 all the time.")
   {
+    // Bulk insert keys
     map.insert(d_pairs.begin(), d_pairs.end());
-    map.find(d_keys.begin(), d_keys.end(), d_results.begin());
-    auto zip = thrust::make_zip_iterator(thrust::make_tuple(d_results.begin(), d_values.begin()));
 
-    REQUIRE(all_of(zip, zip + num_keys, [] __device__(auto const& p) {
-      return thrust::get<0>(p) == thrust::get<1>(p);
-    }));
+    SECTION("non-const view")
+    {
+      REQUIRE(all_of(d_pairs.begin(),
+                     d_pairs.end(),
+                     [view] __device__(cuco::pair_type<Key, Value> const& pair) mutable {
+                       return view.count(pair.first) == 2;
+                     }));
+    }
   }
 }

From d5474723dfc9e6ee51a01600a819ee81c5f80c8a Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Wed, 3 Mar 2021 15:44:08 -0800
Subject: [PATCH 015/267] Add comparison operator to make fancy_iterator and
 iterator comparable

---
 include/cuco/detail/static_multimap.inl       | 4 ++--
 include/cuco/static_multimap.cuh              | 6 +++++-
 tests/static_multimap/static_multimap_test.cu | 5 ++---
 3 files changed, 9 insertions(+), 6 deletions(-)

diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index 91e968886..147efc8fb 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -498,7 +498,7 @@ __device__ size_t static_multimap<Key, Value, Scope, Allocator>::device_view::co
 {
   auto found   = this->find_all(k, hash, key_equal);
   size_t count = 0;
-  while (*found != this->end()) {
+  while (found != this->end()) {
     ++found;
     ++count;
   }
@@ -512,7 +512,7 @@ __device__ size_t static_multimap<Key, Value, Scope, Allocator>::device_view::co
 {
   auto found   = this->find_all(g, k, hash, key_equal);
   size_t count = 0;
-  while (*found != this->end()) {
+  while (found != this->end()) {
     ++found;
     ++count;
   }
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index 128557fba..83fb70260 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -711,7 +711,11 @@ class static_multimap {
         return *this;
       }
 
-      __device__ pair_atomic_type* operator*() { return current_; }
+      __device__ bool operator==(const iterator& it) { return (this->current_ == it); }
+
+      __device__ bool operator!=(const iterator& it) { return this->current_ != it; }
+
+      __device__ pair_atomic_type& operator*() { return *current_; }
 
      private:
       pair_atomic_type* current_;
diff --git a/tests/static_multimap/static_multimap_test.cu b/tests/static_multimap/static_multimap_test.cu
index 5efbd5a06..38c62be6d 100644
--- a/tests/static_multimap/static_multimap_test.cu
+++ b/tests/static_multimap/static_multimap_test.cu
@@ -140,7 +140,7 @@ TEMPLATE_TEST_CASE_SIG("Unique sequence of keys",
       REQUIRE(all_of(d_pairs.begin(),
                      d_pairs.end(),
                      [view] __device__(cuco::pair_type<Key, Value> const& pair) mutable {
-                       return *(view.find_all(pair.first)) != view.end();
+                       return view.find_all(pair.first) != view.end();
                      }));
     }
   }
@@ -193,12 +193,11 @@ TEMPLATE_TEST_CASE_SIG("Each key appears twice",
       REQUIRE(all_of(d_pairs.begin(),
                      d_pairs.end(),
                      [view] __device__(cuco::pair_type<Key, Value> const& pair) mutable {
-                       return *(view.find_all(pair.first)) == view.end();
+                       return view.find_all(pair.first) == view.end();
                      }));
     }
   }
 
-
   SECTION("Counting the number of key/value pairs should return 2 all the time.")
   {
     // Bulk insert keys

From 7b0e3251c3beb4bc4093fa9449423ed076b249ba Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Thu, 4 Mar 2021 08:02:35 -0800
Subject: [PATCH 016/267] Use template to implement fancy_iterator and
 const_fancy_iterator

---
 include/cuco/detail/static_multimap.inl       |  2 +-
 include/cuco/static_multimap.cuh              | 84 ++++++++++---------
 tests/static_multimap/static_multimap_test.cu | 26 +++++-
 3 files changed, 71 insertions(+), 41 deletions(-)

diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index 147efc8fb..71acdda00 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2020, NVIDIA CORPORATION.
+ * Copyright (c) 2021, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index 83fb70260..5f1842e4d 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -595,6 +595,52 @@ class static_multimap {
     using mapped_type    = typename device_view_base::mapped_type;
     using iterator       = typename device_view_base::iterator;
     using const_iterator = typename device_view_base::const_iterator;
+
+    template <typename DataType>
+    struct FancyIterator {
+     public:
+      using data_type      = DataType;
+      using pointer_type   = DataType*;
+      using data_reference = DataType&;
+
+     public:
+      __host__ __device__ FancyIterator(pointer_type current, Key key, device_view& view) noexcept
+        : current_{current},
+          key_{key},
+          end_{view.end()},
+          empty_key_sentinel_{view.get_empty_key_sentinel()}
+      {
+      }
+      __host__ __device__ ~FancyIterator() {}
+
+      __device__ FancyIterator<data_type>& operator++()
+      {
+        ++current_;
+        while (current_->first != key_) {
+          if (current_->first == this->empty_key_sentinel_) {
+            current_ = this->end_;
+            return *this;
+          }
+          ++current_;
+        }
+        return *this;
+      }
+
+      __device__ bool operator==(const pointer_type& it) { return (this->current_ == it); }
+
+      __device__ bool operator!=(const pointer_type& it) { return this->current_ != it; }
+
+      __device__ data_reference operator*() { return *current_; }
+
+     private:
+      pointer_type current_;
+      Key key_;
+      pointer_type end_;
+      Key empty_key_sentinel_;
+    };
+    using fancy_iterator       = FancyIterator<pair_atomic_type>;
+    using const_fancy_iterator = FancyIterator<const pair_atomic_type>;
+
     /**
      * @brief Construct a view of the first `capacity` slots of the
      * slots array pointed to by `slots`.
@@ -687,44 +733,6 @@ class static_multimap {
                          source_device_view.get_empty_value_sentinel());
     }
 
-    struct fancy_iterator {
-      __host__ __device__ fancy_iterator(pair_atomic_type* current,
-                                         Key key,
-                                         device_view& view) noexcept
-        : current_{current},
-          key_{key},
-          end_{view.end()},
-          empty_key_sentinel_{view.get_empty_key_sentinel()}
-      {
-      }
-
-      __device__ fancy_iterator operator++()
-      {
-        ++current_;
-        while (current_->first != key_) {
-          if (current_->first == this->empty_key_sentinel_) {
-            current_ = this->end_;
-            return *this;
-          }
-          ++current_;
-        }
-        return *this;
-      }
-
-      __device__ bool operator==(const iterator& it) { return (this->current_ == it); }
-
-      __device__ bool operator!=(const iterator& it) { return this->current_ != it; }
-
-      __device__ pair_atomic_type& operator*() { return *current_; }
-
-     private:
-      pair_atomic_type* current_;
-      Key key_;
-      pair_atomic_type* const end_;
-      Key const empty_key_sentinel_;
-    };
-    using const_fancy_iterator = fancy_iterator const;
-
     /**
      * @brief Finds the value corresponding to the key `k`.
      *
diff --git a/tests/static_multimap/static_multimap_test.cu b/tests/static_multimap/static_multimap_test.cu
index 38c62be6d..4b9c016d4 100644
--- a/tests/static_multimap/static_multimap_test.cu
+++ b/tests/static_multimap/static_multimap_test.cu
@@ -133,7 +133,12 @@ TEMPLATE_TEST_CASE_SIG("Unique sequence of keys",
   SECTION("Keys are all found.")
   {
     // Bulk insert keys
-    map.insert(d_pairs.begin(), d_pairs.end());
+    thrust::for_each(thrust::device,
+                     d_pairs.begin(),
+                     d_pairs.end(),
+                     [m_view] __device__(cuco::pair_type<Key, Value> const& pair) mutable {
+                       m_view.insert(pair);
+                     });
 
     SECTION("non-const view")
     {
@@ -143,6 +148,13 @@ TEMPLATE_TEST_CASE_SIG("Unique sequence of keys",
                        return view.find_all(pair.first) != view.end();
                      }));
     }
+    SECTION("const view")
+    {
+      REQUIRE(all_of(
+        d_pairs.begin(), d_pairs.end(), [view] __device__(cuco::pair_type<Key, Value> const& pair) {
+          return view.find_all(pair.first) != view.end();
+        }));
+    }
   }
 }
 
@@ -196,8 +208,17 @@ TEMPLATE_TEST_CASE_SIG("Each key appears twice",
                        return view.find_all(pair.first) == view.end();
                      }));
     }
+    /*
+    SECTION("const view")
+    {
+      REQUIRE(all_of(
+        d_pairs.begin(), d_pairs.end(), [view] __device__(cuco::pair_type<Key, Value> const& pair) {
+          return view.find_all(pair.first) == view.end();
+        }));
+    }
+    */
   }
-
+  /*
   SECTION("Counting the number of key/value pairs should return 2 all the time.")
   {
     // Bulk insert keys
@@ -212,4 +233,5 @@ TEMPLATE_TEST_CASE_SIG("Each key appears twice",
                      }));
     }
   }
+  */
 }

From 5143f5a2f5aad9b347e12b75322fd3885ed287c5 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Thu, 4 Mar 2021 08:10:46 -0800
Subject: [PATCH 017/267] Fix a bug in fancy_iterator: add constructor for
 const arguments

---
 include/cuco/static_multimap.cuh              | 9 +++++++++
 tests/static_multimap/static_multimap_test.cu | 2 --
 2 files changed, 9 insertions(+), 2 deletions(-)

diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index 5f1842e4d..f524e1f2b 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -611,6 +611,15 @@ class static_multimap {
           empty_key_sentinel_{view.get_empty_key_sentinel()}
       {
       }
+      __host__ __device__ FancyIterator(pointer_type current,
+                                        const Key key,
+                                        const device_view& view) noexcept
+        : current_{current},
+          key_{key},
+          end_{view.end()},
+          empty_key_sentinel_{view.get_empty_key_sentinel()}
+      {
+      }
       __host__ __device__ ~FancyIterator() {}
 
       __device__ FancyIterator<data_type>& operator++()
diff --git a/tests/static_multimap/static_multimap_test.cu b/tests/static_multimap/static_multimap_test.cu
index 4b9c016d4..174b22269 100644
--- a/tests/static_multimap/static_multimap_test.cu
+++ b/tests/static_multimap/static_multimap_test.cu
@@ -208,7 +208,6 @@ TEMPLATE_TEST_CASE_SIG("Each key appears twice",
                        return view.find_all(pair.first) == view.end();
                      }));
     }
-    /*
     SECTION("const view")
     {
       REQUIRE(all_of(
@@ -216,7 +215,6 @@ TEMPLATE_TEST_CASE_SIG("Each key appears twice",
           return view.find_all(pair.first) == view.end();
         }));
     }
-    */
   }
   /*
   SECTION("Counting the number of key/value pairs should return 2 all the time.")

From e21bc47dd52d95c1360b219545755e57a22c1b59 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Thu, 4 Mar 2021 08:46:19 -0800
Subject: [PATCH 018/267] Updates: use uniform_int_distribution in unit tests

---
 tests/dynamic_map/dynamic_map_test.cu         | 14 +++++++++-----
 tests/static_map/static_map_test.cu           | 12 ++++++++----
 tests/static_multimap/static_multimap_test.cu | 15 ++++++++++-----
 3 files changed, 27 insertions(+), 14 deletions(-)

diff --git a/tests/dynamic_map/dynamic_map_test.cu b/tests/dynamic_map/dynamic_map_test.cu
index fd38ea642..0e52b4b64 100644
--- a/tests/dynamic_map/dynamic_map_test.cu
+++ b/tests/dynamic_map/dynamic_map_test.cu
@@ -33,22 +33,26 @@ static void generate_keys(OutputIt output_begin, OutputIt output_end)
   std::mt19937 gen{rd()};
 
   switch (Dist) {
-    case dist_type::UNIQUE:
+    case dist_type::UNIQUE: {
       for (auto i = 0; i < num_keys; ++i) {
         output_begin[i] = i;
       }
       break;
-    case dist_type::UNIFORM:
+    }
+    case dist_type::UNIFORM: {
+      std::uniform_int_distribution<Key> distribution{0, std::numeric_limits<Key>::max()};
       for (auto i = 0; i < num_keys; ++i) {
-        output_begin[i] = std::abs(static_cast<Key>(gen()));
+        output_begin[i] = distribution(gen);
       }
       break;
-    case dist_type::GAUSSIAN:
+    }
+    case dist_type::GAUSSIAN: {
       std::normal_distribution<> dg{1e9, 1e7};
       for (auto i = 0; i < num_keys; ++i) {
         output_begin[i] = std::abs(static_cast<Key>(dg(gen)));
       }
       break;
+    }
   }
 }
 
@@ -147,4 +151,4 @@ TEMPLATE_TEST_CASE_SIG("Unique sequence of keys",
     REQUIRE(
       none_of(d_contained.begin(), d_contained.end(), [] __device__(bool const& b) { return b; }));
   }
-}
\ No newline at end of file
+}
diff --git a/tests/static_map/static_map_test.cu b/tests/static_map/static_map_test.cu
index 271c435a1..a9d4a4520 100644
--- a/tests/static_map/static_map_test.cu
+++ b/tests/static_map/static_map_test.cu
@@ -59,22 +59,26 @@ static void generate_keys(OutputIt output_begin, OutputIt output_end)
   std::mt19937 gen{rd()};
 
   switch (Dist) {
-    case dist_type::UNIQUE:
+    case dist_type::UNIQUE: {
       for (auto i = 0; i < num_keys; ++i) {
         output_begin[i] = i;
       }
       break;
-    case dist_type::UNIFORM:
+    }
+    case dist_type::UNIFORM: {
+      std::uniform_int_distribution<Key> distribution{0, std::numeric_limits<Key>::max()};
       for (auto i = 0; i < num_keys; ++i) {
-        output_begin[i] = std::abs(static_cast<Key>(gen()));
+        output_begin[i] = distribution(gen);
       }
       break;
-    case dist_type::GAUSSIAN:
+    }
+    case dist_type::GAUSSIAN: {
       std::normal_distribution<> dg{1e9, 1e7};
       for (auto i = 0; i < num_keys; ++i) {
         output_begin[i] = std::abs(static_cast<Key>(dg(gen)));
       }
       break;
+    }
   }
 }
 
diff --git a/tests/static_multimap/static_multimap_test.cu b/tests/static_multimap/static_multimap_test.cu
index 174b22269..465af960a 100644
--- a/tests/static_multimap/static_multimap_test.cu
+++ b/tests/static_multimap/static_multimap_test.cu
@@ -58,27 +58,32 @@ static void generate_keys(OutputIt output_begin, OutputIt output_end)
   std::mt19937 gen{rd()};
 
   switch (Dist) {
-    case dist_type::UNIQUE:
+    case dist_type::UNIQUE: {
       for (auto i = 0; i < num_keys; ++i) {
         output_begin[i] = i;
       }
       break;
-    case dist_type::DUAL:
+    }
+    case dist_type::DUAL: {
       for (auto i = 0; i < num_keys; ++i) {
         output_begin[i] = i % (num_keys / 2);
       }
       break;
-    case dist_type::UNIFORM:
+    }
+    case dist_type::UNIFORM: {
+      std::uniform_int_distribution<Key> distribution{0, std::numeric_limits<Key>::max()};
       for (auto i = 0; i < num_keys; ++i) {
-        output_begin[i] = std::abs(static_cast<Key>(gen()));
+        output_begin[i] = distribution(gen);
       }
       break;
-    case dist_type::GAUSSIAN:
+    }
+    case dist_type::GAUSSIAN: {
       std::normal_distribution<> dg{1e9, 1e7};
       for (auto i = 0; i < num_keys; ++i) {
         output_begin[i] = std::abs(static_cast<Key>(dg(gen)));
       }
       break;
+    }
   }
 }
 

From 32e7fc9d5c3b803a98ad98d899886c5f7842e75a Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Thu, 4 Mar 2021 13:18:07 -0800
Subject: [PATCH 019/267] Add const methods for fancy_iterator

---
 include/cuco/static_multimap.cuh | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index f524e1f2b..7d5db624f 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -635,11 +635,13 @@ class static_multimap {
         return *this;
       }
 
-      __device__ bool operator==(const pointer_type& it) { return (this->current_ == it); }
-
-      __device__ bool operator!=(const pointer_type& it) { return this->current_ != it; }
+      __device__ bool operator==(const pointer_type& it) const { return (this->current_ == it); }
+      __device__ bool operator!=(const pointer_type& it) const { return this->current_ != it; }
 
       __device__ data_reference operator*() { return *current_; }
+      __device__ data_reference operator*() const { return *current_; }
+      __device__ pointer_type operator->() { return current_; }
+      __device__ pointer_type operator->() const { return current_; }
 
      private:
       pointer_type current_;

From dbcfd84b73d5ab00bb1db4e2afa3e266426159ba Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Fri, 5 Mar 2021 06:54:28 -0800
Subject: [PATCH 020/267] Fix a bug: use wrap around handling to find next slot

---
 include/cuco/detail/static_multimap.inl | 28 +++++++++++++++++
 include/cuco/static_multimap.cuh        | 42 +++++++++++++++++++++++--
 2 files changed, 68 insertions(+), 2 deletions(-)

diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index 71acdda00..f31552dba 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -505,6 +505,20 @@ __device__ size_t static_multimap<Key, Value, Scope, Allocator>::device_view::co
   return count;
 }
 
+template <typename Key, typename Value, cuda::thread_scope Scope, typename Allocator>
+template <typename Hash, typename KeyEqual>
+__device__ size_t static_multimap<Key, Value, Scope, Allocator>::device_view::count(
+  Key const& k, Hash hash, KeyEqual key_equal) const noexcept
+{
+  auto found   = this->find_all(k, hash, key_equal);
+  size_t count = 0;
+  while (found != this->end()) {
+    ++found;
+    ++count;
+  }
+  return count;
+}
+
 template <typename Key, typename Value, cuda::thread_scope Scope, typename Allocator>
 template <typename CG, typename Hash, typename KeyEqual>
 __device__ size_t static_multimap<Key, Value, Scope, Allocator>::device_view::count(
@@ -519,4 +533,18 @@ __device__ size_t static_multimap<Key, Value, Scope, Allocator>::device_view::co
   return count;
 }
 
+template <typename Key, typename Value, cuda::thread_scope Scope, typename Allocator>
+template <typename CG, typename Hash, typename KeyEqual>
+__device__ size_t static_multimap<Key, Value, Scope, Allocator>::device_view::count(
+  CG g, Key const& k, Hash hash, KeyEqual key_equal) const noexcept
+{
+  auto found   = this->find_all(g, k, hash, key_equal);
+  size_t count = 0;
+  while (found != this->end()) {
+    ++found;
+    ++count;
+  }
+  return count;
+}
+
 }  // namespace cuco
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index 7d5db624f..cc55073c5 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -607,6 +607,7 @@ class static_multimap {
       __host__ __device__ FancyIterator(pointer_type current, Key key, device_view& view) noexcept
         : current_{current},
           key_{key},
+          view_{view},
           end_{view.end()},
           empty_key_sentinel_{view.get_empty_key_sentinel()}
       {
@@ -616,6 +617,7 @@ class static_multimap {
                                         const device_view& view) noexcept
         : current_{current},
           key_{key},
+          view_{view},
           end_{view.end()},
           empty_key_sentinel_{view.get_empty_key_sentinel()}
       {
@@ -624,13 +626,13 @@ class static_multimap {
 
       __device__ FancyIterator<data_type>& operator++()
       {
-        ++current_;
+        current_ = view_.next_slot(current_);
         while (current_->first != key_) {
           if (current_->first == this->empty_key_sentinel_) {
             current_ = this->end_;
             return *this;
           }
-          ++current_;
+          current_ = view_.next_slot(current_);
         }
         return *this;
       }
@@ -646,6 +648,7 @@ class static_multimap {
      private:
       pointer_type current_;
       Key key_;
+      device_view& view_;
       pointer_type end_;
       Key empty_key_sentinel_;
     };
@@ -1000,6 +1003,23 @@ class static_multimap {
                             Hash hash          = Hash{},
                             KeyEqual key_equal = KeyEqual{}) noexcept;
 
+    /**
+     * @brief Counts the number of matching key/value pairs contained in the multimap.
+     *
+     * @tparam Hash Unary callable type
+     * @tparam KeyEqual Binary callable type
+     * @param k The key to count for
+     * @param hash The unary callable used to hash the key
+     * @param key_equal The binary callable used to compare two keys
+     * @return The total number of the matching key/value pairs
+     * in the multimap
+     */
+    template <typename Hash     = cuco::detail::MurmurHash3_32<key_type>,
+              typename KeyEqual = thrust::equal_to<key_type>>
+    __device__ size_t count(Key const& k,
+                            Hash hash          = Hash{},
+                            KeyEqual key_equal = KeyEqual{}) const noexcept;
+
     /**
      * @brief Counts the number of matching key/value pairs contained in the multimap.
      *
@@ -1017,6 +1037,24 @@ class static_multimap {
               typename KeyEqual = thrust::equal_to<key_type>>
     __device__ size_t
     count(CG g, Key const& k, Hash hash = Hash{}, KeyEqual key_equal = KeyEqual{}) noexcept;
+
+    /**
+     * @brief Counts the number of matching key/value pairs contained in the multimap.
+     *
+     * @tparam CG Cooperative Group type
+     * @tparam Hash Unary callable type
+     * @tparam KeyEqual Binary callable type
+     * @param k The key to count for
+     * @param hash The unary callable used to hash the key
+     * @param key_equal The binary callable used to compare two keys
+     * @return The total number of the matching key/value pairs
+     * in the multimap
+     */
+    template <typename CG,
+              typename Hash     = cuco::detail::MurmurHash3_32<key_type>,
+              typename KeyEqual = thrust::equal_to<key_type>>
+    __device__ size_t
+    count(CG g, Key const& k, Hash hash = Hash{}, KeyEqual key_equal = KeyEqual{}) const noexcept;
   };  // class device_view
 
   /**

From becb953034bb1c3895c9dd15ad95448cc00e03d0 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Fri, 5 Mar 2021 07:19:53 -0800
Subject: [PATCH 021/267] Add multimap::count unit tests

---
 include/cuco/static_multimap.cuh              | 15 ++-----
 tests/static_multimap/static_multimap_test.cu | 44 ++++---------------
 2 files changed, 12 insertions(+), 47 deletions(-)

diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index cc55073c5..d01a37713 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -604,17 +604,7 @@ class static_multimap {
       using data_reference = DataType&;
 
      public:
-      __host__ __device__ FancyIterator(pointer_type current, Key key, device_view& view) noexcept
-        : current_{current},
-          key_{key},
-          view_{view},
-          end_{view.end()},
-          empty_key_sentinel_{view.get_empty_key_sentinel()}
-      {
-      }
-      __host__ __device__ FancyIterator(pointer_type current,
-                                        const Key key,
-                                        const device_view& view) noexcept
+      __host__ __device__ FancyIterator(pointer_type current, Key key, device_view view) noexcept
         : current_{current},
           key_{key},
           view_{view},
@@ -622,6 +612,7 @@ class static_multimap {
           empty_key_sentinel_{view.get_empty_key_sentinel()}
       {
       }
+
       __host__ __device__ ~FancyIterator() {}
 
       __device__ FancyIterator<data_type>& operator++()
@@ -648,7 +639,7 @@ class static_multimap {
      private:
       pointer_type current_;
       Key key_;
-      device_view& view_;
+      device_view view_;
       pointer_type end_;
       Key empty_key_sentinel_;
     };
diff --git a/tests/static_multimap/static_multimap_test.cu b/tests/static_multimap/static_multimap_test.cu
index 465af960a..465d5c4f0 100644
--- a/tests/static_multimap/static_multimap_test.cu
+++ b/tests/static_multimap/static_multimap_test.cu
@@ -87,15 +87,6 @@ static void generate_keys(OutputIt output_begin, OutputIt output_end)
   }
 }
 
-template <typename Iterator>
-static void print(Iterator it_begin, Iterator it_end)
-{
-  const int N = std::distance(it_begin, it_end);
-  for (int i = 0; i < N; i++)
-    std::cout << it_begin[i] << "\t";
-  std::cout << "\n### End of print\n\n";
-}
-
 TEMPLATE_TEST_CASE_SIG("Unique sequence of keys",
                        "",
                        ((typename T, dist_type Dist), T, Dist),
@@ -172,8 +163,8 @@ TEMPLATE_TEST_CASE_SIG("Each key appears twice",
   using Key   = T;
   using Value = T;
 
-  constexpr std::size_t num_keys{50};
-  cuco::static_multimap<Key, Value> map{100, -1, -1};
+  constexpr std::size_t num_keys{50'000'000};
+  cuco::static_multimap<Key, Value> map{100'000'000, -1, -1};
 
   auto m_view = map.get_device_mutable_view();
   auto view   = map.get_device_view();
@@ -198,43 +189,26 @@ TEMPLATE_TEST_CASE_SIG("Each key appears twice",
   thrust::device_vector<Value> d_results(num_keys);
   thrust::device_vector<bool> d_contained(num_keys);
 
-  std::cout << "################ begin\n";
-  for (auto it : h_pairs)
-    std::cout << it.first << ":" << it.second << "\t";
-  std::cout << "\n################ end\n\n";
-
-  SECTION("Cannot find any key in an empty hash map with non-const view")
+  SECTION(
+    "Counting the number of key/value pairs corresponding to each key should always return two.")
   {
+    // Bulk insert keys
+    map.insert(d_pairs.begin(), d_pairs.end());
+
     SECTION("non-const view")
     {
       REQUIRE(all_of(d_pairs.begin(),
                      d_pairs.end(),
                      [view] __device__(cuco::pair_type<Key, Value> const& pair) mutable {
-                       return view.find_all(pair.first) == view.end();
+                       return view.count(pair.first) == 2;
                      }));
     }
     SECTION("const view")
     {
       REQUIRE(all_of(
         d_pairs.begin(), d_pairs.end(), [view] __device__(cuco::pair_type<Key, Value> const& pair) {
-          return view.find_all(pair.first) == view.end();
+          return view.count(pair.first) == 2;
         }));
     }
   }
-  /*
-  SECTION("Counting the number of key/value pairs should return 2 all the time.")
-  {
-    // Bulk insert keys
-    map.insert(d_pairs.begin(), d_pairs.end());
-
-    SECTION("non-const view")
-    {
-      REQUIRE(all_of(d_pairs.begin(),
-                     d_pairs.end(),
-                     [view] __device__(cuco::pair_type<Key, Value> const& pair) mutable {
-                       return view.count(pair.first) == 2;
-                     }));
-    }
-  }
-  */
 }

From 901b1809161d9e7a67e937e3e8ed64d0cd661305 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Fri, 5 Mar 2021 14:43:31 -0800
Subject: [PATCH 022/267] Add host count function

---
 include/cuco/detail/static_multimap.inl       |  51 +++++++-
 .../cuco/detail/static_multimap_kernels.cuh   | 114 ++++++++++++++++++
 include/cuco/static_multimap.cuh              |  52 ++++++++
 3 files changed, 214 insertions(+), 3 deletions(-)

diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index f31552dba..ecdffe952 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -65,9 +65,6 @@ void static_multimap<Key, Value, Scope, Allocator>::insert(InputIt first,
   auto const grid_size  = (tile_size * num_keys + stride * block_size - 1) / (stride * block_size);
   auto view             = get_device_mutable_view();
 
-  int device_id;
-  CUCO_CUDA_TRY(cudaGetDevice(&device_id));
-
   detail::insert<block_size, tile_size>
     <<<grid_size, block_size>>>(first, first + num_keys, view, hash, key_equal);
   CUCO_CUDA_TRY(cudaDeviceSynchronize());
@@ -107,6 +104,54 @@ void static_multimap<Key, Value, Scope, Allocator>::contains(
   CUCO_CUDA_TRY(cudaDeviceSynchronize());
 }
 
+template <typename Key, typename Value, cuda::thread_scope Scope, typename Allocator>
+template <typename InputIt, typename OutputIt, typename Hash, typename KeyEqual>
+void static_multimap<Key, Value, Scope, Allocator>::find_all(InputIt first,
+                                                             InputIt last,
+                                                             OutputIt output_begin,
+                                                             OutputIt output_end,
+                                                             Hash hash,
+                                                             KeyEqual key_equal) noexcept
+{
+  auto num_keys         = std::distance(first, last);
+  auto const block_size = 128;
+  auto const stride     = 1;
+  auto const tile_size  = 4;
+  auto const grid_size  = (tile_size * num_keys + stride * block_size - 1) / (stride * block_size);
+  auto view             = get_device_view();
+
+  detail::find_all<block_size, tile_size, Value>
+    <<<grid_size, block_size>>>(first, last, output_begin, output_end, view, hash, key_equal);
+  CUCO_CUDA_TRY(cudaDeviceSynchronize());
+}
+
+template <typename Key, typename Value, cuda::thread_scope Scope, typename Allocator>
+template <typename InputIt, typename Hash, typename KeyEqual>
+std::size_t static_multimap<Key, Value, Scope, Allocator>::count(InputIt first,
+                                                                 InputIt last,
+                                                                 Hash hash,
+                                                                 KeyEqual key_equal)
+{
+  auto num_keys         = std::distance(first, last);
+  auto const block_size = 128;
+  auto const stride     = 1;
+  auto const tile_size  = 4;
+  auto const grid_size  = (tile_size * num_keys + stride * block_size - 1) / (stride * block_size);
+  auto view             = get_device_view();
+
+  atomic_ctr_type* num_items;
+  CUCO_CUDA_TRY(cudaMallocManaged(&num_items, sizeof(atomic_ctr_type)));
+  *num_items = 0;
+  int device_id;
+  CUCO_CUDA_TRY(cudaGetDevice(&device_id));
+  CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_items, sizeof(atomic_ctr_type), device_id));
+
+  detail::count<block_size, tile_size, Value>
+    <<<grid_size, block_size>>>(first, last, num_items, view, hash, key_equal);
+  CUCO_CUDA_TRY(cudaDeviceSynchronize());
+  return *num_items;
+}
+
 template <typename Key, typename Value, cuda::thread_scope Scope, typename Allocator>
 template <typename Hash, typename KeyEqual>
 __device__ void static_multimap<Key, Value, Scope, Allocator>::device_mutable_view::insert(
diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap_kernels.cuh
index 8c06e0791..f4fcc796e 100644
--- a/include/cuco/detail/static_multimap_kernels.cuh
+++ b/include/cuco/detail/static_multimap_kernels.cuh
@@ -350,5 +350,119 @@ __global__ void contains(
   }
 }
 
+/**
+ * @brief Finds all the values corresponding to all keys in the range `[first, last)`.
+ *
+ * If the key `k = *(first + i)` exists in the map, copies `k` and all associated values to
+ * unspecified locations in `[output_begin, output_end)`. Else, copies `k` and the empty value
+ * sentinel.
+ *
+ * Behavior is undefined if the total number of matching keys exceeds `std::distance(output_begin,
+ * output_end)`. Use `count()` to determine the number of matching keys.
+ *
+ * @tparam block_size The size of the thread block
+ * @tparam Value The type of the mapped value for the map
+ * @tparam InputIt Device accessible input iterator whose `value_type` is
+ * convertible to the map's `key_type`
+ * @tparam OutputIt Device accessible output iterator whose `value_type` is
+ * convertible to the map's `mapped_type`
+ * @tparam viewT Type of device view allowing access of hash map storage
+ * @tparam Hash Unary callable type
+ * @tparam KeyEqual Binary callable type
+ * @param first Beginning of the sequence of keys
+ * @param last End of the sequence of keys
+ * @param output_begin Beginning of the sequence of values retrieved for each key
+ * @param output_end End of the sequence of values retrieved for each key
+ * @param view Device view used to access the hash map's slot storage
+ * @param hash The unary function to apply to hash each key
+ * @param key_equal The binary function to compare two keys for equality
+ */
+template <uint32_t block_size,
+          typename Value,
+          typename InputIt,
+          typename OutputIt,
+          typename viewT,
+          typename Hash,
+          typename KeyEqual>
+__global__ void find_all(InputIt first,
+                         InputIt last,
+                         OutputIt output_begin,
+                         OutputIt output_end,
+                         viewT view,
+                         Hash hash,
+                         KeyEqual key_equal)
+{
+  auto tid     = blockDim.x * blockIdx.x + threadIdx.x;
+  auto key_idx = tid;
+  __shared__ Value writeBuffer[block_size];
+
+  while (first + key_idx < last) {
+    auto key   = *(first + key_idx);
+    auto found = view.find(key, hash, key_equal);
+
+    /*
+     * The ld.relaxed.gpu instruction used in view.find causes L1 to
+     * flush more frequently, causing increased sector stores from L2 to global memory.
+     * By writing results to shared memory and then synchronizing before writing back
+     * to global, we no longer rely on L1, preventing the increase in sector stores from
+     * L2 to global and improving performance.
+     */
+    writeBuffer[threadIdx.x] = found->second.load(cuda::std::memory_order_relaxed);
+    __syncthreads();
+    *(output_begin + key_idx) = writeBuffer[threadIdx.x];
+    key_idx += gridDim.x * blockDim.x;
+  }
+}
+
+/**
+ * @brief Counts the occurrences of keys in `[first, last)` contained in the multimap.
+ *
+ * @tparam block_size The size of the thread block
+ * @tparam tile_size The number of threads in the Cooperative Groups used to perform
+ * inserts
+ * @tparam Value The type of the mapped value for the map
+ * @tparam InputIt Device accessible input iterator whose `value_type` is
+ * convertible to the map's `key_type`
+ * @tparam atomicT Type of atomic storage
+ * @tparam viewT Type of device view allowing access of hash map storage
+ * @tparam Hash Unary callable
+ * @tparam KeyEqual Binary callable
+ * @param first Beginning of the sequence of keys to count
+ * @param last End of the sequence of keys to count
+ * @param num_items The number of all the matches for a sequence of keys
+ * @param view Device view used to access the hash map's slot storage
+ * @param hash Unary function to apply to hash each key
+ * @param key_equal Binary function to compare two keys for equality
+ */
+template <uint32_t block_size,
+          uint32_t tile_size,
+          typename Value,
+          typename InputIt,
+          typename atomicT,
+          typename viewT,
+          typename Hash,
+          typename KeyEqual>
+__global__ void count(
+  InputIt first, InputIt last, atomicT* num_items, viewT view, Hash hash, KeyEqual key_equal)
+{
+  typedef cub::BlockReduce<std::size_t, block_size> BlockReduce;
+  __shared__ typename BlockReduce::TempStorage temp_storage;
+  std::size_t thread_num_items = 0;
+
+  auto tile = cg::tiled_partition<tile_size>(cg::this_thread_block());
+  auto tid  = blockDim.x * blockIdx.x + threadIdx.x;
+  auto it   = first + tid / tile_size;
+
+  while (it < last) {
+    thread_num_items = count(tile, *it, hash, key_equal);
+    it += (gridDim.x * blockDim.x) / tile_size;
+  }
+
+  // compute number of successfully inserted elements for each block
+  // and atomically add to the grand total
+  std::size_t block_num_items = BlockReduce(temp_storage).Sum(thread_num_items);
+  if (threadIdx.x == 0) { *num_items += block_num_items; }
+}
+
 }  // namespace detail
 }  // namespace cuco
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index d01a37713..4bc8451c2 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -237,6 +237,58 @@ class static_multimap {
                 Hash hash          = Hash{},
                 KeyEqual key_equal = KeyEqual{}) noexcept;
 
+  /**
+   * @brief Finds all the values corresponding to all keys in the range `[first, last)`.
+   *
+   * If the key `k = *(first + i)` exists in the map, copies `k` and all associated values to
+   * unspecified locations in `[output_begin, output_end)`. Else, copies `k` and the empty value
+   * sentinel.
+   *
+   * Behavior is undefined if the total number of matching keys exceeds `std::distance(output_begin,
+   * output_end)`. Use `count()` to determine the number of matching keys.
+   *
+   * @tparam InputIt Device accessible input iterator whose `value_type` is
+   * convertible to the map's `key_type`
+   * @tparam OutputIt Device accessible output iterator whose `value_type` is
+   * convertible to the map's `value_type`
+   * @tparam Hash Unary callable type
+   * @tparam KeyEqual Binary callable type
+   * @param first Beginning of the sequence of keys
+   * @param last End of the sequence of keys
+   * @param output_begin Beginning of the sequence of values retrieved for each key
+   * @param output_begin End of the sequence of values retrieved for each key
+   * @param hash The unary function to apply to hash each key
+   * @param key_equal The binary function to compare two keys for equality
+   */
+  template <typename InputIt,
+            typename OutputIt,
+            typename Hash     = cuco::detail::MurmurHash3_32<key_type>,
+            typename KeyEqual = thrust::equal_to<key_type>>
+  void find_all(InputIt first,
+                InputIt last,
+                OutputIt output_begin,
+                OutputIt output_end,
+                Hash hash          = Hash{},
+                KeyEqual key_equal = KeyEqual{}) noexcept;
+
+  /**
+   * @brief Counts the occurrences of keys in `[first, last)` contained in the multimap.
+   *
+   * @tparam Input Device accesible input iterator whose `value_type` is convertible to `key_type`
+   * @tparam Hash Unary callable
+   * @tparam KeyEqual Binary callable
+   * @param first Beginning of the sequence of keys to count
+   * @param last End of the sequence of keys to count
+   * @param hash Unary function to apply to hash each key
+   * @param key_equal Binary function to compare two keys for equality
+   * @return The sum of total occurrences of all keys in `[first,last)`
+   */
+  template <typename InputIt, typename Hash, typename KeyEqual>
+  std::size_t count(InputIt first,
+                    InputIt last,
+                    Hash hash          = Hash{},
+                    KeyEqual key_equal = KeyEqual{});
+
  private:
   class device_view_base {
    protected:

From 0a92baf8fa69520572e629501aa6898aebf151a3 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Sat, 6 Mar 2021 19:19:30 -0800
Subject: [PATCH 023/267] Take begin_ as a member variable instead of the
 entire view

---
 .../cuco/detail/static_multimap_kernels.cuh   |  2 +-
 include/cuco/static_multimap.cuh              | 36 +++++++++++++++----
 2 files changed, 30 insertions(+), 8 deletions(-)

diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap_kernels.cuh
index f4fcc796e..381c25223 100644
--- a/include/cuco/detail/static_multimap_kernels.cuh
+++ b/include/cuco/detail/static_multimap_kernels.cuh
@@ -454,7 +454,7 @@ __global__ void count(
   auto it   = first + tid / tile_size;
 
   while (it < last) {
-    thread_num_items = count(tile, *it, hash, key_equal);
+    thread_num_items = view.count(tile, *it, hash, key_equal);
     it += (gridDim.x * blockDim.x) / tile_size;
   }
 
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index 4bc8451c2..73f65489d 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -283,7 +283,9 @@ class static_multimap {
    * @param key_equal Binary function to compare two keys for equality
    * @return The sum of total occurrences of all keys in `[first,last)`
    */
-  template <typename InputIt, typename Hash, typename KeyEqual>
+  template <typename InputIt,
+            typename Hash     = cuco::detail::MurmurHash3_32<key_type>,
+            typename KeyEqual = thrust::equal_to<key_type>>
   std::size_t count(InputIt first,
                     InputIt last,
                     Hash hash          = Hash{},
@@ -656,11 +658,22 @@ class static_multimap {
       using data_reference = DataType&;
 
      public:
-      __host__ __device__ FancyIterator(pointer_type current, Key key, device_view view) noexcept
+      __host__ __device__ FancyIterator(pointer_type current, Key key, device_view& view) noexcept
+        : current_{current},
+          key_{key},
+          begin_{view.begin_slot()},
+          end_{view.end_slot()},
+          empty_key_sentinel_{view.get_empty_key_sentinel()}
+      {
+      }
+
+      __host__ __device__ FancyIterator(pointer_type current,
+                                        Key key,
+                                        const device_view& view) noexcept
         : current_{current},
           key_{key},
-          view_{view},
-          end_{view.end()},
+          begin_{view.begin_slot()},
+          end_{view.end_slot()},
           empty_key_sentinel_{view.get_empty_key_sentinel()}
       {
       }
@@ -669,17 +682,26 @@ class static_multimap {
 
       __device__ FancyIterator<data_type>& operator++()
       {
-        current_ = view_.next_slot(current_);
+        current_ = next_slot(current_);
         while (current_->first != key_) {
           if (current_->first == this->empty_key_sentinel_) {
             current_ = this->end_;
             return *this;
           }
-          current_ = view_.next_slot(current_);
+          current_ = next_slot(current_);
         }
         return *this;
       }
 
+      __device__ pointer_type next_slot(pointer_type it) noexcept
+      {
+        return (++it < end_) ? it : begin_;
+      }
+      __device__ pointer_type next_slot(pointer_type it) const noexcept
+      {
+        return (++it < end_) ? it : begin_;
+      }
+
       __device__ bool operator==(const pointer_type& it) const { return (this->current_ == it); }
       __device__ bool operator!=(const pointer_type& it) const { return this->current_ != it; }
 
@@ -691,7 +713,7 @@ class static_multimap {
      private:
       pointer_type current_;
       Key key_;
-      device_view view_;
+      pointer_type begin_;
       pointer_type end_;
       Key empty_key_sentinel_;
     };

From c03b7680bf39b084514d742ca2de000badc6380c Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Sun, 7 Mar 2021 09:42:47 -0800
Subject: [PATCH 024/267] Add host count unit test and count global function +
 minor updates

---
 .../cuco/detail/static_multimap_kernels.cuh   | 56 +++++++++++++++++--
 tests/static_multimap/static_multimap_test.cu | 41 +++++++++-----
 2 files changed, 78 insertions(+), 19 deletions(-)

diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap_kernels.cuh
index 381c25223..3701778f9 100644
--- a/include/cuco/detail/static_multimap_kernels.cuh
+++ b/include/cuco/detail/static_multimap_kernels.cuh
@@ -418,11 +418,56 @@ __global__ void find_all(InputIt first,
  * @brief Counts the occurrences of keys in `[first, last)` contained in the multimap.
  *
  * @tparam block_size The size of the thread block
- * @tparam tile_size The number of threads in the Cooperative Groups used to perform
- * inserts
  * @tparam Value The type of the mapped value for the map
- * @tparam InputIt Device accessible input iterator whose `value_type` is
- * convertible to the map's `key_type`
+ * @tparam InputIt Device accessible input iterator whose `value_type` is convertible to the map's
+ * `key_type`
+ * @tparam atomicT Type of atomic storage
+ * @tparam viewT Type of device view allowing access of hash map storage
+ * @tparam Hash Unary callable
+ * @tparam KeyEqual Binary callable
+ * @param first Beginning of the sequence of keys to count
+ * @param last End of the sequence of keys to count
+ * @param num_items The number of all the matches for a sequence of keys
+ * @param view Device view used to access the hash map's slot storage
+ * @param hash Unary function to apply to hash each key
+ * @param key_equal Binary function to compare two keys for equality
+ */
+template <uint32_t block_size,
+          typename Value,
+          typename InputIt,
+          typename atomicT,
+          typename viewT,
+          typename Hash,
+          typename KeyEqual>
+__global__ void count(
+  InputIt first, InputIt last, atomicT* num_items, viewT view, Hash hash, KeyEqual key_equal)
+{
+  typedef cub::BlockReduce<std::size_t, block_size> BlockReduce;
+  __shared__ typename BlockReduce::TempStorage temp_storage;
+  std::size_t thread_num_items = 0;
+
+  auto tid = blockDim.x * blockIdx.x + threadIdx.x;
+  auto it  = first + tid;
+
+  while (it < last) {
+    thread_num_items += view.count(*it, hash, key_equal);
+    it += gridDim.x * blockDim.x;
+  }
+
+  // compute number of successfully inserted elements for each block
+  // and atomically add to the grand total
+  std::size_t block_num_items = BlockReduce(temp_storage).Sum(thread_num_items);
+  if (threadIdx.x == 0) { *num_items += block_num_items; }
+}
+
+/**
+ * @brief Counts the occurrences of keys in `[first, last)` contained in the multimap.
+ *
+ * @tparam block_size The size of the thread block
+ * @tparam tile_size The number of threads in the Cooperative Groups used to perform inserts
+ * @tparam Value The type of the mapped value for the map
+ * @tparam InputIt Device accessible input iterator whose `value_type` is convertible to the map's
+ * `key_type`
  * @tparam atomicT Type of atomic storage
  * @tparam viewT Type of device view allowing access of hash map storage
  * @tparam Hash Unary callable
@@ -454,7 +499,8 @@ __global__ void count(
   auto it   = first + tid / tile_size;
 
   while (it < last) {
-    thread_num_items = view.count(tile, *it, hash, key_equal);
+    auto temp_num = view.count(tile, *it, hash, key_equal);
+    if (tile.thread_rank() == 0) { thread_num_items += temp_num; }
     it += (gridDim.x * blockDim.x) / tile_size;
   }
 
diff --git a/tests/static_multimap/static_multimap_test.cu b/tests/static_multimap/static_multimap_test.cu
index 465d5c4f0..f8869c70a 100644
--- a/tests/static_multimap/static_multimap_test.cu
+++ b/tests/static_multimap/static_multimap_test.cu
@@ -52,34 +52,34 @@ enum class dist_type { UNIQUE, DUAL, UNIFORM, GAUSSIAN };
 template <dist_type Dist, typename Key, typename OutputIt>
 static void generate_keys(OutputIt output_begin, OutputIt output_end)
 {
-  auto num_keys = std::distance(output_begin, output_end);
+  auto num_items = std::distance(output_begin, output_end);
 
   std::random_device rd;
   std::mt19937 gen{rd()};
 
   switch (Dist) {
     case dist_type::UNIQUE: {
-      for (auto i = 0; i < num_keys; ++i) {
+      for (auto i = 0; i < num_items; ++i) {
         output_begin[i] = i;
       }
       break;
     }
     case dist_type::DUAL: {
-      for (auto i = 0; i < num_keys; ++i) {
-        output_begin[i] = i % (num_keys / 2);
+      for (auto i = 0; i < num_items; ++i) {
+        output_begin[i] = i % (num_items / 2);
       }
       break;
     }
     case dist_type::UNIFORM: {
       std::uniform_int_distribution<Key> distribution{0, std::numeric_limits<Key>::max()};
-      for (auto i = 0; i < num_keys; ++i) {
+      for (auto i = 0; i < num_items; ++i) {
         output_begin[i] = distribution(gen);
       }
       break;
     }
     case dist_type::GAUSSIAN: {
       std::normal_distribution<> dg{1e9, 1e7};
-      for (auto i = 0; i < num_keys; ++i) {
+      for (auto i = 0; i < num_items; ++i) {
         output_begin[i] = std::abs(static_cast<Key>(dg(gen)));
       }
       break;
@@ -126,7 +126,7 @@ TEMPLATE_TEST_CASE_SIG("Unique sequence of keys",
   thrust::device_vector<Value> d_results(num_keys);
   thrust::device_vector<bool> d_contained(num_keys);
 
-  SECTION("Keys are all found.")
+  SECTION("All keys should be found.")
   {
     // Bulk insert keys
     thrust::for_each(thrust::device,
@@ -163,19 +163,19 @@ TEMPLATE_TEST_CASE_SIG("Each key appears twice",
   using Key   = T;
   using Value = T;
 
-  constexpr std::size_t num_keys{50'000'000};
+  constexpr std::size_t num_items{50'000'000};
   cuco::static_multimap<Key, Value> map{100'000'000, -1, -1};
 
   auto m_view = map.get_device_mutable_view();
   auto view   = map.get_device_view();
 
-  std::vector<Key> h_keys(num_keys);
-  std::vector<Value> h_values(num_keys);
-  std::vector<cuco::pair_type<Key, Value>> h_pairs(num_keys);
+  std::vector<Key> h_keys(num_items);
+  std::vector<Value> h_values(num_items);
+  std::vector<cuco::pair_type<Key, Value>> h_pairs(num_items);
 
   generate_keys<Dist, Key>(h_keys.begin(), h_keys.end());
 
-  for (auto i = 0; i < num_keys; ++i) {
+  for (auto i = 0; i < num_items; ++i) {
     Key key           = h_keys[i];
     Value val         = i;
     h_pairs[i].first  = key;
@@ -186,8 +186,21 @@ TEMPLATE_TEST_CASE_SIG("Each key appears twice",
   thrust::device_vector<Key> d_keys(h_keys);
   thrust::device_vector<Value> d_values(h_values);
   thrust::device_vector<cuco::pair_type<Key, Value>> d_pairs(h_pairs);
-  thrust::device_vector<Value> d_results(num_keys);
-  thrust::device_vector<bool> d_contained(num_keys);
+  thrust::device_vector<Value> d_results(num_items);
+  thrust::device_vector<bool> d_contained(num_items);
+
+  std::set<Key> temp_set(h_keys.begin(), h_keys.end());
+  std::vector<Key> h_unique_keys(temp_set.begin(), temp_set.end());
+  thrust::device_vector<Key> d_unique_keys(h_unique_keys);
+
+  // bulk function test cases
+  SECTION("Total count should be equal to the number of inserted pairs.")
+  {
+    map.insert(d_pairs.begin(), d_pairs.end());
+    size_t num = map.count(d_unique_keys.begin(), d_unique_keys.end());
+
+    REQUIRE(num == num_items);
+  }
 
   SECTION(
     "Counting the number of key/value pairs corresponding to each key should always return two.")

From dacf4593981d511bef9816d2a2b95939beb58ce6 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Wed, 10 Mar 2021 11:50:13 -0800
Subject: [PATCH 025/267] Add a new copy constructor to cuco::pair

---
 include/cuco/detail/pair.cuh | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/include/cuco/detail/pair.cuh b/include/cuco/detail/pair.cuh
index eec3f3b50..34bc29fdc 100644
--- a/include/cuco/detail/pair.cuh
+++ b/include/cuco/detail/pair.cuh
@@ -15,6 +15,7 @@
  */
 
 #include <thrust/device_reference.h>
+
 #include <thrust/pair.h>
 #include <thrust/tuple.h>
 #include <tuple>
@@ -116,6 +117,9 @@ struct alignas(detail::pair_alignment<First, Second>()) pair {
            thrust::get<1>(thrust::raw_reference_cast(t))}
   {
   }
+  __host__ __device__ constexpr pair(First const& f, Second const& s) noexcept : first{f}, second{s}
+  {
+  }
 };
 
 template <typename K, typename V>

From a1531e184a716f4daafae753b28553573a9143bc Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Wed, 10 Mar 2021 12:54:24 -0800
Subject: [PATCH 026/267] Add bulk find_all function and its unit test

---
 include/cuco/detail/static_multimap.inl       |  11 +-
 .../cuco/detail/static_multimap_kernels.cuh   | 110 +++++++++++++++---
 tests/static_multimap/static_multimap_test.cu |   3 +
 3 files changed, 109 insertions(+), 15 deletions(-)

diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index ecdffe952..0cdb4e02a 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -120,8 +120,15 @@ void static_multimap<Key, Value, Scope, Allocator>::find_all(InputIt first,
   auto const grid_size  = (tile_size * num_keys + stride * block_size - 1) / (stride * block_size);
   auto view             = get_device_view();
 
-  detail::find_all<block_size, tile_size, Value>
-    <<<grid_size, block_size>>>(first, last, output_begin, output_end, view, hash, key_equal);
+  atomic_ctr_type* num_items;
+  CUCO_CUDA_TRY(cudaMallocManaged(&num_items, sizeof(atomic_ctr_type)));
+  *num_items = 0;
+  int device_id;
+  CUCO_CUDA_TRY(cudaGetDevice(&device_id));
+  CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_items, sizeof(atomic_ctr_type), device_id));
+
+  detail::find_all<block_size, tile_size, Key, Value><<<grid_size, block_size>>>(
+    first, last, output_begin, output_end, num_items, view, hash, key_equal);
   CUCO_CUDA_TRY(cudaDeviceSynchronize());
 }
 
diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap_kernels.cuh
index 3701778f9..5348f7b55 100644
--- a/include/cuco/detail/static_multimap_kernels.cuh
+++ b/include/cuco/detail/static_multimap_kernels.cuh
@@ -14,6 +14,8 @@
  * limitations under the License.
  */
 
+#include <cuco/detail/pair.cuh>
+
 namespace cuco {
 namespace detail {
 namespace cg = cooperative_groups;
@@ -361,11 +363,13 @@ __global__ void contains(
  * output_end)`. Use `count()` to determine the number of matching keys.
  *
  * @tparam block_size The size of the thread block
+ * @tparam Key key type
  * @tparam Value The type of the mapped value for the map
  * @tparam InputIt Device accessible input iterator whose `value_type` is
  * convertible to the map's `key_type`
  * @tparam OutputIt Device accessible output iterator whose `value_type` is
  * convertible to the map's `mapped_type`
+ * @tparam atomicT Type of atomic storage
  * @tparam viewT Type of device view allowing access of hash map storage
  * @tparam Hash Unary callable type
  * @tparam KeyEqual Binary callable type
@@ -373,14 +377,17 @@ __global__ void contains(
  * @param last End of the sequence of keys
  * @param output_begin Beginning of the sequence of values retrieved for each key
  * @param output_end End of the sequence of values retrieved for each key
+ * @param num_items Size of the output sequence
  * @param view Device view used to access the hash map's slot storage
  * @param hash The unary function to apply to hash each key
  * @param key_equal The binary function to compare two keys for equality
  */
 template <uint32_t block_size,
+          typename Key,
           typename Value,
           typename InputIt,
           typename OutputIt,
+          typename atomicT,
           typename viewT,
           typename Hash,
           typename KeyEqual>
@@ -388,32 +395,109 @@ __global__ void find_all(InputIt first,
                          InputIt last,
                          OutputIt output_begin,
                          OutputIt output_end,
+                         atomicT* num_items,
                          viewT view,
                          Hash hash,
                          KeyEqual key_equal)
 {
   auto tid     = blockDim.x * blockIdx.x + threadIdx.x;
   auto key_idx = tid;
-  __shared__ Value writeBuffer[block_size];
 
   while (first + key_idx < last) {
-    auto key   = *(first + key_idx);
-    auto found = view.find(key, hash, key_equal);
+    auto key     = *(first + key_idx);
+    auto found   = view.find_all(key, hash, key_equal);
+    size_t count = 0;
 
-    /*
-     * The ld.relaxed.gpu instruction used in view.find causes L1 to
-     * flush more frequently, causing increased sector stores from L2 to global memory.
-     * By writing results to shared memory and then synchronizing before writing back
-     * to global, we no longer rely on L1, preventing the increase in sector stores from
-     * L2 to global and improving performance.
-     */
-    writeBuffer[threadIdx.x] = found->second.load(cuda::std::memory_order_relaxed);
-    __syncthreads();
-    *(output_begin + key_idx) = writeBuffer[threadIdx.x];
+    while (found != view.end()) {
+      size_t index            = (*num_items)++;
+      *(output_begin + index) = cuco::make_pair<Key, Value>(key, (*found).second);
+      ++found;
+      ++count;
+    }
+    if (count == 0) {
+      size_t index            = (*num_items)++;
+      *(output_begin + index) = cuco::make_pair<Key, Value>(key, view.get_empty_value_sentinel());
+    }
     key_idx += gridDim.x * blockDim.x;
   }
 }
 
+/**
+ * @brief Finds all the values corresponding to all keys in the range `[first, last)`.
+ *
+ * If the key `k = *(first + i)` exists in the map, copies `k` and all associated values to
+ * unspecified locations in `[output_begin, output_end)`. Else, copies `k` and the empty value
+ * sentinel.
+ *
+ * Behavior is undefined if the total number of matching keys exceeds `std::distance(output_begin,
+ * output_end)`. Use `count()` to determine the number of matching keys.
+ *
+ * @tparam block_size The size of the thread block
+ * @tparam tile_size The number of threads in the Cooperative Groups used to perform
+ * inserts
+ * @tparam Key key type
+ * @tparam Value The type of the mapped value for the map
+ * @tparam InputIt Device accessible input iterator whose `value_type` is
+ * convertible to the map's `key_type`
+ * @tparam OutputIt Device accessible output iterator whose `value_type` is
+ * convertible to the map's `mapped_type`
+ * @tparam atomicT Type of atomic storage
+ * @tparam viewT Type of device view allowing access of hash map storage
+ * @tparam Hash Unary callable type
+ * @tparam KeyEqual Binary callable type
+ * @param first Beginning of the sequence of keys
+ * @param last End of the sequence of keys
+ * @param output_begin Beginning of the sequence of values retrieved for each key
+ * @param output_end End of the sequence of values retrieved for each key
+ * @param num_items Size of the output sequence
+ * @param view Device view used to access the hash map's slot storage
+ * @param hash The unary function to apply to hash each key
+ * @param key_equal The binary function to compare two keys for equality
+ */
+template <uint32_t block_size,
+          uint32_t tile_size,
+          typename Key,
+          typename Value,
+          typename InputIt,
+          typename OutputIt,
+          typename atomicT,
+          typename viewT,
+          typename Hash,
+          typename KeyEqual>
+__global__ void find_all(InputIt first,
+                         InputIt last,
+                         OutputIt output_begin,
+                         OutputIt output_end,
+                         atomicT* num_items,
+                         viewT view,
+                         Hash hash,
+                         KeyEqual key_equal)
+{
+  auto tile    = cg::tiled_partition<tile_size>(cg::this_thread_block());
+  auto tid     = blockDim.x * blockIdx.x + threadIdx.x;
+  auto key_idx = tid / tile_size;
+
+  while (first + key_idx < last) {
+    auto key     = *(first + key_idx);
+    auto found   = view.find_all(tile, key, hash, key_equal);
+    size_t count = 0;
+
+    if (tile.thread_rank() == 0) {
+      while (found != view.end()) {
+        size_t index            = (*num_items)++;
+        *(output_begin + index) = cuco::make_pair<Key, Value>(key, (*found).second);
+        ++found;
+        ++count;
+      }
+      if (count == 0) {
+        size_t index            = (*num_items)++;
+        *(output_begin + index) = cuco::make_pair<Key, Value>(key, view.get_empty_value_sentinel());
+      }
+    }
+    key_idx += (gridDim.x * blockDim.x) / tile_size;
+  }
+}
+
 /**
  * @brief Counts the occurrences of keys in `[first, last)` contained in the multimap.
  *
diff --git a/tests/static_multimap/static_multimap_test.cu b/tests/static_multimap/static_multimap_test.cu
index f8869c70a..22097d036 100644
--- a/tests/static_multimap/static_multimap_test.cu
+++ b/tests/static_multimap/static_multimap_test.cu
@@ -188,6 +188,7 @@ TEMPLATE_TEST_CASE_SIG("Each key appears twice",
   thrust::device_vector<cuco::pair_type<Key, Value>> d_pairs(h_pairs);
   thrust::device_vector<Value> d_results(num_items);
   thrust::device_vector<bool> d_contained(num_items);
+  thrust::device_vector<cuco::pair_type<Key, Value>> d_outputs(num_items);
 
   std::set<Key> temp_set(h_keys.begin(), h_keys.end());
   std::vector<Key> h_unique_keys(temp_set.begin(), temp_set.end());
@@ -200,6 +201,8 @@ TEMPLATE_TEST_CASE_SIG("Each key appears twice",
     size_t num = map.count(d_unique_keys.begin(), d_unique_keys.end());
 
     REQUIRE(num == num_items);
+
+    map.find_all(d_unique_keys.begin(), d_unique_keys.end(), d_outputs.begin(), d_outputs.end());
   }
 
   SECTION(

From 77fb776d23e75cea48207cd2247cbafa7bcaeb82 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Wed, 10 Mar 2021 13:54:54 -0800
Subject: [PATCH 027/267] Add nvbench package in CMake

---
 CMakeLists.txt            |  2 +-
 benchmarks/CMakeLists.txt | 12 ++++++++++++
 2 files changed, 13 insertions(+), 1 deletion(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index ff25ef46f..1bea1088e 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -35,7 +35,7 @@ endif()
 
 # Note: CUDA 10.2 debian packages are broken for cuda/std/atomic header as it is located in
 # cuda/std/std/atomic
-find_package(CUDAToolkit 10.2 REQUIRED)
+find_package(CUDAToolkit 11.0 REQUIRED)
 
 include(cmake/Modules/CPM.cmake)
 
diff --git a/benchmarks/CMakeLists.txt b/benchmarks/CMakeLists.txt
index 0e7411922..9237de569 100644
--- a/benchmarks/CMakeLists.txt
+++ b/benchmarks/CMakeLists.txt
@@ -14,11 +14,23 @@ CPMAddPackage(
     "RUN_HAVE_STD_REGEX 0" #
 )
 
+CPMAddPackage(
+  NAME nvbench
+  GITHUB_REPOSITORY allisonvacanti/nvbench
+  GIT_TAG main
+  GIT_SHALLOW TRUE
+)
+
 if (benchmark_ADDED)
   # patch google benchmark target
   set_target_properties(benchmark PROPERTIES CXX_STANDARD 14)
 endif()
 
+if (nvbench_ADDED)
+  # nvbench requires c++17
+  set_target_properties(nvbench PROPERTIES CXX_STANDARD 17)
+endif()
+
 ###################################################################################################
 # Auto-detect available GPU compute architectures
 set(GPU_ARCHS "" CACHE STRING "List of GPU architectures (semicolon-separated) to be compiled for. Empty string means to auto-detect the GPUs on the current system")

From af20d2376b5b6678d435a9483472af5db6a6139c Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Wed, 10 Mar 2021 14:54:59 -0800
Subject: [PATCH 028/267] Minor cleanups in static-map-bench

---
 benchmarks/hash_table/static_map_bench.cu | 5 +----
 1 file changed, 1 insertion(+), 4 deletions(-)

diff --git a/benchmarks/hash_table/static_map_bench.cu b/benchmarks/hash_table/static_map_bench.cu
index af8e09de1..52e8eb140 100644
--- a/benchmarks/hash_table/static_map_bench.cu
+++ b/benchmarks/hash_table/static_map_bench.cu
@@ -89,14 +89,11 @@ static void BM_static_map_insert(::benchmark::State& state)
   thrust::device_vector<cuco::pair_type<Key, Value>> d_pairs(h_pairs);
 
   for (auto _ : state) {
-    state.ResumeTiming();
     state.PauseTiming();
     map_type map{size, -1, -1};
     state.ResumeTiming();
 
     map.insert(d_pairs.begin(), d_pairs.end());
-
-    state.PauseTiming();
   }
 
   state.SetBytesProcessed((sizeof(Key) + sizeof(Value)) * int64_t(state.iterations()) *
@@ -189,4 +186,4 @@ BENCHMARK_TEMPLATE(BM_static_map_insert, int64_t, int64_t, dist_type::GAUSSIAN)
 
 BENCHMARK_TEMPLATE(BM_static_map_search_all, int64_t, int64_t, dist_type::GAUSSIAN)
   ->Unit(benchmark::kMillisecond)
-  ->Apply(generate_size_and_occupancy);
\ No newline at end of file
+  ->Apply(generate_size_and_occupancy);

From bf842e1a51abf434658f4f1d4a488a078aad9068 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Fri, 12 Mar 2021 08:19:10 -0800
Subject: [PATCH 029/267] Add static multimap nvbenchmark

---
 benchmarks/CMakeLists.txt                     |  22 ++++
 .../hash_table/static_multimap_bench.cu       | 106 ++++++++++++++++++
 2 files changed, 128 insertions(+)
 create mode 100644 benchmarks/hash_table/static_multimap_bench.cu

diff --git a/benchmarks/CMakeLists.txt b/benchmarks/CMakeLists.txt
index 9237de569..93fd87f38 100644
--- a/benchmarks/CMakeLists.txt
+++ b/benchmarks/CMakeLists.txt
@@ -43,6 +43,7 @@ endif()
 ###################################################################################################
 # - compiler function -----------------------------------------------------------------------------
 
+###################################################################################################
 function(ConfigureBench BENCH_NAME BENCH_SRC)
     add_executable(${BENCH_NAME} "${BENCH_SRC}")
     set_target_properties(${BENCH_NAME} PROPERTIES 
@@ -58,6 +59,23 @@ function(ConfigureBench BENCH_NAME BENCH_SRC)
                                         cuco)
 endfunction(ConfigureBench)
 
+###################################################################################################
+function(ConfigureNVBench BENCH_NAME BENCH_SRC)
+    add_executable(${BENCH_NAME} "${BENCH_SRC}")
+    set_target_properties(${BENCH_NAME} PROPERTIES
+                                        POSITION_INDEPENDENT_CODE ON
+                                        CUDA_ARCHITECTURES ${GPU_ARCHS}
+                                        RUNTIME_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/nvbenchmarks")
+    target_include_directories(${BENCH_NAME} PRIVATE
+                                             "${CMAKE_CURRENT_SOURCE_DIR}")
+                                             #"${NVBench_SOURCE_DIR}")
+    target_compile_options(${BENCH_NAME} PRIVATE --expt-extended-lambda --expt-relaxed-constexpr)
+    target_link_libraries(${BENCH_NAME} PRIVATE
+                                        nvbench::main
+                                        pthread
+                                        cuco)
+endfunction(ConfigureNVBench)
+
 ###################################################################################################
 ### test sources ##################################################################################
 ###################################################################################################
@@ -70,6 +88,10 @@ ConfigureBench(DYNAMIC_MAP_BENCH "${DYNAMIC_MAP_BENCH_SRC}")
 set(STATIC_MAP_BENCH_SRC "${CMAKE_CURRENT_SOURCE_DIR}/hash_table/static_map_bench.cu")
 ConfigureBench(STATIC_MAP_BENCH "${STATIC_MAP_BENCH_SRC}")
 
+###################################################################################################
+set(STATIC_MULTIMAP_BENCH_SRC "${CMAKE_CURRENT_SOURCE_DIR}/hash_table/static_multimap_bench.cu")
+ConfigureNVBench(STATIC_MULTIMAP_BENCH "${STATIC_MULTIMAP_BENCH_SRC}")
+
 ###################################################################################################
 set(RBK_BENCH_SRC "${CMAKE_CURRENT_SOURCE_DIR}/reduce_by_key/reduce_by_key.cu")
 ConfigureBench(RBK_BENCH "${RBK_BENCH_SRC}")
diff --git a/benchmarks/hash_table/static_multimap_bench.cu b/benchmarks/hash_table/static_multimap_bench.cu
new file mode 100644
index 000000000..7cc81ea6c
--- /dev/null
+++ b/benchmarks/hash_table/static_multimap_bench.cu
@@ -0,0 +1,106 @@
+/*
+ * Copyright (c) 2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <nvbench/nvbench.cuh>
+#include <nvbench/test_kernels.cuh>
+
+#include <thrust/device_vector.h>
+#include <random>
+
+#include "cuco/static_multimap.cuh"
+
+template <typename Key, typename OutputIt>
+static void generate_keys(OutputIt output_begin, OutputIt output_end, const std::string& dist)
+{
+  auto num_keys = std::distance(output_begin, output_end);
+
+  std::random_device rd;
+  std::mt19937 gen{rd()};
+
+  if (dist == "UNIQUE") {
+    for (auto i = 0; i < num_keys; ++i) {
+      output_begin[i] = i;
+    }
+  } else if (dist == "UNIFORM") {
+    std::uniform_int_distribution<Key> distribution{0, std::numeric_limits<Key>::max()};
+    for (auto i = 0; i < num_keys; ++i) {
+      output_begin[i] = distribution(gen);
+    }
+  } else if (dist == "GAUSSIAN") {
+    std::normal_distribution<> dg{1e9, 1e7};
+    for (auto i = 0; i < num_keys; ++i) {
+      output_begin[i] = std::abs(static_cast<Key>(dg(gen)));
+    }
+  }
+}
+
+template <typename Key, typename Value>
+void nvbench_static_multimap_insert(nvbench::state& state, nvbench::type_list<Key, Value>)
+{
+  using map_type = cuco::static_multimap<Key, Value>;
+
+  const std::size_t num_keys = state.get_int64("NumInputs");
+  float occupancy            = state.get_int64("Occupancy");
+  std::size_t size           = num_keys / occupancy;
+  const auto dist            = state.get_string("Distribution");
+
+  std::vector<Key> h_keys(num_keys);
+  std::vector<cuco::pair_type<Key, Value>> h_pairs(num_keys);
+
+  generate_keys<Key>(h_keys.begin(), h_keys.end(), dist);
+
+  for (auto i = 0; i < num_keys; ++i) {
+    Key key           = h_keys[i];
+    Value val         = h_keys[i];
+    h_pairs[i].first  = key;
+    h_pairs[i].second = val;
+  }
+
+  thrust::device_vector<cuco::pair_type<Key, Value>> d_pairs(h_pairs);
+
+  state.exec(nvbench::exec_tag::sync | nvbench::exec_tag::timer,
+             [&](nvbench::launch& launch, auto& timer) {
+               map_type map{size, -1, -1};
+               auto m_view = map.get_device_mutable_view();
+
+               auto const block_size = 128;
+               auto const stride     = 1;
+               auto const tile_size  = 4;
+               auto const grid_size =
+                 (tile_size * num_keys + stride * block_size - 1) / (stride * block_size);
+
+               using Hash     = cuco::detail::MurmurHash3_32<Key>;
+               using KeyEqual = thrust::equal_to<Key>;
+
+               Hash hash;
+               KeyEqual key_equal;
+
+               timer.start();
+               cuco::detail::insert<block_size, tile_size>
+                 <<<grid_size, block_size, 0, launch.get_stream()>>>(
+                   d_pairs.begin(), d_pairs.end(), m_view, hash, key_equal);
+               // CUCO_CUDA_TRY(cudaDeviceSynchronize());
+               timer.stop();
+             });
+}
+
+using key_type   = nvbench::type_list<nvbench::int32_t>;
+using value_type = nvbench::type_list<nvbench::int32_t>;
+NVBENCH_BENCH_TYPES(nvbench_static_multimap_insert, NVBENCH_TYPE_AXES(key_type, value_type))
+  .set_type_axes_names({"Key", "Value"})
+  .add_int64_axis("NumInputs", {100'000'000})
+  .add_int64_axis("Occupancy", nvbench::range(10, 90, 10))
+  .add_string_axis("Distribution", {"UNIQUE", "UNIFORM", "GAUSSIAN"});

From c610e1146e1b61ba8e36a7386313b7659b558bf8 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Fri, 12 Mar 2021 09:07:49 -0800
Subject: [PATCH 030/267] Fix the occupancy bug in static multimap benchmark

---
 benchmarks/hash_table/static_multimap_bench.cu | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/benchmarks/hash_table/static_multimap_bench.cu b/benchmarks/hash_table/static_multimap_bench.cu
index 7cc81ea6c..d208acea8 100644
--- a/benchmarks/hash_table/static_multimap_bench.cu
+++ b/benchmarks/hash_table/static_multimap_bench.cu
@@ -53,7 +53,7 @@ void nvbench_static_multimap_insert(nvbench::state& state, nvbench::type_list<Ke
   using map_type = cuco::static_multimap<Key, Value>;
 
   const std::size_t num_keys = state.get_int64("NumInputs");
-  float occupancy            = state.get_int64("Occupancy");
+  auto occupancy             = state.get_float64("Occupancy");
   std::size_t size           = num_keys / occupancy;
   const auto dist            = state.get_string("Distribution");
 
@@ -102,5 +102,5 @@ using value_type = nvbench::type_list<nvbench::int32_t>;
 NVBENCH_BENCH_TYPES(nvbench_static_multimap_insert, NVBENCH_TYPE_AXES(key_type, value_type))
   .set_type_axes_names({"Key", "Value"})
   .add_int64_axis("NumInputs", {100'000'000})
-  .add_int64_axis("Occupancy", nvbench::range(10, 90, 10))
+  .add_float64_axis("Occupancy", nvbench::range(0.1, 0.9, 0.1))
   .add_string_axis("Distribution", {"UNIQUE", "UNIFORM", "GAUSSIAN"});

From 0d1af85dd88663d2c65852d3dddfa040186205f2 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Fri, 12 Mar 2021 10:54:49 -0800
Subject: [PATCH 031/267] Add int64_t into static multimap benchmark

---
 benchmarks/hash_table/static_multimap_bench.cu | 10 ++++++++--
 1 file changed, 8 insertions(+), 2 deletions(-)

diff --git a/benchmarks/hash_table/static_multimap_bench.cu b/benchmarks/hash_table/static_multimap_bench.cu
index d208acea8..e9a4f0411 100644
--- a/benchmarks/hash_table/static_multimap_bench.cu
+++ b/benchmarks/hash_table/static_multimap_bench.cu
@@ -19,6 +19,7 @@
 
 #include <thrust/device_vector.h>
 #include <random>
+#include <type_traits>
 
 #include "cuco/static_multimap.cuh"
 
@@ -50,6 +51,11 @@ static void generate_keys(OutputIt output_begin, OutputIt output_end, const std:
 template <typename Key, typename Value>
 void nvbench_static_multimap_insert(nvbench::state& state, nvbench::type_list<Key, Value>)
 {
+  if (not std::is_same<Key, Value>::value) {
+    state.skip("Key should be the same type as Value.");
+    return;
+  }
+
   using map_type = cuco::static_multimap<Key, Value>;
 
   const std::size_t num_keys = state.get_int64("NumInputs");
@@ -97,8 +103,8 @@ void nvbench_static_multimap_insert(nvbench::state& state, nvbench::type_list<Ke
              });
 }
 
-using key_type   = nvbench::type_list<nvbench::int32_t>;
-using value_type = nvbench::type_list<nvbench::int32_t>;
+using key_type   = nvbench::type_list<nvbench::int32_t, nvbench::int64_t>;
+using value_type = nvbench::type_list<nvbench::int32_t, nvbench::int64_t>;
 NVBENCH_BENCH_TYPES(nvbench_static_multimap_insert, NVBENCH_TYPE_AXES(key_type, value_type))
   .set_type_axes_names({"Key", "Value"})
   .add_int64_axis("NumInputs", {100'000'000})

From 4303e2632f2137c6c5d92fd50e4698abc3eb46b2 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Mon, 15 Mar 2021 17:09:29 -0700
Subject: [PATCH 032/267] Update static multimap benchmark

---
 benchmarks/hash_table/static_multimap_bench.cu | 15 +++++++++++++--
 1 file changed, 13 insertions(+), 2 deletions(-)

diff --git a/benchmarks/hash_table/static_multimap_bench.cu b/benchmarks/hash_table/static_multimap_bench.cu
index e9a4f0411..ac7965aa6 100644
--- a/benchmarks/hash_table/static_multimap_bench.cu
+++ b/benchmarks/hash_table/static_multimap_bench.cu
@@ -105,8 +105,19 @@ void nvbench_static_multimap_insert(nvbench::state& state, nvbench::type_list<Ke
 
 using key_type   = nvbench::type_list<nvbench::int32_t, nvbench::int64_t>;
 using value_type = nvbench::type_list<nvbench::int32_t, nvbench::int64_t>;
+
+NVBENCH_BENCH_TYPES(nvbench_static_multimap_insert, NVBENCH_TYPE_AXES(key_type, value_type))
+  .set_type_axes_names({"Key", "Value"})
+  .set_timeout(100)                        // Custom timeout: 100 s. By default: 15 s.
+  .set_max_noise(3)                        // Custom timeout: 3%. By default: 0.5%.
+  .add_int64_axis("NumInputs", {2 << 27})  // Total number of key/value pairs: 2^28
+  .add_float64_axis("Occupancy", {0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.95})
+  .add_string_axis("Distribution", {"UNIQUE", "UNIFORM", "GAUSSIAN"});
+
 NVBENCH_BENCH_TYPES(nvbench_static_multimap_insert, NVBENCH_TYPE_AXES(key_type, value_type))
   .set_type_axes_names({"Key", "Value"})
-  .add_int64_axis("NumInputs", {100'000'000})
-  .add_float64_axis("Occupancy", nvbench::range(0.1, 0.9, 0.1))
+  .set_timeout(1'500)                      // Custom timeout: 1500 s. By default: 15 s.
+  .set_max_noise(3)                        // Custom timeout: 3%. By default: 0.5%.
+  .add_int64_axis("NumInputs", {2 << 27})  // Total number of key/value pairs: 2^28
+  .add_float64_axis("Occupancy", {1.})
   .add_string_axis("Distribution", {"UNIQUE", "UNIFORM", "GAUSSIAN"});

From 5d96e9ae85f56e087692808a57d4b546cc665eaa Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@yunsongw-mlt.client.nvidia.com>
Date: Mon, 15 Mar 2021 21:41:16 -0400
Subject: [PATCH 033/267] Add a simple notebook for performance analysis

---
 .../analysis/data/static-multimap-data.csv    |  85 ++++++++++++
 .../analysis/notebooks/StaticMultimap.ipynb   | 128 ++++++++++++++++++
 2 files changed, 213 insertions(+)
 create mode 100644 benchmarks/analysis/data/static-multimap-data.csv
 create mode 100644 benchmarks/analysis/notebooks/StaticMultimap.ipynb

diff --git a/benchmarks/analysis/data/static-multimap-data.csv b/benchmarks/analysis/data/static-multimap-data.csv
new file mode 100644
index 000000000..490baea8e
--- /dev/null
+++ b/benchmarks/analysis/data/static-multimap-data.csv
@@ -0,0 +1,85 @@
+Benchmark,Device,Device Name,Key,Value,NumInputs,Occupancy,Distribution,Skipped,Samples,CPU Time (sec),Noise (%),GPU Time (sec),Noise (%)
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,268435456,0.30000000000000004,UNIQUE,No,55,0.17623596332727273,2.8366649185643444,0.1762308269153942,2.836685918207623
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,268435456,0.4,UNIQUE,No,60,0.17160135700000004,2.6937778447414638,0.17159596354166667,2.693864011242942
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,268435456,0.5,UNIQUE,No,62,0.1689210611290323,2.553338295497867,0.16891576976160846,2.553299852912608
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,268435456,0.6,UNIQUE,No,62,0.16677234911290328,2.685202120819898,0.16676685653194304,2.6852709952879823
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,268435456,0.7,UNIQUE,No,62,0.16699080348387105,2.4415898817721717,0.16698551251811364,2.4415778733570503
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,268435456,0.7999999999999999,UNIQUE,No,63,0.1686325047619047,2.0596742093008373,0.16862677777002727,2.059841872290985
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,268435456,0.8999999999999999,UNIQUE,No,57,0.18957879659649124,1.6262733414463166,0.18957367692913926,1.6262072533441363
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,268435456,0.30000000000000004,UNIFORM,No,54,0.17596297272222222,2.66670036622406,0.1759573528148511,2.6668625002495476
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,268435456,0.4,UNIFORM,No,59,0.17142499645762715,2.552197581276359,0.17141963144076078,2.5519825306965047
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,268435456,0.5,UNIFORM,No,60,0.16851585304999997,2.6225798262581113,0.16851056645711263,2.622629492046837
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,268435456,0.6,UNIFORM,No,62,0.16727539333870972,2.6593347443486914,0.1672700207617975,2.659324847836786
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,268435456,0.7,UNIFORM,No,63,0.16745446125396823,2.450011353525469,0.16744873797704304,2.449758983335817
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,268435456,0.7999999999999999,UNIFORM,No,63,0.16961292895238098,2.0937601566650934,0.16960713583325585,2.094047524265341
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,268435456,0.8999999999999999,UNIFORM,No,57,0.19464604971929833,1.5894568800397697,0.19464069754617255,1.589424682018684
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,268435456,0.30000000000000004,GAUSSIAN,No,51,0.1889640750588236,2.6814807943845285,0.18895890060125614,2.681517623019824
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,268435456,0.4,GAUSSIAN,No,53,0.1906565883962264,2.381345219783455,0.19065143110167307,2.3813523629174504
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,268435456,0.5,GAUSSIAN,No,52,0.19739338555769234,2.212527435363628,0.1973881692152757,2.2125861410064234
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,268435456,0.6,GAUSSIAN,No,52,0.215009741,1.7026128226285988,0.21500469119732196,1.7026801785044288
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,268435456,0.7,GAUSSIAN,No,46,0.24394731532608702,1.5865535658264092,0.2439424445110819,1.5865791979144106
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,268435456,0.7999999999999999,GAUSSIAN,No,40,0.3046339542499999,1.070509618047736,0.3046295822143554,1.0704790897602774
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,268435456,0.8999999999999999,GAUSSIAN,No,11,0.5236044275454547,0.46185200251735,0.5235994984019886,0.4617883541121059
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,268435456,0.30000000000000004,UNIQUE,Yes,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,268435456,0.4,UNIQUE,Yes,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,268435456,0.5,UNIQUE,Yes,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,268435456,0.6,UNIQUE,Yes,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,268435456,0.7,UNIQUE,Yes,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,268435456,0.7999999999999999,UNIQUE,Yes,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,268435456,0.8999999999999999,UNIQUE,Yes,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,268435456,0.30000000000000004,UNIFORM,Yes,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,268435456,0.4,UNIFORM,Yes,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,268435456,0.5,UNIFORM,Yes,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,268435456,0.6,UNIFORM,Yes,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,268435456,0.7,UNIFORM,Yes,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,268435456,0.7999999999999999,UNIFORM,Yes,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,268435456,0.8999999999999999,UNIFORM,Yes,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,268435456,0.30000000000000004,GAUSSIAN,Yes,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,268435456,0.4,GAUSSIAN,Yes,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,268435456,0.5,GAUSSIAN,Yes,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,268435456,0.6,GAUSSIAN,Yes,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,268435456,0.7,GAUSSIAN,Yes,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,268435456,0.7999999999999999,GAUSSIAN,Yes,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,268435456,0.8999999999999999,GAUSSIAN,Yes,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,268435456,0.30000000000000004,UNIQUE,Yes,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,268435456,0.4,UNIQUE,Yes,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,268435456,0.5,UNIQUE,Yes,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,268435456,0.6,UNIQUE,Yes,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,268435456,0.7,UNIQUE,Yes,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,268435456,0.7999999999999999,UNIQUE,Yes,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,268435456,0.8999999999999999,UNIQUE,Yes,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,268435456,0.30000000000000004,UNIFORM,Yes,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,268435456,0.4,UNIFORM,Yes,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,268435456,0.5,UNIFORM,Yes,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,268435456,0.6,UNIFORM,Yes,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,268435456,0.7,UNIFORM,Yes,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,268435456,0.7999999999999999,UNIFORM,Yes,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,268435456,0.8999999999999999,UNIFORM,Yes,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,268435456,0.30000000000000004,GAUSSIAN,Yes,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,268435456,0.4,GAUSSIAN,Yes,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,268435456,0.5,GAUSSIAN,Yes,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,268435456,0.6,GAUSSIAN,Yes,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,268435456,0.7,GAUSSIAN,Yes,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,268435456,0.7999999999999999,GAUSSIAN,Yes,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,268435456,0.8999999999999999,GAUSSIAN,Yes,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,268435456,0.30000000000000004,UNIQUE,No,11,0.2406305241818182,0.0054266006238538654,0.24062585171786222,0.005525055604329204
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,268435456,0.4,UNIQUE,No,44,0.21799922827272725,2.951802633679446,0.21799407750909985,2.951922090183025
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,268435456,0.5,UNIQUE,No,45,0.2057523973333333,2.8937447016573135,0.20574722290039058,2.893827623201318
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,268435456,0.6,UNIQUE,No,49,0.20070097910204082,2.9608223532380755,0.20069589233398435,2.960866121711912
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,268435456,0.7,UNIQUE,No,50,0.19529296428,3.1632839061362565,0.19528785949707028,3.1633670129148186
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,268435456,0.7999999999999999,UNIQUE,No,51,0.19828046343137254,3.059201671477376,0.19827551449046416,3.0594245810698806
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,268435456,0.8999999999999999,UNIQUE,No,47,0.2403344038723404,2.4804405530151064,0.2403292054521276,2.480759031056604
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,268435456,0.30000000000000004,UNIFORM,No,11,0.24081009318181817,0.0063007498007621055,0.24080532698197799,0.006430981333279995
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,268435456,0.4,UNIFORM,No,44,0.21724615886363646,2.4495901572184073,0.2172411124489524,2.449660387365714
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,268435456,0.5,UNIFORM,No,46,0.2059849642608696,2.7663304764679935,0.20597933595076856,2.766579820163578
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,268435456,0.6,UNIFORM,No,49,0.20016126432653059,2.6726487249591617,0.20015626463598132,2.6726956413921066
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,268435456,0.7,UNIFORM,No,51,0.19541015386274513,3.1348076152732878,0.19540500117283244,3.1348707560838998
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,268435456,0.7999999999999999,UNIFORM,No,51,0.19906061962745103,2.955834927318549,0.19905554049622773,2.9560088660417967
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,268435456,0.8999999999999999,UNIFORM,No,46,0.24522991017391307,2.354287412814649,0.2452247825290847,2.354340594162305
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,268435456,0.30000000000000004,GAUSSIAN,No,11,0.3264478085454546,0.0045521652511531065,0.32644300426136363,0.004552357559914651
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,268435456,0.4,GAUSSIAN,No,39,0.24420892802564098,0.5307400905876261,0.24420404522235578,0.5307469032050539
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,268435456,0.5,GAUSSIAN,No,11,0.2634309843636364,0.436249346929138,0.2634264221191406,0.43618504993467744
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,268435456,0.6,GAUSSIAN,No,37,0.29589045524324326,1.0290342363948874,0.29588521761507597,1.0289801749851617
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,268435456,0.7,GAUSSIAN,No,34,0.35179509244117646,1.1122657672775487,0.35179062248678766,1.1122794553601718
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,268435456,0.7999999999999999,GAUSSIAN,No,11,0.4641538576363637,0.2949618683075735,0.46414969149502844,0.2949572787835263
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,268435456,0.8999999999999999,GAUSSIAN,No,16,0.8438318679375001,0.5200110881251193,0.8438290519714357,0.5200222667989846
diff --git a/benchmarks/analysis/notebooks/StaticMultimap.ipynb b/benchmarks/analysis/notebooks/StaticMultimap.ipynb
new file mode 100644
index 000000000..d7d058811
--- /dev/null
+++ b/benchmarks/analysis/notebooks/StaticMultimap.ipynb
@@ -0,0 +1,128 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "vocal-glass",
+   "metadata": {},
+   "source": [
+    "# Prep"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "manufactured-kitty",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# !pip install pandas\n",
+    "# !pip install matplotlib\n",
+    "\n",
+    "\n",
+    "# Import libraries\n",
+    "import pandas as pd\n",
+    "import matplotlib\n",
+    "\n",
+    "# Define the data path\n",
+    "datafile='../data/static-multimap-data.csv'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "rising-inspector",
+   "metadata": {},
+   "source": [
+    "# Import Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "editorial-figure",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Read csv file\n",
+    "rawdf = pd.read_csv(datafile)\n",
+    "\n",
+    "# Filter out unrelated data\n",
+    "perfdf = rawdf[rawdf[\"Key\"] == rawdf[\"Value\"]].reset_index(drop=True)\n",
+    "perfdf.drop(columns=['Device', 'Device Name', 'Skipped'], inplace=True)\n",
+    "\n",
+    "# Drop NANs\n",
+    "perfdf.dropna(inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "amber-diploma",
+   "metadata": {},
+   "source": [
+    "# Comput Performance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "marine-floating",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "perfdf['Performance'] = perfdf['NumInputs'] / perfdf['GPU Time (sec)']\n",
+    "#display(perfdf)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "published-designation",
+   "metadata": {},
+   "source": [
+    "# Visualize Performance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "alike-growing",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "perfdf.plot(x=\"Occupancy\", y=\"Performance\");"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From c1c0bc9c5e82c101b6a25c7926a8f7e158f7c0fe Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Wed, 17 Mar 2021 13:19:07 -0700
Subject: [PATCH 034/267] Clean up multimap single insertion benchmark

---
 .../hash_table/static_multimap_bench.cu       | 21 +++++++------------
 1 file changed, 8 insertions(+), 13 deletions(-)

diff --git a/benchmarks/hash_table/static_multimap_bench.cu b/benchmarks/hash_table/static_multimap_bench.cu
index ac7965aa6..0815d1874 100644
--- a/benchmarks/hash_table/static_multimap_bench.cu
+++ b/benchmarks/hash_table/static_multimap_bench.cu
@@ -77,6 +77,9 @@ void nvbench_static_multimap_insert(nvbench::state& state, nvbench::type_list<Ke
 
   thrust::device_vector<cuco::pair_type<Key, Value>> d_pairs(h_pairs);
 
+  state.add_element_count(num_keys, "NumKeys");
+  state.add_global_memory_writes<Key>(num_keys * 2);
+
   state.exec(nvbench::exec_tag::sync | nvbench::exec_tag::timer,
              [&](nvbench::launch& launch, auto& timer) {
                map_type map{size, -1, -1};
@@ -98,7 +101,7 @@ void nvbench_static_multimap_insert(nvbench::state& state, nvbench::type_list<Ke
                cuco::detail::insert<block_size, tile_size>
                  <<<grid_size, block_size, 0, launch.get_stream()>>>(
                    d_pairs.begin(), d_pairs.end(), m_view, hash, key_equal);
-               // CUCO_CUDA_TRY(cudaDeviceSynchronize());
+               CUCO_CUDA_TRY(cudaDeviceSynchronize());
                timer.stop();
              });
 }
@@ -108,16 +111,8 @@ using value_type = nvbench::type_list<nvbench::int32_t, nvbench::int64_t>;
 
 NVBENCH_BENCH_TYPES(nvbench_static_multimap_insert, NVBENCH_TYPE_AXES(key_type, value_type))
   .set_type_axes_names({"Key", "Value"})
-  .set_timeout(100)                        // Custom timeout: 100 s. By default: 15 s.
-  .set_max_noise(3)                        // Custom timeout: 3%. By default: 0.5%.
-  .add_int64_axis("NumInputs", {2 << 27})  // Total number of key/value pairs: 2^28
-  .add_float64_axis("Occupancy", {0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.95})
-  .add_string_axis("Distribution", {"UNIQUE", "UNIFORM", "GAUSSIAN"});
-
-NVBENCH_BENCH_TYPES(nvbench_static_multimap_insert, NVBENCH_TYPE_AXES(key_type, value_type))
-  .set_type_axes_names({"Key", "Value"})
-  .set_timeout(1'500)                      // Custom timeout: 1500 s. By default: 15 s.
-  .set_max_noise(3)                        // Custom timeout: 3%. By default: 0.5%.
-  .add_int64_axis("NumInputs", {2 << 27})  // Total number of key/value pairs: 2^28
-  .add_float64_axis("Occupancy", {1.})
+  .set_timeout(100)                            // Custom timeout: 100 s. By default: 15 s.
+  .set_max_noise(3)                            // Custom timeout: 3%. By default: 0.5%.
+  .add_int64_axis("NumInputs", {100'000'000})  // Total number of key/value pairs: 2^28
+  .add_float64_axis("Occupancy", nvbench::range(0.1, 0.9, 0.1))
   .add_string_axis("Distribution", {"UNIQUE", "UNIFORM", "GAUSSIAN"});

From 272efe5ced4f0ebe44d11b7702a212adda85d5ff Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Wed, 17 Mar 2021 16:38:16 -0700
Subject: [PATCH 035/267] Add multi-value insertion benchmark

---
 .../hash_table/static_multimap_bench.cu       | 87 +++++++++++++++----
 1 file changed, 69 insertions(+), 18 deletions(-)

diff --git a/benchmarks/hash_table/static_multimap_bench.cu b/benchmarks/hash_table/static_multimap_bench.cu
index 0815d1874..d590e610f 100644
--- a/benchmarks/hash_table/static_multimap_bench.cu
+++ b/benchmarks/hash_table/static_multimap_bench.cu
@@ -48,26 +48,28 @@ static void generate_keys(OutputIt output_begin, OutputIt output_end, const std:
   }
 }
 
-template <typename Key, typename Value>
-void nvbench_static_multimap_insert(nvbench::state& state, nvbench::type_list<Key, Value>)
+template <typename Key, typename OutputIt>
+static void generate_multikeys(OutputIt output_begin, OutputIt output_end, const size_t num_reps)
 {
-  if (not std::is_same<Key, Value>::value) {
-    state.skip("Key should be the same type as Value.");
-    return;
-  }
+  auto num_keys = std::distance(output_begin, output_end);
 
-  using map_type = cuco::static_multimap<Key, Value>;
+  std::random_device rd;
+  std::mt19937 gen{rd()};
 
-  const std::size_t num_keys = state.get_int64("NumInputs");
-  auto occupancy             = state.get_float64("Occupancy");
-  std::size_t size           = num_keys / occupancy;
-  const auto dist            = state.get_string("Distribution");
+  std::uniform_int_distribution<Key> distribution{1, static_cast<Key>(num_keys / num_reps)};
+  for (auto i = 0; i < num_keys; ++i) {
+    output_begin[i] = distribution(gen);
+  }
+}
 
-  std::vector<Key> h_keys(num_keys);
+template <typename Key, typename Value>
+void launch_nvbench_insert(nvbench::state& state,
+                           std::vector<Key> const& h_keys,
+                           const std::size_t num_keys,
+                           const std::size_t size)
+{
   std::vector<cuco::pair_type<Key, Value>> h_pairs(num_keys);
 
-  generate_keys<Key>(h_keys.begin(), h_keys.end(), dist);
-
   for (auto i = 0; i < num_keys; ++i) {
     Key key           = h_keys[i];
     Value val         = h_keys[i];
@@ -82,7 +84,7 @@ void nvbench_static_multimap_insert(nvbench::state& state, nvbench::type_list<Ke
 
   state.exec(nvbench::exec_tag::sync | nvbench::exec_tag::timer,
              [&](nvbench::launch& launch, auto& timer) {
-               map_type map{size, -1, -1};
+               cuco::static_multimap<Key, Value> map{size, -1, -1};
                auto m_view = map.get_device_mutable_view();
 
                auto const block_size = 128;
@@ -106,13 +108,62 @@ void nvbench_static_multimap_insert(nvbench::state& state, nvbench::type_list<Ke
              });
 }
 
+template <typename Key, typename Value>
+void nvbench_static_multimap_single_insert(nvbench::state& state, nvbench::type_list<Key, Value>)
+{
+  if (not std::is_same<Key, Value>::value) {
+    state.skip("Key should be the same type as Value.");
+    return;
+  }
+
+  const std::size_t num_keys = state.get_int64("NumInputs");
+  auto occupancy             = state.get_float64("Occupancy");
+  std::size_t size           = num_keys / occupancy;
+  const auto dist            = state.get_string("Distribution");
+
+  std::vector<Key> h_keys(num_keys);
+
+  generate_keys<Key>(h_keys.begin(), h_keys.end(), dist);
+
+  launch_nvbench_insert<Key, Value>(state, h_keys, num_keys, size);
+}
+
+template <typename Key, typename Value>
+void nvbench_static_multimap_multi_insert(nvbench::state& state, nvbench::type_list<Key, Value>)
+{
+  if (not std::is_same<Key, Value>::value) {
+    state.skip("Key should be the same type as Value.");
+    return;
+  }
+
+  using map_type = cuco::static_multimap<Key, Value>;
+
+  const std::size_t num_keys = state.get_int64("NumInputs");
+  auto occupancy             = state.get_float64("Occupancy");
+  const std::size_t size     = num_keys / occupancy;
+  const std::size_t num_reps = state.get_int64("NumReps");
+
+  std::vector<Key> h_keys(num_keys);
+  std::vector<cuco::pair_type<Key, Value>> h_pairs(num_keys);
+
+  generate_multikeys<Key>(h_keys.begin(), h_keys.end(), num_reps);
+
+  launch_nvbench_insert<Key, Value>(state, h_keys, num_keys, size);
+}
+
 using key_type   = nvbench::type_list<nvbench::int32_t, nvbench::int64_t>;
 using value_type = nvbench::type_list<nvbench::int32_t, nvbench::int64_t>;
 
-NVBENCH_BENCH_TYPES(nvbench_static_multimap_insert, NVBENCH_TYPE_AXES(key_type, value_type))
+NVBENCH_BENCH_TYPES(nvbench_static_multimap_single_insert, NVBENCH_TYPE_AXES(key_type, value_type))
   .set_type_axes_names({"Key", "Value"})
-  .set_timeout(100)                            // Custom timeout: 100 s. By default: 15 s.
   .set_max_noise(3)                            // Custom timeout: 3%. By default: 0.5%.
-  .add_int64_axis("NumInputs", {100'000'000})  // Total number of key/value pairs: 2^28
+  .add_int64_axis("NumInputs", {100'000'000})  // Total number of key/value pairs: 100'000'000
   .add_float64_axis("Occupancy", nvbench::range(0.1, 0.9, 0.1))
   .add_string_axis("Distribution", {"UNIQUE", "UNIFORM", "GAUSSIAN"});
+
+NVBENCH_BENCH_TYPES(nvbench_static_multimap_multi_insert, NVBENCH_TYPE_AXES(key_type, value_type))
+  .set_type_axes_names({"Key", "Value"})
+  .set_max_noise(3)                            // Custom timeout: 3%. By default: 0.5%.
+  .add_int64_axis("NumInputs", {100'000'000})  // Total number of key/value pairs: 100'000'000
+  .add_float64_axis("Occupancy", {0.8})
+  .add_int64_power_of_two_axis("NumReps", nvbench::range(0, 8, 1));

From 4116b1affec590853b751815bd259d2c24d1cc5c Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Wed, 17 Mar 2021 19:44:52 -0400
Subject: [PATCH 036/267] Add bandwidth plot in notebook + multi-value
 insertion analysis

---
 .../analysis/data/static-multimap-data.csv    | 230 +++++++++++-------
 .../analysis/notebooks/StaticMultimap.ipynb   |  87 ++++---
 benchmarks/analysis/notebooks/Utils.py        |  65 +++++
 3 files changed, 269 insertions(+), 113 deletions(-)
 create mode 100644 benchmarks/analysis/notebooks/Utils.py

diff --git a/benchmarks/analysis/data/static-multimap-data.csv b/benchmarks/analysis/data/static-multimap-data.csv
index 490baea8e..d4d6dcd26 100644
--- a/benchmarks/analysis/data/static-multimap-data.csv
+++ b/benchmarks/analysis/data/static-multimap-data.csv
@@ -1,85 +1,145 @@
-Benchmark,Device,Device Name,Key,Value,NumInputs,Occupancy,Distribution,Skipped,Samples,CPU Time (sec),Noise (%),GPU Time (sec),Noise (%)
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,268435456,0.30000000000000004,UNIQUE,No,55,0.17623596332727273,2.8366649185643444,0.1762308269153942,2.836685918207623
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,268435456,0.4,UNIQUE,No,60,0.17160135700000004,2.6937778447414638,0.17159596354166667,2.693864011242942
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,268435456,0.5,UNIQUE,No,62,0.1689210611290323,2.553338295497867,0.16891576976160846,2.553299852912608
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,268435456,0.6,UNIQUE,No,62,0.16677234911290328,2.685202120819898,0.16676685653194304,2.6852709952879823
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,268435456,0.7,UNIQUE,No,62,0.16699080348387105,2.4415898817721717,0.16698551251811364,2.4415778733570503
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,268435456,0.7999999999999999,UNIQUE,No,63,0.1686325047619047,2.0596742093008373,0.16862677777002727,2.059841872290985
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,268435456,0.8999999999999999,UNIQUE,No,57,0.18957879659649124,1.6262733414463166,0.18957367692913926,1.6262072533441363
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,268435456,0.30000000000000004,UNIFORM,No,54,0.17596297272222222,2.66670036622406,0.1759573528148511,2.6668625002495476
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,268435456,0.4,UNIFORM,No,59,0.17142499645762715,2.552197581276359,0.17141963144076078,2.5519825306965047
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,268435456,0.5,UNIFORM,No,60,0.16851585304999997,2.6225798262581113,0.16851056645711263,2.622629492046837
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,268435456,0.6,UNIFORM,No,62,0.16727539333870972,2.6593347443486914,0.1672700207617975,2.659324847836786
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,268435456,0.7,UNIFORM,No,63,0.16745446125396823,2.450011353525469,0.16744873797704304,2.449758983335817
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,268435456,0.7999999999999999,UNIFORM,No,63,0.16961292895238098,2.0937601566650934,0.16960713583325585,2.094047524265341
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,268435456,0.8999999999999999,UNIFORM,No,57,0.19464604971929833,1.5894568800397697,0.19464069754617255,1.589424682018684
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,268435456,0.30000000000000004,GAUSSIAN,No,51,0.1889640750588236,2.6814807943845285,0.18895890060125614,2.681517623019824
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,268435456,0.4,GAUSSIAN,No,53,0.1906565883962264,2.381345219783455,0.19065143110167307,2.3813523629174504
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,268435456,0.5,GAUSSIAN,No,52,0.19739338555769234,2.212527435363628,0.1973881692152757,2.2125861410064234
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,268435456,0.6,GAUSSIAN,No,52,0.215009741,1.7026128226285988,0.21500469119732196,1.7026801785044288
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,268435456,0.7,GAUSSIAN,No,46,0.24394731532608702,1.5865535658264092,0.2439424445110819,1.5865791979144106
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,268435456,0.7999999999999999,GAUSSIAN,No,40,0.3046339542499999,1.070509618047736,0.3046295822143554,1.0704790897602774
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,268435456,0.8999999999999999,GAUSSIAN,No,11,0.5236044275454547,0.46185200251735,0.5235994984019886,0.4617883541121059
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,268435456,0.30000000000000004,UNIQUE,Yes,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,268435456,0.4,UNIQUE,Yes,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,268435456,0.5,UNIQUE,Yes,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,268435456,0.6,UNIQUE,Yes,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,268435456,0.7,UNIQUE,Yes,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,268435456,0.7999999999999999,UNIQUE,Yes,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,268435456,0.8999999999999999,UNIQUE,Yes,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,268435456,0.30000000000000004,UNIFORM,Yes,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,268435456,0.4,UNIFORM,Yes,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,268435456,0.5,UNIFORM,Yes,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,268435456,0.6,UNIFORM,Yes,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,268435456,0.7,UNIFORM,Yes,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,268435456,0.7999999999999999,UNIFORM,Yes,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,268435456,0.8999999999999999,UNIFORM,Yes,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,268435456,0.30000000000000004,GAUSSIAN,Yes,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,268435456,0.4,GAUSSIAN,Yes,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,268435456,0.5,GAUSSIAN,Yes,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,268435456,0.6,GAUSSIAN,Yes,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,268435456,0.7,GAUSSIAN,Yes,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,268435456,0.7999999999999999,GAUSSIAN,Yes,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,268435456,0.8999999999999999,GAUSSIAN,Yes,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,268435456,0.30000000000000004,UNIQUE,Yes,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,268435456,0.4,UNIQUE,Yes,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,268435456,0.5,UNIQUE,Yes,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,268435456,0.6,UNIQUE,Yes,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,268435456,0.7,UNIQUE,Yes,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,268435456,0.7999999999999999,UNIQUE,Yes,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,268435456,0.8999999999999999,UNIQUE,Yes,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,268435456,0.30000000000000004,UNIFORM,Yes,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,268435456,0.4,UNIFORM,Yes,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,268435456,0.5,UNIFORM,Yes,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,268435456,0.6,UNIFORM,Yes,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,268435456,0.7,UNIFORM,Yes,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,268435456,0.7999999999999999,UNIFORM,Yes,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,268435456,0.8999999999999999,UNIFORM,Yes,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,268435456,0.30000000000000004,GAUSSIAN,Yes,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,268435456,0.4,GAUSSIAN,Yes,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,268435456,0.5,GAUSSIAN,Yes,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,268435456,0.6,GAUSSIAN,Yes,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,268435456,0.7,GAUSSIAN,Yes,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,268435456,0.7999999999999999,GAUSSIAN,Yes,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,268435456,0.8999999999999999,GAUSSIAN,Yes,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,268435456,0.30000000000000004,UNIQUE,No,11,0.2406305241818182,0.0054266006238538654,0.24062585171786222,0.005525055604329204
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,268435456,0.4,UNIQUE,No,44,0.21799922827272725,2.951802633679446,0.21799407750909985,2.951922090183025
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,268435456,0.5,UNIQUE,No,45,0.2057523973333333,2.8937447016573135,0.20574722290039058,2.893827623201318
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,268435456,0.6,UNIQUE,No,49,0.20070097910204082,2.9608223532380755,0.20069589233398435,2.960866121711912
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,268435456,0.7,UNIQUE,No,50,0.19529296428,3.1632839061362565,0.19528785949707028,3.1633670129148186
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,268435456,0.7999999999999999,UNIQUE,No,51,0.19828046343137254,3.059201671477376,0.19827551449046416,3.0594245810698806
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,268435456,0.8999999999999999,UNIQUE,No,47,0.2403344038723404,2.4804405530151064,0.2403292054521276,2.480759031056604
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,268435456,0.30000000000000004,UNIFORM,No,11,0.24081009318181817,0.0063007498007621055,0.24080532698197799,0.006430981333279995
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,268435456,0.4,UNIFORM,No,44,0.21724615886363646,2.4495901572184073,0.2172411124489524,2.449660387365714
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,268435456,0.5,UNIFORM,No,46,0.2059849642608696,2.7663304764679935,0.20597933595076856,2.766579820163578
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,268435456,0.6,UNIFORM,No,49,0.20016126432653059,2.6726487249591617,0.20015626463598132,2.6726956413921066
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,268435456,0.7,UNIFORM,No,51,0.19541015386274513,3.1348076152732878,0.19540500117283244,3.1348707560838998
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,268435456,0.7999999999999999,UNIFORM,No,51,0.19906061962745103,2.955834927318549,0.19905554049622773,2.9560088660417967
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,268435456,0.8999999999999999,UNIFORM,No,46,0.24522991017391307,2.354287412814649,0.2452247825290847,2.354340594162305
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,268435456,0.30000000000000004,GAUSSIAN,No,11,0.3264478085454546,0.0045521652511531065,0.32644300426136363,0.004552357559914651
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,268435456,0.4,GAUSSIAN,No,39,0.24420892802564098,0.5307400905876261,0.24420404522235578,0.5307469032050539
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,268435456,0.5,GAUSSIAN,No,11,0.2634309843636364,0.436249346929138,0.2634264221191406,0.43618504993467744
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,268435456,0.6,GAUSSIAN,No,37,0.29589045524324326,1.0290342363948874,0.29588521761507597,1.0289801749851617
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,268435456,0.7,GAUSSIAN,No,34,0.35179509244117646,1.1122657672775487,0.35179062248678766,1.1122794553601718
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,268435456,0.7999999999999999,GAUSSIAN,No,11,0.4641538576363637,0.2949618683075735,0.46414969149502844,0.2949572787835263
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,268435456,0.8999999999999999,GAUSSIAN,No,16,0.8438318679375001,0.5200110881251193,0.8438290519714357,0.5200222667989846
+Benchmark,Device,Device Name,Key,Value,NumInputs,Occupancy,Distribution,Skipped,NumKeys,Samples,CPU Time (sec),Noise (%),GPU Time (sec),Noise (%),Elem/s (elem/sec),GlobalMem BW (bytes/sec),BWPeak (%),NumReps (pow2),NumReps
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.1,UNIQUE,No,100000000,11,0.06912646563636364,0.07652550388508265,0.06912139615145596,0.07915160016570952,1446730036.8309128,11573840294.647303,1.2887774700959527,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.2,UNIQUE,No,100000000,118,0.06746609788135592,4.337309557361487,0.06746028699713241,4.337529287854764,1482353610.5657358,11858828884.525887,1.3205116969834447,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.30000000000000004,UNIQUE,No,100000000,126,0.06530046633333332,4.725697765553524,0.06529489698864163,4.725337023009958,1531513251.600588,12252106012.804705,1.3643041366168296,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.4,UNIQUE,No,100000000,137,0.06370433262043793,3.579250420662472,0.06369880186206232,3.579353865055502,1569888240.8580735,12559105926.864588,1.3984893821783009,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.5,UNIQUE,No,100000000,16,0.0629443638125,2.9866222708393324,0.06293836832046508,2.9867299689762548,1588855934.282045,12710847474.25636,1.4153862014342617,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.6,UNIQUE,No,100000000,11,0.062425575272727275,2.582965979313861,0.06241978211836381,2.5839216782841734,1602056216.8957033,12816449735.165627,1.4271452901365658,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.7,UNIQUE,No,100000000,11,0.06241535327272727,2.1984232060706717,0.062409914190118966,2.1984004784526476,1602309525.6207302,12818476204.965841,1.4273709428633927,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.7999999999999999,UNIQUE,No,100000000,11,0.06294936072727274,1.903995887955142,0.06294369680231268,1.9045085877142856,1588721430.1071334,12709771440.857067,1.4152663822932703,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.8999999999999999,UNIQUE,No,100000000,11,0.06938280227272729,1.3427483827102604,0.06937758220325817,1.3427130496541069,1441387791.6215956,11531102332.972765,1.284018485979899,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.1,UNIFORM,No,100000000,11,0.06936386154545454,1.2370474993295733,0.06935821949351918,1.2372128124438435,1441790183.344369,11534321466.754951,1.2843769449689717,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.2,UNIFORM,No,100000000,117,0.06738813333333332,4.3078364477291755,0.06738242953047795,4.3080054523090965,1484066405.6906512,11872531245.52521,1.3220374908162158,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.30000000000000004,UNIFORM,No,100000000,129,0.06515699493798446,4.54398801582476,0.06515145998222886,4.544275477207251,1534885020.646915,12279080165.17532,1.3673077792250883,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.4,UNIFORM,No,100000000,138,0.0639216477753623,3.5016111537825774,0.0639158096866331,3.5021286423015567,1564558134.9947803,12516465079.958242,1.3937412120463764,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.5,UNIFORM,No,100000000,137,0.06275239455474453,3.0750144296542556,0.06274670635696744,3.07525394869748,1593709149.1479683,12749673193.183746,1.4197095470602625,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.6,UNIFORM,No,100000000,11,0.06249900118181817,2.6144095180154534,0.06249369569258256,2.614689386175176,1600161406.5507905,12801291252.406324,1.4254573533270298,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.7,UNIFORM,No,100000000,11,0.06267772136363638,2.476112304445131,0.06267224363847212,2.4765309781095897,1595602681.4175482,12764821451.340385,1.4213963453334773,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.7999999999999999,UNIFORM,No,100000000,11,0.063070525,1.8907418114775623,0.06306490187211469,1.8905582821720457,1585668050.3964574,12685344403.17166,1.4125463675852137,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.8999999999999999,UNIFORM,No,100000000,11,0.06944318990909092,1.595897183851506,0.0694378134987571,1.5957785289634188,1440137512.420231,11521100099.361849,1.2829047110356961,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.1,GAUSSIAN,No,100000000,11,0.06918715736363636,0.061469500784086584,0.06918162675337357,0.061456995625666914,1445470491.1246338,11563763928.99707,1.2876554403547549,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.2,GAUSSIAN,No,100000000,116,0.06800029886206896,4.267386694840163,0.06799465074210333,4.26781866305269,1470703929.0383246,11765631432.306597,1.3101339162613352,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.30000000000000004,GAUSSIAN,No,100000000,125,0.06609113200000001,4.378779126316022,0.06608556872558594,4.379087883539989,1513189671.034542,12105517368.276337,1.347981106608593,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.4,GAUSSIAN,No,100000000,13,0.06598744223076923,2.905408792260974,0.06598183294442984,2.9060914934637796,1515568688.190587,12124549505.524696,1.3501003850044426,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.5,GAUSSIAN,No,100000000,11,0.06511158536363637,2.8937441386991387,0.06510601251775568,2.894017446279962,1535956452.1437716,12287651617.150173,1.3682622328817806,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.6,GAUSSIAN,No,100000000,11,0.06630214227272725,2.0639152150479525,0.06629664611816406,2.0639915389969596,1508371929.1284306,12066975433.027445,1.3436893610394371,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.7,GAUSSIAN,No,100000000,11,0.06947526054545455,1.9360861800005804,0.0694698354547674,1.9363285689016476,1439473684.4469876,11515789475.575901,1.2823133591496112,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.7999999999999999,GAUSSIAN,No,100000000,11,0.07886302727272727,1.447513360543521,0.07885740245472302,1.4475332377223349,1268111767.4072042,10144894139.257633,1.129660568172039,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.8999999999999999,GAUSSIAN,No,100000000,11,0.10860243227272727,0.9435952849951393,0.10859706185080788,0.9436656652011143,920835226.0706773,7366681808.565418,0.8202993390737932,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.1,UNIQUE,Yes,,,,,,,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.2,UNIQUE,Yes,,,,,,,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.30000000000000004,UNIQUE,Yes,,,,,,,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.4,UNIQUE,Yes,,,,,,,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.5,UNIQUE,Yes,,,,,,,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.6,UNIQUE,Yes,,,,,,,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.7,UNIQUE,Yes,,,,,,,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.7999999999999999,UNIQUE,Yes,,,,,,,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.8999999999999999,UNIQUE,Yes,,,,,,,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.1,UNIFORM,Yes,,,,,,,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.2,UNIFORM,Yes,,,,,,,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.30000000000000004,UNIFORM,Yes,,,,,,,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.4,UNIFORM,Yes,,,,,,,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.5,UNIFORM,Yes,,,,,,,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.6,UNIFORM,Yes,,,,,,,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.7,UNIFORM,Yes,,,,,,,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.7999999999999999,UNIFORM,Yes,,,,,,,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.8999999999999999,UNIFORM,Yes,,,,,,,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.1,GAUSSIAN,Yes,,,,,,,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.2,GAUSSIAN,Yes,,,,,,,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.30000000000000004,GAUSSIAN,Yes,,,,,,,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.4,GAUSSIAN,Yes,,,,,,,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.5,GAUSSIAN,Yes,,,,,,,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.6,GAUSSIAN,Yes,,,,,,,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.7,GAUSSIAN,Yes,,,,,,,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.7999999999999999,GAUSSIAN,Yes,,,,,,,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.8999999999999999,GAUSSIAN,Yes,,,,,,,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.1,UNIQUE,Yes,,,,,,,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.2,UNIQUE,Yes,,,,,,,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.30000000000000004,UNIQUE,Yes,,,,,,,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.4,UNIQUE,Yes,,,,,,,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.5,UNIQUE,Yes,,,,,,,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.6,UNIQUE,Yes,,,,,,,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.7,UNIQUE,Yes,,,,,,,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.7999999999999999,UNIQUE,Yes,,,,,,,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.8999999999999999,UNIQUE,Yes,,,,,,,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.1,UNIFORM,Yes,,,,,,,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.2,UNIFORM,Yes,,,,,,,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.30000000000000004,UNIFORM,Yes,,,,,,,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.4,UNIFORM,Yes,,,,,,,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.5,UNIFORM,Yes,,,,,,,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.6,UNIFORM,Yes,,,,,,,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.7,UNIFORM,Yes,,,,,,,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.7999999999999999,UNIFORM,Yes,,,,,,,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.8999999999999999,UNIFORM,Yes,,,,,,,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.1,GAUSSIAN,Yes,,,,,,,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.2,GAUSSIAN,Yes,,,,,,,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.30000000000000004,GAUSSIAN,Yes,,,,,,,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.4,GAUSSIAN,Yes,,,,,,,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.5,GAUSSIAN,Yes,,,,,,,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.6,GAUSSIAN,Yes,,,,,,,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.7,GAUSSIAN,Yes,,,,,,,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.7999999999999999,GAUSSIAN,Yes,,,,,,,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.8999999999999999,GAUSSIAN,Yes,,,,,,,,,,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.1,UNIQUE,No,100000000,11,0.10272163090909091,0.007901706139763768,0.10271557894620029,0.005636848523492517,973562151.1940011,15576994419.104017,1.73453918043401,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.2,UNIQUE,No,100000000,11,0.08642251836363637,0.11914963674706795,0.08641675567626954,0.11904763743988307,1157182993.2451456,18514927891.92233,2.061685777588985,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.30000000000000004,UNIQUE,No,100000000,95,0.08274778317894739,4.567495805865352,0.08274228266665812,4.567790316344297,1208571926.9175549,19337150830.680878,2.1532424581626906,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.4,UNIQUE,No,100000000,104,0.0794820623846154,6.894892224556622,0.07947637161841757,6.89526905847464,1258235598.3753335,20131769574.005337,2.2417253391806824,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.5,UNIQUE,No,100000000,117,0.07611457395726492,6.427858176993963,0.07610873863024588,6.428590728356416,1313909569.3836615,21022553110.138584,2.340916422077504,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.6,UNIQUE,No,100000000,120,0.07420878082500003,5.4233062571217605,0.0742031447092692,5.42370924917713,1347651779.3390548,21562428469.424877,2.401032959198715,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.7,UNIQUE,No,100000000,121,0.07296982550413224,4.743677026026256,0.07296432463590766,4.744092242396731,1370532798.035211,21928524768.563377,2.441798742223509,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.7999999999999999,UNIQUE,No,100000000,122,0.07423772589344262,4.010078132167207,0.07423222763811954,4.010337317376634,1347123792.2091975,21553980675.34716,2.4000922751731713,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.8999999999999999,UNIQUE,No,100000000,111,0.08676809918018015,3.19288219896168,0.08676253282701644,3.1929441156217493,1152571239.4701047,18441139831.521675,2.0534692835485044,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.1,UNIFORM,No,100000000,11,0.1027204400909091,0.009979879569648986,0.10271539375998757,0.010028980644691652,973563906.4351681,15577022502.96269,1.7345423076453252,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.2,UNIFORM,No,100000000,11,0.08625520954545456,0.12258977992327307,0.08624947287819602,0.12261670275207807,1159427375.7617378,18550838012.187805,2.0656844636576,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.30000000000000004,UNIFORM,No,100000000,96,0.08251492306250001,4.623007667563823,0.08250930134455364,4.623304981912717,1211984568.6537364,19391753098.45978,2.159322563878521,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.4,UNIFORM,No,100000000,104,0.0792717475673077,6.8689552100971145,0.07926630364931544,6.869376130645017,1261570117.3907788,20185121878.25246,2.247666258179124,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.5,UNIFORM,No,100000000,116,0.07606569962931033,6.412398265513415,0.07605978018662021,6.413134350951612,1314755311.60674,21036084985.70784,2.342423231910526,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.6,UNIFORM,No,100000000,117,0.07427007210256407,5.325975314764115,0.07426438336494641,5.326818631360499,1346540501.2330725,21544648019.72916,2.3990530594944994,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.7,UNIFORM,No,100000000,121,0.07303455254545453,4.675881339455694,0.07302880266678234,4.676053091266515,1369322737.7187397,21909163803.499836,2.4396428479880625,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.7999999999999999,UNIFORM,No,100000000,122,0.07441630621311475,4.004785027010419,0.07441059669119408,4.004994967160809,1343894612.4165971,21502313798.665554,2.3943390329543135,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.8999999999999999,UNIFORM,No,100000000,111,0.08739033998198202,3.1014807683820287,0.08738475593360691,3.1014415896804164,1144364356.5930183,18309829705.488293,2.0388475566437756,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.1,GAUSSIAN,No,100000000,11,0.10810922845454547,0.009741787450570128,0.10810405245694246,0.009828004378829289,925034702.4671413,14800555239.47426,1.6480806415107279,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.2,GAUSSIAN,No,100000000,11,0.07846991345454546,0.09799187009818086,0.07846344063498757,0.1009385017869323,1274478906.2360985,20391662499.777576,2.270665098054623,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.30000000000000004,GAUSSIAN,No,100000000,11,0.0776798849090909,2.511094069046884,0.0776744946566495,2.511194651039138,1287423889.1677074,20598782226.68332,2.293728422833002,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.4,GAUSSIAN,No,100000000,109,0.07629825366055043,5.509840244736379,0.07629260372896804,5.510217672529852,1310743048.634875,20971888778.158,2.3352748158403562,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.5,GAUSSIAN,No,100000000,115,0.07667099108695652,5.536668533565466,0.07666499209196671,5.536326750104177,1304376316.6379879,20870021066.207806,2.3239315789587867,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.6,GAUSSIAN,No,100000000,114,0.08014078842105259,4.492770484498389,0.08013523985210219,4.49305425984432,1247890443.512246,19966247096.195934,2.223293977181168,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.7,GAUSSIAN,No,100000000,108,0.0881575398703704,3.5359159690407287,0.08815208314966269,3.5360142465180635,1134403140.8789532,18150450254.06325,2.021100236742719,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.7999999999999999,GAUSSIAN,No,100000000,11,0.10628041909090907,2.769544136735993,0.10627425731312146,2.7694711497269293,940961645.1645926,15055386322.633482,1.6764567509346362,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.8999999999999999,GAUSSIAN,No,100000000,11,0.15861997845454548,1.8576216787069537,0.15861453108354048,1.8575564098739714,630459260.6797868,10087348170.876589,1.1232526736740787,,
+nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.8,,No,100000000,11,0.0675892200909091,1.5115737066163348,0.06758353493430398,1.5118362381697914,1479650333.4311697,11837202667.449358,1.3181035609955545,2^0,1
+nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.8,,No,100000000,11,0.0731588810909091,1.700614731775095,0.07315316425670276,1.701025226912277,1366994866.4023423,10935958931.218739,1.217747707385211,2^1,2
+nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.8,,No,100000000,11,0.08682020954545457,1.334484686765813,0.08681472154097122,1.3346523490294244,1151878370.6840105,9215026965.472084,1.0261174197227858,2^2,4
+nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.8,,No,100000000,11,0.11502161018181818,1.068848966854489,0.11501605293967508,1.0687683271719923,869443851.046159,6955550808.369272,0.7745188239792609,2^3,8
+nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.8,,No,100000000,11,0.17184204172727277,0.7761776813538659,0.17183678921786225,0.7762235047043814,581947558.8153337,4655580470.52267,0.518411095010809,2^4,16
+nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.8,,No,100000000,11,0.2828323630909091,0.4670414040414736,0.2828274952281605,0.4670017593190509,353572413.1747826,2828579305.3982606,0.3149697238230318,2^5,32
+nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.8,,No,100000000,11,0.5026626956363637,0.17830799120425211,0.5026588883833452,0.1782977450350701,198942070.47968587,1591536563.837487,0.17722177031043854,2^6,64
+nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.8,,No,100000000,11,0.9214143747272728,0.12028721108259548,0.9214118596857243,0.12032417365194858,108529100.15083598,868232801.2066878,0.09667999942171107,2^7,128
+nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.8,,No,100000000,9,1.7659442706666668,0.07812563688582005,1.7659443088107638,0.07810448771827107,56626927.305166714,453015418.4413337,0.05044445491124458,2^8,256
+nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.8,,Yes,,,,,,,,,,2^0,1
+nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.8,,Yes,,,,,,,,,,2^1,2
+nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.8,,Yes,,,,,,,,,,2^2,4
+nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.8,,Yes,,,,,,,,,,2^3,8
+nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.8,,Yes,,,,,,,,,,2^4,16
+nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.8,,Yes,,,,,,,,,,2^5,32
+nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.8,,Yes,,,,,,,,,,2^6,64
+nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.8,,Yes,,,,,,,,,,2^7,128
+nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.8,,Yes,,,,,,,,,,2^8,256
+nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.8,,Yes,,,,,,,,,,2^0,1
+nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.8,,Yes,,,,,,,,,,2^1,2
+nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.8,,Yes,,,,,,,,,,2^2,4
+nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.8,,Yes,,,,,,,,,,2^3,8
+nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.8,,Yes,,,,,,,,,,2^4,16
+nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.8,,Yes,,,,,,,,,,2^5,32
+nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.8,,Yes,,,,,,,,,,2^6,64
+nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.8,,Yes,,,,,,,,,,2^7,128
+nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.8,,Yes,,,,,,,,,,2^8,256
+nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.8,,No,100000000,117,0.08358124586324789,3.4073378500526617,0.08357501162015475,3.4073037626511162,1196529896.4539328,19144478343.262924,2.131787871390274,2^0,1
+nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.8,,No,100000000,27,0.09553329222222219,2.9961068371423005,0.09552717703360099,2.99579486522814,1046822518.0026594,16749160288.042551,1.8650629240355248,2^1,2
+nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.8,,No,100000000,11,0.12117426200000003,2.607019263443277,0.12116880243474787,2.6070095342757837,825294943.8354996,13204719101.367994,1.4703801023294962,2^2,4
+nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.8,,No,100000000,11,0.17426779272727275,1.5797872197789737,0.17426227361505678,1.5795689435612117,573847671.8196548,9181562749.114477,1.0223910914688832,2^3,8
+nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.8,,No,100000000,11,0.2812710991818182,0.6432035096529316,0.2812656194513494,0.6431494212328667,355535810.5802797,5688572969.284475,0.6334375188502703,2^4,16
+nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.8,,No,100000000,11,0.4875445267272728,0.2664081025716232,0.48753980324485086,0.2663646205828964,205111458.25313935,3281783332.0502295,0.36543518075317016,2^5,32
+nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.8,,No,100000000,11,0.8979136936363638,0.1329969797576101,0.8979087468927559,0.1328683157915315,111369891.81367643,1781918269.018823,0.1984212724730552,2^6,64
+nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.8,,No,100000000,9,1.6986642724444445,0.10934703880104671,1.6986634250217016,0.10937900951870727,58869814.07086129,941917025.1337806,0.10488493099854136,2^7,128
+nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.8,,No,100000000,5,3.2878928112,0.048951349895341265,3.287897314453125,0.048955973451362045,30414575.16340743,486633202.6145189,0.0541878833441552,2^8,256
diff --git a/benchmarks/analysis/notebooks/StaticMultimap.ipynb b/benchmarks/analysis/notebooks/StaticMultimap.ipynb
index d7d058811..bc2c6051a 100644
--- a/benchmarks/analysis/notebooks/StaticMultimap.ipynb
+++ b/benchmarks/analysis/notebooks/StaticMultimap.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "vocal-glass",
+   "id": "exceptional-dining",
    "metadata": {},
    "source": [
     "# Prep"
@@ -11,86 +11,104 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "manufactured-kitty",
+   "id": "radio-fundamental",
    "metadata": {},
    "outputs": [],
    "source": [
     "# !pip install pandas\n",
     "# !pip install matplotlib\n",
     "\n",
-    "\n",
     "# Import libraries\n",
     "import pandas as pd\n",
-    "import matplotlib\n",
     "\n",
-    "# Define the data path\n",
-    "datafile='../data/static-multimap-data.csv'"
+    "from Utils import *"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "rising-inspector",
+   "id": "cleared-delight",
    "metadata": {},
    "source": [
-    "# Import Data"
+    "## Global Parameters"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "editorial-figure",
+   "id": "external-cleaning",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Read csv file\n",
-    "rawdf = pd.read_csv(datafile)\n",
-    "\n",
-    "# Filter out unrelated data\n",
-    "perfdf = rawdf[rawdf[\"Key\"] == rawdf[\"Value\"]].reset_index(drop=True)\n",
-    "perfdf.drop(columns=['Device', 'Device Name', 'Skipped'], inplace=True)\n",
+    "# Define the data path\n",
+    "datafile = '../data/static-multimap-data.csv'\n",
     "\n",
-    "# Drop NANs\n",
-    "perfdf.dropna(inplace=True)"
+    "output_keys = ['Benchmark', 'Label', 'Distribution', 'NumReps', 'NumInputs', \\\n",
+    "               'Occupancy', 'GPU Time (sec)', 'Elem/s (elem/sec)', 'Bandwidth (GB/s)']"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "amber-diploma",
+   "id": "removed-period",
    "metadata": {},
    "source": [
-    "# Comput Performance"
+    "# Import Data"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "marine-floating",
+   "id": "professional-jurisdiction",
    "metadata": {},
    "outputs": [],
    "source": [
-    "perfdf['Performance'] = perfdf['NumInputs'] / perfdf['GPU Time (sec)']\n",
-    "#display(perfdf)"
+    "# Read csv file\n",
+    "rawdf = pd.read_csv(datafile)\n",
+    "\n",
+    "# Filter out skipped tests\n",
+    "perfdf = rawdf[rawdf[\"Key\"] == rawdf[\"Value\"]].reset_index(drop=True)\n",
+    "\n",
+    "# Add labels\n",
+    "perfdf.loc[perfdf['NumReps'].isnull(), 'Label'] = perfdf[\"Key\"] + \" \" + perfdf[\"Distribution\"]\n",
+    "perfdf.loc[perfdf['Distribution'].isnull(), 'Label'] = perfdf[\"Key\"]\n",
+    "\n",
+    "perfdf[\"Bandwidth (GB/s)\"] = perfdf[\"GlobalMem BW (bytes/sec)\"] / (1000 * 1000 * 1000)\n",
+    "\n",
+    "# Trim data frame for visualization\n",
+    "perfdf = perfdf[output_keys]\n",
+    "#perfdf"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "published-designation",
+   "id": "faced-belarus",
    "metadata": {},
    "source": [
-    "# Visualize Performance"
+    "# Visualization"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "alike-growing",
+   "id": "imported-sharing",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 720x360 with 2 Axes>"
       ]
      },
      "metadata": {
@@ -100,7 +118,20 @@
     }
    ],
    "source": [
-    "perfdf.plot(x=\"Occupancy\", y=\"Performance\");"
+    "unique_bms = perfdf[\"Benchmark\"].unique()\n",
+    "num_bm   = len(unique_bms)\n",
+    "    \n",
+    "for bindex, bm in enumerate(unique_bms):\n",
+    "    tmpdf = perfdf[perfdf['Benchmark'].str.contains(bm)]\n",
+    "    \n",
+    "    unique_labels = tmpdf[\"Label\"].unique()\n",
+    "    num_labels = len(unique_labels)\n",
+    "    \n",
+    "    flag = 'multi_insert' in bm\n",
+    "    if (flag):\n",
+    "        plot_insert(bm, tmpdf, \"NumReps\", unique_labels, flag)\n",
+    "    else:\n",
+    "        plot_insert(bm, tmpdf, \"Occupancy\", unique_labels)"
    ]
   }
  ],
diff --git a/benchmarks/analysis/notebooks/Utils.py b/benchmarks/analysis/notebooks/Utils.py
new file mode 100644
index 000000000..c8a7c8879
--- /dev/null
+++ b/benchmarks/analysis/notebooks/Utils.py
@@ -0,0 +1,65 @@
+# Import libraries
+import pandas as pd
+import matplotlib.pyplot as plt
+import matplotlib
+
+# Global parameters
+colors = ['b','r','g','m','y','c']
+styles = ['o','s','v','^','D',">"]
+
+def plot_insert(bm, df, s, unique_labels, flag = False):
+    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5))
+    fig.suptitle(bm)
+    
+    marker_handles = []
+    
+    ax1.set_xlabel(s)
+    ax1.set_ylabel('Performance (number of operations per second)')
+
+    ax2.set_xlabel(s)
+    ax2.set_ylabel('Bandwidth (GB/s)')
+    
+    lax = [ax1, ax2]
+    if flag:
+        lnum = list(df[s])
+        
+        for item in lax:
+            item.set_xscale('log')
+            item.set_xticks(lnum)
+            item.get_xaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter())
+        
+    num_dist = len(df["Distribution"].unique())
+
+    for lindex, lbl in enumerate(unique_labels):
+        tmpdf = df.loc[df['Label'] == lbl]
+
+        x = tmpdf[s]
+        perf = tmpdf["Elem/s (elem/sec)"]
+        bw   = tmpdf['Bandwidth (GB/s)']
+
+        # Get distribution & type index
+        did = lindex % num_dist
+        tid = int(lindex / num_dist)
+
+        if not tid:
+            ax1.plot(x, perf, color=colors[did])
+            ax1.scatter(x, perf, color=colors[did], marker=styles[did])
+
+            ax2.plot(x, bw, color=colors[did])
+            ax2.scatter(x, bw, color=colors[did], marker=styles[did])
+
+            # Add legend
+            marker_handles.append(ax1.plot([], [], c=colors[did], marker=styles[did], \
+                                          label=lbl)[0])
+        else:
+            ax1.plot(x, perf, color=colors[did], linestyle="--")
+            ax1.scatter(x, perf, color=colors[did], marker=styles[did], facecolors='none')
+
+            ax2.plot(x, bw, color=colors[did], linestyle="--")
+            ax2.scatter(x, bw, color=colors[did], marker=styles[did], facecolors='none')
+
+            # Add legend
+            marker_handles.append(ax1.plot([], [], c=colors[did], marker=styles[did], \
+                                          mfc='none', linestyle="--", label=lbl)[0])
+    
+    leg = plt.legend(handles = marker_handles, loc="lower left", ncol=2, frameon=False)

From cdd07acef07eb3d604c17fb3ff8f6a03e40d5690 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Thu, 18 Mar 2021 19:43:51 -0700
Subject: [PATCH 037/267] Add find & find_all benchmarks for staic multimap

---
 .../hash_table/static_multimap_bench.cu       | 167 ++++++++++++++++--
 .../cuco/detail/static_multimap_kernels.cuh   |   6 +-
 2 files changed, 159 insertions(+), 14 deletions(-)

diff --git a/benchmarks/hash_table/static_multimap_bench.cu b/benchmarks/hash_table/static_multimap_bench.cu
index d590e610f..ca7c63950 100644
--- a/benchmarks/hash_table/static_multimap_bench.cu
+++ b/benchmarks/hash_table/static_multimap_bench.cu
@@ -49,7 +49,7 @@ static void generate_keys(OutputIt output_begin, OutputIt output_end, const std:
 }
 
 template <typename Key, typename OutputIt>
-static void generate_multikeys(OutputIt output_begin, OutputIt output_end, const size_t num_reps)
+static void generate_multikeys(OutputIt output_begin, OutputIt output_end, size_t const num_reps)
 {
   auto num_keys = std::distance(output_begin, output_end);
 
@@ -116,10 +116,10 @@ void nvbench_static_multimap_single_insert(nvbench::state& state, nvbench::type_
     return;
   }
 
-  const std::size_t num_keys = state.get_int64("NumInputs");
-  auto occupancy             = state.get_float64("Occupancy");
-  std::size_t size           = num_keys / occupancy;
-  const auto dist            = state.get_string("Distribution");
+  std::size_t const num_keys = state.get_int64("NumInputs");
+  auto const occupancy       = state.get_float64("Occupancy");
+  std::size_t const size     = num_keys / occupancy;
+  auto const dist            = state.get_string("Distribution");
 
   std::vector<Key> h_keys(num_keys);
 
@@ -136,19 +136,151 @@ void nvbench_static_multimap_multi_insert(nvbench::state& state, nvbench::type_l
     return;
   }
 
-  using map_type = cuco::static_multimap<Key, Value>;
+  std::size_t const num_keys = state.get_int64("NumInputs");
+  auto const occupancy       = state.get_float64("Occupancy");
+  std::size_t const size     = num_keys / occupancy;
+  std::size_t const num_reps = state.get_int64("NumReps");
 
-  const std::size_t num_keys = state.get_int64("NumInputs");
-  auto occupancy             = state.get_float64("Occupancy");
-  const std::size_t size     = num_keys / occupancy;
-  const std::size_t num_reps = state.get_int64("NumReps");
+  std::vector<Key> h_keys(num_keys);
+
+  generate_multikeys<Key>(h_keys.begin(), h_keys.end(), num_reps);
+
+  launch_nvbench_insert<Key, Value>(state, h_keys, num_keys, size);
+}
+
+template <typename Key, typename Value>
+void nvbench_static_multimap_find(nvbench::state& state, nvbench::type_list<Key, Value>)
+{
+  if (not std::is_same<Key, Value>::value) {
+    state.skip("Key should be the same type as Value.");
+    return;
+  }
+
+  std::size_t const num_keys = state.get_int64("NumInputs");
+  auto const occupancy       = state.get_float64("Occupancy");
+  std::size_t const size     = num_keys / occupancy;
+
+  std::vector<Key> h_keys(num_keys);
+  std::vector<cuco::pair_type<Key, Value>> h_pairs(num_keys);
+
+  generate_keys<Key>(h_keys.begin(), h_keys.end(), "UNIFORM");
+
+  for (auto i = 0; i < num_keys; ++i) {
+    Key key           = h_keys[i];
+    Value val         = h_keys[i];
+    h_pairs[i].first  = key;
+    h_pairs[i].second = val;
+  }
+
+  thrust::device_vector<Key> d_keys(h_keys);
+  thrust::device_vector<Value> d_results(num_keys);
+  thrust::device_vector<cuco::pair_type<Key, Value>> d_pairs(h_pairs);
+
+  state.add_element_count(num_keys, "NumKeys");
+  state.add_global_memory_writes<Key>(num_keys * 2);
+
+  state.exec(nvbench::exec_tag::sync | nvbench::exec_tag::timer,
+             [&](nvbench::launch& launch, auto& timer) {
+               cuco::static_multimap<Key, Value> map{size, -1, -1};
+               map.insert(d_pairs.begin(), d_pairs.end());
+
+               auto view = map.get_device_view();
+
+               auto const block_size = 128;
+               auto const stride     = 1;
+               auto const tile_size  = 4;
+               auto const grid_size =
+                 (tile_size * num_keys + stride * block_size - 1) / (stride * block_size);
+
+               using Hash     = cuco::detail::MurmurHash3_32<Key>;
+               using KeyEqual = thrust::equal_to<Key>;
+
+               Hash hash;
+               KeyEqual key_equal;
+
+               timer.start();
+               cuco::detail::find<block_size, tile_size, Value>
+                 <<<grid_size, block_size, 0, launch.get_stream()>>>(
+                   d_keys.begin(), d_keys.end(), d_results.begin(), view, hash, key_equal);
+               CUCO_CUDA_TRY(cudaDeviceSynchronize());
+               timer.stop();
+             });
+}
+
+template <typename Key, typename Value>
+void nvbench_static_multimap_find_all(nvbench::state& state, nvbench::type_list<Key, Value>)
+{
+  if (not std::is_same<Key, Value>::value) {
+    state.skip("Key should be the same type as Value.");
+    return;
+  }
+
+  std::size_t const num_keys = state.get_int64("NumInputs");
+  auto const occupancy       = state.get_float64("Occupancy");
+  std::size_t const size     = num_keys / occupancy;
+  std::size_t const num_reps = state.get_int64("NumReps");
 
   std::vector<Key> h_keys(num_keys);
   std::vector<cuco::pair_type<Key, Value>> h_pairs(num_keys);
 
   generate_multikeys<Key>(h_keys.begin(), h_keys.end(), num_reps);
 
-  launch_nvbench_insert<Key, Value>(state, h_keys, num_keys, size);
+  for (auto i = 0; i < num_keys; ++i) {
+    Key key           = h_keys[i];
+    Value val         = h_keys[i];
+    h_pairs[i].first  = key;
+    h_pairs[i].second = val;
+
+    h_keys[i] = i;
+  }
+
+  thrust::device_vector<Key> d_keys(h_keys);
+  thrust::device_vector<cuco::pair_type<Key, Value>> d_results(num_keys);
+  thrust::device_vector<cuco::pair_type<Key, Value>> d_pairs(h_pairs);
+
+  state.add_element_count(num_keys, "NumKeys");
+  state.add_global_memory_writes<Key>(num_keys * 2);
+
+  state.exec(nvbench::exec_tag::sync | nvbench::exec_tag::timer,
+             [&](nvbench::launch& launch, auto& timer) {
+               cuco::static_multimap<Key, Value> map{size, -1, -1};
+               map.insert(d_pairs.begin(), d_pairs.end());
+
+               auto view = map.get_device_view();
+
+               auto const block_size = 128;
+               auto const stride     = 1;
+               auto const tile_size  = 4;
+               auto const grid_size =
+                 (tile_size * num_keys + stride * block_size - 1) / (stride * block_size);
+
+               using Hash     = cuco::detail::MurmurHash3_32<Key>;
+               using KeyEqual = thrust::equal_to<Key>;
+
+               Hash hash;
+               KeyEqual key_equal;
+
+               using atomic_ctr_type = typename cuco::static_multimap<Key, Value>::atomic_ctr_type;
+               atomic_ctr_type* num_items;
+               CUCO_CUDA_TRY(cudaMallocManaged(&num_items, sizeof(atomic_ctr_type)));
+               *num_items = 0;
+               int device_id;
+               CUCO_CUDA_TRY(cudaGetDevice(&device_id));
+               CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_items, sizeof(atomic_ctr_type), device_id));
+
+               timer.start();
+               cuco::detail::find_all<block_size, tile_size, Key, Value>
+                 <<<grid_size, block_size, 0, launch.get_stream()>>>(d_keys.begin(),
+                                                                     d_keys.end(),
+                                                                     d_results.begin(),
+                                                                     d_results.end(),
+                                                                     num_items,
+                                                                     view,
+                                                                     hash,
+                                                                     key_equal);
+               CUCO_CUDA_TRY(cudaDeviceSynchronize());
+               timer.stop();
+             });
 }
 
 using key_type   = nvbench::type_list<nvbench::int32_t, nvbench::int64_t>;
@@ -167,3 +299,16 @@ NVBENCH_BENCH_TYPES(nvbench_static_multimap_multi_insert, NVBENCH_TYPE_AXES(key_
   .add_int64_axis("NumInputs", {100'000'000})  // Total number of key/value pairs: 100'000'000
   .add_float64_axis("Occupancy", {0.8})
   .add_int64_power_of_two_axis("NumReps", nvbench::range(0, 8, 1));
+
+NVBENCH_BENCH_TYPES(nvbench_static_multimap_find, NVBENCH_TYPE_AXES(key_type, value_type))
+  .set_type_axes_names({"Key", "Value"})
+  .set_max_noise(3)                            // Custom timeout: 3%. By default: 0.5%.
+  .add_int64_axis("NumInputs", {100'000'000})  // Total number of key/value pairs: 100'000'000
+  .add_float64_axis("Occupancy", nvbench::range(0.1, 0.9, 0.1));
+
+NVBENCH_BENCH_TYPES(nvbench_static_multimap_find_all, NVBENCH_TYPE_AXES(key_type, value_type))
+  .set_type_axes_names({"Key", "Value"})
+  .set_max_noise(3)                            // Custom timeout: 3%. By default: 0.5%.
+  .add_int64_axis("NumInputs", {100'000'000})  // Total number of key/value pairs: 100'000'000
+  .add_float64_axis("Occupancy", {0.4})
+  .add_int64_power_of_two_axis("NumReps", nvbench::range(0, 8, 1));
diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap_kernels.cuh
index 5348f7b55..d9526d3a0 100644
--- a/include/cuco/detail/static_multimap_kernels.cuh
+++ b/include/cuco/detail/static_multimap_kernels.cuh
@@ -478,11 +478,11 @@ __global__ void find_all(InputIt first,
   auto key_idx = tid / tile_size;
 
   while (first + key_idx < last) {
-    auto key     = *(first + key_idx);
-    auto found   = view.find_all(tile, key, hash, key_equal);
-    size_t count = 0;
+    auto key   = *(first + key_idx);
+    auto found = view.find_all(tile, key, hash, key_equal);
 
     if (tile.thread_rank() == 0) {
+      size_t count = 0;
       while (found != view.end()) {
         size_t index            = (*num_items)++;
         *(output_begin + index) = cuco::make_pair<Key, Value>(key, (*found).second);

From 7a2921db22948e4e4f797f573fcf2820be04c93f Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Fri, 19 Mar 2021 13:35:51 -0700
Subject: [PATCH 038/267] Fix the warp illegal address bug in find_all
 benchmark + adjust find_all API

---
 .../hash_table/static_multimap_bench.cu       | 86 +++++++++----------
 include/cuco/detail/static_multimap.inl       | 13 ++-
 .../cuco/detail/static_multimap_kernels.cuh   | 16 ++--
 include/cuco/static_multimap.cuh              | 15 ++--
 4 files changed, 59 insertions(+), 71 deletions(-)

diff --git a/benchmarks/hash_table/static_multimap_bench.cu b/benchmarks/hash_table/static_multimap_bench.cu
index ca7c63950..a12a425ed 100644
--- a/benchmarks/hash_table/static_multimap_bench.cu
+++ b/benchmarks/hash_table/static_multimap_bench.cu
@@ -235,52 +235,47 @@ void nvbench_static_multimap_find_all(nvbench::state& state, nvbench::type_list<
   }
 
   thrust::device_vector<Key> d_keys(h_keys);
-  thrust::device_vector<cuco::pair_type<Key, Value>> d_results(num_keys);
+  thrust::device_vector<cuco::pair_type<Key, Value>> d_results(2 * num_keys);
   thrust::device_vector<cuco::pair_type<Key, Value>> d_pairs(h_pairs);
 
   state.add_element_count(num_keys, "NumKeys");
   state.add_global_memory_writes<Key>(num_keys * 2);
 
-  state.exec(nvbench::exec_tag::sync | nvbench::exec_tag::timer,
-             [&](nvbench::launch& launch, auto& timer) {
-               cuco::static_multimap<Key, Value> map{size, -1, -1};
-               map.insert(d_pairs.begin(), d_pairs.end());
-
-               auto view = map.get_device_view();
-
-               auto const block_size = 128;
-               auto const stride     = 1;
-               auto const tile_size  = 4;
-               auto const grid_size =
-                 (tile_size * num_keys + stride * block_size - 1) / (stride * block_size);
-
-               using Hash     = cuco::detail::MurmurHash3_32<Key>;
-               using KeyEqual = thrust::equal_to<Key>;
-
-               Hash hash;
-               KeyEqual key_equal;
-
-               using atomic_ctr_type = typename cuco::static_multimap<Key, Value>::atomic_ctr_type;
-               atomic_ctr_type* num_items;
-               CUCO_CUDA_TRY(cudaMallocManaged(&num_items, sizeof(atomic_ctr_type)));
-               *num_items = 0;
-               int device_id;
-               CUCO_CUDA_TRY(cudaGetDevice(&device_id));
-               CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_items, sizeof(atomic_ctr_type), device_id));
-
-               timer.start();
-               cuco::detail::find_all<block_size, tile_size, Key, Value>
-                 <<<grid_size, block_size, 0, launch.get_stream()>>>(d_keys.begin(),
-                                                                     d_keys.end(),
-                                                                     d_results.begin(),
-                                                                     d_results.end(),
-                                                                     num_items,
-                                                                     view,
-                                                                     hash,
-                                                                     key_equal);
-               CUCO_CUDA_TRY(cudaDeviceSynchronize());
-               timer.stop();
-             });
+  state.exec(
+    nvbench::exec_tag::sync | nvbench::exec_tag::timer, [&](nvbench::launch& launch, auto& timer) {
+      cuco::static_multimap<Key, Value> map{size, -1, -1};
+      map.insert(d_pairs.begin(), d_pairs.end());
+
+      auto view = map.get_device_view();
+
+      auto const block_size = 128;
+      auto const stride     = 1;
+      auto const tile_size  = 4;
+      auto const grid_size =
+        (tile_size * num_keys + stride * block_size - 1) / (stride * block_size);
+
+      using Hash     = cuco::detail::MurmurHash3_32<Key>;
+      using KeyEqual = thrust::equal_to<Key>;
+
+      Hash hash;
+      KeyEqual key_equal;
+
+      using atomic_ctr_type = typename cuco::static_multimap<Key, Value>::atomic_ctr_type;
+      atomic_ctr_type* num_items;
+      CUCO_CUDA_TRY(cudaMallocManaged(&num_items, sizeof(atomic_ctr_type)));
+      *num_items = 0;
+      int device_id;
+      CUCO_CUDA_TRY(cudaGetDevice(&device_id));
+      CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_items, sizeof(atomic_ctr_type), device_id));
+
+      timer.start();
+      cuco::detail::find_all<block_size, tile_size, Key, Value>
+        <<<grid_size, block_size, 0, launch.get_stream()>>>(
+          d_keys.begin(), d_keys.end(), d_results.begin(), num_items, view, hash, key_equal);
+      CUCO_CUDA_TRY(cudaDeviceSynchronize());
+      timer.stop();
+      std::cout << "Output size: " << *num_items << std::endl;
+    });
 }
 
 using key_type   = nvbench::type_list<nvbench::int32_t, nvbench::int64_t>;
@@ -288,27 +283,28 @@ using value_type = nvbench::type_list<nvbench::int32_t, nvbench::int64_t>;
 
 NVBENCH_BENCH_TYPES(nvbench_static_multimap_single_insert, NVBENCH_TYPE_AXES(key_type, value_type))
   .set_type_axes_names({"Key", "Value"})
-  .set_max_noise(3)                            // Custom timeout: 3%. By default: 0.5%.
+  .set_max_noise(3)                            // Custom noise: 3%. By default: 0.5%.
   .add_int64_axis("NumInputs", {100'000'000})  // Total number of key/value pairs: 100'000'000
   .add_float64_axis("Occupancy", nvbench::range(0.1, 0.9, 0.1))
   .add_string_axis("Distribution", {"UNIQUE", "UNIFORM", "GAUSSIAN"});
 
 NVBENCH_BENCH_TYPES(nvbench_static_multimap_multi_insert, NVBENCH_TYPE_AXES(key_type, value_type))
   .set_type_axes_names({"Key", "Value"})
-  .set_max_noise(3)                            // Custom timeout: 3%. By default: 0.5%.
+  .set_max_noise(3)                            // Custom noise: 3%. By default: 0.5%.
   .add_int64_axis("NumInputs", {100'000'000})  // Total number of key/value pairs: 100'000'000
   .add_float64_axis("Occupancy", {0.8})
   .add_int64_power_of_two_axis("NumReps", nvbench::range(0, 8, 1));
 
 NVBENCH_BENCH_TYPES(nvbench_static_multimap_find, NVBENCH_TYPE_AXES(key_type, value_type))
   .set_type_axes_names({"Key", "Value"})
-  .set_max_noise(3)                            // Custom timeout: 3%. By default: 0.5%.
+  .set_max_noise(3)                            // Custom noise: 3%. By default: 0.5%.
   .add_int64_axis("NumInputs", {100'000'000})  // Total number of key/value pairs: 100'000'000
   .add_float64_axis("Occupancy", nvbench::range(0.1, 0.9, 0.1));
 
 NVBENCH_BENCH_TYPES(nvbench_static_multimap_find_all, NVBENCH_TYPE_AXES(key_type, value_type))
   .set_type_axes_names({"Key", "Value"})
-  .set_max_noise(3)                            // Custom timeout: 3%. By default: 0.5%.
+  .set_timeout(100)                            // Custom timeout: 100 s. Default is 15 s.
+  .set_max_noise(3)                            // Custom noise: 3%. By default: 0.5%.
   .add_int64_axis("NumInputs", {100'000'000})  // Total number of key/value pairs: 100'000'000
   .add_float64_axis("Occupancy", {0.4})
   .add_int64_power_of_two_axis("NumReps", nvbench::range(0, 8, 1));
diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index 0cdb4e02a..8cfcdc2c5 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -106,12 +106,8 @@ void static_multimap<Key, Value, Scope, Allocator>::contains(
 
 template <typename Key, typename Value, cuda::thread_scope Scope, typename Allocator>
 template <typename InputIt, typename OutputIt, typename Hash, typename KeyEqual>
-void static_multimap<Key, Value, Scope, Allocator>::find_all(InputIt first,
-                                                             InputIt last,
-                                                             OutputIt output_begin,
-                                                             OutputIt output_end,
-                                                             Hash hash,
-                                                             KeyEqual key_equal) noexcept
+OutputIt static_multimap<Key, Value, Scope, Allocator>::find_all(
+  InputIt first, InputIt last, OutputIt output_begin, Hash hash, KeyEqual key_equal) noexcept
 {
   auto num_keys         = std::distance(first, last);
   auto const block_size = 128;
@@ -127,9 +123,10 @@ void static_multimap<Key, Value, Scope, Allocator>::find_all(InputIt first,
   CUCO_CUDA_TRY(cudaGetDevice(&device_id));
   CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_items, sizeof(atomic_ctr_type), device_id));
 
-  detail::find_all<block_size, tile_size, Key, Value><<<grid_size, block_size>>>(
-    first, last, output_begin, output_end, num_items, view, hash, key_equal);
+  detail::find_all<block_size, tile_size, Key, Value>
+    <<<grid_size, block_size>>>(first, last, output_begin, num_items, view, hash, key_equal);
   CUCO_CUDA_TRY(cudaDeviceSynchronize());
+  return output_begin + (*num_items - 1);
 }
 
 template <typename Key, typename Value, cuda::thread_scope Scope, typename Allocator>
diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap_kernels.cuh
index d9526d3a0..7b4ea2bd7 100644
--- a/include/cuco/detail/static_multimap_kernels.cuh
+++ b/include/cuco/detail/static_multimap_kernels.cuh
@@ -356,11 +356,11 @@ __global__ void contains(
  * @brief Finds all the values corresponding to all keys in the range `[first, last)`.
  *
  * If the key `k = *(first + i)` exists in the map, copies `k` and all associated values to
- * unspecified locations in `[output_begin, output_end)`. Else, copies `k` and the empty value
- * sentinel.
+ * unspecified locations in `[output_begin, output_begin + *num_items - 1)`. Else, copies `k` and
+ * the empty value sentinel.
  *
  * Behavior is undefined if the total number of matching keys exceeds `std::distance(output_begin,
- * output_end)`. Use `count()` to determine the number of matching keys.
+ * output_begin + *num_items - 1)`. Use `count()` to determine the number of matching keys.
  *
  * @tparam block_size The size of the thread block
  * @tparam Key key type
@@ -376,7 +376,6 @@ __global__ void contains(
  * @param first Beginning of the sequence of keys
  * @param last End of the sequence of keys
  * @param output_begin Beginning of the sequence of values retrieved for each key
- * @param output_end End of the sequence of values retrieved for each key
  * @param num_items Size of the output sequence
  * @param view Device view used to access the hash map's slot storage
  * @param hash The unary function to apply to hash each key
@@ -394,7 +393,6 @@ template <uint32_t block_size,
 __global__ void find_all(InputIt first,
                          InputIt last,
                          OutputIt output_begin,
-                         OutputIt output_end,
                          atomicT* num_items,
                          viewT view,
                          Hash hash,
@@ -426,11 +424,11 @@ __global__ void find_all(InputIt first,
  * @brief Finds all the values corresponding to all keys in the range `[first, last)`.
  *
  * If the key `k = *(first + i)` exists in the map, copies `k` and all associated values to
- * unspecified locations in `[output_begin, output_end)`. Else, copies `k` and the empty value
- * sentinel.
+ * unspecified locations in `[output_begin, output_begin + *num_items - 1)`. Else, copies `k` and
+ * the empty value sentinel.
  *
  * Behavior is undefined if the total number of matching keys exceeds `std::distance(output_begin,
- * output_end)`. Use `count()` to determine the number of matching keys.
+ * output_begin + *num_items - 1)`. Use `count()` to determine the number of matching keys.
  *
  * @tparam block_size The size of the thread block
  * @tparam tile_size The number of threads in the Cooperative Groups used to perform
@@ -448,7 +446,6 @@ __global__ void find_all(InputIt first,
  * @param first Beginning of the sequence of keys
  * @param last End of the sequence of keys
  * @param output_begin Beginning of the sequence of values retrieved for each key
- * @param output_end End of the sequence of values retrieved for each key
  * @param num_items Size of the output sequence
  * @param view Device view used to access the hash map's slot storage
  * @param hash The unary function to apply to hash each key
@@ -467,7 +464,6 @@ template <uint32_t block_size,
 __global__ void find_all(InputIt first,
                          InputIt last,
                          OutputIt output_begin,
-                         OutputIt output_end,
                          atomicT* num_items,
                          viewT view,
                          Hash hash,
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index 73f65489d..85dbed1e8 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -255,21 +255,20 @@ class static_multimap {
    * @tparam KeyEqual Binary callable type
    * @param first Beginning of the sequence of keys
    * @param last End of the sequence of keys
-   * @param output_begin Beginning of the sequence of values retrieved for each key
-   * @param output_begin End of the sequence of values retrieved for each key
+   * @param output_begin Beginning of the sequence of key/value pairs retrieved for each key
    * @param hash The unary function to apply to hash each key
    * @param key_equal The binary function to compare two keys for equality
+   * @return The iterator indicating the last valid key/value pairs in the output
    */
   template <typename InputIt,
             typename OutputIt,
             typename Hash     = cuco::detail::MurmurHash3_32<key_type>,
             typename KeyEqual = thrust::equal_to<key_type>>
-  void find_all(InputIt first,
-                InputIt last,
-                OutputIt output_begin,
-                OutputIt output_end,
-                Hash hash          = Hash{},
-                KeyEqual key_equal = KeyEqual{}) noexcept;
+  OutputIt find_all(InputIt first,
+                    InputIt last,
+                    OutputIt output_begin,
+                    Hash hash          = Hash{},
+                    KeyEqual key_equal = KeyEqual{}) noexcept;
 
   /**
    * @brief Counts the occurrences of keys in `[first, last)` contained in the multimap.

From 0e815c2225170cb2e2b20dce165960ee4c21b5cc Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Fri, 19 Mar 2021 14:00:57 -0700
Subject: [PATCH 039/267] Minor correction: adjust find_all API in static
 multimap test

---
 tests/static_multimap/static_multimap_test.cu | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/static_multimap/static_multimap_test.cu b/tests/static_multimap/static_multimap_test.cu
index 22097d036..d519fed6c 100644
--- a/tests/static_multimap/static_multimap_test.cu
+++ b/tests/static_multimap/static_multimap_test.cu
@@ -202,7 +202,7 @@ TEMPLATE_TEST_CASE_SIG("Each key appears twice",
 
     REQUIRE(num == num_items);
 
-    map.find_all(d_unique_keys.begin(), d_unique_keys.end(), d_outputs.begin(), d_outputs.end());
+    auto output_end = map.find_all(d_unique_keys.begin(), d_unique_keys.end(), d_outputs.begin());
   }
 
   SECTION(

From a9287964554f79aac4fcbe674cf9bd0a8257b70f Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Mon, 22 Mar 2021 14:47:58 -0400
Subject: [PATCH 040/267] Add complete static multimap benchmark data + update
 notebook

---
 .../analysis/data/static-multimap-data.csv    | 218 ++++++++++++------
 .../analysis/notebooks/StaticMultimap.ipynb   |  38 ++-
 benchmarks/analysis/notebooks/Utils.py        |  11 +-
 3 files changed, 183 insertions(+), 84 deletions(-)

diff --git a/benchmarks/analysis/data/static-multimap-data.csv b/benchmarks/analysis/data/static-multimap-data.csv
index d4d6dcd26..d6473d6c0 100644
--- a/benchmarks/analysis/data/static-multimap-data.csv
+++ b/benchmarks/analysis/data/static-multimap-data.csv
@@ -1,31 +1,31 @@
-Benchmark,Device,Device Name,Key,Value,NumInputs,Occupancy,Distribution,Skipped,NumKeys,Samples,CPU Time (sec),Noise (%),GPU Time (sec),Noise (%),Elem/s (elem/sec),GlobalMem BW (bytes/sec),BWPeak (%),NumReps (pow2),NumReps
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.1,UNIQUE,No,100000000,11,0.06912646563636364,0.07652550388508265,0.06912139615145596,0.07915160016570952,1446730036.8309128,11573840294.647303,1.2887774700959527,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.2,UNIQUE,No,100000000,118,0.06746609788135592,4.337309557361487,0.06746028699713241,4.337529287854764,1482353610.5657358,11858828884.525887,1.3205116969834447,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.30000000000000004,UNIQUE,No,100000000,126,0.06530046633333332,4.725697765553524,0.06529489698864163,4.725337023009958,1531513251.600588,12252106012.804705,1.3643041366168296,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.4,UNIQUE,No,100000000,137,0.06370433262043793,3.579250420662472,0.06369880186206232,3.579353865055502,1569888240.8580735,12559105926.864588,1.3984893821783009,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.5,UNIQUE,No,100000000,16,0.0629443638125,2.9866222708393324,0.06293836832046508,2.9867299689762548,1588855934.282045,12710847474.25636,1.4153862014342617,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.6,UNIQUE,No,100000000,11,0.062425575272727275,2.582965979313861,0.06241978211836381,2.5839216782841734,1602056216.8957033,12816449735.165627,1.4271452901365658,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.7,UNIQUE,No,100000000,11,0.06241535327272727,2.1984232060706717,0.062409914190118966,2.1984004784526476,1602309525.6207302,12818476204.965841,1.4273709428633927,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.7999999999999999,UNIQUE,No,100000000,11,0.06294936072727274,1.903995887955142,0.06294369680231268,1.9045085877142856,1588721430.1071334,12709771440.857067,1.4152663822932703,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.8999999999999999,UNIQUE,No,100000000,11,0.06938280227272729,1.3427483827102604,0.06937758220325817,1.3427130496541069,1441387791.6215956,11531102332.972765,1.284018485979899,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.1,UNIFORM,No,100000000,11,0.06936386154545454,1.2370474993295733,0.06935821949351918,1.2372128124438435,1441790183.344369,11534321466.754951,1.2843769449689717,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.2,UNIFORM,No,100000000,117,0.06738813333333332,4.3078364477291755,0.06738242953047795,4.3080054523090965,1484066405.6906512,11872531245.52521,1.3220374908162158,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.30000000000000004,UNIFORM,No,100000000,129,0.06515699493798446,4.54398801582476,0.06515145998222886,4.544275477207251,1534885020.646915,12279080165.17532,1.3673077792250883,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.4,UNIFORM,No,100000000,138,0.0639216477753623,3.5016111537825774,0.0639158096866331,3.5021286423015567,1564558134.9947803,12516465079.958242,1.3937412120463764,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.5,UNIFORM,No,100000000,137,0.06275239455474453,3.0750144296542556,0.06274670635696744,3.07525394869748,1593709149.1479683,12749673193.183746,1.4197095470602625,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.6,UNIFORM,No,100000000,11,0.06249900118181817,2.6144095180154534,0.06249369569258256,2.614689386175176,1600161406.5507905,12801291252.406324,1.4254573533270298,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.7,UNIFORM,No,100000000,11,0.06267772136363638,2.476112304445131,0.06267224363847212,2.4765309781095897,1595602681.4175482,12764821451.340385,1.4213963453334773,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.7999999999999999,UNIFORM,No,100000000,11,0.063070525,1.8907418114775623,0.06306490187211469,1.8905582821720457,1585668050.3964574,12685344403.17166,1.4125463675852137,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.8999999999999999,UNIFORM,No,100000000,11,0.06944318990909092,1.595897183851506,0.0694378134987571,1.5957785289634188,1440137512.420231,11521100099.361849,1.2829047110356961,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.1,GAUSSIAN,No,100000000,11,0.06918715736363636,0.061469500784086584,0.06918162675337357,0.061456995625666914,1445470491.1246338,11563763928.99707,1.2876554403547549,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.2,GAUSSIAN,No,100000000,116,0.06800029886206896,4.267386694840163,0.06799465074210333,4.26781866305269,1470703929.0383246,11765631432.306597,1.3101339162613352,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.30000000000000004,GAUSSIAN,No,100000000,125,0.06609113200000001,4.378779126316022,0.06608556872558594,4.379087883539989,1513189671.034542,12105517368.276337,1.347981106608593,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.4,GAUSSIAN,No,100000000,13,0.06598744223076923,2.905408792260974,0.06598183294442984,2.9060914934637796,1515568688.190587,12124549505.524696,1.3501003850044426,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.5,GAUSSIAN,No,100000000,11,0.06511158536363637,2.8937441386991387,0.06510601251775568,2.894017446279962,1535956452.1437716,12287651617.150173,1.3682622328817806,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.6,GAUSSIAN,No,100000000,11,0.06630214227272725,2.0639152150479525,0.06629664611816406,2.0639915389969596,1508371929.1284306,12066975433.027445,1.3436893610394371,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.7,GAUSSIAN,No,100000000,11,0.06947526054545455,1.9360861800005804,0.0694698354547674,1.9363285689016476,1439473684.4469876,11515789475.575901,1.2823133591496112,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.7999999999999999,GAUSSIAN,No,100000000,11,0.07886302727272727,1.447513360543521,0.07885740245472302,1.4475332377223349,1268111767.4072042,10144894139.257633,1.129660568172039,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.8999999999999999,GAUSSIAN,No,100000000,11,0.10860243227272727,0.9435952849951393,0.10859706185080788,0.9436656652011143,920835226.0706773,7366681808.565418,0.8202993390737932,,
+Benchmark,Device,Device Name,Key,Value,NumInputs,Occupancy,Distribution,Skipped,NumKeys,Samples,CPU Time (sec),Noise,GPU Time (sec),Noise,Elem/s (elem/sec),GlobalMem BW (bytes/sec),BWPeak,NumReps (pow2),NumReps
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.1,UNIQUE,No,100000000,11,0.06920442581818184,0.00366065791236223,0.06919912719726563,0.0036608494814078744,1445104931.9586136,11560839455.66891,0.01287329792580008,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.2,UNIQUE,No,100000000,11,0.06900785154545455,0.00046053301761491355,0.06900233251398259,0.00046360521920327686,1449226371.8728068,11593810974.982454,0.012910012577259182,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.30000000000000004,UNIQUE,No,100000000,11,0.06708165454545455,0.0006289907616746042,0.06707600264115766,0.0006266077656208109,1490846145.6026042,11926769164.820833,0.013280770253729014,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.4,UNIQUE,No,100000000,11,0.06524690463636364,0.0012178973094829188,0.0652412740534002,0.0012158158228520127,1532772028.9176092,12262176231.340874,0.013654254818607551,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.5,UNIQUE,No,100000000,11,0.06404241445454546,0.00025336208261050397,0.06403677090731534,0.000257293675977584,1561602788.2595239,12492822306.07619,0.013911085271696157,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.6,UNIQUE,No,100000000,11,0.06341475663636366,0.0003825017224831159,0.06340924523093483,0.00038245204062774794,1577057093.7377126,12616456749.901701,0.01404875546730431,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.7,UNIQUE,No,100000000,11,0.06326109672727273,0.00029322812488110874,0.06325564540516247,0.0002949170708644907,1580886565.2936444,12647092522.349155,0.014082869203371262,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.7999999999999999,UNIQUE,No,100000000,11,0.0636532089090909,0.0002999547261766452,0.06364783755215732,0.00029998997010566525,1571145287.0343518,12569162296.274815,0.013996091852857325,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.8999999999999999,UNIQUE,No,100000000,11,0.06988678181818182,0.0006097483572236783,0.06988148429177024,0.0006114044551770101,1430994218.4753613,11447953747.80289,0.012747596729576694,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.1,UNIFORM,No,100000000,11,0.06918830272727274,0.004076046636656547,0.06918292999267578,0.0040787055884692934,1445443261.9518535,11563546095.614828,0.012876311840363575,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.2,UNIFORM,No,100000000,11,0.06897605027272728,0.0004355213737381127,0.06897068301114169,0.00043562856622579555,1449891397.8254466,11599131182.603573,0.012915936767971838,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.30000000000000004,UNIFORM,No,100000000,11,0.06705543254545455,0.0006021725673874137,0.06704993785511364,0.0006033296206601019,1491425692.5351257,11931405540.281006,0.01328593297939643,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.4,UNIFORM,No,100000000,11,0.06428108272727273,0.03127848106145733,0.06427554945512251,0.03128327429446077,1555801558.2553747,12446412466.042997,0.013859406697685421,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.5,UNIFORM,No,100000000,11,0.06403595218181818,0.0008424562226042858,0.06403062612360173,0.0008416345207341068,1561752649.5362494,12494021196.289995,0.013912420267391047,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.6,UNIFORM,No,100000000,11,0.06345626490909091,0.0002616791219334027,0.06345085629549894,0.00026342360999119735,1576022859.8694856,12608182878.955885,0.01403954229501751,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.7,UNIFORM,No,100000000,11,0.06333125536363637,0.0002099589133498871,0.063326021714644,0.00020856218327940887,1579129673.5900152,12633037388.720121,0.014067218443468636,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.7999999999999999,UNIFORM,No,100000000,11,0.06380253554545455,0.00033279867530066725,0.063797061920166,0.0003348330883940887,1567470303.3368123,12539762426.694498,0.013963354327045435,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.8999999999999999,UNIFORM,No,100000000,11,0.07055124263636364,0.005059411064470772,0.07054587346857245,0.005054540167730359,1417517355.4913752,11340138843.931002,0.01262754200658651,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.1,GAUSSIAN,No,100000000,11,0.06919409681818182,0.0005224715930853448,0.06918860764936968,0.0005200745967572223,1445324648.0515208,11562597184.412167,0.012875255202853484,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.2,GAUSSIAN,No,100000000,11,0.06958156254545454,0.0003918305352390283,0.06957549424604936,0.00038972202243796306,1437287669.7987409,11498301358.389927,0.012803660114370197,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.30000000000000004,GAUSSIAN,No,100000000,11,0.0681513359090909,0.0005948989257183495,0.06814571033824574,0.000595526495261821,1467443798.056303,11739550384.450424,0.013072297231829951,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.4,GAUSSIAN,No,100000000,11,0.06685825854545456,0.0007454757001334387,0.06685286365855823,0.0007474593173783835,1495822235.9888155,11966577887.910524,0.013325098310903788,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.5,GAUSSIAN,No,100000000,11,0.06627705681818183,0.0005630770491739888,0.0662715107310902,0.0005626588845737492,1508944022.8059664,12071552182.447731,0.013441989940902637,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.6,GAUSSIAN,No,100000000,11,0.06692918090909092,0.00025382878914624417,0.06692370536110617,0.0002531769891507106,1494238841.9831378,11953910735.865103,0.013310993104895398,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.7,GAUSSIAN,No,100000000,11,0.07026752027272729,0.00026362705904800846,0.07026194624467329,0.00026434211867263,1423245516.8800738,11385964135.04059,0.012678569670040565,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.7999999999999999,GAUSSIAN,No,100000000,11,0.07985989563636364,0.010859783543314126,0.07985459206321023,0.01085614344075895,1252276136.1155453,10018209088.924362,0.011155538555761343,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.8999999999999999,GAUSSIAN,No,100000000,11,0.10914429263636363,0.00045673407432265007,0.10913903808593749,0.0004579464402798095,916262427.7598883,7330099422.079106,0.008162257943984182,,
 nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.1,UNIQUE,Yes,,,,,,,,,,,
 nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.2,UNIQUE,Yes,,,,,,,,,,,
 nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.30000000000000004,UNIQUE,Yes,,,,,,,,,,,
@@ -80,42 +80,42 @@ nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0
 nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.7,GAUSSIAN,Yes,,,,,,,,,,,
 nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.7999999999999999,GAUSSIAN,Yes,,,,,,,,,,,
 nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.8999999999999999,GAUSSIAN,Yes,,,,,,,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.1,UNIQUE,No,100000000,11,0.10272163090909091,0.007901706139763768,0.10271557894620029,0.005636848523492517,973562151.1940011,15576994419.104017,1.73453918043401,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.2,UNIQUE,No,100000000,11,0.08642251836363637,0.11914963674706795,0.08641675567626954,0.11904763743988307,1157182993.2451456,18514927891.92233,2.061685777588985,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.30000000000000004,UNIQUE,No,100000000,95,0.08274778317894739,4.567495805865352,0.08274228266665812,4.567790316344297,1208571926.9175549,19337150830.680878,2.1532424581626906,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.4,UNIQUE,No,100000000,104,0.0794820623846154,6.894892224556622,0.07947637161841757,6.89526905847464,1258235598.3753335,20131769574.005337,2.2417253391806824,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.5,UNIQUE,No,100000000,117,0.07611457395726492,6.427858176993963,0.07610873863024588,6.428590728356416,1313909569.3836615,21022553110.138584,2.340916422077504,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.6,UNIQUE,No,100000000,120,0.07420878082500003,5.4233062571217605,0.0742031447092692,5.42370924917713,1347651779.3390548,21562428469.424877,2.401032959198715,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.7,UNIQUE,No,100000000,121,0.07296982550413224,4.743677026026256,0.07296432463590766,4.744092242396731,1370532798.035211,21928524768.563377,2.441798742223509,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.7999999999999999,UNIQUE,No,100000000,122,0.07423772589344262,4.010078132167207,0.07423222763811954,4.010337317376634,1347123792.2091975,21553980675.34716,2.4000922751731713,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.8999999999999999,UNIQUE,No,100000000,111,0.08676809918018015,3.19288219896168,0.08676253282701644,3.1929441156217493,1152571239.4701047,18441139831.521675,2.0534692835485044,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.1,UNIFORM,No,100000000,11,0.1027204400909091,0.009979879569648986,0.10271539375998757,0.010028980644691652,973563906.4351681,15577022502.96269,1.7345423076453252,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.2,UNIFORM,No,100000000,11,0.08625520954545456,0.12258977992327307,0.08624947287819602,0.12261670275207807,1159427375.7617378,18550838012.187805,2.0656844636576,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.30000000000000004,UNIFORM,No,100000000,96,0.08251492306250001,4.623007667563823,0.08250930134455364,4.623304981912717,1211984568.6537364,19391753098.45978,2.159322563878521,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.4,UNIFORM,No,100000000,104,0.0792717475673077,6.8689552100971145,0.07926630364931544,6.869376130645017,1261570117.3907788,20185121878.25246,2.247666258179124,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.5,UNIFORM,No,100000000,116,0.07606569962931033,6.412398265513415,0.07605978018662021,6.413134350951612,1314755311.60674,21036084985.70784,2.342423231910526,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.6,UNIFORM,No,100000000,117,0.07427007210256407,5.325975314764115,0.07426438336494641,5.326818631360499,1346540501.2330725,21544648019.72916,2.3990530594944994,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.7,UNIFORM,No,100000000,121,0.07303455254545453,4.675881339455694,0.07302880266678234,4.676053091266515,1369322737.7187397,21909163803.499836,2.4396428479880625,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.7999999999999999,UNIFORM,No,100000000,122,0.07441630621311475,4.004785027010419,0.07441059669119408,4.004994967160809,1343894612.4165971,21502313798.665554,2.3943390329543135,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.8999999999999999,UNIFORM,No,100000000,111,0.08739033998198202,3.1014807683820287,0.08738475593360691,3.1014415896804164,1144364356.5930183,18309829705.488293,2.0388475566437756,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.1,GAUSSIAN,No,100000000,11,0.10810922845454547,0.009741787450570128,0.10810405245694246,0.009828004378829289,925034702.4671413,14800555239.47426,1.6480806415107279,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.2,GAUSSIAN,No,100000000,11,0.07846991345454546,0.09799187009818086,0.07846344063498757,0.1009385017869323,1274478906.2360985,20391662499.777576,2.270665098054623,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.30000000000000004,GAUSSIAN,No,100000000,11,0.0776798849090909,2.511094069046884,0.0776744946566495,2.511194651039138,1287423889.1677074,20598782226.68332,2.293728422833002,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.4,GAUSSIAN,No,100000000,109,0.07629825366055043,5.509840244736379,0.07629260372896804,5.510217672529852,1310743048.634875,20971888778.158,2.3352748158403562,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.5,GAUSSIAN,No,100000000,115,0.07667099108695652,5.536668533565466,0.07666499209196671,5.536326750104177,1304376316.6379879,20870021066.207806,2.3239315789587867,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.6,GAUSSIAN,No,100000000,114,0.08014078842105259,4.492770484498389,0.08013523985210219,4.49305425984432,1247890443.512246,19966247096.195934,2.223293977181168,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.7,GAUSSIAN,No,100000000,108,0.0881575398703704,3.5359159690407287,0.08815208314966269,3.5360142465180635,1134403140.8789532,18150450254.06325,2.021100236742719,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.7999999999999999,GAUSSIAN,No,100000000,11,0.10628041909090907,2.769544136735993,0.10627425731312146,2.7694711497269293,940961645.1645926,15055386322.633482,1.6764567509346362,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.8999999999999999,GAUSSIAN,No,100000000,11,0.15861997845454548,1.8576216787069537,0.15861453108354048,1.8575564098739714,630459260.6797868,10087348170.876589,1.1232526736740787,,
-nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.8,,No,100000000,11,0.0675892200909091,1.5115737066163348,0.06758353493430398,1.5118362381697914,1479650333.4311697,11837202667.449358,1.3181035609955545,2^0,1
-nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.8,,No,100000000,11,0.0731588810909091,1.700614731775095,0.07315316425670276,1.701025226912277,1366994866.4023423,10935958931.218739,1.217747707385211,2^1,2
-nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.8,,No,100000000,11,0.08682020954545457,1.334484686765813,0.08681472154097122,1.3346523490294244,1151878370.6840105,9215026965.472084,1.0261174197227858,2^2,4
-nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.8,,No,100000000,11,0.11502161018181818,1.068848966854489,0.11501605293967508,1.0687683271719923,869443851.046159,6955550808.369272,0.7745188239792609,2^3,8
-nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.8,,No,100000000,11,0.17184204172727277,0.7761776813538659,0.17183678921786225,0.7762235047043814,581947558.8153337,4655580470.52267,0.518411095010809,2^4,16
-nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.8,,No,100000000,11,0.2828323630909091,0.4670414040414736,0.2828274952281605,0.4670017593190509,353572413.1747826,2828579305.3982606,0.3149697238230318,2^5,32
-nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.8,,No,100000000,11,0.5026626956363637,0.17830799120425211,0.5026588883833452,0.1782977450350701,198942070.47968587,1591536563.837487,0.17722177031043854,2^6,64
-nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.8,,No,100000000,11,0.9214143747272728,0.12028721108259548,0.9214118596857243,0.12032417365194858,108529100.15083598,868232801.2066878,0.09667999942171107,2^7,128
-nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.8,,No,100000000,9,1.7659442706666668,0.07812563688582005,1.7659443088107638,0.07810448771827107,56626927.305166714,453015418.4413337,0.05044445491124458,2^8,256
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.1,UNIQUE,No,100000000,11,0.10274451318181815,7.919867067043338e-05,0.10273922452059657,7.937319902834375e-05,973338084.5205092,15573409352.328148,0.017341399738464033,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.2,UNIQUE,No,100000000,11,0.08645334581818183,0.001069146654464478,0.08644775668057528,0.0010671316663770792,1156768016.19619,18508288259.13904,0.02060946437065618,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.30000000000000004,UNIQUE,No,100000000,11,0.0849845310909091,0.0009667560971783149,0.08497915302623403,0.0009668284317229073,1176759198.4486933,18828147175.179092,0.020965635662213034,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.4,UNIQUE,No,100000000,11,0.08270579718181818,0.0006225482401469632,0.08270028825239702,0.0006211092880687401,1209185628.166194,19346970050.659103,0.02154335854058926,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.5,UNIQUE,No,100000000,11,0.07897613554545456,0.00040252419577252956,0.07897078774192118,0.0004014339582821938,1266291028.1052647,20260656449.684235,0.022560772308032793,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.6,UNIQUE,No,100000000,11,0.07642653472727273,0.0005395866182401395,0.0764211196899414,0.0005422698066863944,1308538796.6798143,20936620746.87703,0.023313476280640934,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.7,UNIQUE,No,100000000,11,0.07490249536363637,0.000634047536343183,0.07489684989235618,0.0006325369521988604,1335169638.559202,21362714216.94723,0.023787942534193307,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.7999999999999999,UNIQUE,No,100000000,11,0.07593944427272728,0.0009142890604426159,0.07593388227982954,0.0009146761769850632,1316935167.7750735,21070962684.401176,0.023463069551294784,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.8999999999999999,UNIQUE,No,100000000,11,0.08843876872727273,0.00041083894043193324,0.08843329204212536,0.00041137249157570003,1130795854.0361114,18092733664.57778,0.020146733431373135,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.1,UNIFORM,No,100000000,11,0.10274524690909091,6.354018158648775e-05,0.10274024616588245,6.505868237862087e-05,973328405.6818581,15573254490.909729,0.017341227296213263,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.2,UNIFORM,No,100000000,11,0.08623861036363638,0.0010673083587560577,0.08623346016623758,0.0010686851723073897,1159642670.1099992,18554282721.759987,0.020660680411024784,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.30000000000000004,UNIFORM,No,100000000,11,0.08475259200000002,0.0008236603929658194,0.08474735745516691,0.0008206519014245491,1179977795.212105,18879644723.39368,0.02102297953271282,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.4,UNIFORM,No,100000000,11,0.08255245990909091,0.0005501564153902886,0.08254696863347834,0.0005507180339496883,1211431523.8396688,19382904381.4347,0.02158337236031337,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.5,UNIFORM,No,100000000,11,0.07882271636363637,0.0007382712526508775,0.07881737240878019,0.0007369330540988989,1268755820.4980211,20300093127.968338,0.022604686083559384,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.6,UNIFORM,No,100000000,11,0.07642493618181818,0.0021891595178698607,0.07641953832452947,0.0021892748892233535,1308565874.5454836,20937053992.727737,0.023313958711257902,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.7,UNIFORM,No,100000000,11,0.07487487463636364,0.0004947702667866301,0.0748693882335316,0.0004976890232915997,1335659371.0647311,21370549937.035698,0.023796667814009607,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.7999999999999999,UNIFORM,No,100000000,11,0.07613220190909092,0.0005209790978913337,0.0761271383112127,0.0005219125155713814,1313592001.7273405,21017472027.637447,0.023403506302154726,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.8999999999999999,UNIFORM,No,100000000,11,0.08904410554545455,0.0002932838905896244,0.08903856867009943,0.0002917179813264836,1123108799.856321,17969740797.701138,0.020009777648523394,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.1,GAUSSIAN,No,100000000,11,0.10816177872727271,3.566492121809612e-05,0.1081567417491566,3.4091418821815285e-05,924584065.521554,14793345048.344864,0.016472777678191882,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.2,GAUSSIAN,No,100000000,11,0.07844791381818184,0.0007774184979175578,0.07844240153919567,0.0007768336933861818,1274820735.1866012,20397131762.98562,0.022712741148564017,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.30000000000000004,GAUSSIAN,No,100000000,11,0.07857616672727273,0.00271679366288135,0.0785705892389471,0.002718763857120997,1272740868.6713836,20363853898.742138,0.022675685373991297,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.4,GAUSSIAN,No,100000000,11,0.078771252,0.0004900731018220413,0.07876580116965554,0.0004907366567772091,1269586527.5921922,20313384441.475075,0.022619486309724064,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.5,GAUSSIAN,No,100000000,11,0.07914328254545455,0.000779792374350826,0.0791376987804066,0.0007802219776220264,1263620266.208178,20217924259.33085,0.022513188893389716,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.6,GAUSSIAN,No,100000000,11,0.08218435618181817,0.000426789762216608,0.08217897935347124,0.00042696474574037044,1216856193.4783387,19469699095.65342,0.021680020550854096,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.7,GAUSSIAN,No,100000000,11,0.08990060427272728,0.0003185792309776693,0.08989519084583629,0.00032000463514696945,1112406559.8958762,17798504958.33402,0.019819102050596425,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.7999999999999999,GAUSSIAN,No,100000000,11,0.10803566354545456,0.00023774928510362925,0.1080306028886275,0.00023995316819948022,925663629.8058382,14810618076.893412,0.01649201164847916,,
+nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.8999999999999999,GAUSSIAN,No,100000000,11,0.16119024045454547,0.00023327209159478972,0.1611849559437145,0.00023296292641002099,620405294.1201277,9926484705.922043,0.011053401049745719,,
+nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.8,,No,100000000,11,0.06821054463636364,0.004518757391277612,0.06820500876686791,0.004522581115714032,1466167981.0321672,11729343848.257338,0.013060931986104682,2^0,1
+nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.8,,No,100000000,11,0.07414215518181819,0.0002442139124343712,0.0741362963589755,0.0002402154537706555,1348866950.6201093,10790935604.960875,0.012015989796715625,2^1,2
+nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.8,,No,100000000,11,0.087509521,0.00022051151123671586,0.08750433696400034,0.00021913974618767155,1142800499.6042702,9142403996.834162,0.010180306617056285,2^2,4
+nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.8,,No,100000000,11,0.11585742990909091,0.00025986509574229763,0.1158519155328924,0.0002601219294654579,863170881.0339717,6905367048.271773,0.00768930730681631,2^3,8
+nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.8,,No,100000000,11,0.17301705736363637,0.00014263782881344684,0.17301215431906958,0.00014381731895805118,577994074.4254284,4623952595.403427,0.005148892481697445,2^4,16
+nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.8,,No,100000000,11,0.2845625553636364,0.00015372202418767293,0.2845579639781605,0.0001535428523047237,351422250.1524325,2811378001.21946,0.003130543134909782,2^5,32
+nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.8,,No,100000000,11,0.5018689946363637,0.00020770102043540117,0.5018656560724432,0.0002082466096565821,199256511.7577307,1594052094.0618455,0.0017750188119809247,2^6,64
+nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.8,,No,100000000,11,0.9222340671818183,0.000556648194377783,0.9222326438210225,0.0005566477496281188,108432509.59505938,867460076.760475,0.0009659395452809595,2^7,128
+nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.8,,No,100000000,9,1.7673624833333335,0.00038052778852527245,1.767364963107639,0.00038057866158041693,56581409.096265785,452651272.7701263,0.0005040390633575558,2^8,256
 nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.8,,Yes,,,,,,,,,,2^0,1
 nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.8,,Yes,,,,,,,,,,2^1,2
 nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.8,,Yes,,,,,,,,,,2^2,4
@@ -134,12 +134,84 @@ nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.
 nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.8,,Yes,,,,,,,,,,2^6,64
 nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.8,,Yes,,,,,,,,,,2^7,128
 nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.8,,Yes,,,,,,,,,,2^8,256
-nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.8,,No,100000000,117,0.08358124586324789,3.4073378500526617,0.08357501162015475,3.4073037626511162,1196529896.4539328,19144478343.262924,2.131787871390274,2^0,1
-nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.8,,No,100000000,27,0.09553329222222219,2.9961068371423005,0.09552717703360099,2.99579486522814,1046822518.0026594,16749160288.042551,1.8650629240355248,2^1,2
-nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.8,,No,100000000,11,0.12117426200000003,2.607019263443277,0.12116880243474787,2.6070095342757837,825294943.8354996,13204719101.367994,1.4703801023294962,2^2,4
-nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.8,,No,100000000,11,0.17426779272727275,1.5797872197789737,0.17426227361505678,1.5795689435612117,573847671.8196548,9181562749.114477,1.0223910914688832,2^3,8
-nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.8,,No,100000000,11,0.2812710991818182,0.6432035096529316,0.2812656194513494,0.6431494212328667,355535810.5802797,5688572969.284475,0.6334375188502703,2^4,16
-nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.8,,No,100000000,11,0.4875445267272728,0.2664081025716232,0.48753980324485086,0.2663646205828964,205111458.25313935,3281783332.0502295,0.36543518075317016,2^5,32
-nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.8,,No,100000000,11,0.8979136936363638,0.1329969797576101,0.8979087468927559,0.1328683157915315,111369891.81367643,1781918269.018823,0.1984212724730552,2^6,64
-nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.8,,No,100000000,9,1.6986642724444445,0.10934703880104671,1.6986634250217016,0.10937900951870727,58869814.07086129,941917025.1337806,0.10488493099854136,2^7,128
-nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.8,,No,100000000,5,3.2878928112,0.048951349895341265,3.287897314453125,0.048955973451362045,30414575.16340743,486633202.6145189,0.0541878833441552,2^8,256
+nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.8,,No,100000000,11,0.08536571045454547,0.00039028855440837823,0.08536017469926314,0.00039028326257579125,1171506505.8420415,18744104093.472664,0.020872051486638423,2^0,1
+nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.8,,No,100000000,11,0.09729859227272727,0.0005263976466797341,0.09729340570623224,0.0005262994272018765,1027818887.3554293,16445102197.686869,0.018312052582586752,2^1,2
+nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.8,,No,100000000,11,0.12347132945454548,0.00026653618366292287,0.12346609913219106,0.0002663909969661335,809938928.1986897,12959022851.179035,0.014430211805136291,2^2,4
+nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.8,,No,100000000,11,0.17666694827272733,0.00030476914761359364,0.17666187771883876,0.0003038166768234644,566053079.9924599,9056849279.879358,0.010085039195988809,2^3,8
+nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.8,,No,100000000,11,0.2824962571818182,0.00010072454601307116,0.2824916270862926,0.00010048219195278699,353992792.7472804,5663884683.956487,0.006306884135320703,2^4,16
+nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.8,,No,100000000,11,0.48905533781818195,0.00023076981571944953,0.489051230690696,0.00023064481082195662,204477555.16077155,3271640882.572345,0.003643057924044533,2^5,32
+nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.8,,No,100000000,11,0.8969527960000001,0.00012232545382140375,0.896950838955966,0.00012244915401747237,111488830.44292387,1783821287.086782,0.001986331785257338,2^6,64
+nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.8,,No,100000000,9,1.6962511944444445,0.00038631756977580033,1.6962525906032984,0.000386339580966324,58953484.02352828,943255744.3764524,0.0010503400089710711,2^7,128
+nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.8,,No,100000000,5,3.2905120279999998,0.00038547888143643054,3.290520166015625,0.0003854291119151701,30390331.909464173,486245310.55142677,0.000541446905456531,2^8,256
+nvbench_static_multimap_find,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.1,,No,100000000,17,0.030851307941176475,0.0022650732636574984,0.030845771004171935,0.002266629399241108,3241935498.596383,25935483988.771065,0.028879841599525932,,
+nvbench_static_multimap_find,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.2,,No,100000000,19,0.026449221947368422,0.0008253173910985551,0.026443668164704975,0.0008271069798394431,3781623615.042655,30252988920.34124,0.033687496570719205,,
+nvbench_static_multimap_find,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.30000000000000004,,No,100000000,23,0.022465648956521738,0.0007264679211652522,0.022460104403288467,0.000730043899022379,4452339054.370497,35618712434.963974,0.03966237042448062,,
+nvbench_static_multimap_find,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.4,,No,100000000,23,0.022631443739130435,0.011430483669021833,0.02262590333689814,0.011435552785415044,4419713039.1218815,35357704312.97505,0.03937173103550707,,
+nvbench_static_multimap_find,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.5,,No,100000000,22,0.022819085272727278,0.0008192435837584734,0.022813602880998086,0.0008159684844073193,4383349728.739779,35066797829.918236,0.03904779903737688,,
+nvbench_static_multimap_find,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.6,,No,100000000,22,0.023425075772727275,0.0005757309816581208,0.02341948483207009,0.0005752889173025987,4269948750.668604,34159590005.34883,0.038037599332495405,,
+nvbench_static_multimap_find,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.7,,No,100000000,21,0.02472987876190477,0.0005648620931997654,0.024724333717709497,0.0005639783645839629,4044598375.905766,32356787007.246128,0.03603013091421185,,
+nvbench_static_multimap_find,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.7999999999999999,,No,100000000,18,0.027860730388888883,0.0005571224569463895,0.027855303022596572,0.0005533948568001576,3589980691.248584,28719845529.98867,0.031980301197696195,,
+nvbench_static_multimap_find,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.8999999999999999,,No,100000000,14,0.03840460892857143,0.0002420097491422894,0.03839919580732073,0.00024399529926795408,2604221205.615332,20833769644.922657,0.02319894888126543,,
+nvbench_static_multimap_find,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.1,,Yes,,,,,,,,,,,
+nvbench_static_multimap_find,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.2,,Yes,,,,,,,,,,,
+nvbench_static_multimap_find,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.30000000000000004,,Yes,,,,,,,,,,,
+nvbench_static_multimap_find,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.4,,Yes,,,,,,,,,,,
+nvbench_static_multimap_find,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.5,,Yes,,,,,,,,,,,
+nvbench_static_multimap_find,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.6,,Yes,,,,,,,,,,,
+nvbench_static_multimap_find,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.7,,Yes,,,,,,,,,,,
+nvbench_static_multimap_find,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.7999999999999999,,Yes,,,,,,,,,,,
+nvbench_static_multimap_find,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.8999999999999999,,Yes,,,,,,,,,,,
+nvbench_static_multimap_find,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.1,,Yes,,,,,,,,,,,
+nvbench_static_multimap_find,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.2,,Yes,,,,,,,,,,,
+nvbench_static_multimap_find,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.30000000000000004,,Yes,,,,,,,,,,,
+nvbench_static_multimap_find,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.4,,Yes,,,,,,,,,,,
+nvbench_static_multimap_find,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.5,,Yes,,,,,,,,,,,
+nvbench_static_multimap_find,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.6,,Yes,,,,,,,,,,,
+nvbench_static_multimap_find,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.7,,Yes,,,,,,,,,,,
+nvbench_static_multimap_find,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.7999999999999999,,Yes,,,,,,,,,,,
+nvbench_static_multimap_find,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.8999999999999999,,Yes,,,,,,,,,,,
+nvbench_static_multimap_find,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.1,,No,100000000,11,0.068847447,7.054018023164516e-05,0.0688421235518022,7.203100005969825e-05,1452599002.4806857,23241584039.69097,0.025880113356625673,,
+nvbench_static_multimap_find,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.2,,No,100000000,15,0.03524321166666666,0.001650529442398669,0.03523741022745767,0.001648292123721572,2837893005.033556,45406288080.536896,0.050561092592530575,,
+nvbench_static_multimap_find,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.30000000000000004,,No,100000000,16,0.031927686312499995,0.0020041208961081945,0.031922111988067624,0.0020052068452505846,3132624809.955546,50121996959.288734,0.05581215810211563,,
+nvbench_static_multimap_find,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.4,,No,100000000,19,0.026822425842105264,0.0011111774650891613,0.026816889311137956,0.0011088392005559964,3728993278.8165193,59663892461.06431,0.06643730898689637,,
+nvbench_static_multimap_find,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.5,,No,100000000,19,0.026969198526315793,0.0013579985487244533,0.02696380635311729,0.0013671279581181654,3708675202.988876,59338803247.822014,0.06607531362223625,,
+nvbench_static_multimap_find,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.6,,No,100000000,19,0.027730592263157893,0.0012563867319355922,0.027725177162571956,0.0012576373151306354,3606829973.119039,57709279569.904625,0.06426079627136258,,
+nvbench_static_multimap_find,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.7,,No,100000000,17,0.029531005647058822,0.0011698700131234213,0.02952505212671616,0.0011606651967315915,3386954223.5122285,54191267576.195656,0.06034339765379541,,
+nvbench_static_multimap_find,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.7999999999999999,,No,100000000,15,0.03383637233333333,0.0009077916542839276,0.03383104883829753,0.000908598413189415,2955864610.582149,47293833769.314384,0.05266292421932278,,
+nvbench_static_multimap_find,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.8999999999999999,,No,100000000,11,0.04776554863636364,0.0004635063337864464,0.04776010443947532,0.0004571527281544445,2093797766.4334137,33500764262.93462,0.03730397959010501,,
+nvbench_static_multimap_find_all,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.4,,No,100000000,11,0.12107264445454548,0.0002888403006084369,0.12106742720170453,0.00028523266936586786,825986000.6225694,6607888004.980556,0.007358056590494668,2^0,1
+nvbench_static_multimap_find_all,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.4,,No,100000000,11,0.13438666109090908,0.0005865902986289532,0.13438147527521302,0.0005877756359823397,744150187.3319978,5953201498.655982,0.006629045996044735,2^1,2
+nvbench_static_multimap_find_all,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.4,,No,100000000,11,0.14746722963636363,0.0005470865870191734,0.1474622372713956,0.0005474430105601299,678139718.0076406,5425117744.061125,0.006041010885900447,2^2,4
+nvbench_static_multimap_find_all,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.4,,No,100000000,11,0.15730588109090912,0.0004655900701124892,0.15730073408647016,0.0004660007318761322,635724941.6587514,5085799533.270011,0.005663171159303301,2^3,8
+nvbench_static_multimap_find_all,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.4,,No,100000000,11,0.16485765590909093,0.0006792304572982247,0.16485292191938916,0.0006785413570261261,606601319.7442666,4852810557.954133,0.005403731824973869,2^4,16
+nvbench_static_multimap_find_all,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.4,,No,100000000,11,0.17627661436363637,0.0010419775160612906,0.17627154679731888,0.0010428692726222341,567306532.5453933,4538452260.363147,0.005053685616318,2^5,32
+nvbench_static_multimap_find_all,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.4,,No,100000000,11,0.22831155145454549,0.0003346988531024427,0.22830722462047232,0.0003356444486342128,438006288.08937395,3504050304.7149916,0.0039018519107163444,2^6,64
+nvbench_static_multimap_find_all,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.4,,No,100000000,11,0.343012823,0.00013615565054172037,0.34300853382457386,0.0001364498010068651,291537936.05363584,2332303488.4290867,0.0025970811008198744,2^7,128
+nvbench_static_multimap_find_all,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.4,,No,100000000,11,0.5712359422727272,0.00010408496369820208,0.57123291015625,0.0001044324803036856,175059941.7891502,1400479534.3132017,0.0015594706901114436,2^8,256
+nvbench_static_multimap_find_all,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.4,,Yes,,,,,,,,,,2^0,1
+nvbench_static_multimap_find_all,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.4,,Yes,,,,,,,,,,2^1,2
+nvbench_static_multimap_find_all,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.4,,Yes,,,,,,,,,,2^2,4
+nvbench_static_multimap_find_all,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.4,,Yes,,,,,,,,,,2^3,8
+nvbench_static_multimap_find_all,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.4,,Yes,,,,,,,,,,2^4,16
+nvbench_static_multimap_find_all,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.4,,Yes,,,,,,,,,,2^5,32
+nvbench_static_multimap_find_all,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.4,,Yes,,,,,,,,,,2^6,64
+nvbench_static_multimap_find_all,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.4,,Yes,,,,,,,,,,2^7,128
+nvbench_static_multimap_find_all,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.4,,Yes,,,,,,,,,,2^8,256
+nvbench_static_multimap_find_all,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.4,,Yes,,,,,,,,,,2^0,1
+nvbench_static_multimap_find_all,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.4,,Yes,,,,,,,,,,2^1,2
+nvbench_static_multimap_find_all,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.4,,Yes,,,,,,,,,,2^2,4
+nvbench_static_multimap_find_all,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.4,,Yes,,,,,,,,,,2^3,8
+nvbench_static_multimap_find_all,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.4,,Yes,,,,,,,,,,2^4,16
+nvbench_static_multimap_find_all,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.4,,Yes,,,,,,,,,,2^5,32
+nvbench_static_multimap_find_all,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.4,,Yes,,,,,,,,,,2^6,64
+nvbench_static_multimap_find_all,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.4,,Yes,,,,,,,,,,2^7,128
+nvbench_static_multimap_find_all,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.4,,Yes,,,,,,,,,,2^8,256
+nvbench_static_multimap_find_all,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.4,,No,100000000,11,0.12221130081818181,0.0002000734255192767,0.12220602208917791,0.00020057244323789153,818290279.7296404,13092644475.674246,0.014579002988341654,2^0,1
+nvbench_static_multimap_find_all,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.4,,No,100000000,11,0.13576571445454544,0.0003030118102881788,0.13576052301580258,0.00030194008643213716,736591151.6734505,11785458426.775208,0.013123417040932341,2^1,2
+nvbench_static_multimap_find_all,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.4,,No,100000000,11,0.14913550090909092,0.0004339145518877869,0.1491303308660334,0.0004332491773554188,670554403.1135551,10728870449.816881,0.011946878618756327,2^2,4
+nvbench_static_multimap_find_all,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.4,,No,100000000,11,0.15929928772727273,0.0006312398564412787,0.15929455705122514,0.0006315074357204146,627767839.9761174,10044285439.617878,0.01118457525613094,2^3,8
+nvbench_static_multimap_find_all,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.4,,No,100000000,11,0.1686964322727273,0.000658022645946661,0.16869152415882455,0.0006575345582514861,592798011.0361035,9484768176.577656,0.010561538109964786,2^4,16
+nvbench_static_multimap_find_all,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.4,,No,100000000,11,0.21259125472727272,0.00025829907868773946,0.2125865880792791,0.00025848914851093883,470396561.24829185,7526344979.97267,0.008380782519389464,2^5,32
+nvbench_static_multimap_find_all,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.4,,No,100000000,11,0.30872113981818183,0.00015102865045400039,0.30871700772372157,0.0001507158431270966,323921253.1157093,5182740049.851349,0.0057711169668562805,2^6,64
+nvbench_static_multimap_find_all,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.4,,No,100000000,11,0.49863340836363645,0.0001678603470263395,0.49862963312322445,0.0001682559561403036,200549653.20379862,3208794451.260778,0.0035730767745830714,2^7,128
+nvbench_static_multimap_find_all,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.4,,No,100000000,11,0.8690732562727274,0.00011058842912884806,0.869071494362571,0.0001106222336976893,115065331.96482986,1841045311.4372778,0.0020500522371156974,2^8,256
diff --git a/benchmarks/analysis/notebooks/StaticMultimap.ipynb b/benchmarks/analysis/notebooks/StaticMultimap.ipynb
index bc2c6051a..43f622e8f 100644
--- a/benchmarks/analysis/notebooks/StaticMultimap.ipynb
+++ b/benchmarks/analysis/notebooks/StaticMultimap.ipynb
@@ -94,7 +94,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 720x360 with 2 Axes>"
       ]
@@ -106,7 +106,31 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 720x360 with 2 Axes>"
       ]
@@ -118,16 +142,18 @@
     }
    ],
    "source": [
+    "# Get benchmark list\n",
     "unique_bms = perfdf[\"Benchmark\"].unique()\n",
     "num_bm   = len(unique_bms)\n",
-    "    \n",
-    "for bindex, bm in enumerate(unique_bms):\n",
-    "    tmpdf = perfdf[perfdf['Benchmark'].str.contains(bm)]\n",
+    "\n",
+    "# Plot benchmarks\n",
+    "for bm in unique_bms:\n",
+    "    tmpdf = perfdf[perfdf[\"Benchmark\"] == bm]\n",
     "    \n",
     "    unique_labels = tmpdf[\"Label\"].unique()\n",
     "    num_labels = len(unique_labels)\n",
     "    \n",
-    "    flag = 'multi_insert' in bm\n",
+    "    flag = ('find_all' in bm) or ('multi_insert' in bm)\n",
     "    if (flag):\n",
     "        plot_insert(bm, tmpdf, \"NumReps\", unique_labels, flag)\n",
     "    else:\n",
diff --git a/benchmarks/analysis/notebooks/Utils.py b/benchmarks/analysis/notebooks/Utils.py
index c8a7c8879..619ec052c 100644
--- a/benchmarks/analysis/notebooks/Utils.py
+++ b/benchmarks/analysis/notebooks/Utils.py
@@ -7,21 +7,21 @@
 colors = ['b','r','g','m','y','c']
 styles = ['o','s','v','^','D',">"]
 
-def plot_insert(bm, df, s, unique_labels, flag = False):
+def plot_insert(bm, df, xaxis, unique_labels, flag = False):
     fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5))
     fig.suptitle(bm)
     
     marker_handles = []
     
-    ax1.set_xlabel(s)
+    ax1.set_xlabel(xaxis)
     ax1.set_ylabel('Performance (number of operations per second)')
 
-    ax2.set_xlabel(s)
+    ax2.set_xlabel(xaxis)
     ax2.set_ylabel('Bandwidth (GB/s)')
     
     lax = [ax1, ax2]
     if flag:
-        lnum = list(df[s])
+        lnum = list(df[xaxis])
         
         for item in lax:
             item.set_xscale('log')
@@ -33,7 +33,7 @@ def plot_insert(bm, df, s, unique_labels, flag = False):
     for lindex, lbl in enumerate(unique_labels):
         tmpdf = df.loc[df['Label'] == lbl]
 
-        x = tmpdf[s]
+        x = tmpdf[xaxis]
         perf = tmpdf["Elem/s (elem/sec)"]
         bw   = tmpdf['Bandwidth (GB/s)']
 
@@ -63,3 +63,4 @@ def plot_insert(bm, df, s, unique_labels, flag = False):
                                           mfc='none', linestyle="--", label=lbl)[0])
     
     leg = plt.legend(handles = marker_handles, loc="lower left", ncol=2, frameon=False)
+    plt.savefig(bm+'.eps')

From c1c1d962aeff61c11b675dab776d7c8ce61f38ae Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Mon, 22 Mar 2021 12:46:30 -0700
Subject: [PATCH 041/267] Fix an index-related bug + update benchmark & unit
 test for static multimap

---
 .../hash_table/static_multimap_bench.cu       |  5 ++--
 include/cuco/detail/static_multimap.inl       |  2 +-
 tests/static_multimap/static_multimap_test.cu | 28 +++++++------------
 3 files changed, 13 insertions(+), 22 deletions(-)

diff --git a/benchmarks/hash_table/static_multimap_bench.cu b/benchmarks/hash_table/static_multimap_bench.cu
index a12a425ed..dd78f0e06 100644
--- a/benchmarks/hash_table/static_multimap_bench.cu
+++ b/benchmarks/hash_table/static_multimap_bench.cu
@@ -15,11 +15,9 @@
  */
 
 #include <nvbench/nvbench.cuh>
-#include <nvbench/test_kernels.cuh>
 
 #include <thrust/device_vector.h>
 #include <random>
-#include <type_traits>
 
 #include "cuco/static_multimap.cuh"
 
@@ -198,6 +196,7 @@ void nvbench_static_multimap_find(nvbench::state& state, nvbench::type_list<Key,
                Hash hash;
                KeyEqual key_equal;
 
+               // Use timers to explicitly mark the target region
                timer.start();
                cuco::detail::find<block_size, tile_size, Value>
                  <<<grid_size, block_size, 0, launch.get_stream()>>>(
@@ -268,13 +267,13 @@ void nvbench_static_multimap_find_all(nvbench::state& state, nvbench::type_list<
       CUCO_CUDA_TRY(cudaGetDevice(&device_id));
       CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_items, sizeof(atomic_ctr_type), device_id));
 
+      // Use timers to explicitly mark the target region
       timer.start();
       cuco::detail::find_all<block_size, tile_size, Key, Value>
         <<<grid_size, block_size, 0, launch.get_stream()>>>(
           d_keys.begin(), d_keys.end(), d_results.begin(), num_items, view, hash, key_equal);
       CUCO_CUDA_TRY(cudaDeviceSynchronize());
       timer.stop();
-      std::cout << "Output size: " << *num_items << std::endl;
     });
 }
 
diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index 8cfcdc2c5..b25621a3a 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -126,7 +126,7 @@ OutputIt static_multimap<Key, Value, Scope, Allocator>::find_all(
   detail::find_all<block_size, tile_size, Key, Value>
     <<<grid_size, block_size>>>(first, last, output_begin, num_items, view, hash, key_equal);
   CUCO_CUDA_TRY(cudaDeviceSynchronize());
-  return output_begin + (*num_items - 1);
+  return output_begin + *num_items;
 }
 
 template <typename Key, typename Value, cuda::thread_scope Scope, typename Allocator>
diff --git a/tests/static_multimap/static_multimap_test.cu b/tests/static_multimap/static_multimap_test.cu
index d519fed6c..d574c11d9 100644
--- a/tests/static_multimap/static_multimap_test.cu
+++ b/tests/static_multimap/static_multimap_test.cu
@@ -107,7 +107,6 @@ TEMPLATE_TEST_CASE_SIG("Unique sequence of keys",
   auto view   = map.get_device_view();
 
   std::vector<Key> h_keys(num_keys);
-  std::vector<Value> h_values(num_keys);
   std::vector<cuco::pair_type<Key, Value>> h_pairs(num_keys);
 
   generate_keys<Dist, Key>(h_keys.begin(), h_keys.end());
@@ -117,14 +116,9 @@ TEMPLATE_TEST_CASE_SIG("Unique sequence of keys",
     Value val         = h_keys[i];
     h_pairs[i].first  = key;
     h_pairs[i].second = val;
-    h_values[i]       = val;
   }
 
-  thrust::device_vector<Key> d_keys(h_keys);
-  thrust::device_vector<Value> d_values(h_values);
   thrust::device_vector<cuco::pair_type<Key, Value>> d_pairs(h_pairs);
-  thrust::device_vector<Value> d_results(num_keys);
-  thrust::device_vector<bool> d_contained(num_keys);
 
   SECTION("All keys should be found.")
   {
@@ -170,7 +164,6 @@ TEMPLATE_TEST_CASE_SIG("Each key appears twice",
   auto view   = map.get_device_view();
 
   std::vector<Key> h_keys(num_items);
-  std::vector<Value> h_values(num_items);
   std::vector<cuco::pair_type<Key, Value>> h_pairs(num_items);
 
   generate_keys<Dist, Key>(h_keys.begin(), h_keys.end());
@@ -180,29 +173,28 @@ TEMPLATE_TEST_CASE_SIG("Each key appears twice",
     Value val         = i;
     h_pairs[i].first  = key;
     h_pairs[i].second = val;
-    h_values[i]       = val;
   }
-
-  thrust::device_vector<Key> d_keys(h_keys);
-  thrust::device_vector<Value> d_values(h_values);
   thrust::device_vector<cuco::pair_type<Key, Value>> d_pairs(h_pairs);
-  thrust::device_vector<Value> d_results(num_items);
-  thrust::device_vector<bool> d_contained(num_items);
-  thrust::device_vector<cuco::pair_type<Key, Value>> d_outputs(num_items);
+  thrust::device_vector<cuco::pair_type<Key, Value>> d_results(num_items);
 
-  std::set<Key> temp_set(h_keys.begin(), h_keys.end());
-  std::vector<Key> h_unique_keys(temp_set.begin(), temp_set.end());
+  // Get array of unique keys
+  std::set<Key> key_set(h_keys.begin(), h_keys.end());
+  std::vector<Key> h_unique_keys(key_set.begin(), key_set.end());
   thrust::device_vector<Key> d_unique_keys(h_unique_keys);
 
-  // bulk function test cases
+  // Bulk function test cases
   SECTION("Total count should be equal to the number of inserted pairs.")
   {
     map.insert(d_pairs.begin(), d_pairs.end());
+    // Count matching keys
     size_t num = map.count(d_unique_keys.begin(), d_unique_keys.end());
 
     REQUIRE(num == num_items);
 
-    auto output_end = map.find_all(d_unique_keys.begin(), d_unique_keys.end(), d_outputs.begin());
+    auto output_end = map.find_all(d_unique_keys.begin(), d_unique_keys.end(), d_results.begin());
+    auto size       = thrust::distance(d_results.begin(), output_end);
+
+    REQUIRE(size == num_items);
   }
 
   SECTION(

From d82a1321d6b984eb4cfdec57effd37afa43ec078 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Tue, 23 Mar 2021 15:23:56 -0400
Subject: [PATCH 042/267] Remove data folder from git repo

---
 .../analysis/data/static-multimap-data.csv    | 217 ------------------
 .../analysis/notebooks/StaticMultimap.ipynb   |   2 +-
 2 files changed, 1 insertion(+), 218 deletions(-)
 delete mode 100644 benchmarks/analysis/data/static-multimap-data.csv

diff --git a/benchmarks/analysis/data/static-multimap-data.csv b/benchmarks/analysis/data/static-multimap-data.csv
deleted file mode 100644
index d6473d6c0..000000000
--- a/benchmarks/analysis/data/static-multimap-data.csv
+++ /dev/null
@@ -1,217 +0,0 @@
-Benchmark,Device,Device Name,Key,Value,NumInputs,Occupancy,Distribution,Skipped,NumKeys,Samples,CPU Time (sec),Noise,GPU Time (sec),Noise,Elem/s (elem/sec),GlobalMem BW (bytes/sec),BWPeak,NumReps (pow2),NumReps
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.1,UNIQUE,No,100000000,11,0.06920442581818184,0.00366065791236223,0.06919912719726563,0.0036608494814078744,1445104931.9586136,11560839455.66891,0.01287329792580008,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.2,UNIQUE,No,100000000,11,0.06900785154545455,0.00046053301761491355,0.06900233251398259,0.00046360521920327686,1449226371.8728068,11593810974.982454,0.012910012577259182,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.30000000000000004,UNIQUE,No,100000000,11,0.06708165454545455,0.0006289907616746042,0.06707600264115766,0.0006266077656208109,1490846145.6026042,11926769164.820833,0.013280770253729014,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.4,UNIQUE,No,100000000,11,0.06524690463636364,0.0012178973094829188,0.0652412740534002,0.0012158158228520127,1532772028.9176092,12262176231.340874,0.013654254818607551,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.5,UNIQUE,No,100000000,11,0.06404241445454546,0.00025336208261050397,0.06403677090731534,0.000257293675977584,1561602788.2595239,12492822306.07619,0.013911085271696157,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.6,UNIQUE,No,100000000,11,0.06341475663636366,0.0003825017224831159,0.06340924523093483,0.00038245204062774794,1577057093.7377126,12616456749.901701,0.01404875546730431,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.7,UNIQUE,No,100000000,11,0.06326109672727273,0.00029322812488110874,0.06325564540516247,0.0002949170708644907,1580886565.2936444,12647092522.349155,0.014082869203371262,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.7999999999999999,UNIQUE,No,100000000,11,0.0636532089090909,0.0002999547261766452,0.06364783755215732,0.00029998997010566525,1571145287.0343518,12569162296.274815,0.013996091852857325,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.8999999999999999,UNIQUE,No,100000000,11,0.06988678181818182,0.0006097483572236783,0.06988148429177024,0.0006114044551770101,1430994218.4753613,11447953747.80289,0.012747596729576694,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.1,UNIFORM,No,100000000,11,0.06918830272727274,0.004076046636656547,0.06918292999267578,0.0040787055884692934,1445443261.9518535,11563546095.614828,0.012876311840363575,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.2,UNIFORM,No,100000000,11,0.06897605027272728,0.0004355213737381127,0.06897068301114169,0.00043562856622579555,1449891397.8254466,11599131182.603573,0.012915936767971838,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.30000000000000004,UNIFORM,No,100000000,11,0.06705543254545455,0.0006021725673874137,0.06704993785511364,0.0006033296206601019,1491425692.5351257,11931405540.281006,0.01328593297939643,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.4,UNIFORM,No,100000000,11,0.06428108272727273,0.03127848106145733,0.06427554945512251,0.03128327429446077,1555801558.2553747,12446412466.042997,0.013859406697685421,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.5,UNIFORM,No,100000000,11,0.06403595218181818,0.0008424562226042858,0.06403062612360173,0.0008416345207341068,1561752649.5362494,12494021196.289995,0.013912420267391047,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.6,UNIFORM,No,100000000,11,0.06345626490909091,0.0002616791219334027,0.06345085629549894,0.00026342360999119735,1576022859.8694856,12608182878.955885,0.01403954229501751,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.7,UNIFORM,No,100000000,11,0.06333125536363637,0.0002099589133498871,0.063326021714644,0.00020856218327940887,1579129673.5900152,12633037388.720121,0.014067218443468636,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.7999999999999999,UNIFORM,No,100000000,11,0.06380253554545455,0.00033279867530066725,0.063797061920166,0.0003348330883940887,1567470303.3368123,12539762426.694498,0.013963354327045435,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.8999999999999999,UNIFORM,No,100000000,11,0.07055124263636364,0.005059411064470772,0.07054587346857245,0.005054540167730359,1417517355.4913752,11340138843.931002,0.01262754200658651,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.1,GAUSSIAN,No,100000000,11,0.06919409681818182,0.0005224715930853448,0.06918860764936968,0.0005200745967572223,1445324648.0515208,11562597184.412167,0.012875255202853484,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.2,GAUSSIAN,No,100000000,11,0.06958156254545454,0.0003918305352390283,0.06957549424604936,0.00038972202243796306,1437287669.7987409,11498301358.389927,0.012803660114370197,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.30000000000000004,GAUSSIAN,No,100000000,11,0.0681513359090909,0.0005948989257183495,0.06814571033824574,0.000595526495261821,1467443798.056303,11739550384.450424,0.013072297231829951,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.4,GAUSSIAN,No,100000000,11,0.06685825854545456,0.0007454757001334387,0.06685286365855823,0.0007474593173783835,1495822235.9888155,11966577887.910524,0.013325098310903788,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.5,GAUSSIAN,No,100000000,11,0.06627705681818183,0.0005630770491739888,0.0662715107310902,0.0005626588845737492,1508944022.8059664,12071552182.447731,0.013441989940902637,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.6,GAUSSIAN,No,100000000,11,0.06692918090909092,0.00025382878914624417,0.06692370536110617,0.0002531769891507106,1494238841.9831378,11953910735.865103,0.013310993104895398,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.7,GAUSSIAN,No,100000000,11,0.07026752027272729,0.00026362705904800846,0.07026194624467329,0.00026434211867263,1423245516.8800738,11385964135.04059,0.012678569670040565,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.7999999999999999,GAUSSIAN,No,100000000,11,0.07985989563636364,0.010859783543314126,0.07985459206321023,0.01085614344075895,1252276136.1155453,10018209088.924362,0.011155538555761343,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.8999999999999999,GAUSSIAN,No,100000000,11,0.10914429263636363,0.00045673407432265007,0.10913903808593749,0.0004579464402798095,916262427.7598883,7330099422.079106,0.008162257943984182,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.1,UNIQUE,Yes,,,,,,,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.2,UNIQUE,Yes,,,,,,,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.30000000000000004,UNIQUE,Yes,,,,,,,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.4,UNIQUE,Yes,,,,,,,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.5,UNIQUE,Yes,,,,,,,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.6,UNIQUE,Yes,,,,,,,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.7,UNIQUE,Yes,,,,,,,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.7999999999999999,UNIQUE,Yes,,,,,,,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.8999999999999999,UNIQUE,Yes,,,,,,,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.1,UNIFORM,Yes,,,,,,,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.2,UNIFORM,Yes,,,,,,,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.30000000000000004,UNIFORM,Yes,,,,,,,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.4,UNIFORM,Yes,,,,,,,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.5,UNIFORM,Yes,,,,,,,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.6,UNIFORM,Yes,,,,,,,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.7,UNIFORM,Yes,,,,,,,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.7999999999999999,UNIFORM,Yes,,,,,,,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.8999999999999999,UNIFORM,Yes,,,,,,,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.1,GAUSSIAN,Yes,,,,,,,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.2,GAUSSIAN,Yes,,,,,,,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.30000000000000004,GAUSSIAN,Yes,,,,,,,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.4,GAUSSIAN,Yes,,,,,,,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.5,GAUSSIAN,Yes,,,,,,,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.6,GAUSSIAN,Yes,,,,,,,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.7,GAUSSIAN,Yes,,,,,,,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.7999999999999999,GAUSSIAN,Yes,,,,,,,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.8999999999999999,GAUSSIAN,Yes,,,,,,,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.1,UNIQUE,Yes,,,,,,,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.2,UNIQUE,Yes,,,,,,,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.30000000000000004,UNIQUE,Yes,,,,,,,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.4,UNIQUE,Yes,,,,,,,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.5,UNIQUE,Yes,,,,,,,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.6,UNIQUE,Yes,,,,,,,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.7,UNIQUE,Yes,,,,,,,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.7999999999999999,UNIQUE,Yes,,,,,,,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.8999999999999999,UNIQUE,Yes,,,,,,,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.1,UNIFORM,Yes,,,,,,,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.2,UNIFORM,Yes,,,,,,,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.30000000000000004,UNIFORM,Yes,,,,,,,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.4,UNIFORM,Yes,,,,,,,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.5,UNIFORM,Yes,,,,,,,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.6,UNIFORM,Yes,,,,,,,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.7,UNIFORM,Yes,,,,,,,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.7999999999999999,UNIFORM,Yes,,,,,,,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.8999999999999999,UNIFORM,Yes,,,,,,,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.1,GAUSSIAN,Yes,,,,,,,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.2,GAUSSIAN,Yes,,,,,,,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.30000000000000004,GAUSSIAN,Yes,,,,,,,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.4,GAUSSIAN,Yes,,,,,,,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.5,GAUSSIAN,Yes,,,,,,,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.6,GAUSSIAN,Yes,,,,,,,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.7,GAUSSIAN,Yes,,,,,,,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.7999999999999999,GAUSSIAN,Yes,,,,,,,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.8999999999999999,GAUSSIAN,Yes,,,,,,,,,,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.1,UNIQUE,No,100000000,11,0.10274451318181815,7.919867067043338e-05,0.10273922452059657,7.937319902834375e-05,973338084.5205092,15573409352.328148,0.017341399738464033,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.2,UNIQUE,No,100000000,11,0.08645334581818183,0.001069146654464478,0.08644775668057528,0.0010671316663770792,1156768016.19619,18508288259.13904,0.02060946437065618,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.30000000000000004,UNIQUE,No,100000000,11,0.0849845310909091,0.0009667560971783149,0.08497915302623403,0.0009668284317229073,1176759198.4486933,18828147175.179092,0.020965635662213034,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.4,UNIQUE,No,100000000,11,0.08270579718181818,0.0006225482401469632,0.08270028825239702,0.0006211092880687401,1209185628.166194,19346970050.659103,0.02154335854058926,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.5,UNIQUE,No,100000000,11,0.07897613554545456,0.00040252419577252956,0.07897078774192118,0.0004014339582821938,1266291028.1052647,20260656449.684235,0.022560772308032793,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.6,UNIQUE,No,100000000,11,0.07642653472727273,0.0005395866182401395,0.0764211196899414,0.0005422698066863944,1308538796.6798143,20936620746.87703,0.023313476280640934,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.7,UNIQUE,No,100000000,11,0.07490249536363637,0.000634047536343183,0.07489684989235618,0.0006325369521988604,1335169638.559202,21362714216.94723,0.023787942534193307,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.7999999999999999,UNIQUE,No,100000000,11,0.07593944427272728,0.0009142890604426159,0.07593388227982954,0.0009146761769850632,1316935167.7750735,21070962684.401176,0.023463069551294784,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.8999999999999999,UNIQUE,No,100000000,11,0.08843876872727273,0.00041083894043193324,0.08843329204212536,0.00041137249157570003,1130795854.0361114,18092733664.57778,0.020146733431373135,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.1,UNIFORM,No,100000000,11,0.10274524690909091,6.354018158648775e-05,0.10274024616588245,6.505868237862087e-05,973328405.6818581,15573254490.909729,0.017341227296213263,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.2,UNIFORM,No,100000000,11,0.08623861036363638,0.0010673083587560577,0.08623346016623758,0.0010686851723073897,1159642670.1099992,18554282721.759987,0.020660680411024784,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.30000000000000004,UNIFORM,No,100000000,11,0.08475259200000002,0.0008236603929658194,0.08474735745516691,0.0008206519014245491,1179977795.212105,18879644723.39368,0.02102297953271282,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.4,UNIFORM,No,100000000,11,0.08255245990909091,0.0005501564153902886,0.08254696863347834,0.0005507180339496883,1211431523.8396688,19382904381.4347,0.02158337236031337,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.5,UNIFORM,No,100000000,11,0.07882271636363637,0.0007382712526508775,0.07881737240878019,0.0007369330540988989,1268755820.4980211,20300093127.968338,0.022604686083559384,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.6,UNIFORM,No,100000000,11,0.07642493618181818,0.0021891595178698607,0.07641953832452947,0.0021892748892233535,1308565874.5454836,20937053992.727737,0.023313958711257902,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.7,UNIFORM,No,100000000,11,0.07487487463636364,0.0004947702667866301,0.0748693882335316,0.0004976890232915997,1335659371.0647311,21370549937.035698,0.023796667814009607,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.7999999999999999,UNIFORM,No,100000000,11,0.07613220190909092,0.0005209790978913337,0.0761271383112127,0.0005219125155713814,1313592001.7273405,21017472027.637447,0.023403506302154726,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.8999999999999999,UNIFORM,No,100000000,11,0.08904410554545455,0.0002932838905896244,0.08903856867009943,0.0002917179813264836,1123108799.856321,17969740797.701138,0.020009777648523394,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.1,GAUSSIAN,No,100000000,11,0.10816177872727271,3.566492121809612e-05,0.1081567417491566,3.4091418821815285e-05,924584065.521554,14793345048.344864,0.016472777678191882,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.2,GAUSSIAN,No,100000000,11,0.07844791381818184,0.0007774184979175578,0.07844240153919567,0.0007768336933861818,1274820735.1866012,20397131762.98562,0.022712741148564017,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.30000000000000004,GAUSSIAN,No,100000000,11,0.07857616672727273,0.00271679366288135,0.0785705892389471,0.002718763857120997,1272740868.6713836,20363853898.742138,0.022675685373991297,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.4,GAUSSIAN,No,100000000,11,0.078771252,0.0004900731018220413,0.07876580116965554,0.0004907366567772091,1269586527.5921922,20313384441.475075,0.022619486309724064,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.5,GAUSSIAN,No,100000000,11,0.07914328254545455,0.000779792374350826,0.0791376987804066,0.0007802219776220264,1263620266.208178,20217924259.33085,0.022513188893389716,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.6,GAUSSIAN,No,100000000,11,0.08218435618181817,0.000426789762216608,0.08217897935347124,0.00042696474574037044,1216856193.4783387,19469699095.65342,0.021680020550854096,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.7,GAUSSIAN,No,100000000,11,0.08990060427272728,0.0003185792309776693,0.08989519084583629,0.00032000463514696945,1112406559.8958762,17798504958.33402,0.019819102050596425,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.7999999999999999,GAUSSIAN,No,100000000,11,0.10803566354545456,0.00023774928510362925,0.1080306028886275,0.00023995316819948022,925663629.8058382,14810618076.893412,0.01649201164847916,,
-nvbench_static_multimap_single_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.8999999999999999,GAUSSIAN,No,100000000,11,0.16119024045454547,0.00023327209159478972,0.1611849559437145,0.00023296292641002099,620405294.1201277,9926484705.922043,0.011053401049745719,,
-nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.8,,No,100000000,11,0.06821054463636364,0.004518757391277612,0.06820500876686791,0.004522581115714032,1466167981.0321672,11729343848.257338,0.013060931986104682,2^0,1
-nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.8,,No,100000000,11,0.07414215518181819,0.0002442139124343712,0.0741362963589755,0.0002402154537706555,1348866950.6201093,10790935604.960875,0.012015989796715625,2^1,2
-nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.8,,No,100000000,11,0.087509521,0.00022051151123671586,0.08750433696400034,0.00021913974618767155,1142800499.6042702,9142403996.834162,0.010180306617056285,2^2,4
-nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.8,,No,100000000,11,0.11585742990909091,0.00025986509574229763,0.1158519155328924,0.0002601219294654579,863170881.0339717,6905367048.271773,0.00768930730681631,2^3,8
-nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.8,,No,100000000,11,0.17301705736363637,0.00014263782881344684,0.17301215431906958,0.00014381731895805118,577994074.4254284,4623952595.403427,0.005148892481697445,2^4,16
-nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.8,,No,100000000,11,0.2845625553636364,0.00015372202418767293,0.2845579639781605,0.0001535428523047237,351422250.1524325,2811378001.21946,0.003130543134909782,2^5,32
-nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.8,,No,100000000,11,0.5018689946363637,0.00020770102043540117,0.5018656560724432,0.0002082466096565821,199256511.7577307,1594052094.0618455,0.0017750188119809247,2^6,64
-nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.8,,No,100000000,11,0.9222340671818183,0.000556648194377783,0.9222326438210225,0.0005566477496281188,108432509.59505938,867460076.760475,0.0009659395452809595,2^7,128
-nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.8,,No,100000000,9,1.7673624833333335,0.00038052778852527245,1.767364963107639,0.00038057866158041693,56581409.096265785,452651272.7701263,0.0005040390633575558,2^8,256
-nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.8,,Yes,,,,,,,,,,2^0,1
-nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.8,,Yes,,,,,,,,,,2^1,2
-nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.8,,Yes,,,,,,,,,,2^2,4
-nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.8,,Yes,,,,,,,,,,2^3,8
-nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.8,,Yes,,,,,,,,,,2^4,16
-nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.8,,Yes,,,,,,,,,,2^5,32
-nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.8,,Yes,,,,,,,,,,2^6,64
-nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.8,,Yes,,,,,,,,,,2^7,128
-nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.8,,Yes,,,,,,,,,,2^8,256
-nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.8,,Yes,,,,,,,,,,2^0,1
-nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.8,,Yes,,,,,,,,,,2^1,2
-nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.8,,Yes,,,,,,,,,,2^2,4
-nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.8,,Yes,,,,,,,,,,2^3,8
-nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.8,,Yes,,,,,,,,,,2^4,16
-nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.8,,Yes,,,,,,,,,,2^5,32
-nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.8,,Yes,,,,,,,,,,2^6,64
-nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.8,,Yes,,,,,,,,,,2^7,128
-nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.8,,Yes,,,,,,,,,,2^8,256
-nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.8,,No,100000000,11,0.08536571045454547,0.00039028855440837823,0.08536017469926314,0.00039028326257579125,1171506505.8420415,18744104093.472664,0.020872051486638423,2^0,1
-nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.8,,No,100000000,11,0.09729859227272727,0.0005263976466797341,0.09729340570623224,0.0005262994272018765,1027818887.3554293,16445102197.686869,0.018312052582586752,2^1,2
-nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.8,,No,100000000,11,0.12347132945454548,0.00026653618366292287,0.12346609913219106,0.0002663909969661335,809938928.1986897,12959022851.179035,0.014430211805136291,2^2,4
-nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.8,,No,100000000,11,0.17666694827272733,0.00030476914761359364,0.17666187771883876,0.0003038166768234644,566053079.9924599,9056849279.879358,0.010085039195988809,2^3,8
-nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.8,,No,100000000,11,0.2824962571818182,0.00010072454601307116,0.2824916270862926,0.00010048219195278699,353992792.7472804,5663884683.956487,0.006306884135320703,2^4,16
-nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.8,,No,100000000,11,0.48905533781818195,0.00023076981571944953,0.489051230690696,0.00023064481082195662,204477555.16077155,3271640882.572345,0.003643057924044533,2^5,32
-nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.8,,No,100000000,11,0.8969527960000001,0.00012232545382140375,0.896950838955966,0.00012244915401747237,111488830.44292387,1783821287.086782,0.001986331785257338,2^6,64
-nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.8,,No,100000000,9,1.6962511944444445,0.00038631756977580033,1.6962525906032984,0.000386339580966324,58953484.02352828,943255744.3764524,0.0010503400089710711,2^7,128
-nvbench_static_multimap_multi_insert,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.8,,No,100000000,5,3.2905120279999998,0.00038547888143643054,3.290520166015625,0.0003854291119151701,30390331.909464173,486245310.55142677,0.000541446905456531,2^8,256
-nvbench_static_multimap_find,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.1,,No,100000000,17,0.030851307941176475,0.0022650732636574984,0.030845771004171935,0.002266629399241108,3241935498.596383,25935483988.771065,0.028879841599525932,,
-nvbench_static_multimap_find,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.2,,No,100000000,19,0.026449221947368422,0.0008253173910985551,0.026443668164704975,0.0008271069798394431,3781623615.042655,30252988920.34124,0.033687496570719205,,
-nvbench_static_multimap_find,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.30000000000000004,,No,100000000,23,0.022465648956521738,0.0007264679211652522,0.022460104403288467,0.000730043899022379,4452339054.370497,35618712434.963974,0.03966237042448062,,
-nvbench_static_multimap_find,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.4,,No,100000000,23,0.022631443739130435,0.011430483669021833,0.02262590333689814,0.011435552785415044,4419713039.1218815,35357704312.97505,0.03937173103550707,,
-nvbench_static_multimap_find,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.5,,No,100000000,22,0.022819085272727278,0.0008192435837584734,0.022813602880998086,0.0008159684844073193,4383349728.739779,35066797829.918236,0.03904779903737688,,
-nvbench_static_multimap_find,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.6,,No,100000000,22,0.023425075772727275,0.0005757309816581208,0.02341948483207009,0.0005752889173025987,4269948750.668604,34159590005.34883,0.038037599332495405,,
-nvbench_static_multimap_find,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.7,,No,100000000,21,0.02472987876190477,0.0005648620931997654,0.024724333717709497,0.0005639783645839629,4044598375.905766,32356787007.246128,0.03603013091421185,,
-nvbench_static_multimap_find,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.7999999999999999,,No,100000000,18,0.027860730388888883,0.0005571224569463895,0.027855303022596572,0.0005533948568001576,3589980691.248584,28719845529.98867,0.031980301197696195,,
-nvbench_static_multimap_find,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.8999999999999999,,No,100000000,14,0.03840460892857143,0.0002420097491422894,0.03839919580732073,0.00024399529926795408,2604221205.615332,20833769644.922657,0.02319894888126543,,
-nvbench_static_multimap_find,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.1,,Yes,,,,,,,,,,,
-nvbench_static_multimap_find,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.2,,Yes,,,,,,,,,,,
-nvbench_static_multimap_find,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.30000000000000004,,Yes,,,,,,,,,,,
-nvbench_static_multimap_find,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.4,,Yes,,,,,,,,,,,
-nvbench_static_multimap_find,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.5,,Yes,,,,,,,,,,,
-nvbench_static_multimap_find,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.6,,Yes,,,,,,,,,,,
-nvbench_static_multimap_find,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.7,,Yes,,,,,,,,,,,
-nvbench_static_multimap_find,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.7999999999999999,,Yes,,,,,,,,,,,
-nvbench_static_multimap_find,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.8999999999999999,,Yes,,,,,,,,,,,
-nvbench_static_multimap_find,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.1,,Yes,,,,,,,,,,,
-nvbench_static_multimap_find,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.2,,Yes,,,,,,,,,,,
-nvbench_static_multimap_find,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.30000000000000004,,Yes,,,,,,,,,,,
-nvbench_static_multimap_find,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.4,,Yes,,,,,,,,,,,
-nvbench_static_multimap_find,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.5,,Yes,,,,,,,,,,,
-nvbench_static_multimap_find,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.6,,Yes,,,,,,,,,,,
-nvbench_static_multimap_find,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.7,,Yes,,,,,,,,,,,
-nvbench_static_multimap_find,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.7999999999999999,,Yes,,,,,,,,,,,
-nvbench_static_multimap_find,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.8999999999999999,,Yes,,,,,,,,,,,
-nvbench_static_multimap_find,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.1,,No,100000000,11,0.068847447,7.054018023164516e-05,0.0688421235518022,7.203100005969825e-05,1452599002.4806857,23241584039.69097,0.025880113356625673,,
-nvbench_static_multimap_find,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.2,,No,100000000,15,0.03524321166666666,0.001650529442398669,0.03523741022745767,0.001648292123721572,2837893005.033556,45406288080.536896,0.050561092592530575,,
-nvbench_static_multimap_find,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.30000000000000004,,No,100000000,16,0.031927686312499995,0.0020041208961081945,0.031922111988067624,0.0020052068452505846,3132624809.955546,50121996959.288734,0.05581215810211563,,
-nvbench_static_multimap_find,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.4,,No,100000000,19,0.026822425842105264,0.0011111774650891613,0.026816889311137956,0.0011088392005559964,3728993278.8165193,59663892461.06431,0.06643730898689637,,
-nvbench_static_multimap_find,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.5,,No,100000000,19,0.026969198526315793,0.0013579985487244533,0.02696380635311729,0.0013671279581181654,3708675202.988876,59338803247.822014,0.06607531362223625,,
-nvbench_static_multimap_find,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.6,,No,100000000,19,0.027730592263157893,0.0012563867319355922,0.027725177162571956,0.0012576373151306354,3606829973.119039,57709279569.904625,0.06426079627136258,,
-nvbench_static_multimap_find,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.7,,No,100000000,17,0.029531005647058822,0.0011698700131234213,0.02952505212671616,0.0011606651967315915,3386954223.5122285,54191267576.195656,0.06034339765379541,,
-nvbench_static_multimap_find,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.7999999999999999,,No,100000000,15,0.03383637233333333,0.0009077916542839276,0.03383104883829753,0.000908598413189415,2955864610.582149,47293833769.314384,0.05266292421932278,,
-nvbench_static_multimap_find,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.8999999999999999,,No,100000000,11,0.04776554863636364,0.0004635063337864464,0.04776010443947532,0.0004571527281544445,2093797766.4334137,33500764262.93462,0.03730397959010501,,
-nvbench_static_multimap_find_all,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.4,,No,100000000,11,0.12107264445454548,0.0002888403006084369,0.12106742720170453,0.00028523266936586786,825986000.6225694,6607888004.980556,0.007358056590494668,2^0,1
-nvbench_static_multimap_find_all,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.4,,No,100000000,11,0.13438666109090908,0.0005865902986289532,0.13438147527521302,0.0005877756359823397,744150187.3319978,5953201498.655982,0.006629045996044735,2^1,2
-nvbench_static_multimap_find_all,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.4,,No,100000000,11,0.14746722963636363,0.0005470865870191734,0.1474622372713956,0.0005474430105601299,678139718.0076406,5425117744.061125,0.006041010885900447,2^2,4
-nvbench_static_multimap_find_all,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.4,,No,100000000,11,0.15730588109090912,0.0004655900701124892,0.15730073408647016,0.0004660007318761322,635724941.6587514,5085799533.270011,0.005663171159303301,2^3,8
-nvbench_static_multimap_find_all,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.4,,No,100000000,11,0.16485765590909093,0.0006792304572982247,0.16485292191938916,0.0006785413570261261,606601319.7442666,4852810557.954133,0.005403731824973869,2^4,16
-nvbench_static_multimap_find_all,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.4,,No,100000000,11,0.17627661436363637,0.0010419775160612906,0.17627154679731888,0.0010428692726222341,567306532.5453933,4538452260.363147,0.005053685616318,2^5,32
-nvbench_static_multimap_find_all,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.4,,No,100000000,11,0.22831155145454549,0.0003346988531024427,0.22830722462047232,0.0003356444486342128,438006288.08937395,3504050304.7149916,0.0039018519107163444,2^6,64
-nvbench_static_multimap_find_all,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.4,,No,100000000,11,0.343012823,0.00013615565054172037,0.34300853382457386,0.0001364498010068651,291537936.05363584,2332303488.4290867,0.0025970811008198744,2^7,128
-nvbench_static_multimap_find_all,0,Tesla V100-SXM2-32GB,I32,I32,100000000,0.4,,No,100000000,11,0.5712359422727272,0.00010408496369820208,0.57123291015625,0.0001044324803036856,175059941.7891502,1400479534.3132017,0.0015594706901114436,2^8,256
-nvbench_static_multimap_find_all,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.4,,Yes,,,,,,,,,,2^0,1
-nvbench_static_multimap_find_all,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.4,,Yes,,,,,,,,,,2^1,2
-nvbench_static_multimap_find_all,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.4,,Yes,,,,,,,,,,2^2,4
-nvbench_static_multimap_find_all,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.4,,Yes,,,,,,,,,,2^3,8
-nvbench_static_multimap_find_all,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.4,,Yes,,,,,,,,,,2^4,16
-nvbench_static_multimap_find_all,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.4,,Yes,,,,,,,,,,2^5,32
-nvbench_static_multimap_find_all,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.4,,Yes,,,,,,,,,,2^6,64
-nvbench_static_multimap_find_all,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.4,,Yes,,,,,,,,,,2^7,128
-nvbench_static_multimap_find_all,0,Tesla V100-SXM2-32GB,I32,I64,100000000,0.4,,Yes,,,,,,,,,,2^8,256
-nvbench_static_multimap_find_all,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.4,,Yes,,,,,,,,,,2^0,1
-nvbench_static_multimap_find_all,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.4,,Yes,,,,,,,,,,2^1,2
-nvbench_static_multimap_find_all,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.4,,Yes,,,,,,,,,,2^2,4
-nvbench_static_multimap_find_all,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.4,,Yes,,,,,,,,,,2^3,8
-nvbench_static_multimap_find_all,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.4,,Yes,,,,,,,,,,2^4,16
-nvbench_static_multimap_find_all,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.4,,Yes,,,,,,,,,,2^5,32
-nvbench_static_multimap_find_all,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.4,,Yes,,,,,,,,,,2^6,64
-nvbench_static_multimap_find_all,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.4,,Yes,,,,,,,,,,2^7,128
-nvbench_static_multimap_find_all,0,Tesla V100-SXM2-32GB,I64,I32,100000000,0.4,,Yes,,,,,,,,,,2^8,256
-nvbench_static_multimap_find_all,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.4,,No,100000000,11,0.12221130081818181,0.0002000734255192767,0.12220602208917791,0.00020057244323789153,818290279.7296404,13092644475.674246,0.014579002988341654,2^0,1
-nvbench_static_multimap_find_all,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.4,,No,100000000,11,0.13576571445454544,0.0003030118102881788,0.13576052301580258,0.00030194008643213716,736591151.6734505,11785458426.775208,0.013123417040932341,2^1,2
-nvbench_static_multimap_find_all,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.4,,No,100000000,11,0.14913550090909092,0.0004339145518877869,0.1491303308660334,0.0004332491773554188,670554403.1135551,10728870449.816881,0.011946878618756327,2^2,4
-nvbench_static_multimap_find_all,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.4,,No,100000000,11,0.15929928772727273,0.0006312398564412787,0.15929455705122514,0.0006315074357204146,627767839.9761174,10044285439.617878,0.01118457525613094,2^3,8
-nvbench_static_multimap_find_all,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.4,,No,100000000,11,0.1686964322727273,0.000658022645946661,0.16869152415882455,0.0006575345582514861,592798011.0361035,9484768176.577656,0.010561538109964786,2^4,16
-nvbench_static_multimap_find_all,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.4,,No,100000000,11,0.21259125472727272,0.00025829907868773946,0.2125865880792791,0.00025848914851093883,470396561.24829185,7526344979.97267,0.008380782519389464,2^5,32
-nvbench_static_multimap_find_all,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.4,,No,100000000,11,0.30872113981818183,0.00015102865045400039,0.30871700772372157,0.0001507158431270966,323921253.1157093,5182740049.851349,0.0057711169668562805,2^6,64
-nvbench_static_multimap_find_all,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.4,,No,100000000,11,0.49863340836363645,0.0001678603470263395,0.49862963312322445,0.0001682559561403036,200549653.20379862,3208794451.260778,0.0035730767745830714,2^7,128
-nvbench_static_multimap_find_all,0,Tesla V100-SXM2-32GB,I64,I64,100000000,0.4,,No,100000000,11,0.8690732562727274,0.00011058842912884806,0.869071494362571,0.0001106222336976893,115065331.96482986,1841045311.4372778,0.0020500522371156974,2^8,256
diff --git a/benchmarks/analysis/notebooks/StaticMultimap.ipynb b/benchmarks/analysis/notebooks/StaticMultimap.ipynb
index 43f622e8f..c91fbfe04 100644
--- a/benchmarks/analysis/notebooks/StaticMultimap.ipynb
+++ b/benchmarks/analysis/notebooks/StaticMultimap.ipynb
@@ -39,7 +39,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Define the data path\n",
+    "# Specify the data path\n",
     "datafile = '../data/static-multimap-data.csv'\n",
     "\n",
     "output_keys = ['Benchmark', 'Label', 'Distribution', 'NumReps', 'NumInputs', \\\n",

From 41eedb1f560e2e16e0ec2daf64090f48dbd41b4e Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Wed, 24 Mar 2021 09:47:04 -0700
Subject: [PATCH 043/267] Add multi-insertion CG size benchmark

---
 .../hash_table/static_multimap_bench.cu       | 103 ++++++++++++++++--
 1 file changed, 96 insertions(+), 7 deletions(-)

diff --git a/benchmarks/hash_table/static_multimap_bench.cu b/benchmarks/hash_table/static_multimap_bench.cu
index dd78f0e06..15570770d 100644
--- a/benchmarks/hash_table/static_multimap_bench.cu
+++ b/benchmarks/hash_table/static_multimap_bench.cu
@@ -21,6 +21,10 @@
 
 #include "cuco/static_multimap.cuh"
 
+/**
+ * @brief Generates input keys with a specific distribution.
+ *
+ */
 template <typename Key, typename OutputIt>
 static void generate_keys(OutputIt output_begin, OutputIt output_end, const std::string& dist)
 {
@@ -46,6 +50,10 @@ static void generate_keys(OutputIt output_begin, OutputIt output_end, const std:
   }
 }
 
+/**
+ * @brief Generates input keys by a given number of repetitions per key.
+ *
+ */
 template <typename Key, typename OutputIt>
 static void generate_multikeys(OutputIt output_begin, OutputIt output_end, size_t const num_reps)
 {
@@ -60,11 +68,15 @@ static void generate_multikeys(OutputIt output_begin, OutputIt output_end, size_
   }
 }
 
-template <typename Key, typename Value>
+/**
+ * @brief Helper function to launch insertion benchmark.
+ *
+ */
+template <typename Key, typename Value, size_t cg_size>
 void launch_nvbench_insert(nvbench::state& state,
                            std::vector<Key> const& h_keys,
-                           const std::size_t num_keys,
-                           const std::size_t size)
+                           std::size_t const num_keys,
+                           std::size_t const size)
 {
   std::vector<cuco::pair_type<Key, Value>> h_pairs(num_keys);
 
@@ -87,7 +99,7 @@ void launch_nvbench_insert(nvbench::state& state,
 
                auto const block_size = 128;
                auto const stride     = 1;
-               auto const tile_size  = 4;
+               auto const tile_size  = cg_size;
                auto const grid_size =
                  (tile_size * num_keys + stride * block_size - 1) / (stride * block_size);
 
@@ -106,6 +118,14 @@ void launch_nvbench_insert(nvbench::state& state,
              });
 }
 
+/**
+ * @brief A benchmark evaluating single-value insertion performance by varying occupancy:
+ * - Total number of insertions is fixed at 100'000'000
+ * - Map occupancy: 0.1, ... , 0.8, 0.9
+ * - CG size is fixed at 4
+ * - Three different key distributions are tested: Unique, Uniform and Gaussian
+ *
+ */
 template <typename Key, typename Value>
 void nvbench_static_multimap_single_insert(nvbench::state& state, nvbench::type_list<Key, Value>)
 {
@@ -118,14 +138,25 @@ void nvbench_static_multimap_single_insert(nvbench::state& state, nvbench::type_
   auto const occupancy       = state.get_float64("Occupancy");
   std::size_t const size     = num_keys / occupancy;
   auto const dist            = state.get_string("Distribution");
+  std::size_t const cg_size  = 4;
 
   std::vector<Key> h_keys(num_keys);
 
   generate_keys<Key>(h_keys.begin(), h_keys.end(), dist);
 
-  launch_nvbench_insert<Key, Value>(state, h_keys, num_keys, size);
+  launch_nvbench_insert<Key, Value, cg_size>(state, h_keys, num_keys, size);
 }
 
+/**
+ * @brief A benchmark evaluating multi-value insertion performance by varing number of repetitions
+ * per key:
+ * - Total number of insertions is fixed at 100'000'000
+ * - Map occupancy is fixed at 0.7
+ * - CG size is fixed at 4
+ * - Unique key distributions is tested
+ * - Number of repetitions per key: 1, ... , 128, 256
+ *
+ */
 template <typename Key, typename Value>
 void nvbench_static_multimap_multi_insert(nvbench::state& state, nvbench::type_list<Key, Value>)
 {
@@ -134,6 +165,38 @@ void nvbench_static_multimap_multi_insert(nvbench::state& state, nvbench::type_l
     return;
   }
 
+  std::size_t const num_keys = state.get_int64("NumInputs");
+  auto const occupancy       = state.get_float64("Occupancy");
+  std::size_t const size     = num_keys / occupancy;
+  std::size_t const num_reps = state.get_int64("NumReps");
+  std::size_t const cg_size  = 4;
+
+  std::vector<Key> h_keys(num_keys);
+
+  generate_multikeys<Key>(h_keys.begin(), h_keys.end(), num_reps);
+
+  launch_nvbench_insert<Key, Value, cg_size>(state, h_keys, num_keys, size);
+}
+
+/**
+ * @brief A benchmark evaluating multi-value insertion performance by varing number of repetitions
+ * per key and CUDA CG size:
+ * - Total number of insertions is fixed at 100'000'000
+ * - Map occupancy is fixed at 0.7
+ * - Unique key distributions is tested
+ * - Number of repetitions per key: 1, ... , 128, 256
+ * - CG size: 1, ... , 16, 32
+ *
+ */
+template <typename Key, typename Value, nvbench::int32_t CGSize>
+void nvbench_static_multimap_insert_cgsize(
+  nvbench::state& state, nvbench::type_list<Key, Value, nvbench::enum_type<CGSize>>)
+{
+  if (not std::is_same<Key, Value>::value) {
+    state.skip("Key should be the same type as Value.");
+    return;
+  }
+
   std::size_t const num_keys = state.get_int64("NumInputs");
   auto const occupancy       = state.get_float64("Occupancy");
   std::size_t const size     = num_keys / occupancy;
@@ -143,9 +206,16 @@ void nvbench_static_multimap_multi_insert(nvbench::state& state, nvbench::type_l
 
   generate_multikeys<Key>(h_keys.begin(), h_keys.end(), num_reps);
 
-  launch_nvbench_insert<Key, Value>(state, h_keys, num_keys, size);
+  launch_nvbench_insert<Key, Value, CGSize>(state, h_keys, num_keys, size);
 }
 
+/**
+ * @brief A benchmark evaluating single-value retrieval performance by varing occupancy:
+ * - 100'000'000 uniformed keys are inserted
+ * - Map occupancy: 0.1, ... , 0.8, 0.9
+ * - CG size is fixed at 4
+ *
+ */
 template <typename Key, typename Value>
 void nvbench_static_multimap_find(nvbench::state& state, nvbench::type_list<Key, Value>)
 {
@@ -206,6 +276,15 @@ void nvbench_static_multimap_find(nvbench::state& state, nvbench::type_list<Key,
              });
 }
 
+/**
+ * @brief A benchmark evaluating multi-value retrieval performance by varing number of repetitions
+ * per key:
+ * - 100'000'000 keys are inserted
+ * - Map occupancy is fixed at 0.3
+ * - CG size is fixed at 4
+ * - Number of repetitions per key: 1, ... , 128, 256
+ *
+ */
 template <typename Key, typename Value>
 void nvbench_static_multimap_find_all(nvbench::state& state, nvbench::type_list<Key, Value>)
 {
@@ -279,6 +358,7 @@ void nvbench_static_multimap_find_all(nvbench::state& state, nvbench::type_list<
 
 using key_type   = nvbench::type_list<nvbench::int32_t, nvbench::int64_t>;
 using value_type = nvbench::type_list<nvbench::int32_t, nvbench::int64_t>;
+using cg_size    = nvbench::enum_type_list<1, 2, 4, 8, 16, 32>;
 
 NVBENCH_BENCH_TYPES(nvbench_static_multimap_single_insert, NVBENCH_TYPE_AXES(key_type, value_type))
   .set_type_axes_names({"Key", "Value"})
@@ -291,7 +371,7 @@ NVBENCH_BENCH_TYPES(nvbench_static_multimap_multi_insert, NVBENCH_TYPE_AXES(key_
   .set_type_axes_names({"Key", "Value"})
   .set_max_noise(3)                            // Custom noise: 3%. By default: 0.5%.
   .add_int64_axis("NumInputs", {100'000'000})  // Total number of key/value pairs: 100'000'000
-  .add_float64_axis("Occupancy", {0.8})
+  .add_float64_axis("Occupancy", {0.7})
   .add_int64_power_of_two_axis("NumReps", nvbench::range(0, 8, 1));
 
 NVBENCH_BENCH_TYPES(nvbench_static_multimap_find, NVBENCH_TYPE_AXES(key_type, value_type))
@@ -307,3 +387,12 @@ NVBENCH_BENCH_TYPES(nvbench_static_multimap_find_all, NVBENCH_TYPE_AXES(key_type
   .add_int64_axis("NumInputs", {100'000'000})  // Total number of key/value pairs: 100'000'000
   .add_float64_axis("Occupancy", {0.4})
   .add_int64_power_of_two_axis("NumReps", nvbench::range(0, 8, 1));
+
+NVBENCH_BENCH_TYPES(nvbench_static_multimap_insert_cgsize,
+                    NVBENCH_TYPE_AXES(key_type, value_type, cg_size))
+  .set_type_axes_names({"Key", "Value", "CGSize"})
+  .set_timeout(100)                            // Custom timeout: 100 s. Default is 15 s.
+  .set_max_noise(3)                            // Custom noise: 3%. By default: 0.5%.
+  .add_int64_axis("NumInputs", {100'000'000})  // Total number of key/value pairs: 100'000'000
+  .add_float64_axis("Occupancy", {0.7})
+  .add_int64_power_of_two_axis("NumReps", nvbench::range(0, 8, 1));

From 3889d1950def5ab977400e29acce46b4b5af337d Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Wed, 24 Mar 2021 15:57:52 -0400
Subject: [PATCH 044/267] Update notebook: add multimap CG size analysis +
 update comments

---
 .../analysis/notebooks/StaticMultimap.ipynb   | 214 ++++++++++++++++--
 benchmarks/analysis/notebooks/Utils.py        |  45 ++--
 2 files changed, 216 insertions(+), 43 deletions(-)

diff --git a/benchmarks/analysis/notebooks/StaticMultimap.ipynb b/benchmarks/analysis/notebooks/StaticMultimap.ipynb
index c91fbfe04..ff6a5ec89 100644
--- a/benchmarks/analysis/notebooks/StaticMultimap.ipynb
+++ b/benchmarks/analysis/notebooks/StaticMultimap.ipynb
@@ -5,7 +5,7 @@
    "id": "exceptional-dining",
    "metadata": {},
    "source": [
-    "# Prep"
+    "# Preparation"
    ]
   },
   {
@@ -29,7 +29,7 @@
    "id": "cleared-delight",
    "metadata": {},
    "source": [
-    "## Global Parameters"
+    "### Global Parameters"
    ]
   },
   {
@@ -42,7 +42,7 @@
     "# Specify the data path\n",
     "datafile = '../data/static-multimap-data.csv'\n",
     "\n",
-    "output_keys = ['Benchmark', 'Label', 'Distribution', 'NumReps', 'NumInputs', \\\n",
+    "output_keys = ['Benchmark', 'Label', 'Distribution', 'CGSize', 'NumReps', 'NumInputs', \\\n",
     "               'Occupancy', 'GPU Time (sec)', 'Elem/s (elem/sec)', 'Bandwidth (GB/s)']"
    ]
   },
@@ -51,7 +51,7 @@
    "id": "removed-period",
    "metadata": {},
    "source": [
-    "# Import Data"
+    "### Import Data"
    ]
   },
   {
@@ -67,15 +67,18 @@
     "# Filter out skipped tests\n",
     "perfdf = rawdf[rawdf[\"Key\"] == rawdf[\"Value\"]].reset_index(drop=True)\n",
     "\n",
+    "# Set 'Int64' as CG Size type. Default is 'float64'.\n",
+    "perfdf['CGSize'] = perfdf['CGSize'].astype('Int64')\n",
+    "\n",
     "# Add labels\n",
-    "perfdf.loc[perfdf['NumReps'].isnull(), 'Label'] = perfdf[\"Key\"] + \" \" + perfdf[\"Distribution\"]\n",
-    "perfdf.loc[perfdf['Distribution'].isnull(), 'Label'] = perfdf[\"Key\"]\n",
+    "perfdf.loc[perfdf['NumReps'].isnull() & perfdf['CGSize'].isnull(), 'Label'] = perfdf[\"Key\"] + \" \" + perfdf[\"Distribution\"]\n",
+    "perfdf.loc[perfdf['Distribution'].isnull() & perfdf['CGSize'].isnull(), 'Label'] = perfdf[\"Key\"]\n",
+    "perfdf.loc[perfdf['CGSize'].notnull(), 'Label'] = perfdf[\"Key\"] + \" \" + perfdf['CGSize'].astype(str)\n",
     "\n",
     "perfdf[\"Bandwidth (GB/s)\"] = perfdf[\"GlobalMem BW (bytes/sec)\"] / (1000 * 1000 * 1000)\n",
     "\n",
     "# Trim data frame for visualization\n",
-    "perfdf = perfdf[output_keys]\n",
-    "#perfdf"
+    "perfdf = perfdf[output_keys]"
    ]
   },
   {
@@ -86,15 +89,56 @@
     "# Visualization"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "careful-produce",
+   "metadata": {},
+   "source": [
+    "### Visualization Parameters"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 4,
    "id": "imported-sharing",
    "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "nvbench_static_multimap_single_insert\n",
+      "nvbench_static_multimap_multi_insert\n",
+      "nvbench_static_multimap_find\n",
+      "nvbench_static_multimap_find_all\n",
+      "nvbench_static_multimap_insert_cgsize\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Get benchmark list\n",
+    "unique_bms = perfdf[\"Benchmark\"].unique()\n",
+    "for it in unique_bms:\n",
+    "    print(it)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "blocked-confidence",
+   "metadata": {},
+   "source": [
+    "### Single-Value Insertion"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "periodic-champion",
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 720x360 with 2 Axes>"
       ]
@@ -103,10 +147,34 @@
       "needs_background": "light"
      },
      "output_type": "display_data"
-    },
+    }
+   ],
+   "source": [
+    "for bm in unique_bms:\n",
+    "    tmpdf = perfdf[perfdf[\"Benchmark\"] == bm]    \n",
+    "    unique_labels = tmpdf[\"Label\"].unique()\n",
+    "    \n",
+    "    if \"single_insert\" in bm:\n",
+    "        plot_perf(bm, tmpdf, \"Occupancy\", unique_labels)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "spatial-patent",
+   "metadata": {},
+   "source": [
+    "### Multi-Value Insertion "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "homeless-grave",
+   "metadata": {},
+   "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 720x360 with 2 Axes>"
       ]
@@ -115,10 +183,35 @@
       "needs_background": "light"
      },
      "output_type": "display_data"
-    },
+    }
+   ],
+   "source": [
+    "for bm in unique_bms:\n",
+    "    tmpdf = perfdf[perfdf[\"Benchmark\"] == bm]\n",
+    "    unique_labels = tmpdf[\"Label\"].unique()\n",
+    "    \n",
+    "    flag = \"multi_insert\" in bm\n",
+    "    if flag:\n",
+    "        plot_perf(bm, tmpdf, \"NumReps\", unique_labels, flag)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "apparent-skiing",
+   "metadata": {},
+   "source": [
+    "### Multi-Value Insertion by Varying CG Sizes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "aquatic-accreditation",
+   "metadata": {},
+   "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 720x360 with 2 Axes>"
       ]
@@ -130,7 +223,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 720x360 with 2 Axes>"
       ]
@@ -142,22 +235,93 @@
     }
    ],
    "source": [
-    "# Get benchmark list\n",
-    "unique_bms = perfdf[\"Benchmark\"].unique()\n",
-    "num_bm   = len(unique_bms)\n",
-    "\n",
-    "# Plot benchmarks\n",
     "for bm in unique_bms:\n",
     "    tmpdf = perfdf[perfdf[\"Benchmark\"] == bm]\n",
     "    \n",
+    "    flag = \"insert_cgsize\" in bm\n",
+    "    if flag:\n",
+    "        # Plot INT32 performance\n",
+    "        tmp_int32df = tmpdf[tmpdf[\"Label\"].str.contains('I32')]\n",
+    "        unique_labels = tmp_int32df[\"Label\"].unique()\n",
+    "        plot_perf(bm+'_INT32', tmp_int32df, \"NumReps\", unique_labels, flag, True)\n",
+    "        \n",
+    "        # Plot INT64 Performance\n",
+    "        tmp_int64df = tmpdf[tmpdf[\"Label\"].str.contains('I64')]\n",
+    "        unique_labels = tmp_int64df[\"Label\"].unique()\n",
+    "        plot_perf(bm+'_INT64', tmp_int64df, \"NumReps\", unique_labels, flag, True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "supposed-kruger",
+   "metadata": {},
+   "source": [
+    "### Single-Value Retrieval"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "middle-omaha",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for bm in unique_bms:\n",
+    "    tmpdf = perfdf[perfdf[\"Benchmark\"] == bm]    \n",
+    "    unique_labels = tmpdf[\"Label\"].unique()\n",
+    "    \n",
+    "    if bm == 'nvbench_static_multimap_find':\n",
+    "        plot_perf(bm, tmpdf, \"Occupancy\", unique_labels)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "wrong-skiing",
+   "metadata": {},
+   "source": [
+    "### Multi-Value Retrieval"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "peaceful-bernard",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for bm in unique_bms:\n",
+    "    tmpdf = perfdf[perfdf[\"Benchmark\"] == bm]\n",
     "    unique_labels = tmpdf[\"Label\"].unique()\n",
-    "    num_labels = len(unique_labels)\n",
     "    \n",
-    "    flag = ('find_all' in bm) or ('multi_insert' in bm)\n",
-    "    if (flag):\n",
-    "        plot_insert(bm, tmpdf, \"NumReps\", unique_labels, flag)\n",
-    "    else:\n",
-    "        plot_insert(bm, tmpdf, \"Occupancy\", unique_labels)"
+    "    flag = \"find_all\" in bm\n",
+    "    if flag:\n",
+    "        plot_perf(bm, tmpdf, \"NumReps\", unique_labels, flag)"
    ]
   }
  ],
diff --git a/benchmarks/analysis/notebooks/Utils.py b/benchmarks/analysis/notebooks/Utils.py
index 619ec052c..8c5173187 100644
--- a/benchmarks/analysis/notebooks/Utils.py
+++ b/benchmarks/analysis/notebooks/Utils.py
@@ -7,20 +7,25 @@
 colors = ['b','r','g','m','y','c']
 styles = ['o','s','v','^','D',">"]
 
-def plot_insert(bm, df, xaxis, unique_labels, flag = False):
+def plot_perf(bm, df, xaxis, unique_labels, is_multivalue = False, is_singletype = False):
     fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5))
     fig.suptitle(bm)
     
     marker_handles = []
     
+    # Frist sub-figure shows performance (#ops/s)
     ax1.set_xlabel(xaxis)
     ax1.set_ylabel('Performance (number of operations per second)')
 
+    # Second sub-figure shows bandwidth
     ax2.set_xlabel(xaxis)
     ax2.set_ylabel('Bandwidth (GB/s)')
     
+    # Custom x-axis for multi-value test cases:
+    # - power-of-two ticks
+    # - log-scale
     lax = [ax1, ax2]
-    if flag:
+    if is_multivalue:
         lnum = list(df[xaxis])
         
         for item in lax:
@@ -28,8 +33,12 @@ def plot_insert(bm, df, xaxis, unique_labels, flag = False):
             item.set_xticks(lnum)
             item.get_xaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter())
         
-    num_dist = len(df["Distribution"].unique())
+    if not is_singletype:
+        num_style = len(df["Distribution"].unique())
+    else:
+        num_style = len(df["CGSize"].unique())
 
+    # Iterate over labels and label indices
     for lindex, lbl in enumerate(unique_labels):
         tmpdf = df.loc[df['Label'] == lbl]
 
@@ -37,30 +46,30 @@ def plot_insert(bm, df, xaxis, unique_labels, flag = False):
         perf = tmpdf["Elem/s (elem/sec)"]
         bw   = tmpdf['Bandwidth (GB/s)']
 
-        # Get distribution & type index
-        did = lindex % num_dist
-        tid = int(lindex / num_dist)
+        # Get style & type index
+        sid = lindex % num_style
+        tid = int(lindex / num_style)
 
-        if not tid:
-            ax1.plot(x, perf, color=colors[did])
-            ax1.scatter(x, perf, color=colors[did], marker=styles[did])
+        if (not tid) or is_singletype:
+            ax1.plot(x, perf, color=colors[sid])
+            ax1.scatter(x, perf, color=colors[sid], marker=styles[sid])
 
-            ax2.plot(x, bw, color=colors[did])
-            ax2.scatter(x, bw, color=colors[did], marker=styles[did])
+            ax2.plot(x, bw, color=colors[sid])
+            ax2.scatter(x, bw, color=colors[sid], marker=styles[sid])
 
             # Add legend
-            marker_handles.append(ax1.plot([], [], c=colors[did], marker=styles[did], \
+            marker_handles.append(ax1.plot([], [], c=colors[sid], marker=styles[sid], \
                                           label=lbl)[0])
         else:
-            ax1.plot(x, perf, color=colors[did], linestyle="--")
-            ax1.scatter(x, perf, color=colors[did], marker=styles[did], facecolors='none')
+            ax1.plot(x, perf, color=colors[sid], linestyle="--")
+            ax1.scatter(x, perf, color=colors[sid], marker=styles[sid], facecolors='none')
 
-            ax2.plot(x, bw, color=colors[did], linestyle="--")
-            ax2.scatter(x, bw, color=colors[did], marker=styles[did], facecolors='none')
+            ax2.plot(x, bw, color=colors[sid], linestyle="--")
+            ax2.scatter(x, bw, color=colors[sid], marker=styles[sid], facecolors='none')
 
             # Add legend
-            marker_handles.append(ax1.plot([], [], c=colors[did], marker=styles[did], \
+            marker_handles.append(ax1.plot([], [], c=colors[sid], marker=styles[sid], \
                                           mfc='none', linestyle="--", label=lbl)[0])
     
     leg = plt.legend(handles = marker_handles, loc="lower left", ncol=2, frameon=False)
-    plt.savefig(bm+'.eps')
+    plt.savefig(bm+'.eps')
\ No newline at end of file

From 59c234245644b464e3b28860e4d77ba4fc1d54b9 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Mon, 29 Mar 2021 09:22:46 -0700
Subject: [PATCH 045/267] Use per block output cache instead of global atomic
 writing

---
 .../cuco/detail/static_multimap_kernels.cuh   | 165 ++++++++++++++----
 1 file changed, 135 insertions(+), 30 deletions(-)

diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap_kernels.cuh
index 7b4ea2bd7..ca8bf59fd 100644
--- a/include/cuco/detail/static_multimap_kernels.cuh
+++ b/include/cuco/detail/static_multimap_kernels.cuh
@@ -20,6 +20,34 @@ namespace cuco {
 namespace detail {
 namespace cg = cooperative_groups;
 
+/**
+ * @brief Flushes shared memory buffer into the output sequence.
+ *
+ * @tparam Key key type
+ * @tparam Value value type
+ * @tparam atomicT Type of atomic storage
+ * @tparam OutputIt Device accessible output iterator whose `value_type` is
+ * convertible to the map's `mapped_type`
+ * @param output_size Number of valid output in the buffer
+ * @param output_buffer Shared memory buffer of the key/value pair sequence
+ * @param num_items Size of the output sequence
+ * @param output_begin Beginning of the output sequence of key/value pairs
+ */
+template <typename Key, typename Value, typename atomicT, typename OutputIt>
+__inline__ __device__ void flush_output_buffer(size_t output_size,
+                                               cuco::pair_type<Key, Value>* output_buffer,
+                                               atomicT* num_items,
+                                               OutputIt output_begin)
+{
+  __shared__ size_t offset;
+  if (threadIdx.x == 0) { offset = (*num_items += output_size); }
+  __syncthreads();
+
+  for (auto index = threadIdx.x; index < output_size; index += blockDim.x) {
+    *(output_begin + offset + index) = output_buffer[index];
+  }
+}
+
 /**
  * @brief Initializes each slot in the flat `slots` storage to contain `k` and `v`.
  *
@@ -401,22 +429,57 @@ __global__ void find_all(InputIt first,
   auto tid     = blockDim.x * blockIdx.x + threadIdx.x;
   auto key_idx = tid;
 
-  while (first + key_idx < last) {
-    auto key     = *(first + key_idx);
-    auto found   = view.find_all(key, hash, key_equal);
-    size_t count = 0;
-
-    while (found != view.end()) {
-      size_t index            = (*num_items)++;
-      *(output_begin + index) = cuco::make_pair<Key, Value>(key, (*found).second);
-      ++found;
-      ++count;
-    }
-    if (count == 0) {
-      size_t index            = (*num_items)++;
-      *(output_begin + index) = cuco::make_pair<Key, Value>(key, view.get_empty_value_sentinel());
-    }
-    key_idx += gridDim.x * blockDim.x;
+  constexpr uint32_t step        = 1;
+  constexpr uint32_t buffer_size = block_size * 16;
+  auto const end                 = view.end();
+
+  __shared__ cuco::pair_type<Key, Value> output_buffer[buffer_size];
+  __shared__ uint32_t block_counter;  // TODO: do we really need uint32_t?
+
+  if (0 == threadIdx.x) { block_counter = 0; }
+
+  if (first + key_idx < last) {
+    auto key   = *(first + key_idx);
+    auto found = view.find_all(key, hash, key_equal);
+
+    bool running     = true;
+    bool found_match = false;
+
+    while (__syncthreads_or(running)) {
+      if (running) {
+        if (found == end) {
+          running = false;
+        } else {
+          found_match = true;
+
+          auto index           = atomicAdd_block(&block_counter, step);
+          output_buffer[index] = cuco::make_pair<Key, Value>(key, (*found).second);
+
+          ++found;
+        }
+
+        if ((not running) && (not found_match)) {
+          auto index           = atomicAdd_block(&block_counter, step);
+          output_buffer[index] = cuco::make_pair<Key, Value>(key, view.get_empty_value_sentinel());
+        }
+      }  // if (running)
+
+      __syncthreads();
+
+      if ((block_counter + block_size) > buffer_size) {
+        flush_output_buffer<Key, Value, atomicT, OutputIt>(
+          block_counter, output_buffer, num_items, output_begin);
+        __syncthreads();
+        if (0 == threadIdx.x) { block_counter = 0; }
+        __syncthreads();
+      }
+    }  // while syncthreads_or
+  }
+
+  // Final flush of output cache
+  if (block_counter > 0) {
+    flush_output_buffer<Key, Value, atomicT, OutputIt>(
+      block_counter, output_buffer, num_items, output_begin);
   }
 }
 
@@ -451,6 +514,7 @@ __global__ void find_all(InputIt first,
  * @param hash The unary function to apply to hash each key
  * @param key_equal The binary function to compare two keys for equality
  */
+
 template <uint32_t block_size,
           uint32_t tile_size,
           typename Key,
@@ -473,24 +537,65 @@ __global__ void find_all(InputIt first,
   auto tid     = blockDim.x * blockIdx.x + threadIdx.x;
   auto key_idx = tid / tile_size;
 
-  while (first + key_idx < last) {
+  constexpr uint32_t step        = 1;
+  constexpr uint32_t buffer_size = block_size * 16;
+  auto const end                 = view.end();
+
+  __shared__ cuco::pair_type<Key, Value> output_buffer[buffer_size];
+  __shared__ uint32_t block_counter;  // TODO: do we really need uint32_t?
+
+  if (0 == threadIdx.x) { block_counter = 0; }
+
+  if (first + key_idx < last) {
     auto key   = *(first + key_idx);
     auto found = view.find_all(tile, key, hash, key_equal);
 
-    if (tile.thread_rank() == 0) {
-      size_t count = 0;
-      while (found != view.end()) {
-        size_t index            = (*num_items)++;
-        *(output_begin + index) = cuco::make_pair<Key, Value>(key, (*found).second);
-        ++found;
-        ++count;
-      }
-      if (count == 0) {
-        size_t index            = (*num_items)++;
-        *(output_begin + index) = cuco::make_pair<Key, Value>(key, view.get_empty_value_sentinel());
+    bool running     = true;
+    bool found_match = false;
+
+    // Loop over all matches for each key
+    while (__syncthreads_or(running)) {
+      if (running) {
+        if (found == end) {
+          running = false;
+        } else {
+          found_match = true;
+
+          // First lane of each CG performs scalar retrieval
+          if (tile.thread_rank() == 0) {
+            auto index           = atomicAdd_block(&block_counter, step);
+            output_buffer[index] = cuco::make_pair<Key, Value>(key, (*found).second);
+
+            ++found;
+          }
+          // Stall other lanes
+          else {
+            running = false;
+          }
+        }
+
+        if ((not running) && (not found_match) && (tile.thread_rank() == 0)) {
+          auto index           = atomicAdd_block(&block_counter, step);
+          output_buffer[index] = cuco::make_pair<Key, Value>(key, view.get_empty_value_sentinel());
+        }
+      }  // if (running)
+
+      __syncthreads();
+
+      if ((block_counter + block_size / tile_size) > buffer_size) {
+        flush_output_buffer<Key, Value, atomicT, OutputIt>(
+          block_counter, output_buffer, num_items, output_begin);
+        __syncthreads();
+        if (0 == threadIdx.x) { block_counter = 0; }
+        __syncthreads();
       }
-    }
-    key_idx += (gridDim.x * blockDim.x) / tile_size;
+    }  // while sync_or
+  }
+
+  // Final flush of output cache
+  if (block_counter > 0) {
+    flush_output_buffer<Key, Value, atomicT, OutputIt>(
+      block_counter, output_buffer, num_items, output_begin);
   }
 }
 

From 8d8ab41f727892fc73d4e3615168a4892d382efd Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Mon, 29 Mar 2021 10:28:36 -0700
Subject: [PATCH 046/267] Pass output buffer size as template parameter

---
 include/cuco/detail/static_multimap.inl         | 15 ++++++++-------
 include/cuco/detail/static_multimap_kernels.cuh | 14 ++++++++------
 2 files changed, 16 insertions(+), 13 deletions(-)

diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index b25621a3a..891a94105 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -109,12 +109,13 @@ template <typename InputIt, typename OutputIt, typename Hash, typename KeyEqual>
 OutputIt static_multimap<Key, Value, Scope, Allocator>::find_all(
   InputIt first, InputIt last, OutputIt output_begin, Hash hash, KeyEqual key_equal) noexcept
 {
-  auto num_keys         = std::distance(first, last);
-  auto const block_size = 128;
-  auto const stride     = 1;
-  auto const tile_size  = 4;
-  auto const grid_size  = (tile_size * num_keys + stride * block_size - 1) / (stride * block_size);
-  auto view             = get_device_view();
+  auto num_keys          = std::distance(first, last);
+  auto const block_size  = 128;
+  auto const buffer_size = block_size * 16;
+  auto const stride      = 1;
+  auto const tile_size   = 4;
+  auto const grid_size   = (tile_size * num_keys + stride * block_size - 1) / (stride * block_size);
+  auto view              = get_device_view();
 
   atomic_ctr_type* num_items;
   CUCO_CUDA_TRY(cudaMallocManaged(&num_items, sizeof(atomic_ctr_type)));
@@ -123,7 +124,7 @@ OutputIt static_multimap<Key, Value, Scope, Allocator>::find_all(
   CUCO_CUDA_TRY(cudaGetDevice(&device_id));
   CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_items, sizeof(atomic_ctr_type), device_id));
 
-  detail::find_all<block_size, tile_size, Key, Value>
+  detail::find_all<block_size, tile_size, buffer_size, Key, Value>
     <<<grid_size, block_size>>>(first, last, output_begin, num_items, view, hash, key_equal);
   CUCO_CUDA_TRY(cudaDeviceSynchronize());
   return output_begin + *num_items;
diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap_kernels.cuh
index ca8bf59fd..7214e35b1 100644
--- a/include/cuco/detail/static_multimap_kernels.cuh
+++ b/include/cuco/detail/static_multimap_kernels.cuh
@@ -391,6 +391,7 @@ __global__ void contains(
  * output_begin + *num_items - 1)`. Use `count()` to determine the number of matching keys.
  *
  * @tparam block_size The size of the thread block
+ * @tparam buffer_size Size of the output buffer
  * @tparam Key key type
  * @tparam Value The type of the mapped value for the map
  * @tparam InputIt Device accessible input iterator whose `value_type` is
@@ -410,6 +411,7 @@ __global__ void contains(
  * @param key_equal The binary function to compare two keys for equality
  */
 template <uint32_t block_size,
+          uint32_t buffer_size,
           typename Key,
           typename Value,
           typename InputIt,
@@ -429,9 +431,8 @@ __global__ void find_all(InputIt first,
   auto tid     = blockDim.x * blockIdx.x + threadIdx.x;
   auto key_idx = tid;
 
-  constexpr uint32_t step        = 1;
-  constexpr uint32_t buffer_size = block_size * 16;
-  auto const end                 = view.end();
+  constexpr uint32_t step = 1;
+  auto const end          = view.end();
 
   __shared__ cuco::pair_type<Key, Value> output_buffer[buffer_size];
   __shared__ uint32_t block_counter;  // TODO: do we really need uint32_t?
@@ -496,6 +497,7 @@ __global__ void find_all(InputIt first,
  * @tparam block_size The size of the thread block
  * @tparam tile_size The number of threads in the Cooperative Groups used to perform
  * inserts
+ * @tparam buffer_size Size of the output buffer
  * @tparam Key key type
  * @tparam Value The type of the mapped value for the map
  * @tparam InputIt Device accessible input iterator whose `value_type` is
@@ -517,6 +519,7 @@ __global__ void find_all(InputIt first,
 
 template <uint32_t block_size,
           uint32_t tile_size,
+          uint32_t buffer_size,
           typename Key,
           typename Value,
           typename InputIt,
@@ -537,9 +540,8 @@ __global__ void find_all(InputIt first,
   auto tid     = blockDim.x * blockIdx.x + threadIdx.x;
   auto key_idx = tid / tile_size;
 
-  constexpr uint32_t step        = 1;
-  constexpr uint32_t buffer_size = block_size * 16;
-  auto const end                 = view.end();
+  constexpr uint32_t step = 1;
+  auto const end          = view.end();
 
   __shared__ cuco::pair_type<Key, Value> output_buffer[buffer_size];
   __shared__ uint32_t block_counter;  // TODO: do we really need uint32_t?

From cc1e862e06ac0dab2aa5dea09514945f946ed152 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Mon, 29 Mar 2021 12:40:03 -0700
Subject: [PATCH 047/267] Create a folder for static multimap benchmarks + add
 find_all-specific benchmark

---
 benchmarks/CMakeLists.txt                     |   6 +-
 .../static_multimap/find_all_bench.cu         | 145 ++++++++++++++++++
 .../static_multimap_bench.cu                  |   9 +-
 3 files changed, 155 insertions(+), 5 deletions(-)
 create mode 100644 benchmarks/hash_table/static_multimap/find_all_bench.cu
 rename benchmarks/hash_table/{ => static_multimap}/static_multimap_bench.cu (98%)

diff --git a/benchmarks/CMakeLists.txt b/benchmarks/CMakeLists.txt
index 93fd87f38..24e60de9b 100644
--- a/benchmarks/CMakeLists.txt
+++ b/benchmarks/CMakeLists.txt
@@ -89,9 +89,13 @@ set(STATIC_MAP_BENCH_SRC "${CMAKE_CURRENT_SOURCE_DIR}/hash_table/static_map_benc
 ConfigureBench(STATIC_MAP_BENCH "${STATIC_MAP_BENCH_SRC}")
 
 ###################################################################################################
-set(STATIC_MULTIMAP_BENCH_SRC "${CMAKE_CURRENT_SOURCE_DIR}/hash_table/static_multimap_bench.cu")
+set(STATIC_MULTIMAP_BENCH_SRC "${CMAKE_CURRENT_SOURCE_DIR}/hash_table/static_multimap/static_multimap_bench.cu")
 ConfigureNVBench(STATIC_MULTIMAP_BENCH "${STATIC_MULTIMAP_BENCH_SRC}")
 
+###################################################################################################
+set(FIND_ALL_BENCH_SRC "${CMAKE_CURRENT_SOURCE_DIR}/hash_table/static_multimap/find_all_bench.cu")
+ConfigureNVBench(FIND_ALL_BENCH "${FIND_ALL_BENCH_SRC}")
+
 ###################################################################################################
 set(RBK_BENCH_SRC "${CMAKE_CURRENT_SOURCE_DIR}/reduce_by_key/reduce_by_key.cu")
 ConfigureBench(RBK_BENCH "${RBK_BENCH_SRC}")
diff --git a/benchmarks/hash_table/static_multimap/find_all_bench.cu b/benchmarks/hash_table/static_multimap/find_all_bench.cu
new file mode 100644
index 000000000..29db8fe98
--- /dev/null
+++ b/benchmarks/hash_table/static_multimap/find_all_bench.cu
@@ -0,0 +1,145 @@
+/*
+ * Copyright (c) 2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <nvbench/nvbench.cuh>
+
+#include <thrust/device_vector.h>
+#include <random>
+
+#include "cuco/static_multimap.cuh"
+
+/**
+ * @brief Generates input keys by a given number of repetitions per key.
+ *
+ */
+template <typename Key, typename OutputIt>
+static void generate_multikeys(OutputIt output_begin, OutputIt output_end, size_t const num_reps)
+{
+  auto num_keys = std::distance(output_begin, output_end);
+
+  std::random_device rd;
+  std::mt19937 gen{rd()};
+
+  std::uniform_int_distribution<Key> distribution{1, static_cast<Key>(num_keys / num_reps)};
+  for (auto i = 0; i < num_keys; ++i) {
+    output_begin[i] = distribution(gen);
+  }
+}
+
+/**
+ * @brief A benchmark evaluating multi-value retrieval performance by varing number of repetitions
+ * per key:
+ * - 100'000'000 keys are inserted
+ * - Map occupancy is fixed at 0.3
+ * - CG size is fixed at 4
+ * - Number of repetitions per key: 1, ... , 128, 256
+ *
+ */
+template <typename Key, typename Value, nvbench::int32_t CGSize, nvbench::int32_t BufferSize>
+void nvbench_find_all(
+  nvbench::state& state,
+  nvbench::type_list<Key, Value, nvbench::enum_type<CGSize>, nvbench::enum_type<BufferSize>>)
+{
+  if (not std::is_same<Key, Value>::value) {
+    state.skip("Key should be the same type as Value.");
+    return;
+  }
+
+  std::size_t const num_keys = state.get_int64("NumInputs");
+  auto const occupancy       = state.get_float64("Occupancy");
+  std::size_t const size     = num_keys / occupancy;
+  std::size_t const num_reps = state.get_int64("NumReps");
+
+  std::vector<Key> h_keys(num_keys);
+  std::vector<cuco::pair_type<Key, Value>> h_pairs(num_keys);
+
+  generate_multikeys<Key>(h_keys.begin(), h_keys.end(), num_reps);
+
+  for (auto i = 0; i < num_keys; ++i) {
+    Key key           = h_keys[i];
+    Value val         = h_keys[i];
+    h_pairs[i].first  = key;
+    h_pairs[i].second = val;
+
+    h_keys[i] = i;
+  }
+
+  thrust::device_vector<Key> d_keys(h_keys);
+  thrust::device_vector<cuco::pair_type<Key, Value>> d_results(2 * num_keys);
+  thrust::device_vector<cuco::pair_type<Key, Value>> d_pairs(h_pairs);
+
+  state.add_element_count(num_keys, "NumKeys");
+  state.add_global_memory_writes<Key>(num_keys * 2);
+
+  state.exec(
+    nvbench::exec_tag::sync | nvbench::exec_tag::timer, [&](nvbench::launch& launch, auto& timer) {
+      cuco::static_multimap<Key, Value> map{size, -1, -1};
+      map.insert(d_pairs.begin(), d_pairs.end());
+
+      auto view = map.get_device_view();
+
+      auto const block_size  = 128;
+      auto const buffer_size = block_size * BufferSize;
+      auto const stride      = 1;
+      auto const grid_size = (CGSize * num_keys + stride * block_size - 1) / (stride * block_size);
+
+      using Hash     = cuco::detail::MurmurHash3_32<Key>;
+      using KeyEqual = thrust::equal_to<Key>;
+
+      Hash hash;
+      KeyEqual key_equal;
+
+      using atomic_ctr_type = typename cuco::static_multimap<Key, Value>::atomic_ctr_type;
+      atomic_ctr_type* num_items;
+      CUCO_CUDA_TRY(cudaMallocManaged(&num_items, sizeof(atomic_ctr_type)));
+      *num_items = 0;
+      int device_id;
+      CUCO_CUDA_TRY(cudaGetDevice(&device_id));
+      CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_items, sizeof(atomic_ctr_type), device_id));
+
+      // Use timers to explicitly mark the target region
+      timer.start();
+      cuco::detail::find_all<block_size, CGSize, buffer_size, Key, Value>
+        <<<grid_size, block_size, 0, launch.get_stream()>>>(
+          d_keys.begin(), d_keys.end(), d_results.begin(), num_items, view, hash, key_equal);
+      CUCO_CUDA_TRY(cudaDeviceSynchronize());
+      timer.stop();
+    });
+}
+
+using key_type    = nvbench::type_list<nvbench::int32_t, nvbench::int64_t>;
+using value_type  = nvbench::type_list<nvbench::int32_t, nvbench::int64_t>;
+using cg_size     = nvbench::enum_type_list<1, 2, 4, 8, 16, 32>;
+using buffer_size = nvbench::enum_type_list<1, 2, 4, 8, 16>;
+
+NVBENCH_BENCH_TYPES(nvbench_find_all,
+                    NVBENCH_TYPE_AXES(key_type, value_type, cg_size, nvbench::enum_type_list<16>))
+  .set_type_axes_names({"Key", "Value", "CGSize", "BufferSize"})
+  .set_timeout(100)                            // Custom timeout: 100 s. Default is 15 s.
+  .set_max_noise(3)                            // Custom noise: 3%. By default: 0.5%.
+  .add_int64_axis("NumInputs", {100'000'000})  // Total number of key/value pairs: 100'000'000
+  .add_float64_axis("Occupancy", {0.4})
+  .add_int64_power_of_two_axis("NumReps", nvbench::range(0, 8, 1));
+
+NVBENCH_BENCH_TYPES(
+  nvbench_find_all,
+  NVBENCH_TYPE_AXES(key_type, value_type, nvbench::enum_type_list<8>, buffer_size))
+  .set_type_axes_names({"Key", "Value", "CGSize", "BufferSize"})
+  .set_timeout(100)                            // Custom timeout: 100 s. Default is 15 s.
+  .set_max_noise(3)                            // Custom noise: 3%. By default: 0.5%.
+  .add_int64_axis("NumInputs", {100'000'000})  // Total number of key/value pairs: 100'000'000
+  .add_float64_axis("Occupancy", {0.4})
+  .add_int64_power_of_two_axis("NumReps", nvbench::range(0, 8, 1));
diff --git a/benchmarks/hash_table/static_multimap_bench.cu b/benchmarks/hash_table/static_multimap/static_multimap_bench.cu
similarity index 98%
rename from benchmarks/hash_table/static_multimap_bench.cu
rename to benchmarks/hash_table/static_multimap/static_multimap_bench.cu
index 15570770d..f813f9615 100644
--- a/benchmarks/hash_table/static_multimap_bench.cu
+++ b/benchmarks/hash_table/static_multimap/static_multimap_bench.cu
@@ -326,9 +326,10 @@ void nvbench_static_multimap_find_all(nvbench::state& state, nvbench::type_list<
 
       auto view = map.get_device_view();
 
-      auto const block_size = 128;
-      auto const stride     = 1;
-      auto const tile_size  = 4;
+      auto const block_size  = 128;
+      auto const buffer_size = block_size * 16;
+      auto const stride      = 1;
+      auto const tile_size   = 4;
       auto const grid_size =
         (tile_size * num_keys + stride * block_size - 1) / (stride * block_size);
 
@@ -348,7 +349,7 @@ void nvbench_static_multimap_find_all(nvbench::state& state, nvbench::type_list<
 
       // Use timers to explicitly mark the target region
       timer.start();
-      cuco::detail::find_all<block_size, tile_size, Key, Value>
+      cuco::detail::find_all<block_size, tile_size, buffer_size, Key, Value>
         <<<grid_size, block_size, 0, launch.get_stream()>>>(
           d_keys.begin(), d_keys.end(), d_results.begin(), num_items, view, hash, key_equal);
       CUCO_CUDA_TRY(cudaDeviceSynchronize());

From a5a33746fc2c62de594cae5789cde57b6f12659c Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Mon, 29 Mar 2021 15:36:35 -0700
Subject: [PATCH 048/267] Fix memory leak bugs

---
 .../hash_table/static_multimap/find_all_bench.cu     |  2 ++
 .../static_multimap/static_multimap_bench.cu         |  2 ++
 include/cuco/detail/static_multimap.inl              | 12 ++++++++++--
 include/cuco/detail/static_multimap_kernels.cuh      |  2 +-
 4 files changed, 15 insertions(+), 3 deletions(-)

diff --git a/benchmarks/hash_table/static_multimap/find_all_bench.cu b/benchmarks/hash_table/static_multimap/find_all_bench.cu
index 29db8fe98..7a19f137e 100644
--- a/benchmarks/hash_table/static_multimap/find_all_bench.cu
+++ b/benchmarks/hash_table/static_multimap/find_all_bench.cu
@@ -117,6 +117,8 @@ void nvbench_find_all(
           d_keys.begin(), d_keys.end(), d_results.begin(), num_items, view, hash, key_equal);
       CUCO_CUDA_TRY(cudaDeviceSynchronize());
       timer.stop();
+
+      CUCO_CUDA_TRY(cudaFree(num_items));
     });
 }
 
diff --git a/benchmarks/hash_table/static_multimap/static_multimap_bench.cu b/benchmarks/hash_table/static_multimap/static_multimap_bench.cu
index f813f9615..7f381e2a3 100644
--- a/benchmarks/hash_table/static_multimap/static_multimap_bench.cu
+++ b/benchmarks/hash_table/static_multimap/static_multimap_bench.cu
@@ -354,6 +354,8 @@ void nvbench_static_multimap_find_all(nvbench::state& state, nvbench::type_list<
           d_keys.begin(), d_keys.end(), d_results.begin(), num_items, view, hash, key_equal);
       CUCO_CUDA_TRY(cudaDeviceSynchronize());
       timer.stop();
+
+      CUCO_CUDA_TRY(cudaFree(num_items));
     });
 }
 
diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index 891a94105..41baac2ce 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -127,7 +127,11 @@ OutputIt static_multimap<Key, Value, Scope, Allocator>::find_all(
   detail::find_all<block_size, tile_size, buffer_size, Key, Value>
     <<<grid_size, block_size>>>(first, last, output_begin, num_items, view, hash, key_equal);
   CUCO_CUDA_TRY(cudaDeviceSynchronize());
-  return output_begin + *num_items;
+
+  auto output_end = output_begin + *num_items;
+  CUCO_CUDA_TRY(cudaFree(num_items));
+
+  return output_end;
 }
 
 template <typename Key, typename Value, cuda::thread_scope Scope, typename Allocator>
@@ -154,7 +158,11 @@ std::size_t static_multimap<Key, Value, Scope, Allocator>::count(InputIt first,
   detail::count<block_size, tile_size, Value>
     <<<grid_size, block_size>>>(first, last, num_items, view, hash, key_equal);
   CUCO_CUDA_TRY(cudaDeviceSynchronize());
-  return *num_items;
+
+  size_t result = *num_items;
+  CUCO_CUDA_TRY(cudaFree(num_items));
+
+  return result;
 }
 
 template <typename Key, typename Value, cuda::thread_scope Scope, typename Allocator>
diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap_kernels.cuh
index 7214e35b1..c7147bb4c 100644
--- a/include/cuco/detail/static_multimap_kernels.cuh
+++ b/include/cuco/detail/static_multimap_kernels.cuh
@@ -34,7 +34,7 @@ namespace cg = cooperative_groups;
  * @param output_begin Beginning of the output sequence of key/value pairs
  */
 template <typename Key, typename Value, typename atomicT, typename OutputIt>
-__inline__ __device__ void flush_output_buffer(size_t output_size,
+__inline__ __device__ void flush_output_buffer(uint32_t output_size,
                                                cuco::pair_type<Key, Value>* output_buffer,
                                                atomicT* num_items,
                                                OutputIt output_begin)

From 07b25ffc983493fee1a1e4b6120ee5cd76475b47 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Tue, 30 Mar 2021 16:25:37 -0700
Subject: [PATCH 049/267] Fix the illegal memory access bug related to cache
 flushing

---
 include/cuco/detail/static_multimap_kernels.cuh | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap_kernels.cuh
index c7147bb4c..4c5922569 100644
--- a/include/cuco/detail/static_multimap_kernels.cuh
+++ b/include/cuco/detail/static_multimap_kernels.cuh
@@ -40,7 +40,9 @@ __inline__ __device__ void flush_output_buffer(uint32_t output_size,
                                                OutputIt output_begin)
 {
   __shared__ size_t offset;
-  if (threadIdx.x == 0) { offset = (*num_items += output_size); }
+  if (threadIdx.x == 0) {
+    offset = num_items->fetch_add(output_size, cuda::std::memory_order_relaxed);
+  }
   __syncthreads();
 
   for (auto index = threadIdx.x; index < output_size; index += blockDim.x) {

From d8244e92a299006644539d0bc4e7d4dfb24f530a Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Tue, 6 Apr 2021 10:46:34 -0700
Subject: [PATCH 050/267] Parallel retrieval in find_all

---
 .../cuco/detail/static_multimap_kernels.cuh   | 80 ++++++++-----------
 include/cuco/static_multimap.cuh              |  2 +-
 2 files changed, 34 insertions(+), 48 deletions(-)

diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap_kernels.cuh
index 4c5922569..04e71bb7a 100644
--- a/include/cuco/detail/static_multimap_kernels.cuh
+++ b/include/cuco/detail/static_multimap_kernels.cuh
@@ -543,63 +543,49 @@ __global__ void find_all(InputIt first,
   auto key_idx = tid / tile_size;
 
   constexpr uint32_t step = 1;
-  auto const end          = view.end();
 
-  __shared__ cuco::pair_type<Key, Value> output_buffer[buffer_size];
-  __shared__ uint32_t block_counter;  // TODO: do we really need uint32_t?
-
-  if (0 == threadIdx.x) { block_counter = 0; }
+  const int lane_id = tile.thread_rank();
 
-  if (first + key_idx < last) {
-    auto key   = *(first + key_idx);
-    auto found = view.find_all(tile, key, hash, key_equal);
+  while (first + key_idx < last) {
+    auto key          = *(first + key_idx);
+    auto current_slot = view.initial_slot(tile, key, hash);
 
     bool running     = true;
     bool found_match = false;
 
-    // Loop over all matches for each key
-    while (__syncthreads_or(running)) {
-      if (running) {
-        if (found == end) {
-          running = false;
-        } else {
-          found_match = true;
-
-          // First lane of each CG performs scalar retrieval
-          if (tile.thread_rank() == 0) {
-            auto index           = atomicAdd_block(&block_counter, step);
-            output_buffer[index] = cuco::make_pair<Key, Value>(key, (*found).second);
-
-            ++found;
-          }
-          // Stall other lanes
-          else {
-            running = false;
-          }
+    while (tile.any(running)) {
+      auto const existing_key  = current_slot->first.load(cuda::std::memory_order_relaxed);
+      auto existing_value      = current_slot->second.load(cuda::std::memory_order_relaxed);
+      auto const slot_is_empty = (existing_key == view.get_empty_key_sentinel());
+      auto const equals        = key_equal(existing_key, key);
+      auto const exists        = tile.ballot(not slot_is_empty and equals);
+
+      if (exists) {
+        found_match      = true;
+        auto num_matches = __popc(exists);
+        uint32_t output_idx;
+        if (0 == lane_id) {
+          output_idx = num_items->fetch_add(num_matches, cuda::std::memory_order_relaxed);
         }
-
-        if ((not running) && (not found_match) && (tile.thread_rank() == 0)) {
-          auto index           = atomicAdd_block(&block_counter, step);
-          output_buffer[index] = cuco::make_pair<Key, Value>(key, view.get_empty_value_sentinel());
+        output_idx = tile.shfl(output_idx, 0);
+        if (equals) {
+          auto lane_offset = __popc(exists & ((1 << lane_id) - 1));
+          Key k            = key;
+          *(output_begin + output_idx + lane_offset) =
+            cuco::make_pair<Key, Value>(std::move(k), std::move(existing_value));
+        }
+      } else if (tile.any(slot_is_empty)) {
+        running = false;
+        if ((not found_match) && (lane_id == 0)) {
+          auto output_idx = num_items->fetch_add(step, cuda::std::memory_order_relaxed);
+          *(output_begin + output_idx) =
+            cuco::make_pair<Key, Value>(key, view.get_empty_key_sentinel());
         }
-      }  // if (running)
-
-      __syncthreads();
-
-      if ((block_counter + block_size / tile_size) > buffer_size) {
-        flush_output_buffer<Key, Value, atomicT, OutputIt>(
-          block_counter, output_buffer, num_items, output_begin);
-        __syncthreads();
-        if (0 == threadIdx.x) { block_counter = 0; }
-        __syncthreads();
       }
-    }  // while sync_or
-  }
+      current_slot = view.next_slot(tile, current_slot);
+    }
 
-  // Final flush of output cache
-  if (block_counter > 0) {
-    flush_output_buffer<Key, Value, atomicT, OutputIt>(
-      block_counter, output_buffer, num_items, output_begin);
+    key_idx += (gridDim.x * blockDim.x) / tile_size;
   }
 }
 
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index 85dbed1e8..120557d13 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -306,7 +306,7 @@ class static_multimap {
     Key empty_key_sentinel_{};      ///< Key value that represents an empty slot
     Value empty_value_sentinel_{};  ///< Initial Value of empty slot
 
-   protected:
+   public:
     __host__ __device__ device_view_base(pair_atomic_type* slots,
                                          std::size_t capacity,
                                          Key empty_key_sentinel,

From 161f77d7fe56cfac9b57b732ad1858cde0b6d5e9 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Wed, 7 Apr 2021 08:55:03 -0700
Subject: [PATCH 051/267] Use reinterpret_cast instead of atomic loads
 (temporary unsafe solution)

---
 include/cuco/detail/static_multimap_kernels.cuh | 17 ++++++++++-------
 1 file changed, 10 insertions(+), 7 deletions(-)

diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap_kernels.cuh
index 04e71bb7a..146cac23d 100644
--- a/include/cuco/detail/static_multimap_kernels.cuh
+++ b/include/cuco/detail/static_multimap_kernels.cuh
@@ -554,10 +554,14 @@ __global__ void find_all(InputIt first,
     bool found_match = false;
 
     while (tile.any(running)) {
-      auto const existing_key  = current_slot->first.load(cuda::std::memory_order_relaxed);
-      auto existing_value      = current_slot->second.load(cuda::std::memory_order_relaxed);
-      auto const slot_is_empty = (existing_key == view.get_empty_key_sentinel());
-      auto const equals        = key_equal(existing_key, key);
+      // TODO: Replace reinterpret_cast with atomic ref when it's available. The current
+      // implementation is unsafe!
+      static_assert(sizeof(Key) == sizeof(cuda::atomic<Key>));
+      static_assert(sizeof(Value) == sizeof(cuda::atomic<Value>));
+      pair<Key, Value> slot_contents = *reinterpret_cast<pair<Key, Value> const*>(current_slot);
+
+      auto const slot_is_empty = (slot_contents.first == view.get_empty_key_sentinel());
+      auto const equals        = key_equal(slot_contents.first, key);
       auto const exists        = tile.ballot(not slot_is_empty and equals);
 
       if (exists) {
@@ -572,7 +576,7 @@ __global__ void find_all(InputIt first,
           auto lane_offset = __popc(exists & ((1 << lane_id) - 1));
           Key k            = key;
           *(output_begin + output_idx + lane_offset) =
-            cuco::make_pair<Key, Value>(std::move(k), std::move(existing_value));
+            cuco::make_pair<Key, Value>(std::move(k), std::move(slot_contents.second));
         }
       } else if (tile.any(slot_is_empty)) {
         running = false;
@@ -583,8 +587,7 @@ __global__ void find_all(InputIt first,
         }
       }
       current_slot = view.next_slot(tile, current_slot);
-    }
-
+    }  // while running
     key_idx += (gridDim.x * blockDim.x) / tile_size;
   }
 }

From 1f79fbedef79fc57543d44bbe7e3c8b3858c58ea Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Thu, 8 Apr 2021 08:35:00 -0700
Subject: [PATCH 052/267] Fix a bug: guard key_equal invocation against the
 empty key sentinel

---
 include/cuco/detail/static_multimap_kernels.cuh | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap_kernels.cuh
index 146cac23d..bf6bc6a05 100644
--- a/include/cuco/detail/static_multimap_kernels.cuh
+++ b/include/cuco/detail/static_multimap_kernels.cuh
@@ -561,8 +561,9 @@ __global__ void find_all(InputIt first,
       pair<Key, Value> slot_contents = *reinterpret_cast<pair<Key, Value> const*>(current_slot);
 
       auto const slot_is_empty = (slot_contents.first == view.get_empty_key_sentinel());
-      auto const equals        = key_equal(slot_contents.first, key);
-      auto const exists        = tile.ballot(not slot_is_empty and equals);
+      auto equals              = false;
+      if (not slot_is_empty and key_equal(slot_contents.first, key)) { equals = true; }
+      auto const exists = tile.ballot(not slot_is_empty and equals);
 
       if (exists) {
         found_match      = true;

From a3bfca335b3da77878ca438a68b2210a4f1faf44 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Thu, 8 Apr 2021 13:52:30 -0700
Subject: [PATCH 053/267] Add compute_prime function for double hashing

---
 include/cuco/detail/prime.cuh | 55 +++++++++++++++++++++++++++++++++++
 1 file changed, 55 insertions(+)
 create mode 100644 include/cuco/detail/prime.cuh

diff --git a/include/cuco/detail/prime.cuh b/include/cuco/detail/prime.cuh
new file mode 100644
index 000000000..01cbeba95
--- /dev/null
+++ b/include/cuco/detail/prime.cuh
@@ -0,0 +1,55 @@
+/*
+ * Copyright (c) 2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+namespace cuco {
+namespace detail {
+
+constexpr bool is_prime(std::size_t num) noexcept
+{
+  bool flag = true;
+  // 0 and 1 are not prime numbers
+  if (num == 0 || num == 1) {
+    flag = false;
+  } else {
+    for (auto i = 2; i <= num / 2; ++i) {
+      if (num % i == 0) {
+        flag = false;
+        break;
+      }
+    }
+  }
+  return flag;
+}
+
+/**
+ * @brief Computes the smallest prime number greater than or equal to `num`.
+ *
+ * @param num
+ * @return The smallest prime number greater than or equal to `num`.
+ */
+constexpr std::size_t compute_prime(std::size_t num) noexcept
+{
+  while (not is_prime(num)) {
+    num++;
+  }
+  return num;
+}
+
+}  // namespace detail
+
+}  // namespace cuco

From 2db4cb86f402f978cd4a6be94d743afa5611ce24 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Thu, 8 Apr 2021 14:06:38 -0700
Subject: [PATCH 054/267] Use double hashing in multimap instead of linear
 probing

---
 include/cuco/detail/static_multimap.inl       | 281 ++++++++++++------
 .../cuco/detail/static_multimap_kernels.cuh   |   6 +-
 include/cuco/static_multimap.cuh              |   9 +-
 3 files changed, 194 insertions(+), 102 deletions(-)

diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index 41baac2ce..5f773dbb8 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -26,17 +26,22 @@ enum class insert_result {
   DUPLICATE  ///< Insert did not succeed, key is already present
 };
 
-template <typename Key, typename Value, cuda::thread_scope Scope, typename Allocator>
-static_multimap<Key, Value, Scope, Allocator>::static_multimap(std::size_t capacity,
-                                                               Key empty_key_sentinel,
-                                                               Value empty_value_sentinel,
-                                                               Allocator const& alloc)
-  : capacity_{capacity},
-    empty_key_sentinel_{empty_key_sentinel},
+template <typename Key,
+          typename Value,
+          std::size_t CGSize,
+          cuda::thread_scope Scope,
+          typename Allocator>
+static_multimap<Key, Value, CGSize, Scope, Allocator>::static_multimap(std::size_t capacity,
+                                                                       Key empty_key_sentinel,
+                                                                       Value empty_value_sentinel,
+                                                                       Allocator const& alloc)
+  : empty_key_sentinel_{empty_key_sentinel},
     empty_value_sentinel_{empty_value_sentinel},
     slot_allocator_{alloc}
 {
-  slots_ = std::allocator_traits<slot_allocator_type>::allocate(slot_allocator_, capacity);
+  auto min_prime  = cuco::detail::compute_prime(capacity / CGSize);
+  this->capacity_ = min_prime * CGSize;
+  slots_          = std::allocator_traits<slot_allocator_type>::allocate(slot_allocator_, capacity);
 
   auto constexpr block_size = 256;
   auto constexpr stride     = 4;
@@ -45,76 +50,92 @@ static_multimap<Key, Value, Scope, Allocator>::static_multimap(std::size_t capac
     <<<grid_size, block_size>>>(slots_, empty_key_sentinel, empty_value_sentinel, capacity);
 }
 
-template <typename Key, typename Value, cuda::thread_scope Scope, typename Allocator>
-static_multimap<Key, Value, Scope, Allocator>::~static_multimap()
+template <typename Key,
+          typename Value,
+          std::size_t CGSize,
+          cuda::thread_scope Scope,
+          typename Allocator>
+static_multimap<Key, Value, CGSize, Scope, Allocator>::~static_multimap()
 {
   std::allocator_traits<slot_allocator_type>::deallocate(slot_allocator_, slots_, capacity_);
 }
 
-template <typename Key, typename Value, cuda::thread_scope Scope, typename Allocator>
+template <typename Key,
+          typename Value,
+          std::size_t CGSize,
+          cuda::thread_scope Scope,
+          typename Allocator>
 template <typename InputIt, typename Hash, typename KeyEqual>
-void static_multimap<Key, Value, Scope, Allocator>::insert(InputIt first,
-                                                           InputIt last,
-                                                           Hash hash,
-                                                           KeyEqual key_equal)
+void static_multimap<Key, Value, CGSize, Scope, Allocator>::insert(InputIt first,
+                                                                   InputIt last,
+                                                                   Hash hash,
+                                                                   KeyEqual key_equal)
 {
   auto num_keys         = std::distance(first, last);
   auto const block_size = 128;
   auto const stride     = 1;
-  auto const tile_size  = 4;
-  auto const grid_size  = (tile_size * num_keys + stride * block_size - 1) / (stride * block_size);
+  auto const grid_size  = (CGSize * num_keys + stride * block_size - 1) / (stride * block_size);
   auto view             = get_device_mutable_view();
 
-  detail::insert<block_size, tile_size>
+  detail::insert<block_size, CGSize>
     <<<grid_size, block_size>>>(first, first + num_keys, view, hash, key_equal);
   CUCO_CUDA_TRY(cudaDeviceSynchronize());
 }
 
-template <typename Key, typename Value, cuda::thread_scope Scope, typename Allocator>
+template <typename Key,
+          typename Value,
+          std::size_t CGSize,
+          cuda::thread_scope Scope,
+          typename Allocator>
 template <typename InputIt, typename OutputIt, typename Hash, typename KeyEqual>
-void static_multimap<Key, Value, Scope, Allocator>::find(
+void static_multimap<Key, Value, CGSize, Scope, Allocator>::find(
   InputIt first, InputIt last, OutputIt output_begin, Hash hash, KeyEqual key_equal) noexcept
 {
   auto num_keys         = std::distance(first, last);
   auto const block_size = 128;
   auto const stride     = 1;
-  auto const tile_size  = 4;
-  auto const grid_size  = (tile_size * num_keys + stride * block_size - 1) / (stride * block_size);
+  auto const grid_size  = (CGSize * num_keys + stride * block_size - 1) / (stride * block_size);
   auto view             = get_device_view();
 
-  detail::find<block_size, tile_size, Value>
+  detail::find<block_size, CGSize, Value>
     <<<grid_size, block_size>>>(first, last, output_begin, view, hash, key_equal);
   CUCO_CUDA_TRY(cudaDeviceSynchronize());
 }
 
-template <typename Key, typename Value, cuda::thread_scope Scope, typename Allocator>
+template <typename Key,
+          typename Value,
+          std::size_t CGSize,
+          cuda::thread_scope Scope,
+          typename Allocator>
 template <typename InputIt, typename OutputIt, typename Hash, typename KeyEqual>
-void static_multimap<Key, Value, Scope, Allocator>::contains(
+void static_multimap<Key, Value, CGSize, Scope, Allocator>::contains(
   InputIt first, InputIt last, OutputIt output_begin, Hash hash, KeyEqual key_equal) noexcept
 {
   auto num_keys         = std::distance(first, last);
   auto const block_size = 128;
   auto const stride     = 1;
-  auto const tile_size  = 4;
-  auto const grid_size  = (tile_size * num_keys + stride * block_size - 1) / (stride * block_size);
+  auto const grid_size  = (CGSize * num_keys + stride * block_size - 1) / (stride * block_size);
   auto view             = get_device_view();
 
-  detail::contains<block_size, tile_size>
+  detail::contains<block_size, CGSize>
     <<<grid_size, block_size>>>(first, last, output_begin, view, hash, key_equal);
   CUCO_CUDA_TRY(cudaDeviceSynchronize());
 }
 
-template <typename Key, typename Value, cuda::thread_scope Scope, typename Allocator>
+template <typename Key,
+          typename Value,
+          std::size_t CGSize,
+          cuda::thread_scope Scope,
+          typename Allocator>
 template <typename InputIt, typename OutputIt, typename Hash, typename KeyEqual>
-OutputIt static_multimap<Key, Value, Scope, Allocator>::find_all(
+OutputIt static_multimap<Key, Value, CGSize, Scope, Allocator>::find_all(
   InputIt first, InputIt last, OutputIt output_begin, Hash hash, KeyEqual key_equal) noexcept
 {
   auto num_keys          = std::distance(first, last);
   auto const block_size  = 128;
   auto const buffer_size = block_size * 16;
   auto const stride      = 1;
-  auto const tile_size   = 4;
-  auto const grid_size   = (tile_size * num_keys + stride * block_size - 1) / (stride * block_size);
+  auto const grid_size   = (CGSize * num_keys + stride * block_size - 1) / (stride * block_size);
   auto view              = get_device_view();
 
   atomic_ctr_type* num_items;
@@ -124,7 +145,7 @@ OutputIt static_multimap<Key, Value, Scope, Allocator>::find_all(
   CUCO_CUDA_TRY(cudaGetDevice(&device_id));
   CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_items, sizeof(atomic_ctr_type), device_id));
 
-  detail::find_all<block_size, tile_size, buffer_size, Key, Value>
+  detail::find_all<block_size, CGSize, buffer_size, Key, Value>
     <<<grid_size, block_size>>>(first, last, output_begin, num_items, view, hash, key_equal);
   CUCO_CUDA_TRY(cudaDeviceSynchronize());
 
@@ -134,18 +155,21 @@ OutputIt static_multimap<Key, Value, Scope, Allocator>::find_all(
   return output_end;
 }
 
-template <typename Key, typename Value, cuda::thread_scope Scope, typename Allocator>
+template <typename Key,
+          typename Value,
+          std::size_t CGSize,
+          cuda::thread_scope Scope,
+          typename Allocator>
 template <typename InputIt, typename Hash, typename KeyEqual>
-std::size_t static_multimap<Key, Value, Scope, Allocator>::count(InputIt first,
-                                                                 InputIt last,
-                                                                 Hash hash,
-                                                                 KeyEqual key_equal)
+std::size_t static_multimap<Key, Value, CGSize, Scope, Allocator>::count(InputIt first,
+                                                                         InputIt last,
+                                                                         Hash hash,
+                                                                         KeyEqual key_equal)
 {
   auto num_keys         = std::distance(first, last);
   auto const block_size = 128;
   auto const stride     = 1;
-  auto const tile_size  = 4;
-  auto const grid_size  = (tile_size * num_keys + stride * block_size - 1) / (stride * block_size);
+  auto const grid_size  = (CGSize * num_keys + stride * block_size - 1) / (stride * block_size);
   auto view             = get_device_view();
 
   atomic_ctr_type* num_items;
@@ -155,7 +179,7 @@ std::size_t static_multimap<Key, Value, Scope, Allocator>::count(InputIt first,
   CUCO_CUDA_TRY(cudaGetDevice(&device_id));
   CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_items, sizeof(atomic_ctr_type), device_id));
 
-  detail::count<block_size, tile_size, Value>
+  detail::count<block_size, CGSize, Value>
     <<<grid_size, block_size>>>(first, last, num_items, view, hash, key_equal);
   CUCO_CUDA_TRY(cudaDeviceSynchronize());
 
@@ -165,9 +189,13 @@ std::size_t static_multimap<Key, Value, Scope, Allocator>::count(InputIt first,
   return result;
 }
 
-template <typename Key, typename Value, cuda::thread_scope Scope, typename Allocator>
+template <typename Key,
+          typename Value,
+          std::size_t CGSize,
+          cuda::thread_scope Scope,
+          typename Allocator>
 template <typename Hash, typename KeyEqual>
-__device__ void static_multimap<Key, Value, Scope, Allocator>::device_mutable_view::insert(
+__device__ void static_multimap<Key, Value, CGSize, Scope, Allocator>::device_mutable_view::insert(
   value_type const& insert_pair, Hash hash, KeyEqual key_equal) noexcept
 {
   auto current_slot{initial_slot(insert_pair.first, hash)};
@@ -202,9 +230,13 @@ __device__ void static_multimap<Key, Value, Scope, Allocator>::device_mutable_vi
   }
 }
 
-template <typename Key, typename Value, cuda::thread_scope Scope, typename Allocator>
+template <typename Key,
+          typename Value,
+          std::size_t CGSize,
+          cuda::thread_scope Scope,
+          typename Allocator>
 template <typename CG, typename Hash, typename KeyEqual>
-__device__ void static_multimap<Key, Value, Scope, Allocator>::device_mutable_view::insert(
+__device__ void static_multimap<Key, Value, CGSize, Scope, Allocator>::device_mutable_view::insert(
   CG g, value_type const& insert_pair, Hash hash, KeyEqual key_equal) noexcept
 {
   auto current_slot = initial_slot(g, insert_pair.first, hash);
@@ -267,12 +299,15 @@ __device__ void static_multimap<Key, Value, Scope, Allocator>::device_mutable_vi
   }
 }
 
-template <typename Key, typename Value, cuda::thread_scope Scope, typename Allocator>
+template <typename Key,
+          typename Value,
+          std::size_t CGSize,
+          cuda::thread_scope Scope,
+          typename Allocator>
 template <typename Hash, typename KeyEqual>
-__device__ typename static_multimap<Key, Value, Scope, Allocator>::device_view::iterator
-static_multimap<Key, Value, Scope, Allocator>::device_view::find(Key const& k,
-                                                                 Hash hash,
-                                                                 KeyEqual key_equal) noexcept
+__device__ typename static_multimap<Key, Value, CGSize, Scope, Allocator>::device_view::iterator
+static_multimap<Key, Value, CGSize, Scope, Allocator>::device_view::find(
+  Key const& k, Hash hash, KeyEqual key_equal) noexcept
 {
   auto current_slot = initial_slot(k, hash);
 
@@ -288,12 +323,16 @@ static_multimap<Key, Value, Scope, Allocator>::device_view::find(Key const& k,
   }
 }
 
-template <typename Key, typename Value, cuda::thread_scope Scope, typename Allocator>
+template <typename Key,
+          typename Value,
+          std::size_t CGSize,
+          cuda::thread_scope Scope,
+          typename Allocator>
 template <typename Hash, typename KeyEqual>
-__device__ typename static_multimap<Key, Value, Scope, Allocator>::device_view::const_iterator
-static_multimap<Key, Value, Scope, Allocator>::device_view::find(Key const& k,
-                                                                 Hash hash,
-                                                                 KeyEqual key_equal) const noexcept
+__device__
+  typename static_multimap<Key, Value, CGSize, Scope, Allocator>::device_view::const_iterator
+  static_multimap<Key, Value, CGSize, Scope, Allocator>::device_view::find(
+    Key const& k, Hash hash, KeyEqual key_equal) const noexcept
 {
   auto current_slot = initial_slot(k, hash);
 
@@ -309,13 +348,15 @@ static_multimap<Key, Value, Scope, Allocator>::device_view::find(Key const& k,
   }
 }
 
-template <typename Key, typename Value, cuda::thread_scope Scope, typename Allocator>
+template <typename Key,
+          typename Value,
+          std::size_t CGSize,
+          cuda::thread_scope Scope,
+          typename Allocator>
 template <typename CG, typename Hash, typename KeyEqual>
-__device__ typename static_multimap<Key, Value, Scope, Allocator>::device_view::iterator
-static_multimap<Key, Value, Scope, Allocator>::device_view::find(CG g,
-                                                                 Key const& k,
-                                                                 Hash hash,
-                                                                 KeyEqual key_equal) noexcept
+__device__ typename static_multimap<Key, Value, CGSize, Scope, Allocator>::device_view::iterator
+static_multimap<Key, Value, CGSize, Scope, Allocator>::device_view::find(
+  CG g, Key const& k, Hash hash, KeyEqual key_equal) noexcept
 {
   auto current_slot = initial_slot(g, k, hash);
 
@@ -346,13 +387,16 @@ static_multimap<Key, Value, Scope, Allocator>::device_view::find(CG g,
   }
 }
 
-template <typename Key, typename Value, cuda::thread_scope Scope, typename Allocator>
+template <typename Key,
+          typename Value,
+          std::size_t CGSize,
+          cuda::thread_scope Scope,
+          typename Allocator>
 template <typename CG, typename Hash, typename KeyEqual>
-__device__ typename static_multimap<Key, Value, Scope, Allocator>::device_view::const_iterator
-static_multimap<Key, Value, Scope, Allocator>::device_view::find(CG g,
-                                                                 Key const& k,
-                                                                 Hash hash,
-                                                                 KeyEqual key_equal) const noexcept
+__device__
+  typename static_multimap<Key, Value, CGSize, Scope, Allocator>::device_view::const_iterator
+  static_multimap<Key, Value, CGSize, Scope, Allocator>::device_view::find(
+    CG g, Key const& k, Hash hash, KeyEqual key_equal) const noexcept
 {
   auto current_slot = initial_slot(g, k, hash);
 
@@ -385,12 +429,16 @@ static_multimap<Key, Value, Scope, Allocator>::device_view::find(CG g,
   }
 }
 
-template <typename Key, typename Value, cuda::thread_scope Scope, typename Allocator>
+template <typename Key,
+          typename Value,
+          std::size_t CGSize,
+          cuda::thread_scope Scope,
+          typename Allocator>
 template <typename Hash, typename KeyEqual>
-__device__ typename static_multimap<Key, Value, Scope, Allocator>::device_view::fancy_iterator
-static_multimap<Key, Value, Scope, Allocator>::device_view::find_all(Key const& k,
-                                                                     Hash hash,
-                                                                     KeyEqual key_equal) noexcept
+__device__
+  typename static_multimap<Key, Value, CGSize, Scope, Allocator>::device_view::fancy_iterator
+  static_multimap<Key, Value, CGSize, Scope, Allocator>::device_view::find_all(
+    Key const& k, Hash hash, KeyEqual key_equal) noexcept
 {
   auto current_slot = initial_slot(k, hash);
 
@@ -408,11 +456,16 @@ static_multimap<Key, Value, Scope, Allocator>::device_view::find_all(Key const&
   }
 }
 
-template <typename Key, typename Value, cuda::thread_scope Scope, typename Allocator>
+template <typename Key,
+          typename Value,
+          std::size_t CGSize,
+          cuda::thread_scope Scope,
+          typename Allocator>
 template <typename Hash, typename KeyEqual>
-__device__ typename static_multimap<Key, Value, Scope, Allocator>::device_view::const_fancy_iterator
-static_multimap<Key, Value, Scope, Allocator>::device_view::find_all(
-  Key const& k, Hash hash, KeyEqual key_equal) const noexcept
+__device__
+  typename static_multimap<Key, Value, CGSize, Scope, Allocator>::device_view::const_fancy_iterator
+  static_multimap<Key, Value, CGSize, Scope, Allocator>::device_view::find_all(
+    Key const& k, Hash hash, KeyEqual key_equal) const noexcept
 {
   auto current_slot = initial_slot(k, hash);
 
@@ -430,13 +483,16 @@ static_multimap<Key, Value, Scope, Allocator>::device_view::find_all(
   }
 }
 
-template <typename Key, typename Value, cuda::thread_scope Scope, typename Allocator>
+template <typename Key,
+          typename Value,
+          std::size_t CGSize,
+          cuda::thread_scope Scope,
+          typename Allocator>
 template <typename CG, typename Hash, typename KeyEqual>
-__device__ typename static_multimap<Key, Value, Scope, Allocator>::device_view::fancy_iterator
-static_multimap<Key, Value, Scope, Allocator>::device_view::find_all(CG g,
-                                                                     Key const& k,
-                                                                     Hash hash,
-                                                                     KeyEqual key_equal) noexcept
+__device__
+  typename static_multimap<Key, Value, CGSize, Scope, Allocator>::device_view::fancy_iterator
+  static_multimap<Key, Value, CGSize, Scope, Allocator>::device_view::find_all(
+    CG g, Key const& k, Hash hash, KeyEqual key_equal) noexcept
 {
   auto current_slot = initial_slot(g, k, hash);
 
@@ -467,11 +523,16 @@ static_multimap<Key, Value, Scope, Allocator>::device_view::find_all(CG g,
   }
 }
 
-template <typename Key, typename Value, cuda::thread_scope Scope, typename Allocator>
+template <typename Key,
+          typename Value,
+          std::size_t CGSize,
+          cuda::thread_scope Scope,
+          typename Allocator>
 template <typename CG, typename Hash, typename KeyEqual>
-__device__ typename static_multimap<Key, Value, Scope, Allocator>::device_view::const_fancy_iterator
-static_multimap<Key, Value, Scope, Allocator>::device_view::find_all(
-  CG g, Key const& k, Hash hash, KeyEqual key_equal) const noexcept
+__device__
+  typename static_multimap<Key, Value, CGSize, Scope, Allocator>::device_view::const_fancy_iterator
+  static_multimap<Key, Value, CGSize, Scope, Allocator>::device_view::find_all(
+    CG g, Key const& k, Hash hash, KeyEqual key_equal) const noexcept
 {
   auto current_slot = initial_slot(g, k, hash);
 
@@ -504,9 +565,13 @@ static_multimap<Key, Value, Scope, Allocator>::device_view::find_all(
   }
 }
 
-template <typename Key, typename Value, cuda::thread_scope Scope, typename Allocator>
+template <typename Key,
+          typename Value,
+          std::size_t CGSize,
+          cuda::thread_scope Scope,
+          typename Allocator>
 template <typename Hash, typename KeyEqual>
-__device__ bool static_multimap<Key, Value, Scope, Allocator>::device_view::contains(
+__device__ bool static_multimap<Key, Value, CGSize, Scope, Allocator>::device_view::contains(
   Key const& k, Hash hash, KeyEqual key_equal) noexcept
 {
   auto current_slot = initial_slot(k, hash);
@@ -522,9 +587,13 @@ __device__ bool static_multimap<Key, Value, Scope, Allocator>::device_view::cont
   }
 }
 
-template <typename Key, typename Value, cuda::thread_scope Scope, typename Allocator>
+template <typename Key,
+          typename Value,
+          std::size_t CGSize,
+          cuda::thread_scope Scope,
+          typename Allocator>
 template <typename CG, typename Hash, typename KeyEqual>
-__device__ bool static_multimap<Key, Value, Scope, Allocator>::device_view::contains(
+__device__ bool static_multimap<Key, Value, CGSize, Scope, Allocator>::device_view::contains(
   CG g, Key const& k, Hash hash, KeyEqual key_equal) noexcept
 {
   auto current_slot = initial_slot(g, k, hash);
@@ -549,9 +618,13 @@ __device__ bool static_multimap<Key, Value, Scope, Allocator>::device_view::cont
   }
 }
 
-template <typename Key, typename Value, cuda::thread_scope Scope, typename Allocator>
+template <typename Key,
+          typename Value,
+          std::size_t CGSize,
+          cuda::thread_scope Scope,
+          typename Allocator>
 template <typename Hash, typename KeyEqual>
-__device__ size_t static_multimap<Key, Value, Scope, Allocator>::device_view::count(
+__device__ size_t static_multimap<Key, Value, CGSize, Scope, Allocator>::device_view::count(
   Key const& k, Hash hash, KeyEqual key_equal) noexcept
 {
   auto found   = this->find_all(k, hash, key_equal);
@@ -563,9 +636,13 @@ __device__ size_t static_multimap<Key, Value, Scope, Allocator>::device_view::co
   return count;
 }
 
-template <typename Key, typename Value, cuda::thread_scope Scope, typename Allocator>
+template <typename Key,
+          typename Value,
+          std::size_t CGSize,
+          cuda::thread_scope Scope,
+          typename Allocator>
 template <typename Hash, typename KeyEqual>
-__device__ size_t static_multimap<Key, Value, Scope, Allocator>::device_view::count(
+__device__ size_t static_multimap<Key, Value, CGSize, Scope, Allocator>::device_view::count(
   Key const& k, Hash hash, KeyEqual key_equal) const noexcept
 {
   auto found   = this->find_all(k, hash, key_equal);
@@ -577,9 +654,13 @@ __device__ size_t static_multimap<Key, Value, Scope, Allocator>::device_view::co
   return count;
 }
 
-template <typename Key, typename Value, cuda::thread_scope Scope, typename Allocator>
+template <typename Key,
+          typename Value,
+          std::size_t CGSize,
+          cuda::thread_scope Scope,
+          typename Allocator>
 template <typename CG, typename Hash, typename KeyEqual>
-__device__ size_t static_multimap<Key, Value, Scope, Allocator>::device_view::count(
+__device__ size_t static_multimap<Key, Value, CGSize, Scope, Allocator>::device_view::count(
   CG g, Key const& k, Hash hash, KeyEqual key_equal) noexcept
 {
   auto found   = this->find_all(g, k, hash, key_equal);
@@ -591,9 +672,13 @@ __device__ size_t static_multimap<Key, Value, Scope, Allocator>::device_view::co
   return count;
 }
 
-template <typename Key, typename Value, cuda::thread_scope Scope, typename Allocator>
+template <typename Key,
+          typename Value,
+          std::size_t CGSize,
+          cuda::thread_scope Scope,
+          typename Allocator>
 template <typename CG, typename Hash, typename KeyEqual>
-__device__ size_t static_multimap<Key, Value, Scope, Allocator>::device_view::count(
+__device__ size_t static_multimap<Key, Value, CGSize, Scope, Allocator>::device_view::count(
   CG g, Key const& k, Hash hash, KeyEqual key_equal) const noexcept
 {
   auto found   = this->find_all(g, k, hash, key_equal);
diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap_kernels.cuh
index bf6bc6a05..f1b3f1968 100644
--- a/include/cuco/detail/static_multimap_kernels.cuh
+++ b/include/cuco/detail/static_multimap_kernels.cuh
@@ -554,8 +554,8 @@ __global__ void find_all(InputIt first,
     bool found_match = false;
 
     while (tile.any(running)) {
-      // TODO: Replace reinterpret_cast with atomic ref when it's available. The current
-      // implementation is unsafe!
+      // TODO: Replace reinterpret_cast with atomic ref when possible. The current implementation is
+      // unsafe!
       static_assert(sizeof(Key) == sizeof(cuda::atomic<Key>));
       static_assert(sizeof(Value) == sizeof(cuda::atomic<Value>));
       pair<Key, Value> slot_contents = *reinterpret_cast<pair<Key, Value> const*>(current_slot);
@@ -569,11 +569,13 @@ __global__ void find_all(InputIt first,
         found_match      = true;
         auto num_matches = __popc(exists);
         uint32_t output_idx;
+        // First lane gets the CG-level offset
         if (0 == lane_id) {
           output_idx = num_items->fetch_add(num_matches, cuda::std::memory_order_relaxed);
         }
         output_idx = tile.shfl(output_idx, 0);
         if (equals) {
+          // Each match computes its lane-level offset
           auto lane_offset = __popc(exists & ((1 << lane_id) - 1));
           Key k            = key;
           *(output_begin + output_idx + lane_offset) =
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index 120557d13..68ca6686f 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -34,6 +34,7 @@
 #include <cuco/detail/error.hpp>
 #include <cuco/detail/hash_functions.cuh>
 #include <cuco/detail/pair.cuh>
+#include <cuco/detail/prime.cuh>
 #include <cuco/detail/static_multimap_kernels.cuh>
 
 namespace cuco {
@@ -106,6 +107,7 @@ namespace cuco {
  */
 template <typename Key,
           typename Value,
+          std::size_t CGSize       = 16,
           cuda::thread_scope Scope = cuda::thread_scope_device,
           typename Allocator       = cuco::cuda_allocator<char>>
 class static_multimap {
@@ -305,6 +307,7 @@ class static_multimap {
     std::size_t capacity_{};        ///< Total number of slots
     Key empty_key_sentinel_{};      ///< Key value that represents an empty slot
     Value empty_value_sentinel_{};  ///< Initial Value of empty slot
+    std::size_t step_size_{};
 
    public:
     __host__ __device__ device_view_base(pair_atomic_type* slots,
@@ -375,6 +378,7 @@ class static_multimap {
     template <typename CG, typename Hash>
     __device__ iterator initial_slot(CG g, Key const& k, Hash hash) noexcept
     {
+      step_size_ = (hash(k) % (capacity_ / g.size() - 1) + 1) * g.size();
       return &slots_[(hash(k) + g.thread_rank()) % capacity_];
     }
 
@@ -393,6 +397,7 @@ class static_multimap {
     template <typename CG, typename Hash>
     __device__ const_iterator initial_slot(CG g, Key const& k, Hash hash) const noexcept
     {
+      step_size_ = (hash(k) % (capacity_ / g.size() - 1) + 1) * g.size();
       return &slots_[(hash(k) + g.thread_rank()) % capacity_];
     }
 
@@ -434,7 +439,7 @@ class static_multimap {
     __device__ iterator next_slot(CG g, iterator s) noexcept
     {
       uint32_t index = s - slots_;
-      return &slots_[(index + g.size()) % capacity_];
+      return &slots_[(index + step_size_) % capacity_];
     }
 
     /**
@@ -452,7 +457,7 @@ class static_multimap {
     __device__ const_iterator next_slot(CG g, const_iterator s) const noexcept
     {
       uint32_t index = s - slots_;
-      return &slots_[(index + g.size()) % capacity_];
+      return &slots_[(index + step_size_) % capacity_];
     }
 
    public:

From 77b952ce2ddb698887fd918be6047dc50a19e339 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Fri, 9 Apr 2021 13:25:22 -0700
Subject: [PATCH 055/267] Add per CG shared memory buffer for find_all

---
 include/cuco/detail/static_multimap.inl       |  2 +-
 .../cuco/detail/static_multimap_kernels.cuh   | 64 +++++++++++++------
 2 files changed, 44 insertions(+), 22 deletions(-)

diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index 5f773dbb8..69cbc0c70 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -133,7 +133,7 @@ OutputIt static_multimap<Key, Value, CGSize, Scope, Allocator>::find_all(
 {
   auto num_keys          = std::distance(first, last);
   auto const block_size  = 128;
-  auto const buffer_size = block_size * 16;
+  auto const buffer_size = CGSize * 2;
   auto const stride      = 1;
   auto const grid_size   = (CGSize * num_keys + stride * block_size - 1) / (stride * block_size);
   auto view              = get_device_view();
diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap_kernels.cuh
index f1b3f1968..78c20251e 100644
--- a/include/cuco/detail/static_multimap_kernels.cuh
+++ b/include/cuco/detail/static_multimap_kernels.cuh
@@ -33,19 +33,24 @@ namespace cg = cooperative_groups;
  * @param num_items Size of the output sequence
  * @param output_begin Beginning of the output sequence of key/value pairs
  */
-template <typename Key, typename Value, typename atomicT, typename OutputIt>
-__inline__ __device__ void flush_output_buffer(uint32_t output_size,
+template <uint32_t cg_size,
+          typename CG,
+          typename Key,
+          typename Value,
+          typename atomicT,
+          typename OutputIt>
+__inline__ __device__ void flush_output_buffer(CG const& cg,
+                                               uint32_t const output_size,
                                                cuco::pair_type<Key, Value>* output_buffer,
                                                atomicT* num_items,
                                                OutputIt output_begin)
 {
-  __shared__ size_t offset;
-  if (threadIdx.x == 0) {
-    offset = num_items->fetch_add(output_size, cuda::std::memory_order_relaxed);
-  }
-  __syncthreads();
+  std::size_t offset;
+  const auto lane_id = cg.thread_rank();
+  if (0 == lane_id) { offset = num_items->fetch_add(output_size, cuda::std::memory_order_relaxed); }
+  cg.shfl(offset, 0);
 
-  for (auto index = threadIdx.x; index < output_size; index += blockDim.x) {
+  for (auto index = lane_id; index < output_size; index += cg_size) {
     *(output_begin + offset + index) = output_buffer[index];
   }
 }
@@ -542,9 +547,14 @@ __global__ void find_all(InputIt first,
   auto tid     = blockDim.x * blockIdx.x + threadIdx.x;
   auto key_idx = tid / tile_size;
 
-  constexpr uint32_t step = 1;
+  constexpr uint32_t num_cgs = block_size / tile_size;
+  const uint32_t cg_id       = threadIdx.x / tile_size;
+  const uint32_t lane_id     = tile.thread_rank();
+
+  __shared__ cuco::pair_type<Key, Value> output_buffer[num_cgs][buffer_size];
+  __shared__ uint32_t cg_counter[num_cgs];
 
-  const int lane_id = tile.thread_rank();
+  if (lane_id == 0) { cg_counter[cg_id] = 0; }
 
   while (first + key_idx < last) {
     auto key          = *(first + key_idx);
@@ -566,33 +576,45 @@ __global__ void find_all(InputIt first,
       auto const exists = tile.ballot(not slot_is_empty and equals);
 
       if (exists) {
-        found_match      = true;
-        auto num_matches = __popc(exists);
-        uint32_t output_idx;
-        // First lane gets the CG-level offset
-        if (0 == lane_id) {
-          output_idx = num_items->fetch_add(num_matches, cuda::std::memory_order_relaxed);
-        }
-        output_idx = tile.shfl(output_idx, 0);
+        found_match         = true;
+        auto num_matches    = __popc(exists);
+        uint32_t output_idx = cg_counter[cg_id];
         if (equals) {
           // Each match computes its lane-level offset
           auto lane_offset = __popc(exists & ((1 << lane_id) - 1));
           Key k            = key;
-          *(output_begin + output_idx + lane_offset) =
+          output_buffer[cg_id][output_idx + lane_offset] =
             cuco::make_pair<Key, Value>(std::move(k), std::move(slot_contents.second));
         }
+        if (0 == lane_id) { cg_counter[cg_id] += num_matches; }
       } else if (tile.any(slot_is_empty)) {
         running = false;
         if ((not found_match) && (lane_id == 0)) {
-          auto output_idx = num_items->fetch_add(step, cuda::std::memory_order_relaxed);
-          *(output_begin + output_idx) =
+          auto output_idx = cg_counter[cg_id]++;
+          output_buffer[cg_id][output_idx] =
             cuco::make_pair<Key, Value>(key, view.get_empty_key_sentinel());
         }
       }
+
+      tile.sync();
+
+      // Flush it the next iteration won't fit into buffer
+      if ((cg_counter[cg_id] + tile_size) > buffer_size) {
+        flush_output_buffer<tile_size>(
+          tile, cg_counter[cg_id], output_buffer[cg_id], num_items, output_begin);
+        // First lane reset CG-level counter
+        if (lane_id == 0) { cg_counter[cg_id] = 0; }
+      }
       current_slot = view.next_slot(tile, current_slot);
     }  // while running
     key_idx += (gridDim.x * blockDim.x) / tile_size;
   }
+
+  // Final flush of output buffer
+  if (cg_counter[cg_id] > 0) {
+    flush_output_buffer<tile_size>(
+      tile, cg_counter[cg_id], output_buffer[cg_id], num_items, output_begin);
+  }
 }
 
 /**

From 8386d947ef845f24c53a397f5031548806154098 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Mon, 12 Apr 2021 11:19:44 -0700
Subject: [PATCH 056/267] Fix a bug: add an offset to hash2 function

---
 include/cuco/static_multimap.cuh | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index 68ca6686f..d20034660 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -379,7 +379,7 @@ class static_multimap {
     __device__ iterator initial_slot(CG g, Key const& k, Hash hash) noexcept
     {
       step_size_ = (hash(k) % (capacity_ / g.size() - 1) + 1) * g.size();
-      return &slots_[(hash(k) + g.thread_rank()) % capacity_];
+      return &slots_[(hash(k + 1) + g.thread_rank()) % capacity_];
     }
 
     /**
@@ -398,7 +398,7 @@ class static_multimap {
     __device__ const_iterator initial_slot(CG g, Key const& k, Hash hash) const noexcept
     {
       step_size_ = (hash(k) % (capacity_ / g.size() - 1) + 1) * g.size();
-      return &slots_[(hash(k) + g.thread_rank()) % capacity_];
+      return &slots_[(hash(k + 1) + g.thread_rank()) % capacity_];
     }
 
     /**

From aeeb88bb0ec8db34f52ace98bcf21a4fe29aedf4 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Mon, 12 Apr 2021 11:38:59 -0700
Subject: [PATCH 057/267] Update multimap count function without using fancy
 iterator

---
 .../cuco/detail/static_multimap_kernels.cuh   | 47 ++++++++++++-------
 1 file changed, 29 insertions(+), 18 deletions(-)

diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap_kernels.cuh
index 78c20251e..21020c624 100644
--- a/include/cuco/detail/static_multimap_kernels.cuh
+++ b/include/cuco/detail/static_multimap_kernels.cuh
@@ -571,9 +571,8 @@ __global__ void find_all(InputIt first,
       pair<Key, Value> slot_contents = *reinterpret_cast<pair<Key, Value> const*>(current_slot);
 
       auto const slot_is_empty = (slot_contents.first == view.get_empty_key_sentinel());
-      auto equals              = false;
-      if (not slot_is_empty and key_equal(slot_contents.first, key)) { equals = true; }
-      auto const exists = tile.ballot(not slot_is_empty and equals);
+      auto const equals        = (not slot_is_empty and key_equal(slot_contents.first, key));
+      auto const exists        = tile.ballot(equals);
 
       if (exists) {
         found_match         = true;
@@ -693,24 +692,36 @@ template <uint32_t block_size,
 __global__ void count(
   InputIt first, InputIt last, atomicT* num_items, viewT view, Hash hash, KeyEqual key_equal)
 {
-  typedef cub::BlockReduce<std::size_t, block_size> BlockReduce;
-  __shared__ typename BlockReduce::TempStorage temp_storage;
-  std::size_t thread_num_items = 0;
+  auto tile    = cg::tiled_partition<tile_size>(cg::this_thread_block());
+  auto tid     = blockDim.x * blockIdx.x + threadIdx.x;
+  auto key_idx = tid / tile_size;
 
-  auto tile = cg::tiled_partition<tile_size>(cg::this_thread_block());
-  auto tid  = blockDim.x * blockIdx.x + threadIdx.x;
-  auto it   = first + tid / tile_size;
+  const int lane_id = tile.thread_rank();
 
-  while (it < last) {
-    auto temp_num = view.count(tile, *it, hash, key_equal);
-    if (tile.thread_rank() == 0) { thread_num_items += temp_num; }
-    it += (gridDim.x * blockDim.x) / tile_size;
-  }
+  while (first + key_idx < last) {
+    auto key          = *(first + key_idx);
+    auto current_slot = view.initial_slot(tile, key, hash);
 
-  // compute number of successfully inserted elements for each block
-  // and atomically add to the grand total
-  std::size_t block_num_items = BlockReduce(temp_storage).Sum(thread_num_items);
-  if (threadIdx.x == 0) { *num_items += block_num_items; }
+    bool running     = true;
+    bool found_match = false;
+
+    while (tile.any(running)) {
+      auto const current_key = current_slot->first.load(cuda::std::memory_order_relaxed);
+
+      auto const slot_is_empty = (current_key == view.get_empty_key_sentinel());
+      auto const equals        = (not slot_is_empty and key_equal(current_key, key));
+      auto const exists        = tile.ballot(equals);
+
+      if (exists) {
+        found_match      = true;
+        auto num_matches = __popc(exists);
+        // First lane gets the CG-level offset
+        if (0 == lane_id) { num_items->fetch_add(num_matches, cuda::std::memory_order_relaxed); }
+      }
+      current_slot = view.next_slot(tile, current_slot);
+    }  // while running
+    key_idx += (gridDim.x * blockDim.x) / tile_size;
+  }
 }
 
 }  // namespace detail

From 565d784622dfac0e893899322ff5b7898b96f43d Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Mon, 12 Apr 2021 11:43:42 -0700
Subject: [PATCH 058/267] Remove unused variable

---
 include/cuco/detail/static_multimap_kernels.cuh | 2 --
 1 file changed, 2 deletions(-)

diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap_kernels.cuh
index 21020c624..8a9cd14a1 100644
--- a/include/cuco/detail/static_multimap_kernels.cuh
+++ b/include/cuco/detail/static_multimap_kernels.cuh
@@ -703,7 +703,6 @@ __global__ void count(
     auto current_slot = view.initial_slot(tile, key, hash);
 
     bool running     = true;
-    bool found_match = false;
 
     while (tile.any(running)) {
       auto const current_key = current_slot->first.load(cuda::std::memory_order_relaxed);
@@ -713,7 +712,6 @@ __global__ void count(
       auto const exists        = tile.ballot(equals);
 
       if (exists) {
-        found_match      = true;
         auto num_matches = __popc(exists);
         // First lane gets the CG-level offset
         if (0 == lane_id) { num_items->fetch_add(num_matches, cuda::std::memory_order_relaxed); }

From bf33f3802d82400d68fd572e1ba18d2179caf787 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Mon, 12 Apr 2021 11:53:16 -0700
Subject: [PATCH 059/267] Remove device count tests in find_all

---
 tests/static_multimap/static_multimap_test.cu | 23 -------------------
 1 file changed, 23 deletions(-)

diff --git a/tests/static_multimap/static_multimap_test.cu b/tests/static_multimap/static_multimap_test.cu
index d574c11d9..6ff2dfc59 100644
--- a/tests/static_multimap/static_multimap_test.cu
+++ b/tests/static_multimap/static_multimap_test.cu
@@ -196,27 +196,4 @@ TEMPLATE_TEST_CASE_SIG("Each key appears twice",
 
     REQUIRE(size == num_items);
   }
-
-  SECTION(
-    "Counting the number of key/value pairs corresponding to each key should always return two.")
-  {
-    // Bulk insert keys
-    map.insert(d_pairs.begin(), d_pairs.end());
-
-    SECTION("non-const view")
-    {
-      REQUIRE(all_of(d_pairs.begin(),
-                     d_pairs.end(),
-                     [view] __device__(cuco::pair_type<Key, Value> const& pair) mutable {
-                       return view.count(pair.first) == 2;
-                     }));
-    }
-    SECTION("const view")
-    {
-      REQUIRE(all_of(
-        d_pairs.begin(), d_pairs.end(), [view] __device__(cuco::pair_type<Key, Value> const& pair) {
-          return view.count(pair.first) == 2;
-        }));
-    }
-  }
 }

From 2e064100c144114a64ddf91f77f80ed0a81a5ad5 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Mon, 12 Apr 2021 12:00:37 -0700
Subject: [PATCH 060/267] Update multimap benchmarks in accordance with double
 hashing

---
 .../static_multimap/find_all_bench.cu           |  4 ++--
 .../static_multimap/static_multimap_bench.cu    | 17 ++++++++---------
 2 files changed, 10 insertions(+), 11 deletions(-)

diff --git a/benchmarks/hash_table/static_multimap/find_all_bench.cu b/benchmarks/hash_table/static_multimap/find_all_bench.cu
index 7a19f137e..8222ec7c7 100644
--- a/benchmarks/hash_table/static_multimap/find_all_bench.cu
+++ b/benchmarks/hash_table/static_multimap/find_all_bench.cu
@@ -86,13 +86,13 @@ void nvbench_find_all(
 
   state.exec(
     nvbench::exec_tag::sync | nvbench::exec_tag::timer, [&](nvbench::launch& launch, auto& timer) {
-      cuco::static_multimap<Key, Value> map{size, -1, -1};
+      cuco::static_multimap<Key, Value, CGSize> map{size, -1, -1};
       map.insert(d_pairs.begin(), d_pairs.end());
 
       auto view = map.get_device_view();
 
       auto const block_size  = 128;
-      auto const buffer_size = block_size * BufferSize;
+      auto const buffer_size = CGSize * BufferSize;
       auto const stride      = 1;
       auto const grid_size = (CGSize * num_keys + stride * block_size - 1) / (stride * block_size);
 
diff --git a/benchmarks/hash_table/static_multimap/static_multimap_bench.cu b/benchmarks/hash_table/static_multimap/static_multimap_bench.cu
index 7f381e2a3..f32e64ce1 100644
--- a/benchmarks/hash_table/static_multimap/static_multimap_bench.cu
+++ b/benchmarks/hash_table/static_multimap/static_multimap_bench.cu
@@ -94,14 +94,13 @@ void launch_nvbench_insert(nvbench::state& state,
 
   state.exec(nvbench::exec_tag::sync | nvbench::exec_tag::timer,
              [&](nvbench::launch& launch, auto& timer) {
-               cuco::static_multimap<Key, Value> map{size, -1, -1};
+               cuco::static_multimap<Key, Value, cg_size> map{size, -1, -1};
                auto m_view = map.get_device_mutable_view();
 
                auto const block_size = 128;
                auto const stride     = 1;
-               auto const tile_size  = cg_size;
                auto const grid_size =
-                 (tile_size * num_keys + stride * block_size - 1) / (stride * block_size);
+                 (cg_size * num_keys + stride * block_size - 1) / (stride * block_size);
 
                using Hash     = cuco::detail::MurmurHash3_32<Key>;
                using KeyEqual = thrust::equal_to<Key>;
@@ -110,7 +109,7 @@ void launch_nvbench_insert(nvbench::state& state,
                KeyEqual key_equal;
 
                timer.start();
-               cuco::detail::insert<block_size, tile_size>
+               cuco::detail::insert<block_size, cg_size>
                  <<<grid_size, block_size, 0, launch.get_stream()>>>(
                    d_pairs.begin(), d_pairs.end(), m_view, hash, key_equal);
                CUCO_CUDA_TRY(cudaDeviceSynchronize());
@@ -249,14 +248,14 @@ void nvbench_static_multimap_find(nvbench::state& state, nvbench::type_list<Key,
 
   state.exec(nvbench::exec_tag::sync | nvbench::exec_tag::timer,
              [&](nvbench::launch& launch, auto& timer) {
-               cuco::static_multimap<Key, Value> map{size, -1, -1};
+               auto const tile_size  = 16;
+               cuco::static_multimap<Key, Value, tile_size> map{size, -1, -1};
                map.insert(d_pairs.begin(), d_pairs.end());
 
                auto view = map.get_device_view();
 
                auto const block_size = 128;
                auto const stride     = 1;
-               auto const tile_size  = 4;
                auto const grid_size =
                  (tile_size * num_keys + stride * block_size - 1) / (stride * block_size);
 
@@ -321,15 +320,15 @@ void nvbench_static_multimap_find_all(nvbench::state& state, nvbench::type_list<
 
   state.exec(
     nvbench::exec_tag::sync | nvbench::exec_tag::timer, [&](nvbench::launch& launch, auto& timer) {
-      cuco::static_multimap<Key, Value> map{size, -1, -1};
+      auto const tile_size   = 4;
+      cuco::static_multimap<Key, Value, tile_size> map{size, -1, -1};
       map.insert(d_pairs.begin(), d_pairs.end());
 
       auto view = map.get_device_view();
 
       auto const block_size  = 128;
-      auto const buffer_size = block_size * 16;
+      auto const buffer_size = tile_size * 2;
       auto const stride      = 1;
-      auto const tile_size   = 4;
       auto const grid_size =
         (tile_size * num_keys + stride * block_size - 1) / (stride * block_size);
 

From 7a6d60fd4f0b9a87f55aabdf08a8398718b7a729 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Mon, 12 Apr 2021 12:06:12 -0700
Subject: [PATCH 061/267] Fix a bug in multimap count: add end condition for
 key iterations

---
 include/cuco/detail/static_multimap_kernels.cuh | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap_kernels.cuh
index 8a9cd14a1..d332490f1 100644
--- a/include/cuco/detail/static_multimap_kernels.cuh
+++ b/include/cuco/detail/static_multimap_kernels.cuh
@@ -702,7 +702,7 @@ __global__ void count(
     auto key          = *(first + key_idx);
     auto current_slot = view.initial_slot(tile, key, hash);
 
-    bool running     = true;
+    bool running = true;
 
     while (tile.any(running)) {
       auto const current_key = current_slot->first.load(cuda::std::memory_order_relaxed);
@@ -715,6 +715,8 @@ __global__ void count(
         auto num_matches = __popc(exists);
         // First lane gets the CG-level offset
         if (0 == lane_id) { num_items->fetch_add(num_matches, cuda::std::memory_order_relaxed); }
+      } else if (tile.any(slot_is_empty)) {
+        running = false;
       }
       current_slot = view.next_slot(tile, current_slot);
     }  // while running

From 0e2f5dea716f31a862c0ec699d539bb93fad5a87 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Tue, 13 Apr 2021 12:43:25 -0700
Subject: [PATCH 062/267] Fix a bug: use adjusted capacity for slots allocation

---
 include/cuco/detail/prime.cuh           | 25 ++++++++++++++++++++++++-
 include/cuco/detail/static_multimap.inl |  7 +++----
 include/cuco/static_multimap.cuh        |  8 ++++----
 3 files changed, 31 insertions(+), 9 deletions(-)

diff --git a/include/cuco/detail/prime.cuh b/include/cuco/detail/prime.cuh
index 01cbeba95..6dd96fa71 100644
--- a/include/cuco/detail/prime.cuh
+++ b/include/cuco/detail/prime.cuh
@@ -19,6 +19,15 @@
 namespace cuco {
 namespace detail {
 
+// safe division
+#define SDIV(x, y) (((x) + (y)-1) / (y))
+
+/**
+ * @brief Indicates whether the input `num` is a prime number.
+ *
+ * @param num
+ * @return A boolean indicating whether the input `num` is a prime number
+ */
 constexpr bool is_prime(std::size_t num) noexcept
 {
   bool flag = true;
@@ -40,7 +49,7 @@ constexpr bool is_prime(std::size_t num) noexcept
  * @brief Computes the smallest prime number greater than or equal to `num`.
  *
  * @param num
- * @return The smallest prime number greater than or equal to `num`.
+ * @return The smallest prime number greater than or equal to `num`
  */
 constexpr std::size_t compute_prime(std::size_t num) noexcept
 {
@@ -50,6 +59,20 @@ constexpr std::size_t compute_prime(std::size_t num) noexcept
   return num;
 }
 
+/**
+ * @brief Calculates the adjusted/valid capacity based on `CGSize` and the initial `capacity`.
+ *
+ * @tparam CGSize Cooperative Group size
+ * @param capacity The initially requested capacity
+ * @return An adjusted capacity greater than or equal to `capacity`
+ */
+template <std::size_t CGSize>
+constexpr std::size_t get_valid_capacity(std::size_t capacity) noexcept
+{
+  auto const min_prime = compute_prime(SDIV(capacity, CGSize));
+  return min_prime * CGSize;
+}
+
 }  // namespace detail
 
 }  // namespace cuco
diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index 69cbc0c70..622dee15b 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -35,13 +35,12 @@ static_multimap<Key, Value, CGSize, Scope, Allocator>::static_multimap(std::size
                                                                        Key empty_key_sentinel,
                                                                        Value empty_value_sentinel,
                                                                        Allocator const& alloc)
-  : empty_key_sentinel_{empty_key_sentinel},
+  : capacity_{cuco::detail::get_valid_capacity<CGSize>(capacity)},
+    empty_key_sentinel_{empty_key_sentinel},
     empty_value_sentinel_{empty_value_sentinel},
     slot_allocator_{alloc}
 {
-  auto min_prime  = cuco::detail::compute_prime(capacity / CGSize);
-  this->capacity_ = min_prime * CGSize;
-  slots_          = std::allocator_traits<slot_allocator_type>::allocate(slot_allocator_, capacity);
+  slots_ = std::allocator_traits<slot_allocator_type>::allocate(slot_allocator_, get_capacity());
 
   auto constexpr block_size = 256;
   auto constexpr stride     = 4;
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index d20034660..9898f3c20 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -378,8 +378,8 @@ class static_multimap {
     template <typename CG, typename Hash>
     __device__ iterator initial_slot(CG g, Key const& k, Hash hash) noexcept
     {
-      step_size_ = (hash(k) % (capacity_ / g.size() - 1) + 1) * g.size();
-      return &slots_[(hash(k + 1) + g.thread_rank()) % capacity_];
+      step_size_ = (hash(k + 1) % (capacity_ / g.size() - 1) + 1) * g.size();
+      return &slots_[(hash(k) + g.thread_rank()) % capacity_];
     }
 
     /**
@@ -397,8 +397,8 @@ class static_multimap {
     template <typename CG, typename Hash>
     __device__ const_iterator initial_slot(CG g, Key const& k, Hash hash) const noexcept
     {
-      step_size_ = (hash(k) % (capacity_ / g.size() - 1) + 1) * g.size();
-      return &slots_[(hash(k + 1) + g.thread_rank()) % capacity_];
+      step_size_ = (hash(k + 1) % (capacity_ / g.size() - 1) + 1) * g.size();
+      return &slots_[(hash(k) + g.thread_rank()) % capacity_];
     }
 
     /**

From 8659e2a28ed520383dd2ccfb598103c321215fdd Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Tue, 13 Apr 2021 14:04:40 -0700
Subject: [PATCH 063/267] Fix a bug in per CG cache writing: shfl offset
 properly

---
 include/cuco/detail/static_multimap_kernels.cuh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap_kernels.cuh
index d332490f1..a3c9b09c6 100644
--- a/include/cuco/detail/static_multimap_kernels.cuh
+++ b/include/cuco/detail/static_multimap_kernels.cuh
@@ -48,7 +48,7 @@ __inline__ __device__ void flush_output_buffer(CG const& cg,
   std::size_t offset;
   const auto lane_id = cg.thread_rank();
   if (0 == lane_id) { offset = num_items->fetch_add(output_size, cuda::std::memory_order_relaxed); }
-  cg.shfl(offset, 0);
+  offset = cg.shfl(offset, 0);
 
   for (auto index = lane_id; index < output_size; index += cg_size) {
     *(output_begin + offset + index) = output_buffer[index];

From b9e06cf40009191c99558356f942e70a38c29e8d Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Tue, 13 Apr 2021 15:34:01 -0700
Subject: [PATCH 064/267] Use CG memcpy_async to write output

---
 .../static_multimap/find_all_bench.cu         |  2 +-
 .../cuco/detail/static_multimap_kernels.cuh   | 58 ++++++++++++++++---
 tests/static_multimap/static_multimap_test.cu |  5 +-
 3 files changed, 53 insertions(+), 12 deletions(-)

diff --git a/benchmarks/hash_table/static_multimap/find_all_bench.cu b/benchmarks/hash_table/static_multimap/find_all_bench.cu
index 8222ec7c7..fa87dbce3 100644
--- a/benchmarks/hash_table/static_multimap/find_all_bench.cu
+++ b/benchmarks/hash_table/static_multimap/find_all_bench.cu
@@ -114,7 +114,7 @@ void nvbench_find_all(
       timer.start();
       cuco::detail::find_all<block_size, CGSize, buffer_size, Key, Value>
         <<<grid_size, block_size, 0, launch.get_stream()>>>(
-          d_keys.begin(), d_keys.end(), d_results.begin(), num_items, view, hash, key_equal);
+          d_keys.begin(), d_keys.end(), d_results.data().get(), num_items, view, hash, key_equal);
       CUCO_CUDA_TRY(cudaDeviceSynchronize());
       timer.stop();
 
diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap_kernels.cuh
index a3c9b09c6..9af21f7d1 100644
--- a/include/cuco/detail/static_multimap_kernels.cuh
+++ b/include/cuco/detail/static_multimap_kernels.cuh
@@ -16,6 +16,8 @@
 
 #include <cuco/detail/pair.cuh>
 
+#include <cooperative_groups/memcpy_async.h>
+
 namespace cuco {
 namespace detail {
 namespace cg = cooperative_groups;
@@ -39,18 +41,56 @@ template <uint32_t cg_size,
           typename Value,
           typename atomicT,
           typename OutputIt>
-__inline__ __device__ void flush_output_buffer(CG const& cg,
-                                               uint32_t const output_size,
-                                               cuco::pair_type<Key, Value>* output_buffer,
-                                               atomicT* num_items,
-                                               OutputIt output_begin)
+__inline__ __device__ std::enable_if_t<std::is_pointer<OutputIt>::value, void> flush_output_buffer(
+  CG const& g,
+  uint32_t const num_outputs,
+  cuco::pair_type<Key, Value>* output_buffer,
+  atomicT* num_items,
+  OutputIt output_begin)
+{
+  std::size_t offset;
+  const auto lane_id = g.thread_rank();
+  if (0 == lane_id) { offset = num_items->fetch_add(num_outputs, cuda::std::memory_order_relaxed); }
+  offset = g.shfl(offset, 0);
+
+  cg::memcpy_async(g,
+                   output_begin + offset,
+                   output_buffer,
+                   cuda::aligned_size_t<16>(sizeof(cuco::pair_type<Key, Value>) * num_outputs));
+}
+
+/**
+ * @brief Flushes shared memory buffer into the output sequence.
+ *
+ * @tparam Key key type
+ * @tparam Value value type
+ * @tparam atomicT Type of atomic storage
+ * @tparam OutputIt Device accessible output iterator whose `value_type` is
+ * convertible to the map's `mapped_type`
+ * @param output_size Number of valid output in the buffer
+ * @param output_buffer Shared memory buffer of the key/value pair sequence
+ * @param num_items Size of the output sequence
+ * @param output_begin Beginning of the output sequence of key/value pairs
+ */
+template <uint32_t cg_size,
+          typename CG,
+          typename Key,
+          typename Value,
+          typename atomicT,
+          typename OutputIt>
+__inline__ __device__ std::enable_if_t<not std::is_pointer<OutputIt>::value, void>
+flush_output_buffer(CG const& g,
+                    uint32_t const num_outputs,
+                    cuco::pair_type<Key, Value>* output_buffer,
+                    atomicT* num_items,
+                    OutputIt output_begin)
 {
   std::size_t offset;
-  const auto lane_id = cg.thread_rank();
-  if (0 == lane_id) { offset = num_items->fetch_add(output_size, cuda::std::memory_order_relaxed); }
-  offset = cg.shfl(offset, 0);
+  const auto lane_id = g.thread_rank();
+  if (0 == lane_id) { offset = num_items->fetch_add(num_outputs, cuda::std::memory_order_relaxed); }
+  offset = g.shfl(offset, 0);
 
-  for (auto index = lane_id; index < output_size; index += cg_size) {
+  for (auto index = lane_id; index < num_outputs; index += cg_size) {
     *(output_begin + offset + index) = output_buffer[index];
   }
 }
diff --git a/tests/static_multimap/static_multimap_test.cu b/tests/static_multimap/static_multimap_test.cu
index 6ff2dfc59..7c1916ab5 100644
--- a/tests/static_multimap/static_multimap_test.cu
+++ b/tests/static_multimap/static_multimap_test.cu
@@ -191,8 +191,9 @@ TEMPLATE_TEST_CASE_SIG("Each key appears twice",
 
     REQUIRE(num == num_items);
 
-    auto output_end = map.find_all(d_unique_keys.begin(), d_unique_keys.end(), d_results.begin());
-    auto size       = thrust::distance(d_results.begin(), output_end);
+    auto output_begin = d_results.data().get();
+    auto output_end   = map.find_all(d_unique_keys.begin(), d_unique_keys.end(), output_begin);
+    auto size         = thrust::distance(output_begin, output_end);
 
     REQUIRE(size == num_items);
   }

From 730ad038265fe6a0d794e04ac41f771fb662119f Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Wed, 14 Apr 2021 14:49:54 -0700
Subject: [PATCH 065/267] Use alignof to get rid of misaligned memory access
 error

---
 include/cuco/detail/static_multimap_kernels.cuh | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap_kernels.cuh
index 9af21f7d1..535566fcd 100644
--- a/include/cuco/detail/static_multimap_kernels.cuh
+++ b/include/cuco/detail/static_multimap_kernels.cuh
@@ -56,7 +56,8 @@ __inline__ __device__ std::enable_if_t<std::is_pointer<OutputIt>::value, void> f
   cg::memcpy_async(g,
                    output_begin + offset,
                    output_buffer,
-                   cuda::aligned_size_t<16>(sizeof(cuco::pair_type<Key, Value>) * num_outputs));
+                   cuda::aligned_size_t<alignof(cuco::pair_type<Key, Value>)>(
+                     sizeof(cuco::pair_type<Key, Value>) * num_outputs));
 }
 
 /**

From 19170bc285c39462c7920f618923869f8ebc072f Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Thu, 22 Apr 2021 07:31:23 -0700
Subject: [PATCH 066/267] Use reinterpret_case instead of atomic loads in
 multimap count

---
 include/cuco/detail/static_multimap.inl         | 2 +-
 include/cuco/detail/static_multimap_kernels.cuh | 9 +++++----
 2 files changed, 6 insertions(+), 5 deletions(-)

diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index 622dee15b..b9a215a09 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -178,7 +178,7 @@ std::size_t static_multimap<Key, Value, CGSize, Scope, Allocator>::count(InputIt
   CUCO_CUDA_TRY(cudaGetDevice(&device_id));
   CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_items, sizeof(atomic_ctr_type), device_id));
 
-  detail::count<block_size, CGSize, Value>
+  detail::count<block_size, CGSize, Key, Value>
     <<<grid_size, block_size>>>(first, last, num_items, view, hash, key_equal);
   CUCO_CUDA_TRY(cudaDeviceSynchronize());
 
diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap_kernels.cuh
index 535566fcd..a39a4b4b8 100644
--- a/include/cuco/detail/static_multimap_kernels.cuh
+++ b/include/cuco/detail/static_multimap_kernels.cuh
@@ -638,7 +638,7 @@ __global__ void find_all(InputIt first,
 
       tile.sync();
 
-      // Flush it the next iteration won't fit into buffer
+      // Flush if the next iteration won't fit into buffer
       if ((cg_counter[cg_id] + tile_size) > buffer_size) {
         flush_output_buffer<tile_size>(
           tile, cg_counter[cg_id], output_buffer[cg_id], num_items, output_begin);
@@ -724,6 +724,7 @@ __global__ void count(
  */
 template <uint32_t block_size,
           uint32_t tile_size,
+          typename Key,
           typename Value,
           typename InputIt,
           typename atomicT,
@@ -746,11 +747,11 @@ __global__ void count(
     bool running = true;
 
     while (tile.any(running)) {
-      auto const current_key = current_slot->first.load(cuda::std::memory_order_relaxed);
+      pair<Key, Value> slot_contents = *reinterpret_cast<pair<Key, Value> const*>(current_slot);
+      auto const& current_key        = slot_contents.first;
 
       auto const slot_is_empty = (current_key == view.get_empty_key_sentinel());
-      auto const equals        = (not slot_is_empty and key_equal(current_key, key));
-      auto const exists        = tile.ballot(equals);
+      auto const exists        = tile.ballot(not slot_is_empty and key_equal(current_key, key));
 
       if (exists) {
         auto num_matches = __popc(exists);

From 91b600aeb56756f263eaf49426983b121421d56f Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Thu, 22 Apr 2021 07:45:48 -0700
Subject: [PATCH 067/267] Add stream arguments to multimap bulk functions

---
 include/cuco/detail/static_multimap.inl | 47 +++++++++++++++----------
 include/cuco/static_multimap.cuh        | 26 +++++++++-----
 2 files changed, 45 insertions(+), 28 deletions(-)

diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index b9a215a09..da97e6bd5 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -65,10 +65,8 @@ template <typename Key,
           cuda::thread_scope Scope,
           typename Allocator>
 template <typename InputIt, typename Hash, typename KeyEqual>
-void static_multimap<Key, Value, CGSize, Scope, Allocator>::insert(InputIt first,
-                                                                   InputIt last,
-                                                                   Hash hash,
-                                                                   KeyEqual key_equal)
+void static_multimap<Key, Value, CGSize, Scope, Allocator>::insert(
+  InputIt first, InputIt last, Hash hash, KeyEqual key_equal, cudaStream_t stream)
 {
   auto num_keys         = std::distance(first, last);
   auto const block_size = 128;
@@ -77,7 +75,7 @@ void static_multimap<Key, Value, CGSize, Scope, Allocator>::insert(InputIt first
   auto view             = get_device_mutable_view();
 
   detail::insert<block_size, CGSize>
-    <<<grid_size, block_size>>>(first, first + num_keys, view, hash, key_equal);
+    <<<grid_size, block_size, 0, stream>>>(first, first + num_keys, view, hash, key_equal);
   CUCO_CUDA_TRY(cudaDeviceSynchronize());
 }
 
@@ -87,8 +85,12 @@ template <typename Key,
           cuda::thread_scope Scope,
           typename Allocator>
 template <typename InputIt, typename OutputIt, typename Hash, typename KeyEqual>
-void static_multimap<Key, Value, CGSize, Scope, Allocator>::find(
-  InputIt first, InputIt last, OutputIt output_begin, Hash hash, KeyEqual key_equal) noexcept
+void static_multimap<Key, Value, CGSize, Scope, Allocator>::find(InputIt first,
+                                                                 InputIt last,
+                                                                 OutputIt output_begin,
+                                                                 Hash hash,
+                                                                 KeyEqual key_equal,
+                                                                 cudaStream_t stream)
 {
   auto num_keys         = std::distance(first, last);
   auto const block_size = 128;
@@ -97,7 +99,7 @@ void static_multimap<Key, Value, CGSize, Scope, Allocator>::find(
   auto view             = get_device_view();
 
   detail::find<block_size, CGSize, Value>
-    <<<grid_size, block_size>>>(first, last, output_begin, view, hash, key_equal);
+    <<<grid_size, block_size, 0, stream>>>(first, last, output_begin, view, hash, key_equal);
   CUCO_CUDA_TRY(cudaDeviceSynchronize());
 }
 
@@ -107,8 +109,12 @@ template <typename Key,
           cuda::thread_scope Scope,
           typename Allocator>
 template <typename InputIt, typename OutputIt, typename Hash, typename KeyEqual>
-void static_multimap<Key, Value, CGSize, Scope, Allocator>::contains(
-  InputIt first, InputIt last, OutputIt output_begin, Hash hash, KeyEqual key_equal) noexcept
+void static_multimap<Key, Value, CGSize, Scope, Allocator>::contains(InputIt first,
+                                                                     InputIt last,
+                                                                     OutputIt output_begin,
+                                                                     Hash hash,
+                                                                     KeyEqual key_equal,
+                                                                     cudaStream_t stream)
 {
   auto num_keys         = std::distance(first, last);
   auto const block_size = 128;
@@ -117,7 +123,7 @@ void static_multimap<Key, Value, CGSize, Scope, Allocator>::contains(
   auto view             = get_device_view();
 
   detail::contains<block_size, CGSize>
-    <<<grid_size, block_size>>>(first, last, output_begin, view, hash, key_equal);
+    <<<grid_size, block_size, 0, stream>>>(first, last, output_begin, view, hash, key_equal);
   CUCO_CUDA_TRY(cudaDeviceSynchronize());
 }
 
@@ -127,8 +133,12 @@ template <typename Key,
           cuda::thread_scope Scope,
           typename Allocator>
 template <typename InputIt, typename OutputIt, typename Hash, typename KeyEqual>
-OutputIt static_multimap<Key, Value, CGSize, Scope, Allocator>::find_all(
-  InputIt first, InputIt last, OutputIt output_begin, Hash hash, KeyEqual key_equal) noexcept
+OutputIt static_multimap<Key, Value, CGSize, Scope, Allocator>::find_all(InputIt first,
+                                                                         InputIt last,
+                                                                         OutputIt output_begin,
+                                                                         Hash hash,
+                                                                         KeyEqual key_equal,
+                                                                         cudaStream_t stream)
 {
   auto num_keys          = std::distance(first, last);
   auto const block_size  = 128;
@@ -145,7 +155,8 @@ OutputIt static_multimap<Key, Value, CGSize, Scope, Allocator>::find_all(
   CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_items, sizeof(atomic_ctr_type), device_id));
 
   detail::find_all<block_size, CGSize, buffer_size, Key, Value>
-    <<<grid_size, block_size>>>(first, last, output_begin, num_items, view, hash, key_equal);
+    <<<grid_size, block_size, 0, stream>>>(
+      first, last, output_begin, num_items, view, hash, key_equal);
   CUCO_CUDA_TRY(cudaDeviceSynchronize());
 
   auto output_end = output_begin + *num_items;
@@ -160,10 +171,8 @@ template <typename Key,
           cuda::thread_scope Scope,
           typename Allocator>
 template <typename InputIt, typename Hash, typename KeyEqual>
-std::size_t static_multimap<Key, Value, CGSize, Scope, Allocator>::count(InputIt first,
-                                                                         InputIt last,
-                                                                         Hash hash,
-                                                                         KeyEqual key_equal)
+std::size_t static_multimap<Key, Value, CGSize, Scope, Allocator>::count(
+  InputIt first, InputIt last, Hash hash, KeyEqual key_equal, cudaStream_t stream)
 {
   auto num_keys         = std::distance(first, last);
   auto const block_size = 128;
@@ -179,7 +188,7 @@ std::size_t static_multimap<Key, Value, CGSize, Scope, Allocator>::count(InputIt
   CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_items, sizeof(atomic_ctr_type), device_id));
 
   detail::count<block_size, CGSize, Key, Value>
-    <<<grid_size, block_size>>>(first, last, num_items, view, hash, key_equal);
+    <<<grid_size, block_size, 0, stream>>>(first, last, num_items, view, hash, key_equal);
   CUCO_CUDA_TRY(cudaDeviceSynchronize());
 
   size_t result = *num_items;
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index 9898f3c20..09569afdc 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -182,7 +182,11 @@ class static_multimap {
   template <typename InputIt,
             typename Hash     = cuco::detail::MurmurHash3_32<key_type>,
             typename KeyEqual = thrust::equal_to<key_type>>
-  void insert(InputIt first, InputIt last, Hash hash = Hash{}, KeyEqual key_equal = KeyEqual{});
+  void insert(InputIt first,
+              InputIt last,
+              Hash hash           = Hash{},
+              KeyEqual key_equal  = KeyEqual{},
+              cudaStream_t stream = 0);
 
   /**
    * @brief Finds the values corresponding to all keys in the range `[first, last)`.
@@ -209,8 +213,9 @@ class static_multimap {
   void find(InputIt first,
             InputIt last,
             OutputIt output_begin,
-            Hash hash          = Hash{},
-            KeyEqual key_equal = KeyEqual{}) noexcept;
+            Hash hash           = Hash{},
+            KeyEqual key_equal  = KeyEqual{},
+            cudaStream_t stream = 0);
 
   /**
    * @brief Indicates whether the keys in the range `[first, last)` are contained in the map.
@@ -236,8 +241,9 @@ class static_multimap {
   void contains(InputIt first,
                 InputIt last,
                 OutputIt output_begin,
-                Hash hash          = Hash{},
-                KeyEqual key_equal = KeyEqual{}) noexcept;
+                Hash hash           = Hash{},
+                KeyEqual key_equal  = KeyEqual{},
+                cudaStream_t stream = 0);
 
   /**
    * @brief Finds all the values corresponding to all keys in the range `[first, last)`.
@@ -269,8 +275,9 @@ class static_multimap {
   OutputIt find_all(InputIt first,
                     InputIt last,
                     OutputIt output_begin,
-                    Hash hash          = Hash{},
-                    KeyEqual key_equal = KeyEqual{}) noexcept;
+                    Hash hash           = Hash{},
+                    KeyEqual key_equal  = KeyEqual{},
+                    cudaStream_t stream = 0);
 
   /**
    * @brief Counts the occurrences of keys in `[first, last)` contained in the multimap.
@@ -289,8 +296,9 @@ class static_multimap {
             typename KeyEqual = thrust::equal_to<key_type>>
   std::size_t count(InputIt first,
                     InputIt last,
-                    Hash hash          = Hash{},
-                    KeyEqual key_equal = KeyEqual{});
+                    Hash hash           = Hash{},
+                    KeyEqual key_equal  = KeyEqual{},
+                    cudaStream_t stream = 0);
 
  private:
   class device_view_base {

From 6e07991984a21874a044dc1158099dd3713e8067 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Thu, 22 Apr 2021 08:37:08 -0700
Subject: [PATCH 068/267] Add multimap count benchmark + update default
 benchmarking parameters

---
 .../static_multimap/find_all_bench.cu         |  30 ++--
 .../static_multimap/static_multimap_bench.cu  | 160 +++++++++---------
 include/cuco/detail/static_multimap.inl       |  16 +-
 include/cuco/static_multimap.cuh              |  20 +--
 4 files changed, 118 insertions(+), 108 deletions(-)

diff --git a/benchmarks/hash_table/static_multimap/find_all_bench.cu b/benchmarks/hash_table/static_multimap/find_all_bench.cu
index fa87dbce3..30e4a6168 100644
--- a/benchmarks/hash_table/static_multimap/find_all_bench.cu
+++ b/benchmarks/hash_table/static_multimap/find_all_bench.cu
@@ -30,12 +30,8 @@ static void generate_multikeys(OutputIt output_begin, OutputIt output_end, size_
 {
   auto num_keys = std::distance(output_begin, output_end);
 
-  std::random_device rd;
-  std::mt19937 gen{rd()};
-
-  std::uniform_int_distribution<Key> distribution{1, static_cast<Key>(num_keys / num_reps)};
   for (auto i = 0; i < num_keys; ++i) {
-    output_begin[i] = distribution(gen);
+    output_begin[i] = (i % (num_keys / num_reps)) + 1;
   }
 }
 
@@ -43,8 +39,7 @@ static void generate_multikeys(OutputIt output_begin, OutputIt output_end, size_
  * @brief A benchmark evaluating multi-value retrieval performance by varing number of repetitions
  * per key:
  * - 100'000'000 keys are inserted
- * - Map occupancy is fixed at 0.3
- * - CG size is fixed at 4
+ * - Map occupancy is fixed at 0.4
  * - Number of repetitions per key: 1, ... , 128, 256
  *
  */
@@ -73,11 +68,13 @@ void nvbench_find_all(
     Value val         = h_keys[i];
     h_pairs[i].first  = key;
     h_pairs[i].second = val;
-
-    h_keys[i] = i;
   }
 
-  thrust::device_vector<Key> d_keys(h_keys);
+  // Get an array of unique keys
+  std::set<Key> key_set(h_keys.begin(), h_keys.end());
+  std::vector<Key> h_unique_keys(key_set.begin(), key_set.end());
+  thrust::device_vector<Key> d_unique_keys(h_unique_keys);
+
   thrust::device_vector<cuco::pair_type<Key, Value>> d_results(2 * num_keys);
   thrust::device_vector<cuco::pair_type<Key, Value>> d_pairs(h_pairs);
 
@@ -89,6 +86,7 @@ void nvbench_find_all(
       cuco::static_multimap<Key, Value, CGSize> map{size, -1, -1};
       map.insert(d_pairs.begin(), d_pairs.end());
 
+      timer.start();
       auto view = map.get_device_view();
 
       auto const block_size  = 128;
@@ -111,10 +109,14 @@ void nvbench_find_all(
       CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_items, sizeof(atomic_ctr_type), device_id));
 
       // Use timers to explicitly mark the target region
-      timer.start();
       cuco::detail::find_all<block_size, CGSize, buffer_size, Key, Value>
-        <<<grid_size, block_size, 0, launch.get_stream()>>>(
-          d_keys.begin(), d_keys.end(), d_results.data().get(), num_items, view, hash, key_equal);
+        <<<grid_size, block_size, 0, launch.get_stream()>>>(d_unique_keys.begin(),
+                                                            d_unique_keys.end(),
+                                                            d_results.data().get(),
+                                                            num_items,
+                                                            view,
+                                                            hash,
+                                                            key_equal);
       CUCO_CUDA_TRY(cudaDeviceSynchronize());
       timer.stop();
 
@@ -128,7 +130,7 @@ using cg_size     = nvbench::enum_type_list<1, 2, 4, 8, 16, 32>;
 using buffer_size = nvbench::enum_type_list<1, 2, 4, 8, 16>;
 
 NVBENCH_BENCH_TYPES(nvbench_find_all,
-                    NVBENCH_TYPE_AXES(key_type, value_type, cg_size, nvbench::enum_type_list<16>))
+                    NVBENCH_TYPE_AXES(key_type, value_type, cg_size, nvbench::enum_type_list<2>))
   .set_type_axes_names({"Key", "Value", "CGSize", "BufferSize"})
   .set_timeout(100)                            // Custom timeout: 100 s. Default is 15 s.
   .set_max_noise(3)                            // Custom noise: 3%. By default: 0.5%.
diff --git a/benchmarks/hash_table/static_multimap/static_multimap_bench.cu b/benchmarks/hash_table/static_multimap/static_multimap_bench.cu
index f32e64ce1..10ff3efe9 100644
--- a/benchmarks/hash_table/static_multimap/static_multimap_bench.cu
+++ b/benchmarks/hash_table/static_multimap/static_multimap_bench.cu
@@ -59,12 +59,8 @@ static void generate_multikeys(OutputIt output_begin, OutputIt output_end, size_
 {
   auto num_keys = std::distance(output_begin, output_end);
 
-  std::random_device rd;
-  std::mt19937 gen{rd()};
-
-  std::uniform_int_distribution<Key> distribution{1, static_cast<Key>(num_keys / num_reps)};
   for (auto i = 0; i < num_keys; ++i) {
-    output_begin[i] = distribution(gen);
+    output_begin[i] = (i % (num_keys / num_reps)) + 1;
   }
 }
 
@@ -97,22 +93,8 @@ void launch_nvbench_insert(nvbench::state& state,
                cuco::static_multimap<Key, Value, cg_size> map{size, -1, -1};
                auto m_view = map.get_device_mutable_view();
 
-               auto const block_size = 128;
-               auto const stride     = 1;
-               auto const grid_size =
-                 (cg_size * num_keys + stride * block_size - 1) / (stride * block_size);
-
-               using Hash     = cuco::detail::MurmurHash3_32<Key>;
-               using KeyEqual = thrust::equal_to<Key>;
-
-               Hash hash;
-               KeyEqual key_equal;
-
                timer.start();
-               cuco::detail::insert<block_size, cg_size>
-                 <<<grid_size, block_size, 0, launch.get_stream()>>>(
-                   d_pairs.begin(), d_pairs.end(), m_view, hash, key_equal);
-               CUCO_CUDA_TRY(cudaDeviceSynchronize());
+               map.insert(d_pairs.begin(), d_pairs.end(), launch.get_stream());
                timer.stop();
              });
 }
@@ -121,7 +103,7 @@ void launch_nvbench_insert(nvbench::state& state,
  * @brief A benchmark evaluating single-value insertion performance by varying occupancy:
  * - Total number of insertions is fixed at 100'000'000
  * - Map occupancy: 0.1, ... , 0.8, 0.9
- * - CG size is fixed at 4
+ * - CG size is fixed at 8
  * - Three different key distributions are tested: Unique, Uniform and Gaussian
  *
  */
@@ -137,7 +119,7 @@ void nvbench_static_multimap_single_insert(nvbench::state& state, nvbench::type_
   auto const occupancy       = state.get_float64("Occupancy");
   std::size_t const size     = num_keys / occupancy;
   auto const dist            = state.get_string("Distribution");
-  std::size_t const cg_size  = 4;
+  std::size_t const cg_size  = 8;
 
   std::vector<Key> h_keys(num_keys);
 
@@ -151,7 +133,7 @@ void nvbench_static_multimap_single_insert(nvbench::state& state, nvbench::type_
  * per key:
  * - Total number of insertions is fixed at 100'000'000
  * - Map occupancy is fixed at 0.7
- * - CG size is fixed at 4
+ * - CG size is fixed at 8
  * - Unique key distributions is tested
  * - Number of repetitions per key: 1, ... , 128, 256
  *
@@ -168,7 +150,7 @@ void nvbench_static_multimap_multi_insert(nvbench::state& state, nvbench::type_l
   auto const occupancy       = state.get_float64("Occupancy");
   std::size_t const size     = num_keys / occupancy;
   std::size_t const num_reps = state.get_int64("NumReps");
-  std::size_t const cg_size  = 4;
+  std::size_t const cg_size  = 8;
 
   std::vector<Key> h_keys(num_keys);
 
@@ -212,7 +194,7 @@ void nvbench_static_multimap_insert_cgsize(
  * @brief A benchmark evaluating single-value retrieval performance by varing occupancy:
  * - 100'000'000 uniformed keys are inserted
  * - Map occupancy: 0.1, ... , 0.8, 0.9
- * - CG size is fixed at 4
+ * - CG size is fixed at 8
  *
  */
 template <typename Key, typename Value>
@@ -248,44 +230,28 @@ void nvbench_static_multimap_find(nvbench::state& state, nvbench::type_list<Key,
 
   state.exec(nvbench::exec_tag::sync | nvbench::exec_tag::timer,
              [&](nvbench::launch& launch, auto& timer) {
-               auto const tile_size  = 16;
-               cuco::static_multimap<Key, Value, tile_size> map{size, -1, -1};
+               auto const cg_size = 8;
+               cuco::static_multimap<Key, Value, cg_size> map{size, -1, -1};
                map.insert(d_pairs.begin(), d_pairs.end());
 
-               auto view = map.get_device_view();
-
-               auto const block_size = 128;
-               auto const stride     = 1;
-               auto const grid_size =
-                 (tile_size * num_keys + stride * block_size - 1) / (stride * block_size);
-
-               using Hash     = cuco::detail::MurmurHash3_32<Key>;
-               using KeyEqual = thrust::equal_to<Key>;
-
-               Hash hash;
-               KeyEqual key_equal;
-
                // Use timers to explicitly mark the target region
                timer.start();
-               cuco::detail::find<block_size, tile_size, Value>
-                 <<<grid_size, block_size, 0, launch.get_stream()>>>(
-                   d_keys.begin(), d_keys.end(), d_results.begin(), view, hash, key_equal);
-               CUCO_CUDA_TRY(cudaDeviceSynchronize());
+               map.find(d_keys.begin(), d_keys.end(), d_results.begin(), launch.get_stream());
                timer.stop();
              });
 }
 
 /**
- * @brief A benchmark evaluating multi-value retrieval performance by varing number of repetitions
+ * @brief A benchmark evaluating multi-value count performance by varing number of repetitions
  * per key:
  * - 100'000'000 keys are inserted
- * - Map occupancy is fixed at 0.3
- * - CG size is fixed at 4
+ * - Map occupancy is fixed at 0.4
+ * - CG size is fixed at 8
  * - Number of repetitions per key: 1, ... , 128, 256
  *
  */
 template <typename Key, typename Value>
-void nvbench_static_multimap_find_all(nvbench::state& state, nvbench::type_list<Key, Value>)
+void nvbench_static_multimap_count(nvbench::state& state, nvbench::type_list<Key, Value>)
 {
   if (not std::is_same<Key, Value>::value) {
     state.skip("Key should be the same type as Value.");
@@ -307,11 +273,13 @@ void nvbench_static_multimap_find_all(nvbench::state& state, nvbench::type_list<
     Value val         = h_keys[i];
     h_pairs[i].first  = key;
     h_pairs[i].second = val;
-
-    h_keys[i] = i;
   }
 
-  thrust::device_vector<Key> d_keys(h_keys);
+  // Get an array of unique keys
+  std::set<Key> key_set(h_keys.begin(), h_keys.end());
+  std::vector<Key> h_unique_keys(key_set.begin(), key_set.end());
+  thrust::device_vector<Key> d_unique_keys(h_unique_keys);
+
   thrust::device_vector<cuco::pair_type<Key, Value>> d_results(2 * num_keys);
   thrust::device_vector<cuco::pair_type<Key, Value>> d_pairs(h_pairs);
 
@@ -320,41 +288,73 @@ void nvbench_static_multimap_find_all(nvbench::state& state, nvbench::type_list<
 
   state.exec(
     nvbench::exec_tag::sync | nvbench::exec_tag::timer, [&](nvbench::launch& launch, auto& timer) {
-      auto const tile_size   = 4;
-      cuco::static_multimap<Key, Value, tile_size> map{size, -1, -1};
+      auto const cg_size = 8;
+      cuco::static_multimap<Key, Value, cg_size> map{size, -1, -1};
       map.insert(d_pairs.begin(), d_pairs.end());
 
-      auto view = map.get_device_view();
+      // Use timers to explicitly mark the target region
+      timer.start();
+      auto count = map.count(d_unique_keys.begin(), d_unique_keys.end(), launch.get_stream());
+      timer.stop();
+    });
+}
+
+/**
+ * @brief A benchmark evaluating multi-value retrieval performance by varing number of repetitions
+ * per key:
+ * - 100'000'000 keys are inserted
+ * - Map occupancy is fixed at 0.4
+ * - CG size is fixed at 8
+ * - Number of repetitions per key: 1, ... , 128, 256
+ *
+ */
+template <typename Key, typename Value>
+void nvbench_static_multimap_find_all(nvbench::state& state, nvbench::type_list<Key, Value>)
+{
+  if (not std::is_same<Key, Value>::value) {
+    state.skip("Key should be the same type as Value.");
+    return;
+  }
+
+  std::size_t const num_keys = state.get_int64("NumInputs");
+  auto const occupancy       = state.get_float64("Occupancy");
+  std::size_t const size     = num_keys / occupancy;
+  std::size_t const num_reps = state.get_int64("NumReps");
+
+  std::vector<Key> h_keys(num_keys);
+  std::vector<cuco::pair_type<Key, Value>> h_pairs(num_keys);
+
+  generate_multikeys<Key>(h_keys.begin(), h_keys.end(), num_reps);
+
+  for (auto i = 0; i < num_keys; ++i) {
+    Key key           = h_keys[i];
+    Value val         = h_keys[i];
+    h_pairs[i].first  = key;
+    h_pairs[i].second = val;
+  }
 
-      auto const block_size  = 128;
-      auto const buffer_size = tile_size * 2;
-      auto const stride      = 1;
-      auto const grid_size =
-        (tile_size * num_keys + stride * block_size - 1) / (stride * block_size);
+  // Get an array of unique keys
+  std::set<Key> key_set(h_keys.begin(), h_keys.end());
+  std::vector<Key> h_unique_keys(key_set.begin(), key_set.end());
+  thrust::device_vector<Key> d_unique_keys(h_unique_keys);
 
-      using Hash     = cuco::detail::MurmurHash3_32<Key>;
-      using KeyEqual = thrust::equal_to<Key>;
+  thrust::device_vector<cuco::pair_type<Key, Value>> d_results(2 * num_keys);
+  thrust::device_vector<cuco::pair_type<Key, Value>> d_pairs(h_pairs);
 
-      Hash hash;
-      KeyEqual key_equal;
+  state.add_element_count(num_keys, "NumKeys");
+  state.add_global_memory_writes<Key>(num_keys * 2);
 
-      using atomic_ctr_type = typename cuco::static_multimap<Key, Value>::atomic_ctr_type;
-      atomic_ctr_type* num_items;
-      CUCO_CUDA_TRY(cudaMallocManaged(&num_items, sizeof(atomic_ctr_type)));
-      *num_items = 0;
-      int device_id;
-      CUCO_CUDA_TRY(cudaGetDevice(&device_id));
-      CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_items, sizeof(atomic_ctr_type), device_id));
+  state.exec(
+    nvbench::exec_tag::sync | nvbench::exec_tag::timer, [&](nvbench::launch& launch, auto& timer) {
+      auto const cg_size = 8;
+      cuco::static_multimap<Key, Value, cg_size> map{size, -1, -1};
+      map.insert(d_pairs.begin(), d_pairs.end());
 
       // Use timers to explicitly mark the target region
       timer.start();
-      cuco::detail::find_all<block_size, tile_size, buffer_size, Key, Value>
-        <<<grid_size, block_size, 0, launch.get_stream()>>>(
-          d_keys.begin(), d_keys.end(), d_results.begin(), num_items, view, hash, key_equal);
-      CUCO_CUDA_TRY(cudaDeviceSynchronize());
+      map.find_all(
+        d_unique_keys.begin(), d_unique_keys.end(), d_results.begin(), launch.get_stream());
       timer.stop();
-
-      CUCO_CUDA_TRY(cudaFree(num_items));
     });
 }
 
@@ -382,6 +382,14 @@ NVBENCH_BENCH_TYPES(nvbench_static_multimap_find, NVBENCH_TYPE_AXES(key_type, va
   .add_int64_axis("NumInputs", {100'000'000})  // Total number of key/value pairs: 100'000'000
   .add_float64_axis("Occupancy", nvbench::range(0.1, 0.9, 0.1));
 
+NVBENCH_BENCH_TYPES(nvbench_static_multimap_count, NVBENCH_TYPE_AXES(key_type, value_type))
+  .set_type_axes_names({"Key", "Value"})
+  .set_timeout(100)                            // Custom timeout: 100 s. Default is 15 s.
+  .set_max_noise(3)                            // Custom noise: 3%. By default: 0.5%.
+  .add_int64_axis("NumInputs", {100'000'000})  // Total number of key/value pairs: 100'000'000
+  .add_float64_axis("Occupancy", {0.4})
+  .add_int64_power_of_two_axis("NumReps", nvbench::range(0, 8, 1));
+
 NVBENCH_BENCH_TYPES(nvbench_static_multimap_find_all, NVBENCH_TYPE_AXES(key_type, value_type))
   .set_type_axes_names({"Key", "Value"})
   .set_timeout(100)                            // Custom timeout: 100 s. Default is 15 s.
diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index da97e6bd5..e1988e12a 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -66,7 +66,7 @@ template <typename Key,
           typename Allocator>
 template <typename InputIt, typename Hash, typename KeyEqual>
 void static_multimap<Key, Value, CGSize, Scope, Allocator>::insert(
-  InputIt first, InputIt last, Hash hash, KeyEqual key_equal, cudaStream_t stream)
+  InputIt first, InputIt last, cudaStream_t stream, Hash hash, KeyEqual key_equal)
 {
   auto num_keys         = std::distance(first, last);
   auto const block_size = 128;
@@ -88,9 +88,9 @@ template <typename InputIt, typename OutputIt, typename Hash, typename KeyEqual>
 void static_multimap<Key, Value, CGSize, Scope, Allocator>::find(InputIt first,
                                                                  InputIt last,
                                                                  OutputIt output_begin,
+                                                                 cudaStream_t stream,
                                                                  Hash hash,
-                                                                 KeyEqual key_equal,
-                                                                 cudaStream_t stream)
+                                                                 KeyEqual key_equal)
 {
   auto num_keys         = std::distance(first, last);
   auto const block_size = 128;
@@ -112,9 +112,9 @@ template <typename InputIt, typename OutputIt, typename Hash, typename KeyEqual>
 void static_multimap<Key, Value, CGSize, Scope, Allocator>::contains(InputIt first,
                                                                      InputIt last,
                                                                      OutputIt output_begin,
+                                                                     cudaStream_t stream,
                                                                      Hash hash,
-                                                                     KeyEqual key_equal,
-                                                                     cudaStream_t stream)
+                                                                     KeyEqual key_equal)
 {
   auto num_keys         = std::distance(first, last);
   auto const block_size = 128;
@@ -136,9 +136,9 @@ template <typename InputIt, typename OutputIt, typename Hash, typename KeyEqual>
 OutputIt static_multimap<Key, Value, CGSize, Scope, Allocator>::find_all(InputIt first,
                                                                          InputIt last,
                                                                          OutputIt output_begin,
+                                                                         cudaStream_t stream,
                                                                          Hash hash,
-                                                                         KeyEqual key_equal,
-                                                                         cudaStream_t stream)
+                                                                         KeyEqual key_equal)
 {
   auto num_keys          = std::distance(first, last);
   auto const block_size  = 128;
@@ -172,7 +172,7 @@ template <typename Key,
           typename Allocator>
 template <typename InputIt, typename Hash, typename KeyEqual>
 std::size_t static_multimap<Key, Value, CGSize, Scope, Allocator>::count(
-  InputIt first, InputIt last, Hash hash, KeyEqual key_equal, cudaStream_t stream)
+  InputIt first, InputIt last, cudaStream_t stream, Hash hash, KeyEqual key_equal)
 {
   auto num_keys         = std::distance(first, last);
   auto const block_size = 128;
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index 09569afdc..35c27987e 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -184,9 +184,9 @@ class static_multimap {
             typename KeyEqual = thrust::equal_to<key_type>>
   void insert(InputIt first,
               InputIt last,
+              cudaStream_t stream = 0,
               Hash hash           = Hash{},
-              KeyEqual key_equal  = KeyEqual{},
-              cudaStream_t stream = 0);
+              KeyEqual key_equal  = KeyEqual{});
 
   /**
    * @brief Finds the values corresponding to all keys in the range `[first, last)`.
@@ -213,9 +213,9 @@ class static_multimap {
   void find(InputIt first,
             InputIt last,
             OutputIt output_begin,
+            cudaStream_t stream = 0,
             Hash hash           = Hash{},
-            KeyEqual key_equal  = KeyEqual{},
-            cudaStream_t stream = 0);
+            KeyEqual key_equal  = KeyEqual{});
 
   /**
    * @brief Indicates whether the keys in the range `[first, last)` are contained in the map.
@@ -241,9 +241,9 @@ class static_multimap {
   void contains(InputIt first,
                 InputIt last,
                 OutputIt output_begin,
+                cudaStream_t stream = 0,
                 Hash hash           = Hash{},
-                KeyEqual key_equal  = KeyEqual{},
-                cudaStream_t stream = 0);
+                KeyEqual key_equal  = KeyEqual{});
 
   /**
    * @brief Finds all the values corresponding to all keys in the range `[first, last)`.
@@ -275,9 +275,9 @@ class static_multimap {
   OutputIt find_all(InputIt first,
                     InputIt last,
                     OutputIt output_begin,
+                    cudaStream_t stream = 0,
                     Hash hash           = Hash{},
-                    KeyEqual key_equal  = KeyEqual{},
-                    cudaStream_t stream = 0);
+                    KeyEqual key_equal  = KeyEqual{});
 
   /**
    * @brief Counts the occurrences of keys in `[first, last)` contained in the multimap.
@@ -296,9 +296,9 @@ class static_multimap {
             typename KeyEqual = thrust::equal_to<key_type>>
   std::size_t count(InputIt first,
                     InputIt last,
+                    cudaStream_t stream = 0,
                     Hash hash           = Hash{},
-                    KeyEqual key_equal  = KeyEqual{},
-                    cudaStream_t stream = 0);
+                    KeyEqual key_equal  = KeyEqual{});
 
  private:
   class device_view_base {

From 44c0fc1f115806eb95c03edc405e9b8e59e595b9 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Thu, 22 Apr 2021 09:16:20 -0700
Subject: [PATCH 069/267] Use is_contiguous_iterator to detect when to use
 cg::memcpy_async

---
 .../static_multimap/static_multimap_bench.cu  |  2 +-
 .../cuco/detail/static_multimap_kernels.cuh   | 19 ++++++++++---------
 2 files changed, 11 insertions(+), 10 deletions(-)

diff --git a/benchmarks/hash_table/static_multimap/static_multimap_bench.cu b/benchmarks/hash_table/static_multimap/static_multimap_bench.cu
index 10ff3efe9..39fb66667 100644
--- a/benchmarks/hash_table/static_multimap/static_multimap_bench.cu
+++ b/benchmarks/hash_table/static_multimap/static_multimap_bench.cu
@@ -353,7 +353,7 @@ void nvbench_static_multimap_find_all(nvbench::state& state, nvbench::type_list<
       // Use timers to explicitly mark the target region
       timer.start();
       map.find_all(
-        d_unique_keys.begin(), d_unique_keys.end(), d_results.begin(), launch.get_stream());
+        d_unique_keys.begin(), d_unique_keys.end(), d_results.data().get(), launch.get_stream());
       timer.stop();
     });
 }
diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap_kernels.cuh
index a39a4b4b8..91342b2fb 100644
--- a/include/cuco/detail/static_multimap_kernels.cuh
+++ b/include/cuco/detail/static_multimap_kernels.cuh
@@ -14,9 +14,10 @@
  * limitations under the License.
  */
 
-#include <cuco/detail/pair.cuh>
-
 #include <cooperative_groups/memcpy_async.h>
+#include <thrust/type_traits/is_contiguous_iterator.h>
+
+#include <cuco/detail/pair.cuh>
 
 namespace cuco {
 namespace detail {
@@ -41,12 +42,12 @@ template <uint32_t cg_size,
           typename Value,
           typename atomicT,
           typename OutputIt>
-__inline__ __device__ std::enable_if_t<std::is_pointer<OutputIt>::value, void> flush_output_buffer(
-  CG const& g,
-  uint32_t const num_outputs,
-  cuco::pair_type<Key, Value>* output_buffer,
-  atomicT* num_items,
-  OutputIt output_begin)
+__inline__ __device__ std::enable_if_t<thrust::is_contiguous_iterator<OutputIt>::value, void>
+flush_output_buffer(CG const& g,
+                    uint32_t const num_outputs,
+                    cuco::pair_type<Key, Value>* output_buffer,
+                    atomicT* num_items,
+                    OutputIt output_begin)
 {
   std::size_t offset;
   const auto lane_id = g.thread_rank();
@@ -79,7 +80,7 @@ template <uint32_t cg_size,
           typename Value,
           typename atomicT,
           typename OutputIt>
-__inline__ __device__ std::enable_if_t<not std::is_pointer<OutputIt>::value, void>
+__inline__ __device__ std::enable_if_t<not thrust::is_contiguous_iterator<OutputIt>::value, void>
 flush_output_buffer(CG const& g,
                     uint32_t const num_outputs,
                     cuco::pair_type<Key, Value>* output_buffer,

From e540a3782efcc227f1cdb180f4dc6999e676f9ba Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Fri, 23 Apr 2021 12:21:24 -0700
Subject: [PATCH 070/267] Optimize multimap count: getting rid of cg.any,
 ballot, popc + using cub block reduce

---
 .../cuco/detail/static_multimap_kernels.cuh   | 34 +++++++++----------
 1 file changed, 17 insertions(+), 17 deletions(-)

diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap_kernels.cuh
index 91342b2fb..a99388390 100644
--- a/include/cuco/detail/static_multimap_kernels.cuh
+++ b/include/cuco/detail/static_multimap_kernels.cuh
@@ -736,35 +736,35 @@ __global__ void count(
   InputIt first, InputIt last, atomicT* num_items, viewT view, Hash hash, KeyEqual key_equal)
 {
   auto tile    = cg::tiled_partition<tile_size>(cg::this_thread_block());
-  auto tid     = blockDim.x * blockIdx.x + threadIdx.x;
+  auto tid     = block_size * blockIdx.x + threadIdx.x;
   auto key_idx = tid / tile_size;
 
-  const int lane_id = tile.thread_rank();
+  typedef cub::BlockReduce<std::size_t, block_size> BlockReduce;
+  __shared__ typename BlockReduce::TempStorage temp_storage;
+  std::size_t thread_num_items = 0;
 
   while (first + key_idx < last) {
     auto key          = *(first + key_idx);
     auto current_slot = view.initial_slot(tile, key, hash);
 
-    bool running = true;
-
-    while (tile.any(running)) {
-      pair<Key, Value> slot_contents = *reinterpret_cast<pair<Key, Value> const*>(current_slot);
-      auto const& current_key        = slot_contents.first;
+    while (true) {
+      pair<Key, Value> slot_contents =
+        *reinterpret_cast<cuco::pair_type<Key, Value> const*>(current_slot);
+      auto const& current_key = slot_contents.first;
 
       auto const slot_is_empty = (current_key == view.get_empty_key_sentinel());
-      auto const exists        = tile.ballot(not slot_is_empty and key_equal(current_key, key));
+      auto const equals        = not slot_is_empty and key_equal(current_key, key);
+
+      if (equals) { thread_num_items++; }
+
+      if (tile.any(slot_is_empty)) { break; }
 
-      if (exists) {
-        auto num_matches = __popc(exists);
-        // First lane gets the CG-level offset
-        if (0 == lane_id) { num_items->fetch_add(num_matches, cuda::std::memory_order_relaxed); }
-      } else if (tile.any(slot_is_empty)) {
-        running = false;
-      }
       current_slot = view.next_slot(tile, current_slot);
-    }  // while running
-    key_idx += (gridDim.x * blockDim.x) / tile_size;
+    }  // while true
+    key_idx += (gridDim.x * block_size) / tile_size;
   }
+  auto const block_num_items = BlockReduce(temp_storage).Sum(thread_num_items);
+  if (threadIdx.x == 0) { num_items->fetch_add(block_num_items, cuda::std::memory_order_relaxed); }
 }
 
 }  // namespace detail

From d8c7ce1aa03de6a7d52372f30d008a2836994a2d Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Fri, 23 Apr 2021 13:41:04 -0700
Subject: [PATCH 071/267] Minor improvement: end key iteration whenever an
 empty slot shows up

---
 include/cuco/detail/static_multimap_kernels.cuh | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap_kernels.cuh
index a99388390..3d345f2cd 100644
--- a/include/cuco/detail/static_multimap_kernels.cuh
+++ b/include/cuco/detail/static_multimap_kernels.cuh
@@ -628,7 +628,8 @@ __global__ void find_all(InputIt first,
             cuco::make_pair<Key, Value>(std::move(k), std::move(slot_contents.second));
         }
         if (0 == lane_id) { cg_counter[cg_id] += num_matches; }
-      } else if (tile.any(slot_is_empty)) {
+      }
+      if (tile.any(slot_is_empty)) {
         running = false;
         if ((not found_match) && (lane_id == 0)) {
           auto output_idx = cg_counter[cg_id]++;

From 3c1e01d6b4af84229c9adad4a0133dc3f10080be Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Fri, 23 Apr 2021 14:44:20 -0700
Subject: [PATCH 072/267] Remove one unnessary cg sync in multimap find_all

---
 include/cuco/detail/static_multimap_kernels.cuh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap_kernels.cuh
index 3d345f2cd..d34dd4402 100644
--- a/include/cuco/detail/static_multimap_kernels.cuh
+++ b/include/cuco/detail/static_multimap_kernels.cuh
@@ -605,7 +605,7 @@ __global__ void find_all(InputIt first,
     bool running     = true;
     bool found_match = false;
 
-    while (tile.any(running)) {
+    while (running) {
       // TODO: Replace reinterpret_cast with atomic ref when possible. The current implementation is
       // unsafe!
       static_assert(sizeof(Key) == sizeof(cuda::atomic<Key>));

From 36f6cffd60679e2bd0ace7f02c4c149c2acb0bf7 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Mon, 26 Apr 2021 10:39:46 -0700
Subject: [PATCH 073/267] Use cg::any in multimap insert for simplicity

---
 include/cuco/detail/static_multimap.inl | 5 +----
 1 file changed, 1 insertion(+), 4 deletions(-)

diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index e1988e12a..c5d471a9c 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -290,11 +290,8 @@ __device__ void static_multimap<Key, Value, CGSize, Scope, Allocator>::device_mu
         // so we need to try the next empty slot in the window
       }
 
-      uint32_t res_status = g.shfl(static_cast<uint32_t>(status), src_lane);
-      status              = static_cast<insert_result>(res_status);
-
       // successful insert
-      if (status == insert_result::SUCCESS) { return; }
+      if (g.any(status == insert_result::SUCCESS)) { return; }
       // if we've gotten this far, a different key took our spot
       // before we could insert. We need to retry the insert on the
       // same window

From 0aac02a94d7d4b16079e14a2d42f9fec8e16b903 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Mon, 26 Apr 2021 11:12:35 -0700
Subject: [PATCH 074/267] Fix a bug in multimap constructor: using the adjusted
 capcacity

---
 include/cuco/detail/static_multimap.inl | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index c5d471a9c..5cf81c167 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -44,9 +44,9 @@ static_multimap<Key, Value, CGSize, Scope, Allocator>::static_multimap(std::size
 
   auto constexpr block_size = 256;
   auto constexpr stride     = 4;
-  auto const grid_size      = (capacity + stride * block_size - 1) / (stride * block_size);
+  auto const grid_size      = (get_capacity() + stride * block_size - 1) / (stride * block_size);
   detail::initialize<atomic_key_type, atomic_mapped_type>
-    <<<grid_size, block_size>>>(slots_, empty_key_sentinel, empty_value_sentinel, capacity);
+    <<<grid_size, block_size>>>(slots_, empty_key_sentinel, empty_value_sentinel, get_capacity());
 }
 
 template <typename Key,

From 7f83783b37f780bff98739f2edf59b4a6f8209ea Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Wed, 28 Apr 2021 10:29:44 -0700
Subject: [PATCH 075/267] Use vector load to optimize multimap count and
 find_all

---
 include/cuco/detail/prime.cuh                 |  4 +-
 include/cuco/detail/static_multimap.inl       | 31 +++----
 .../cuco/detail/static_multimap_kernels.cuh   | 91 +++++++++++--------
 include/cuco/static_multimap.cuh              | 18 +++-
 4 files changed, 84 insertions(+), 60 deletions(-)

diff --git a/include/cuco/detail/prime.cuh b/include/cuco/detail/prime.cuh
index 6dd96fa71..0c8365edf 100644
--- a/include/cuco/detail/prime.cuh
+++ b/include/cuco/detail/prime.cuh
@@ -69,8 +69,8 @@ constexpr std::size_t compute_prime(std::size_t num) noexcept
 template <std::size_t CGSize>
 constexpr std::size_t get_valid_capacity(std::size_t capacity) noexcept
 {
-  auto const min_prime = compute_prime(SDIV(capacity, CGSize));
-  return min_prime * CGSize;
+  auto const min_prime = compute_prime(SDIV(capacity, CGSize * 2));
+  return min_prime * CGSize * 2;
 }
 
 }  // namespace detail
diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index 5cf81c167..6ba9dfbdf 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -248,32 +248,32 @@ __device__ void static_multimap<Key, Value, CGSize, Scope, Allocator>::device_mu
   CG g, value_type const& insert_pair, Hash hash, KeyEqual key_equal) noexcept
 {
   auto current_slot = initial_slot(g, insert_pair.first, hash);
-
   while (true) {
-    key_type const existing_key = current_slot->first.load(cuda::memory_order_relaxed);
-
+    // key_type const existing_key = current_slot->first.load(cuda::memory_order_relaxed);
+    auto const tmp = *reinterpret_cast<uint4 const*>(current_slot);
+    pair<Key, Value> arr[2];
+    memcpy(&arr[0], &tmp, 2 * sizeof(pair<Key, Value>));
     // The user provide `key_equal` can never be used to compare against `empty_key_sentinel` as the
     // sentinel is not a valid key value. Therefore, first check for the sentinel
-    auto const slot_is_empty         = (existing_key == this->get_empty_key_sentinel());
-    auto const window_contains_empty = g.ballot(slot_is_empty);
-
+    auto const first_slot_is_empty  = (arr[0].first == this->get_empty_key_sentinel());
+    auto const second_slot_is_empty = (arr[1].first == this->get_empty_key_sentinel());
+    // auto const slot_is_empty         = (existing_key == this->get_empty_key_sentinel());
+    auto const window_contains_empty = g.ballot(first_slot_is_empty or second_slot_is_empty);
     if (window_contains_empty) {
       // the first lane in the group with an empty slot will attempt the insert
       insert_result status{insert_result::CONTINUE};
       uint32_t src_lane = __ffs(window_contains_empty) - 1;
-
       if (g.thread_rank() == src_lane) {
         using cuda::std::memory_order_relaxed;
-        auto expected_key   = this->get_empty_key_sentinel();
-        auto expected_value = this->get_empty_value_sentinel();
-        auto& slot_key      = current_slot->first;
-        auto& slot_value    = current_slot->second;
-
+        auto expected_key    = this->get_empty_key_sentinel();
+        auto expected_value  = this->get_empty_value_sentinel();
+        auto insert_location = current_slot + !(first_slot_is_empty);
+        auto& slot_key       = insert_location->first;
+        auto& slot_value     = insert_location->second;
         bool key_success =
           slot_key.compare_exchange_strong(expected_key, insert_pair.first, memory_order_relaxed);
         bool value_success = slot_value.compare_exchange_strong(
           expected_value, insert_pair.second, memory_order_relaxed);
-
         if (key_success) {
           while (not value_success) {
             value_success =
@@ -285,9 +285,8 @@ __device__ void static_multimap<Key, Value, CGSize, Scope, Allocator>::device_mu
         } else if (value_success) {
           slot_value.store(this->get_empty_value_sentinel(), memory_order_relaxed);
         }
-
-        // another key was inserted in the slot we wanted to try
-        // so we need to try the next empty slot in the window
+        // another key was inserted in both slots we wanted to try
+        // so we need to try the next empty slots in the window
       }
 
       // successful insert
diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap_kernels.cuh
index d34dd4402..191bebc16 100644
--- a/include/cuco/detail/static_multimap_kernels.cuh
+++ b/include/cuco/detail/static_multimap_kernels.cuh
@@ -200,6 +200,13 @@ __global__ void insert(InputIt first, InputIt last, viewT view, Hash hash, KeyEq
     view.insert(tile, insert_pair, hash, key_equal);
     it += (gridDim.x * blockDim.x) / tile_size;
   }
+  if (threadIdx.x == 0 && blockIdx.x == 0) {
+    for(auto i = 0; i < view.get_capacity(); i++){
+      auto slot = view.get_slots() + i;
+      //if (slot->first != -1)
+        //printf("%ld:\t%ld\n", long(slot->first), long(slot->second));
+    }
+  }
 }
 
 /**
@@ -606,30 +613,40 @@ __global__ void find_all(InputIt first,
     bool found_match = false;
 
     while (running) {
-      // TODO: Replace reinterpret_cast with atomic ref when possible. The current implementation is
-      // unsafe!
-      static_assert(sizeof(Key) == sizeof(cuda::atomic<Key>));
-      static_assert(sizeof(Value) == sizeof(cuda::atomic<Value>));
-      pair<Key, Value> slot_contents = *reinterpret_cast<pair<Key, Value> const*>(current_slot);
+      auto const tmp = *reinterpret_cast<uint4 const*>(current_slot);
+      pair<Key, Value> arr[2];
+      memcpy(&arr[0], &tmp, 2 * sizeof(pair<Key, Value>));
+
+      auto const first_slot_is_empty  = (arr[0].first == view.get_empty_key_sentinel());
+      auto const second_slot_is_empty = (arr[1].first == view.get_empty_key_sentinel());
+      auto const first_equals         = (not first_slot_is_empty and key_equal(arr[0].first, key));
+      auto const second_equals        = (not second_slot_is_empty and key_equal(arr[1].first, key));
+      auto const first_exists = tile.ballot(first_equals);
+      auto const second_exists = tile.ballot(second_equals);
+
+      if (first_exists or second_exists) {
+        found_match         = true;
 
-      auto const slot_is_empty = (slot_contents.first == view.get_empty_key_sentinel());
-      auto const equals        = (not slot_is_empty and key_equal(slot_contents.first, key));
-      auto const exists        = tile.ballot(equals);
+        auto num_first_matches = __popc(first_exists);
+        auto num_second_matches = __popc(second_exists);
 
-      if (exists) {
-        found_match         = true;
-        auto num_matches    = __popc(exists);
         uint32_t output_idx = cg_counter[cg_id];
-        if (equals) {
-          // Each match computes its lane-level offset
-          auto lane_offset = __popc(exists & ((1 << lane_id) - 1));
+
+        if (first_equals) {
+          auto lane_offset = __popc(first_exists & ((1 << lane_id) - 1));
           Key k            = key;
           output_buffer[cg_id][output_idx + lane_offset] =
-            cuco::make_pair<Key, Value>(std::move(k), std::move(slot_contents.second));
+            cuco::make_pair<Key, Value>(std::move(k), std::move(arr[0].second));
         }
-        if (0 == lane_id) { cg_counter[cg_id] += num_matches; }
+        if (second_equals) {
+          auto lane_offset = __popc(second_exists & ((1 << lane_id) - 1));
+          Key k            = key;
+          output_buffer[cg_id][output_idx + num_first_matches + lane_offset] =
+            cuco::make_pair<Key, Value>(std::move(k), std::move(arr[1].second));
+        }
+        if (0 == lane_id) {cg_counter[cg_id] += (num_first_matches + num_second_matches);}
       }
-      if (tile.any(slot_is_empty)) {
+      if (tile.any(first_slot_is_empty or second_slot_is_empty)) {
         running = false;
         if ((not found_match) && (lane_id == 0)) {
           auto output_idx = cg_counter[cg_id]++;
@@ -640,13 +657,11 @@ __global__ void find_all(InputIt first,
 
       tile.sync();
 
-      // Flush if the next iteration won't fit into buffer
-      if ((cg_counter[cg_id] + tile_size) > buffer_size) {
         flush_output_buffer<tile_size>(
           tile, cg_counter[cg_id], output_buffer[cg_id], num_items, output_begin);
         // First lane reset CG-level counter
         if (lane_id == 0) { cg_counter[cg_id] = 0; }
-      }
+
       current_slot = view.next_slot(tile, current_slot);
     }  // while running
     key_idx += (gridDim.x * blockDim.x) / tile_size;
@@ -736,36 +751,38 @@ template <uint32_t block_size,
 __global__ void count(
   InputIt first, InputIt last, atomicT* num_items, viewT view, Hash hash, KeyEqual key_equal)
 {
-  auto tile    = cg::tiled_partition<tile_size>(cg::this_thread_block());
-  auto tid     = block_size * blockIdx.x + threadIdx.x;
-  auto key_idx = tid / tile_size;
+  auto tile         = cg::tiled_partition<tile_size>(cg::this_thread_block());
+  auto tid          = blockDim.x * blockIdx.x + threadIdx.x;
+  auto key_idx      = tid / tile_size;
 
-  typedef cub::BlockReduce<std::size_t, block_size> BlockReduce;
+  using BlockReduce = cub::BlockReduce<int, block_size>;
   __shared__ typename BlockReduce::TempStorage temp_storage;
-  std::size_t thread_num_items = 0;
+
+  int thread_counter{0};
 
   while (first + key_idx < last) {
     auto key          = *(first + key_idx);
     auto current_slot = view.initial_slot(tile, key, hash);
 
     while (true) {
-      pair<Key, Value> slot_contents =
-        *reinterpret_cast<cuco::pair_type<Key, Value> const*>(current_slot);
-      auto const& current_key = slot_contents.first;
-
-      auto const slot_is_empty = (current_key == view.get_empty_key_sentinel());
-      auto const equals        = not slot_is_empty and key_equal(current_key, key);
+      auto const tmp = *reinterpret_cast<uint4 const*>(current_slot);
+      pair<Key, Value> arr[2];
+      memcpy(&arr[0], &tmp, 2 * sizeof(pair<Key, Value>));
 
-      if (equals) { thread_num_items++; }
+      auto const first_slot_is_empty  = (arr[0].first == view.get_empty_key_sentinel());
+      auto const second_slot_is_empty = (arr[1].first == view.get_empty_key_sentinel());
+      auto const first_equals         = (not first_slot_is_empty and key_equal(arr[0].first, key));
+      auto const second_equals        = (not second_slot_is_empty and key_equal(arr[1].first, key));
+      thread_counter += (first_equals + second_equals);
 
-      if (tile.any(slot_is_empty)) { break; }
+      if (tile.any(first_slot_is_empty or second_slot_is_empty)) { break; }
 
       current_slot = view.next_slot(tile, current_slot);
-    }  // while true
-    key_idx += (gridDim.x * block_size) / tile_size;
+    }
+    key_idx += (gridDim.x * blockDim.x) / tile_size;
   }
-  auto const block_num_items = BlockReduce(temp_storage).Sum(thread_num_items);
-  if (threadIdx.x == 0) { num_items->fetch_add(block_num_items, cuda::std::memory_order_relaxed); }
+  auto const block_count = BlockReduce(temp_storage).Sum(thread_counter);
+  if (threadIdx.x == 0) { num_items->fetch_add(block_count, cuda::std::memory_order_relaxed); }
 }
 
 }  // namespace detail
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index 35c27987e..36a12fc7a 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -107,7 +107,7 @@ namespace cuco {
  */
 template <typename Key,
           typename Value,
-          std::size_t CGSize       = 16,
+          std::size_t CGSize       = 8,
           cuda::thread_scope Scope = cuda::thread_scope_device,
           typename Allocator       = cuco::cuda_allocator<char>>
 class static_multimap {
@@ -386,8 +386,12 @@ class static_multimap {
     template <typename CG, typename Hash>
     __device__ iterator initial_slot(CG g, Key const& k, Hash hash) noexcept
     {
-      step_size_ = (hash(k + 1) % (capacity_ / g.size() - 1) + 1) * g.size();
-      return &slots_[(hash(k) + g.thread_rank()) % capacity_];
+      auto const cg_size = g.size();
+      step_size_            = (hash(k + 1) % (capacity_ / (cg_size * 2) - 1) + 1) * cg_size * 2;
+      auto const h          = hash(k);
+      auto const slot_index = h  % (capacity_ / (cg_size * 2)) * cg_size * 2 + g.thread_rank() * 2;
+      auto const p          = begin_slot() + slot_index;
+      return p;
     }
 
     /**
@@ -405,8 +409,12 @@ class static_multimap {
     template <typename CG, typename Hash>
     __device__ const_iterator initial_slot(CG g, Key const& k, Hash hash) const noexcept
     {
-      step_size_ = (hash(k + 1) % (capacity_ / g.size() - 1) + 1) * g.size();
-      return &slots_[(hash(k) + g.thread_rank()) % capacity_];
+      auto const cg_size = g.size();
+      step_size_            = (hash(k + 1) % (capacity_ / (cg_size * 2) - 1) + 1) * cg_size * 2;
+      auto const h          = hash(k);
+      auto const slot_index = h  % (capacity_ / (cg_size * 2)) * cg_size * 2 + g.thread_rank() * 2;
+      auto const p          = begin_slot() + slot_index;
+      return p;
     }
 
     /**

From bceedcd058bfbf872576d16913b32989b2004a8e Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Sun, 2 May 2021 21:23:37 -0400
Subject: [PATCH 076/267] Update multimap benchmarks: remove uninterested
 benchmarks, evaluate both RANDOM and CYCLE key distributions

---
 .../static_multimap/static_multimap_bench.cu  | 223 ++++++++----------
 1 file changed, 98 insertions(+), 125 deletions(-)

diff --git a/benchmarks/hash_table/static_multimap/static_multimap_bench.cu b/benchmarks/hash_table/static_multimap/static_multimap_bench.cu
index 39fb66667..f0373b9de 100644
--- a/benchmarks/hash_table/static_multimap/static_multimap_bench.cu
+++ b/benchmarks/hash_table/static_multimap/static_multimap_bench.cu
@@ -26,44 +26,27 @@
  *
  */
 template <typename Key, typename OutputIt>
-static void generate_keys(OutputIt output_begin, OutputIt output_end, const std::string& dist)
+static void generate_keys(OutputIt output_begin,
+                          OutputIt output_end,
+                          size_t const num_reps,
+                          const std::string& dist)
 {
   auto num_keys = std::distance(output_begin, output_end);
 
-  std::random_device rd;
-  std::mt19937 gen{rd()};
-
-  if (dist == "UNIQUE") {
-    for (auto i = 0; i < num_keys; ++i) {
-      output_begin[i] = i;
-    }
-  } else if (dist == "UNIFORM") {
-    std::uniform_int_distribution<Key> distribution{0, std::numeric_limits<Key>::max()};
+  if (dist == "RANDOM") {
+    std::random_device rd;
+    std::mt19937 gen{rd()};
+    std::uniform_int_distribution<Key> distribution{1, static_cast<Key>(num_keys / num_reps)};
     for (auto i = 0; i < num_keys; ++i) {
       output_begin[i] = distribution(gen);
     }
-  } else if (dist == "GAUSSIAN") {
-    std::normal_distribution<> dg{1e9, 1e7};
+  } else if (dist == "CYCLE") {
     for (auto i = 0; i < num_keys; ++i) {
-      output_begin[i] = std::abs(static_cast<Key>(dg(gen)));
+      output_begin[i] = (i % (num_keys / num_reps)) + 1;
     }
   }
 }
 
-/**
- * @brief Generates input keys by a given number of repetitions per key.
- *
- */
-template <typename Key, typename OutputIt>
-static void generate_multikeys(OutputIt output_begin, OutputIt output_end, size_t const num_reps)
-{
-  auto num_keys = std::distance(output_begin, output_end);
-
-  for (auto i = 0; i < num_keys; ++i) {
-    output_begin[i] = (i % (num_keys / num_reps)) + 1;
-  }
-}
-
 /**
  * @brief Helper function to launch insertion benchmark.
  *
@@ -78,7 +61,7 @@ void launch_nvbench_insert(nvbench::state& state,
 
   for (auto i = 0; i < num_keys; ++i) {
     Key key           = h_keys[i];
-    Value val         = h_keys[i];
+    Value val         = i;
     h_pairs[i].first  = key;
     h_pairs[i].second = val;
   }
@@ -99,47 +82,17 @@ void launch_nvbench_insert(nvbench::state& state,
              });
 }
 
-/**
- * @brief A benchmark evaluating single-value insertion performance by varying occupancy:
- * - Total number of insertions is fixed at 100'000'000
- * - Map occupancy: 0.1, ... , 0.8, 0.9
- * - CG size is fixed at 8
- * - Three different key distributions are tested: Unique, Uniform and Gaussian
- *
- */
-template <typename Key, typename Value>
-void nvbench_static_multimap_single_insert(nvbench::state& state, nvbench::type_list<Key, Value>)
-{
-  if (not std::is_same<Key, Value>::value) {
-    state.skip("Key should be the same type as Value.");
-    return;
-  }
-
-  std::size_t const num_keys = state.get_int64("NumInputs");
-  auto const occupancy       = state.get_float64("Occupancy");
-  std::size_t const size     = num_keys / occupancy;
-  auto const dist            = state.get_string("Distribution");
-  std::size_t const cg_size  = 8;
-
-  std::vector<Key> h_keys(num_keys);
-
-  generate_keys<Key>(h_keys.begin(), h_keys.end(), dist);
-
-  launch_nvbench_insert<Key, Value, cg_size>(state, h_keys, num_keys, size);
-}
-
 /**
  * @brief A benchmark evaluating multi-value insertion performance by varing number of repetitions
  * per key:
- * - Total number of insertions is fixed at 100'000'000
- * - Map occupancy is fixed at 0.7
- * - CG size is fixed at 8
- * - Unique key distributions is tested
+ * - Total number of insertions: 100'000'000
+ * - Map occupancy: 0.8
+ * - CG size: 8
  * - Number of repetitions per key: 1, ... , 128, 256
  *
  */
 template <typename Key, typename Value>
-void nvbench_static_multimap_multi_insert(nvbench::state& state, nvbench::type_list<Key, Value>)
+void nvbench_static_multimap_insert(nvbench::state& state, nvbench::type_list<Key, Value>)
 {
   if (not std::is_same<Key, Value>::value) {
     state.skip("Key should be the same type as Value.");
@@ -148,13 +101,15 @@ void nvbench_static_multimap_multi_insert(nvbench::state& state, nvbench::type_l
 
   std::size_t const num_keys = state.get_int64("NumInputs");
   auto const occupancy       = state.get_float64("Occupancy");
-  std::size_t const size     = num_keys / occupancy;
   std::size_t const num_reps = state.get_int64("NumReps");
-  std::size_t const cg_size  = 8;
+  auto const dist            = state.get_string("Distribution");
+
+  std::size_t const size    = num_keys / occupancy;
+  std::size_t const cg_size = 8;
 
   std::vector<Key> h_keys(num_keys);
 
-  generate_multikeys<Key>(h_keys.begin(), h_keys.end(), num_reps);
+  generate_keys<Key>(h_keys.begin(), h_keys.end(), num_reps, dist);
 
   launch_nvbench_insert<Key, Value, cg_size>(state, h_keys, num_keys, size);
 }
@@ -162,9 +117,8 @@ void nvbench_static_multimap_multi_insert(nvbench::state& state, nvbench::type_l
 /**
  * @brief A benchmark evaluating multi-value insertion performance by varing number of repetitions
  * per key and CUDA CG size:
- * - Total number of insertions is fixed at 100'000'000
- * - Map occupancy is fixed at 0.7
- * - Unique key distributions is tested
+ * - Total number of insertions: 100'000'000
+ * - Map occupancy: 0.8
  * - Number of repetitions per key: 1, ... , 128, 256
  * - CG size: 1, ... , 16, 32
  *
@@ -180,25 +134,29 @@ void nvbench_static_multimap_insert_cgsize(
 
   std::size_t const num_keys = state.get_int64("NumInputs");
   auto const occupancy       = state.get_float64("Occupancy");
-  std::size_t const size     = num_keys / occupancy;
   std::size_t const num_reps = state.get_int64("NumReps");
+  auto const dist            = state.get_string("Distribution");
+
+  std::size_t const size = num_keys / occupancy;
 
   std::vector<Key> h_keys(num_keys);
 
-  generate_multikeys<Key>(h_keys.begin(), h_keys.end(), num_reps);
+  generate_keys<Key>(h_keys.begin(), h_keys.end(), num_reps, dist);
 
   launch_nvbench_insert<Key, Value, CGSize>(state, h_keys, num_keys, size);
 }
 
 /**
- * @brief A benchmark evaluating single-value retrieval performance by varing occupancy:
- * - 100'000'000 uniformed keys are inserted
- * - Map occupancy: 0.1, ... , 0.8, 0.9
- * - CG size is fixed at 8
+ * @brief A benchmark evaluating multi-value count performance by varing number of repetitions
+ * per key:
+ * - 100'000'000 keys are inserted
+ * - Map occupancy: 0.8
+ * - CG size: 8
+ * - Number of repetitions per key: 1, ... , 128, 256
  *
  */
 template <typename Key, typename Value>
-void nvbench_static_multimap_find(nvbench::state& state, nvbench::type_list<Key, Value>)
+void nvbench_static_multimap_count(nvbench::state& state, nvbench::type_list<Key, Value>)
 {
   if (not std::is_same<Key, Value>::value) {
     state.skip("Key should be the same type as Value.");
@@ -207,51 +165,58 @@ void nvbench_static_multimap_find(nvbench::state& state, nvbench::type_list<Key,
 
   std::size_t const num_keys = state.get_int64("NumInputs");
   auto const occupancy       = state.get_float64("Occupancy");
-  std::size_t const size     = num_keys / occupancy;
+  std::size_t const num_reps = state.get_int64("NumReps");
+  auto const dist            = state.get_string("Distribution");
 
-  std::vector<Key> h_keys(num_keys);
-  std::vector<cuco::pair_type<Key, Value>> h_pairs(num_keys);
+  std::size_t const size = num_keys / occupancy;
 
-  generate_keys<Key>(h_keys.begin(), h_keys.end(), "UNIFORM");
+  std::vector<Key> h_keys(num_keys);
+  generate_keys<Key>(h_keys.begin(), h_keys.end(), num_reps, dist);
 
+  std::vector<cuco::pair_type<Key, Value>> h_pairs(num_keys);
   for (auto i = 0; i < num_keys; ++i) {
     Key key           = h_keys[i];
     Value val         = h_keys[i];
     h_pairs[i].first  = key;
     h_pairs[i].second = val;
+
+    if (dist == "RANDOM") { h_keys[i] = i; }
   }
 
-  thrust::device_vector<Key> d_keys(h_keys);
-  thrust::device_vector<Value> d_results(num_keys);
+  // Get an array of unique keys
+  std::set<Key> key_set(h_keys.begin(), h_keys.end());
+  std::vector<Key> h_unique_keys(key_set.begin(), key_set.end());
+  thrust::device_vector<Key> d_unique_keys(h_unique_keys);
+
   thrust::device_vector<cuco::pair_type<Key, Value>> d_pairs(h_pairs);
 
   state.add_element_count(num_keys, "NumKeys");
   state.add_global_memory_writes<Key>(num_keys * 2);
 
-  state.exec(nvbench::exec_tag::sync | nvbench::exec_tag::timer,
-             [&](nvbench::launch& launch, auto& timer) {
-               auto const cg_size = 8;
-               cuco::static_multimap<Key, Value, cg_size> map{size, -1, -1};
-               map.insert(d_pairs.begin(), d_pairs.end());
+  state.exec(
+    nvbench::exec_tag::sync | nvbench::exec_tag::timer, [&](nvbench::launch& launch, auto& timer) {
+      auto const cg_size = 8;
+      cuco::static_multimap<Key, Value, cg_size> map{size, -1, -1};
+      map.insert(d_pairs.begin(), d_pairs.end());
 
-               // Use timers to explicitly mark the target region
-               timer.start();
-               map.find(d_keys.begin(), d_keys.end(), d_results.begin(), launch.get_stream());
-               timer.stop();
-             });
+      // Use timers to explicitly mark the target region
+      timer.start();
+      auto count = map.count(d_unique_keys.begin(), d_unique_keys.end(), launch.get_stream());
+      timer.stop();
+    });
 }
 
 /**
- * @brief A benchmark evaluating multi-value count performance by varing number of repetitions
+ * @brief A benchmark evaluating multi-value retrieval performance by varing number of repetitions
  * per key:
  * - 100'000'000 keys are inserted
- * - Map occupancy is fixed at 0.4
- * - CG size is fixed at 8
+ * - Map occupancy: 0.8
+ * - CG size: 8
  * - Number of repetitions per key: 1, ... , 128, 256
  *
  */
 template <typename Key, typename Value>
-void nvbench_static_multimap_count(nvbench::state& state, nvbench::type_list<Key, Value>)
+void nvbench_static_multimap_find_all(nvbench::state& state, nvbench::type_list<Key, Value>)
 {
   if (not std::is_same<Key, Value>::value) {
     state.skip("Key should be the same type as Value.");
@@ -260,19 +225,22 @@ void nvbench_static_multimap_count(nvbench::state& state, nvbench::type_list<Key
 
   std::size_t const num_keys = state.get_int64("NumInputs");
   auto const occupancy       = state.get_float64("Occupancy");
-  std::size_t const size     = num_keys / occupancy;
   std::size_t const num_reps = state.get_int64("NumReps");
+  auto const dist            = state.get_string("Distribution");
 
-  std::vector<Key> h_keys(num_keys);
-  std::vector<cuco::pair_type<Key, Value>> h_pairs(num_keys);
+  std::size_t const size = num_keys / occupancy;
 
-  generate_multikeys<Key>(h_keys.begin(), h_keys.end(), num_reps);
+  std::vector<Key> h_keys(num_keys);
+  generate_keys<Key>(h_keys.begin(), h_keys.end(), num_reps, dist);
 
+  std::vector<cuco::pair_type<Key, Value>> h_pairs(num_keys);
   for (auto i = 0; i < num_keys; ++i) {
     Key key           = h_keys[i];
     Value val         = h_keys[i];
     h_pairs[i].first  = key;
     h_pairs[i].second = val;
+
+    if (dist == "RANDOM") { h_keys[i] = i; }
   }
 
   // Get an array of unique keys
@@ -294,22 +262,23 @@ void nvbench_static_multimap_count(nvbench::state& state, nvbench::type_list<Key
 
       // Use timers to explicitly mark the target region
       timer.start();
-      auto count = map.count(d_unique_keys.begin(), d_unique_keys.end(), launch.get_stream());
+      map.find_all(
+        d_unique_keys.begin(), d_unique_keys.end(), d_results.data().get(), launch.get_stream());
       timer.stop();
     });
 }
 
 /**
- * @brief A benchmark evaluating multi-value retrieval performance by varing number of repetitions
- * per key:
+ * @brief A benchmark evaluating multi-value retrieval performance (`count` + `find_all`) by varing
+ * number of repetitions per key:
  * - 100'000'000 keys are inserted
- * - Map occupancy is fixed at 0.4
- * - CG size is fixed at 8
+ * - Map occupancy: 0.8
+ * - CG size: 8
  * - Number of repetitions per key: 1, ... , 128, 256
  *
  */
 template <typename Key, typename Value>
-void nvbench_static_multimap_find_all(nvbench::state& state, nvbench::type_list<Key, Value>)
+void nvbench_static_multimap_retrieve(nvbench::state& state, nvbench::type_list<Key, Value>)
 {
   if (not std::is_same<Key, Value>::value) {
     state.skip("Key should be the same type as Value.");
@@ -318,19 +287,22 @@ void nvbench_static_multimap_find_all(nvbench::state& state, nvbench::type_list<
 
   std::size_t const num_keys = state.get_int64("NumInputs");
   auto const occupancy       = state.get_float64("Occupancy");
-  std::size_t const size     = num_keys / occupancy;
   std::size_t const num_reps = state.get_int64("NumReps");
+  auto const dist            = state.get_string("Distribution");
 
-  std::vector<Key> h_keys(num_keys);
-  std::vector<cuco::pair_type<Key, Value>> h_pairs(num_keys);
+  std::size_t const size = num_keys / occupancy;
 
-  generate_multikeys<Key>(h_keys.begin(), h_keys.end(), num_reps);
+  std::vector<Key> h_keys(num_keys);
+  generate_keys<Key>(h_keys.begin(), h_keys.end(), num_reps, dist);
 
+  std::vector<cuco::pair_type<Key, Value>> h_pairs(num_keys);
   for (auto i = 0; i < num_keys; ++i) {
     Key key           = h_keys[i];
     Value val         = h_keys[i];
     h_pairs[i].first  = key;
     h_pairs[i].second = val;
+
+    if (dist == "RANDOM") { h_keys[i] = i; }
   }
 
   // Get an array of unique keys
@@ -352,6 +324,7 @@ void nvbench_static_multimap_find_all(nvbench::state& state, nvbench::type_list<
 
       // Use timers to explicitly mark the target region
       timer.start();
+      auto count = map.count(d_unique_keys.begin(), d_unique_keys.end(), launch.get_stream());
       map.find_all(
         d_unique_keys.begin(), d_unique_keys.end(), d_results.data().get(), launch.get_stream());
       timer.stop();
@@ -362,40 +335,39 @@ using key_type   = nvbench::type_list<nvbench::int32_t, nvbench::int64_t>;
 using value_type = nvbench::type_list<nvbench::int32_t, nvbench::int64_t>;
 using cg_size    = nvbench::enum_type_list<1, 2, 4, 8, 16, 32>;
 
-NVBENCH_BENCH_TYPES(nvbench_static_multimap_single_insert, NVBENCH_TYPE_AXES(key_type, value_type))
-  .set_type_axes_names({"Key", "Value"})
-  .set_max_noise(3)                            // Custom noise: 3%. By default: 0.5%.
-  .add_int64_axis("NumInputs", {100'000'000})  // Total number of key/value pairs: 100'000'000
-  .add_float64_axis("Occupancy", nvbench::range(0.1, 0.9, 0.1))
-  .add_string_axis("Distribution", {"UNIQUE", "UNIFORM", "GAUSSIAN"});
-
-NVBENCH_BENCH_TYPES(nvbench_static_multimap_multi_insert, NVBENCH_TYPE_AXES(key_type, value_type))
+NVBENCH_BENCH_TYPES(nvbench_static_multimap_insert, NVBENCH_TYPE_AXES(key_type, value_type))
   .set_type_axes_names({"Key", "Value"})
   .set_max_noise(3)                            // Custom noise: 3%. By default: 0.5%.
   .add_int64_axis("NumInputs", {100'000'000})  // Total number of key/value pairs: 100'000'000
-  .add_float64_axis("Occupancy", {0.7})
+  .add_float64_axis("Occupancy", {0.8})
+  .add_string_axis("Distribution", {"CYCLE", "RANDOM"})
   .add_int64_power_of_two_axis("NumReps", nvbench::range(0, 8, 1));
 
-NVBENCH_BENCH_TYPES(nvbench_static_multimap_find, NVBENCH_TYPE_AXES(key_type, value_type))
+NVBENCH_BENCH_TYPES(nvbench_static_multimap_count, NVBENCH_TYPE_AXES(key_type, value_type))
   .set_type_axes_names({"Key", "Value"})
+  .set_timeout(100)                            // Custom timeout: 100 s. Default is 15 s.
   .set_max_noise(3)                            // Custom noise: 3%. By default: 0.5%.
   .add_int64_axis("NumInputs", {100'000'000})  // Total number of key/value pairs: 100'000'000
-  .add_float64_axis("Occupancy", nvbench::range(0.1, 0.9, 0.1));
+  .add_float64_axis("Occupancy", {0.8})
+  .add_string_axis("Distribution", {"CYCLE", "RANDOM"})
+  .add_int64_power_of_two_axis("NumReps", nvbench::range(0, 8, 1));
 
-NVBENCH_BENCH_TYPES(nvbench_static_multimap_count, NVBENCH_TYPE_AXES(key_type, value_type))
+NVBENCH_BENCH_TYPES(nvbench_static_multimap_find_all, NVBENCH_TYPE_AXES(key_type, value_type))
   .set_type_axes_names({"Key", "Value"})
   .set_timeout(100)                            // Custom timeout: 100 s. Default is 15 s.
   .set_max_noise(3)                            // Custom noise: 3%. By default: 0.5%.
   .add_int64_axis("NumInputs", {100'000'000})  // Total number of key/value pairs: 100'000'000
-  .add_float64_axis("Occupancy", {0.4})
+  .add_float64_axis("Occupancy", {0.8})
+  .add_string_axis("Distribution", {"CYCLE", "RANDOM"})
   .add_int64_power_of_two_axis("NumReps", nvbench::range(0, 8, 1));
 
-NVBENCH_BENCH_TYPES(nvbench_static_multimap_find_all, NVBENCH_TYPE_AXES(key_type, value_type))
+NVBENCH_BENCH_TYPES(nvbench_static_multimap_retrieve, NVBENCH_TYPE_AXES(key_type, value_type))
   .set_type_axes_names({"Key", "Value"})
   .set_timeout(100)                            // Custom timeout: 100 s. Default is 15 s.
   .set_max_noise(3)                            // Custom noise: 3%. By default: 0.5%.
   .add_int64_axis("NumInputs", {100'000'000})  // Total number of key/value pairs: 100'000'000
-  .add_float64_axis("Occupancy", {0.4})
+  .add_float64_axis("Occupancy", {0.8})
+  .add_string_axis("Distribution", {"CYCLE", "RANDOM"})
   .add_int64_power_of_two_axis("NumReps", nvbench::range(0, 8, 1));
 
 NVBENCH_BENCH_TYPES(nvbench_static_multimap_insert_cgsize,
@@ -404,5 +376,6 @@ NVBENCH_BENCH_TYPES(nvbench_static_multimap_insert_cgsize,
   .set_timeout(100)                            // Custom timeout: 100 s. Default is 15 s.
   .set_max_noise(3)                            // Custom noise: 3%. By default: 0.5%.
   .add_int64_axis("NumInputs", {100'000'000})  // Total number of key/value pairs: 100'000'000
-  .add_float64_axis("Occupancy", {0.7})
+  .add_float64_axis("Occupancy", {0.8})
+  .add_string_axis("Distribution", {"CYCLE", "RANDOM"})
   .add_int64_power_of_two_axis("NumReps", nvbench::range(0, 8, 1));

From d84d8b307d112fefa9995989d45dfd5b0bcedb8a Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Mon, 3 May 2021 13:11:49 -0400
Subject: [PATCH 077/267] Use vector load for 64-bit variables

---
 include/cuco/detail/static_multimap.inl       |  75 ---------
 .../cuco/detail/static_multimap_kernels.cuh   |  82 ++++++++--
 include/cuco/static_multimap.cuh              | 146 +++++++++++++++++-
 3 files changed, 205 insertions(+), 98 deletions(-)

diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index 6ba9dfbdf..705e9e092 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -16,16 +16,6 @@
 
 namespace cuco {
 
-/**---------------------------------------------------------------------------*
- * @brief Enumeration of the possible results of attempting to insert into
- *a hash bucket
- *---------------------------------------------------------------------------**/
-enum class insert_result {
-  CONTINUE,  ///< Insert did not succeed, continue trying to insert
-  SUCCESS,   ///< New pair inserted successfully
-  DUPLICATE  ///< Insert did not succeed, key is already present
-};
-
 template <typename Key,
           typename Value,
           std::size_t CGSize,
@@ -238,71 +228,6 @@ __device__ void static_multimap<Key, Value, CGSize, Scope, Allocator>::device_mu
   }
 }
 
-template <typename Key,
-          typename Value,
-          std::size_t CGSize,
-          cuda::thread_scope Scope,
-          typename Allocator>
-template <typename CG, typename Hash, typename KeyEqual>
-__device__ void static_multimap<Key, Value, CGSize, Scope, Allocator>::device_mutable_view::insert(
-  CG g, value_type const& insert_pair, Hash hash, KeyEqual key_equal) noexcept
-{
-  auto current_slot = initial_slot(g, insert_pair.first, hash);
-  while (true) {
-    // key_type const existing_key = current_slot->first.load(cuda::memory_order_relaxed);
-    auto const tmp = *reinterpret_cast<uint4 const*>(current_slot);
-    pair<Key, Value> arr[2];
-    memcpy(&arr[0], &tmp, 2 * sizeof(pair<Key, Value>));
-    // The user provide `key_equal` can never be used to compare against `empty_key_sentinel` as the
-    // sentinel is not a valid key value. Therefore, first check for the sentinel
-    auto const first_slot_is_empty  = (arr[0].first == this->get_empty_key_sentinel());
-    auto const second_slot_is_empty = (arr[1].first == this->get_empty_key_sentinel());
-    // auto const slot_is_empty         = (existing_key == this->get_empty_key_sentinel());
-    auto const window_contains_empty = g.ballot(first_slot_is_empty or second_slot_is_empty);
-    if (window_contains_empty) {
-      // the first lane in the group with an empty slot will attempt the insert
-      insert_result status{insert_result::CONTINUE};
-      uint32_t src_lane = __ffs(window_contains_empty) - 1;
-      if (g.thread_rank() == src_lane) {
-        using cuda::std::memory_order_relaxed;
-        auto expected_key    = this->get_empty_key_sentinel();
-        auto expected_value  = this->get_empty_value_sentinel();
-        auto insert_location = current_slot + !(first_slot_is_empty);
-        auto& slot_key       = insert_location->first;
-        auto& slot_value     = insert_location->second;
-        bool key_success =
-          slot_key.compare_exchange_strong(expected_key, insert_pair.first, memory_order_relaxed);
-        bool value_success = slot_value.compare_exchange_strong(
-          expected_value, insert_pair.second, memory_order_relaxed);
-        if (key_success) {
-          while (not value_success) {
-            value_success =
-              slot_value.compare_exchange_strong(expected_value = this->get_empty_value_sentinel(),
-                                                 insert_pair.second,
-                                                 memory_order_relaxed);
-          }
-          status = insert_result::SUCCESS;
-        } else if (value_success) {
-          slot_value.store(this->get_empty_value_sentinel(), memory_order_relaxed);
-        }
-        // another key was inserted in both slots we wanted to try
-        // so we need to try the next empty slots in the window
-      }
-
-      // successful insert
-      if (g.any(status == insert_result::SUCCESS)) { return; }
-      // if we've gotten this far, a different key took our spot
-      // before we could insert. We need to retry the insert on the
-      // same window
-    }
-    // if there are no empty slots in the current window,
-    // we move onto the next window
-    else {
-      current_slot = next_slot(g, current_slot);
-    }
-  }
-}
-
 template <typename Key,
           typename Value,
           std::size_t CGSize,
diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap_kernels.cuh
index 191bebc16..dc0889bf5 100644
--- a/include/cuco/detail/static_multimap_kernels.cuh
+++ b/include/cuco/detail/static_multimap_kernels.cuh
@@ -201,10 +201,10 @@ __global__ void insert(InputIt first, InputIt last, viewT view, Hash hash, KeyEq
     it += (gridDim.x * blockDim.x) / tile_size;
   }
   if (threadIdx.x == 0 && blockIdx.x == 0) {
-    for(auto i = 0; i < view.get_capacity(); i++){
+    for (auto i = 0; i < view.get_capacity(); i++) {
       auto slot = view.get_slots() + i;
-      //if (slot->first != -1)
-        //printf("%ld:\t%ld\n", long(slot->first), long(slot->second));
+      // if (slot->first != -1)
+      // printf("%ld:\t%ld\n", long(slot->first), long(slot->second));
     }
   }
 }
@@ -621,13 +621,13 @@ __global__ void find_all(InputIt first,
       auto const second_slot_is_empty = (arr[1].first == view.get_empty_key_sentinel());
       auto const first_equals         = (not first_slot_is_empty and key_equal(arr[0].first, key));
       auto const second_equals        = (not second_slot_is_empty and key_equal(arr[1].first, key));
-      auto const first_exists = tile.ballot(first_equals);
-      auto const second_exists = tile.ballot(second_equals);
+      auto const first_exists         = tile.ballot(first_equals);
+      auto const second_exists        = tile.ballot(second_equals);
 
       if (first_exists or second_exists) {
-        found_match         = true;
+        found_match = true;
 
-        auto num_first_matches = __popc(first_exists);
+        auto num_first_matches  = __popc(first_exists);
         auto num_second_matches = __popc(second_exists);
 
         uint32_t output_idx = cg_counter[cg_id];
@@ -644,7 +644,7 @@ __global__ void find_all(InputIt first,
           output_buffer[cg_id][output_idx + num_first_matches + lane_offset] =
             cuco::make_pair<Key, Value>(std::move(k), std::move(arr[1].second));
         }
-        if (0 == lane_id) {cg_counter[cg_id] += (num_first_matches + num_second_matches);}
+        if (0 == lane_id) { cg_counter[cg_id] += (num_first_matches + num_second_matches); }
       }
       if (tile.any(first_slot_is_empty or second_slot_is_empty)) {
         running = false;
@@ -657,10 +657,10 @@ __global__ void find_all(InputIt first,
 
       tile.sync();
 
-        flush_output_buffer<tile_size>(
-          tile, cg_counter[cg_id], output_buffer[cg_id], num_items, output_begin);
-        // First lane reset CG-level counter
-        if (lane_id == 0) { cg_counter[cg_id] = 0; }
+      flush_output_buffer<tile_size>(
+        tile, cg_counter[cg_id], output_buffer[cg_id], num_items, output_begin);
+      // First lane reset CG-level counter
+      if (lane_id == 0) { cg_counter[cg_id] = 0; }
 
       current_slot = view.next_slot(tile, current_slot);
     }  // while running
@@ -747,13 +747,14 @@ template <uint32_t block_size,
           typename atomicT,
           typename viewT,
           typename Hash,
-          typename KeyEqual>
+          typename KeyEqual,
+          typename std::enable_if<sizeof(Key) == 4>::type* = nullptr>
 __global__ void count(
   InputIt first, InputIt last, atomicT* num_items, viewT view, Hash hash, KeyEqual key_equal)
 {
-  auto tile         = cg::tiled_partition<tile_size>(cg::this_thread_block());
-  auto tid          = blockDim.x * blockIdx.x + threadIdx.x;
-  auto key_idx      = tid / tile_size;
+  auto tile    = cg::tiled_partition<tile_size>(cg::this_thread_block());
+  auto tid     = blockDim.x * blockIdx.x + threadIdx.x;
+  auto key_idx = tid / tile_size;
 
   using BlockReduce = cub::BlockReduce<int, block_size>;
   __shared__ typename BlockReduce::TempStorage temp_storage;
@@ -779,7 +780,54 @@ __global__ void count(
 
       current_slot = view.next_slot(tile, current_slot);
     }
-    key_idx += (gridDim.x * blockDim.x) / tile_size;
+    key_idx += (gridDim.x * block_size) / tile_size;
+  }
+  auto const block_count = BlockReduce(temp_storage).Sum(thread_counter);
+  if (threadIdx.x == 0) { num_items->fetch_add(block_count, cuda::std::memory_order_relaxed); }
+}
+
+template <uint32_t block_size,
+          uint32_t tile_size,
+          typename Key,
+          typename Value,
+          typename InputIt,
+          typename atomicT,
+          typename viewT,
+          typename Hash,
+          typename KeyEqual,
+          typename std::enable_if<sizeof(Key) == 8>::type* = nullptr>
+__global__ void count(
+  InputIt first, InputIt last, atomicT* num_items, viewT view, Hash hash, KeyEqual key_equal)
+{
+  auto tile    = cg::tiled_partition<tile_size>(cg::this_thread_block());
+  auto tid     = blockDim.x * blockIdx.x + threadIdx.x;
+  auto key_idx = tid / tile_size;
+
+  using BlockReduce = cub::BlockReduce<int, block_size>;
+  __shared__ typename BlockReduce::TempStorage temp_storage;
+
+  int thread_counter{0};
+
+  while (first + key_idx < last) {
+    auto key          = *(first + key_idx);
+    auto current_slot = view.initial_slot(tile, key, hash);
+
+    while (true) {
+      auto const tmp = *reinterpret_cast<ulonglong4 const*>(current_slot);
+      pair<Key, Value> arr[2];
+      memcpy(&arr[0], &tmp, 2 * sizeof(pair<Key, Value>));
+
+      auto const first_slot_is_empty  = (arr[0].first == view.get_empty_key_sentinel());
+      auto const second_slot_is_empty = (arr[1].first == view.get_empty_key_sentinel());
+      auto const first_equals         = (not first_slot_is_empty and key_equal(arr[0].first, key));
+      auto const second_equals        = (not second_slot_is_empty and key_equal(arr[1].first, key));
+      thread_counter += (first_equals + second_equals);
+
+      if (tile.any(first_slot_is_empty or second_slot_is_empty)) { break; }
+
+      current_slot = view.next_slot(tile, current_slot);
+    }
+    key_idx += (gridDim.x * block_size) / tile_size;
   }
   auto const block_count = BlockReduce(temp_storage).Sum(thread_counter);
   if (threadIdx.x == 0) { num_items->fetch_add(block_count, cuda::std::memory_order_relaxed); }
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index 36a12fc7a..10160b09f 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -39,6 +39,16 @@
 
 namespace cuco {
 
+/**---------------------------------------------------------------------------*
+ * @brief Enumeration of the possible results of attempting to insert into
+ *a hash bucket
+ *---------------------------------------------------------------------------**/
+enum class insert_result {
+  CONTINUE,  ///< Insert did not succeed, continue trying to insert
+  SUCCESS,   ///< New pair inserted successfully
+  DUPLICATE  ///< Insert did not succeed, key is already present
+};
+
 /**
  * @brief A GPU-accelerated, unordered, associative container of key-value
  * pairs with unique keys.
@@ -386,10 +396,10 @@ class static_multimap {
     template <typename CG, typename Hash>
     __device__ iterator initial_slot(CG g, Key const& k, Hash hash) noexcept
     {
-      auto const cg_size = g.size();
+      auto const cg_size    = g.size();
       step_size_            = (hash(k + 1) % (capacity_ / (cg_size * 2) - 1) + 1) * cg_size * 2;
       auto const h          = hash(k);
-      auto const slot_index = h  % (capacity_ / (cg_size * 2)) * cg_size * 2 + g.thread_rank() * 2;
+      auto const slot_index = h % (capacity_ / (cg_size * 2)) * cg_size * 2 + g.thread_rank() * 2;
       auto const p          = begin_slot() + slot_index;
       return p;
     }
@@ -409,10 +419,10 @@ class static_multimap {
     template <typename CG, typename Hash>
     __device__ const_iterator initial_slot(CG g, Key const& k, Hash hash) const noexcept
     {
-      auto const cg_size = g.size();
+      auto const cg_size    = g.size();
       step_size_            = (hash(k + 1) % (capacity_ / (cg_size * 2) - 1) + 1) * cg_size * 2;
       auto const h          = hash(k);
-      auto const slot_index = h  % (capacity_ / (cg_size * 2)) * cg_size * 2 + g.thread_rank() * 2;
+      auto const slot_index = h % (capacity_ / (cg_size * 2)) * cg_size * 2 + g.thread_rank() * 2;
       auto const p          = begin_slot() + slot_index;
       return p;
     }
@@ -646,11 +656,135 @@ class static_multimap {
      */
     template <typename CG,
               typename Hash     = cuco::detail::MurmurHash3_32<key_type>,
-              typename KeyEqual = thrust::equal_to<key_type>>
+              typename KeyEqual = thrust::equal_to<key_type>,
+              typename U        = Key,
+              typename std::enable_if<sizeof(U) == 4>::type* = nullptr>
     __device__ void insert(CG g,
                            value_type const& insert_pair,
                            Hash hash          = Hash{},
-                           KeyEqual key_equal = KeyEqual{}) noexcept;
+                           KeyEqual key_equal = KeyEqual{})
+    {
+      auto current_slot = initial_slot(g, insert_pair.first, hash);
+      while (true) {
+        // key_type const existing_key = current_slot->first.load(cuda::memory_order_relaxed);
+        auto const tmp = *reinterpret_cast<uint4 const*>(current_slot);
+        pair<Key, Value> arr[2];
+        memcpy(&arr[0], &tmp, 2 * sizeof(pair<Key, Value>));
+        // The user provide `key_equal` can never be used to compare against `empty_key_sentinel` as
+        // the sentinel is not a valid key value. Therefore, first check for the sentinel
+        auto const first_slot_is_empty  = (arr[0].first == this->get_empty_key_sentinel());
+        auto const second_slot_is_empty = (arr[1].first == this->get_empty_key_sentinel());
+        // auto const slot_is_empty         = (existing_key == this->get_empty_key_sentinel());
+        auto const window_contains_empty = g.ballot(first_slot_is_empty or second_slot_is_empty);
+        if (window_contains_empty) {
+          // the first lane in the group with an empty slot will attempt the insert
+          insert_result status{insert_result::CONTINUE};
+          uint32_t src_lane = __ffs(window_contains_empty) - 1;
+          if (g.thread_rank() == src_lane) {
+            using cuda::std::memory_order_relaxed;
+            auto expected_key    = this->get_empty_key_sentinel();
+            auto expected_value  = this->get_empty_value_sentinel();
+            auto insert_location = current_slot + !(first_slot_is_empty);
+            auto& slot_key       = insert_location->first;
+            auto& slot_value     = insert_location->second;
+            bool key_success     = slot_key.compare_exchange_strong(
+              expected_key, insert_pair.first, memory_order_relaxed);
+            bool value_success = slot_value.compare_exchange_strong(
+              expected_value, insert_pair.second, memory_order_relaxed);
+            if (key_success) {
+              while (not value_success) {
+                value_success = slot_value.compare_exchange_strong(
+                  expected_value = this->get_empty_value_sentinel(),
+                  insert_pair.second,
+                  memory_order_relaxed);
+              }
+              status = insert_result::SUCCESS;
+            } else if (value_success) {
+              slot_value.store(this->get_empty_value_sentinel(), memory_order_relaxed);
+            }
+            // another key was inserted in both slots we wanted to try
+            // so we need to try the next empty slots in the window
+          }
+
+          // successful insert
+          if (g.any(status == insert_result::SUCCESS)) { return; }
+          // if we've gotten this far, a different key took our spot
+          // before we could insert. We need to retry the insert on the
+          // same window
+        }
+        // if there are no empty slots in the current window,
+        // we move onto the next window
+        else {
+          current_slot = next_slot(g, current_slot);
+        }
+      }
+    }
+
+    template <typename CG,
+              typename Hash     = cuco::detail::MurmurHash3_32<key_type>,
+              typename KeyEqual = thrust::equal_to<key_type>,
+              typename U        = Key,
+              typename std::enable_if<sizeof(U) == 8>::type* = nullptr>
+    __device__ void insert(CG g,
+                           value_type const& insert_pair,
+                           Hash hash          = Hash{},
+                           KeyEqual key_equal = KeyEqual{})
+    {
+      auto current_slot = initial_slot(g, insert_pair.first, hash);
+      while (true) {
+        // key_type const existing_key = current_slot->first.load(cuda::memory_order_relaxed);
+        auto const tmp = *reinterpret_cast<ulonglong4 const*>(current_slot);
+        pair<Key, Value> arr[2];
+        memcpy(&arr[0], &tmp, 2 * sizeof(pair<Key, Value>));
+        // The user provide `key_equal` can never be used to compare against `empty_key_sentinel` as
+        // the sentinel is not a valid key value. Therefore, first check for the sentinel
+        auto const first_slot_is_empty  = (arr[0].first == this->get_empty_key_sentinel());
+        auto const second_slot_is_empty = (arr[1].first == this->get_empty_key_sentinel());
+        // auto const slot_is_empty         = (existing_key == this->get_empty_key_sentinel());
+        auto const window_contains_empty = g.ballot(first_slot_is_empty or second_slot_is_empty);
+        if (window_contains_empty) {
+          // the first lane in the group with an empty slot will attempt the insert
+          insert_result status{insert_result::CONTINUE};
+          uint32_t src_lane = __ffs(window_contains_empty) - 1;
+          if (g.thread_rank() == src_lane) {
+            using cuda::std::memory_order_relaxed;
+            auto expected_key    = this->get_empty_key_sentinel();
+            auto expected_value  = this->get_empty_value_sentinel();
+            auto insert_location = current_slot + !(first_slot_is_empty);
+            auto& slot_key       = insert_location->first;
+            auto& slot_value     = insert_location->second;
+            bool key_success     = slot_key.compare_exchange_strong(
+              expected_key, insert_pair.first, memory_order_relaxed);
+            bool value_success = slot_value.compare_exchange_strong(
+              expected_value, insert_pair.second, memory_order_relaxed);
+            if (key_success) {
+              while (not value_success) {
+                value_success = slot_value.compare_exchange_strong(
+                  expected_value = this->get_empty_value_sentinel(),
+                  insert_pair.second,
+                  memory_order_relaxed);
+              }
+              status = insert_result::SUCCESS;
+            } else if (value_success) {
+              slot_value.store(this->get_empty_value_sentinel(), memory_order_relaxed);
+            }
+            // another key was inserted in both slots we wanted to try
+            // so we need to try the next empty slots in the window
+          }
+
+          // successful insert
+          if (g.any(status == insert_result::SUCCESS)) { return; }
+          // if we've gotten this far, a different key took our spot
+          // before we could insert. We need to retry the insert on the
+          // same window
+        }
+        // if there are no empty slots in the current window,
+        // we move onto the next window
+        else {
+          current_slot = next_slot(g, current_slot);
+        }
+      }
+    }
 
   };  // class device mutable view
 

From 349126d6fb734634690e7328b1df411366fba9fd Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Mon, 3 May 2021 14:00:13 -0400
Subject: [PATCH 078/267] Update benchmark CMake: using c++17

---
 benchmarks/CMakeLists.txt | 10 +---------
 1 file changed, 1 insertion(+), 9 deletions(-)

diff --git a/benchmarks/CMakeLists.txt b/benchmarks/CMakeLists.txt
index 24e60de9b..604d10ec7 100644
--- a/benchmarks/CMakeLists.txt
+++ b/benchmarks/CMakeLists.txt
@@ -21,15 +21,7 @@ CPMAddPackage(
   GIT_SHALLOW TRUE
 )
 
-if (benchmark_ADDED)
-  # patch google benchmark target
-  set_target_properties(benchmark PROPERTIES CXX_STANDARD 14)
-endif()
-
-if (nvbench_ADDED)
-  # nvbench requires c++17
-  set_target_properties(nvbench PROPERTIES CXX_STANDARD 17)
-endif()
+set_target_properties(benchmark PROPERTIES CXX_STANDARD 17)
 
 ###################################################################################################
 # Auto-detect available GPU compute architectures

From 789ca200e1559a19564d08ba8609dd2e0c3aa500 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Mon, 3 May 2021 16:59:05 -0400
Subject: [PATCH 079/267] Updates:   - Compile-time benchmark skip by using
 SFINAE: shorter compilation time   - Use c++17 if constexpr to simplify
 vector loads in multimap   - Resolve conflicts with upstream updates (i.e.
 bitwise_compare)   - Remove fancy iterator   - Remove view find_all functions
   - Update unit test

---
 .../static_multimap/find_all_bench.cu         |  15 +-
 .../static_multimap/static_multimap_bench.cu  |  74 ++--
 include/cuco/detail/bitwise_compare.cuh       |   3 +-
 include/cuco/detail/pair.cuh                  |   6 +-
 include/cuco/detail/static_map_kernels.cuh    |   6 +-
 include/cuco/detail/static_multimap.inl       | 237 +++++--------
 .../cuco/detail/static_multimap_kernels.cuh   |  73 +---
 include/cuco/static_multimap.cuh              | 328 ++----------------
 tests/static_multimap/static_multimap_test.cu |  61 ----
 9 files changed, 193 insertions(+), 610 deletions(-)

diff --git a/benchmarks/hash_table/static_multimap/find_all_bench.cu b/benchmarks/hash_table/static_multimap/find_all_bench.cu
index 30e4a6168..21de4a26d 100644
--- a/benchmarks/hash_table/static_multimap/find_all_bench.cu
+++ b/benchmarks/hash_table/static_multimap/find_all_bench.cu
@@ -44,15 +44,10 @@ static void generate_multikeys(OutputIt output_begin, OutputIt output_end, size_
  *
  */
 template <typename Key, typename Value, nvbench::int32_t CGSize, nvbench::int32_t BufferSize>
-void nvbench_find_all(
+std::enable_if_t<(sizeof(Key) == sizeof(Value)), void> nvbench_find_all(
   nvbench::state& state,
   nvbench::type_list<Key, Value, nvbench::enum_type<CGSize>, nvbench::enum_type<BufferSize>>)
 {
-  if (not std::is_same<Key, Value>::value) {
-    state.skip("Key should be the same type as Value.");
-    return;
-  }
-
   std::size_t const num_keys = state.get_int64("NumInputs");
   auto const occupancy       = state.get_float64("Occupancy");
   std::size_t const size     = num_keys / occupancy;
@@ -124,6 +119,14 @@ void nvbench_find_all(
     });
 }
 
+template <typename Key, typename Value, nvbench::int32_t CGSize, nvbench::int32_t BufferSize>
+std::enable_if_t<(sizeof(Key) != sizeof(Value)), void> nvbench_find_all(
+  nvbench::state& state,
+  nvbench::type_list<Key, Value, nvbench::enum_type<CGSize>, nvbench::enum_type<BufferSize>>)
+{
+  state.skip("Key should be the same type as Value.");
+}
+
 using key_type    = nvbench::type_list<nvbench::int32_t, nvbench::int64_t>;
 using value_type  = nvbench::type_list<nvbench::int32_t, nvbench::int64_t>;
 using cg_size     = nvbench::enum_type_list<1, 2, 4, 8, 16, 32>;
diff --git a/benchmarks/hash_table/static_multimap/static_multimap_bench.cu b/benchmarks/hash_table/static_multimap/static_multimap_bench.cu
index f0373b9de..c93fbb3ef 100644
--- a/benchmarks/hash_table/static_multimap/static_multimap_bench.cu
+++ b/benchmarks/hash_table/static_multimap/static_multimap_bench.cu
@@ -92,13 +92,9 @@ void launch_nvbench_insert(nvbench::state& state,
  *
  */
 template <typename Key, typename Value>
-void nvbench_static_multimap_insert(nvbench::state& state, nvbench::type_list<Key, Value>)
+std::enable_if_t<(sizeof(Key) == sizeof(Value)), void> nvbench_static_multimap_insert(
+  nvbench::state& state, nvbench::type_list<Key, Value>)
 {
-  if (not std::is_same<Key, Value>::value) {
-    state.skip("Key should be the same type as Value.");
-    return;
-  }
-
   std::size_t const num_keys = state.get_int64("NumInputs");
   auto const occupancy       = state.get_float64("Occupancy");
   std::size_t const num_reps = state.get_int64("NumReps");
@@ -114,6 +110,13 @@ void nvbench_static_multimap_insert(nvbench::state& state, nvbench::type_list<Ke
   launch_nvbench_insert<Key, Value, cg_size>(state, h_keys, num_keys, size);
 }
 
+template <typename Key, typename Value>
+std::enable_if_t<(sizeof(Key) != sizeof(Value)), void> nvbench_static_multimap_insert(
+  nvbench::state& state, nvbench::type_list<Key, Value>)
+{
+  state.skip("Key should be the same type as Value.");
+}
+
 /**
  * @brief A benchmark evaluating multi-value insertion performance by varing number of repetitions
  * per key and CUDA CG size:
@@ -124,14 +127,9 @@ void nvbench_static_multimap_insert(nvbench::state& state, nvbench::type_list<Ke
  *
  */
 template <typename Key, typename Value, nvbench::int32_t CGSize>
-void nvbench_static_multimap_insert_cgsize(
+std::enable_if_t<(sizeof(Key) == sizeof(Value)), void> nvbench_static_multimap_insert_cgsize(
   nvbench::state& state, nvbench::type_list<Key, Value, nvbench::enum_type<CGSize>>)
 {
-  if (not std::is_same<Key, Value>::value) {
-    state.skip("Key should be the same type as Value.");
-    return;
-  }
-
   std::size_t const num_keys = state.get_int64("NumInputs");
   auto const occupancy       = state.get_float64("Occupancy");
   std::size_t const num_reps = state.get_int64("NumReps");
@@ -146,6 +144,13 @@ void nvbench_static_multimap_insert_cgsize(
   launch_nvbench_insert<Key, Value, CGSize>(state, h_keys, num_keys, size);
 }
 
+template <typename Key, typename Value, nvbench::int32_t CGSize>
+std::enable_if_t<(sizeof(Key) != sizeof(Value)), void> nvbench_static_multimap_insert_cgsize(
+  nvbench::state& state, nvbench::type_list<Key, Value, nvbench::enum_type<CGSize>>)
+{
+  state.skip("Key should be the same type as Value.");
+}
+
 /**
  * @brief A benchmark evaluating multi-value count performance by varing number of repetitions
  * per key:
@@ -156,13 +161,9 @@ void nvbench_static_multimap_insert_cgsize(
  *
  */
 template <typename Key, typename Value>
-void nvbench_static_multimap_count(nvbench::state& state, nvbench::type_list<Key, Value>)
+std::enable_if_t<(sizeof(Key) == sizeof(Value)), void> nvbench_static_multimap_count(
+  nvbench::state& state, nvbench::type_list<Key, Value>)
 {
-  if (not std::is_same<Key, Value>::value) {
-    state.skip("Key should be the same type as Value.");
-    return;
-  }
-
   std::size_t const num_keys = state.get_int64("NumInputs");
   auto const occupancy       = state.get_float64("Occupancy");
   std::size_t const num_reps = state.get_int64("NumReps");
@@ -206,6 +207,13 @@ void nvbench_static_multimap_count(nvbench::state& state, nvbench::type_list<Key
     });
 }
 
+template <typename Key, typename Value>
+std::enable_if_t<(sizeof(Key) != sizeof(Value)), void> nvbench_static_multimap_count(
+  nvbench::state& state, nvbench::type_list<Key, Value>)
+{
+  state.skip("Key should be the same type as Value.");
+}
+
 /**
  * @brief A benchmark evaluating multi-value retrieval performance by varing number of repetitions
  * per key:
@@ -216,13 +224,9 @@ void nvbench_static_multimap_count(nvbench::state& state, nvbench::type_list<Key
  *
  */
 template <typename Key, typename Value>
-void nvbench_static_multimap_find_all(nvbench::state& state, nvbench::type_list<Key, Value>)
+std::enable_if_t<(sizeof(Key) == sizeof(Value)), void> nvbench_static_multimap_find_all(
+  nvbench::state& state, nvbench::type_list<Key, Value>)
 {
-  if (not std::is_same<Key, Value>::value) {
-    state.skip("Key should be the same type as Value.");
-    return;
-  }
-
   std::size_t const num_keys = state.get_int64("NumInputs");
   auto const occupancy       = state.get_float64("Occupancy");
   std::size_t const num_reps = state.get_int64("NumReps");
@@ -268,6 +272,13 @@ void nvbench_static_multimap_find_all(nvbench::state& state, nvbench::type_list<
     });
 }
 
+template <typename Key, typename Value>
+std::enable_if_t<(sizeof(Key) != sizeof(Value)), void> nvbench_static_multimap_find_all(
+  nvbench::state& state, nvbench::type_list<Key, Value>)
+{
+  state.skip("Key should be the same type as Value.");
+}
+
 /**
  * @brief A benchmark evaluating multi-value retrieval performance (`count` + `find_all`) by varing
  * number of repetitions per key:
@@ -278,13 +289,9 @@ void nvbench_static_multimap_find_all(nvbench::state& state, nvbench::type_list<
  *
  */
 template <typename Key, typename Value>
-void nvbench_static_multimap_retrieve(nvbench::state& state, nvbench::type_list<Key, Value>)
+std::enable_if_t<(sizeof(Key) == sizeof(Value)), void> nvbench_static_multimap_retrieve(
+  nvbench::state& state, nvbench::type_list<Key, Value>)
 {
-  if (not std::is_same<Key, Value>::value) {
-    state.skip("Key should be the same type as Value.");
-    return;
-  }
-
   std::size_t const num_keys = state.get_int64("NumInputs");
   auto const occupancy       = state.get_float64("Occupancy");
   std::size_t const num_reps = state.get_int64("NumReps");
@@ -331,6 +338,13 @@ void nvbench_static_multimap_retrieve(nvbench::state& state, nvbench::type_list<
     });
 }
 
+template <typename Key, typename Value>
+std::enable_if_t<(sizeof(Key) != sizeof(Value)), void> nvbench_static_multimap_retrieve(
+  nvbench::state& state, nvbench::type_list<Key, Value>)
+{
+  state.skip("Key should be the same type as Value.");
+}
+
 using key_type   = nvbench::type_list<nvbench::int32_t, nvbench::int64_t>;
 using value_type = nvbench::type_list<nvbench::int32_t, nvbench::int64_t>;
 using cg_size    = nvbench::enum_type_list<1, 2, 4, 8, 16, 32>;
diff --git a/include/cuco/detail/bitwise_compare.cuh b/include/cuco/detail/bitwise_compare.cuh
index aac3b8a9e..267f5b6d3 100644
--- a/include/cuco/detail/bitwise_compare.cuh
+++ b/include/cuco/detail/bitwise_compare.cuh
@@ -13,6 +13,7 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
+#pragma once
 
 #include <cstdint>
 #include <type_traits>
@@ -74,4 +75,4 @@ __host__ __device__ constexpr bool bitwise_compare(T const& lhs, T const& rhs)
 }
 
 }  // namespace detail
-}  // namespace cuco
\ No newline at end of file
+}  // namespace cuco
diff --git a/include/cuco/detail/pair.cuh b/include/cuco/detail/pair.cuh
index 34bc29fdc..1299ee16c 100644
--- a/include/cuco/detail/pair.cuh
+++ b/include/cuco/detail/pair.cuh
@@ -14,8 +14,9 @@
  * limitations under the License.
  */
 
-#include <thrust/device_reference.h>
+#pragma once
 
+#include <thrust/device_reference.h>
 #include <thrust/pair.h>
 #include <thrust/tuple.h>
 #include <tuple>
@@ -117,9 +118,6 @@ struct alignas(detail::pair_alignment<First, Second>()) pair {
            thrust::get<1>(thrust::raw_reference_cast(t))}
   {
   }
-  __host__ __device__ constexpr pair(First const& f, Second const& s) noexcept : first{f}, second{s}
-  {
-  }
 };
 
 template <typename K, typename V>
diff --git a/include/cuco/detail/static_map_kernels.cuh b/include/cuco/detail/static_map_kernels.cuh
index f709071a1..c243414e8 100644
--- a/include/cuco/detail/static_map_kernels.cuh
+++ b/include/cuco/detail/static_map_kernels.cuh
@@ -55,7 +55,7 @@ __global__ void initialize(pair_atomic_type* const slots, Key k, Value v, std::s
  *
  * Each space in `slots` that can hold a key value pair is initialized to a
  * `pair_atomic_type` containing the key `k` and the value `v`.
- * 
+ *
  * @tparam atomic_key_type Type of the `Key` atomic container
  * @tparam atomic_mapped_type Type of the `Value` atomic container
  * @tparam Key key type
@@ -418,5 +418,5 @@ __global__ void contains(
   }
 }
 
-} // namespace detail
-} // namespace cuco
+}  // namespace detail
+}  // namespace cuco
diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index 705e9e092..52664a3e4 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -14,8 +14,20 @@
  * limitations under the License.
  */
 
+#include <cuco/detail/bitwise_compare.cuh>
+
 namespace cuco {
 
+/**---------------------------------------------------------------------------*
+ * @brief Enumeration of the possible results of attempting to insert into
+ *a hash bucket
+ *---------------------------------------------------------------------------**/
+enum class insert_result {
+  CONTINUE,  ///< Insert did not succeed, continue trying to insert
+  SUCCESS,   ///< New pair inserted successfully
+  DUPLICATE  ///< Insert did not succeed, key is already present
+};
+
 template <typename Key,
           typename Value,
           std::size_t CGSize,
@@ -228,133 +240,75 @@ __device__ void static_multimap<Key, Value, CGSize, Scope, Allocator>::device_mu
   }
 }
 
-template <typename Key,
-          typename Value,
-          std::size_t CGSize,
-          cuda::thread_scope Scope,
-          typename Allocator>
-template <typename Hash, typename KeyEqual>
-__device__ typename static_multimap<Key, Value, CGSize, Scope, Allocator>::device_view::iterator
-static_multimap<Key, Value, CGSize, Scope, Allocator>::device_view::find(
-  Key const& k, Hash hash, KeyEqual key_equal) noexcept
-{
-  auto current_slot = initial_slot(k, hash);
-
-  while (true) {
-    auto const existing_key = current_slot->first.load(cuda::std::memory_order_relaxed);
-    // Key doesn't exist, return end()
-    if (existing_key == this->get_empty_key_sentinel()) { return this->end(); }
-
-    // Key exists, return iterator to location
-    if (key_equal(existing_key, k)) { return current_slot; }
-
-    current_slot = next_slot(current_slot);
-  }
-}
-
-template <typename Key,
-          typename Value,
-          std::size_t CGSize,
-          cuda::thread_scope Scope,
-          typename Allocator>
-template <typename Hash, typename KeyEqual>
-__device__
-  typename static_multimap<Key, Value, CGSize, Scope, Allocator>::device_view::const_iterator
-  static_multimap<Key, Value, CGSize, Scope, Allocator>::device_view::find(
-    Key const& k, Hash hash, KeyEqual key_equal) const noexcept
-{
-  auto current_slot = initial_slot(k, hash);
-
-  while (true) {
-    auto const existing_key = current_slot->first.load(cuda::std::memory_order_relaxed);
-    // Key doesn't exist, return end()
-    if (existing_key == this->get_empty_key_sentinel()) { return this->end(); }
-
-    // Key exists, return iterator to location
-    if (key_equal(existing_key, k)) { return current_slot; }
-
-    current_slot = next_slot(current_slot);
-  }
-}
-
 template <typename Key,
           typename Value,
           std::size_t CGSize,
           cuda::thread_scope Scope,
           typename Allocator>
 template <typename CG, typename Hash, typename KeyEqual>
-__device__ typename static_multimap<Key, Value, CGSize, Scope, Allocator>::device_view::iterator
-static_multimap<Key, Value, CGSize, Scope, Allocator>::device_view::find(
-  CG g, Key const& k, Hash hash, KeyEqual key_equal) noexcept
+__device__ void static_multimap<Key, Value, CGSize, Scope, Allocator>::device_mutable_view::insert(
+  CG g, value_type const& insert_pair, Hash hash, KeyEqual key_equal) noexcept
 {
-  auto current_slot = initial_slot(g, k, hash);
-
+  auto current_slot = initial_slot(g, insert_pair.first, hash);
   while (true) {
-    auto const existing_key = current_slot->first.load(cuda::std::memory_order_relaxed);
-
-    // The user provide `key_equal` can never be used to compare against `empty_key_sentinel` as the
-    // sentinel is not a valid key value. Therefore, first check for the sentinel
-    auto const slot_is_empty = (existing_key == this->get_empty_key_sentinel());
-
-    // the key we were searching for was found by one of the threads,
-    // so we return an iterator to the entry
-    auto const exists = g.ballot(not slot_is_empty and key_equal(existing_key, k));
-    if (exists) {
-      uint32_t src_lane = __ffs(exists) - 1;
-      // TODO: This shouldn't cast an iterator to an int to shuffle. Instead, get the index of the
-      // current_slot and shuffle that instead.
-      intptr_t res_slot = g.shfl(reinterpret_cast<intptr_t>(current_slot), src_lane);
-      return reinterpret_cast<iterator>(res_slot);
+    // key_type const existing_key = current_slot->first.load(cuda::memory_order_relaxed);
+    pair<Key, Value> arr[2];
+    if constexpr (sizeof(Key) == 4) {
+      auto const tmp = *reinterpret_cast<uint4 const*>(current_slot);
+      memcpy(&arr[0], &tmp, 2 * sizeof(pair<Key, Value>));
+    } else {
+      auto const tmp = *reinterpret_cast<ulonglong4 const*>(current_slot);
+      memcpy(&arr[0], &tmp, 2 * sizeof(pair<Key, Value>));
     }
 
-    // we found an empty slot, meaning that the key we're searching for isn't present
-    if (g.ballot(slot_is_empty)) { return this->end(); }
-
-    // otherwise, all slots in the current window are full with other keys, so we move onto the
-    // next window
-    current_slot = next_slot(g, current_slot);
-  }
-}
-
-template <typename Key,
-          typename Value,
-          std::size_t CGSize,
-          cuda::thread_scope Scope,
-          typename Allocator>
-template <typename CG, typename Hash, typename KeyEqual>
-__device__
-  typename static_multimap<Key, Value, CGSize, Scope, Allocator>::device_view::const_iterator
-  static_multimap<Key, Value, CGSize, Scope, Allocator>::device_view::find(
-    CG g, Key const& k, Hash hash, KeyEqual key_equal) const noexcept
-{
-  auto current_slot = initial_slot(g, k, hash);
-
-  while (true) {
-    auto const existing_key = current_slot->first.load(cuda::std::memory_order_relaxed);
-
-    // The user provide `key_equal` can never be used to compare against `empty_key_sentinel` as the
-    // sentinel is not a valid key value. Therefore, first check for the sentinel
-    auto const slot_is_empty = (existing_key == this->get_empty_key_sentinel());
+    // The user provide `key_equal` can never be used to compare against `empty_key_sentinel` as
+    // the sentinel is not a valid key value. Therefore, first check for the sentinel
+    auto const first_slot_is_empty =
+      (detail::bitwise_compare(arr[0].first, this->get_empty_value_sentinel()));
+    auto const second_slot_is_empty =
+      (detail::bitwise_compare(arr[1].first, this->get_empty_value_sentinel()));
+    auto const window_contains_empty = g.ballot(first_slot_is_empty or second_slot_is_empty);
+    if (window_contains_empty) {
+      // the first lane in the group with an empty slot will attempt the insert
+      insert_result status{insert_result::CONTINUE};
+      uint32_t src_lane = __ffs(window_contains_empty) - 1;
+      if (g.thread_rank() == src_lane) {
+        using cuda::std::memory_order_relaxed;
+        auto expected_key    = this->get_empty_key_sentinel();
+        auto expected_value  = this->get_empty_value_sentinel();
+        auto insert_location = current_slot + !(first_slot_is_empty);
+        auto& slot_key       = insert_location->first;
+        auto& slot_value     = insert_location->second;
+        bool key_success =
+          slot_key.compare_exchange_strong(expected_key, insert_pair.first, memory_order_relaxed);
+        bool value_success = slot_value.compare_exchange_strong(
+          expected_value, insert_pair.second, memory_order_relaxed);
+        if (key_success) {
+          while (not value_success) {
+            value_success =
+              slot_value.compare_exchange_strong(expected_value = this->get_empty_value_sentinel(),
+                                                 insert_pair.second,
+                                                 memory_order_relaxed);
+          }
+          status = insert_result::SUCCESS;
+        } else if (value_success) {
+          slot_value.store(this->get_empty_value_sentinel(), memory_order_relaxed);
+        }
+        // another key was inserted in both slots we wanted to try
+        // so we need to try the next empty slots in the window
+      }
 
-    // the key we were searching for was found by one of the threads, so we return an iterator to
-    // the entry
-    auto const exists = g.ballot(not slot_is_empty and key_equal(existing_key, k));
-    if (exists) {
-      uint32_t src_lane = __ffs(exists) - 1;
-      // TODO: This shouldn't cast an iterator to an int to shuffle. Instead, get the index of the
-      // current_slot and shuffle that instead.
-      intptr_t res_slot = g.shfl(reinterpret_cast<intptr_t>(current_slot), src_lane);
-      return reinterpret_cast<const_iterator>(res_slot);
+      // successful insert
+      if (g.any(status == insert_result::SUCCESS)) { return; }
+      // if we've gotten this far, a different key took our spot
+      // before we could insert. We need to retry the insert on the
+      // same window
+    }
+    // if there are no empty slots in the current window,
+    // we move onto the next window
+    else {
+      current_slot = next_slot(g, current_slot);
     }
-
-    // we found an empty slot, meaning that the key we're searching
-    // for isn't in this submap, so we should move onto the next one
-    if (g.ballot(slot_is_empty)) { return this->end(); }
-
-    // otherwise, all slots in the current window are full with other keys,
-    // so we move onto the next window in the current submap
-
-    current_slot = next_slot(g, current_slot);
   }
 }
 
@@ -364,22 +318,21 @@ template <typename Key,
           cuda::thread_scope Scope,
           typename Allocator>
 template <typename Hash, typename KeyEqual>
-__device__
-  typename static_multimap<Key, Value, CGSize, Scope, Allocator>::device_view::fancy_iterator
-  static_multimap<Key, Value, CGSize, Scope, Allocator>::device_view::find_all(
-    Key const& k, Hash hash, KeyEqual key_equal) noexcept
+__device__ typename static_multimap<Key, Value, CGSize, Scope, Allocator>::device_view::iterator
+static_multimap<Key, Value, CGSize, Scope, Allocator>::device_view::find(
+  Key const& k, Hash hash, KeyEqual key_equal) noexcept
 {
   auto current_slot = initial_slot(k, hash);
 
   while (true) {
     auto const existing_key = current_slot->first.load(cuda::std::memory_order_relaxed);
     // Key doesn't exist, return end()
-    if (existing_key == this->get_empty_key_sentinel()) {
-      return fancy_iterator{this->end(), k, *this};
+    if (detail::bitwise_compare(existing_key, this->get_empty_key_sentinel())) {
+      return this->end();
     }
 
     // Key exists, return iterator to location
-    if (key_equal(existing_key, k)) { return fancy_iterator{current_slot, k, *this}; }
+    if (key_equal(existing_key, k)) { return current_slot; }
 
     current_slot = next_slot(current_slot);
   }
@@ -392,8 +345,8 @@ template <typename Key,
           typename Allocator>
 template <typename Hash, typename KeyEqual>
 __device__
-  typename static_multimap<Key, Value, CGSize, Scope, Allocator>::device_view::const_fancy_iterator
-  static_multimap<Key, Value, CGSize, Scope, Allocator>::device_view::find_all(
+  typename static_multimap<Key, Value, CGSize, Scope, Allocator>::device_view::const_iterator
+  static_multimap<Key, Value, CGSize, Scope, Allocator>::device_view::find(
     Key const& k, Hash hash, KeyEqual key_equal) const noexcept
 {
   auto current_slot = initial_slot(k, hash);
@@ -401,12 +354,12 @@ __device__
   while (true) {
     auto const existing_key = current_slot->first.load(cuda::std::memory_order_relaxed);
     // Key doesn't exist, return end()
-    if (existing_key == this->get_empty_key_sentinel()) {
-      return const_fancy_iterator{this->end(), k, *this};
+    if (detail::bitwise_compare(existing_key, this->get_empty_key_sentinel())) {
+      return this->end();
     }
 
     // Key exists, return iterator to location
-    if (key_equal(existing_key, k)) { return const_fancy_iterator{current_slot, k, *this}; }
+    if (key_equal(existing_key, k)) { return current_slot; }
 
     current_slot = next_slot(current_slot);
   }
@@ -418,10 +371,9 @@ template <typename Key,
           cuda::thread_scope Scope,
           typename Allocator>
 template <typename CG, typename Hash, typename KeyEqual>
-__device__
-  typename static_multimap<Key, Value, CGSize, Scope, Allocator>::device_view::fancy_iterator
-  static_multimap<Key, Value, CGSize, Scope, Allocator>::device_view::find_all(
-    CG g, Key const& k, Hash hash, KeyEqual key_equal) noexcept
+__device__ typename static_multimap<Key, Value, CGSize, Scope, Allocator>::device_view::iterator
+static_multimap<Key, Value, CGSize, Scope, Allocator>::device_view::find(
+  CG g, Key const& k, Hash hash, KeyEqual key_equal) noexcept
 {
   auto current_slot = initial_slot(g, k, hash);
 
@@ -430,7 +382,8 @@ __device__
 
     // The user provide `key_equal` can never be used to compare against `empty_key_sentinel` as the
     // sentinel is not a valid key value. Therefore, first check for the sentinel
-    auto const slot_is_empty = (existing_key == this->get_empty_key_sentinel());
+    auto const slot_is_empty =
+      detail::bitwise_compare(existing_key, this->get_empty_key_sentinel());
 
     // the key we were searching for was found by one of the threads,
     // so we return an iterator to the entry
@@ -440,11 +393,11 @@ __device__
       // TODO: This shouldn't cast an iterator to an int to shuffle. Instead, get the index of the
       // current_slot and shuffle that instead.
       intptr_t res_slot = g.shfl(reinterpret_cast<intptr_t>(current_slot), src_lane);
-      return fancy_iterator{reinterpret_cast<iterator>(res_slot), k, *this};
+      return reinterpret_cast<iterator>(res_slot);
     }
 
     // we found an empty slot, meaning that the key we're searching for isn't present
-    if (g.ballot(slot_is_empty)) { return fancy_iterator{this->end(), k, *this}; }
+    if (g.ballot(slot_is_empty)) { return this->end(); }
 
     // otherwise, all slots in the current window are full with other keys, so we move onto the
     // next window
@@ -459,8 +412,8 @@ template <typename Key,
           typename Allocator>
 template <typename CG, typename Hash, typename KeyEqual>
 __device__
-  typename static_multimap<Key, Value, CGSize, Scope, Allocator>::device_view::const_fancy_iterator
-  static_multimap<Key, Value, CGSize, Scope, Allocator>::device_view::find_all(
+  typename static_multimap<Key, Value, CGSize, Scope, Allocator>::device_view::const_iterator
+  static_multimap<Key, Value, CGSize, Scope, Allocator>::device_view::find(
     CG g, Key const& k, Hash hash, KeyEqual key_equal) const noexcept
 {
   auto current_slot = initial_slot(g, k, hash);
@@ -470,7 +423,8 @@ __device__
 
     // The user provide `key_equal` can never be used to compare against `empty_key_sentinel` as the
     // sentinel is not a valid key value. Therefore, first check for the sentinel
-    auto const slot_is_empty = (existing_key == this->get_empty_key_sentinel());
+    auto const slot_is_empty =
+      detail::bitwise_compare(existing_key, this->get_empty_key_sentinel());
 
     // the key we were searching for was found by one of the threads, so we return an iterator to
     // the entry
@@ -480,12 +434,12 @@ __device__
       // TODO: This shouldn't cast an iterator to an int to shuffle. Instead, get the index of the
       // current_slot and shuffle that instead.
       intptr_t res_slot = g.shfl(reinterpret_cast<intptr_t>(current_slot), src_lane);
-      return const_fancy_iterator{reinterpret_cast<iterator>(res_slot), k, *this};
+      return reinterpret_cast<const_iterator>(res_slot);
     }
 
     // we found an empty slot, meaning that the key we're searching
     // for isn't in this submap, so we should move onto the next one
-    if (g.ballot(slot_is_empty)) { return const_fancy_iterator{this->end(), k, *this}; }
+    if (g.ballot(slot_is_empty)) { return this->end(); }
 
     // otherwise, all slots in the current window are full with other keys,
     // so we move onto the next window in the current submap
@@ -508,7 +462,7 @@ __device__ bool static_multimap<Key, Value, CGSize, Scope, Allocator>::device_vi
   while (true) {
     auto const existing_key = current_slot->first.load(cuda::std::memory_order_relaxed);
 
-    if (existing_key == empty_key_sentinel_) { return false; }
+    if (detail::bitwise_compare(existing_key, empty_key_sentinel_)) { return false; }
 
     if (key_equal(existing_key, k)) { return true; }
 
@@ -532,7 +486,8 @@ __device__ bool static_multimap<Key, Value, CGSize, Scope, Allocator>::device_vi
 
     // The user provide `key_equal` can never be used to compare against `empty_key_sentinel` as the
     // sentinel is not a valid key value. Therefore, first check for the sentinel
-    auto const slot_is_empty = (existing_key == this->get_empty_key_sentinel());
+    auto const slot_is_empty =
+      detail::bitwise_compare(existing_key, this->get_empty_key_sentinel());
 
     // the key we were searching for was found by one of the threads, so we return an iterator to
     // the entry
diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap_kernels.cuh
index dc0889bf5..18c44c25f 100644
--- a/include/cuco/detail/static_multimap_kernels.cuh
+++ b/include/cuco/detail/static_multimap_kernels.cuh
@@ -16,6 +16,7 @@
 
 #include <cooperative_groups/memcpy_async.h>
 #include <thrust/type_traits/is_contiguous_iterator.h>
+#include <cuda/barrier>
 
 #include <cuco/detail/pair.cuh>
 
@@ -191,7 +192,7 @@ template <uint32_t block_size,
 __global__ void insert(InputIt first, InputIt last, viewT view, Hash hash, KeyEqual key_equal)
 {
   auto tile = cg::tiled_partition<tile_size>(cg::this_thread_block());
-  auto tid  = blockDim.x * blockIdx.x + threadIdx.x;
+  auto tid  = block_size * blockIdx.x + threadIdx.x;
   auto it   = first + tid / tile_size;
 
   while (it < last) {
@@ -203,8 +204,6 @@ __global__ void insert(InputIt first, InputIt last, viewT view, Hash hash, KeyEq
   if (threadIdx.x == 0 && blockIdx.x == 0) {
     for (auto i = 0; i < view.get_capacity(); i++) {
       auto slot = view.get_slots() + i;
-      // if (slot->first != -1)
-      // printf("%ld:\t%ld\n", long(slot->first), long(slot->second));
     }
   }
 }
@@ -613,9 +612,14 @@ __global__ void find_all(InputIt first,
     bool found_match = false;
 
     while (running) {
-      auto const tmp = *reinterpret_cast<uint4 const*>(current_slot);
       pair<Key, Value> arr[2];
-      memcpy(&arr[0], &tmp, 2 * sizeof(pair<Key, Value>));
+      if constexpr (sizeof(Key) == 4) {
+        auto const tmp = *reinterpret_cast<uint4 const*>(current_slot);
+        memcpy(&arr[0], &tmp, 2 * sizeof(pair<Key, Value>));
+      } else {
+        auto const tmp = *reinterpret_cast<ulonglong4 const*>(current_slot);
+        memcpy(&arr[0], &tmp, 2 * sizeof(pair<Key, Value>));
+      }
 
       auto const first_slot_is_empty  = (arr[0].first == view.get_empty_key_sentinel());
       auto const second_slot_is_empty = (arr[1].first == view.get_empty_key_sentinel());
@@ -747,55 +751,7 @@ template <uint32_t block_size,
           typename atomicT,
           typename viewT,
           typename Hash,
-          typename KeyEqual,
-          typename std::enable_if<sizeof(Key) == 4>::type* = nullptr>
-__global__ void count(
-  InputIt first, InputIt last, atomicT* num_items, viewT view, Hash hash, KeyEqual key_equal)
-{
-  auto tile    = cg::tiled_partition<tile_size>(cg::this_thread_block());
-  auto tid     = blockDim.x * blockIdx.x + threadIdx.x;
-  auto key_idx = tid / tile_size;
-
-  using BlockReduce = cub::BlockReduce<int, block_size>;
-  __shared__ typename BlockReduce::TempStorage temp_storage;
-
-  int thread_counter{0};
-
-  while (first + key_idx < last) {
-    auto key          = *(first + key_idx);
-    auto current_slot = view.initial_slot(tile, key, hash);
-
-    while (true) {
-      auto const tmp = *reinterpret_cast<uint4 const*>(current_slot);
-      pair<Key, Value> arr[2];
-      memcpy(&arr[0], &tmp, 2 * sizeof(pair<Key, Value>));
-
-      auto const first_slot_is_empty  = (arr[0].first == view.get_empty_key_sentinel());
-      auto const second_slot_is_empty = (arr[1].first == view.get_empty_key_sentinel());
-      auto const first_equals         = (not first_slot_is_empty and key_equal(arr[0].first, key));
-      auto const second_equals        = (not second_slot_is_empty and key_equal(arr[1].first, key));
-      thread_counter += (first_equals + second_equals);
-
-      if (tile.any(first_slot_is_empty or second_slot_is_empty)) { break; }
-
-      current_slot = view.next_slot(tile, current_slot);
-    }
-    key_idx += (gridDim.x * block_size) / tile_size;
-  }
-  auto const block_count = BlockReduce(temp_storage).Sum(thread_counter);
-  if (threadIdx.x == 0) { num_items->fetch_add(block_count, cuda::std::memory_order_relaxed); }
-}
-
-template <uint32_t block_size,
-          uint32_t tile_size,
-          typename Key,
-          typename Value,
-          typename InputIt,
-          typename atomicT,
-          typename viewT,
-          typename Hash,
-          typename KeyEqual,
-          typename std::enable_if<sizeof(Key) == 8>::type* = nullptr>
+          typename KeyEqual>
 __global__ void count(
   InputIt first, InputIt last, atomicT* num_items, viewT view, Hash hash, KeyEqual key_equal)
 {
@@ -813,9 +769,14 @@ __global__ void count(
     auto current_slot = view.initial_slot(tile, key, hash);
 
     while (true) {
-      auto const tmp = *reinterpret_cast<ulonglong4 const*>(current_slot);
       pair<Key, Value> arr[2];
-      memcpy(&arr[0], &tmp, 2 * sizeof(pair<Key, Value>));
+      if constexpr (sizeof(Key) == 4) {
+        auto const tmp = *reinterpret_cast<uint4 const*>(current_slot);
+        memcpy(&arr[0], &tmp, 2 * sizeof(pair<Key, Value>));
+      } else {
+        auto const tmp = *reinterpret_cast<ulonglong4 const*>(current_slot);
+        memcpy(&arr[0], &tmp, 2 * sizeof(pair<Key, Value>));
+      }
 
       auto const first_slot_is_empty  = (arr[0].first == view.get_empty_key_sentinel());
       auto const second_slot_is_empty = (arr[1].first == view.get_empty_key_sentinel());
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index 10160b09f..73fef6bac 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -24,10 +24,13 @@
 #include <memory>
 
 #include <cuco/allocator.hpp>
-#include <cuco/detail/cuda_memcmp.cuh>
-#ifndef CUDART_VERSION
-#error CUDART_VERSION Undefined!
-#elif (CUDART_VERSION >= 11000)  // including with CUDA 10.2 leads to compilation errors
+
+#if defined(CUDART_VERSION) && (CUDART_VERSION >= 11000) && defined(__CUDA_ARCH__) && \
+  (__CUDA_ARCH__ >= 700)
+#define CUCO_HAS_CUDA_BARRIER
+#endif
+
+#if defined(CUCO_HAS_CUDA_BARRIER)
 #include <cuda/barrier>
 #endif
 
@@ -39,16 +42,6 @@
 
 namespace cuco {
 
-/**---------------------------------------------------------------------------*
- * @brief Enumeration of the possible results of attempting to insert into
- *a hash bucket
- *---------------------------------------------------------------------------**/
-enum class insert_result {
-  CONTINUE,  ///< Insert did not succeed, continue trying to insert
-  SUCCESS,   ///< New pair inserted successfully
-  DUPLICATE  ///< Insert did not succeed, key is already present
-};
-
 /**
  * @brief A GPU-accelerated, unordered, associative container of key-value
  * pairs with unique keys.
@@ -57,7 +50,9 @@ enum class insert_result {
  * concurrent insert and find) from threads in device code.
  *
  * Current limitations:
- * - Requires keys that are Arithmetic
+ * - Requires keys and values that are trivially copyable and have unique object representations
+ * - Comparisons against the "sentinel" values will always be done with bitwise comparisons
+ * Therefore, the objects must have unique, bitwise object representations (e.g., no padding bits).
  * - Does not support erasing keys
  * - Capacity is fixed and will not grow automatically
  * - Requires the user to specify sentinel values for both key and mapped value
@@ -121,7 +116,14 @@ template <typename Key,
           cuda::thread_scope Scope = cuda::thread_scope_device,
           typename Allocator       = cuco::cuda_allocator<char>>
 class static_multimap {
-  static_assert(std::is_arithmetic<Key>::value, "Unsupported, non-arithmetic key type.");
+  template <typename T>
+  static constexpr bool is_CAS_safe =
+    std::is_trivially_copyable_v<T>and std::has_unique_object_representations_v<T>;
+
+  static_assert(is_CAS_safe<Key>,
+                "Key type must be trivially copyable and have unique object representation.");
+  static_assert(is_CAS_safe<Value>,
+                "Value type must be trivially copyable and have unique object representation.");
 
  public:
   using value_type         = cuco::pair_type<Key, Value>;
@@ -656,135 +658,11 @@ class static_multimap {
      */
     template <typename CG,
               typename Hash     = cuco::detail::MurmurHash3_32<key_type>,
-              typename KeyEqual = thrust::equal_to<key_type>,
-              typename U        = Key,
-              typename std::enable_if<sizeof(U) == 4>::type* = nullptr>
-    __device__ void insert(CG g,
-                           value_type const& insert_pair,
-                           Hash hash          = Hash{},
-                           KeyEqual key_equal = KeyEqual{})
-    {
-      auto current_slot = initial_slot(g, insert_pair.first, hash);
-      while (true) {
-        // key_type const existing_key = current_slot->first.load(cuda::memory_order_relaxed);
-        auto const tmp = *reinterpret_cast<uint4 const*>(current_slot);
-        pair<Key, Value> arr[2];
-        memcpy(&arr[0], &tmp, 2 * sizeof(pair<Key, Value>));
-        // The user provide `key_equal` can never be used to compare against `empty_key_sentinel` as
-        // the sentinel is not a valid key value. Therefore, first check for the sentinel
-        auto const first_slot_is_empty  = (arr[0].first == this->get_empty_key_sentinel());
-        auto const second_slot_is_empty = (arr[1].first == this->get_empty_key_sentinel());
-        // auto const slot_is_empty         = (existing_key == this->get_empty_key_sentinel());
-        auto const window_contains_empty = g.ballot(first_slot_is_empty or second_slot_is_empty);
-        if (window_contains_empty) {
-          // the first lane in the group with an empty slot will attempt the insert
-          insert_result status{insert_result::CONTINUE};
-          uint32_t src_lane = __ffs(window_contains_empty) - 1;
-          if (g.thread_rank() == src_lane) {
-            using cuda::std::memory_order_relaxed;
-            auto expected_key    = this->get_empty_key_sentinel();
-            auto expected_value  = this->get_empty_value_sentinel();
-            auto insert_location = current_slot + !(first_slot_is_empty);
-            auto& slot_key       = insert_location->first;
-            auto& slot_value     = insert_location->second;
-            bool key_success     = slot_key.compare_exchange_strong(
-              expected_key, insert_pair.first, memory_order_relaxed);
-            bool value_success = slot_value.compare_exchange_strong(
-              expected_value, insert_pair.second, memory_order_relaxed);
-            if (key_success) {
-              while (not value_success) {
-                value_success = slot_value.compare_exchange_strong(
-                  expected_value = this->get_empty_value_sentinel(),
-                  insert_pair.second,
-                  memory_order_relaxed);
-              }
-              status = insert_result::SUCCESS;
-            } else if (value_success) {
-              slot_value.store(this->get_empty_value_sentinel(), memory_order_relaxed);
-            }
-            // another key was inserted in both slots we wanted to try
-            // so we need to try the next empty slots in the window
-          }
-
-          // successful insert
-          if (g.any(status == insert_result::SUCCESS)) { return; }
-          // if we've gotten this far, a different key took our spot
-          // before we could insert. We need to retry the insert on the
-          // same window
-        }
-        // if there are no empty slots in the current window,
-        // we move onto the next window
-        else {
-          current_slot = next_slot(g, current_slot);
-        }
-      }
-    }
-
-    template <typename CG,
-              typename Hash     = cuco::detail::MurmurHash3_32<key_type>,
-              typename KeyEqual = thrust::equal_to<key_type>,
-              typename U        = Key,
-              typename std::enable_if<sizeof(U) == 8>::type* = nullptr>
+              typename KeyEqual = thrust::equal_to<key_type>>
     __device__ void insert(CG g,
                            value_type const& insert_pair,
                            Hash hash          = Hash{},
-                           KeyEqual key_equal = KeyEqual{})
-    {
-      auto current_slot = initial_slot(g, insert_pair.first, hash);
-      while (true) {
-        // key_type const existing_key = current_slot->first.load(cuda::memory_order_relaxed);
-        auto const tmp = *reinterpret_cast<ulonglong4 const*>(current_slot);
-        pair<Key, Value> arr[2];
-        memcpy(&arr[0], &tmp, 2 * sizeof(pair<Key, Value>));
-        // The user provide `key_equal` can never be used to compare against `empty_key_sentinel` as
-        // the sentinel is not a valid key value. Therefore, first check for the sentinel
-        auto const first_slot_is_empty  = (arr[0].first == this->get_empty_key_sentinel());
-        auto const second_slot_is_empty = (arr[1].first == this->get_empty_key_sentinel());
-        // auto const slot_is_empty         = (existing_key == this->get_empty_key_sentinel());
-        auto const window_contains_empty = g.ballot(first_slot_is_empty or second_slot_is_empty);
-        if (window_contains_empty) {
-          // the first lane in the group with an empty slot will attempt the insert
-          insert_result status{insert_result::CONTINUE};
-          uint32_t src_lane = __ffs(window_contains_empty) - 1;
-          if (g.thread_rank() == src_lane) {
-            using cuda::std::memory_order_relaxed;
-            auto expected_key    = this->get_empty_key_sentinel();
-            auto expected_value  = this->get_empty_value_sentinel();
-            auto insert_location = current_slot + !(first_slot_is_empty);
-            auto& slot_key       = insert_location->first;
-            auto& slot_value     = insert_location->second;
-            bool key_success     = slot_key.compare_exchange_strong(
-              expected_key, insert_pair.first, memory_order_relaxed);
-            bool value_success = slot_value.compare_exchange_strong(
-              expected_value, insert_pair.second, memory_order_relaxed);
-            if (key_success) {
-              while (not value_success) {
-                value_success = slot_value.compare_exchange_strong(
-                  expected_value = this->get_empty_value_sentinel(),
-                  insert_pair.second,
-                  memory_order_relaxed);
-              }
-              status = insert_result::SUCCESS;
-            } else if (value_success) {
-              slot_value.store(this->get_empty_value_sentinel(), memory_order_relaxed);
-            }
-            // another key was inserted in both slots we wanted to try
-            // so we need to try the next empty slots in the window
-          }
-
-          // successful insert
-          if (g.any(status == insert_result::SUCCESS)) { return; }
-          // if we've gotten this far, a different key took our spot
-          // before we could insert. We need to retry the insert on the
-          // same window
-        }
-        // if there are no empty slots in the current window,
-        // we move onto the next window
-        else {
-          current_slot = next_slot(g, current_slot);
-        }
-      }
-    }
+                           KeyEqual key_equal = KeyEqual{}) noexcept;
 
   };  // class device mutable view
 
@@ -804,76 +682,6 @@ class static_multimap {
     using iterator       = typename device_view_base::iterator;
     using const_iterator = typename device_view_base::const_iterator;
 
-    template <typename DataType>
-    struct FancyIterator {
-     public:
-      using data_type      = DataType;
-      using pointer_type   = DataType*;
-      using data_reference = DataType&;
-
-     public:
-      __host__ __device__ FancyIterator(pointer_type current, Key key, device_view& view) noexcept
-        : current_{current},
-          key_{key},
-          begin_{view.begin_slot()},
-          end_{view.end_slot()},
-          empty_key_sentinel_{view.get_empty_key_sentinel()}
-      {
-      }
-
-      __host__ __device__ FancyIterator(pointer_type current,
-                                        Key key,
-                                        const device_view& view) noexcept
-        : current_{current},
-          key_{key},
-          begin_{view.begin_slot()},
-          end_{view.end_slot()},
-          empty_key_sentinel_{view.get_empty_key_sentinel()}
-      {
-      }
-
-      __host__ __device__ ~FancyIterator() {}
-
-      __device__ FancyIterator<data_type>& operator++()
-      {
-        current_ = next_slot(current_);
-        while (current_->first != key_) {
-          if (current_->first == this->empty_key_sentinel_) {
-            current_ = this->end_;
-            return *this;
-          }
-          current_ = next_slot(current_);
-        }
-        return *this;
-      }
-
-      __device__ pointer_type next_slot(pointer_type it) noexcept
-      {
-        return (++it < end_) ? it : begin_;
-      }
-      __device__ pointer_type next_slot(pointer_type it) const noexcept
-      {
-        return (++it < end_) ? it : begin_;
-      }
-
-      __device__ bool operator==(const pointer_type& it) const { return (this->current_ == it); }
-      __device__ bool operator!=(const pointer_type& it) const { return this->current_ != it; }
-
-      __device__ data_reference operator*() { return *current_; }
-      __device__ data_reference operator*() const { return *current_; }
-      __device__ pointer_type operator->() { return current_; }
-      __device__ pointer_type operator->() const { return current_; }
-
-     private:
-      pointer_type current_;
-      Key key_;
-      pointer_type begin_;
-      pointer_type end_;
-      Key empty_key_sentinel_;
-    };
-    using fancy_iterator       = FancyIterator<pair_atomic_type>;
-    using const_fancy_iterator = FancyIterator<const pair_atomic_type>;
-
     /**
      * @brief Construct a view of the first `capacity` slots of the
      * slots array pointed to by `slots`.
@@ -1059,102 +867,6 @@ class static_multimap {
     __device__ const_iterator
     find(CG g, Key const& k, Hash hash = Hash{}, KeyEqual key_equal = KeyEqual{}) const noexcept;
 
-    /**
-     * @brief Finds all values corresponding to the key `k`.
-     *
-     * Returns a fancy iterator to the first pair whose key is equivalent to `k`.
-     * If no such pair exists, returns `end()`.
-     *
-     * @tparam Hash Unary callable type
-     * @tparam KeyEqual Binary callable type
-     * @param k The key to search for
-     * @param hash The unary callable used to hash the key
-     * @param key_equal The binary callable used to compare two keys
-     * for equality
-     * @return A fancy iterator to the position at which the first key/value pair
-     * containing `k` was inserted
-     */
-    template <typename Hash     = cuco::detail::MurmurHash3_32<key_type>,
-              typename KeyEqual = thrust::equal_to<key_type>>
-    __device__ fancy_iterator find_all(Key const& k,
-                                       Hash hash          = Hash{},
-                                       KeyEqual key_equal = KeyEqual{}) noexcept;
-
-    /**
-     * @brief Finds all values corresponding to the key `k`.
-     *
-     * Returns a const fancy iterator to the first pair whose key is equivalent to `k`.
-     * If no such pair exists, returns `end()`.
-     *
-     * @tparam Hash Unary callable type
-     * @tparam KeyEqual Binary callable type
-     * @param k The key to search for
-     * @param hash The unary callable used to hash the key
-     * @param key_equal The binary callable used to compare two keys
-     * for equality
-     * @return A const fancy iterator to the position at which the first key/value pair
-     * containing `k` was inserted
-     */
-    template <typename Hash     = cuco::detail::MurmurHash3_32<key_type>,
-              typename KeyEqual = thrust::equal_to<key_type>>
-    __device__ const_fancy_iterator find_all(Key const& k,
-                                             Hash hash          = Hash{},
-                                             KeyEqual key_equal = KeyEqual{}) const noexcept;
-
-    /**
-     * @brief Finds all values corresponding to the key `k`.
-     *
-     * Returns a fancy iterator to the first pair whose key is equivalent to `k`.
-     * If no such pair exists, returns `end()`. Uses the CUDA Cooperative Groups API to
-     * to leverage multiple threads to perform a single find. This provides a
-     * significant boost in throughput compared to the non Cooperative Group
-     * `find_all` at moderate to high load factors.
-     *
-     * @tparam CG Cooperative Group type
-     * @tparam Hash Unary callable type
-     * @tparam KeyEqual Binary callable type
-     * @param g The Cooperative Group used to perform the find
-     * @param k The key to search for
-     * @param hash The unary callable used to hash the key
-     * @param key_equal The binary callable used to compare two keys
-     * for equality
-     * @return A fancy iterator to the position at which the first key/value pair
-     * containing `k` was inserted
-     */
-    template <typename CG,
-              typename Hash     = cuco::detail::MurmurHash3_32<key_type>,
-              typename KeyEqual = thrust::equal_to<key_type>>
-    __device__ fancy_iterator
-    find_all(CG g, Key const& k, Hash hash = Hash{}, KeyEqual key_equal = KeyEqual{}) noexcept;
-
-    /**
-     * @brief Finds all values corresponding to the key `k`.
-     *
-     * Returns a const_iterator to the first pair whose key is equivalent to `k`.
-     * If no such pair exists, returns `end()`. Uses the CUDA Cooperative Groups API to
-     * to leverage multiple threads to perform a single find. This provides a
-     * significant boost in throughput compared to the non Cooperative Group
-     * `find_all` at moderate to high load factors.
-     *
-     * @tparam CG Cooperative Group type
-     * @tparam Hash Unary callable type
-     * @tparam KeyEqual Binary callable type
-     * @param g The Cooperative Group used to perform the find
-     * @param k The key to search for
-     * @param hash The unary callable used to hash the key
-     * @param key_equal The binary callable used to compare two keys
-     * for equality
-     * @return A const fancy iterator to the position at which the first key/value pair
-     * containing `k` was inserted
-     */
-    template <typename CG,
-              typename Hash     = cuco::detail::MurmurHash3_32<key_type>,
-              typename KeyEqual = thrust::equal_to<key_type>>
-    __device__ const_fancy_iterator find_all(CG g,
-                                             Key const& k,
-                                             Hash hash          = Hash{},
-                                             KeyEqual key_equal = KeyEqual{}) const noexcept;
-
     /**
      * @brief Indicates whether the key `k` was inserted into the map.
      *
diff --git a/tests/static_multimap/static_multimap_test.cu b/tests/static_multimap/static_multimap_test.cu
index 7c1916ab5..bdf75b88d 100644
--- a/tests/static_multimap/static_multimap_test.cu
+++ b/tests/static_multimap/static_multimap_test.cu
@@ -87,67 +87,6 @@ static void generate_keys(OutputIt output_begin, OutputIt output_end)
   }
 }
 
-TEMPLATE_TEST_CASE_SIG("Unique sequence of keys",
-                       "",
-                       ((typename T, dist_type Dist), T, Dist),
-                       (int32_t, dist_type::UNIQUE),
-                       (int64_t, dist_type::UNIQUE),
-                       (int32_t, dist_type::UNIFORM),
-                       (int64_t, dist_type::UNIFORM),
-                       (int32_t, dist_type::GAUSSIAN),
-                       (int64_t, dist_type::GAUSSIAN))
-{
-  using Key   = T;
-  using Value = T;
-
-  constexpr std::size_t num_keys{50'000'000};
-  cuco::static_multimap<Key, Value> map{100'000'000, -1, -1};
-
-  auto m_view = map.get_device_mutable_view();
-  auto view   = map.get_device_view();
-
-  std::vector<Key> h_keys(num_keys);
-  std::vector<cuco::pair_type<Key, Value>> h_pairs(num_keys);
-
-  generate_keys<Dist, Key>(h_keys.begin(), h_keys.end());
-
-  for (auto i = 0; i < num_keys; ++i) {
-    Key key           = h_keys[i];
-    Value val         = h_keys[i];
-    h_pairs[i].first  = key;
-    h_pairs[i].second = val;
-  }
-
-  thrust::device_vector<cuco::pair_type<Key, Value>> d_pairs(h_pairs);
-
-  SECTION("All keys should be found.")
-  {
-    // Bulk insert keys
-    thrust::for_each(thrust::device,
-                     d_pairs.begin(),
-                     d_pairs.end(),
-                     [m_view] __device__(cuco::pair_type<Key, Value> const& pair) mutable {
-                       m_view.insert(pair);
-                     });
-
-    SECTION("non-const view")
-    {
-      REQUIRE(all_of(d_pairs.begin(),
-                     d_pairs.end(),
-                     [view] __device__(cuco::pair_type<Key, Value> const& pair) mutable {
-                       return view.find_all(pair.first) != view.end();
-                     }));
-    }
-    SECTION("const view")
-    {
-      REQUIRE(all_of(
-        d_pairs.begin(), d_pairs.end(), [view] __device__(cuco::pair_type<Key, Value> const& pair) {
-          return view.find_all(pair.first) != view.end();
-        }));
-    }
-  }
-}
-
 TEMPLATE_TEST_CASE_SIG("Each key appears twice",
                        "",
                        ((typename T, dist_type Dist), T, Dist),

From 9afd723f3ba8b12eef1fcda0785d7cf0b94f6ca6 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Mon, 3 May 2021 20:57:51 -0400
Subject: [PATCH 080/267] Update performance analysis jupyter notebook

---
 .../analysis/notebooks/StaticMultimap.ipynb   | 206 ++++++++++++++----
 1 file changed, 158 insertions(+), 48 deletions(-)

diff --git a/benchmarks/analysis/notebooks/StaticMultimap.ipynb b/benchmarks/analysis/notebooks/StaticMultimap.ipynb
index ff6a5ec89..b5f5526c1 100644
--- a/benchmarks/analysis/notebooks/StaticMultimap.ipynb
+++ b/benchmarks/analysis/notebooks/StaticMultimap.ipynb
@@ -71,9 +71,8 @@
     "perfdf['CGSize'] = perfdf['CGSize'].astype('Int64')\n",
     "\n",
     "# Add labels\n",
-    "perfdf.loc[perfdf['NumReps'].isnull() & perfdf['CGSize'].isnull(), 'Label'] = perfdf[\"Key\"] + \" \" + perfdf[\"Distribution\"]\n",
-    "perfdf.loc[perfdf['Distribution'].isnull() & perfdf['CGSize'].isnull(), 'Label'] = perfdf[\"Key\"]\n",
-    "perfdf.loc[perfdf['CGSize'].notnull(), 'Label'] = perfdf[\"Key\"] + \" \" + perfdf['CGSize'].astype(str)\n",
+    "perfdf['Label'] = perfdf[\"Key\"] + \"_\" + perfdf[\"Distribution\"]\n",
+    "perfdf.loc[perfdf['CGSize'].notnull(), 'Label'] += \"_\" + perfdf['CGSize'].astype(str)\n",
     "\n",
     "perfdf[\"Bandwidth (GB/s)\"] = perfdf[\"GlobalMem BW (bytes/sec)\"] / (1000 * 1000 * 1000)\n",
     "\n",
@@ -107,10 +106,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "nvbench_static_multimap_single_insert\n",
-      "nvbench_static_multimap_multi_insert\n",
-      "nvbench_static_multimap_find\n",
+      "nvbench_static_multimap_insert\n",
+      "nvbench_static_multimap_count\n",
       "nvbench_static_multimap_find_all\n",
+      "nvbench_static_multimap_retrieve\n",
       "nvbench_static_multimap_insert_cgsize\n"
      ]
     }
@@ -124,21 +123,33 @@
   },
   {
    "cell_type": "markdown",
-   "id": "blocked-confidence",
+   "id": "spatial-patent",
    "metadata": {},
    "source": [
-    "### Single-Value Insertion"
+    "### insert "
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "periodic-champion",
+   "id": "homeless-grave",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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MGgUnn2w9bNdfDw89ZMmccy5xREtZEZrMzEzNzs4OO4ztZGTkfgk0WoMGlox16QJ7723Tz6tXT0p4zpVqIjJNVTPDjiMeknUM++03mDLFErPp0+32558wfz40bWonh2+9ZQnZ2LHw5ZdbT0ZnzYIDDoAff4TatRMeqnMpLb/jV4E9YSJSD9gX2AVYC8wGslV1c1yjLOXyugT64IN2OeCLL+z2+efwyiv2eloa7LnntonZHnvYduecK4769a0nK9KbpWqXJzMy7PmSJfDqq7BihT1v1syOP++/bz1nhxxilyf79AklfOfKhDyTMBE5GLgKqA3MAH4HKgPHA01F5AXgLlX9KwlxlngFXQLt3Bkuusger1hhZ52RxOz5521cB0CNGrDXXlsTsy5d7GDqnHPFIQK77bb1+cCBMGAAnH8+/PUXNG8O//1n488Ali7dmqA55xIjz8uRInIHMEpVtyv4ICLlge5AOVV9MY/PPx6853dV3TOP9xwE3ANUAFao6oEFBVxSL0cWx+bN8MMP1ksWScy+/trGeoCdue6999besvbtbXyIl8RwZYVfjkyczz+33rLp07eOFfviCzvWVKgAvXvb+LTmzcON07nSKr/jV8LGhInIAcA/wBO5JWEiUgv4FOimqotFpJ6q/l7QfkvaASxR1qyxg2J0YrZkib1WoYIN8l+82GZHRVStCqNHeyLmUo8nYYl1zTU2IeC002xg/nPPwbXXwoIF8Nhj1kN2yilw661bL2c652JTpCRMRAbmt1NVHRlDwxnA63kkYRcCu6jqtQXtJ1pJPIAlyy+/bB1Xdu+9uZfNSE+HhQuTHppzCeVJWOJ9842NEatUyRKu9HTb/ttvMHKkJWMzZliv+9q1Vg7DOVewoiZhNwQPdwc6A68Gz48FvlTVM2JoOIO8k7B7sMuQrYEawL2q+kRB+yypB7BkS0uzgba5eeMNW3/Oy2G4VOFJWPjWrdtaJuPww+1+yBA48EA/1jiXn/yOX3nOw1PVYao6DGgIdFTVy1X1cqAT0DgOcZUP9nUMcCRwnYi0yO2NItJPRLJFJHv58uVxaLr0a5zHN1CunFXRbt8eJk7c9nKlc84VVSQB27wZjjjCylgcfDDst5+d+JWyakfOlQixFEOoD/wX9fy/YFtxLQXeVtV/VXUF8BHQLrc3qupoVc1U1cy6devGoenSb/hwGwMWrWpVGDMGxo2DDRvg9NOhRQsrurh2bShhOudSTFoaDB5s5S7uv99mUR5zjF2udM4VTixJ2BPAlyIyVESGAl8A4+PQ9iRgPxEpLyJVgS7Ad3HYb5mQlWWD8NPT7VJAero979vXbrNnWz2yevXgwgutCONtt9lSJs45V1xVqkD//jazO7JUEliv2BNP2Imgcy5/Mc2OFJFOwH7B049UdUYMn5kIHATUAX4DbsDGgKGqDwfvGQycBWwGHlPVewrab2kdTxEWVfjwQ0vA3n4bdtgBLrgALrsMdt457Oici42PCSs9evWCZ5+1WZRXXglnnrn1UqZzZVGxS1SISDnsEuSW4q651Q9LhlQ/gCXSjBmWjL3wgpW5OOssGDTIljBxriTzJKz0ULVK+8OH22zuBg3g9tvhjAKncjmXmoo0MD/qwxdjPVnvAq8Dk4N7V8p06GBnqN9/b5csH3/cxoyddhrMnBl2dM65VCBii45/9pktIt6ypQ3mB6tBtmqVPV6/3i5bnnMOXH01zJsXWsjOhSaWMWGXAruramtVbauqbVS1baIDc4nTvDk88ojVExs0CCZPtgTt6KPho498lpNzrvhE4NBDbS3K3r1t20MP2fjVwYNtVuX48bakG9jzSZPCi9e5MMSShC0BfDh3CmrQAEaMsMr7w4dDdrbV/Nl3XyvauNmXaHfOxUGkjtgRR8CRR8Jdd9nwiDZtrNfs1lvtmHPBBT6g35UtsSRhC4ApInK1iAyM3BIdmEueWrVs2ZJFi+CBB2DZMjjuODtA+iwn51y8tG1rSyJ16QIHHWQlLs46y17be2+oU8eHRriyJZYkbDE2HqwiVtk+cnMppkoVK2fxww/w1FNWD6hvX2jWDEaNsvUsJ0ywWU9paXY/YULYUTvnSpt69Sz5mj/flkQCK5/z559QrVq4sTmXTOULekNQNR8RqR48/yfRQblwlS9vdchOP91q/tx6K1xyiS1Rsm7d1p6xRYugXz977IuGO+di1bs3DBtm5XMiC4IfdBCsWOHlLFzZEsvsyD1FZAYwB5gjItNEpHXiQ3NhE7FK2FOnwscf2xJIOS9NrlljyZlzzsXqpJNsIlDz5nDyyTY4//ffoWJFe/zee2FH6FxyxHI5cjQwUFXTVTUduBx4NLFhuZJmv/2sFyw3i0OpGOecK61EbFLQ9OlwwgnW2754MUybZkWkjzwS7r3XZ2q71BdLElZNVT+IPFHVKYBftS+D8lo0fNddkxuHcy41pKfbsIfDDoNy5Wz86eefQ/fucMst8McfYUfoXGLFNDtSRK4TkYzgdi02Y9KVMbktGg6waZPVHHPOueKqUQNeesmSsZ12slI5v/8edlTOJUYsSdjZQF3gJeBFbC3IsxMZlCuZcls0/LrrYO1a6NrV6v4451xxpaVBkyb2+KaboF07q8DvXKopMAlT1T9V9RJV7aiqnVT1MlX9MxnBuZInK8t6vTZvtvsbb4RPPrG1KA880JYpcc65eDnxROuBP+ggGDMm7Gici69YZke+KyK1op7vKCJvJzQqV6q0amVnqRkZcNRRXjvMORc/bdrAV1/BAQfAuefCxRd7AWmXOmK5HFlHVVdFngS9YPUSFpErlXbd1cpY7LcfnHEG3H67z2xyzsVH7drw5pswcKCtezt7dtgRORcfsSRhm0Vky7w4EUkH/L9Xt52aNeGtt+DUU+HKK+Gyy2zQvnPOFVf58rbm5HffQYcOtu2338KNybniiiUJGwJMFZEnReQp4CPg6sSG5UqrSpXg6adhwAC47z7o1Svv+mLOOVdYTZva/csvw267wbPPhhuPc8URy7JFb4lIR2DvYNNlqroisWG50iwtzdaDa9gQLr/cppe/8grsuGPYkTnnUkXXrtYj1qsXfP21zaIsVy7sqJwrnFgG5gvQDeioqq8DVUVkr4RHVgw//2w9MZ062dIYkyaFHVHZNHAgTJxog/b33x+WLAk7Iudcqth5Z3j/fVu/9tZboUcPWLUq7KicK5xYLkc+CHQFTgue/w08kLCIimnZMjtDSkuDBx+EPn1g8GC4++6wIyubevWycWJLltj34gNqnXPxUrGiDdR/6CF45x071jhXmsSShHVR1f7AOtgyO7JiQqMqhnvvheOOswGcnTvbIPG337au6n/+CTu6sumQQ2zmpKrNnvzww7Ajcq5wRKSRiHwgIt+KyBwRuTTYXjso4/NDcO8X3UNw/vnw/fd20gc+YN+VHrEkYRtEpBzBjEgRqQtsTmhUxfDZZ7YgLMDUqTZjLyvLxgoMGwZTpsC//4YaYpnUtq19N7vsAkccAc8/H3ZEzhXKRuByVW2FjY/tLyKtgKuA91S1OfBe8NyFIDJgf9Ysezx8uJfJcSVfLEnYfcDLQD0RGQ5MBW5JaFTF0KABzJ1rj3fayS5HlisHK1fCnXfCwQfbHylYQjZwIIwdC9nZsGZNaGGXCY0bW2Ic6aG8776wI3IuNqq6TFWnB4//Br4DdgWOA8YHbxsPHB9KgG6Lpk3tasi118Ipp/gVEFeyxTI7coKITAMOBQQ4XlW/S3hkRXThhXD66Tb+qH17Kxp6+eU2M+/hhy0Ba9vW3jt7to0liJRQELE/4C++sOKA8+bZ8jzNmlmNmvx8/73NCJw1y95/2WU2McBtq3ZtePddK+h66aWwdCncdpuN4XOuNBCRDKAD8AVQX1WXBS/9CtTP4zP9gH4AjRs3zu0tLk6qVoWnnrKZk1deacfxV17ZuhalcyVJLLMjmwI/qeoDwGzg8OhljEqaAw6wmTJHHw0tWlgl9+XL4Ykn7FLYkUfaHynARRfZWdLcufDCC3DDDdCly9ZSCrfcAnvsAdWr2x90nz65D/CfMcPabdzYetsiszJ9HcXcVakCzz0H/fvDHXfY7/W//8KOyrmCiUh14EWsVM9f0a+pqpJHIWtVHa2qmaqaWbdu3SREWraJwKBB8MYbsHgxjB699bUlS6z6/rx54cXnXIRoARfNRWQmkAlkAJOBV4HWqnp0ooPLTWZmpmZnZxf4vv/+g/nz7ZJk/VzPTQv2/ffw5ZfWuzV7tt3vsAN8+6293rMn/Pqr9ebsvbf1wrVpY0ncpEk2GSCGUMssVRgxAq6+Gg49FF56yX6/zuUkItNUNTPkGCoArwNvq+rIYNtc4CBVXSYiDYApqrp7fvuJ9Rjm4mPhQjsZF4Ezz7TErHNnqy2299621m21amFH6VJZfsevmJYtUtWNwInA/ao6GGgQQ6OPi8jvIpJvUQIR6SwiG0WkZwyxxKxiRVtYuqgJGEDLltZLc8cddua0dClMm7b19d2DQ+2iRVa1+cADracN4NhjLWlbu7bo7ac6EbjqKhg/3mZMHnCAlRhxrqQJ6iWOAb6LJGCBV4G+weO+gFclLGEyMqBCBbjxRrviEakduXixXeUYPDjsCF1ZFuvsyNOAPthZIECFGD43Divymqdg1uUI4J0Y9lciVKmy9fHw4TbQfPfdbQmNyZOtNhnAN9/YuomPPgp//ZX7vpzp0wdef916Lrt2tR5I50qYfYHewCEiMjO4HQ3chg3R+AE4LHjuSqCnn7ZjzYQJcNBBsHq1DR956inYuDHs6FxZFUsSdhZWrHW4qv4kIk2AJwv6kKp+BPxRwNsuxsZX/B5DHCVW//42uLxjR8jMhD//hIsvtl64Sy+1rvCLL946a9Nt78gjrTds7VrYd1/49NOwI3JuK1Wdqqqiqm1VtX1we0NVV6rqoaraXFUPU9WCjnkuJKtX27jfF1+0S5EnnAA1atjQFR+T6sJSYBKmqt+q6iWqOjF4/pOqjihuwyKyK3AC8FBx9xW2/v1tTNMee9iMzN12gz33hJ9+sjFlJ5xgVZ332MO6wF3uOnWyWmI77WS/z1deCTsi51yqOOwwePJJOPFEGwLxySfQu7cddyKTtZxLtjALA9wDXKmqBRZ+FZF+IpItItnLly9PfGSFlJZmlyYXLoTHH4cffrDSFxUr2gDQJ56wGTljx9oMSrCZO3ff7Wud5bTbbnZwbNcOTjrJyoo451xx3Xij1Sa86CIbI3b88fDRR3ZJ0rmwhJmEZQLPiMhCoCfwoIgcn9sbS8v07po17ZJknTrbv1a/PvQNhu9u2mSzJgcOhIYNbVZlZMalg7p1bWHeo4+GCy6wg2V6uiW7GRk2psM55wqjeXObWFW7Nowbt7UmZJs2NrbXuTDkm4SJSDkRSch5gqo2UdUMVc0AXgAuVNVXEtFWSVOunFXrnzYNTj7Zes9at4bHHgs7spKjalWb7HDwwVtnMqnaTNR+/TwRc84VXoMG1iM2aZL1gDVtaifDRxyx7cx355Il3yRMVTcB+xVlxyIyEfgM2F1ElorIOSJyvoicX5T9paKOHe0S5ZIlVhg2Ut7i/fftAPFHGR/iW748/Pjj9tvXrIEhQ5Ifj3Mu9dx0E9SrBz16wM8/hx2NK2tiKdb6ELZG2vPAlqWvVfWlxIaWu7JQ6PCKK6w2WZUqtrzPxRdbl3lZlJaW+yK8IraklCsbSkKx1ngpC8ew0mbWLNhnH1tl5aOPvHiri6/iFmutDKwEDgGODW7d4xeey+n2220KdVaW1bBp29bq2+T07782y+emm6yYbComJXkts+fL7znn4qVNG3jmGZg508oKOZcssSzgfVYyAnHbatvWCr2OGAFjxljZBoD16+H+++2s7dRTrSTGnnva5bkRI6xgbCqdxQ0fbmPA1qzZui0tDYYNCy8m51zqOeYYO6ndd9+wI3FlSSwLeLcQkfciyw+JSFsRuTbxoTmwmTyDB8PZZ9vzKVOsvMW++1oR2GHDbDxZdraNa7jjjlDDjbusLFt8Nz3dLkHWqWM9fj/9FHZkzrlUc8YZ0KSJHWNmzAg7GlcWxHI58lHgamADgKp+A/RKZFAub0ceaWMWKlSwpZE6drQ1K5cvt+TshRfCjjD+srKsBtvmzfZz9u4NN98Mn38edmTOuVR0++3QpYuXrnCJF0sSVlVVv8yxzVfaClHLljZof+FCGDnS6mpFapOJhBpaUowaZfXVzjgD/vkn7Gicc6nmf/+zHrETToAFC8KOxqWyWJKwFSLSFFAAEekJLEtoVC5fdetaRflnn4UBA6z3S8QG6FeoACtXhh1hYtWsacuPLFhgP79zzsXTjjvC669bYe3u3W3dSecSIZYkrD/wCNBSRH4GLgO81lfIRo+2LvPu3eHqq+2y5IIFMHu2JWgffBB2hIm1//5w5ZVW4NbXmHTOxVvz5vDSS7YMXe/eYUfjUlUsC3gvUNXDgLpAS1XdT1UXJT40l5/dd4fvv7cZktWq2czIWbNsnFS1arYA9jXXwIYNYUeaOMOGQYcOcN558OuvYUfjnEs1Bx1kK5pcfnnYkbhUVWCJChHZCbgBq5yvIjIVuFFVU/yiV8lXter2Z2gdO8L06XDZZXDrrbbtlluSHlpSVKxoyxd17GizRydPLhtj4pxzyRN9jP3pJxsr5ly8xHI58hlgOXASttD2cuDZRAbliqdaNasx9vLLNmMS4O+/c688X9rtsYeV5XjzTXjoobCjcc6lqueft4r6b78ddiQulcSShDVQ1ZtU9afgdjNQP9GBueI7/nirM7Zhg12ezMpKzQGm/ftDt26WcH7/fdjROOdS0VFHQatWcMop8O23YUfjUkUsSdg7ItJLRNKC2ymAnwuUImlplpA995wN2v/kk7Ajii8RG7dRtaolmv/9F3ZEzrlUU706vPaalQfq3t1qFjpXXLEkYecBTwPrg9szwP9E5G8R+SuRwbn4KFfOBul/8ok9PuAAGDoUNqZQtbcGDewS7PTpvqSRcy4xGjeGSZPgl1/gxBNT6xjqwhHL7MgaqpqmqhWCW1qwrYaq7pCMIF18dOliS3FkZVmvWKr1GJ1wgg3Qv+02r3TtnEuMLl1sjcmsLChf4NQ25/IXS0+YSyE77ABPPAGffmqX7/75J7XqbN1zD2Rk2Iymv7yf1jmXAKeeCucH1TJXrAg3Fle6eRJWRtWqZfejRlkP0lln2QzK0q5GDXjqKVi8GC65JOxonHOp7IsvrGTFSy+FHYkrrTwJK+MGD4brrrPesY4d4auvwo6o+Lp2hSFD7JJBKi5o7pwrGdq2hdatbR3badPCjsaVRgUmYSLSVEQqBY8PEpFLRKRWwiNzSVG+PNx4oy1ztH497LMPPP102FEV33XXQefOthDvzz+HHY1zLhVVqWLDOerWhR49/FjjCi+WnrAXgU0i0gwYDTTCZku6FHLAAfD113ZG17Vr2NEUX4UKdlly3Tq71Lp5c9gROedS0c47W+mKv/6yROzff8OOyJUmsSRhm1V1I3ACMEpVBwMNEhuWC8OOO8LYsTbGQdWSl5dfDjuqomvRAkaOhHfftbFvzjmXCG3bwsSJsPfetpyac7GKJQnbICKnAX2B14NtFRIXkisJVq2yBcFPPNFmAa1ZE3ZERdOvnxVWvPJKmDMn7Gicc6mqe3d44AHrhS+tx0uXfLEkYWcBXYHhqvqTiDQBnkxsWC5sO+5oZSyuuAIeeQQ6dbIaYy+8AEcfbZcsb7gB/vgj7EjzJwJjxkDNmlbXZ/36sCNyzqWyRYtseaMnngg7ElcaxFKs9VtVvURVJwbPf1LVEYkPzYWtYkUYMcIu561eDQcfDNdfD337WkHUJUtgv/1K/nqU9epZIvb11zZg3znnEmWXXaBpUzjvPC8a7QoWy+zIfUXkXRGZJyILROQnEVmQjOBcyXDYYfD++7Bpk82iPOkk2GMPW6+xTRtLcEq67t1tpuSdd8KUKWFH45xLVRUqwPPPQ3q61WBcsMCOnX//bWNtnYsWy+XIMcBIYD+gM5AZ3LsyZMECuwRZvz7ceit06ADz51tCVlrO9u66C5o1gz59bMybc84lQu3a8PrrlnztvbcdN+vXt5PX558POzpXksSShK1W1TdV9XdVXRm5FfQhEXlcRH4Xkdl5vJ4lIt+IyCwR+VRE2hU6epc0O+8MP/5opR569LCxVYcdBtnZ9lppUK0aTJhgi+/27x92NM65VNaihfXAb9gAb75ppSseeQQGDIC33w47OldSxJKEfSAid4hIVxHpGLnF8LlxQLd8Xv8JOFBV2wA3YTXIXAnVoYONrRo61M7m3nnH1kwbOdJ6w0qLzp1tQsHTT9uUcuecS4R162DyZJvQ1Lmz9YodeKANibjrrrCjcyVFLElYF+wS5C3AXcHtzoI+pKofAXnOnVPVT1X1z+Dp50DDGGJxIRGx9dE+/RQaNrSZhlWqWMX9K6+0A0xpcfXVdmn1ggtsjUnnnIu3lSttclNGhhVyPeQQq66fmWlDOZwDKF/QG1T14CTEcQ7wZhLaccXQoAH83//ZFOzVq61HbMoUWLsWypULO7rYlS8PTz4J7dvbTM/33oM0X0XVORdH9erZ/Zw5dmny77/hwgttvd62bcONzZUcscyOrCkiI0UkO7jdJSI14xWAiByMJWFX5vOefpH2ly9fHq+mXRGlp9tBpEIFOPxwGyMGNoNy7dpwY4tV06Zw772WRI4cGXY0zrlUU6ECDBkCPXvChx/C7bfDr7/CNddYb7xzENvlyMeBv4FTgttfwNh4NC4ibYHHgOPyG+yvqqNVNVNVM+vWrRuPpl2cLVoE3brZ+LD//gs7mticdZZNIR8yxGqIOedcPF10kSVdV14JvXrZCey6dV5R320VSxLWVFVvUNUFwW0YsFtxGxaRxsBLQG9VnVfc/blwpafD/ffbLKCsLNi4MeyICiYCo0fbdPKsLDs4OudcPPXuDdOm2RixOXOsF/7mm8OOypUUBY4JA9aKyH6qOhWseCtQ4EUnEZkIHATUEZGlwA0Ea06q6sPA9cBOwIMiArBRVTOL8kO4kqFfP/jnH7j8cisH8fjjJX+sVZ06MG6c9eJdfTXcfXfYEblEEZHKQHdgf2AX7Dg2G5isqr6yqEu4qlXhtddscpNzEFsSdgEwPhgHJtiMxzML+pCqnlbA6+cC58bQvitFBg60AahDh8Jxx9nlvpLuyCPtssE998Axx1j9M5daRGQYloBNAb4AfgcqAy2A24IE7XJV/Sa0IF2ZsMcedr9+Pfz2GzRuHG48LlyxzI6cCbQTkR2C538lOihXul1/PeyzT+lKZkaMsFmSffvCrFl2idKllC9V9YY8XhspIvUA/+/QJc0xx1itxa++skH8rmzK82KRiJwR3A8UkYFYr9W5Uc+dy5WIzZoUgdmzYdSosCMqWNWq8NRTsHw5nH++r/GWalR1cs5tIpIWdXL5u6pmJz8yV1b1728Tgu4ssOqmS2X5jdipFtzXyOVWPcFxuRTx8MNwySWlIxHr2BFuvNHWdnvqqbCjcYkgIk+LyA4iUg0bD/atiAwOOy5X9pxwgs0mHzYM5s4NOxoXljyTMFV9JHj4f6o6LPoGvJec8Fxpd889cPzxloiNjUthk8QaPBj2398mGTRsaBMLMjJszUmXEloFQyqOxwpENwF6hxqRK7Puv99WHjnvPFuX15U9scxdy60PoxT0a7iSoHx5eOYZuzx57rnWy1SSlSsHJ55o5Sp+/tkuSy5aZEmZJ2IpoYKIVMCSsFdVdQPgF59dKHbe2daRXLPGSli4sie/MWFdReRyoG5kHFhwGwqUokVqXNgqVYKXX7bB+mPHlvzxVvfcs/22NWusqKsr9R4BFmLDLT4SkXSsALVzoTjrLPj8c/A65GVTfj1hFbGxX+XZdjzYX0DPxIfmUkm1ajB5si0CLlKyE7G8FvX2xb5Lr+CkUlT1PlXdVVWPVlUFFgPJWB/XuVyJ2BWDlSvtBLAkHxtd/OVZokJVPwQ+FJFxqrooiTG5FLXDDnb/559wyilWNbpLl3Bjyk3jxnYJMrftrtTqAzwgIvOAt4C3VPXXIBErBes7uFT39NMwYIBdouzVK+xoXLLEMiZsjYjcISJviMj7kVvCI3Mpa906WLjQqtSXxDUbhw+3khXRKle27a50UtULVLUjMBTYERgnIp+JyC0icoCI+BALF6oLL4TOnW0Sk48PKztiScImAN9js4iGYeMpvkpgTC7FNWgA//d/UKOGDdgvadOzs7JsTcn0dHuelgbNm9t2V7qp6veqereqdgMOAaYCJ2NV9J0LTbly8NhjdqVgwICwo3HJEksStpOqjgE2qOqHqno2dvByrsjS0y0RE7HK+gsXhh3RtrKyLCZVuOMOq6L/1lthR+XiRUSqAq2Br1T1Yl+31pUEbdvaGrZPPunHm7IiliRsQ3C/TESOEZEOgC/q4oqtRQt4912oVw82bQo7mrz17w9Nm8KgQbDRRw+VSiLSQ0QWish0ETkamAPcD8wSkb4xfP5xEfldRGZHbRsqIj+LyMzgdnQCfwRXRgwZAhdfDG3ahB2JS4ZYkrCbg8W7LwcGAY8B3lnq4qJtW8jOtiRHFf4qgcUCKlWytSXnzIExY8KOxhXRTcARwP+A54BDVXVvoC12XCvIOKBbLtvvVtX2we2NeAXryq5KleC++2DXXcOOxCVDvklYMFi1uaquVtXZqnqwqnZS1VeTFJ8rA0Ts/pJL4OCDYfXqcOPJzYknwn772eLkJTFRdAXarKrzVPUr4CdVXQC2ZiQxzI5U1Y+APxIco3Nb/PILHHoofPZZ2JG4RMo3CVPVTcBpSYrFlXFHH21jr445Bv79N+xotiUCI0fC77/DbbeFHY0rgjQR2VFEdgI2B49ri0htYrsikJeLROSb4HLljnGK1Tlq1IAffoBzzoH168OOxiVKLAefT0TkfhHZX0Q6Rm4Jj8yVOUcdZbVyPvvM1ptcty7siLbVubMN2B85Mvc6Yq5EqwlMA7KBHYDpwfNpWBHqongIaAq0B5YBd+X1RhHpJyLZIpK9fPnyIjbnypIaNeCRR+C77+CWW8KOxiWKaAHleUXkg1w2q6qGMkMyMzNTs7Ozw2jaJcn48XDmmXDyyfDcc2FHs63Fi2H33e3ypK8lmTwiMi3sGYwikgG8rqp7Fua1nPwY5grjjDPsODh9OuxZ4L8uVxLld/zKs2J+hKr6kh4uqfr2hbVrbbA+WOHC11+3gfvHHBPuGmuNG8PAgXZmeumlsNde4cXiYheMb62iqv8Ez/fGlmYDmKGqfxdhnw1UdVnw9ARgdn7vd64o7rkH3n4bhg6FF14IOxoXbwVejhSR+iIyRkTeDJ63EpFzEh+aK8vOP98KuT7zDGRkwGuvwZtvWlmL8ePDje2qq6ysxsCBvs5bKTICuDDq+URgMHAdcG1BHxaRicBnwO4isjQ4Bt4uIrNE5Bts/UmfNe7irk4deOMNGDcu7EhcIhTYE4ZNzR4LDAmezwOeBXyyvkuoZcugXz8bpJ+eDnfeCfPmwT77wIEHWnIWhho14Kab4H//swXJTzopnDhcoRwKdI56vkpVjxURAT4u6MOqmtsEJT8GuqToHPzLXbfOZmfXqxduPC5+YhmYX0dVnwM2A6jqRqAEl9Z0qeLFF23s1cUX22D44cNtPNYpp8Dzz4cb29ln2/iMK67wmUulRFpw7Iq4EmxwK1A9nJCci52qnXyecYb3wKeSWJKwf4Np3QpbxlKUwEpOLtX89x9UqwZ3320Hnuuvh/fft21hJz7ly8Ndd8GCBfDAA+HG4mJSUUS2zIJU1XcAgkLUlUOLyrkYidh42XffhSeeCDsaFy+xJGEDgVeBpiLyCfAEcHFCo3IO6N7deryWL4eHH7ZesNNPh6eegh49wo4OjjgCunWDG2+EFSvCjsYV4FHgWRFpHNkgIunY2LDHQovKuUI4/3wrGj1gAPz2W9jRuHgoMAlT1enAgcA+2JIfrVX1m0QH5lyLFlZFv1MnWzbowANhwwa7FNi2bdjRmTvvhL//tkTMlVyqOhI7mZwqIitFZCXwEfCaqt4ZbnTOxSYtDR591MbJXnJJ2NG4eIhldmRl4BJs7bVhQP9gm3MJd+21MGmSXZqsWdNmCd1yS8mpqN+6NZx3Hjz0EMydG3Y0Lj+q+rCqNgYygAxVTVfVh0IOy7lCadnShmYsWGAngK50i+Vy5BNAa2AUcH/w+MmCPhQs4/G7iORaO0fMfSIyP1j2w6vwu1x16mRLBY0YAV262Liw9HSYOTPsyMywYVClig3SdyWTiJwhImkAqvp3zrpgItJURPYLJzrnCueKK2xlkRpFXevBlRixJGF7quo5qvpBcDsPS8QKMg7ols/rRwHNg1s/bAkQ5wrUti1UrAi9epWMHrH69eHqq+HVV+GD3NaXcCXBTsCM4OSwv4icIiJ9RORGEfkQuB3wUTauVKhQwSYHrVjh9cNKu1iSsOnBjEgARKQLtv5avlT1I+CPfN5yHPCEms+BWiLSIIZ4XBlXp44Nzp83z6rWlwSXXWbV9C+/HDZvDjsal5Oq3gt0xAbi18XqhnUEfgZ6q+pJqvpDiCE6V2j33QdnnQUffhh2JK6oYknCOgGfishCEVmIVY3uHFUpuqh2BZZEPV8abHOuQIccYr1PY8bAs8+GHY1djrztNpgxA54s8GK9C4OqblLVd1V1qKr+T1UvU9VHVHVx2LE5VxRXXWXLu517ri315kqfWJKwbkATbIbkgcHjbkB34NjEhbaViPQTkWwRyV6+fHkymnSlwNChsPfeMG1a2JGYXr1sLclrrikZl0mdc6mtalUYPRrmz7exqa70iaVExSKgFpZwHQvUUtVFkVsx2v4ZaBT1vGGwLbcYRqtqpqpm1g1z9WZXolSoYIP0b7897EiMiFX2/+UXK+TqnHOJdsghcM45Vi5n+vSwo3GFFUuJikuBCUC94PaUiMSjWOurQJ9gluTewGpVXRaH/boypEoVu58+HUaNCjcWgH33hZ49bSbnL7+EHY1zriy4806rpr/zzmFH4gorlsuR5wBdVPV6Vb0e2Bs4r6APichEbPzY7iKyVETOEZHzReT84C1vAAuA+Vg16wuL9BM4h1XUv/TSkjE78bbbYONGuO66sCNxOYlIJRE5XUSuEZHrI7ew48ppwgRboD4tze4nTAg7IleS1apl42N32SXsSFxhxZKECdsu2L0p2JYvVT1NVRuoagVVbaiqY4JiiQ8Hr6uq9lfVpqraRlULnHHpXF7uvtsq7J9xRvhLCDVtaouOjx1bcmqZuS0mYTOzNwL/Rt1KjAkToF8/WLTIFmpetMieeyLmCrJokS2l9oPP8y01ysfwnrHAFyLycvD8eGBMwiJyrgiqVYOJE22g/tlnW5V9KfBUIXGuvdbq91x+Ofzf/4Ubi9tGQ1XNr35h6IYMgTVrtt22Zo0l9pGVI3K7VapUvHYnTLC2Fy+2civDh0NWVvH26ZKrYkX44gubLXnFFbB6NRx0kPeQlWQFJmGqOlJEpgCRatJnqeqMhEblXBF06GCD9C+7zMpW9OoVXiy1atnszYsvhsmTbTFyVyJ8KiJtVHVW2IHkZXEeBTP+/NNOMPJSuXLeCVpet1q17P7dd+2EIVLmINL7Bp6IlSYNGtiwjGHDYOlSOyb27w8DB/rwiJJKVDXsGAolMzNTs7P9yqXLnarV6Tr9dKsoHaYNG6BNG3s8a5bN5nRFIyLTVDWzGJ+fBSh24tkcG4+6HhtaoaqatCXhCzqGZWRYEpRTw4bw8cewapX1cBR0y/m+opRNqVnTZvpmZNhSYY0aFb/HzSXO5s02LKNSJUvCvv3WjoP77mvr2x5+eNgRlk35Hb9C/m/KufgSgT597PGKFTZ7slq1cGKpUAHuuAN69LBaPv37hxOHA6yuYakwfLj1QkVfkqxa1SZ8ZGQUfb8bN8Jff+WesEX+ZnJavdoubUWI2Ay8SFIWfYtsK8zfm18Cja/PP7dj3ssv2/JuN94IjzxiPWFPPulJWEmUZxImIpVUdX0yg3EuXv7+G9q3h6OOgkcfDS+O7t3h4IPhhhvsP5datcKLpSyL1DQUkSdVtXf0ayLyJNA71w+GIJKExDs5KV8eate2W07XXZd771vjxjBlir22cKHdR25ffQUvvmg9vtF22mnbpCxnklarliVzkQkIkWTTL4EW35o19vtt1gxef92KR4Nt8wLSJVN+PWGfAR1zO2g5V9LVqGFn97feamd/p5wSThwidjmnUye45ZaSU1i2DGsd/UREymFLs5UoWVnJTUTy6n275RZo0sRuudm8GZYt2zY5iyRr330Hb721/SSDGjUsGfvxx+2X2lmzxpJPT8KKpmtXuwQ5e7YVcQX7nT78MJx5ZqihuTzkl4RVFJHTgX1E5MScL6rqS4kLy7niGzbMKur362dnhMW5lFMcHTpYIcV774ULLsj7PzSXOCJyNXANUEVE/opsBv4DRocWWAlR1N63tDTYdVe77bPP9q+rwsqV2/eiLVpkiUJuFi2y0i5t29r+XeyqVbNFvQ89FM47D+rUsWXU6tWz8j2u5MlzYL6I7AdkAadg1e2jqarmM08ncXxgviuMn36yy5KtW8NHH4U3WP/nn23AbPfuJWPB8dKmuAPzo/Zzq6peHY+YisqPYSavCQgRdepYMnHooXDYYX7yUhhz5sD48Taj9qef7JLy9OmW2Lrky+/4VeDsSBE5R1VLTF0wP4C5wpo40cafTJhgs73CMmyYla345JPcew1c3uIwO7Jjfq+ratJW3fNjmMk5JgzsEuiIEfZ3+n//Z7fI8l+77bY1ITvkEEvSXMH++AN23x2aN4epU713MQzFTcIqAucDBwSbPgQeVtUNeX8qcfwA5opCNfyCqf/+a71hjRrBZ5+FH09pEockLLKgVWUgE/gauxzZFshW1a7FjzI2fgzbqqDZkaowd+7WhOyDD2yGJ9hl/khStv/+lsC53I0fb2PCHn1029muLjmKm4Q9BlQAxgebegObVDWUr9IPYK6oFi2CQYOsXk5YZ9HjxsFZZ1nvXJjFZEubOF6OfAm4IVKsVUT2BIaqas/i7jtWfgwruo0bYdq0rUnZp5/aKgIVK1rvciQpy8wMv05gSaJqs7QrV4Y33/QTwGQrbhL2taq2K2hbsvgBzBXVjBm2rFG3bvDKK+EciDZvtpmSf/4J339vB0VXsDgmYXNUNecMye22JZIfw+JnzRq7xBZJymbOtIRjhx1suZ7DDrNby5Zb/97Lam2yFStgxx2hXLmwIyl7ilusdZOINFXVH4Od7ca2C3o7VypEL2v04IPhFE9NS7OSFYcearMlr7wy+TGUcd8EvftPBc+zgG9CjMcVQ9WqcMQRdgNLND74YGtS9mowpWyXXexvrmpVuzS3bp1tL0u1ySK9/ytWwG+/2WQlF75YesIOxRbxXoCNoUjH1o/8IN8PJoifRbriUIVjj7UD9JdfhjdbqEcPm7E0f75NH3f5i2NPWGXgAraOcf0IeEhV1xV337HyY1jy/PQTvPee/b2/954lILlJT7cyGqlO1cr1/Puv9RpWrBh2RGVDsS5HBjuoBOwePJ0bZiV9P4C54lq+3JKvzp23nikn29y5sOeeVsvnwQfDiaE0iVcSVhL4MSwcmzfnfymulC2jXGSTJ1upnFtvhauuCjuasiG/41dMk1VVdb2qfhPcfCkjV6rVrWuVvCdMCC+G3Xe3wq2PPGIVrl1iichzwf0sEfkm5y3s+FzipaVZj1duypWzv8X//ktuTGE45hg44QRbV7Is9P6VdF4xxJVJ7drZ8ilr10JYnRLXX28xDB4cTvtlzKXBfXfg2FxurgwYPnz7UhaVKlkh2PPPt1pao0enfjJ2772WlF58cdnpASyp8k3CxDRKVjDOJdv559vakvlV7k6UOnVs4eQ33oB3301++2WJqi4LHh4GVFTVRdG3MGNzyZOVZUlWerrNlkxPhzFjYN486x1v0AD+9z+r5/fYY9svTp4qGjWy4tGVKsF6v7YVqlgG5s9S1TZJiqdAPp7CxdOCBbasUdu2NlA+2bWF1q+HVq1szbcZM3z6eF7iODB/GLA/kAFMwwbmf6yqM4u771j5MazkUoW334YbbrCJOxkZcO210KcPVKgQdnTxVRIKWJcVxR0TNl1EOsc5JudKhN12s7Egn3xiYySSrVIlW6Zl1iwYOzb57Zc1qnqDqh4CtAY+BgZjyZhziFgdwc8/twHsdepYhfndd4fHH0+tnrFIAjZ3rvX6uXDEkoR1AT4TkR+DQayzfCCrSyWnnWZLetx8s/WGJdtJJ8G++1o1/8aNbaxGRka4EwdSlYhcKyJvAu8AzYBBQMNwo3IljQgcfbT1hr3+Ouy0E5xzjhV9HTvWKveninvusWEZ3/j/6qGIJQk7EmgKHIINYI0MbHUuZYwaBSefDLvumvy2RWxc2urVsGSJXSaIFJH0RCzuTgR2Av4PeAmYFDVezLltiNhswi+/hNdeg1q14OyzLRkbPz41krHhw62S/vnnWxkPl1wFJmHBoNVGwCHB4zWxfM650qR6dXj2WZsdpZr8GUO5XYpcs8aWV3Hxo6odscH5XwKHA7NEZGq4UbmSTsRqa2Vnw6RJtizSmWfCHnvAE0+U7mSsdm2480747DObpOCSq8BkSkRuAK4Erg42VWDrkh/OpZS1a+HEE+126KFw/PHw8suJT8oWLy7cdlc0wYLdWUBf4FTgZ+D9UINypYaIrXYxbZqtP1u9OvTta5Nrnnyy9CZjffrAAQfYMmrLl4cdTdkSS4/WCUAP4F8AVf0FqJHIoJwLy+bN8P77Vkn/pJPsNmSIzZZKpMaNC7fdFdltwA7AfcAeqnqwql4fckyulBGB446D6dPtJK1qVUtkWreGp56CTaVsdWUReOghu9RapUrY0ZQtsSRh/6nVsVAAEakW685FpJuIzBWR+SKy3QIJItJYRD4QkRnBoP+jYw/dufh7+mnIzLSq+g88YEnYhx/amLFlCRw5lFsRySpVbLuLH1XtrqojVPVTVU2huW4uDCLWWz59Orz4IlSuDL17WzL29NOWjE2YYBNtSvqEm1at7LJk9ephR1K2xJKEPScijwC1ROQ8bEDrowV9SETKAQ8ARwGtgNNEpFWOt10LPKeqHYBegK+i50L14Yd2EH3ySVtO6PLLLSHbf3/49NPEtRtdRDLi9NNtuyu+vJYr8mWLXDykpdkQhhkz4IUXrKZYVpb1ZJ99tk20KS0TbqZOtcQy1VcNKCliGZh/J/AC8CLQArheVUfFsO+9gPmqukBV/wOeAY7LuXvs0gBATeCXWAN3LhHq1bP11A4/3EpGvPCCjZFYuNBeS6SsLGtn82bYbz+7JLpqVWLbLEMis7rfCm5Zwe2N4OZcsaWlWe/511/D88/bsSNnMlPSJ9ysXm2TD0aODDuSsiHWWY6zsMKGHwWPY7ErsCTq+dJgW7ShwBkishQ7EF4c476dS4izz7axEdnZcNNNMHOmjfHYvNlqeSWDCNx3H6xYEU4B2VQUtTzR4ap6harOCm5XAUeEHZ9LLWlp0LNn3sVdS/KEm+gFvn/6KexoUl8ssyPPxaZznwj0BD4XkbPj1P5pwDhVbQgcDTwpItvFJCL9RCRbRLKX+9QNl0B77mljwbp3hy5doGtXmDgR+vdP7hIfHTpYpe5Ro+C775LXbhkgIrJv1JN98JI7LkGihxdE22WX5MZRWL7Ad/LEcvAZDHRQ1TNVtS/QCStZUZCfsfpiEQ2DbdHOAZ4DUNXPgMpAnZw7UtXRqpqpqpl169aNoWnniq5nTztTffRReOcduOgiuPBC6xFLpuHDbU3JAQP8QBhH5wAPishCEVmEjUON10mlc9vIbcINwN9/w8cfJz+eWDVqZD1hkyfDu++GHU1qiyUJWwn8HfX872BbQb4CmotIExGpiA28fzXHexYDhwKIyB5YEuZdXS50FSvCXntZZeysLBujddFFyb2MULcuDB1qCwpPnpy8dlOZqk5T1XZAO6CtqrZX1elhx+VSU/SEGxG7HzECdt4ZDjkEHnyw5J5gXXKJFaI99NCwI0ltonn8CxCRgcHD9kAbYBI2kP444BtVPbPAnVvJiXuAcsDjqjpcRG4EslX11WC25KNA9WDfV6jqO/ntMzMzU7Ozswv+yZyLo59+grZtrXzFe+9ZV30ybNgA7drZ/ezZtuB3WSQi01Q1Mw77qQScBGQA5SPbVTVpo+/8GOZWr7YEbfJkW5PygQdK9t/2+vUlO76SLr/jV37/ldQIbj8CrxDUCcOSsZiG66nqG6raQlWbqurwYNv1qvpq8PhbVd1XVdsFZ6T5JmDOhaVJExsnMWWKLXibLBUqWHvz51v7rtgmYSeSG7EC1JGbc0lTs6bNfr72Wlsq6MAD4eecg3VKiKlTrQfPF/hOjPJ5vaCqw5IZiHMl3Vln2WzJLl2S2+4RR9hSKTfdZDXMGjRIbvsppqGqdgs7COfS0uxvukMHW/ooM9NK4iRrFnasWrWyorPnn28JWbKuApQVscyOzBSRl0Vkuhc3dGVZpHRE5CCZzLEcd91l9Yauvrrg97p8fSoibcIOwrmIE0+Ezz+3SvUHHwyPPBJ2RNuqXduOP77Ad2LEktNOAMZi4yiOjbo5VyZt3mwzFpNZcLFZM2tz/Hj44ovktZuC9gOmBcupfROppB92UK5sa90avvwSDjvMepz+9z8bh1VS9O5tl0yvvBJ+/z3saFJLLEnYclV9VVV/ihQ8DIoeOlcmpaXZFPPbbrPu+WQZMsQuRV5yiSWCrkiOAppjBVqPZWsl/XyJyOMi8ruIzI7aVltE3hWRH4L7HRMWtUt5O+4Ir71mvd2jR9vsyUSuV1sYkQW+//kHnnsu7GhSSyxJ2A0i8piInCYiJ0ZuCY/MuRLs7rttMd4+fSwhS4YaNSzx+/JLW9vSFV7USeRabLJR5FaQcUDOsWRXAe+panPgveC5c0VWrhzccoslOjNn2jixzz8POyqzxx4wZ46V6nHxE0sSdhZWpqIbWy9Fdk9gTM6VeDVqWA2dhQvtMmGynHGGTQy46qrkJX+pRER6iMgP2AzvD4GFwJsFfU5VPwL+yLH5OGB88Hg8cHzcAnVl2sknW/JVqZJdBiwpY7GaN7f7H37wBb7jJZYkrHNQrb6vqp4V3LzCtCvz9tvPxkg8/XTyirimpdnkgF9/tWrcrtBuAvYG5qlqE6xYdFH7GuqrauSC0a9A/TjE5xwAbdrYGrYHHmhLmPXvXzISnx9/tOXd7ror7EhSQyxJ2KdBUVXnXA7Dhtllg8aNk9fmXnvBmWfaJdH585PXborYoKorgTQRSVPVD4BiF4FVq3qd52VNX//WFUXt2vDGGzB4sFXXP/RQ+O23cGNq2tTW1r3pJl/gOx5iScL2Bmb6bCLntlexIrRoYeUqPvggeWUrbrnF2h44sOD3um2sEpHqwEfABBG5l6IXa/1NRBoABPd5zhvz9W9dUZUvD7ffDhMnwrRp0KkTfPVVuDHde6+NX7voopK77FJpEUsS1o0izCZyrix5/XWbzTR2bHLaa9AArrvOZlO9/XZy2kwRxwFrgAHAW9iKIEU9nr0K9A0e98Wq8TuXEL16waef2ioa++8P48aFF0vDhrbA9xtvwMsvhxdHKoglCdM8bs65wDHHwEEHwaWXJq+L/tJLrX7YZZfZ2pKuYKr6r6puVtWNwGRgVHB5Ml8iMhH4DNhdRJaKyDnAbcDhwUD/w4LnziVM+/bWC7bvvraCxyWXhPe3f/HF1is3b1447aeKWJKwycDrwf17wAJimE3kXFmSlmZnpmlpVrZi06bEt1mpko0L+/57uP/+xLdXmonI3iIyRUReEpEOQb2v2dglxQKXMVLV01S1gapWUNWGqjpGVVeq6qGq2lxVD1PVnLMnnYu7OnWs93vgQBg1Cg4/PJwCquXLWxX9q7wwS7EUmISpahtVbRvcNwf2ws4InXNR0tPtoDh1Ktx5Z3LaPOYY6NYNhg71StYFuB+4BZgIvA+cq6o7AwcAt4YZmHOFVb68zU586ilbQSMz08aLJVuFCnb/wQcwa1by208FhV6KU1WnA0lewti50qF3bzjvvK31dBJNxHrD1qxJ7jJKpVB5VX1HVZ8HflXVzwFU9fuQ43KuyLKy4JNP7Diw336WlCXbmjU2Xu2MM+CGG+D662HGjOTHUVrFsoD3wKjbIBF5GvglCbE5V+qI2JIjJyZxTYmWLW1syJgxMH168totZaIXelqb4zUf4+pKrY4drZ7Y3nvbSeDAgbBxY/Lar1rVSmd8840VmN240UpYDB2avBhKs1h6wmpE3SphY8OOS2RQzqWCkSOT1zt13XU2VuSSS3zKeB7aichfIvI30DZ4HHneJuzgnCuOunXhnXdsss7dd0O7dtCokY1RzciACRMS1/aCBTZGrWtXW1LtssusduLo0X6JMhblC3qDqg5LRiDOpZoffoBHHrGBswcdlNi2atWy2mHnnQfPPAOnnZbY9kobVS0XdgzOJVKFCnDPPbBunR13IhYtgn797HFWVvzbfe016NnTlm9r29buJ0yA00+HV16xyv8ub7FcjmwhIqNF5B0ReT9yS0ZwzpVmd95p1aX79oXVqxPf3lln2aWJwYPh36KWH3XOlWpvvbX9tkSOGS1f3pZTatnSaoftt5/1xq9fv3XgvstbLJcjnwdmANcCg6Nuzrl8VKsGTz4JS5faZYJEK1fO1pX8+We4zStWOVcm5bWObaLWtz3hBJg0Cb77zspVXHCB1UqcONEWInf5iyUJ26iqD6nql6o6LXJLeGTOpYC997Yz0CeegDlzEt/evvvaZYA77vB13Zwri/Jax7ZevcS0t8sudvK3zz52ubNvX7ss2aCBle1x+YslCXtNRC4UkQYiUjtyS3hkzqWI666zWUOtWyenvREjrFds0KDktOecKzmGD7cZi9FEbEjEp58mps0zzrCi0QceaAP0b7sNZs+2caouf7EkYX2xy4+fAtOCW3Yig3IulVSoAHvtZY9nzEj87MWGDeGaa+Cll+B9H73pXJmSlWUzE9PTLflKT7eeqsaNrbDzZwkqtV6/vk0AOP98W9g7K8vGiCWqvVQhWsrms2dmZmp2tueArvSZOhUOOAAefnjrbKVEWbsWWrWC6tUt8Stf4Dzokk1EpqlqZthxxIMfw1wYfvnFZmn/+quVs9h778S2t3o1dOhgj2fOhB12SGx7JVl+x688e8JEZL8CdrqDiOxZ3OCcKyv22QcOO8ymcM+fn9i2qlSxZU1mz952urpzrmzaZRdbXqh+fTjySFvuKJFq1rRSFYsXwwsvJLat0iy/y5EnicinInK9iBwjInuJyAEicraIPIkt6l0lSXE6V+qlpcHYsbbwdu/eia9qfcIJcMghNiZt5crEtuWcK/l23dUSsbp14YgjEp+Ide1qE5LOPjux7ZRmeSZhqjoA6A4sA04GbgIGAs2BR1T1AFX9KilROpcidt0VHnzQBuonuoyECNx7L/z1l63n5pxzDRvClClbE7Evv0xse7vvbvczZ/qM7dzkOzBfVf9Q1UdV9UxVPVJVj1fVq1V1aiw7F5FuIjJXROaLyFV5vOcUEflWROYE61I6l9J69bIzwx13THxbe+5pdXseftjWdnPOuYYNrUesTh1LxL5KcHfK2rV2CTQrK7nrWpYGscyOLBIRKQc8ABwFtAJOE5FWOd7THLga2FdVWwOXJSoe50qSMWOgf//ktDVsmCV8l17q60o650yjRpaI1a5tS6slcq5IlSrWK//ZZ3DTTYlrpzRKWBIG7AXMV9UFqvof8AzbL/x9HvCAqv4JoKq/JzAe50qcZ59N/KXC2rXtwDdlCrz4YmLbcs6VHo0b23EhkohNS2AZ9l69rJDrzTfbTHFnEpmE7QosiXq+NNgWrQXQQkQ+EZHPRaRbbjsSkX4iki0i2cuXL09QuM4l3yefWIL0f/+X2HbOO88W0h00yC4NOOccWCL2wQdQq5bN3k5kIjZqFDRpYsVdV61KXDulSSwLeFcVketE5NHgeXMR6R6n9stjA/0PAk4DHhWRWjnfpKqjVTVTVTPr1q0bp6adC99tt9nCt2edZWtMZmfb2o/xVr68FWxctMiWNHLOuYj0dOsRq1XLesSmT09MOzVqwNNP29Jq1aolpo3SJpaesLHAeqBr8Pxn4OYYPvcz0CjqecNgW7SlwKuqukFVfwLmYUmZc2VC1aq2yPcvv0DTptZj1batLXz711/xbeugg6BnT0v8liwp8O3OuTIkPd16xHbYwXrEZsxITDt77WXLGVWoAJs3J6aN0iSWJKypqt4ObABQ1TWAxPC5r4DmItJERCoCvYBXc7znFawXDBGpg12eXBBT5M6liEWLbOD8f/9Z+YqlS+1AeMEF8W/rzjttcP4VV8R/38650i0jw3rEatSwRGzmzMS19dVXNns70YWrS7pYkrD/RKQKoAAi0hTrGcuXqm4ELgLeBr4DnlPVOSJyo4j0CN72NrBSRL4FPgAGq6qXlXRlysMPwwMPwJtvWnHDKlXgnntg8uT4F1lNT7cE7Jln4OOP47tv51zpF0nEqleHQw9NXCJWvz4sW2ZlKzZsSEwbpUEsSdgNwFtAIxGZALwHxHQerapvqGoLVW2qqsODbder6qvBY1XVgaraSlXbqOozRfw5nCu1fvsNmje3xXUBZs2CihWtd+yPP+Lf3pVX2vT0Sy6BTZviv3/nXOnWpIldmqxWzRKxr7+OfxuNG9uSal9+aWV0yqoCkzBVfRc4ETgTmAhkquqUxIblXNmx//7w/PP2+OefoUsXOPVUuzzZpEn826ta1Qbnz5xp9cqccy6n3XazHrGqVROXiJ1yik1KuuUW+PDD+O+/NIhlduQJwEZVnayqrwMbReT4hEfmXBlx5ZU2OP/yy218xIEHwqRJVsm6fPnEtHnKKbacyAUX2PJGGRm22K5zzkVEErEqVSwRS8SqG/fdB82awRNPxH/fpUFMlyNVdXXkiaquwi5ROufioHFjW0tSBIYMsUH5XbvCU0/BRx8lps2nn7YJAZHZSYsWQb9+nog557bVtKldmqxc2RKxWbPiu//q1a0X7NFH47vf0iKWJCy39yTo/Ny5sqlhQ5u5OHWqVdF/8007+PXsaeUr4m3IEFi3bttta9bYdueci9asmfWIVaoEhxwCs2fHd/8NGkBampXOefPN+O67pIslCcsWkZEi0jS4jQQSWFPXOVezpl2SPO88qFcv/vtfvLhw251zZVuzZtYjVrFiYhIxsPVtTz4Zfvgh/vsuqWJJwi4G/gOeDW7rgSQtPexc2bX77jB8uI0L++OP+C6+3bhx7tsbNcp9u3PONW9uiVj58paIzZkT3/3fd58leaefbhOTyoJYZkf+q6pXRZYNUtWrVfXfZATnnLPirW3awMiR8dvn8OE26ymnLl3i14ZzLvW0aGGXJiOJ2Lffxm/fDRva2LDsbLihjIw8j2V2ZAsRGS0i74jI+5FbMoJzzsGuu9pA/SuugHfeic8+s7Jg9Ggr3ipiPWNdu8KLLyZ+MXHnXOnWooX1iJUrZ4nYd9/Fb98nnQTnngsjRpSNshWxDLB/HngYeAzw0o7OJZkIjBsH8+ZBr1623EfTpsXfb1aW3SL++cd6wk47zdaNa9iw+G0451LT7rvD++/DwQfb7YMPYI894rPve+6B2rWhQ4f47K8ki2VM2EZVfUhVv1TVaZFbwiNzzm1RvTq88oolZMcfbwlTItp48UWbNXnyyWVnTIZzrmhatrTkC6wnfdddbZZjcesOVqtmPWE77GDHoXiOhy1pYknCXhORC0WkgYjUjtwSHplzbhu77WblK9LTYePGxLTRsiU8/rjVLRs0KDFtOOdSR8uWMHAgrF5t5XRU41d38PffITMztVf2iCUJ6wsMBj7FSlNMA7ITGZRzLneHHQavvQa1am0ttBpvJ58MAwbAqFEwcWJi2nDOpY4HH9x+WzzqDtapYyV6Lr0U5s4t3r5KqlhmRzbJ5bZbMoJzzm1PxM4Q99vPErJEGDEC9t3XBsjGexq6cy61JKruYFoajB9vyyalatmKWHrCEJE9ReQUEekTuSU6MOdc3mrUsANSVhZ8/33891+hAjz3nLVz4onw11/xb8M5lxryqjtYv37x973rrnY5cvp0uPba4u+vpImlRMUNwKjgdjBwO9AjwXE55/JRpQq8/LLdH3ccrFoV/zZ22cXGoP34I5xzTmoPjnXOFV1udQdFbJzYZ58Vf//HHQf/+x+89x6sX1/8/ZUksfSE9QQOBX5V1bOAdkDNhEblnCtQo0bwwguwYIH1iG1KQAGZAw+EW2+1du65J/77d86VfjnrDqan2/GiYUM4/PD41Pu6+2749FNbvzKVxJKErVXVzcBGEdkB+B3wxU2cKwH239+W+vjhBxsnlgiDBllZjMGD4eOPE9OGc650y8qChQttwtDChXDJJZZ8NW4MRx0F775bvP1XqWIJ2OrVluClSs98rAt41wIexWZGTgfi0MHonIuH88+HmTOhQYPE7D9SLLZJEzj1VPj118S045xLLQ0a2BJHzZvDscfC5MnF3+fEiTZ7e/To4u+rJIhlduSFqrpKVR8GDgf6BpclnXMlgIiNx1i3zhKyb76Jfxs1a1oh11WrrGp/ouqUOedSS716Vll/zz3hhBNsLGtx9OsHRxxhiVh2ti3l9tlniSvZk2ixzo5sKyI9gI5AMxE5MbFhOecKa/VqK1lx/PGwcmX899+2LTzyiF1iuOaa+O/fOZeadtrJ1qTNzLQ6hM8+W/R9paVZz3y5crD33nDTTXDeedCqVekspxPL7MjHgceBk4Bjg1v3BMflnCuk+vXtLPPnn+2yYSJ6q3r3hgsugDvugJdeiv/+nXOpqVYtePttqz94+unwxBNF39dvv0H58jYZ6YADYNYsuOoq6NEjMROUEimWnrC9VTVTVfuq6lnB7eyER+acK7S99rLeqvfegyuuSEwbd98NnTvDmWfaouLOOReLGjXgzTfhkEPs+PHoo0Xbz9ixtlTSAw/AhRfakIwzz7RFv6dMiWPASRBLEvaZiLRKeCTOubg480ybmTRmjK3lFm+VKlnJiooV4aST4N9/49+Gcy41Va1qwyaOOsrGd91/f+H3sWoV7LyzJWC77gobNmydnPTnn/GOOLFiScKewBKxuSLyjYjMEpEEDP11zsXLnXfCjBlWcDURGje2xXnnzLHJAKkyXdw5l3iVK9twhuOPh4svtuNVYRx2mB1/IoPxhwyBrl1tAsABB8Q93ISKJQkbA/QGurF1PNixiQzKOVc8FSrAbrtZcvTQQzaGIt6OPBKGDoWnnrJLoM45F6tKlWxptFNOsRqEN98c+2dPPdUG6B95pJWs2HFHGwv23382Pqw0iSUJW66qr6rqT6q6KHKLZeci0i3oQZsvIlfl876TRERFJDPmyJ1zBVq0CC6/HHr2TMzit9dea5cVLr0Uvvwy/vt3zqWuChWsR6t3b7juOjuexNKrXrGijS3r1cuGRnz/vc24bNkSune3mZilhWgBP7GIPAjUAl4DtqzapKr5zo0SkXLAPKy22FLgK+A0Vf02x/tqAJOBisBFqpqd334zMzM1Ozvftzjnojz7rB2szj/fesXi7Y8/oGNHuzQwfTrUqRP/NkRkmqqmxEmaH8Oc29bmzbY25GOP2Qodt99ug+0La/lyOPRQWz1kwYLt17MMS37Hr/IxfL4KlnwdEbVNgYImqO8FzFfVBUEQzwDHAd/meN9NwAhgcAyxOOcK6dRTbXzYiBHQoYMNho2n2rWtkOs++9jSJW+8YTV8nHMuFmlpNqShcmUbH7ZuHdx7r20vjLp1bVzYwoUlJwErSL5JWNCbtVJVBxVh37sCS6KeLwW65Nh/R6CRqk4WEU/CnEuQ4cPh66/tsuGxx8Z/iaNOnWyWU79+cOONMGxYfPdfkonIQuBvYBOwMVV67JxLprQ0Wwe3UiW46y5Yvx4efrjwiVidOlt74+++24q4Hnlk/OONl3yTMFXdJCL7JqJhEUkDRgJnxvDefkA/gMaNGyciHOdSWrly8PTTMG1a4taYPPdc+PRTS8K6dIGjj05MOyXUwaq6IuwgnCvNRKwQdOXKduK4bh08/rgVZi2s9eutIOx338Err0C3bnEPNy5iyTFnisirItJbRE6M3GL43M9Ao6jnDYNtETWAPYEpwZnk3sCruQ3OV9XRQcHYzLp168bQtHMupx13tKndAFOn2gEunkSseGK7dnDGGXZJwDnnCkPEZkredBM8+aQdSzZsKPx+KlWyAfqtWlkpjDffjHuocRFLElYZWAkcQuGWLfoKaC4iTUSkItALeDXyoqquVtU6qpqhqhnA50CPggbmO+eKZ8ECOOggGwj73HN2sHvppaId6HKqWtVmK23ebDMy453olVAKvCMi04Jee+dcMV17rfWKPfuslbFYv77gz+QUWbOydWtLxN54I+5hFluBSVjUUkVnFWbZIlXdCFwEvA18BzynqnNE5MZgMXDnXAh2283Ghj3xhBU5XLfOxk506mSzi4qrWTPb97RpVrm/DNhPVTsCRwH9RWS7cpEi0k9EskUke3k8fsnOlQGDBsGoUXY58cQTi3ZSV7s2vPsutG0LS5YU/P5ki2UB74Yi8rKI/B7cXhSRhrHsXFXfUNUWqtpUVYcH265X1Vdzee9B3gvmXHL8+is0bw4//WSXKD/6yNZzu/rq+Oy/Rw/b16OP2jpvqUxVfw7ufwdexmaG53yPD6lwrgguushmTr75pk0qKsoyabVr23jV//3Pnq9eHd8YiyOWy5FjscuIuwS314JtzrlSSNUuP77zDrRoASefbGuxXXmlXUqMlxtvtMTuwgttXbdUJCLVglqHiEg1rJTP7HCjci619OtnJ3Pvv28Tfv7+u/D7qFDB7j//HDIybP3KkiCWJKyuqo5V1Y3BbRzgp3LOlXI77ACTJsEVV9ig/XgrX96WFKld2xb6XrUq/m2UAPWBqSLyNfAlMFlV3wo5JudSTt++NsP7k0/giCOKfjxp2dKGTJx0Ery63TW55IslCVspImeISLngdgY2UN85VwqJ2AHo9tvtkuQVV1jv2MCBVsw1nurVg+efh8WLoU+frQvupgpVXaCq7YJb68iwC+dc/J16qh1Ppk2zYRSPPGK9Wmlpdj9hQsH7qFXLxoi1b2+ThyZNSmzMBYklCTsbOAX4FVgG9ATOSmRQzrnEuuMOO/gcfLANzj/gAJs5NGWKvRbL+m2x2mcfK7742muW+DnnXFGdcAK8/DJ88w1ccIGtj6tq9/36FS4R69DBhmN89VXCw85TnkmYiIwIHu6lqj1Uta6q1lPV41V1cZLic84lQIMGVkH/wgutMOKAAbB0qU0Fv+IKm9W4aVP82rv4YjuLveYaqF+/cGeuzjkX7ZhjbAhFzpPFNWvspDIWNWvauNjrr7e1b8OSXx3ao0XkKuBq4PkkxeOcS5KKFe0sMNrEidCokfVcLV1qzytXLn5bInD44VaX7PffbVvkzBVszUnnnItVXpVeFheii6hmTatHBna8+/prS/CSKb/LkW8BfwJtReQvEfk7+j5J8TnnkigtzRbQveceG79VlOVC8nLTTcU7c3XOuYi8VjBs1Cj37QW56ior6Prii0UOqUjyTMJUdbCq1sJm++ygqjWi75MXonMu2S691MZdlC9vNcUWLCj+PvM6Qy3MmatzzoGtLVm16vbbK1Qo2vHqwQdhr71s2EQ8S/UUJN+B+SJSDvCEy7kyKC04OvTpA127Fn/wal5nrnltd865vGRlwejRkJ5uwx3S020s68qVNvNx4sTC7W+HHeCtt2DvvaFXL5uFmQz5JmGqugnYLCI1kxOOc66kue8+O+M86CCYPLno+8ntzLVqVdvunHOFlZUFCxfa0ImFC+Hee60wdNu2cPrpcNZZ8M8/se+vRg2rzN+1K4wYEd/JSXmJpUTFP8AsERkjIvdFbokOzDlXMrRsCZ99Zvc9etjZZ1HkduY6erQPynfOxU96upXaue46GD/e1sSdMSP2z9eoYeV63n4bypWLb7me3MSShL0EXAd8BEyLujnnyoidd4YPP7RK1ffcU7SFdGH7M1dPwJxz8Va+vC2b9v77ttbk3nvbcSvWhKpGDdhpJ1i/3hYOL+ylzULFWtAbVHW8iFQBGqvq3MSF4pwryapXt2U+Vq60shXr11uPVsWKYUfmnHPbO+ggKztxzjlWC/Hdd2HcOKgb48KLGzfCH3/AGWdYAnf66fGPscCeMBE5FpiJlaxARNqLSAlYcck5l2wVKlivmCqceSZ07w5/ecEa51wJtdNONtP7/vvhvfdsvNh778X22WrV7NLk/vtD7962iPiYMXDeeTB0qNU6LK5YLkcOBfYCVgGo6kxgt+I37ZwrrSLFV99/35Y8+uWXsCNyzrnciUD//vDll1Zp//DDbfWODRsK/my1ajYhaZ994OyzbaJSp062gHhmpo0/K45YkrANqro6x7YUW4bXOVdYZ59tB6cff7QxF3PmhB2Rc87lrW1bK7Vz7rlw663Ww/XTTwV/rlo12HdfK6czZgycf76NMRs3zh4XZ/B+LEnYHBE5HSgnIs1FZBTwadGbdM6liiOPtAH7GzbAscfGdmbpnHNhqVbNZmU/9xx8/73VFHvmmYI/9+67NkA/M3PrtqOPtoH/P/5Y9HhiScIuBloD64GngdXAZUVv0jmXSjp2tBIWTz1lY8acc66kO/lkqynWujWcdpoN3v/337zfX7UqrM5xTXDDBlt6rUqVoseRZxImIpVF5DLgdmAx0FVVO6vqtapaxAnqzrlUlJFhYybAihzedVfi6+s451xxZGTARx/ZIt5jx9pYr5kzc39v7962/m2k+KuqHefat4dddy16DPn1hI0HMoFZwFHAnUVvxjlXFmzeDNOmwaBBNiU8GRWnnXOuqMqXt+Tqvffg77+hSxcbfJ/zJPLcc6FNG9htN+s569gRnn7akrfiyC8Ja6WqZ6jqI0BP4IDiNeWcS3VpaTa+YsAAW0Lk1FNh7dqwo0o9EybYWXxamt1PmFD8fVavXh2ARYsW0bFjR9q3b0/r1q15+OGHAVizZg3HHHMMLVu2pHXr1lx11VX57m/o0KHsuuuutG/fnlatWjExR8XLV155BRHh+++/37Jt4cKFiAijRo3asu2iiy5i3LhxAJx55pk0adKEdu3a0aJFC/r06cPSpUu3vHf16tX06dOHZs2a0bRpU/r06cPq4BpSZN/XXnvtlvevWLGCChUqcNFFFxXhN1Y8EyfCnntaVfY994xPQdDIdwiwePFijjjiCPbYYw9atWrFwoULt3nvJZdcss37c1OWvsODD7aaYkceCZdeaquDrFix9fW0NHjkEZg61caCjRxp7y/u2rf5JWFbhtiq6sbiNeOcKyvS0uwANXIkvPQSdOvmPWLxNGEC9OtnNYpU7b5fv/gkYgANGjTgs88+Y+bMmXzxxRfcdttt/BLUIBk0aBDff/89M2bM4JNPPuHNN9/Md18DBgxg5syZTJo0if/9739siJq5MXHiRPbbb7/t/mOvV68e9957L//991+u+7zjjjv4+uuvmTt3Lh06dOCQQw7Z8t5zzjmH3Xbbjfnz5/Pjjz/SpEkTzj333C2fbdKkCZOjFkB9/vnnad26deF+QXEwcSIMGQKjRtnqE6NG2fN4Vmbv06cPgwcP5rvvvuPLL7+kXr16W17Lzs7mzz//jGk/Zek7rFMHJk2ynrB33rHZlO+/v+17WrSwS5MHH2zHuuLKr2J+OxGJlGEUoErwXABV1R2K37xzLlUNGACNGlnF6XLltm7ftMmq7edczNuZyy7Le1wKwOef2+8v2po1NrD40Udz/0z79jalPhYVo5ZAWL9+PZs3W0WiqlWrcvDBB295T8eOHbfpwchP8+bNqVq1Kn/++Sf16tXjn3/+YerUqXzwwQcce+yxDBs2bMt769aty7777sv48eM577zz8tyniDBgwABefvll3nzzTVq3bs20adN49tlnt7zn+uuvp1mzZvz444+UK1eOqlWrsscee5CdnU1mZibPPvssp5xyypYkM54OOmj7baecAhdeaJe/atSAqB+bGjVg8GC71LViBfTsue1nC1OP6ttvv2Xjxo0cfvjhwLY9ZJs2bWLw4ME8/fTTvPzyyzHvs6x8hyJw8cVW/7BXLzjsMLj6aivOmoiJR3nmcapaTlV3CG41VLV81GNPwJxzBerZ03ppAF5/Hc46C+rVg9q1oUMHWyTXFU7OBKyg7UWxZMkS2rZtS6NGjbjyyivZZZddtnl91apVvPbaaxx66KEx7W/69Ok0b958S2/MpEmT6NatGy1atGCnnXZi2rRtlyO+8sorufPOO9kUQxdqx44d+f777/n2229p37495aIy/nLlytG+fXvmRBWx69WrF8888wxLliyhXLly2/1syTB3LtSsue22mjXjV/R43rx51KpVixNPPJEOHTowePDgLb/L+++/nx49etCgQYNC7bOsfYft2kF2ttVDvOUWOPBAW+823kMBClw70jnnimvTJujb16Z4P/YY9OkDb75p96+9BnvtFXaEJUdBPVYZGbkvl5KeXvzq3RGNGjXim2++4ZdffuH444+nZ8+e1K9fH4CNGzdy2mmncckll7DbbvkvnnL33XczduxY5s2bx2uvvbZl+8SJE7n00ksB+w914sSJdOrUacvru+22G126dOHpp58uMFYt5DTcbt26cd1111G/fn1OPfXUQn22MPL7LvbYw3rBgo5FAD74wHpgwC6LFee73LhxIx9//DEzZsygcePGnHrqqYwbN46jjjqK559/nimF2HlZ/g6rVbPj1eGH28lk69a2nmTkKmtkKABAVlbR2ojDFc28iUg3EZkrIvNFZLtRnCIyUES+FZFvROQ9EUlPZDzOuXD89ZfV1GnVymYZPfEEHHMMXHcd3H132NGVLsOHb38pt2pV2x5vu+yyC3vuuScff/zxlm39+vWjefPmXHbZZQV+fsCAAcyZM4cXX3yRc845h3Xr1vHHH3/w/vvvc+6555KRkcEdd9zBc889t91/xNdccw0jRowo8D/oGTNmbBl8PnPmzC2XTwE2b97MzJkzadWq1ZZtFStWpFOnTtx11130zHnNL0mGDLHLxx98YH8XH3xgz4cMic/+GzZsSPv27dltt90oX748xx9/PNOnT2fGjBnMnz+fZs2akZGRwZo1a2jWrFm++/Lv0CYYzZy5bQIWsWZN8b63hCVhIlIOeAArb9EKOE1EWuV42wwgU1XbAi9gNcmccylmyRKbRfTJJ3DUURC5EtK5M8yfH25spU1WllX8Tk+38Svp6fa8qGfiOS1dupS1wZTWP//8k6lTp7L77rsDcO2117J69WruiXWAWaBHjx5kZmYyfvx4XnjhBXr37s2iRYtYuHAhS5YsoUmTJtskegAtW7akVatW2/S+RFNV7rvvPpYtW0a3bt1o1qwZHTp04Oabb97ynptvvpmOHTtul2hcfvnljBgxgtq1axfq54iX006zpPnii6FyZbsfPty2x0Pnzp1ZtWoVy5cvB+D999+nVatWHHPMMfz6668sXLiQhQsXUrVqVebH+AdY1r/DJk22T8AiFi8u+n4T2RO2FzBfVReo6n/AM8Bx0W9Q1Q9UdU3w9HOgYQLjcc6FpEkT+Pln6xF79VWbBg4286ht23BjK42ysmx8yubNdh+vBAzgu+++o0uXLrRr144DDzyQQYMG0aZNG5YuXcrw4cP59ttvt5SweOyxx2Le7/XXX8/IkSOZOHEiJ5xwwjavnXTSSdvNsAMYMmTIdoP/Bw8evKW8wVdffcUHH3ywZTLBmDFjmDdvHk2bNqVp06bMmzePMWPGbLff1q1b07dv35hjT4TTToPZs+1S/ezZ8UvAwMZR3XnnnRx66KG0adMGVc13gHysyvp3mJ7HtbrilKmQwl6LjXnHIj2Bbqp6bvC8N9BFVXMt5iEi9wO/qurNub0ekZmZqdnZ2XGP1zmXWDfcYAt+33svNG9u5Suuu87WnmyVs488BxGZpqqZ+b+rdPBjmHOlU6Q8zJo1W7dVrVpwT3R+x68SMTBfRM7AqvMfmMfr/YB+AI2LWxnNOReKoUOhfn07iC1bBvvua7MjC0rAnHOuJIgkWkOG2CXIxo3tMnJxeqITmYT9DDSKet4w2LYNETkMGAIcqKq5TrJW1dHAaLCzyPiH6pxLNBGrkXThhWFH4uJp+PDhPP/889tsO/nkkxkSr1HmLuH8O4xdVlZ8L/8n8nJkeWAecCiWfH0FnK6qc6Le0wEbkN9NVX+IZb/ele9c2eOXI51zpVV+x6+EDcwPljq6CHgb+A54TlXniMiNItIjeNsdQHXgeRGZKSKvJioe55xzzrmSJKFjwlT1DeCNHNuuj3p8WCLbd84555wrqRJarNU555xzzuXOkzDnnHPOuRB4Euacc845FwJPwpxzzjnnQuBJmHPOOedcCBJWJyxRRGQ5sAioCazO8XJu25oAPyUhtHi2mdvPEY/28ttvYV/Lrc3Cxl1Ysfyc8Ygheh/xarOg9+TVZl6fi/Xffyzi+TcSawz5tZnbPtJVtW5xAispCnkMK43HL0jMMawwf0OxbM/ZZkk4fsUrjsg+4nnMjPX/iFh+r/E8fuXWZlEVJoa82izc8UtVS+UNGB3jtn9DiK1Ybeb2c8Sjvfz2W9jXcmuzsHEn4vcajxii9xGvNgt6T15t5vW5WP/9x/PfT2F/d0VtM9H/jkrKLZbvsDQev4ryHcbj76wwfyu5tVkSjl/xiiOyj3geM2P9PyKW32s8j1+F+d3G83efV5uF/TlK8+XI12LcVhol6ufIb79Ffa0o70ukeMRQ2H3E8v6C3pPX64XZXlZ//6VVSf0O4yERP0e8/4aK+r5ES/bfUDx+P4V9raT+20/68avUXY4sLBH5V1WrpXKbZeFn9DZTp72w2iyNysp3UxbaLAs/o7dZeKW5JyxWL5WBNsvCz+htpk57YbVZGpWV76YstFkWfkZvs5BSvifMOeecc64kKgs9Yc4555xzJU7KJmEiMk9ENovIuiS1t5eI/Cki60VknYi8mIx2g7YriMgaEfktSe29EvyM60RkkYjUTEAbuX5/IvJ81O/4izi2V1NE/hGRtcG+pwTbfxKR/4Jt80SkSrzaDPafLiJLg59pvYicF/XaqyKiItKimG1s97sUkS+D9taKyC8ikh5sryIi84Ofd72IvFWE9nL9WxCRKSKyKWhzrYhcH/WZk0Tk76h/V3H/N1Wa+PEroe2l3PEr2HfSj2GpePwK9pO0Y1jKJmHAfcAZSWxvPdBfVSth9UOOFZFjk9T2C8DvyWhIRDoBxwANVLUy9m/orgQ0td33JyIDgEOAOkHbZ8Wxvb+ApqpaBdgRyBSRc4CxQCWgSnA/Jo5tArwH/F/w76Y28A7YQQDYB9gUhzZy+1t4Cdgh+HkXA88G2+8AKgS/312Aw0Rkv0K2l9/fwhuqWiW43QggIpWAp4Czg3b3BNYUss1U48evBEjh4xeEcwxLxeMXJPEYlrJJmKrej305yWrva1V9Oni8DFgJtEp0uyKSCeyP/UNNFgF2DP7hVQTmx7uBPL6/gcBtqvp38J5v49ieqmrkTLwq9rehqnpj8JoCM4DG8WpTRBoB6QQHY1X9V1UXBS+/BFwQj3Zy+12q6m2quj54OgWoH3kJqBR8tzsCm4FfCtleYf8WrgSWqerzwWfmq+qGwrSZavz4ldhmSbHjV7C/pB7DUvX4Few/acewlE3CwhRk3nWxzDjRXgEuxv6xJZyqTgMmAT8Ca4E1qnpbMtoG6gDHBF3uq0Skbzx3HlwWWQusAL5R1cejXqsCHAFMjGOT+2BnXD8El2O+F5G6IjIcWB75g06CM4E3g8dXBDGtBX4AnlXVBUXdcS5/C0cF3fjzRCQj2NYheO+K4PcwuajtueLz41fCJPT4BUk/hqX88QsSfwzzJCzORKQ+8DZwl6r+nOC2hgGrVHVCItvJ0WYGcDCwB1ANqCwiDyareezspgZwGfCYiEi8dq6qG4Lu7QxgdxE5PurlGcBcVX0gXu1hZ+HVgFtUtSp24JgMXAr0iGM7eRKRd7D/APsHm/oGz6sBrYFTReTAIu47599Cf+ySSHXs8tM7wVvLA42wg3oj4AARGVSkH8gVix+/Ets8CTx+QdKPYSl9/Ar2n/hjmMah1H9JvQH7AeuS2F4V7AxkUpLa+xTYGNw2YV2xCxLc5l3YH3Lk+SPA7GR8f8ByYEDU8w1AywS1/R7wWvD4fWAZUC7ObbQBNkY97w/8gR1EIt+rBvdt4vm7DLY9BvwN7BS1bRbwcNTzecDdRWgv37+F6HiAe4H5Ua/9H/B6Ir7X0nTz41dC2iwTx69g/wk9hqXy8Sv4bFKOYd4TFifBGc1sYImqHpeMNlV1H1Utr6rlgcuB31V1twQ3OwdIF5Gdgp/5UOC7BLcZ8Q5wPICIHIGdWc6Nx45FpGXUDJsdgUxguoiMCx63UtV4DDLdQlVnAf+KSLdg08nYH3Ja1Pe6KWh7VjzbFpEhQG+gs6qujHppCfadIiJ1sTEfUwu571z/FkSkXdTbBgGR8St3A7sE/6YqYV372YX7iVxx+PErKRJ2/Ar2mdRjWKoev4LPJu8YlqgsPOwbsIitZ1cbgbEJbu+CoK21Ubfrk/jzXgb8lqS2pmDX3dcBC4Aayfj+sC7mBUG7a4CBcWzvpGCfa4P9vxdsV+yMNfKdvhfnn/NU4N9g38uAjByvbwRaJOB3+V/wOPJzzQneWx87kK0LvuNC90jl9bcQ9d2tBX4F2kV95sHgtXXAF8n4d1ySb378SmhbKXf8CtpM+jEsFY9fwX6SdgzzivnOOeeccyHwy5HOOeeccyHwJMw555xzLgSehDnnnHPOhcCTMOecc865EHgS5pxzzjkXAk/CXFyJiIrIXVHPB4nI0Djte6iI/CwiM0XkWxE5LR77dc65CD+GuWTyJMzF23rgRBGpk6D9362q7YHjgEdEpEKC2nHOlU1+DHNJ40mYi7eNwGhgQM4XRGSciPSMev5PcH+QiHwoIpNEZIGI3CYiWSLypYjMEpGmOfelqj9ghQl3DPYxWES+EpFvgjXpEJGMYFHZCSLynYi8ICJVg9duC85EvxGROxPxi3DOlUp+DHNJ40mYS4QHgCwRqVmIz7QDzscW1u2NVVneC1sf7OKcbxaRjsAPqvp7sARIc2AvoD3QSUQOCN66O/Cgqu4B/AVcKCI7AScArVW1LXBzEX5G51zq8mOYSwpPwlzcqepfwBPAJYX42FequkxV1wM/snV1+llARtT7BojIHOALYHiw7YjgNgOYDrTEDmhga399Ejx+Clt0dTW2tMQYETkROxt1zjnAj2EueTwJc4lyD3AOtl5axEaCf3MikgZUjHptfdTjzVHPNwPlo167W1VbY+ukjRGRythCuLeqavvg1kxVxwTvz7kul6rqRuyM8wWgO/BW0X5E51wKuwc/hrkE8yTMJYSq/gE8hx3EIhYCnYLHPYAiD0hV1VexVer7Am8DZ4tIdQAR2VVE6gVvbSwiXYPHpwNTg/fVVNU3sHEf7Yoah3MuNfkxzCVD+YLf4lyR3QVcFPX8UWCSiHyNnbn9W8z93wg8jY3B2AP4TEQA/gHOADYBc4H+IvI48C3wEFAziCNyBjqwmHE451KTH8NcQolqzp5O51KDiGQAr6vqnmHH4pxzheXHsNTnlyOdc84550LgPWHOOeeccyHwnjDnnHPOuRB4Euacc845FwJPwpxzzjnnQuBJmHPOOedcCDwJc84555wLgSdhzjnnnHMh+H9PtHHqfR5QvAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 720x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 720x360 with 2 Axes>"
       ]
@@ -151,30 +162,49 @@
    ],
    "source": [
     "for bm in unique_bms:\n",
-    "    tmpdf = perfdf[perfdf[\"Benchmark\"] == bm]    \n",
-    "    unique_labels = tmpdf[\"Label\"].unique()\n",
+    "    flag = \"nvbench_static_multimap_insert\" == bm\n",
     "    \n",
-    "    if \"single_insert\" in bm:\n",
-    "        plot_perf(bm, tmpdf, \"Occupancy\", unique_labels)"
+    "    if flag:\n",
+    "        tmpdf = perfdf[perfdf[\"Benchmark\"] == bm]\n",
+    "        \n",
+    "        randomdf = tmpdf[tmpdf[\"Distribution\"] == \"RANDOM\"].reset_index(drop=True)\n",
+    "        unique_labels = randomdf[\"Label\"].unique()\n",
+    "        plot_perf(bm + \"_RANDOM\", randomdf, \"NumReps\", unique_labels, flag)\n",
+    "        \n",
+    "        cycledf = tmpdf[tmpdf[\"Distribution\"] == \"CYCLE\"].reset_index(drop=True)\n",
+    "        unique_labels = cycledf[\"Label\"].unique()\n",
+    "        plot_perf(bm + \"_CYCLE\", cycledf, \"NumReps\", unique_labels, flag)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "spatial-patent",
+   "id": "excessive-occupation",
    "metadata": {},
    "source": [
-    "### Multi-Value Insertion "
+    "### count"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "homeless-grave",
+   "id": "descending-enterprise",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 720x360 with 2 Axes>"
       ]
@@ -187,12 +217,18 @@
    ],
    "source": [
     "for bm in unique_bms:\n",
-    "    tmpdf = perfdf[perfdf[\"Benchmark\"] == bm]\n",
-    "    unique_labels = tmpdf[\"Label\"].unique()\n",
+    "    flag = \"count\" in bm\n",
     "    \n",
-    "    flag = \"multi_insert\" in bm\n",
     "    if flag:\n",
-    "        plot_perf(bm, tmpdf, \"NumReps\", unique_labels, flag)"
+    "        tmpdf = perfdf[perfdf[\"Benchmark\"] == bm]\n",
+    "        \n",
+    "        randomdf = tmpdf[tmpdf[\"Distribution\"] == \"RANDOM\"].reset_index(drop=True)\n",
+    "        unique_labels = randomdf[\"Label\"].unique()\n",
+    "        plot_perf(bm + \"_RANDOM\", randomdf, \"NumReps\", unique_labels, flag)\n",
+    "        \n",
+    "        cycledf = tmpdf[tmpdf[\"Distribution\"] == \"CYCLE\"].reset_index(drop=True)\n",
+    "        unique_labels = cycledf[\"Label\"].unique()\n",
+    "        plot_perf(bm + \"_CYCLE\", cycledf, \"NumReps\", unique_labels, flag)"
    ]
   },
   {
@@ -200,18 +236,18 @@
    "id": "apparent-skiing",
    "metadata": {},
    "source": [
-    "### Multi-Value Insertion by Varying CG Sizes"
+    "### insert by varying CG sizes"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "aquatic-accreditation",
+   "id": "varying-stranger",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 720x360 with 2 Axes>"
       ]
@@ -223,7 +259,31 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 720x360 with 2 Axes>"
       ]
@@ -236,38 +296,63 @@
    ],
    "source": [
     "for bm in unique_bms:\n",
-    "    tmpdf = perfdf[perfdf[\"Benchmark\"] == bm]\n",
-    "    \n",
     "    flag = \"insert_cgsize\" in bm\n",
     "    if flag:\n",
+    "        tmpdf = perfdf[perfdf[\"Benchmark\"] == bm]\n",
+    "        \n",
     "        # Plot INT32 performance\n",
     "        tmp_int32df = tmpdf[tmpdf[\"Label\"].str.contains('I32')]\n",
-    "        unique_labels = tmp_int32df[\"Label\"].unique()\n",
-    "        plot_perf(bm+'_INT32', tmp_int32df, \"NumReps\", unique_labels, flag, True)\n",
+    "        \n",
+    "        randomdf = tmp_int32df[tmp_int32df[\"Distribution\"] == \"RANDOM\"].reset_index(drop=True)\n",
+    "        unique_labels = randomdf[\"Label\"].unique()\n",
+    "        plot_perf(bm + \"_INT32_RANDOM\", randomdf, \"NumReps\", unique_labels, flag, True)\n",
+    "        \n",
+    "        cycledf = tmp_int32df[tmp_int32df[\"Distribution\"] == \"CYCLE\"].reset_index(drop=True)\n",
+    "        unique_labels = cycledf[\"Label\"].unique()\n",
+    "        plot_perf(bm + \"_INT32_CYCLE\", cycledf, \"NumReps\", unique_labels, flag, True)\n",
+    "\n",
     "        \n",
     "        # Plot INT64 Performance\n",
     "        tmp_int64df = tmpdf[tmpdf[\"Label\"].str.contains('I64')]\n",
-    "        unique_labels = tmp_int64df[\"Label\"].unique()\n",
-    "        plot_perf(bm+'_INT64', tmp_int64df, \"NumReps\", unique_labels, flag, True)"
+    "        \n",
+    "        randomdf = tmp_int64df[tmp_int64df[\"Distribution\"] == \"RANDOM\"].reset_index(drop=True)\n",
+    "        unique_labels = randomdf[\"Label\"].unique()\n",
+    "        plot_perf(bm + \"_INT64_RANDOM\", randomdf, \"NumReps\", unique_labels, flag, True)\n",
+    "        \n",
+    "        cycledf = tmp_int64df[tmp_int64df[\"Distribution\"] == \"CYCLE\"].reset_index(drop=True)\n",
+    "        unique_labels = cycledf[\"Label\"].unique()\n",
+    "        plot_perf(bm + \"_INT64_CYCLE\", cycledf, \"NumReps\", unique_labels, flag, True)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "supposed-kruger",
+   "id": "wrong-skiing",
    "metadata": {},
    "source": [
-    "### Single-Value Retrieval"
+    "### find_all"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "middle-omaha",
+   "id": "peaceful-bernard",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 720x360 with 2 Axes>"
       ]
@@ -280,30 +365,49 @@
    ],
    "source": [
     "for bm in unique_bms:\n",
-    "    tmpdf = perfdf[perfdf[\"Benchmark\"] == bm]    \n",
-    "    unique_labels = tmpdf[\"Label\"].unique()\n",
+    "    flag = \"find_all\" in bm\n",
     "    \n",
-    "    if bm == 'nvbench_static_multimap_find':\n",
-    "        plot_perf(bm, tmpdf, \"Occupancy\", unique_labels)"
+    "    if flag:\n",
+    "        tmpdf = perfdf[perfdf[\"Benchmark\"] == bm]\n",
+    "        \n",
+    "        randomdf = tmpdf[tmpdf[\"Distribution\"] == \"RANDOM\"].reset_index(drop=True)\n",
+    "        unique_labels = randomdf[\"Label\"].unique()\n",
+    "        plot_perf(bm + \"_RANDOM\", randomdf, \"NumReps\", unique_labels, flag)\n",
+    "        \n",
+    "        cycledf = tmpdf[tmpdf[\"Distribution\"] == \"CYCLE\"].reset_index(drop=True)\n",
+    "        unique_labels = cycledf[\"Label\"].unique()\n",
+    "        plot_perf(bm + \"_CYCLE\", cycledf, \"NumReps\", unique_labels, flag)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "wrong-skiing",
+   "id": "musical-merchandise",
    "metadata": {},
    "source": [
-    "### Multi-Value Retrieval"
+    "### retrieve"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "peaceful-bernard",
+   "id": "bound-pulse",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 720x360 with 2 Axes>"
       ]
@@ -316,12 +420,18 @@
    ],
    "source": [
     "for bm in unique_bms:\n",
-    "    tmpdf = perfdf[perfdf[\"Benchmark\"] == bm]\n",
-    "    unique_labels = tmpdf[\"Label\"].unique()\n",
+    "    flag = \"retrieve\" in bm\n",
     "    \n",
-    "    flag = \"find_all\" in bm\n",
     "    if flag:\n",
-    "        plot_perf(bm, tmpdf, \"NumReps\", unique_labels, flag)"
+    "        tmpdf = perfdf[perfdf[\"Benchmark\"] == bm]\n",
+    "        \n",
+    "        randomdf = tmpdf[tmpdf[\"Distribution\"] == \"RANDOM\"].reset_index(drop=True)\n",
+    "        unique_labels = randomdf[\"Label\"].unique()\n",
+    "        plot_perf(bm + \"_RANDOM\", randomdf, \"NumReps\", unique_labels, flag)\n",
+    "        \n",
+    "        cycledf = tmpdf[tmpdf[\"Distribution\"] == \"CYCLE\"].reset_index(drop=True)\n",
+    "        unique_labels = cycledf[\"Label\"].unique()\n",
+    "        plot_perf(bm + \"_CYCLE\", cycledf, \"NumReps\", unique_labels, flag)"
    ]
   }
  ],

From b43bfbde21434eb15320b36a779737fa602a800a Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Wed, 5 May 2021 21:41:35 -0400
Subject: [PATCH 081/267] Benchmark updates: remove insert CG benchmark, add
 count-varying-matching-rate benchmark

---
 .../static_multimap/static_multimap_bench.cu  | 106 +++++++++++-------
 1 file changed, 67 insertions(+), 39 deletions(-)

diff --git a/benchmarks/hash_table/static_multimap/static_multimap_bench.cu b/benchmarks/hash_table/static_multimap/static_multimap_bench.cu
index c93fbb3ef..1362b9eca 100644
--- a/benchmarks/hash_table/static_multimap/static_multimap_bench.cu
+++ b/benchmarks/hash_table/static_multimap/static_multimap_bench.cu
@@ -117,40 +117,6 @@ std::enable_if_t<(sizeof(Key) != sizeof(Value)), void> nvbench_static_multimap_i
   state.skip("Key should be the same type as Value.");
 }
 
-/**
- * @brief A benchmark evaluating multi-value insertion performance by varing number of repetitions
- * per key and CUDA CG size:
- * - Total number of insertions: 100'000'000
- * - Map occupancy: 0.8
- * - Number of repetitions per key: 1, ... , 128, 256
- * - CG size: 1, ... , 16, 32
- *
- */
-template <typename Key, typename Value, nvbench::int32_t CGSize>
-std::enable_if_t<(sizeof(Key) == sizeof(Value)), void> nvbench_static_multimap_insert_cgsize(
-  nvbench::state& state, nvbench::type_list<Key, Value, nvbench::enum_type<CGSize>>)
-{
-  std::size_t const num_keys = state.get_int64("NumInputs");
-  auto const occupancy       = state.get_float64("Occupancy");
-  std::size_t const num_reps = state.get_int64("NumReps");
-  auto const dist            = state.get_string("Distribution");
-
-  std::size_t const size = num_keys / occupancy;
-
-  std::vector<Key> h_keys(num_keys);
-
-  generate_keys<Key>(h_keys.begin(), h_keys.end(), num_reps, dist);
-
-  launch_nvbench_insert<Key, Value, CGSize>(state, h_keys, num_keys, size);
-}
-
-template <typename Key, typename Value, nvbench::int32_t CGSize>
-std::enable_if_t<(sizeof(Key) != sizeof(Value)), void> nvbench_static_multimap_insert_cgsize(
-  nvbench::state& state, nvbench::type_list<Key, Value, nvbench::enum_type<CGSize>>)
-{
-  state.skip("Key should be the same type as Value.");
-}
-
 /**
  * @brief A benchmark evaluating multi-value count performance by varing number of repetitions
  * per key:
@@ -214,6 +180,61 @@ std::enable_if_t<(sizeof(Key) != sizeof(Value)), void> nvbench_static_multimap_c
   state.skip("Key should be the same type as Value.");
 }
 
+template <typename Key, typename Value>
+std::enable_if_t<(sizeof(Key) == sizeof(Value)), void> nvbench_static_multimap_count_analysis(
+  nvbench::state& state, nvbench::type_list<Key, Value>)
+{
+  std::size_t const num_keys = state.get_int64("NumInputs");
+  auto const occupancy       = state.get_float64("Occupancy");
+  std::size_t const num_reps = state.get_int64("NumReps");
+  auto const matching_rate   = state.get_float64("MatchingRate");
+
+  std::size_t const size = num_keys / occupancy;
+
+  std::vector<Key> h_keys(num_keys);
+  generate_keys<Key>(h_keys.begin(), h_keys.end(), num_reps, "RANDOM");
+
+  std::vector<cuco::pair_type<Key, Value>> h_pairs(num_keys);
+
+  std::random_device rd;
+  std::mt19937 gen{rd()};
+  auto tmp_max = static_cast<double>(num_keys / num_reps) / matching_rate;
+  std::uniform_int_distribution<Key> distribution{1, static_cast<Key>(tmp_max)};
+
+  for (auto i = 0; i < num_keys; ++i) {
+    Key key           = h_keys[i];
+    h_pairs[i].first  = key;
+    h_pairs[i].second = i;
+
+    h_keys[i] = distribution(gen);
+  }
+
+  thrust::device_vector<Key> d_keys(h_keys);
+  thrust::device_vector<cuco::pair_type<Key, Value>> d_pairs(h_pairs);
+
+  state.add_element_count(num_keys, "NumKeys");
+  state.add_global_memory_writes<Key>(num_keys * 2);
+
+  state.exec(nvbench::exec_tag::sync | nvbench::exec_tag::timer,
+             [&](nvbench::launch& launch, auto& timer) {
+               auto const cg_size = 8;
+               cuco::static_multimap<Key, Value, cg_size> map{size, -1, -1};
+               map.insert(d_pairs.begin(), d_pairs.end());
+
+               // Use timers to explicitly mark the target region
+               timer.start();
+               auto count = map.count(d_keys.begin(), d_keys.end(), launch.get_stream());
+               timer.stop();
+             });
+}
+
+template <typename Key, typename Value>
+std::enable_if_t<(sizeof(Key) != sizeof(Value)), void> nvbench_static_multimap_count_analysis(
+  nvbench::state& state, nvbench::type_list<Key, Value>)
+{
+  state.skip("Key should be the same type as Value.");
+}
+
 /**
  * @brief A benchmark evaluating multi-value retrieval performance by varing number of repetitions
  * per key:
@@ -347,7 +368,6 @@ std::enable_if_t<(sizeof(Key) != sizeof(Value)), void> nvbench_static_multimap_r
 
 using key_type   = nvbench::type_list<nvbench::int32_t, nvbench::int64_t>;
 using value_type = nvbench::type_list<nvbench::int32_t, nvbench::int64_t>;
-using cg_size    = nvbench::enum_type_list<1, 2, 4, 8, 16, 32>;
 
 NVBENCH_BENCH_TYPES(nvbench_static_multimap_insert, NVBENCH_TYPE_AXES(key_type, value_type))
   .set_type_axes_names({"Key", "Value"})
@@ -384,12 +404,20 @@ NVBENCH_BENCH_TYPES(nvbench_static_multimap_retrieve, NVBENCH_TYPE_AXES(key_type
   .add_string_axis("Distribution", {"CYCLE", "RANDOM"})
   .add_int64_power_of_two_axis("NumReps", nvbench::range(0, 8, 1));
 
-NVBENCH_BENCH_TYPES(nvbench_static_multimap_insert_cgsize,
-                    NVBENCH_TYPE_AXES(key_type, value_type, cg_size))
-  .set_type_axes_names({"Key", "Value", "CGSize"})
+NVBENCH_BENCH_TYPES(nvbench_static_multimap_count_analysis, NVBENCH_TYPE_AXES(key_type, value_type))
+  .set_type_axes_names({"Key", "Value"})
   .set_timeout(100)                            // Custom timeout: 100 s. Default is 15 s.
   .set_max_noise(3)                            // Custom noise: 3%. By default: 0.5%.
   .add_int64_axis("NumInputs", {100'000'000})  // Total number of key/value pairs: 100'000'000
   .add_float64_axis("Occupancy", {0.8})
-  .add_string_axis("Distribution", {"CYCLE", "RANDOM"})
+  .add_float64_axis("MatchingRate", {0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1})
+  .add_int64_power_of_two_axis("NumReps", {3});
+
+NVBENCH_BENCH_TYPES(nvbench_static_multimap_count_analysis, NVBENCH_TYPE_AXES(key_type, value_type))
+  .set_type_axes_names({"Key", "Value"})
+  .set_timeout(100)                            // Custom timeout: 100 s. Default is 15 s.
+  .set_max_noise(3)                            // Custom noise: 3%. By default: 0.5%.
+  .add_int64_axis("NumInputs", {100'000'000})  // Total number of key/value pairs: 100'000'000
+  .add_float64_axis("Occupancy", {0.8})
+  .add_float64_axis("MatchingRate", {0.5})
   .add_int64_power_of_two_axis("NumReps", nvbench::range(0, 8, 1));

From ac5655f136c028b775218a4eeab88b113d0ccca4 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Thu, 6 May 2021 09:46:14 -0700
Subject: [PATCH 082/267] Customize benchmark name

---
 benchmarks/hash_table/static_multimap/static_multimap_bench.cu | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/benchmarks/hash_table/static_multimap/static_multimap_bench.cu b/benchmarks/hash_table/static_multimap/static_multimap_bench.cu
index 1362b9eca..2b174fe7e 100644
--- a/benchmarks/hash_table/static_multimap/static_multimap_bench.cu
+++ b/benchmarks/hash_table/static_multimap/static_multimap_bench.cu
@@ -405,6 +405,7 @@ NVBENCH_BENCH_TYPES(nvbench_static_multimap_retrieve, NVBENCH_TYPE_AXES(key_type
   .add_int64_power_of_two_axis("NumReps", nvbench::range(0, 8, 1));
 
 NVBENCH_BENCH_TYPES(nvbench_static_multimap_count_analysis, NVBENCH_TYPE_AXES(key_type, value_type))
+  .set_name("count_varying_matching_rate")
   .set_type_axes_names({"Key", "Value"})
   .set_timeout(100)                            // Custom timeout: 100 s. Default is 15 s.
   .set_max_noise(3)                            // Custom noise: 3%. By default: 0.5%.
@@ -414,6 +415,7 @@ NVBENCH_BENCH_TYPES(nvbench_static_multimap_count_analysis, NVBENCH_TYPE_AXES(ke
   .add_int64_power_of_two_axis("NumReps", {3});
 
 NVBENCH_BENCH_TYPES(nvbench_static_multimap_count_analysis, NVBENCH_TYPE_AXES(key_type, value_type))
+  .set_name("count_varying_num_reps")
   .set_type_axes_names({"Key", "Value"})
   .set_timeout(100)                            // Custom timeout: 100 s. Default is 15 s.
   .set_max_noise(3)                            // Custom noise: 3%. By default: 0.5%.

From a94d2c5a8b56f94587e9d886e59fc6fbb967d05e Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Thu, 6 May 2021 13:11:33 -0400
Subject: [PATCH 083/267] Add in-depth count analysis in notebook

---
 .../analysis/notebooks/StaticMultimap.ipynb   | 153 +++++++-----------
 benchmarks/analysis/notebooks/Utils.py        |   9 +-
 2 files changed, 65 insertions(+), 97 deletions(-)

diff --git a/benchmarks/analysis/notebooks/StaticMultimap.ipynb b/benchmarks/analysis/notebooks/StaticMultimap.ipynb
index b5f5526c1..356cedd29 100644
--- a/benchmarks/analysis/notebooks/StaticMultimap.ipynb
+++ b/benchmarks/analysis/notebooks/StaticMultimap.ipynb
@@ -2,7 +2,6 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "exceptional-dining",
    "metadata": {},
    "source": [
     "# Preparation"
@@ -11,7 +10,6 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "radio-fundamental",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -26,7 +24,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cleared-delight",
    "metadata": {},
    "source": [
     "### Global Parameters"
@@ -35,20 +32,18 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "external-cleaning",
    "metadata": {},
    "outputs": [],
    "source": [
     "# Specify the data path\n",
     "datafile = '../data/static-multimap-data.csv'\n",
     "\n",
-    "output_keys = ['Benchmark', 'Label', 'Distribution', 'CGSize', 'NumReps', 'NumInputs', \\\n",
+    "output_keys = ['Benchmark', 'Label', 'Distribution', 'MatchingRate', 'NumReps', 'NumInputs', \\\n",
     "               'Occupancy', 'GPU Time (sec)', 'Elem/s (elem/sec)', 'Bandwidth (GB/s)']"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "removed-period",
    "metadata": {},
    "source": [
     "### Import Data"
@@ -57,7 +52,6 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "professional-jurisdiction",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -67,12 +61,10 @@
     "# Filter out skipped tests\n",
     "perfdf = rawdf[rawdf[\"Key\"] == rawdf[\"Value\"]].reset_index(drop=True)\n",
     "\n",
-    "# Set 'Int64' as CG Size type. Default is 'float64'.\n",
-    "perfdf['CGSize'] = perfdf['CGSize'].astype('Int64')\n",
     "\n",
     "# Add labels\n",
-    "perfdf['Label'] = perfdf[\"Key\"] + \"_\" + perfdf[\"Distribution\"]\n",
-    "perfdf.loc[perfdf['CGSize'].notnull(), 'Label'] += \"_\" + perfdf['CGSize'].astype(str)\n",
+    "perfdf['Label'] = perfdf[\"Key\"]\n",
+    "perfdf.loc[perfdf['Distribution'].notnull(), 'Label'] += \"_\" + perfdf['Distribution']\n",
     "\n",
     "perfdf[\"Bandwidth (GB/s)\"] = perfdf[\"GlobalMem BW (bytes/sec)\"] / (1000 * 1000 * 1000)\n",
     "\n",
@@ -82,7 +74,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "faced-belarus",
    "metadata": {},
    "source": [
     "# Visualization"
@@ -90,7 +81,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "careful-produce",
    "metadata": {},
    "source": [
     "### Visualization Parameters"
@@ -99,7 +89,6 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "imported-sharing",
    "metadata": {},
    "outputs": [
     {
@@ -110,7 +99,8 @@
       "nvbench_static_multimap_count\n",
       "nvbench_static_multimap_find_all\n",
       "nvbench_static_multimap_retrieve\n",
-      "nvbench_static_multimap_insert_cgsize\n"
+      "count_varying_matching_rate\n",
+      "count_varying_num_reps\n"
      ]
     }
    ],
@@ -123,7 +113,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "spatial-patent",
    "metadata": {},
    "source": [
     "### insert "
@@ -132,12 +121,11 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "homeless-grave",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 720x360 with 2 Axes>"
       ]
@@ -149,7 +137,7 @@
     },
     {
      "data": {
-      "image/png": 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sz02WcxzasGE7doOuWGGJF7MqZd9+awNsAfbYwypjWRWzAw6wczhX0nlLWOxMmGDXqO+/h912s30//2xjyLZts8Sx/frZV79JdK7witoSdgJwsarulPBBREoDJwHHAm9GJcokF+k4tGrVbNDtiSfa861bbVxZVqVs0qS8W8sOPhjq199+IfTZmM654jrkEEuFsd9+dv1YuxZeecXGoa1ZA489Bv/3f/b6/ffDcceFHbFzqcPHhCWg/FrLata01rLy5WHMGBt4myW3ljfnUoG3hMXe1Kl2A1iunFXKGjSw/Rs3wqhR8MADcM89cMIJNqtcZHvLmXMub/ldv/Lrjrwmv5Oq6oNRiK3QEvUCFks5W8smTtyeKyin9HQfx+FSj1fCwpf1r0LEZm4+/jhceCFcddX2Cptzbmf5Xb/ym9NXOdgygcuAvYLtUqB1tIN0ecvqlrz0Upu+PmdO3scuWJB7ugznnCsOke1DIbp1s0H9Q4dCo0bWcvbtt+HG51wyyrMSpqqDVHUQUAdorap9VbUv0AaoV9CJReRZEVkmIjPyOaadiEwTkZki8nlRvoGSKj0979fq1YNbboHly+MXj3Ou5NhvP7sh/O03uPZaGDsWHszWN5Jko1ycC00kC3jXBDZle74p2FeQEUCHvF4UkarAEKCjqjYDPDdzIQwevPPMyQoVLB3GEUfA7bdbRe2KK2DevHBidM6ltr32grvvtsTVWZWwmTOhSRN48skdZ4s753YWSSXsBeBbEblVRG4FvgGeL+hNqvoF8Hc+h5wNjM6afamqyyKIxQW6dbNB+Onp2/OUDRsGt94Kb71liRm7drV9jRrZ46lTw47aOZeKKlfenm1/7VpbZunyy7e3yi9dGmp4ziWsAithqjoYuBD4J9guUNU7o1B2Y2A3EflMRKaIyHlROGeJ0q2bDcLfts2+Zp8Vuc8+8Mwztr9vX3j/fcuCfdxxMG6cdxc452KjbVv45htby/Kww2x9y6ZNbQmmnCZPtlmXL764fQa4cyVJJC1hANOA14G3gBUiUuCYsAiUxsaXnQj8H3CziDTO7UAR6Skik0Vk8nIf6FQotWvDvfdad8Hdd8P06XDMMZYI9rXXbOalc85Fk4gtRD5mDPz0EwwZYml1VOGaa+B//7PlkLp0sclEr79ui44n4aRR54qlwEqYiFwJLAU+Ad4D3g++Ftci4GNVXauqfwFfAC1zO1BVh6lqpqpm1qhRIwpFlzxVqsD119tA2mHDbHHfM8+Exo1t7Mb69WFH6JxLRY0b27UG4I8/4KWXLPv+6NE2fOKBByw/2aOPwjnneCu9K1kiaQnrAzRR1Waq2kJV91PVFlEo+23gMBEpLSIVgAOB2VE4r8tH+fJw8cU2ZuzNN6F6dRu7kZ5u3QZ/5zeKzznnimGvvazlq3lzS/R6wQW2ZuXEiXD66dYy/+OPYUfpXPxEUgn7HVhV2BOLyChgItBERBaJyEUicqmIXAqgqrOBj4AfgW+Bp1U1z3QWLrpKlYLOnS0z/2efQWYm3HyzDaS95hrrvnTOuWjbZRcbJvHoo/Duu3DUUdCihXVhli7tQyRcyZLf2pFZ5gGficj7wH+L5BSUMV9VuxZ0YlW9D7gvghhcjIjAkUfa9uOPcN99dnF87DE4+2zLAdS8edhROudSSefOlnH/44/hpJNs35tvwp9/WnoL50qKSFrCFmLjwcqyPYt+5VgG5cLRooXNUvr1V+jVC954w5IynnQSfPmljdUYORIyMiAtzb6OHBl21M65ZHPhhVCpks3YHjgQune3rslVq6B9e1iyJOwInYuPiBfwFpFKAKoa6kTiZF13LRmtWAFPPGGtYn/9ZbOXFi6ETdlS9/qi4S4efO3I1LNtm6XL+fxz2GMPy2X45Zc2a7JaNXj7bdh//7CjdK74irSAd7Y3NwdeBHYPdv0FnKeqM6MaZYT8AhZ/69bBc8/ZQr1btuz8ui8a7mLNK2Elx9Sp0LGjTRJ66y3LbehcMivqAt5ZhgHXqGq6qqYDfYHh0QzQJbYKFax7MrcKGFjrmHPORcP++8N331k+Qx8f5lJdJJWwiqr6adYTVf0MqBiziFzCymvR8Lp14xuHcy617bmndUemp1u35aOPei5Dl5oiqYTNE5GbRSQj2G7CZky6Eia3RcPBco+tWBH/eJxzqe/LL6FPH0tl4QP2XaqJpBJ2IVADGA28CVQP9rkSJrdFw6+80pIvHnIIzPOquXMuyo480rLrT59uy61NnRp2RM5FTyQLeP+jqr1VtbWqtlHVq1T1n3gE5xJPzkXDH33U1oH76y846CD49tuwI3TOpZpTT4Wvv7abv8MOs2WOnEsFkawd+YmIVM32fDcR+TimUbmkcthhMGGC5f1p186yYDvnXDS1amU3eQccALvvXuDhziWFSLojq6vqyqwnQSvYHjGLyCWlJk1s/bdmzaBTJ1sU3DnnomnPPeHTT+3GDyzLvg/Yd8kskkrYNhGpl/VERNIBX+fe7aRmTVuH8oQTbFHw66+3bkvnnIsWEfv6889wxhk2ZuzPP8ONybmiiqQSNgD4SkReFJGXgC+AG2IbVvFt3gyzZ8OyZWFHUrJUrGgJFi+9FO6918aQbdxY8Pucc64wmjSxAfuzZlkX5fffhx2Rc4UXycD8j4DWwKvAK0AbVU3oMWEvvWQz9zp2tD/U00+HlSvDjqrkKF0ahgyBu++GV16xjNf/+FQO51yUnXKKDdgvVcq6KN98M+yInCucSAbmC9ABaK2q7wEVRKRtzCMroi+/hP794b334JdfYNEiW4fs/PPDjqxkEbHuyJdfhkmT4NBDLZWFc85FU8uWNmC/VSu/2XbJJ5LuyCHAwUDX4Plq4ImYRVRMQ4bAgAHQujUsXw4ffQQXXQRffeXL64Sha1cYO9bGbBx0kHcZOOeir2ZN+OILu9aDTRLyAfsuGURSCTtQVXsBG+C/2ZFlYxpVMfzxx/b1xiZNgi5doG1by+iemWkpFKZNs9eXLYOZM2HDhrCiLRmOPNIqwWXLwhFHwIcfhh2Rcy7VlC5tX5cvh2OP9QH7LjlEUgnbLCKlCGZEikgNIGHnvB18MIwZY4/bt7eWl8ceg112sT/MzZu3L73zxhvQvLk9T0+3BWMvu8z+iAFWr4ZNmyIrd/Roaw4vVcoqgc8+G+3vLLk1a2Z3p40bw8knw3BfAt45FwM1asDIkT5g3yWHSCphjwJvAXuIyGDgK+DOmEZVDH36WCWsXz+YMQPmzLGs7nfcYX+YX39tFQGAE0+0fQMHwuGHW6Xr1VehXDl7/c47rfK2997QoYMt0fPoo7Bly45lvvOOlXvvvdaq9txzcN998NRTcf3WE17t2vD551YZ7tkTbr4Z1JOdOOeiLOeA/TfeCDsi53InGsF/QRHZB2gPCDBOVWfHOrC8ZGZm6uTJk/M9ZvFiqwR98QXssYelS+jUqfBlff45jBtnFblffrENYNUqG3h+0UU2EWD5cqvE/d//wT77WJfn1KnWFfrbb9vz2jizebO1OD7zDJx7Ljz9tHVVOpcXEZmiqplhxxENkVzDXHQsXWpLHmVm2g30tm22zNqPP0LDhnYjXqZM2FG6VJff9avASpiI7A0sUtWNItIOaAG8kD2LfjyFeQFTtVQLWUtmPPWU/UGPHg3ly8O6ddbK9tNPVvEqV86Oz+r+dNupwuDB1hp29NH2M6xSJeyoXKIKuxImInWBF4Ca2NCMYar6iIjsjqXvyQDmA2cUtLauV8Lia+NGaxFbtw6OOsp6Mtq3h+++s7HCn3wCe+0VdpQuleV3/YqkO/JNYKuINASeAuoCL0cxvqQhsuOaZZdcAq+/DgceaKkYFi+GDz6w4776yv7YO3WyCsbmzaGFnZBE4Kab4PnnrcXysMPg99/Djsq5PG0B+qpqU+AgoJeINAX6Y70DjYBxwXOXQMqVs0H7N99sWfbLlrXhKl9+CZ07w1VXhR2hK8kiWrZIVbcAnYHHVfVaoFZsw0ouN9wAV1xhTdx161oF7IILbPme2bPhtNOgXj2rdPhsnR2dd56lEVm40CZV/PBD2BE5tzNV/VNVvw8erwZmA3sBpwDPB4c9D3QKJUBXoNGjbZjK7Nk2YP/nn+G66yynpK/q4cIS6ezIrsB5wHvBPu9Fz+bkk228wQ03WNfjxRfDjTfCu+/amLB33oE2bWygf1Zrz8qVOw/wL6nat7eKK9jYuk8+CTce5/IjIhnA/sA3QE1Vzbq1WoJ1V7oEtHWrrd4xYYL1TJx8sk3GUvUJQi48kVTCLsCStQ5W1d9EpD7wYmzDSj6nnmqD8bdssTutCy6w/aVL2x/7e+9ZBeyAA2x/v35Qvz7ceqtl9S/p9tvP8rplZFgL4ogRYUfk3M5EpBI2ROMqVf03+2tqA2xz/XcuIj1FZLKITF6elQPHxdWpp8KDD9q15q23YP58OPNMm61dvnzY0bmSKpK1I2epam9VHRU8/01V7ynofSLyrIgsE5EZBRx3gIhsEZEukYednPbaa/tMyVNPtRxlt922fZ3L//0v3PjCVqeOjdNo184qsYMG+R2qSxwiUgargI1U1dHB7qUiUit4vRawLLf3quowVc1U1cwaNWrEJ2C3g0GDrMX96KNh/HibMblwITzySNiRuZIskpawohqBrTmZpyAJ7D3A2BjGkZBOPNEyx//6q62x+O238Pbb9poqLFkSbnxhqVIF3n8fune3VsIePXxSgwtfsIbuM8BsVX0w20vvAN2Dx92Bt+Mdm4tM9eo2I/LSSy2f48UXW69F9eo2LtW5MMSsEqaqXwB/F3DYldidZa53jyVB/fo2VmzhQrj9dtv3xRfWKtS5M3z8seW2KUnKlrWEt7fcYisPtGljExvS0qy7cuTIsCN0JdChwLnA0SIyLdhOAO4GjhWRX4BjgucuQZUta12Qgwdba3vFirbW8MknW15I5+It30qYiJQSkftjUbCI7AWcCjwZi/Mnm7JloWpVe9ygAfTta03nHTpYUsG77oK1a3d8z7Ztlkz2uedg+vS4hxxTItZ90KOHfW+//24thAsWWLZ9r4i5eFLVr1RVVLWFqrYKtg9UdYWqtlfVRqp6jKoWdOPpEswdd9g19rTTYN68sKNxJU2+lTBV3QocFqOyHwauV9UC23lK2qDWunXhnnus4vHKK9b68/DD2zM7L1xor+2/P1x7LXz6KRx/PJx9dup13eU2U3LdOrt7dc654qpSxWayb9tmY3P//bfg9zgXLZF0R04VkXdE5FwR6Zy1RaHsTOAVEZkPdAGGiEin3A4sqYNay5WzpvPx47cnGdy6FY44wpZH2mMPG8vwwgs2tmz5ckuVkUoWLizcfuecK6yGDS3x9k8/efJWF1+RVMLKAyuAo4GTg+2k4hasqvVVNUNVM4A3gMtVdUxxz5uqsroqVS0f2caNNpuybl045xzLO3bLLanXTVevXuH2O+dcUbRvbyufDBoUdiSuJIkkRcUFuWwXFvQ+ERkFTASaiMgiEblIRC4VkUujEXhJVbq0NZlXqWJjpS67DN58E77+GipXhvXrw44wugYP3nntzawlj5xzLprOOMNubLdtg++/DzsaVxIUWAkTkcYiMi4r35eItBCRAv8FqmpXVa2lqmVUtY6qPqOqQ1V1aC7Hnq+qbxTtWyh59tzTxonNmGFjxX791fKOPfkktG5tXZapols3GDbMcqmJQM0gH/k334Qbl3Mudd1+uy2jNmlS2JG4VBdJd+Rw4AZgM4Cq/gicFcugXP5EYMgQ6N3bct689RacdJIN0H/jDWtWX7w47Cijp1s3y269bZvlT7vuOnj6acsn5pxz0XbFFZYmqFOn7UvNORcLkVTCKqjqtzn2+aqHITvwQFvsOj3duiU7d4Zp06zV6LvvoFWr1E1AOGiQLT3SowesWBF2NM65VFOtmq35u26dVcTWrQs7IpeqIqmE/SUiexOsiRYsL/Rn/m9x8VCrlg3SHzoULrrIxk517w6TJ9trxx9vr6fa0j/lysGLL1oF7PLLw47GOZeKmjWDUaNsTeBLLgk7GpeqIqmE9QKeAvYRkcXAVYAPrk9g++5rY6YuucS6LrPWq0wlLVtai9hrr1kuNeeci7YTT4QnnrAJUM7FgmiEzSQiUhFIU9XVsQ0pf5mZmTp58uQwQ0gqqlYJ++ILS2PRsWPYEUXPli1w+OGWQ23GDKhdO+yIXKyIyBRVzQw7jmjwa1jyWrwY9tor7Chcssnv+hXJ7MhqIvIo8CXwmYg8IiLVoh2ki42sVrD77oNTToFrroFNm8KNKVpKl7ZEtRs2WHdsqnW7OucSx/Dh0Lixjb11Lloi6Y58BVgOnIZltl8OvBrLoFz0vfEGXHklPPQQHHpo6qyR1qiRVTA/+sgmJTjnXCycfDLsvrv1JixdGnY0LlVEUgmrpaq3q+pvwXYHUDPWgbnoKlfOljR680345Rdbd/Lnn8OOKjouuwyOOcYWPf/117Cjcc6loj33tBmTK1ZYXsaNG8OOyKWCSCphY0XkLBFJC7YzgI9jHZiLjc6dbbbPpZdaK1IqSEuDZ5+17snu3VMrWa1zLnHsvz88/zxMnGgTn3wIhCuuSCphFwMvAxuD7RXgEhFZLSK+3nwSql8f7rnHKi8LF1py1zlzwo6qeOrWhcces+WbHnww7Gicc6mqSxdbTu3II1Nz5rmLr0jWjqysqmnB8kNlgseVg23XeATpYmfBAkv62qaNLV6bzM45x1r6brrJZks651ws3HgjXHCBPfZErq44ImkJcyns8MNttk+rVrY8UI8eyXtREbHEtVWrwrnnps4sUOdcYvrwQ2jQAGbNCjsSl6y8EuaoU8fWnbzxRhtbdfvtYUdUdDVq2CzJadOS+/twziW+5s3t68kn+xJqrmi8EuYAG9Q+eDD8739WGQNYtSo5B56ecgqcfz7ceaetHOCcc7FQty6MGWNJXLt0gc2bw47IJZtIkrXuLSLlgsftRKS3iFSNeWQuFEcfDZUrw/r1cNhhNttwzZqwoyq8hx+2Fr7zzkve7lXnXOI76CBL5PrZZ9CnT9jRuGQTSUvYm8BWEWkIDAPqYrMlXQorWxZOPx1GjrRB+z/8EHZEhVOlCjz3nM367N8/7Gicc6ns3HPhuussH+O2bWFH45JJJJWwbaq6BTgVeExVrwVqxTYsF7ZSpeCWW2DcOFi9Gg48EB5/HG69FdLTrZLTpUtiJ3w9+mjo3dtSV4wbF3Y0zrlUdvfdtiJJWppXxFzkIqmEbRaRrkB34L1gX5nYheQSSbt2Nsi9XTsYONCSFL73HsydCwcfbPv/+CPcGPNz113QpIlNJ1+1KuxonHOpKitn2A8/WFLXuXPDjcclh0gqYRcABwODVfU3EakPvBjbsFwi2WMPGDLEHr/1FtSrB7//bssEdeliaSESVYUKtsj3H3/4eA3nXOxVrgyLFtkak37j5woSSbLWWaraW1VHBc9/U9V7Yh+aSySzZ0PbtlapufFGG7Q/YYK1hE2fHnZ0+WvbFm64wZYbGTMm7Gicc6msQQN44w1bo7drV19GzeUvktmRh4rIJyIyR0TmichvIjIvHsG5xNG4sa05uXGjjRWrUwdOPBHefddeS3Q332xdBD17wrJlYUfjnEtlRx1lY2g//BCuvNLGi112GTzzjM/WdjuKpDvyGeBB4DDgACAz+OpKkEaN4NBDLWXFli02LkzEuvpOOCHs6ApWtqzFumqVLV6ejPnPnHPJ45JLbIb500/Dr79Cs2Y2nKNtW/jrr7Cjc4kikkrYKlX9UFWXqeqKrC3mkbmE8+KLULMmNG1qW6NGsOuuNjU7GSo1zZtbQtq33rLvxTnnYum336z1a/hwuOIKu3k9/HCbMOQcgGgB/z1F5G6gFDAa2Ji1X1W/j21oucvMzNTJkyeHUbQLbNtm4xzKlIHvvrOBqPvsE3ZUkdm61boKfvjBFvmuWzfsiFwkRGSKqmaGHUc0+DWsZFi2zGZm//UXLF9uLfBPPQVLl1oLWSKn93HRld/1K5KWsAOxLsg7gQeC7f7oheeSTVqaVcAADjjAKmCqtmbj2rXhxlaQUqVgxAirjF1wgefzcc7FRtmydp3ZuBH+/tvGh/XpY3kXd9kl7OhcoohkduRRuWxHF/Q+EXlWRJaJyIw8Xu8mIj+KyHQRmSAiLYvyDbjEMG2aDTw97TTYtCnsaPLXoAE8+KAlcM1KveGcc9FUtarNHr/rLth3X5sc9Oqr1i3ZrVvY0blEEcnsyCoi8qCITA62B0SkSgTnHgF0yOf134AjVXU/4HZsSSSXpPbf31rCPv7Y1mtM9GnZF18Mxx9v49nmzAk7GudcKho61GaQt24NP/0EpUtbN+SFF4YdmUsUkXRHPgusBs4Itn+B5wp6k6p+Afydz+sTVPWf4OkkoE4EsbgEdtFFcN992+/2EnmwvojNWipf3iqNW7aEHZFzLtXUrg3ffw8PPGDLqD33HGzYAPf7gB4XKB3BMXur6mnZng8SkWlRjuMi4MMon9OFoF8/WLHCKmOXXAKtWoUdUd5q17buyK5d4d57LQmtc85FU1qaVcCylC8Pxx0XXjwusURSCVsvIoep6ldgyVuB9dEKQESOwiphh+VzTE+gJ0C9evWiVbSLkTvvhLPOgpZJMMrvrLMsi/6tt1q+s0SuNLriEZHywEnA4UBt7Do2A3hfVWeGGZsrObp0sa9ZY2fLlg0vFhe+SLojLwOeEJH5IrIAeBy4NBqFi0gL4GnglPxyj6nqMFXNVNXMGjVqRKNoF0Mi2ytgo0fbckGJ7IknoFo1OPdcm8nkUo+IDAK+xtbB/QZ4CngN2ALcHawK0iLEEF0Jsno1tGljeQtdyVZgS5iqTgNaisiuwfN/o1GwiNTDco+dq6o+NDoFqVqSwrFjoUoV6NQp7IhyV62aJVQ88UQYONCWGHEp51tVHZjHaw+KyB6AN7O7uKhc2Vrd77oLzjjDsum7kinPZK0ico6qviQi1+T2uqo+mO+JRUYB7YDqwFJgIFAmeO9QEXkaOA1YELxlSyTJGD3RYXJZswaOOcbWnfzoI0uUmqh69rTB+l9+aUs0ucQRi2StIpIGVIrWjWWk/BrmwBK47ruvrTzy1VeWw9ClpqIma60YfK2cy1apoEJVtauq1lLVMqpaR1WfUdWhqjo0eL2Hqu6mqq2CLSWyYbsdVaoE778PDRtCx46QyP97HngAMjJsfcw1a8KOxsWCiLwsIruKSEVsPNgsEbk27LhcyVOjBjz8MEyaBE8+GXY0Lix5VsJU9ang4f9UdVD2DRgXn/BcKqhWzbokq1e3tdMSVeXKNn5t3jy41v8tp6qmQctXJ2xGdn3g3FAjciVWt27wf/8Hr72W2Cl9XOxEMjD/sQj3OZenvfaCKVNszFUiO/xwuOYaS7JYs6ZNL8/IgJEjw47MRUkZESmDVcLeUdXNgP/7c6EQsWvLuHH22JU8eQ7MF5GDgUOAGjnGhe2KLejtXKHsvrt9nT4deve2u79EnOzarJldEJcts+cLFth4MfDlRlLAU8B84AfgCxFJxxJQOxeKatXs6z//WFb9gw8ONx4XX/m1hJXFxn6VZsfxYP8CXWIfmktVq1bBN99Ahw7wbwL++xs0aOeugXXrYMCAcOJxxSciB4uIqOqjqrqXqp6gNitpIZDA00VcSXHeeTZu9q+/wo7ExVOeLWGq+jnwuYiMUNUFeR3nXGEddhi88QaccoptH35oWaQTxcKFhdvvksJ5WL7DOcBHwEequiSoiPmiVS50gwdb7rC+fRM/t6KLnkjGhK0TkftE5AMRGZ+1xTwyl9JOOMEuNJ9/DmeemVhrN+a1KIMv1pC8VPUyVW0N3ArsBowQkYkicqeIHCEiPsTChapFC7j+enjhBfjkk7CjcfESSSVsJPATNotoEDae4rsYxuRKiLPPhkcfhbVrEytT/eDBUKHCjvvKlvXs1qlAVX9S1YdUtQNwNPAVcDqWRd+5UN10EzRubOvurl0bdjQuHiKphFVT1WeAzar6uapeiF28nCu2K66Ajz+GihWtIpYI07S7dYNhwyA93Z6XLm2TCs44I9y4XPSISAWgGfCdql7peQpdIihf3lYZadbMK2ElRSSVsM3B1z9F5EQR2R/YPYYxuRKmVClbS+3II23x70TQrRvMn2+VwjFjYMkSS1vhkpOIdAzWv/1eRE4AZmLr4E4Xke4hh+fcf444At59F/bYI+xIXDxEUgm7Q0SqAH2BftiC21fHNCpX4lSsaM3wN92UeNmjTzjBll669VabRu6S0u3AccAl2MLd7VX1IKAFdl1zLqHMmwe9esHmzQUf65JXvpWwYLBqI1VdpaozVPUoVW2jqu/EKT5XQqSl2SLaJ59sF55Ro8KOaDsRW9Lon3/g9tvDjsYV0TZVnaOq3wG/qeo8AFVdhs+OdAnoxx9hyBC4//6wI3GxlG8lTFW3Al3jFIsr4cqUgVdftaz1551nY8USRYsWcNFF8Pjj8MsvYUfjiiBNRHYTkWrAtuDx7iKyOxH0CIjIsyKyTERmZNt3q4gsFpFpwXZCLL8BV7J06gSnnWZ5C+fMCTsaFyuRdEd+LSKPi8jhItI6a4t5ZK5E2mUXeOcdS1rYsGHY0ezo9tuhXDmbRu6SThVgCjAZW/Xj++D5FCwJdUFGAB1y2f+QqrYKtg+iFKtzADz2mF0Te/aEbdvCjsbFQp7JWrNpFXy9Lds+xWdIuhipUgXefNMeb9gAd9xhC4CrQufOcNVVdmGKtz33hBtusMz5n39uEwlcclDVjGK+/wsRKdY5nCusWrWsO7JHD8sfdv75YUfkoq3ASpiq+pIeLhSqNlV78WIbL1a3Ljz4IJx0kiUzTIukHTfKrr7aZklecw189104MbjCC8a37qKqa4LnB2FLswFMVdXVRTz1FSJyHtbC1ldVfeqGi6oLL7TZ4507hx2Ji4VIxkLUFJFnROTD4HlTEbko9qG5ku7zz+1rxYpw883WPfnmmzZAPqzxYrvsAnffDd9/Dy+9FE4MrkjuAS7P9nwUcC1wM3BTEc/5JLA31lvwJ/BAXgeKSE8RmSwik5cvX17E4lxJJGKt/7vumlgri7joiOQ+fgTwMVA7eD4HuCpG8Tj3n2++sbu/Dz+E5ctt5uSWLXDiifZaWM46C9q2hRtv9ISKSaQ98GC25ytV9WQsbcWhRTmhqi5V1a2qug0YDrTN59hhqpqpqpk1atQoSnGuhFu4EFq1grfeCjsSF02RVMKqq+prwDYAVd0CbI1pVM4Be+0Fs2dbheell6z16aab4Kef7LWwpKVZt+jixZa6wiWFtODaleV6gGAB70pFOaGI1Mr29FRgRl7HOldctWrZ6h29esHKlWFH46IlkkrY2mBat8J/YylWxTQq57BWsGnTbAmhk06ylieAr76yRb/DdOihcPrpcM898Mcf4cbiIlJWRP6bBamqYwGCRNTlC3qziIwCJgJNRGRRMCTjXhGZLiI/AkfhSaxdDJUpA08/DUuXQv/+YUfjoiWSStg1wDvA3iLyNfACcGVMo3IOW0R77Fh49lm7C3z6afj0U/joI6hUpLaL6Lr7buseHTAg7EhcBIYDr4pIvawdIpKOjQ17uqA3q2pXVa2lqmVUtY6qPqOq56rqfqraQlU7quqfMYzfOTIzbVLQU0/BF1+EHY2LhgIrYar6PXAkcAi25EczVf0x1oE5B9C0KUyaBFOm2GzEyZPh/fehS5fwF/tu0AD69IHnn7euUpe4VPVB7GbyKxFZISIrgC+Ad1XVc5K7pDFokF17nngi7EhcNEQyO7I80Btbe20Q0CvY51zc1K0L9YI2jAoVbHBqIiyoPWAAVKsGffuGXyl0+VPVoapaD8gAMlQ1XVUTbKVS5/JXoYKl6Bk5MuxIXDRE0h35AtAMeAx4PHj8YiyDci4/vXtDhw7WLD9zZrixVKkCt90Gn31mmf5dYhKRc0QkDUBVV+fMCyYie4vIYeFE51zhNGhgg/T//tsW+nbJK5KM+c1VtWm255+KyKxYBeRcQdLSYMQIW8+xa1f49lsoH2Lb7MUX2/Ii114Lxx8PZcsW/B4Xd9WAqSKStVTRcmxAfkNsuMVfgA93dklDFdq1s2vfxIlQqlTYEbmiiKQl7PtgRiQAInIglh3audDUrAnPPWfpKr76KtxYSpe2VBW//AJPeudWQlLVR4DW2ED8GljesNbAYuBcVT1NVX1pdpc0RGzG+Hff2U2gS06iBQxkEZHZQBNgYbCrHvAzsAVLs9Mij/c9C5wELFPV5rm8LsAjwAnAOuD8YBJAvjIzM3XyZK8DOrNoEdSpE3YUdlfaoYNdEOfOhd13Dzui1CIiU1Q1M+w4osGvYS5aVC2J9aef2tCMjIywI3K5ye/6FUlLWAegPtZkf2TwuANWwTo5n/eNCI7Ly/FAo2DriS0B4lyhZFXA3nsPliwJLw4RW2h31Sq4/fbw4nDOlRwiMGSIDdG49FKfHJSMIklRsQCoilW4TgaqquqCrC2f930B/J3PqU8BXlAzCaiaIwO1cxFZsgTOOAMuuAC2bQsvjv32gx494PHHYc6c8OJwzpUc9erBXXdZMldfRi35RJKiog8wEtgj2F4SkWgka90L+D3b80XBvtxi8MVvXZ723NNaoT76KPyxEbfdZgNlr78+3DiccyVHr142OzsRkli7womkO/Ii4EBVvUVVbwEOAi6ObVg78sVvXUEuu8zGRlx3HfzwQ3hx1Kxpg2XHjLG0FS6xiEg5ETlbRG4UkVuytrDjcq44RGz79Ve4776wo3GFEUklTNhxwe6twb7iWgzUzfa8TrDPuUITseWNqlWDs8+GjRvDi+Wqq6yL4JprYKsvdZ9o3saGQmwB1mbbEsrIkTbIOi3NvnpiTheJkSPtRvTDD8OOxEUqkjxhzwHfiMhbwfNOwDNRKPsd4AoReQU4EFjla6+54qheHV58ERYuDDdX1y672LqSZ59t8Zx/fnixuJ3UUdX8JgyFbuRI6NkT1q2z5wsWWC46gG7dYlvugAH291OvHgweHNvyXPRdfz28+ipcdBGcdZb9Dh1zDHTqZKl0XOIpMEUFgIi0BrKySX+pqlMjeM8ooB1QHVgKDATKgC0fEqSoeBybQbkOuEBVC5y37dO7XaQ2bQqvMqYKhxxi/0B/+QUqVgwnjlQRrRQVIjIMeExVp0chrCIp6BqWkWG/NzmJWOWoalXbqlTZ/jivfVnPq1TJ/59wzoof2PI4w4Z5RSzZ3HYbDBwIbdrAeefZZ7vbbjZmzBNJhyO/61dElbBE4pUwF4kxY6xbcMIEqF07nBgmTrSK2MCBcOut4cSQKopbCROR6YBirf+NgHnARmxoRZ75DmOhoGtYWlreqQbOOw9WrrRUKCtXbt9WrSq43EqV8q6ovfQS/Pvvzu9JT4f58ws+t0sM69ZZRf3YY61FbOJEyMyE9u2he3ebQe7iL7/rlzdQupS0776wfLn90xo71v6xxdvBB8OZZ8K991p30l65zv11cXJS2AFEql693FvC0tPh+edzf8/WrbB69c6Vs9wqbFn7/vwTZs+257lVwMDiaNPGWucyMiyGrMcZGbDrrsX5Tl20TZhg175hw2x9yaZNbTmjiy+G0aO9EpaI8qyEiUg5VQ1xeLNzRdekCTzyiF18HnjA1nUMw913W6vcgAG23qULR1ZOQxF5UVXPzf6aiLwInJvrG0MweHDuXYODB+f9nlKltrdspacXvsz0dBsLllOlSjbjd/ZsG+y9fv2Or++2246VspxbQZU0H4cWXRUqWAW7cuUdf1/+/ddec4knv5awiUDr3C5aziWDiy6y3GE33ghHHWXN8vGWkWHdovfcA1deaa0KLlTNsj8RkVJAQn0qWZWQeFZO7rwz94rf0KHby1W11uX5821bsGD7459/ho8/3vH9sGMlLWcr2rff2t9G9gkIPXvaY6+IFc2BB9rP89VXrRV+2jRLIL1ihX2WLvHkOSZMRGYAdwK3Azu1I6jq6NiGljsfE+YK4++/oVUruPxy6N8/nBhWrYJGjaxr4NNPbYC1K5wojAm7AbgR2AWbCAQ2HmwTMExVbyh+lJFJ1GtYcVulVOGvv7ZXzHJW1ObPjyyju49DK55p0yxnYv361iL2wQd27Zk5M+zISq4iDcwXkcOAbsAZWDqJ7FRVL4xqlBFK1AuYS1wrV1o3TZiGDrWEsm+9ZdPFXeFEcXbkXfGscOWmpF7DVK1FJqtCdvrpeR/bu7cNJj/iiPD/dpPRpk02Fvaffyx59QMPWEvlcceFHVnJVKzZkSJykapGIy9YVJTUC5grvm+/te6UE0+Mf9lbtkDLlnZxnDnTp4oXVhRawlrn97qqfl/UcxeWX8NMXqk4ype31uL1621CTWYmHH20VcoOOcTHNhXWhg22rq0ITJ8O5cqFHVHJk9/1K5I5Yy+KSG8ReSPYrhSRMlGO0bmYUrUM9ueck/sA5FgrXdruRufOhSFD4l++44FgewL4BhgGDA8ePxFiXCXW4ME7V6gqVICnn7YWnM8+s+7RMmVsbdhjj7UxZu3awe23w9dfw+bNYUSeXMqXh8cft3yFzz4bdjQup0hawp7GkqxmTY4+F9iqqj1iHFuu/C7SFdWvv9r4sNatYfx4m1EWbx06wDffWGWsWrX4l5+sotgdORoYmJWsVUSaA7eqapfinjtSfg3bLtJxaGvWwJdf2t/tuHE27knVkiAfcYS1kh19tLU2h5GOJhm8+y4cf7xnzg9DcVvCDlDV7qo6PtguAA6IbojOxd7ee8MTT8AXX1jqiDA88IBNF7/ttnDKdzTJni1fVWcA+4YYT4nWrZuND9u2zb7mNRGgUiWrQNx3H3z/vQ0reOMNS0D622/Qr5/dXNWoAV26wJNP2ozN3NoYSuq6nCefbBWwNWvCjsRlF0klbKuI7J31REQasOOC3s4ljXPPtTXVBg60Aavx1qyZTcMfMsT+Sbi4+1FEnhaRdsE2HPgx7KBc4VSrBqedZjdVs2fDokW2TmvHjjb28/LLYZ99oG5dS9g8YgT8/vv25ZkWLLAKWlZajJJSEZsxw25G33sv7Ehclki6I9tji3jPw6Z0p2PrPH4a+/B25k35rrhWrrSxEb17h9M0v2wZNGxoucvefjv+5SejKHZHlgcuA44Idn0BPKmqG4p77kj5NSy2VG3oQVbX5fjxljoD7O99y5ad31NS0mJs2mRDMjZssAlCu+wSdkQlQ7HXjhSRckCT4OnPYWbS9wuYi6Z//w1n6ZV77rG8ZePG2VgWl79oVcISgV/D4mvbNmsBGj8err4692NE7LiS4NNP7Zpzyy0waFDY0ZQMxR0ThqpuVNUfg82XMnIpYc4caNwYXn45/mX36WN333372rp/LrZE5LXg63QR+THnFnZ8LnbS0qBFC8vOn9eSTlWrRpZINhUcdRScfbbdCM6dG3Y0zueRuBKrQQMbH3HZZTa4N57Kl7eL4LRp8MIL8S27hOoTfD0JODmXzZUAuaXFSEuzlBj169vf5OrV4cQWT/ffb/nCwrgBdTvKtxImpm68gnEunkqX3j4gt1u33MeKxNIZZ8BBB9kUfZ+xFFuq+mfw8BigrKouyL6FGZuLn27dYNgwaxETsa8vvABffWWzK/v3txmTgwfbcmOpqlYtm5h0881hR+LyrYSpDRj7IE6xOBd3GRnw1FMwcaIlgIwnEXjoIfjzT5t67+KiHvCUiMwTkdeD5NOtwg7KxU9uaTEOPRQ++ggmTYKDD4abbrJrw6BB1kqWijIy7Br0668lpys2EUXSHfm9iHheMJeyzjrL8g3NmBH/wbkHHWTl33efTbN3saWqA1X1aKAZ8CVwLTAl3KhcojjwQEvfMHkyHHkk3HqrVVZuusnWvUw1f/5pSxrF+wbUbRdJJexAYKKI/BoMYp3uA1ldqnnqKUv+GEa27bvvtsrfjTfGv+ySRkRuEpEPgbFAQ6AfUCfcqFyiadMGxoyxMZvHHWfdkxkZ1l25bFnIwUVRrVpw5pmWRHr27LCjKZki+Zfzf8DewNHYANasga3OpYxy5bY3zd92W+6ZtmMlPd3WtXzxRbsolrRM3nHWGagG/A8YDbydbbyYczto2RJef90Wvj7pJLj3XhvA37cvLFkSdnTRcc89tiLBFVfE97rnTIGVsGDQal3g6ODxukje51wyevNNy6Z/ySU2VuSyy2y8WKzVr29flywpmZm840VVW2OD878FjgWmi8hX4UblEl3z5jBqFMyaZZn6H37Y/mb79IHFi8OOrnj22MNa+saPh1dfDTuakqfAypSIDASuB24IdpUBXoplUM6FpXdv2G03eOYZGyvRoIHNYnz00diWO3jwzvvWrbOZky56ggW7uwHdgTOBxcD4UINySWOffWw25c8/Q9eutmxSgwbQq5ctQp6sLrkE2rb1pdTCEEmL1qlAR2AtgKr+AVSOZVDOheWNN2xJoSpVrFWsTx+bvj5wYGwH5uZ1AU/mC3uCuhvYFXgU2FdVj1LVW0KOySWZhg1t6bNffoHzz4fhw23fJZfEP+dgNJQqBV9+adc5F1+RVMI2BakqFEBEKsY2JOfC8/HH1g04fLjNkHr4YRuzdcgh8MUXsSu3Xr3C7XdFo6onqeo9qjpBVTeHHY9LbvXr26SeuXPh4ottofBGjeDCC5MvG33Zsvb1q69sXUkXH5FUwl4TkaeAqiJyMTagdXhsw3IuHFWq2GK/p50GL71k3QwAy5fHdo3J3DJ5ly+fezelK7y8livyZYtcNNSrZ12T8+bZNWPUKGjSBM47z9LPZGQkx4Sb9euhc2dr0fNB+vERycD8+4E3gDeBxsAtqvpYJCcXkQ4i8rOIzBWR/rm8Xk9EPhWRqcHF8ITCfgPORVP37vDYY3Yx7dYNKla0u9ulSy1vUKxkz+SdpU0b2++iImtW90fB1i3YPsATUrso2WsveOQR65K8+mp47TW47jqbaJMME2522cVS5nz9tS+nFi+iEVR3RWRPoC3WJfmdqhY4OVdESgFzsBlIi4DvgK6qOivbMcOAqar6pIg0BT5Q1Yz8zpuZmamTJ08uMGbniurJJy1nV9u2VvmaNcty6bz4YvxiuOkmawWbMMEyeJd0IjJFVTOjcJ6pqrp/jn3fB7Mm48KvYSVH3bq5J2FOT7ds/Ylo2zY47DDrTv35Z5uo5Ionv+tXJLMje2DTuTsDXYBJInJhBOW2Beaq6jxV3QS8ApyS4xjFBskCVAH+iOC8zsXUZZdt71Z46CHo0cPuXGM5Jiyn/v2hdm2bGBDvLP4pTkTk0GxPDsFT7rgYySt9xYIEXq00LQ2GDLGJSDfdFHY0qa90BMdcC+yvqisARKQaMAF4toD37QX8nu35Iiz7fna3AmNF5EqgIpa/x7nQ7bYbdOxojw84AMaOtVlQP/5oiQ1jrVIlS6J47rnWLXD++bEvs4S4CHhWRKoAAvwDRHJT6Vyh1auXe4WrXDnLCbjnnvGPKRKtWsH111sOMRdbkdwBrgBWZ3u+OtgXDV2BEapaBzgBeFFEdopJRHqKyGQRmbx8+fIoFe1cZCpVsnFh8+fb+I54OftsW1uyf3/499/4lZvKVHWKqrYEWgItVLWVqn4fdlwuNeU24SZrFmJmps3ATlR33glXXRV2FKkvz0qYiFwjItcAc4FvROTWIHHrJGysV0EWY5n2s9QJ9mV3EfAagKpOBMoD1XOeSFWHqWqmqmbWqFEjgqKdi67DDrOlhaZOhQ0b4lNmWpoliV261C6IrvhEpJyInA30AvqIyC0i4nnCXExkn3AjYl+ffRa++QZKl4bDD4eXXw47yryp2lCMRI4x2eXXElY52H4FxhDkCQPeBiJJR/cd0EhE6otIWeAs4J0cxywE2gOIyL5YJcybulxCGjzYEhqWLx+/Mg84wLoiH3oo+fIOJai3sbGpW7AE1FmbczHRrZu1om/bZl+7dbM1Kb/7zib/dOtmXX9bt4Ydae6GD4crr7TUPS76IpodWeSTW8qJh4FSwLOqOlhEbgMmq+o7wYzI4UAlrJJ3naqOze+cPrPIhW35csumf+ml8Snvzz+hcWNo3x7GjIlPmYkmirMjZ6hq82jEVFR+DXNZNm2yyTdDh8IJJ1iLU5UqYUe1o5kzbYxY1soArvCKOzsyU0TeEpHvC5vcUFU/UNXGqrq3qg4O9t2iqu8Ej2ep6qGq2jIYm5FvBcy5RPDUUzaD8t1341NerVo2S+ntt+GTT+JTZgqbICL7hR2Ec2Djw5580raxY20M6JxIBvvEUbNmNjbs6adh0qSwo0k9BbaEicjP2AzJ6cB/k+VVNZRJtn4X6cK2aZN1Ey5daneJ1arFvsyNG+1iWK4cTJsGZcrEvsxEEsWWsFlAQ2xIxUZshqSqaovinjtSfg1zufn8c+jSBbZsgVdegf/7v7Aj2m7NGlu8fI89rBu1VKmwI0ouxWoJA5ar6juq+puqLsjaohyjc0mjbFlLG/H333DFFfEps1w5ePBBSxw7dGh8ykxRxwONgOOwDPpZmfSdC9WRR1oFp14965p88MHEWTqoUiXrAbj5Zpsw5KInkh/nQBF5WkS6ikjnrC3mkTmXwFq2hIED7Y71jTfiU+bJJ8Oxx8Itt/gg2aLKdhO5HhuHmrU5F7qMDFsy6NRToW9fG4cVr9nYBTnxRItLJOxIUksklbALgFZAB+yOMevu0bkS7frrbTv88PiUJ2KzJFevtoqYKzwR6Sgiv2DdkZ8D84EPI3jfsyKyTERmZNu3u4h8IiK/BF99gRdXbJUq2ZqTgwZZi3u7dvBHAq0lc++9tsC3i45IKmEHBDm6uqvqBcHmGaZdiVe6tC12W7OmTT+PR9dBs2Zw+eXWNfBjRNNjXA63AwcBc1S1PpYiJ5LhxiOwG9Hs+gPjVLURMC547lyxpaXZjdbo0TBjhiV2/fbbsKMyq1ZZ7rN4LuOWyiKphE0IUkk453KxYgUccQS89FJ8yrv1VltW6aqrEmfMSBLZHCzBliYiaar6KVDggH9V/QL4O8fuU4Dng8fPA52iGahzp54KEyfamNAjjoAXXww7IhgwwJLOXn45bN4cdjTJL5JK2EHANBH5OUhPMT3SFBXOlQRVq1pX4ZVXwqJFsS9v993h9tvh00/hrbdiX16KWSkilYAvgJEi8ghFT9ZaU1X/DB4vAWpGI0DnsttvPxuwf/DBcN55cO214SZ2rVABHnnEZoY/+mh4caSKSFJUpOe231NUOLfdr79CixY2PuzDD2M/eHXLFmjd2saHzZ4d3yz+YYhiioqK2KD8NKAbUAUYGbSOFfTeDOC9rGSvIrJSVatme/0fVc11XJiI9AR6AtSrV6/NgtxWdXYuH5s3w9VXwxNPQIcOMGqU3QCGQRU6drQuyUWLoHLlcOJIFsVNUaF5bM65wN57w333wccfxyerdOnS8PDDtgzKAw/EvrxUoaprVXWbqm4B3gcei6QCloelIlILIPi6LJ9yff1bVyxlysDjj9t40P/9Dw48EH7+OZxYROCxx2D8eK+AFVcklbD3gfeCr+OAeUQwm8i5kubSS21poSeftIH6sXb00dC5sy3uvXhx7MtLZiJykIh8JiKjRWT/YJbjDKwilXPAfaTeAboHj7tj61I6F1M9e1rl559/bO3JD0P6b5yRAW3a2OO1vvpqkRVYCVPV/VS1RfC1EdAWmBj70JxLLmlpMHIkfPVV/BIa3n+/jQ/p7/PyCvI4cCcwChgP9FDVPYEjgLsKerOIjMKue01EZJGIXATcDRwbpLw4JnjuXMwdfriNE2vQwPJ33XdfeJN0br3VZm9u3BhO+cmu0P8qVPV74MAYxOJc0qtZEypWhPXrbRmSWKtfH/r1s5mZE/3WKD+lVXWsqr4OLFHVSQCq+lMkb1bVrqpaS1XLqGodVX1GVVeoantVbaSqx6hqztmTzsVMerrd8HXpAtddZ4P216+PfxwHHQQ//eTDIooqkgW8r8m29RORl4EESh3nXOLp188Gz8ZjzEb//lC7NvTpE59u0CSV/SeT81+Vj3F1SaliRXj1VbjjDrsRO+KI+A9N6NDBWuNuuQV23dW288+HJUviG0eyiqQlrHK2rRw2NuyUWAblXLK76SbYZRe7GG3ZEtuyKlWCe+6x7okXXohtWUmspYj8KyKrgRbB46zn+4UdnHNFJWK5u8aMsRapzEzLtp+RYcMiMjJsmESsbN1qE4RE4JBDYO5cW+j7qKO8izISkYwJG5RtG6yqI1U1QVazci4x1aplU8knTbJxW7F29tnWLdC/P/z7b+zLSzaqWkpVd1XVyqpaOnic9bxM2PE5V1ynnGJDErZts3FaCxbYOLEFC2wwf6wqYh9+aC1yt99u3aPr1tnSRnvtZRn/Xf4i6Y5sLCLDRGSsiIzP2uIRnHPJ7Kyz4LTTbKHv6dNjW1ZamiVQXLrUZks650qe5s2hbNmd969bZ61lsTBrlk0UuOYaa4nLyLD9hx9ur7n8lY7gmNeBocDTQIh5ep1LLiKWrmLjxvgkU23b1ro/H3oIevSAhg1jX6ZzLrHkNSZs4cLYlLfPPtYVWqYM1KljrW9jx8LXX0P37gW+vcSLZEzYFlV9UlW/VdUpWVvMI3MuBdSoAe++C40axae8O++0O+G+feNTnnMusdSrl/v+unVjU94JJ1iesL59YflyGDHCBuv/8IP1BLj8RVIJe1dELheRWiKye9YW88icSyF//w1du8KUGN++1KplkwLeecfuRp1zJcvgwba+Y04NGsRm9nTp0pbB/++/rSvyssssVc+aNbFrfUslkVTCugPXAhOAKcHmizc6Vwgits5a9+6wIcbTWq66ypZRuvpqW2/OOVdydOsGw4ZZHjERaxk75RT47DO45JLYVMRq1LAWsDVrLFfZ5Mk2BOPss2HTpuiXl0oimR1ZP5etQTyCcy5V7LYbPPMMzJxpA/VjqVw5S5w4axYMHRrbspxziadbN0sbsW2bzY586y1rIX/6aZspGat8giK21alj17spU2I3ISBV5FkJE5HD8nujiOwqIs2jH5JzqalDBxswf//9MGFCbMvq2BGOPdYSKP71V2zLcs4lNhG47Ta4+WarHPXoEfvEzp06WQWsffvYlpPs8psdeZqI3At8hHVBLgfKAw2Bo4B0wIf/OlcIDzwAn3xiGfW//toujrEgYrMkW7a0itiQIbEpxzmXHEQsiWtWhUzVWsZKlYpdmXfcsf2xauyud8ksz0qYql4dDMA/DTgdqIUt9zEbeEpVv4pPiM6ljl13ta6B2rVjf0Fq1gwuv9ySxl56KbRoEdvynHOJLXtFbNAgqxg980xsK2JgK3p88w28+aZXxHLKN09YsCDt8GBzzkXB/vvb161bYdEiG0AbK7feCi+/bIP1x43zC6Bzzq4LIvZVFZ59NrYVsfLl7eZzyBDo1St25SSjSGZHFpmIdBCRn0Vkroj0z+OYM0RklojMDBYHd65EuOgiOPJIWL06dmXsvrstJ/Lpp76EiHNuu4EDrTXshRfgggvspjBWeveG44+3XGIzZsSunGQUs0qYiJQCngCOB5oCXUWkaY5jGgE3AIeqajPgqljF41yi6dkTfv899olVL74Y9tvPxqGtXx/bspxzyeOWW+wm7cUXbbWNWFXERCyFRdWqli/Rr0PbxbIlrC0wV1Xnqeom4BXglBzHXAw8oar/AKjqshjG41xCOeQQqxgNHw4ffRS7ckqXhocftinrDz4Yu3Kcc8nnpptsAP1LL1kew1hVxPbYwypiP/8MX34ZmzKSUSQLeFcQkZtFZHjwvJGInBTBufcCfs/2fFGwL7vGQGMR+VpEJolIh0gDdy4VDBoETZta1+SyZfDbb/Dvv9Ev5+ijoXNnW9Yor7XlnHMl04ABlml/5Eg47zzYsiU25XToAPPmwXHHxeb8ySiSlrDngI3AwcHzxcAdeR9eKKWBRkA7oCswXESq5jxIRHqKyGQRmbx8+fIoFe1c+MqXtzEZ27bZbMZ27SzD9SWXRL/J/v777S63f66jM51zJdmNN8Jdd9lEnnPPjV1FrE4d+/rBB/DHH7EpI5lEUgnbW1XvBTYDqOo6IJI5VouB7EuG1gn2ZbcIeEdVN6vqb8AcrFK2A1UdpqqZqppZo0aNCIp2LnksX26Lbn/yiWW3/vVXWLEC+vSJbjn161v350svwcSJ0T23cy759e8Pd98Nr7wS24rY8uVw+unW/RnrpLGJLpJK2CYR2QVQABHZG2sZK8h3QCMRqS8iZYGzgHdyHDMGawVDRKpj3ZPzIorcuRTx6KPWTdikiXULbNtm48Reew1WroxuWf37W46y3r394uec29n111ter1deseWPYlERq1HDxqn+73+WwLoki6QSNhDLml9XREYC44DrCnqTqm4BrgA+xhK8vqaqM0XkNhHpGBz2MbBCRGYBnwLXquqKInwfziWt33+3rsh586zL8OyzoXJlqFbN7hijqVIlu8BOnmzdoM45l9N118F999mN4Nlnw+bN0S+jRw8bp3rjjXY9KqlEVQs+SKQacBDWDTlJVUNbjS4zM1Mnl+RPzKWcHj0sYevNN8Nzz8GFF1oz/UcfwcKF1lUZTdu2waGH2iSAOXMsi3+iE5EpqpoZdhzR4NcwlyweeMCGMHTpYmPFypSJ7vn//tuWVttlF/jhB/uaivK7fuWbMT9486nAeFV9P3heVUQ6qeqY6IbpXMl03XVw+OGQlmYLb3foAM8/b3l7ol0BAyvnkUfgwANtRtQ990S/DOdc8uvb13J89e1rmfVHjYpuRWz33W2M6ty5NkmpJIqoO1JVV2U9UdWVWBelcy4KGje2vDlz58KZZ8KmTdC8Obz/PqxdG5sy27a1it+999pFNiPDpqc751x211xj+QXffBPOOiv6XZNHHmkpekRgYySjzVNMgS1h5F5Ri+R9zrkINW5sXZFZli+3vGEVK8amvJEjdxyHsWCBZfAHG4zrnHNZrr7aKklXX203iq+8Ev1W+vfeg8sug6++iu16uokmkpawySLyoIjsHWwPAlNiHZhzJVmNGjZYXxVefz36d58DBuych2zdOtvvnHM5XXWVDWN4663tLfbR1LQprFoF55wT23UsE00klbArgU3Aq8G2EfB10J2Lg2++gTPOsDvQaFq4sHD7nXOud29LqTNmjF2XolkRa9AAnnzSWsLuvDN65010BVbCVHWtqvbPSpaqqjeoaoxGqjjnsjvoIJud9MQT8Mwz0TtvvXq578/KZu2cc7m58kp47DF4+21LuBrNili3btYSNmgQTJgQvfMmskjWjmwsIsNEZKyIjM/a4hGcc86WEjn2WLj88uhluh88GCpU2Hl/rVrWBeqcc3m54gp4/HF45x1LXxHNAfVPPGFjwsaNi945E1kkA+xfB4YCTwMlqKfWucRQurQNhD3gALvg/fJL7hWowsgafD9ggHVB1qsHBx9s5Tz0kM2Ics65vPTqZeluLr/crktvvAHlyhX/vLvuClOnJkf+wmiIpBK2RVWfjHkkzrk87b67Nf/Pm1f8CliWbt12nAmpane0110HmZlwxBHRKcc5l5ouu8xmTV52mQ2d+PtvWwGkXj1rbS/qTOusCtjkyXa+U0+NXsyJJpKB+e+KyOUiUktEds/aYh6Zc24HzZtbMleA2bOj320oYmky9t7bZj/9+Wd0z++cSz2XXgoXXADTplmruur2lDfFzT14441w3nmWQzFVRVIJ6w5cC0zAUlNMAXzNDedCMnEi7LefzSSKtipVYPRo+PdfG3QbizXjnHOpZXwuo8SjkfLmmWcsQ3/XrtFPiZEoIpkdWT+XrUE8gnPO7ezAA+H446FPH/jii+ifv1kzePpp+Ppr65p0zrn8xCrlTd26MHy4dUsOTNF1eiJpCUNEmovIGSJyXtYW68Ccc7lLS7P11vbe2wbExiK3V9eulhPo4YdtsL5zzuUllilvTjsNLr7Y1ridNKn450s0kaSoGAg8FmxHAfcCHWMcl3MuH1Wq2ED9jRtt0OqGDdEv47774NBDoUcPmDkz+ud3zqWGvFLelC0L//xT/PM/9JBtmZnFP1eiiaQlrAvQHliiqhcALYEqMY3KOVegJk1s4Otpp0VnanhOZcvCa69BpUrQubONE3POuZy6dYNhwyy/l4h9vfpqm9nYvj389Vfxzl+xog2/KF3aKnWplMswkkrYelXdBmwRkV2BZUDd2IblnIvESSfZDCIRWBuDdSxq14ZXX4Vff7UZUKl08XPORU+3bjB/PmzbZl8ffNBa62fPhqOOgqVLi1/G/Pm2xuTQocU/V6KIdAHvqsBwbGbk90CU8nY756Lh+++hfn343/+if+4jj7TxGKNHwwMPRP/8zrnU1KEDvP++5Tc88khYvLh456tXD1q2tGTSqTJEIpLZkZer6kpVHQocC3QPuiWdcwmicWOoWdPye82bF/3zX3ONTQK4/nr47LPon985l5qOPho+/hj++MMSQC9YUPRzpaXBiBFQubJNHorFWNh4i3R2ZAsR6Qi0BhqKSOfYhuWcK4xKlWDMGOsK6NQJ1qyJ7vlF4NlnrbJ35pnFv6N1zpUchx1mrfR//20VseIkX91zT6uITZ8OrVvbzWfDhnD77cmZSyyS2ZHPAs8CpwEnB9tJMY7LOVdIe+9t47dmzozN+K3Kla1Lcu1aS+SajBc851w42ra1pK5r11rX5E8/Ff1cBx1k16OtWy159euvW/qK7t2jF2+8RNISdpCqZqpqd1W9INgujHlkzrlCO+44G79VunRsKkn77mstYhMnQr9+0T+/cy517b+/DWfYutUqYtOnF+08zz5rS7j99BM0aGDnffNN+PTT4lXuwhBJJWyiiDSNeSTOuajo2xdeftnSVsRiNuMZZ9j088ceK/7acM65kqV5c/j8c1uOqF07m1RUWD/8AMcea8Mk/vjDxoetX2/dnj/+GPWQYyqSStgLWEXsZxH5UUSmi0iSfZvOlRwits2bB4cfDj//HP0y7rnHzt2zJ8yYEf3zO+dSV5MmtuRa5co2cP+bbwr3/r33tqWMwK4/o0fDMcfAt9/a+LBkEkkl7BngXKAD28eDnRzLoJxzxVeqlFXAOnWKfqLVMmVs/Nmuu1oi11Wront+51xqa9DAKmLVq1sF6ssvI3/vxRdbIunnnrMcZCNGWOvYP//kvYRSooqkErZcVd9R1d9UdUHWFsnJRaRD0II2V0T653PcaSKiIpKCixI4F470dBuw+ssvcM45NnMymmrVsgvhvHlw/vmeyNU5Vzj16llFrE4dyyk2blxk79trL/jwQ3j+eWtNu/hiy8y/ZYu1rC1fHtu4oymSSthUEXlZRLqKSOesraA3iUgp4AngeKAp0DW3sWUiUhnoAxSyQdI5V5B27WzNtXffhUGDon/+ww+H+++39Bj33hv98zvnUlvt2jZGbO+94cQT4YMPIntf69Y2yP/vv237+GO7zlWoYMMxkkUklbBdgI3AcRQuRUVbYK6qzlPVTcArwCm5HHc7cA+QAmnXnEs8V1xhKSvef98W/I62Pn0sd9iNN0Z+J+ucc1n22MNmNjZrZsMnxoyJ/L0VKtg6t2DdmhMnWhfnpk2wbFksoo2ufCthQWvWimypKQqTomIv4PdszxcF+7KfvzVQV1XfL2zgzrnIiMCQIdbsH4uFvkXg6adtsG3XrrZob0kiIvODCUvTRGRy2PE4l4yqVbObuDZtbHWOV18t2nmyWsEuvNBa6hM9sXS+lTBV3QocGouCRSQNeBDoG8GxPUVksohMXp5Mnb3OJYjy5e2O8d9/rWXsn3+ie/5KlWyG0vr1lsg1Fi1uCe4oVW2lqj6u1bkiqloVxo6FQw6Bs8+GF14o+rkuu8zSV7Rrl9g3hpF0R04TkXdE5NzCjAkDFgN1sz2vE+zLUhloDnwmIvOBg4B3chucr6rDgoSxmTVq1IigaOdcbn76CYYNg7POsoSJ0bTPPjZL6ZtvbK1J55wrrMqVbdD90UfbhJ9hw4p2nkMPtQrdsmWWGLY4a1bGUiSVsPLACuBoCjcm7DugkYjUF5GywFnAO1kvquoqVa2uqhmqmgFMAjqqqjfnOxcjbdta1+TYsXDDDbbUx4gR8N130ZndeNpplkl/yBB48cXiny9JKDBWRKaISM+wg3Eu2VWsaIPsjz8eLrnEEkMXxcEHwyef2MD9Tp2iP0M8GkoXdICqXlCUE6vqFhG5AvgYKAU8q6ozReQ2YLKqvpP/GZxzsdCjh1W+7rvPKkrHHgu33QaNGtnSH5UqFe/8d91llbpLLoGWLaFFi+jEncAOU9XFIrIH8ImI/KSqX2Q/IKic9QSol2yJjJwLQfny8NZb1mrfuzds2ADXXlv487Rta2PNtm2DtEianeIskgW864jIWyKyLNjeFJE6kZxcVT9Q1caqureqDg723ZJbBUxV23krmHPxUaoU1KwJu+xig+p/+cVmFN18c/HPXbq0DardbTdL5LpyZfHPmchUdXHwdRnwFjYzPOcxPqTCuUIqW9auJWedBdddB7ffXrQW+zZt4IAD7PHjj9v1LlFEUi98DutGrB1s7wb7nHNJSBVGjbK8Ol98YRe6UqXgjjvgpZeiU0bNmpYodsECOO+8xOwGiAYRqRjkOkREKmKpfHwhJ+eipEwZuy517w633AIDBhR96MRff1m+xHbtYrOcW1FEUgmroarPqeqWYBsB+K2cc0lswwZLjlinjl3Q+veHRYtsdmO0HHIIPPigje24++7onTfB1AS+EpEfgG+B91X1o5Bjci6llCoFzz5ra9XedRf07Vu0ilj16paPbPNmq4jNnh31UAstkkrYChE5R0RKBds52EB951wSErEBr089Zc+XLNk+NiwzygkWrrjCcofddJMNkE01QTLqlsHWLGvYhXMuutLSYOhQGx/20EPQq1fRWtibN7dM+6pWEZs5M9qRFk4klbALgTOAJcCfQBegSIP1nXOJ4b77rJXqvPPg7bet8rVtG0yYEN1ZjSIwfLhlwu7aFRYujN65nXMliwg8/DBcfz08+aQt3p2ebhW0jAwYOTKy8zRtaksllSkDU6bEMuKC5VkJE5F7godtVbWjqtZQ1T1UtZOq+qXUuSTWuDFMnw6tWsH331tOnjlz4LDDrGL2wAPRK6tiRZt1uXmzZcIugYlcnXNRImJdkp0725jWhQutVWvBAuuujLQi1qSJ5U087zx7viGkhRPzawk7QUQEuCFewTjn4mf33S2p6rBhtv5jRgZ89JElSGzePLplNW68PR9Z9eqFv3N1zrksIrm3YK1bZwP3I5WVjuezz6BhQ7shjbf8KmEfAf8ALUTkXxFZnf1rnOJzzsVR2bLw3HPwf/9nz99+G9asic65162z9BVr1hTtztU557LkNbShKEMe0tPt2tS+vd0oxlOelTBVvVZVq2KzfXZV1crZv8YvROdcGBYssHUg27WzwfvFNWAAbNmy477C3rk65xxAXjmPK1UqfNdi/fo2Rmy33eCYYyyZdbzkOzBfREoBXuFyrgRKT7eM1bNn2/IfP/1UvPNF887VOVeyDR4MFSrsuK90aVi92hKzzihktr70dKuI1agBxx0Xvzxi+VbCVHUrsE1EqsQnHOdcIjnxRLswrVtneb++/LLo58rrztVX8XHOFVa3bjaeNT3dxoilp9u40w8+sEW7DzjA1rAtTD6xunXtetenjy3jFg+RpKhYA0wXkWdE5NGsLdaBOecSQ2YmTJxod4jFmc6d251rhQq23znnCqtbN5g/39LrzJ9vz48/Hn780dJX9OoFp5ximfIjtddetjxSWpoNySjOjWckIqmEjQZuBr4ApmTbnHMlRIMGNnOoTx97njUtvDByu3MdNsz2O+dctNSsCe+/D488YsuztWgB//tf4c9z5ZU2SWncuOjHmKXASpiqPg+8BkxS1eezttiF5JxLRBUrWuVp3jxLYXHVVbB1a+HOkdudq3PORZuIZdf/9luoWtVWBLn2Wti0KfJzPP20pa446SQYOzY2cRZYCRORk4FpWMoKRKSViLwTm3Ccc4kuIwMuvhgefdRmT0ZzvUnnnIumli1h8mS49FK4/36bZDRnTmTv3WMPGD/eErt27AjvvQdvvAH9+tmKI8uXFz++SLojbwXaAisBVHUa0KD4RTvnklFammXUf+QRGDPGsu0XZsyFc87FU4UKtszRW29ZC/z++9uC4JEMqahe3Spi++4LZ59tla8aNWz2ZbNmxU9nEUklbLOqrsqxrwjLZjrnUknv3nZXOG2a3WE651wi69TJBu0feCBcdBGceSb880/B79t9dzjhBLvh/PpruO46q8QNGWIJpws7Pja7SCphM0XkbKCUiDQSkceACUUv0jmXKjp3tjvB226z54UdI+acc/G0117wySdw993WMtayZWQzIMeOhb59be3bHj0sbc9pp8GKFfDbb0WPJ5JK2JVAM2Aj8DKwCriq6EU651JJy5a23NHy5dCmjS115JxziapUKbj+epgwAcqVs1VBbr4ZNm/O+z1lylgm/k2bbM3dChXspnPTJnutqPKshIlIeRG5CrgXWAgcrKoHqOpNqhrSeuPOuUSlapWxzp3hiSfCjsY55/J3wAEwdSp07w533AFHHGGzv3PTtSvcdZdV2po1s31PPmmD9uvWLXoM+bWEPQ9kAtOB4wEf9eGcy9Mee8Cnn1qW/SuusHET23z0qHMugVWqZOO7Ro2yJdpatYKXX975uEsvtWtc48b2uF07eOgheO654pWfXyWsqaqeo6pPAV2AI4pXlHMu1VWsaOMsLrsM7rvPMk+76Bs50lKFpKXZ15Eji3/OSpUqAbBgwQJat25Nq1ataNasGUOHDgVg3bp1nHjiieyzzz40a9aM/v37F3jOF154gebNm7Pffvux//77c//99zNgwACuv/76/45ZsGABDRo0YOXKlXz77bccccQRNGnShP33358ePXqwbt06RowYwRVXXLHT+TMyMthvv/1o1aoVrVq1onfv3sX/QRTSqFGWN69UKfs6alTxz5n1WQAsXLiQ4447jn333ZemTZsyf/78HY7t3bv3DsfnpSR8FsVx1lk2yahFC8tfeN558O+/218vUwZeeQVGj4b99rM8iT/9FIXljVQ11w34Pr/nYW1t2rRR51xi27ZNdcgQ1aVLo3M+YLImwPUnGltxr2EvvaRaoYKqdQDbVqGC7S+OihUrqqrqxo0bdcOGDaqqunr1ak1PT9fFixfr2rVrdfz48f8dc9hhh+kHH3yQ5/k++OAD3X///XXx4sWqqrphwwYdNmyYrlu3Ths3bqyzZs1SVdVTTjlFX3rpJV2yZInWq1dPJ0yY8N85Xn/9dV2yZIk+99xz2qtXr53KSE9P1+XLlxfvGy+Gl19WrV9fdfx41U2b7Gv9+ra/OLI+C1XVI488UseOHauq9nmsXbv2v9e+++47Peecc3Y4Pjcl4bOIls2bVW+9VTUtTbVBA9VJk4p/zvyuX6XzqZ+1FJGseqAAuwTPxepuumsx63/OuRQlYq1hYINdr7rKuifT020w68aNO68j6cxVV9kdeV4mTbKfX3br1tmU++HDc39Pq1bw8MORlV+2bNn/Hm/cuJFtQZ9yhQoVOOqoo/47pnXr1ixatCjP89x1113cf//91K5dG4By5cpx8cUXA/DQQw/Rq1cv+vXrx+rVq+nWrRu33HIL3bt35+CDD/7vHF26dIks6Bhq127nfWecAZdfbi29lSvDoEHbX6tc2TKzd+1q+fNyfguffRZ52bNmzWLLli0ce+yxwI4tZFu3buXaa6/l5Zdf5q233sr3PKnyWcRD6dIwcCAcc4y1iB16qH2+/ftba2e05dkdqaqlVHXXYKusqqWzPfYKmHMuInPm2BiLgw6CCy+0cRW7724JEz/+OOzokk/OClhB+4vi999/p0WLFtStW5frr7/+v3/eWVauXMm7775L+/bt8zzHjBkzaNOmTa6vnXDCCey22250796dIUOGFHh8fo466qj/usAeeuihQr+/OH7+GapU2XFflSrwxx/ROf+cOXOoWrUqnTt3Zv/99+faa69la5AH5vHHH6djx47UqlWrwPOUhM8i2g491G6GTj8dbrrJcoT9/nv0hwLk1xLmnHPF1qyZJTg88EB44QV45hk491z48EMbd/Huu9C2bdhRJo6CWqwyMmDBgp33p6cXrpUlP3Xr1uXHH3/kjz/+oFOnTnTp0oWaNWsCsGXLFrp27Urv3r1p0KDoi6f06tWL9evX06RJk2LF+umnn1K9evVinSM/+f1M993XWkmCBsIgHlv4GSzbenE+ky1btvDll18ydepU6tWrx5lnnsmIESM4/vjjef311/ksSh94snwW8Va1qt1AHn889Opln/fmzdvXn1ywwJK1QtHXwY0kT1iRiUgHEflZROaKyE6jOEXkGhGZJSI/isg4EUmPZTzOuXDUqmVdlE2bWrfZqFE2i/Lmm22GkYvc4ME7d+VWqGD7o6127do0b96cL7Nls+zZsyeNGjXiqquuyve9zZo1Y8qUKXm+npaWRlpaWsTHJ6IBA+z3+dNP7Z/zp5/a8wEDonP+OnXq0KpVKxo0aEDp0qXp1KkT33//PVOnTmXu3Lk0bNiQjIwM1q1bR8OGDfM8T0n4LGJFxG4Wp07dsQKWZd264n3eMauEiUgp4AksvUVToKuINM1x2FQgU1VbAG9gOcmccynm99+hXj1rETv11O15dg44AObODTe2ZNOtGwwbZi1fIvZ12LCi34nntGjRItYHq7L/888/fPXVV/+1kNx0002sWrWKhyMYYHbDDTdw7bXXsmTJEgA2bdrE008/nefxV1xxBc8//zzffPPNf/tGjx7N0qVLi/HdxFbXrlb5vfJKKF/evg4ebPuj4YADDmDlypUsD1aKHj9+PE2bNuXEE09kyZIlzJ8/n/nz51OhQgXm5vOHVBI+i1hr2HDnCliWhQuLft5Ydke2Beaq6jwAEXkFOAWYlXWAqn6a7fhJwDkxjMc5F5L69WHxYpvy/frr2/ePH29Twl3hdOsWvUpXTrNnz6Zv376ICKpKv3792G+//Vi0aBGDBw9mn332oXXr1oD9s+7Ro0eu5znhhBNYunQpxxxzDKqKiHDhhRfmWW7NmjV55ZVX6NevH8uWLSMtLY0jjjiCDh06ADBixAjGjBnz3/GTgpWTjzrqKEoFI6ZbtGjBCy+8EI0fQ8S6do1epSunUqVKcf/999O+ffusmbX/DagvjJLyWcRaenruQwHq1Sv6OUWLs/JkficW6QJ0UNUewfNzgQNVdecEI/b648ASVb0jv/NmZmbq5MmTox6vcy62Bg6E99+HRx6x3DqjR1t35OefWzdlfkRkiqpmxifS2PJrmHPJaeRIGwO2bt32fRUqFNwSnd/1KyEG5ovIOVh2/iPzeL0n0BOgXnGqnM650Nx6K9SsaRexP/+02Ucff1xwBcw55xJBVkVrwADrgqxXz7qfi9MqHctK2GIg+4pKdYJ9OxCRY4ABwJGqmuska1UdBgwDu4uMfqjOuVgTsdxKl18ediQumgYPHszr2fuYgdNPP50B0Rqd7iLmn0XsRXsoQCy7I0sDc4D2WOXrO+BsVZ2Z7Zj9sQH5HVT1l0jO6035zpU83h3pnEtW+V2/YjY7UlW3AFcAHwOzgddUdaaI3CYiHYPD7gMqAa+LyDQReSdW8TjnnHPOJZKYjglT1Q+AD3LsuyXb42NiWb5zzjnnXKKKabJW55xzzjmXO6+EOeecc86FwCthzjnnnHMh8EqYc84551wIvBLmnHPOOReCmOUJixURWQ4sAKoAq3K8nNu++sBvcQgtmmXm9n1Eo7z8zlvY13Irs7BxF1Yk32c0Ysh+jmiVWdAxeZWZ1/si/f2PRDT/RiKNIb8ycztHuqrWKE5giaKQ17BkvH5BbK5hhfkbimR/zjIT4foVrTiyzhHNa2ak/yMi+blG8/qVW5lFVZgY8iqzcNcvVU3KDRgW4b61IcRWrDJz+z6iUV5+5y3sa7mVWdi4Y/FzjUYM2c8RrTILOiavMvN6X6S//9H8/Snsz66oZcb69yhRtkg+w2S8fhXlM4zG31lh/lZyKzMRrl/RiiPrHNG8Zkb6PyKSn2s0r1+F+dlG82efV5mF/T6SuTvy3Qj3JaNYfR/5nbeorxXluFiKRgyFPUckxxd0TF6vF2Z/Sf35J6tE/QyjIRbfR7T/hop6XKzF+28oGj+fwr6WqL/7cb9+JV13ZGGJyFpVrZjKZZaE79HLTJ3ywiozGZWUz6YklFkSvkcvs/CSuSUsUqNLQJkl4Xv0MlOnvLDKTEYl5bMpCWWWhO/RyyyklG8Jc84555xLRCWhJcw555xzLuGkbCVMROaIyDYR2RCn8tqKyD8islFENojIm/EoNyi7jIisE5GlcSpvTPA9bhCRBSJSJQZl5Pr5icjr2X7G30SxvCoiskZE1gfn/izY/5uIbAr2zRGRXaJVZnD+dBFZFHxPG0Xk4myvvSMiKiKNi1nGTj9LEfk2KG+9iPwhIunB/l1EZG7w/W4UkY+KUF6ufwsi8pmIbA3KXC8it2R7z2kisjrb71XUf6eSiV+/Ylpeyl2/gnPH/RqWitev4Dxxu4albCUMeBQ4J47lbQR6qWo5LH/IySJycpzKfgNYFo+CRKQNcCJQS1XLY79DD8SgqJ0+PxG5GjgaqB6UfUEUy/sX2FtVdwF2AzJF5CLgOaAcsEvw9ZkolgkwDvhf8HuzOzAW7CIAHAJsjUIZuf0tjAZ2Db7fhcCrwf77gDLBz7c2cIyIHFbI8vL7W/hAVXcJttsARKQc8BJwYVBuc2BdIctMNX79ioEUvn5BONewVLx+QRyvYSlbCVPVx7EPJ17l/aCqLweP/wRWAE1jXa6IZAKHY7+o8SLAbsEvXllgbrQLyOPzuwa4W1VXB8fMimJ5qqpZd+IVsL8NVdXbgtcUmArUi1aZIlIXSCe4GKvqWlVdELw8GrgsGuXk9rNU1btVdWPw9DOgZtZLQLngs90N2Ab8UcjyCvu3cD3wp6q+HrxnrqpuLkyZqcavX7EtlhS7fgXni+s1LFWvX8H543YNS9lKWJiCmncNrGYca2OAK7FftphT1SnA28CvwHpgnareHY+ygerAiUGT+0oR6R7NkwfdIuuBv4AfVfXZbK/tAhwHjIpikYdgd1y/BN0xP4lIDREZDCzP+oOOg/OBD4PH1wUxrQd+AV5V1XlFPXEufwvHB834c0QkI9i3f3DsX8HP4f2ilueKz69fMRPT6xfE/RqW8tcviP01zCthUSYiNYGPgQdUdXGMyxoErFTVkbEsJ0eZGcBRwL5ARaC8iAyJV/HY3U1l4CrgaRGRaJ1cVTcHzdsZQBMR6ZTt5anAz6r6RLTKw+7CKwJ3qmoF7MLxPtAH6BjFcvIkImOxf4C9gl3dg+cVgWbAmSJyZBHPnfNvoRfWJVIJ634aGxxaGqiLXdTrAkeISL8ifUOuWPz6FdviieH1C+J+DUvp61dw/thfwzQKqf4TdQMOAzbEsbxdsDuQt+NU3gRgS7BtxZpi58W4zAewP+Ss508BM+Lx+QHLgauzPd8M7BOjsscB7waPxwN/AqWiXMZ+wJZsz3sBf2MXkazPVYOv+0XzZxnsexpYDVTLtm86MDTb8znAQ0UoL9+/hezxAI8Ac7O99j/gvVh8rsm0+fUrJmWWiOtXcP6YXsNS+foVvDcu1zBvCYuS4I5mBvC7qp4SjzJV9RBVLa2qpYG+wDJVbRDjYmcC6SJSLfie2wOzY1xmlrFAJwAROQ67s/w5GicWkX2yzbDZDcgEvheREcHjpqoajUGm/1HV6cBaEekQ7Dod+0NOy/a5bg3Knh7NskVkAHAucICqrsj20u/YZ4qI1MDGfHxVyHPn+rcgIi2zHdYPyBq/8hBQO/idKoc17U8u3HfkisOvX3ERs+tXcM64XsNS9foVvDd+17BY1cLD3oAFbL+72gI8F+PyLgvKWp9tuyWO3+9VwNI4lfUZ1u++AZgHVI7H54c1Mc8Lyl0HXBPF8k4Lzrk+OP+4YL9id6xZn+m4KH+fZwJrg3P/CWTkeH0L0DgGP8tNweOs72tmcGxN7EK2IfiMC90ildffQrbPbj2wBGiZ7T1Dgtc2AN/E4/c4kTe/fsW0rJS7fgVlxv0alorXr+A8cbuGecZ855xzzrkQeHekc84551wIvBLmnHPOORcCr4Q555xzzoXAK2HOOeeccyHwSphzzjnnXAi8EuaiSkRURB7I9ryfiNwapXPfKiKLRWSaiMwSka7ROK9zzmXxa5iLJ6+EuWjbCHQWkeoxOv9DqtoKOAV4SkTKxKgc51zJ5NcwFzdeCXPRtgUYBlyd8wURGSEiXbI9XxN8bScin4vI2yIyT0TuFpFuIvKtiEwXkb1znktVf8ESE+4WnONaEflORH4M1qRDRDKCRWVHishsEXlDRCoEr90d3In+KCL3x+IH4ZxLSn4Nc3HjlTAXC08A3USkSiHe0xK4FFtY91wsy3JbbH2wK3MeLCKtgV9UdVmwBEgjoC3QCmgjIkcEhzYBhqjqvsC/wOUiUg04FWimqi2AO4rwPTrnUpdfw1xceCXMRZ2q/gu8APQuxNu+U9U/VXUj8CvbV6efDmRkO+5qEZkJfAMMDvYdF2xTge+BfbALGtjaX18Hj1/CFl1dhS0t8YyIdMbuRp1zDvBrmIsfr4S5WHkYuAhbLy3LFoLfORFJA8pme21jtsfbsj3fBpTO9tpDqtoMWyftGREpjy2Ee5eqtgq2hqr6THB8znW5VFW3YHecbwAnAR8V7Vt0zqWwh/FrmIsxr4S5mFDVv4HXsItYlvlAm+BxR6DIA1JV9R1slfruwMfAhSJSCUBE9hKRPYJD64nIwcHjs4GvguOqqOoH2LiPlkWNwzmXmvwa5uKhdMGHOFdkDwBXZHs+HHhbRH7A7tzWFvP8twEvY2Mw9gUmigjAGuAcYCvwM9BLRJ4FZgFPAlWCOLLuQK8pZhzOudTk1zAXU6Kas6XTudQgIhnAe6raPOxYnHOusPwalvq8O9I555xzLgTeEuacc845FwJvCXPOOeecC4FXwpxzzjnnQuCVMOecc865EHglzDnnnHMuBF4Jc84555wLgVfCnHPOOedC8P+2vVsrly3sRgAAAABJRU5ErkJggg==\n",
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\n",
       "text/plain": [
        "<Figure size 720x360 with 2 Axes>"
       ]
@@ -178,7 +166,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "excessive-occupation",
    "metadata": {},
    "source": [
     "### count"
@@ -187,12 +174,11 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "descending-enterprise",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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37tC/v806Gjw47Kiccy53RYvCo4/aEOX//mcNsbvu8oaYc8ko20aYiFyS0xNV9a7Yh5M4++4L06bBSSfBWWdZQ2z48LCjcs653BUpAg8+aA2x55+3mZTVqoUdlXMur3JKsC8XXNKB84ADgstQLDcs6ZUuDa++akuCXHihdes751wyEIG774Z586wBpmprTzrnkkdOw5GjAERkOtBCVTcEt28AXkhIdAmQcSZ55plwzTXw999w883ete+cK/hEdjXALroI/vgDHn8ciqVs8SHnUks0X9XawD+Zbv8DpMUlmpAULw5PPWXTv2+5xRpi99zjDTHnUp2I1AKeAvYHdgJjVfVeEXkOODh4WAVgvao2i/D85cAGYAewXVXTExD2HjKKUt9/v+WITZhgxzXnXMEWTSPsaeAzEZkCKHASdtBKKUWLwiOPWEPsnnssR+yRR2y7cy5lbQcuVdV5IlIOmCsiM1T13znTInIn8GcOr9FBVdfFO9DcXH219exfein8848t/F2yZNhROedykmsjTFVHi8g04Khg0yBV/SK+YYVDxGYZlStna7Zt3Gg9ZH5G6VxqUtU1wJrg+gYRWYzlvn4NICICnAJ0DC3IPLjkEmt4XXAB9OsHkyd7j75zBVm0mQPzsQNVMQARqR2pflgqENlVmfqqq2DTJjujLFUq7Micc/EkImlAc+DTTJvbAWtVdWk2T1Nguogo8Iiqjo1vlLkbNswaYlWregPMuYIu10aYiAwHrgfWYnkPgh14msY3tHBdeSWULWtnlD162MK5ZcqEHZVzLh5EpCxWeHqEqv6V6a7+wKQcnnqkqv4kIlWBGSLyjap+EOH1hwBDAGrXrh3DyCM7++xd16dNs7Vyy5WL+26dc3kUzRqQFwEHq2pjVW2qqoeqako3wDIMG2Yzjd55xyrs/5lTVohzLimJSHGsATZBVSdn2l4M6AU8l91zVfWn4OcvwBSgdTaPG6uq6aqaXqVKlViGn6OffrISPMcdB+vXJ2y3zrkoRdMIW0XOSakp7cwz4dln4ZNPoFMn+O23sCNyzsVKkPP1GLA4QgHqY4FvVHV1Ns8tEyTzIyJlgOOAhfGMN69q1IBJk2DuXDj2WPj997Ajcs5lFk0j7HtgpohcLSKXZFziHVhBcvLJNhy5cCG0bw8//xx2RM65GDkSOB3oKCLzg0vX4L5+ZBmKFJEaIjI1uFkNmC0iXwKfAW+o6rREBR6tk07adfzq0AF+/TXsiJxzGaJJzF8ZXEoEl0Kpe3d44w048URo186GKBOQ2uGciyNVnY3luUa678wI234CugbXvwcOi2d8sdKtG7z2mh2/nn3Wl2hzrqCIpkRFRuX8cnZT/457VAVUp04wfTp07bqrIVavXthROedc7jp3hgUL4MAD7baqz550Lmy5DkeKSBMR+QLLdVgkInNFpHH8QyuYjjgC3n3Xaoi1aweLFoUdkXPOReegg6zhtXAhtGkDy5eHHZFzhVs0OWFjgUtUtY6q1gEuBcbFN6yCrUUL+OADO5gdc4wlvTrnXLLYsgWWLLHj17JlYUfjXOEVTSOsjKq+l3FDVWcChb5iVqNG1hArUwY6doQPPww7Iueci056Orz3nvXoH3MMfPONDU9+9x2sjjgX1DkXD1HNjhSR60QkLbhcC/wQ78CSQb16MHs2VKtmdXiuvhrS0qBIEfs5YULYETrnXGTNmsHMmbB9u6VZ1K9vDbLmzeHoo61B5pyLr2gaYYOBKsDk4LIfMCieQSWTWrWsR6xSJbj1Vlixws4oV6yAIUO8IeacK7iaNIEXXrDl2UaPhlWrrMDrSSdZgert28OO0LnUlmsjTFX/UNULVbVFcBmhqn8kIrhksf/+kbdv2gQjRyY2Fuecy4uPP4aBA6FvX1sVZNEiuPhiW3vyrbfCjs651BbN7MgZIlIh0+2KIuJfzSx+/DHy9pUpucy5cy5V/PgjHHKIXR861FIrfvjBtmV3XHPOxUY0w5H7qer6jBtBL1jVuEWUpLIr3OoFXZ1zBVnbtvDKK5ZGceONNgTZrRu8+abd55yLn2gaYTtF5N+mhIjUATR+ISWn0aOhdOndt4nAFVeEE49zzkWjd2/YvBn694dffoGrrrLZkgANG4Ybm3OpLppG2EhsfbSnReRp4APg6viGlXwGDICxY6FOHWt87b8/FC8O48fD34V2jQHnXEFXogS8/baV3RkxAl59FU49FdasgeuuCzs651KbqObeqSUi+wFtsDXWPlbVdVE8pxbwFLA/sBMYq6r3ZnlMe+AVdpW8mKyqN+b0uunp6TpnzpxcYy4IXn/d1mo77jg7sBUvHnZEziUnEZmrqulhxxELyXIMu+8+O37VqRN2JM4lt5yOX9Ek5gvQBWihqq8BpUWkdRT73Q5cqqoNsQbcMBFpFOFxs1S1WXDJsQGWbLp3h4cfhmnTrFxFFO1d55wrEC680BpgO3fC4sVhR+NcaopmOPJBoC3QP7i9AXggtyep6hpVnRdc3wAsBg7IZ5xJ65xz4Prr4Ykn7KdzziWTkSPh8MNt8W/nXGxF0wg7XFWHAVvg39mRJfKyExFJA5oDn0a4u62IfCkib2a3MLiIDBGROSIy59dff83LrguE66+Hs86Cm26CRx4JOxrnnIvesGFQtiyccAKsXRt2NM6llmgaYdtEpCjBjEgRqYLleEVFRMoCLwEjVPWvLHfPA+qo6mHA/cDLkV5DVceqarqqplepUiXaXRcYIjYs2bUrnH++5Yc551wyqFkTXnvNZk727GkzKZ1zsRFNI+w+YApQVURGA7OBW6J5cREpjjXAJqjq5Kz3q+pfqvp3cH0qUDyYBJByihWD55+Hli2hXz+rUu2cc8mgZUt45hn45BMr6Oqci41iuT1AVSeIyFygEzY7sqeq5pqmGST0PwYsVtW7snnM/sBaVdUg2b8I8Fte3kAyKVPGZkwecYR17X/4IRx8cNhROedc7nr1gvvvt4W/nXOxEc3syIOAH1T1AWAh0DnzMkY5OBI4HegoIvODS1cRGSoiGedSfYCFIvIl1uPWT6OpmZHEqla12ZJFitgCuT//HHZEzjkXnQsugKOOsuu+JJtzey+a4ciXgB0iUg94FKgLTMztSao6W1VFVZtmKkExVVUfVtWHg8eMUdXGqnqYqrZR1Y/26t0kiXr14I03LMeiWzfYsCHsiJxzLnpPPgkNGsBHheKI7Vz8RLVskapuB3oB96rqxUD1+IaV+lq1ghdegC+/hD59YNu2sCNyzrnodO9u6+L27GmLfTvn8ifa2ZH9gTOA14NtXvs9Brp2taWOpk+Hs8/2Yq7OueRQubLlt27fbg2yP/8MOyLnklM0jbBBWLHW0ar6g4jUBZ6Jb1iFx+DBcOON8NRTcO21YUfjXOEiIrVE5D0RWSwii0TkomD7DSLyY+Z81mye30VEvhWRZSJyVWKjD1eDBvDSS7BkCfTta5X1nXN5E83syK+BCzPd/gG4NZ5BFTbXXgurV8Mtt8ABB1gtMedcQmQsrzZPRMoBc0VkRnDf3ap6R3ZPDOonPgB0BlYDn4vIq8Exs1Do0MEKUO/caZONnHN5k2sjzMWfCDzwAKxZY7OPatSwXAvnXHyp6hpgTXB9g4jkZXm11sAyVf0eQESeBU4ECk0jDKw3P8Nvv9lQpXMuOn7uUkAUKwaTJkHr1tC/v886ci7RIiyvdoGIfCUi40WkYoSnHACsynR7NYVwfdwMs2ZBWhq8+WbYkTiXPHJshIlIURG5PVHBFHZlytjyILVqWTHXb74JOyLnCocIy6s9BBwENMN6yu6M9LQI2yJOr0n29W+j0by5ld/p2xcWLgw7GueSQ46NMFXdAbQMqt+7BKhSxYq5FitmxVzXrAk7IudSW6Tl1VR1raruUNWdwDhs6DGr1UCtTLdrAj9F2keyr38bjbJl7SSybFmbMemLfTuXu2iGI78AXhGR00WkV8Yl3oEVZgceCFOnwrp1Vsbir6zLnjvnYiK75dVEJHMtxJOw1UKy+hyoLyJ1RaQE0A94NZ7xFnRZF/vesiXsiJwr2KJphFXC1nPsCJwQXLrHMyhnC+a++KJ16/fuDf/8E3ZEzqWkiMurAbeJyAIR+QroAFwMICI1RGQqQFDE+gLgLWAx8LyqLgrlXRQgGYt9t25tPfrOuexJsi3VmJ6ernPmzAk7jIR58kk480w47TSrJeYDw64wEpG5qpoedhyxUNiOYZs2QenSYUfhXHhyOn5Fs4B3AxF5R0QWBrebioiXFU2QgQNh9Gg7s7zmmrCjcc656H3/PRxyCEzMdbVh5wqnaIYjxwFXA9sAVPUrLPfBJcjVV8PQoXDrrTBmTNjROOdcdGrWhLp1YdAgL7vjXCTRNMJKq+pnWbZtj0cwLjIRa3ydeCJceCFMnhx2RM45l7sSJex45Yt9OxdZNI2wdSJyEEH9GxHpQ1Bh2iVO0aLWpd+mDZx6KsyeHXZEzjmXu4zFvrdt88W+ncsqmkbYMOAR4BAR+REYAQyNZ1AustKlbfp3Whr06AFfF6rFUZxzyergg3f1iCXZXDDn4irXRpiqfq+qxwJVgENU9ShVXRH/0FwklStbMdeSJeHooy3nokgRa5hNmBB2dM45F1mHDlb/sEIF6xVzzkU3O7KyiNwHzAJmisi9IuJLtIYoLc0W+v7tN/jxRzuzXLEChgzxhphzruASseHIDh0sz1XVCrtu2hR2ZM6FI5rhyGeBX4HeQJ/g+nPxDMrlbty4Pbdt2gQjRyY+Fueci1bZstajf9FFNjx5yCFQvTqcey5s3Bh2dM4lVlQV81X1JlX9IbjcDFSIc1wuFytX5m27c84VBEWLwmWXWRrF77/D++/Dd9/Bhg1w9tlhR+dcYkXTCHtPRPqJSJHgcgrwRrwDczmrXTtv251zrqAYP97qH5YvDyecADt2wKOPwvTplmLhXGERTSPsXGAisDW4PAtcIiIbRMSXlg7J6NGRlwLp3z/xsTjnXF788AO0b2+zvStUgD/+sOPZQQfBqlVhR+dc4kQzO7KcqhZR1eLBpUiwrZyq7puIIN2eBgyAsWOhTh1Ldq1Vy2ZKPvwwLF0adnTOOZe9li1tlnfLlvDFF5YX9vPPduw6+OCwo3MucaLpCXMF1IABsHw57NxpuWAffADFiln3/vr1YUfnnHORXXQRPP209eh/952VrmjSBDp3hooVw47OucTxRlgKqVsXXnrJDmr9+1uehXPOFTS1a9tJ45Il0KkTXHedzZp86y1Ytizs6JxLHG+EpZijj4YHH7Su/iuuCDsa55yLrH59ePJJq3E4dy7MnGk9+X36wObNYUfnXGJEU6z1IBEpGVxvLyIXikiFuEfm8u2cc2D4cLjrLpuF5JxzBV1aGjzzDHz1FQwbFnY0ziVGND1hLwE7RKQe8BhQF5st6Qqwu+6CY4+FoUPhww/DjsY553J3/PFw7bXw4ovWQ+ZcqoumEbZTVbcDJwH3qOrFQPX4huX2VrFi8PzzdnbZq5cf0JxzyeH66603rE6dsCNxLv6iaYRtE5H+wEDg9WBb8fiF5GKlYkV49VXYuhVOPBH+/jvsiJxzLmdFi9rJoyo89pjVEHMuVUXTCBsEtAVGq+oPIlIXeCa+YblYOeQQePZZWLAABg60chbOOVfQLV0K550HZ5zhxy2XuqIp1vq1ql6oqpOC2z+o6q3xD83FSpcucMcdMHkyjBoVdjTOOZe7Bg0st/X11+F//ws7GufiI5rZkUeKyAwRWSIi34vIDyLyfSKCc7EzYgQMGgQ33mi5Ys45EJFaIvKeiCwWkUUiclGw/XYR+UZEvhKRKdnNCBeR5SKyQETmi8ichAZfCAwbBv36WbL+O++EHY1zsRfNcORjwF3AUUArID346ZKICDz0EBx5JJx5JsybF3ZEzhUI24FLVbUh0AYYJiKNgBlAE1VtCiwBrs7hNTqoajNVTY9/uIWLCIwbZ0sZDRgAGzeGHZFzsRVNI+xPVX1TVX9R1d8yLnGPzMVcyZI2JFmliiXqr1kTdkTOhUtV16jqvOD6BmAxcICqTg9mhQN8AtRMZFwTJlhyepEi9nPChETuvWApW9ZWAnn8cShTJuxonIutaBph7wVd821FpEXGJe6RubioWhVeeQV+/x1OOgm2bAk7IucKBhFJA5oDn2a5azDwZjZPU2C6iMwVkSGxiGPCBBgyxMrKqNrPIUMKd0OsYUOrIQa2LJtzqSKaRtjh2BDkLcCdweWOeAbl4qtZM1s899NP7eCuGnZEzoVLRMpihalHqOpfmbaPxIYss2sCHamqLYDjsaHMo7N5/SEiMkdE5vz66685xjJyJGzatPu2TZtse2H39ts2NPnCC2FH4lxsRDM7skOES8dEBOfip1cvS9J/+mmbOelcYSUixbEG2ARVnZxp+0CgOzBANfKpiqr+FPz8BZgCtM7mcWNVNV1V06tUqZJjPCtX5m17YXL00dC6NQweDN98E3Y0zu29aGZHlheRuzLO4kTkThEpn4jgXHxdey2ccgpceaVNA3eusBERwSYfLVbVuzJt7wJcCfRQ1U3ZPLeMiJTLuA4cByzc25hq1468/YAD9vaVk1+JEja7u1Qp6N3bC1C75BfNcOR4YANwSnD5C3g8nkG5xBCxZNfmzeHUU2HRorAjci7hjgROBzoGZSbmi0hXYAxQDpgRbHsYQERqiMjU4LnVgNki8iXwGfCGqk7b24BGj4bSpffcvmkTfP753r568qtZEyZNgsWL4dxzPZ3CJbdiUTzmIFXtnen2KBGZH6d4XIKVLm2J+q1aQY8e8NlnULly2FE5lxiqOhuQCHdNjbAtY/ixa3D9e+CwWMc0YID9HDnShiBr14ZzzoFHH4WjjoIHH4Szzor1XpPLscfCLbfYEkfOJbNoGmGbReSo4GCFiBwJbI5vWC6RataEKVOgfXvo0wemT4fivjqoSyIiUgrL32oH1MCOUQux3qmk6+MdMGBXYyzD0KHQvz+cfbb1iN17r5WdKayuumrX9Z07rZyHc8kmmj/b84AHgsrQK7Bu+qHxDcslWps2VhRx5ky46KKwo3EueiJyA/Ahtsbtp8AjwPPYrMZbgxU/moYXYWxUrgxvvmmNj0cesZOmH38MO6rwzZgBLVrAunVhR+Jc3uXaE6aq84HDRGTf4PZfOT/DJavTT4eFC+G226BJEzj//LAjci4qn6vqDdncd5eIVAWySXdPLkWLwv/9H6Sn28oXLVtauYZ27cKOLDyVKll+2GmnwRtv+BClSy7Z9oSJyGnBz0tE5BLgbODsTLddCrrlFujWDS68EN59N+xonMudqr6RdZuIFMl04viLqqbUuo69e1udv/LloWNHuO++wpug3rIl3H8/vPUW3Hxz2NE4lzc5DUdmLBBRLsKlbJzjciEpWhQmTrSCiCef7NWpXfIQkYkism9QLuJr4FsRuTzsuOKlUSObSNO1q6UQnHHGnkVeC4tzzrH3P2qUNcacSxbZNsJU9ZHg6tuqOirzBfD17FPYvvvCa6/Z9RNOgL98ANolh0ZBukRPbHZjbaz8RMoqX94m1dx0ky1rdOSR8MMPYUeVeCLw0EOWRuE1D10yiSYx//4ot7kUcuCB8OKLsHSpzcjasSPsiJzLVfGg+n1P4BVV3Yat7ZjSihSxwstvvAHLl9vw3PTpYUeVeKVLwwcf2NCsc8kip5ywtiJyKVAlIw8suNwAeOpjIdChg+VaTJ0KV18ddjTO5eoRYDmWSvGBiNTBiksXCscfD3PmWMmZLl0sgb+w5YlVqGC9Yt98A7feGnY0zuUup56wEljuVzF2zwf7C+iT2wuLSC0ReU9EFovIIhHZo/CBmPtEZJmIfCUiLfL3Nly8DB1qsyRvvx2eeirsaJzbU3DCKKp6n6oeoKpdg7UeVwIdwo4vkQ46CD7+GPr1g2uusQT+DRvCjirxnnrKThwnZLfsunMFRLYlKlT1feB9EXlCVVfk47W3A5eq6rxgfbW5IjJDVb/O9JjjgfrB5XDgoeCnK0DuucemgJ9zDtSvD23bhh2Rc7sZiNUyXAJMA6ap6s9BQ2x7uKElXpky1vho1Qouv9wWvJ4yBQ45JOzIEmfUKJg9G4YMgWbNoHHjsCNyLrJocsI2icjtIjJVRN7NuOT2JFVdo6rzgusbgMVA1iVoTwSeUvMJUEFEquf1Tbj4Kl7cahHVqmVDHjVrWh5KWpqfabrwqepQVW0B3ABUBJ4QkY9F5BYROVpECl36hAhcfDG8/Tb89ps1xF5+OeyoEqd4cXjuOShXrvD2BrrkEE0jbALwDVAXGIXlXORpGVkRSQOaY9WsMzsAWJXp9mr2bKi5AqByZTur/PNPq9KtCitW2DZviLmCQFW/UdW7VbUL0BGYDZzMnsedQqN9e5g713rBTjrJEvgLyySb6tWtIbZsmeeHuYIrmkZYZVV9DNimqu+r6mCgTbQ7EJGywEvAiAjV9iMtnLtHKqmIDBGROSIy59dff4121y7GHnxwz22bNtlCw84VFCJSGmiMVdIfrqrpYccUplq1bNbg2WfD6NHQvTv8/nvYUSXGMcfYxKLrrgs7Eucii6YRti34uUZEuolIc6BmNC8eTBd/CZigqpMjPGQ1UCvT7ZrAT1kfpKpjVTVdVdOrVKkSza5dHKxcmbftziWCiPQI1radJyJdgUXYGrcLRGRgyOEVCKVK2dqwjzwC77xj+WJffhl2VIlx3HH2/tevh6++Cjsa53YXTSPsZhEpD1wKXAY8Clyc25NERIDHgMWqelc2D3sVOCOYJdkG+FNV10QXuku02tmsvpfdducS5CbgOOBcbOHuTqraBmiKHbNcYMgQ6xXbutUm2EycGHZEidOvnzXIhg+3VQYuvBC+/TbsqFxhl2MjLEhora+qf6rqQlXtoKotVfXVKF77SKxadUcRmR9cuorIUBEZGjxmKvA9sAwYB/iS0QXY6NFWEDGri/YoPuJcQu1U1SWq+jnwg6p+D7ZmJIVwdmRu2rSxPLFWrWDAAEvg37Yt9+clu/POg7Vr4ZVX4NxzoWJFOOoom0XpXFhybISp6g6gR35eWFVnq6qoalNVbRZcpqrqw6r6cPAYVdVhqnqQqh6aaovsppoBA2DsWKhTx2Zf1ahh0+GfeAI2bgw7OleIFRGRiiJSGdgZXK8kIpWIrre/0KlWzWZOXnSRlaDp3NlyPtPSUnfm88SJcMopsGqVLX4+ahSMGWNlPJwLS7Z1wjL5SETGAM8B//6rzSg/4QqXAQPskmH6dCtbcfbZdpCTSFMtnIuv8sBcdk30yXxsKmQ146NXvLg1wNLT4ayzbJgyo8J+xsxn2P37nszefdfy4MqXt9UEjjzSylcMHGgTjCL18jsXb9E0wo4Ift6YaZtiU8BdIXfccXDLLXDVVXYwv/TSsCNyhY2qpoUdQzI77TS44gpYkyUbN2Pmc6o0wipVsvI6990HRYtC8+bw669QsiSUKBF2dK6wyrURpqqFatkPl3dXXGFr1l1xBRx2GBx7bNgRucIkyF3dR1X/Dm63wZZdA/giKBbtcvDzz5G3p9LM57PPtqHHV16Bhx6CLVvgjDOskVksmu4I5+Ig13wJEakmIo+JyJvB7UYiclb8Q3PJQgQefxwaNoS+feGHH8KOyBUy/2P3ST2TgMuB64BrQ4koyWQ3w7lWrcjbk9Ell9jyRXXrQocOu+qneQPMhSmapNUngLeAGsHtJcCIOMXjklTZsrYsys6d0KuXDWU4lyCdgMxlcNar6glY2YojwwkpuWQ38/ngg+07nQqKFoUHHoBFi2zlgM8+g5NPhvvvt94x58IQTSNsP1V9HtgJoKrbgUKy8IXLi3r1bEbVl19aUq96SrRLjCLBcSnDlWCzr4GyOT1RRGqJyHsislhEFonIRcH2SiIyQ0SWBj8rZvP8LiLyrYgsE5GrYvWGEi3rzOfataFbN5gxwxLXU6mERfXq0KmT9YjdcYflsp55pvfgu3BE0wjbGEz9Vvg33+LPuEblklbXrnDTTdYYu/fesKNxhUQJESmXcUNVpwMERaZL5fLc7cClqtoQW45tmIg0Aq4C3lHV+sA7we3dBLloDwDHA42A/sFzk9KAAbB8ufV8rVgBr71mk26eecZmEW7ZEnaEsVeyJDz/vJ0wnnKKFbF1LpGiaYRdglW2P0hEPgSeAobHNSqX1K6+2hYLvuwyeO+9sKNxhcA44DkR+TezSUTqYLlh43J6oqquySi3EyTwLwYOAE4Engwe9iTQM8LTWwPLVPV7Vf0HeDZ4XkoQse/ygw/C669bKZq/sq7+mwLq1rVah7/8kloTEVxyiGZ25DwROQY4GKvD862qplDntIu1IkXgySfh8MPt7HLuXF/ayMWPqt4lIpuA2SJSBuu13wjcqqoPRfs6IpIGNAc+BaplLKGmqmtEpGqEpxwArMp0ezVweDavPQQYAlA7yb4M550HFSrYTMJOneDNN2G//cKOKrZ69oT//Af22SfsSFxhE83syFLAhdj6bKOw7vrcuvhdIVeuHEyZAv/8Y4n6mzeHHZFLZcFKHLWBOkBdVa2TxwZYWeAlYISqRtvfE6k0ccRMSFUdq6rpqppepUqVaMMqMPr3t4k3CxfC0Udbva1Us88+lvt29dWwbFnY0bjCIprhyKeAxsD9wBgs9+HpeAblUsPBB1s+ydy5MHSoJ+q7+BCR00SkCICq/p21LpiIHCQiR+Xw/OJYA2yCqk4ONq8VkerB/dWBXyI8dTWQuYhDTeCn/L+Tgq1bN5g2DVavtjUXU7GhsnYtPPKIzZpMxRw4V/BE0wg7WFXPUtX3gssQoEG8A3Op4YQT4IYb4KmnbHq4c3FQGfhCRMaLyDAROUVEzhCRG0XkfeA2YG2kJ4qIAI8Bi1U1c5mLV4GBwfWBQKQiBp8D9UWkroiUAPoFz0tZxxxjeZ5//20Nsa++Cjui2KpZ045V8+fbwubOxVs0jbAvghmRAIjI4cCH8QvJpZrrrrPG2MUXW3FE52JJVe8FWmCJ+FWwumEtgB+B01W1t6ouzebpRwKnAx1FZH5w6QrcCnQWkaVA5+A2IlJDRKYG+90OXIDVUVwMPK+qi+L1PguKli1h1ixbe/KYY+Djj8OOKLa6d7fK+g8/DJMmhR2NS3WiuYwRichiLCk/Y95IbeyAsxMrxdM0rhFmkZ6ernPmzEnkLl0M/PkntG4N69fb8GTNmmFH5JKJiMxV1fSw44iFVDmGrVhhS5T99JPli3XuHHZEsbNtG7RvD0uXWtkOX9zb7Y2cjl/RLNjQJcbxJMTq1fDRR1CtGrRrZzP2XHjKl7cDdevWVnPo/fehlE/vcC5p1akDs2fbrMJu3azXqHfvsKOKjeLF4bnn4PffvQHm4ivXpomqrgAqACcElwqquiLjEuf48kwVrrkGmjaFZ5+Fiy6CQw+F778POzLXsKHlW3z2GQwb5on6ziW7atVg5kxo1crK0YwfH3ZEsVOzpv0fgdQbcnUFRzQlKi4CJgBVg8szIlJgi7W+9BK8+qp1I0+eDF98AWefbVOsXfhOOsnWbRs/3mYhOeeSW4UKMH26DUeedRbcdVeuT0kqkyfDEUfA014TwMVBNDlhXwFtVXVjcLsM8HGic8Ey5JZPccIJ1uA69VSbYvzRR5Y8mpYGb79tZRNcuHbsgB49bF26996DI32JZZeLaHLCRKQk0BtII1OqhareGN/o8iZVcsKy+ucfOO00eOEFO9G68Uarup/stm+3IrVz5sDnn0OjpF2YyoUlp+NXNJlSwu4Ldu8gcpHCAuHvv6FSJbs+dqx9eXr3tnH9DRtyfq5LjKJFbW3JOnWgTx9L7HUuBl7Blg3ajlXMz7i4BChRwvLCzjoLbr4ZLrzQ1qFMdsWK2fsqU8bqh230vygXQ9Ek5j8OfCoiU4LbPbG6OgXS8cfDo49asui558KmTXZGtnkzvPgiHHIIlC0bdpSuQgWrqN+mjTXE3nvPFtN1bi/UVNWknEiUKooWhXHjoGJFuOMOmw09frwluiezGjVg4kQ47jjLZ33iibAjcqkimsT8u4BBwO/AH8AgVb0nznHl2/nn2yKsxx9vvS1//23LURxzDPzvf7b+mSsYmjSxg9nHH9sECuf20kcicmjYQRR2InDbbXDLLbZiRu/eqVF9/thj4dZbbSaoc7ESTU8YqjoPmBfnWGKibFkrfzBxos3aqVbNplE3bGj/7DOmG//yi61/1rx5qOEWen36wFVX2cGtZUs455ywI3LJRkQWYGs2FgMGicj3wFYsbSLhtQydNcSuvtp6vIcNs5PiV1+1NWWT2RVX7Lq+fbsNVTq3N3JNzC9oYpXUetFFcP/99k//5pshCdfUTRk7dtjZ5XvvWQO6TZvcn+MKl5wSW0WkTk7PLWildFI1MT87EyfaCETz5vDmm7DffmFHtPeeeALuucdO8D29xeUmX4n5wUyjlDVqFIwYYfkKDRrAffdZlWSXeEWL2oG6Zk0buvj557AjcskkU83CmzPXMMzYFnZ8hd2pp1qh5oUL4eijbQQi2dWpAwsWwNChXu/Q7Z2ccsI+BhCRlKyOUqGC1bP58ksrNHjRRdZ97sJRqZIl6q9fbzOQ/vkn7IhcEmqc+YaIFAVahhSLy6R7d5g2zVYyOeooWLYs7Ij2TocOdiI/YYJNBHMuv3JqhJUQkYHAESLSK+slUQHGW6NG8NZbdqaWkRy+bBn88EOoYRVKTZtaz+Ts2XDJJWFH45KFiFwtIhuApiLyV3DZAPyCla1wBcAxx1jKwYYN1hD76quwI9o711xjsyWHD7eTeefyI6dG2FCgDbsvWZRx6R73yBJIBE48EWrVstsXX2yJ/P/9r9eESbS+feHyy+GBB+Dxx8OOxiUDVf0/VS0H3K6q+waXcqpaWVW9f7sAadkSZs2yhPZjjoEbbrBC2kWK2M8JE0IOMA+KFLHZn1WqwAcfhB2NS1bRVMw/S1ULTF2wRCS1rl4NV165K0/pjjtsXbRUqP6cDLZvt9lUs2bZpVWrsCNyYcslMb9FTs8NZncXGIUtMT+S5cttAs7atbtvL13aimwPGBBKWPmyYUPyz/p08bW3FfOfFpELReTF4DJcRJK89F7Oata0M7JZs+wsp18/X+cwkYoVs8XXq1eHXr2snIhzObgzuDwAfAqMBcYF1+8LMS6XjbS0yAVcN22CkSMTHs5eyWiAzZwJTz4ZaiguCUXTCHsQS259MLi0AB6KZ1AFxVFH2Vph48fbmmhgeQzr1oUbV2FQubIl6v/2mw1b1KmTnEMWLv5UtYOqdgBWAC1UNV1VWwLNgSRPAU9d2c2SXLkysXHEyl13wZAhMHdu2JG4ZBJNI6yVqg5U1XeDyyCg0AwQFS0KgwZZLRhVm27doAGMGWPDZi5+mjWzz/6bb+zArAorVtiBzhtiLoJDVHVBxg1VXQg0Cy8cl5PatfO2vaB7/HErDn7KKfDnn2FH45JFNI2wHSJyUMYNETmQ3Rf0LjRE4LnnoEULmxHTvLnN9gGrMfbSS3D99ZasuXlzuLGmijfe2HNbMg5ZuIRYLCKPikh7ETlGRMYBi8MOykU2evSuFUwy69078bHEQuXK9v9h5UoYPNjrh7noRNMIuxx4T0Rmisj7wLvApfENq+Bq3BhmzIDJk21dyo4dbWHwVq3g3nvtMRMm2LqIKwpUne7klN3QRLIOWbi4GgQsAi4CRgBfB9tcATRggCXh16ljJ7i1asGBB9pKJq+/HnZ0+dO2rS3BNnmylT1yLjdRLVsUVM8/GFuL7RtV3RrvwLJTkGYWbd5siZhffmkHkTPPtMZX6dK2FNLcuZbX5PIvLS1yY7Z2bW/kFiY5zS5KNgXpGFbQrF8PnTtb7u2UKdC1a9gR5Z2qjYr06mV5rM7ldPyKdgHvrUCSl9aLvX32sWUr9t8f3n7bEsjLlrUKyiNG2Bpp//wDJUqEHWnyGj3acsA2bdp9e8OGsHOnH+QciMjzqnpKpoW8d+MLeCePChVg+nQ49lg46STrTTr++LCjyhsR6NPHrq9YYbMnK1UKNyZXcPka8DFQpAiUKmVnbkOHWhXlCy7wnIBYyKgXNHKkDUHWqmWrHEybBqefbsmw3sgt9IK1LvJeRFpExgfP+0VVmwTbnsN6/sGKVa9X1WYRnrsc2IDlyG5PlZ66sFWsaCkfmRtiXbqEHVXebdxotdBatLD3sXIlpKdDt2424cs5yCUnTEytRAWTrE4+2fIA2rWDOXNg2DCbPbnPPrBlS9jRJb8BA6y4486ddmY5dap93hMnQo8evqpBYaeqa4KrnYASERbxzskTwG7/4lW1r6o2CxpeLwGTc3h+h+Cx3gCLoUqVbHShYUPo2dOWlks2ZcrY7O6pU+Gee6yHbPRo+z+xYUPY0bmCIsdGmFrC2MuJCSV5jRoFCxZA69a2DMeiRXDAAdY423ffsKNLPSK2osGjj9oZc6dOVk/MFXppwCMi8p2IPB8Ulm6W0xNU9QPg90j3iYgApwCTYh2oy13mhtiJJ9owZbKZPdtK7XzzDfznP/Dxx1C3Lvzvf2FH5gqKaDJqPhGRQlMXLD8qVICPPrIGWKVKNhT5/fcwbpzdv2iR5Qh45ffYOussS4CdP9/OLletCjsiFyZV/a+qdgSaALOxmd17UzqzHbBWVZdmt0tguojMFZEhe7Efl43Kla0hdsgh1hB7++2wI4reunU2aWvGDJsB2rev/Q+45BI7bjkH0TXCOmANse9E5CsRWSAinqSfRdGiNtZ/1VVW5yZzntLChTbl+tBD4c03w4sxFWUMVfz4IxxxBCz2qlCFlohcKyJvAtOBesBlQM29eMn+5NwLdqSqtgCOB4aJyNE5xDZEROaIyJxff/11L0IqfDIaYvXrwwknwDvvhB1RdDLWGt53XytjdMwxdpK+Y4dPKHK7RPOncDxwINAROAFLYj0hnkGlmr59LVesWjWbcn3hhV7MNZaOOQY++MAK5h51FHz6adgRuZD0AioDb2N5XK9myhfLExEpFrzec9k9RlV/Cn7+AkwBWufw2LHBckrpVapUyU9Ihdp++1njK6Mh9u67YUeUu8qVrX7kAw9YYe9nnrGT9ZtusqFJ5yCKRliQ2FoL6Bhc3xTN89zumjSBzz6Diy+2YoT33x92RKnlsMNsSLhiRSugm4yJvG7vBL1SnYDPgM7AAhGZnc+XOxaribg60p0iUkZEymVcB44DFuZzXy4KVapYQ+ygg6B7912rlRRkDz8M991nM+Yvu8yOU7NmwSuvePqEM7k2pkTkeuBK4OpgU3HgmXgGlapKlbJFXmfOhIuCSfWrVtmsP7f3DjzQEmEbNLCD9MSJYUfkEklEmgCnAQOBvsBqbIWPnJ4zCfgYOFhEVovIWcFd/cgyFCkiNURkanCzGjBbRL7EGn1vqOq0mL0ZF1FGQ6xuXUv/mDkz7IhyVq+epUgMHmyx33uvlddZtw46dPCGmIuiYr6IzAeaA/NUtXmw7auwCiCmUrXpDRush+yQQ+CJJ6B69bAjSg1//mm5YjNn2kHvwgvDjsjtrWgq5ovIG8AHwCzgc1XdlpDg8iiVjmFhWbvWeryXL7c826OzzcYrmD791HrH9tvPjlO1vBBUSsvp+BXNsOI/QakKDV6sTCyDK8zKloVrrrHu6UMPtS5qt/fKl7cD80knWY/jtdd64dzCQFW7qer/VPWjgtoAc7FRrZrlhdWpY3m2s2aFHVHeHH64ldxYt85WBHGFVzSNsOdF5BGggoicgyW9jotvWIWDCJx7LsybZ2sh9uxpFfe3hrYyZ+ooVQpeeAHOOccKJJ57LmzfHnZULh4yZmxndwk7PhcfGQ2xWrVsaaPZ+c3+C8nhh9vQ6qOPhh2JC1Ouyxap6h0i0hn4C2gA/FdVZ8Q9skLkkEPgk0/guuusQVa8eNgRpYaiReGRR6BqVWuIrVtneWKlSoUdmYuxjOWKhgU/nw5+DsAmErkUtf/+1hDr0MEaYtOmwZFHhh1V9NKDAaodO6y80fDhdkLuCo9oZzkuwPIsPgiuuxgrUcKqKL/5ptWQ+eknuPtuT9rfWyJw882WGzZliq1B9+efYUflYinT8kRHquoVqroguFwFeDGAFFe9us2UrFHDvt8ffRR2RHn33XdW3Lt9e1tj0hUe0cyOPBub/dML6IMVbh0c78AKq2JB3+STT1pl5c6dYXXESfIuLy68ECZMgA8/tAPd2rVhR+TioIyIHJVxQ0SOADyHtRDIaIhVr24NsY8/DjuivGnQwCrr//67HZ9W5LbiqUsZ0fSEXQ40V9UzVXUg0BIrWeHi6Kqr4LHHbBZN06a+zEUsnHoqvPYaLFliQxbffx92RC7GzgIeEJHlIrIceBDwE8ZCokYNa4hVq2bFUD/5JOyI8qZVK1sZ4I8/bHjVG2KFQzSNsNVA5jXfNwC5VjcRkfEi8ouIRCxgKCLtReRPEZkfXP4bXciFg4jVlvniC6sS3afPrrUoXf516WI5JH/8YcsczZ8fdkQuVlR1rqoeBjQFDlPVZqo6L+y4XOIccIA1xKpWtYZYsq2ekZ5uPWL//OM1xAqLbBPzReSS4OqPwKci8gpWpuJEbHgyN08AY4CncnjMLFXtnsP9hV79+jbr58474eSTbdv27buGLcHyxnwtsugdfrh9pscdZ0sevfqq/XTJTURKAr2BNKCYBIv3qeqNIYblEqxmTWuItW9v3/EZM6B1tgtKFTzp6ZYjVrKk3d64Ecr4oHrKyulfd7ng8h3wMkGdMOAVINf12FT1A+D3vYzPYbMlr7oKKlSwM6SjjrJk8zvusJk0xYrZF9cXB49ew4aWwFujhp0xv/xy2BG5GHgFO0ncDmzMdHGFTK1a1hCrXNkaYp9/HnZEeZPRABs3zmpILl8eajgujrLtCVPVUQnYf9tg2Y+fgMtUdVGkB4nIEGAIQO1CPn9361ZbsuO666zY6/PP20HmzTdh0CB47jnv1YlWrVrWI9atG/TuDWPHwlln5f48V2DVVNUuYQfhCobata0affv2NsHp7bd3lYRIFi1bwvr19h5mzoS0tHDjcbEXzezIdBGZIiLzYlwAcR5QJ8jhuB/rbYtIVceqarqqplepUiUGu05e5crB449bAwygf39riHXvbiUubr893PiSTeXKVjCxc2c4+2y49Vavrp/EPhKRQ8MOwhUctWtbj1jFivYdnzs37IjypkULazz+9Zc1xLxHLPVEk0k0AXgcy7U4IdNlr6jqX6r6d3B9KlBcRPbb29ctDH79FfbdF776Cho3tkKkW7dC27bwzTdhR5d8ypSxvLBTT4Wrr7bSIF6fLSkdBcwVkW+Dk8UFXjHf1aljDbHy5a0hdvPN1qNUpIj9nDAh7AhzlrUhtmFDrk9xSSTXivnAr6r6aqx3LCL7A2tVVUWkNdYg/C3W+0lFVatabtiOHfD++1YJvmRJ+6LWrRt2dMmpRAl4+mlbUPeee+Czz6w+26pVdjY9ejQMGBB2lC4Xx4cdgCuY0tKsIdaqlaVyZFixYtfajQX5+53REPvoIxsNcakjmkbY9SLyKPAO8O+qhqo6OacnicgkoD2wn4isBq4HigfPfRgr/HqeiGwHNgP9goXCXS5KloTLLrPZkg8/bImbL7wAl15qS/K88w506hR2lMmnSBFrgK1ZY59nhmQ5UBd2QdV8RKQq4ItTud3Urbsr4T2zTZtg5MiC/91u0cIuYKU3qlSBAw8MNya396JphA0CDsEaUBmDNArk2AhT1f653D8GK2Hh8uGKK+yM6PTTrZHQqhU89JDlhB13HNxyiz0mmKXvoiRivWBZJcuBujATkR7AnUAN4BegDrAYaBxmXK7gWJPNvP5kWipo2zbo189GQmbO9IZYsosmJ+ywICl+oKoOCi5ehTpkInD++Vb9fetWm+V35pl2hnTyyVbSondvXycxP7I7ICfTgbqQugloAyxR1bpAJ+DDcENyBUl2k+uTadJ98eIwebLVD2vf3lf+SHbRNMI+EZFGcY/ExUTZsjBpki3+/eGHVhne5U12B+TSpeHvvxMbi8uTbar6G1BERIqo6ntAs5BjcgXI6NH2Pc5MxCbjJJPmzS3tJKMh9t13YUfk8iuaRthRwHyfcZQ8RGDECFi2zBJSd+60bmsXnUgH6mLFbEgyPd1mpboCab2IlAU+ACaIyL1Y4VbnAEsnGDvWZkyKwP77Wx7tmDHw889hR5c3zZrtaojdc0/Y0bj8iqYR1gWoDxyHlaboTgxKVLj4y5hF8/TTtiDsiBGWT+BylvVAXacOPPGEHfD++suWQHn4Ya8nVgCdCGwCLgamYat9+LHK7WbAAKu3tXOn5Yi9/Tb89BMce6zNNE8mzZpZCspdd4UdicuvaBphms3FJYlTT7UG2L33WmMsu+RUt0vmA/Xy5Xa7Qwdb8Lt9ezjvPOjb13PuChJV3aiqO1V1O/AGcH8wPOlcto44wuoELltmk5rWrw87orypV8/yxNautSXYli0LOyKXF9E0wt4AXg9+vgN8D/gqhUmkeHHLEZs0Cb74wvIJZs8OO6rkVLUqTJ1qlfUnT7Yp48m2Ll2qEZE2IjJTRCaLSHMRWQgsBNaKiC9j5HLVsaN9nxcuhOOPT86CqL/8YisCdOhga0727m3v65Zb/GSxIMu1Eaaqh6pq0+BnfaA14P/Ck1C/flZ+oXJl2O6ZMvlWpAhceSV88IF9jkceaY1cH54MzRjgFmAS8C5wtqruDxwN/F9OTxSR8SLyS9Bwy9h2g4j8KCLzg0vXbJ7bJciVXSYiV8Xu7bgwdO0Kzz5rJ1U9esDmzWFHlDeHHmopE7//bjPnW7e249SiRdCunaVSuIInmp6w3ajqPKBVHGJxCdC4sSWWt29vt198MTnP+gqCI46wnsWuXW121Yknwm8++BWGYqo6XVVfAH5W1U8AVDWaRbyewPJes7pbVZsFl6lZ7xSRosADWJX+RkB/n0We/Hr1gqeespVIevWy8j/JpFYtKFrU8oHvv9+GKp95Bho0gMceCzs6F0k0C3hfkulymYhMBH5NQGwuTooWtZ/Ll9sC4Icf7mtO5lelSjBlCtx3H7z1liXK+lBvwmVe6TNr/0WO/ZOq+gHwez722RpYpqrfq+o/wLPYxACX5E491Ybzpk2z0YNkmsz0+edWuDujiGuZMja5qG9fa1i6gieanrBymS4lsdwwP9ikgLQ0azisW2df3JdeCjui5CQCw4fbum4lS1ov4y23+CLgCXSYiPwlIhuApsH1jNuH5vM1LwhK8owXkYoR7j8AWJXp9upgm0sBZ51lJ1YvvwxnnGHV6ZNB1ap2ct2kiTW69t/fevNee83ucwVPNDlhozJdRqvqBFXdkojgXPx17Ajz5tkwZZ8+tjSPy5+WLe2zPPlk+xz/85/kqz2UjFS1qKruq6rlVLVYcD3jdvF8vORDwEFYodc12FJIWUVaECzbXjcRGSIic0Rkzq+/+kBCMhg+3CbgPPssnHNOcpxUNWtmOb+33LKr4fjf/1qZonXr4J9/Qg3PRRDNcGQDERkrItNF5N2MSyKCc4lRs6adNZ1/PtSoEXY0yW3ffWHiRBvOmD3bDopvvx12VC4vVHWtqu5Q1Z3AOGzoMavVQK1Mt2sCP+XwmmOD5d/Sq1SpEtuAXdxceSVcdx08/jhceGHBn3wjYiMab71l9Q2bN4fx46FbN0ub8BJFBU80C3i/ADwMPAokSaesy6uSJeGBB3bdfu01qFIF2rQJL6ZkJQJnn22fXd++Vnvommvghhus8r4r2ESkuqpm/Ks6CSt3kdXnQH0RqQv8CPQDTk1QiC6BRo2y1TLuvNNW0vjf/+w7XlDVqgWzZtm6wn/8YSeCJUvC88/DoEHWY//KK5aC4sIXzb+E7ar6UNwjcQXGzp3Whb1okZVeOP/8gn3QKaiaNLGSIBdeaEshvf++1WqrWTPsyFwGEZkEtAf2E5HVwPVAexFphg0vLgfODR5bA3hUVbuq6nYRuQB4CygKjFfVRYl/By7eROD2260hdvvtlux+/fVhR5W7Bg12v33KKdCwoeW4lS0bTkxuT6K59K+KyA3AL8AU4N8Ju6qanxlFey09PV3nzJkTxq4LlT/+gNNPhzfegNNOg0ce2XM9RRe9CRNg6FA7I33iCejePeyIkouIzFXV9LDjiAU/hiWnnTth8GB48km47Ta4/PKwI8ofVWtYqlquWL9+UKJE2FGltpyOX9HMjhwIXA58BMwNLn4ESXEVK9pSHjfdZA2Itm2tntjatbb80ahRNhuwoOdIFBQDBlg161q14IQT4NJLPUnWuWRSpIjV2jrlFLjiit3TN5JJxqjGJ5/AwIGeJxa2aGZH1o1wOTARwblwFSkC114Lb75pM/0+/BAaNbL1E7dutW7tQYOSY9ZQQdCgAXz8MQwbZgvuHnUUfP992FE556JVtKgVP+3RAy64wBL2k1XbtvDcc3Y8b9nSjk0u8bJthInIUTk9UUT2FZEmsQ/JFTT/+Y/1iA0caMmptWvb7a++ggULbM01F51SpWDMGJvBtGSJzV568cWwo3LORat4cWu8HHec1RN79tmwI8q/U06xHrF99oFjjrGhVpdYOfWE9RaRj0TkvyLSTURai8jRIjJYRJ7GFvXeJ0FxupDNnm0VmL//Hm680Ra53bzZenW8yGve9eplZ6ANG1pdsfPPt7PqtDTrgUxLs2Fg51zBU6qUlXxo185yZl9+OeyI8u/QQ63S/rHHWo0xl1jZzo5U1YuDStF9gJOB6tiSIIuBR1TVF2cpRIoUsWHHG2+0vKYLLrApzqed5jMn8ystzaaSjxxps64efnhXjt2KFTBkiF0fMCC0EJ1z2ShdGl5/HTp3tlI0r75qowbJqFIlm4SVcSx//nlLl/C6kfGXY06Yqv6hquNU9UxV/Y+q9lTVq70BVvgceSSsWgXvvmvVoz/4wPLCbr7ZhtRc/hQvbjOtqlbdc5LDpk2+goFzBVm5cpYz26gR9OxpazYmq4wG2O+/2wlgerrniSVCNLMjnaNECRseO+UUO+ubNMkaDYceChddFHZ0yS+7lWxWrEhsHM65vKlYEaZPh7p1rfRMsjdcKlWyHvrSpS1PbNy4sCNKbd4Ic1Hr0AGWLrXcgQMOgKlT4csvrYG2bp0l7q9bF3aUyal27cjbixWzxH0vBeJcwVWlCrzzji2YffzxtoZsMsvIE+vY0XrFzj/fj0Hx4o0wlycVK9pw5OWX23IYGebMsRlD6enwxRehhZe0Ro/esxhuiRJ2UD/5ZGjd2g7yzrmCqXp1+46WL28zJxcl+foJFStantjVV9tJouf+xkc0C3iXFpHrRGRccLu+iHi9b7ebLl1sBuWOHXDEET6zL68GDICxY23RXRH7OX48LF9uFfZ/+cV6IDt3tgavc67gqVPHGmIlSkCnTjZykMyKFoVbboGrrrLb776b/MOtBU00PWGPY8sVtQ1urwZujltELmmlp1tV+MMP37XUkYvegAHW6Nq5034OGGAHwYEDrabYPfdYWYtWrax37Ntvw43XObenevXg7bfthLRTJ/supwJVa4wdc4ydMLrYiKYRdpCq3gZsA1DVzYB3TLqIqlaFGTNsgdtevcKOJnWULGkTIL77zj7badOgcWPL11i9OuzonHOZNWpkx8ENG+yktGbN5K//J2LHnY4d4dxz7bJ1a+7PczmLphH2j4jsAyiAiBxEpoW8ncuqeHG44QZLVt22bde6iW7v7buvfbbffWe12p54AurXt7Xsfv897OiccxmaNYMRIyyV4McfrScpo/5fsjbEMuqJXX219YZ16GANTZd/0TTCrgemAbVEZALwDnBFXKNyKWP1assVO/JIeOqpsKNJHVWr2vDkkiVWMuSOO2xFg//7P9i4MezonHMQeW3JZK//l5En9sIL1htftqylULzwgqVJ9O5tjcwdO8KONDlEs4D3DKAXcCYwCUhX1ZnxDculirp1LZH8yCMtt+nCC613zMVGWpr1hn31leVqXHON5aQ89JB/zs6FbeXKvG1PJn36WA0xEejfHy69FLp1g5NOgvvvt7xgL2uRu2hmR54EbFfVN1T1dWC7iPSMe2QuZVSpAm+9BZdcYl/Os88OO6LU06QJvPIKfPihDU+ef76tS/nss3aW6pxLvOzq/5UrB9u3JzaWePniC6sZuWoVfPSR9YbNnGm10mbNCju6gi+q4UhV/TPjhqqux4YonYtasWJw553WTX3JJWFHk7qOOALef9/yNsqUsTPUli0todbPSp1LrEj1/4oWhb/+snIzP/8cTlyx9M47cOaZlic2bhy0aWOFXk8+2WsbRiOaRlikx2S78LdzOTn1VDjsMLs+YoTVwnKxJQJdu9oZ6oQJdsA//nhLovUaP84lTqT6f08+aSkEn35qyfvvvRd2lHunUiVYs8byxF5+2VZNOfpo65WvVCns6Aq+aBphc0TkLhE5SEQOFJG7AZ/r5vbK1q2wcCGcdRYMGwb//BN2RKmnSBFr9C5eDGPGwDffWE9Zz55WzXvCBMspS/ap884VZJHq/w0cCJ99BhUqWBHm0aOTN22gd2/rfX/zTTjxRKtfOHSo5an26wcLFsDff4cdZcEVTSNsOPAP8BzwArAFGBbPoFzqK1nShsguuwwefNBqz6RC13xBVKKENXSXLYObb7Yz70MPtX8EK1akxtR555JNkyY2aalvX7j2WktqT8a1d8uXhylTrG5Y8+bWC/bKK5aPWrmyNcwOPhiefjp5G5rxJJpkiSLp6ek6x9dtSSnPPguDB1sS68KFlj/m4ue336ycxV9/7XlfnToFs8K3iMxV1fSw44gFP4a5zFRtdZGLLrLSM889Zz3WyWb7dhti3bnT8sKKF7ftH31k723OHFsD99577f7CJKfjVzSzIxuIyFgRmS4i72ZcYh+mK6z69bNcpTvv9AZYIlSunH2BxRUrbPjSOZcYIjZ89/HH1mt9zDFw113JN5GmWDErRdSu3a4GGFiD8tNPLQ9u1Spo29YaZs5EMxz5AvAFcC1weaaLczFz2GHWHQ9W48qXxIiv7KbOgy25cuihcOONlkeWykRkvIj8IiILM227XUS+EZGvRGSKiFTI5rnLRWSBiMwXEe/acnulRQtbWeSEE6zmVq9esH592FHFRpEiu9bAfeABa4iBNcY2bQo3trBF0wjbrqoPqepnqjo34xL3yFyh9dNPu5bE+OknS/QcPdrymRYtCju61BBp6nzp0lbH7f77oWJFWx6pYUNo2tQ++yVLQgk13p4AumTZNgNooqpNgSXA1Tk8v4OqNkuVoVIXrgoV4KWXrCfs9dd3NcxSRdmyVsNQBP78E7p0gUMOsZSUZOv5i5VoGmGvicj5IlJdRCplXOIemSu0brrJ8iK+/NK+oG3aWMLqH39Ap05w661hR5j8Ik2dHzvW1qO84AL44ANbcuq++yzx9rrrLLm2WTNrwC1dGvY7iA1V/QD4Pcu26aqaUUrzE6BmwgNzhZYIXHyxfQe3bbPhvIceSr1GSvny8Nprlh7Rv78NY6ZSgzNauSbmi8gPETarqh4Yn5By5kmthcebb0L37pZfsHQp1Kpl9WiaNbOKzA0bhh1h4bF6tZ2hP//8rnyOZs3glFOsKGO9evHdfzwT80UkDXhdVZtEuO814DlVfSbCfT8AfwAKPKKqY3PYxxBgCEDt2rVbrlixIkbRu1S2bh2ccYYdC/v3twT+cuXCjiq2duywNTZHjrT3u2QJHHRQ2FHF1l4l5qtq3QiXUBpgrnD55hs7AI0fbw0wsANQ//42JdolTs2aNsPpww9t3bu774Z99rG1KuvXt6r8t94K330XdqSxIyIjge1AdoU7jlTVFsDxwDAROTq711LVsaqarqrpVapUiUO0LhXtt58NS44ebaMDrVpZ3a1UUrSoLWW3ZImVschogL31FmzZEm5siRDNcCQi0kREThGRMzIu8Q7MuaJF7XLqqXZ79mxLKJ8/37rsXThq1bLVDj76yGZT3nmn9VZefbX1iKWnw223wQ+R+tCThIgMBLoDAzSb4QJV/Sn4+QswBWiduAhdYVGkiJ3svPOO5VEdfrjNNEw15cvvOtYvX26rfjRqZCfcqTYUm1k0JSquB+4PLh2A24AecY7LOU46CSZPtiKjYAuBN2xo1ZmfecaX4CkIate2tUA/+cQOnHfcYQ3nK6+0WmStWsHtt++qPZYMVfpFpAtwJdBDVSPO3RKRMiJSLuM6cBywMNJjnYuF9u1tKbI2bWDQIFttJFVnFqalWTHv0qVtlminTlaBPxVF0xPWB+gE/Kyqg4DDgJJxjco5rMfl9tutwN/gwXZ98WK7vn69JayOGBF2lC5DnTo2tf7TT+H77603DOCKK6BuXeslGzSoYFXpF5FJwMfAwSKyWkTOAsYA5YAZQfmJh4PH1hCRqcFTqwGzReRL4DPgDVWdFsJbcIXI/vvDjBlWYX/8eGuQfftt2FHFR+fONurxwAM2SeuII/Ys2aFqJ3i//hpCgDESTWL+Z6raWkTmYj1hG4CFqto4EQFm5Yn5hc/q1bYwrKotgVG7thUbvekmy1W68EK7b+dO64VxBcv338OLL9oMy0hrhEZTpd8r5ju3u2nT4LTTrJ7io4/a8kep6vffbeSjWzc71r/4Iuy7r52E//mn9QgedRSMGwfVq4cd7Z72KjEfW8C7AjAOW7h7Hnbm51xC1KxpZROGD99VZLRcOetpufBCuz1hgvWYffJJeHG6yA480HrDtm2LfP/KlYmNx7lU0KWLDU82bWqrjgwblroFritV2lXM+9NPbVZ2t27W8Fy92mbNH3YY9OiRfPlj0cyOPF9V16vqw0BnYGAwLOlcgbHvvrYAeNu2liuRzN3TqSq7Kv05Ve93zmWvVi0r13PppfDgg7ZsUDJPiIlGmzZWFqd8eRg1Co4/3oYrb7oJ/v47+U7Eo50d2VREegAtgHoi0iu+YTmXNz16WEmLyy+Hp56CBg1g4sSwo3KZZVelf/TocOJxLhUUL24TYqZMsUlMLVrYZJmCPgFmb5QqBf/7n5XK+fRT6N3besAaNky+QtLRzI4cD4wHegMnBJfu0Twv65psWe4XEblPRJYFa7S1yGPszu0mY4jyyy+heXOrYwXJ1z2dqrKr0j9gQNiROZf8evaEefNsybG77y5YE2BirXVrK2A7YoSlM7z8suWFvf++pT4cc4zlyf35Z9iR5i6axPyvVbVRnl/YChf+DTyVTSXqrsBwoCtwOHCvqh6e2+t6UquLRsaftYj1tCxfDv/3f1b80CUfT8x3Ljp16kTOs4xmAkyy2LDB6qUdfbQVev3tNxuabNzYZmI//bSNjJQqZaMkV15pPYRh2dvE/I9FJM+NsEhrsmVxItZAU1X9BKggIgVwXoNLRiK7Crpu3WrFDRs0gIcftmUynHMuFa1aFXl7Kq2UVa6cra1ZvryVvRk1CgYOtGWdrrkGvv4aPvsMzjkH3n3XGmlgn8GcOQVrdCSaRtiTWEPs22DYcIGIxKJs2gFA5j+X1cE252LqxhttiLJZMzjvPOvKnj8/7Kiccy72spvoUrQovPpqYmOJp/32s7ywBQts9Y5zz7UcOLAT8Fat4L774KefoGNH2z5mjG1v3NhGRgrCzOxoGmHjgdOBLuzKBzshBvuOtPBMxPapiAwRkTkiMudXn/bm8qFRI1v249lnbeak94Y551JRpAkwJUta/awTT7RLKvWK5aZ48V31I6+5xnrLKle262lp4Ze1iKYRtlJVX1XVH1R1RcYlBvteDdTKdLsm8FOkB/rity4WRKyuzHff2YLTYFO7fYjSOZcqIk2AeeyxXatYvP22nZTedlv2tftSVcWKNklh1iz7P3DDDdCkya7UlauusiK427cnLqZoEvMfBCoArwH/loJT1cm5vrhIGvB6Non53YAL2JWYf5+q5roArie1ulj55x+rMfPuu9Yoe/BBG6oEOzh99ZXVH6tfP9w4nSfmOxcrK1daketXXrEGyEMPWbX5wm7tWitx8ccftjzUqafC6adbEdiMRtqmTbZu8SefQI0aVpOybt3cX3tvE/P3wRpfx5G3EhV7rMkmIkNFZGjwkKnA98AyrBr/+VHE4lzMlChhZ4WTJlneQJs2lsj55JN29njmmbZo7hFHpM6sIudc4Va7tpV0eOUV+OsvaNfOGhPr1oUdWbiqVbPK+y+9ZP8L7r/fSh1NmWL3//GHFQN/9VX7uXmznbS/887e7TfHnjARKQrcqqqX791uYsfPIl08bNhgM2zGjYNixWDqVJsCvWMH3HWXFYD98stdiZ8usbwnzLnY27jRJi7ddZfNNLztNjv59OOczah87jk44wwoWxb+8x9YuNAS+nv1sm3Tplmv4jff5PyZ5bsnTFV3YFXynUtp5cpZ1enTTrM12Fq3ti/Xhx/CZZfZYz76KNwYnXMulsqUsRmGX3xhQ3FnnWWFThdGLLFeuFSuDOefb40tsIYWWCmMLl3s+n/+A1u2WH5ZfkXT3p0vIq+KyOki0ivjkv9dOldw/fmnLTi9ejW8+KIdkI4+2r6IP/8cdnTOORd7TZpYtfnHHoPFi20Y7sorrafMmYwVPmbNsoR+sNzhjRutMZtf0TTCKgG/AR3JQ06Yc8moXTt44QWoWdPWYbvvPptV9MkntkDs3pzxOOdcQVWkCAwebD0+Z5xhQ5ONGqVWbbG9ccYZcPPNlqh/7LFW1uK226wSf40a+X/dYrk9QFUH5f/lnUsup59uZzv9+lnXfKVKlivRrJklsR4QlBP+7js7MyqW6zfIOeeSx377WY/YoEEwdOiu2mL33Zd9IdjCYPBgW5vzwAOhQwdrrIrA66/v3etGs4B3TRGZEizGvVZEXhKRmnu3W+cKptKlYeZM646/5RYr7nrTTfZFmzXL1iLbts3OhA45xBaJ/eefsKN2zrnYOuooyxW77TaYMcNyxm6/vfDVFstQpIiVMfrkEzjpJKu+/8UXUKtW7s/N8XWjeMzjwKtADWxZodeCbc6lpHLlrGjfzJnw2mvQu/euOjFg1ZfvvhsqVLCSFvXq2XTmzZvDitg552KveHG4/HJbi/HYY+GKK2z47cMPw44sPAcdBP37W65wLGaRRvMSVVT1cVXdHlyeALxsvSu0ihSBnj3h889tinJams2knD497Miccy726tSxumIvv2yTl446Cs4+e9fC2C7/ommErROR00SkaHA5DUvUd65QE7Epyh98AB9/DCcEK6recQdcf70foJxzqeXEE61X7PLLraj1wQfD44/Dzp1hR5a8ommEDQZOAX4G1gB9gm3OuUCbNru6pr/+2gog1qljBysvbeGcSxVly1qe2Lx5lhc7eLCtLPK//9moQJEi9nPChJADTRLZNsJE5H/B1cNVtYeqVlHVqqraM0YLeDuXksaPhwUL7KzxrrvsgPToo2FH5SIRkfHBpKOFmbZVEpEZIrI0+Fkxm+d2EZFvRWSZiFyVuKidC9+hh9oowGOPWYL6VVfBihVWumHFClso2xtiucupJ6yriBQHrk5UMM6liiZN7AD07bdW9qJpU9u+ahUsXRpubG43TwBdsmy7CnhHVesD7wS3dxMs6fYAcDzQCOgvIo3iG6pzBUtGbbEKFfa8b9MmGDky4SElnZwaYdOAdUBTEflLRDZk/pmg+JxLavXq2XqUrVvb7Ztusi78/v2ttyyzH36A55+3mUc5LOnqYkhVPwB+z7L5RODJ4PqTQM8IT20NLFPV71X1H+DZ4HnOFTo//hh5+4oVvgRSbrJthKnq5apaHnhDVfdV1XKZfyYwRudSxk032VqUr79uvWMnngiffmrrVbZqZY2wIUMgPR1++insaAutaqq6BiD4WTXCYw4AVmW6vTrY5lyhk10RVxEbtuzcGd580xP4I8kxMT/oct+LVZGcc5lVq2YJrCtW2Ppjs2bZGm3z5llP2Isv2plj9+7Wze8KLImwLdv+SxEZIiJzRGTOr7/+GsewnEu80aOt0HVmpUtbcdNbbrHJSl27QuPG8MgjNlTpTI6NMFXdAWwSkfIJise5QqFSJStjsWIFbNkC//0vfPml1d+ZMAGGD4fPPvOZlSFZKyLVAYKfv0R4zGogc63smkC2fZeqOlZV01U1vUoVL7PoUsuAAbbcW5061vuVsdj10KFw9dV2gvnMM7bQ9dCh1nM2cqT39kN0JSq2AAtE5DERuS/jEu/AnCsMypWDrVutUbZ+veVWnH66LYWxeTNMngzbt4cdZaHzKjAwuD4QeCXCYz4H6otIXREpAfQLnudcoTRgACxfbkOOy5fb7QwlStjtzz+3GZXt2sH//Z/NHD/9dBsJKKyiaYS9AVwHfADMzXRxzsVAly5WwqJ7d1sYfPZsy6HYts3qjWUsmbRkSeFdty1eRGQS8DFwsIisFpGzgFuBziKyFOgc3EZEaojIVABV3Q5cALwFLAaeV9VFYbwH55KFiDXApkyxWeLnnWdV+Fu2tFpjr7wCO3aEHWViiUYxDUtE9gFqq+q38Q8pZ+np6Tpnzpyww3AuZn77zdYhq18fevSAb76xKtQTJtiZYoMGdnaZlma5FH362OzKdu1is3ZZMhCRuaqaHnYcseDHMOd2Wb/eao3ddx+sXGlrM150EQwaZIVhU0FOx69cD+EicgIwHytZgYg0ExHvdncuRipXthmSxx0H778PxYrBJ5/Y7QYN7DGqMGaM9ZA9/bSdNdaqBRMnhhq6c87tlQoV4NJLbRTgueegShVbi7dmTVswfNWqXF8iqUVzHn0DVhNnPYCqzgfqxi0i5wqhsmXh/PNtPbZbbrGzwcyKFrVeskmT4Jdf7GerVpZLBtZ7NnKk1+RxziWnYsXglFNsHd6PP7Z1ee+6C+rWhX797EQ1FUXTCNuuqn9m2ealJJ0LSZkydlB6+WXLJwP46CO49VaryXPooTZl/LvvQg3TOefypU0b6xX77jsYMcJqjLVpA0ccYWV8MiYrZaRsJPN6ldE0whaKyKlAURGpLyL3Ax/FOS7nXB4MHmzTvceMgfLl4dprrTGWUY9n69Y9n6NqZ5dPPglz5niVfudcwVKnDtxxB6xeDffeC2vXwskn20okAwbAOeck/3qV0TTChgONga3AROBPYEQcY3LO5UO1alZ5f/ZsOyBNmrSrgOLRR1se2SOPwLp18Ndf0KkTnHYavP22DQN06QJ//x3qW3DOuT2UK2d5YkuW2MzKOnUsH3bz5t0fl4zrVWbbCBORUiIyArgNWAm0VdVWqnqtqm5JVIDOubyrXduWRAKbWdmtm51FDh0K1atbL1nx4rbA+NNP23Tx/faD664LN27nnMtO0aLQs6dNYMrOihV2opkscuoJexJIBxYAxwN3JCQi51xMFSliFfm//hq++AIuucRmHHXubPetXGnDmH36WIPMOecKujp1sr+valWbuHTddTYyUJALXufUCGukqqep6iNAH+DoBMXknIsDEWjWzBL4ixaFs86y7bNnW/Jrr15Ws+yII6yh5kuKFFzxSEguGxRlWrFiBS1btqRZs2Y0btyYhx9++N/HDBgwgIMPPpgmTZowePBgtuVQPfiJJ56gSpUqNGvWjEMOOYS77757t/u/+OILRIS33nprt+0iwqWXXvrv7TvuuIMbbrgBgBtuuIEDDjiAZs2aUb9+fXr16sXXX3/972P/+ecfRowYwUEHHUT9+vU58cQTWb169W6vffrpp/97e/v27VSpUoXu3bvn4ZOKjUmToEkT+y42aWK391bZTIW1Vq5cyXHHHUfDhg1p1KgRy5cv3+2xw4cP3+3xkRTk3+Gu9Sq3A1WA7uyzD4waZZcSJWymebt2Vgaod29bSmnFilw/xoTKqRH277crqA7tnEsBIraY7vjxdvvUU61nrH9/q0tWpAg89JBNGQcYN856ye64wxpsvvhuuCZMsATkeCUkV69enY8++oj58+fz6aefcuutt/JT0CIfMGAA33zzDQsWLGDz5s08+uijOb5W3759mT9/Ph9++CGjR49mVaaiT5MmTeKoo45iUpbWR8mSJZk8eTLrshlTuvjii5k/fz5Lly6lb9++dOzYkYxF0a+55ho2bNjAkiVLWLp0KT179qRXr15kFCUvU6YMCxcuZHOQTDRjxgwOOOCA/H1Qe2HSJMtduv9+Wzv2/vvtdiwaYhnOOOMMLr/8chYvXsxnn31G1apV/71vzpw5rF+/PqrXKai/w169NjN2LFStOgM4gH32sWPVf/9rPWAffmgnlS++aDmvn38O555rJy0NG9qJ57Rp4R/PiuVw32Ei8ldwXYB9gtsCqKruG/fonHNxcfvt0KEDfPUVtG0Ls2ZZA2vmTDjwQFseqXhxe+zGjTaM+dJLdrtYMevqnz3bGmzr19uMzIzllXKzY4fN1syYNOB2N2IEzJ+f/f2ffLLnbNdNm6xnc9y4yM9p1gzuuSe6/ZcoUeLf61u3bmXnzp3/3u7ateu/11u3br1bD0VOKleuTL169VizZg21atVCVXnxxReZMWMG7dq1Y8uWLZQqVQqAYsWKMWTIEO6++25Gjx6d4+v27duXN954g4kTJ3LOOefw+OOP88MPP1C0aFEABg0axPjx43n33Xfp1KkTAMcffzxvvPEGffr0YdKkSfTv359Zs2ZF9+HkQfv2e2475RSrB3jTTZZsPmrUrvvKlYPLL7eToXXr7MQns5kzo9/3119/zfbt2+ncuTOwew/Zjh07uPzyy5k4cSJTpkyJ+jUL4u9wwIA+vPXWJBo3tt9h5vUqwQrB9u5tF1WrpzhtGrz1lk1SuvdeKFnSJi516WK1yRo1iv5YFgvZ9oSpalFV3Te4lFPVYpmuewPMuSTWoAEsWACHHWaL57ZqBV9+aQ0w2NUAA2sUfPedJfa/8or9o2jWbNeSST16WA7GCSdY9/+778KGDXvuc9s2uOYae2ylStC8uR0MXd5EKjeS0/b8WLVqFU2bNqVWrVpceeWV1KhRY7f7t23bxtNPP02XjEJ1uVi5ciVbtmyhadOmAHz44YfUrVuXgw46iPbt2zN16tTdHj9s2DAmTJjAn39mLVG5pxYtWvDNN9+wbNkyateuzb777v7vKT09nUWLdi3r2a9fP5599lm2bNnCV199xeGHHx7Ve4ilb7+1E5fMypePXQrAkiVLqFChAr169aJ58+Zcfvnl7AgWZRwzZgw9evSgevXqeXrNZP8dilgP2MUXW0Ps99/t5/nnw48/WtX+Jk1sUtPZZ8MLL8Aff+z5OrFOBcipJ8w5l8IqVbLcr2hVrWoNrh49dt9+1lk2W+njj+H1121bt267rr/wgh3cxoyxxtzcuXage/NNOOMMeO01aN06Nu8pFeTWY5WWFjmvpU6dvPWW5KRWrVp89dVX/PTTT/Ts2ZM+ffpQrVq1f+8///zzOfroo2nXrl2Or/Pcc8/x3nvv8e233zJu3Lh/e0omTZpEv379APuH+vTTT9OrV69/n7fvvvtyxhlncN9997HPPvvkuI+MYSpVRSJ0YWTd3rRpU5YvX86kSZN269mLtZx+Fw0bWi9Yhw67tr33Hgwfbtf322/vfpfbt29n1qxZfPHFF9SuXZu+ffvyxBNPcPzxx/PCCy8wMw8vnqq/w332sZ6v//zHbq9aZSeFb71lQ5iPPWYNrcMP3/W4pUtthnnGEGZGKgCwRy9ctArJ8r/OuXgZONDyyxYv3nV2ecUVdt8ff9gQTKNG8OCD1hv2xBN2MOvWzXI3suT6ulzsSkjepXRp2x5rNWrUoHHjxrsN140aNYpff/2Vu+66K9fn9+3bl0WLFjFr1iwuvfRSfv75Z3bs2MFLL73EjTfeSFpaGsOHD+fNN99kQ5bu0xEjRvDYY4+xcePGHPfxxRdf0LBhQ+rVq8eKFSv2eJ158+bRqFGj3bb16NGDyy67jP79++f6HuJh5Eg7eXnvPftOvPee3Y5VjauaNWvSvHlzDjzwQIoVK0bPnj2ZN28eX3zxBcuWLaNevXqkpaWxadMm6tWrl+NrFZbfYa1au3rA1q2znLJrr7X0iVGjLG3jzDP3zCHb29pk3ghzzsVMxYp2xnh0MJe6fHlrnN14o923bp3lwyxdave3agXLloUXbzIaMMBmedWpY0MsderY7fyeiWe1evXqfxPX//jjDz788EMOPvhgAB599FHeeustJk2aRJEi0f/7aNu2Laeffjr33nsvb7/9NocddhirVq1i+fLlrFixgt69e/Pyyy/v9pxKlSpxyimn8Nhjj2X7ui+99BLTp0+nf//+lClThoEDB3LJJZf8O/T21FNPsWnTJjp27Ljb8wYPHsx///tfDj300KjfQyz172+N5uHDoVQp+zl6tG2PhVatWvHHH3/8m+z+7rvv0qhRI7p168bPP//M8uXLWb58OaVLl2ZZlF/AwvQ7LFbMZomPGmWrivz6q02ayJQeuZuVK/O/L2+EOefipkgROOQQyytThalTLZH/2GPt/nffhSDFxOXBgAGwfLn9U1i+PHYNMIDFixdz+OGHc9hhh3HMMcdw2WWX/fuPbujQoaxdu5a2bdvSrFkzbrzxxqhf98orr+Txxx9n7NixnHTSSbvd17t3byZOnLjHcy699NI9Ztjdfffd/5Y3eOaZZ3j33XepUqUKAP/3f/9HqVKlaNCgAfXr1+eFF15gypQpewxx1axZk4suuijq2OOhf39YuNB6WhYujF0DDKBo0aLccccddOrUiUMPPRRV5Zxzztnr1y2sv8PKlW293uxqk9Wunf/XFk2yBePS09N1zpw5YYfhnMuj66+HN96wGUn168PkyTYc+f77NlyZExGZq6rpiYk0vvwY5lxyyigPk3lIsnTp3Huiczp+eWK+cy4hbrjB1rccMgTWrIEjj7Qk2NwaYM45VxBkNLRGjrQhyNq1bRh5b3qivRHmnEsIEZsOfv75YUfiYunxxx/n3nvv3W3bkUceyQMPPBBSRC6v/HcYvQEDYjv878ORzrkCz4cjnXPJKqfjlyfmO+ecc86FwBthzjnnnHMh8EaYc85lISIHi8j8TJe/RGRElse0F5E/Mz3mvyGF65xLUp6Y75xzWajqt0AzABEpCvwIRFrteJaqdk9gaM65FOI9Yc45l7NOwHeqGmHFRuecyz9vhDnnXM76AZOyua+tiHwpIm+KSONEBuWcS37eCHPOuWyISAmgB/BChLvnAXVU9TDgfuDlHF5niIjMEZE5Gev5Oedc0tUJE5FfgRVAeeDPLHdH2lYX+CEBocVyn5HeRyz2l9Pr5vW+SPvMa9x5Fc37jEUMmV8jVvvM7THZ7TO750X79x+NWH5Hoo0hp31Geo06qlplbwLLDxE5ERimqsdF8djlQLqqrsvlcXk5hiXj8QvicwzLy3comu1Z91kQjl+xiiPjNWJ5zIz2f0Q0n2ssj1+R9plfeYkhu33m7filqkl5AcZGuW1jCLHt1T4jvY9Y7C+n183rfZH2mde44/G5xiKGzK8Rq33m9pjs9pnd86L9+4/l309eP7v87jPef0d5fD/PAoOyuW9/dp3ItgZWZtzO72eVdVsyHr/y8zuMxfcsL9+VSPssCMevWMWR8RqxPGZG+z8ims81lsevvHy2sfzss9tnXt9HMs+OfC3KbckoXu8jp9fN7335eVw8xSKGvL5GNI/P7THZ3Z+X7YX1848LESkNdAbOzbRtKICqPgz0Ac4Tke3AZqCfBkfhKBXU32EsxON9xPo7lN/HxVuiv0Ox+Hzyel9B/dtP+PEr6YYj80pENqpqmVTeZ2F4j77P1NlfWPtMRoXld1MY9lkY3qPvM+8KQ2L+5EKwz8LwHn2fqbO/sPaZjArL76Yw7LMwvEffZx6lfE+Yc84551xBVBh6wpxzzjnnCpyUbYSJyBIR2SkiWxK0v9Yi8oeIbBWRLSLyUiL2G+y7uIhsEpG1Cdrfy8F73CIiK0SkfBz2EfH3JyIviMg/wb4/jeH+yovI3yKyOXjtmcH2z4Lf6WYR+UlE6sRqn8Hr1xGR1cE+torIOZnue01EVEQa7OU+9vgss3tfIrKPiCwLPoOtIjItH/uL+F0QkZkisiPY5+bMay2KSG8R2ZDp7yrmf1PJxI9fcd1fyh2/gtdO+DEsFY9fwesk7BiWso0w4D7gtATubytWT6gkVj/kBBE5IUH7fhH4JRE7EpGWQDeguqqWwv6G7ozDrvb4/YnIxUBHoHKw70Ex3N9fwEGqug9QEUgXkbOwcf99g+0rgediuE+Ad4C3g7+bSsB0sIMA0BbYEYN9RPouZPe+bgeKB59vDeBYETkqj/vL6bswVVX3CS43AohISeAZYHCw3ybApjzuM9X48SsOUvj4BeEcw1Lx+AUJPIalbCNMVcdgv5xE7e9LVZ0YXF8D/AY0ivd+RSQdaIf9oSaKABWDP7wSwLJY7yCb398lwK2quiF4zNcx3J+qasaZeGnsu6Gqequqbg22zwSqxWqfInIAUIfgYKyqG3XX+oSTgfNisZ9In2UO70uBksHvtiKwE/gpj/vL63fhSmCNqr4QPGeZqm7Lyz5TjR+/4rtbUuz4FbxeQo9hqXr8Cl4/YcewlG2EhSloeVfBWsbx9jIwHPtjiztVnQu8AnyH1UbapKq3JmLfwH5At6DLfb2IDIzliwfDIpuBdcBXqjo+y0POBN6M4S6Pws64lgbDMd+ISBURuRn4NeMLnQBnsut9XRHEtBlYCjynqt/n94UjfBeOD7rxl4hIWrCtefDYdcHn8EZ+9+f2nh+/4iauxy9I+DEs5Y9fEP9jmDfCYkxEqgFvAXeq6o9x3tcoYL2qTojnfrLsMw3oADQEygClROTBRO0eqACUA0YAj4qIxOrFVXVb0L2dBhwsIj3/3bHIdOwfxbBY7Q87Cy8D3KKqpbEDxxvYe+sRw/1kK8L7GhjcLgM0BvqKyDH5fO2s34VhwD5AWWz4aXrw0GJALeCI4OfRInJZvt6Q2yt+/Irv7onj8QsSfgxL6eNX8PrxP4ZFKqOfKhespb4lgfvbBzsDeSVB+/sI2B5cdmBdsd/HeZ93At9muv0IsDARvz/gV2BEptvbgEPitO93gdeC62OBDVguRyz3cSiwPdPtYcDv2EEk4/eqwc9DY/lZZve+gAXAw5luLwHuzsf+cvwuZI4HuBdYlum+t4HX4/F7TaaLH7/iss9CcfwKXj+ux7BUPn4Fz03IMcx7wmIkOKNZCKxS1RMTsU9VPUJVi6lqMeBS4BdVPTDOu10E1BGRysF77gQsjvM+M0wHTgIQkeOwM8tvY/HCInJIphk2FYGWwDwRGYmdXbVS1d9isa8MqroA2CgiXYJNJ2Nf5CKZfq87gEbBY2Mmh/e1CugkpgqW8zE7j68d8bsgIodlethlQEb+yt1AjeBvqiTWtT8nr+/J5Z8fvxIibsev4DUTegxL1eNX8PqJO4bFqxUe9gVYwa6zq+3A43He33nBvjZnuvw3ge93BLA2QfuaiY27bwG+B8ol4veHdTF/H+x3E3BJDPfXO3jNzcHrvxNs/yfYf8bvdFGM32dfYGPw2muAtCz3bwcaxOGzjPi+sATXVcFnsJV89Ehl913I9LvbDPwMHJbpOQ8G920BPk3E33FBvvjxK677SrnjV7DPhB/DUvH4FbxOwo5hXjHfOeeccy4EPhzpnHPOORcCb4Q555xzzoXAG2HOOeeccyHwRphzzjnnXAi8Eeacc845FwJvhLmYEhEVkTsz3b5MRG6I0WvfICI/ish8EflaRPrH4nWdcy6DH8NcInkjzMXaVqCXiOwXp9e/W1WbAScCj4hI8TjtxzlXOPkxzCWMN8JcrG3HlpO4OOsdIvKEiPTJdPvv4Gd7EXlfRJ4PFkW9VUQGiMhnIrJARA7K+lqquhQrTFgxeI3LReRzEfkqWJMOEUkLFpV9Mtj+ooiUDu67NTgT/UpE7ojHB+GcS0p+DHMJ440wFw8PAANEpHwennMYcBG2HtnpWJXl1sCjwPCsDxaRFsBSVf0lWAKkPtAaaAa0FJGjg4ceDIxV1abAX8D5IlIJWz6kcbD95ny8R+dc6vJjmEsIb4S5mFPVv4CngAvz8LTPVXWNqm4FvmPX6vQLgLRMj7tYRL4FPgVuCLYdF1y+AOYBh2AHNLC1vz4Mrj+DLbr6F7a0xKMi0gs7G3XOOcCPYS5xvBHm4uUe4CxsvbQM2wn+5oIFUktkum9rpus7M93eCRTLdN/dqnowtmbZUyJSClsI9/9UtVlwqaeqjwWPz7oul6rqduyM8yWgJzAtX+/QOZfK7sGPYS7OvBHm4kJVfweexw5iGZYDLYPrJwL5TkhV1cnYKvUDgbeAwSJSFkBEDhCRqsFDa4tI2+B6f2B28LjyqjoVWzi4WX7jcM6lJj+GuUQolvtDnMu3O4ELMt0eB7wiIp8B7wAb9/L1bwQmAg2Dy8d2csrfwGnADmAxMFBEHgGWAg8B5YM4Ms5A90jAdc45/Bjm4kxUs/Z0OpcaRCQNeF1Vm4Qdi3PO5ZUfw1KfD0c655xzzoXAe8Kcc84550LgPWHOOeeccyHwRphzzjnnXAi8Eeacc845FwJvhDnnnHPOhcAbYc4555xzIfBGmHPOOedcCP4fcXRQyD5WtFQAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 720x360 with 2 Axes>"
       ]
@@ -204,7 +190,7 @@
     },
     {
      "data": {
-      "image/png": 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ZATnn0sOyZXDzzfDGG3DIIfD115aI75xz6WDWLGjXDr76KrW9X/HyGo7smtcTVfXxxIfjnCvqVK3hdfPNsHo13HefXUXuvHPUkTnnXP6i7v2Kl9dwZGxA4SCgKVYZGuAc4IdkBuWcK5rmzYMbbrAhyKOOsunahx0WdVTOORdOUej9ipfXcOQDACLyJdBYVdcE9+8HPkpJdM65IiErC55/Hu64w77v2xduvBFKl446Muecy19WFjzzjJ3DdtrJaha2bRtN71e8MMVaqwOb4+5vDrblSUTKicgPIjJFRGaIyAM57HO1iCwTkZ+C23XhQ3fOpcIvv0Dz5pZ0f8wxMH26DUV6A8w5lw5mzYIWLeCmm+Ckk2DGDLj66ugbYBCuWOurwA8iMjy43xoYFOJ5m4CTVXWtiJQFxovIJ6r6Xbb93lLVTqEjds6lxObN8Oij0KMHVKwYbd6Ec84VVFHt/YqXbyNMVXuJyEjg+GDTNao6OcTzFFgb3C0b3HJY+tI5V9T8+CNce60t13HJJbZ4bfV8+7+dc65o+OMPy/368suikfuVm7BrR/4EDAWGA8tFJNSa4SJSWkR+wgq9jlbV73PY7UIRmSoi74hIrZDxOOeSYN06W+vx6KNh+XJ4/3146y1vgDnn0kNs5mODBjBlivV+jRiRmAbYsmVw//1WiLpdO7tYLax8G2EiciOwBBgNjMCS8keEeXFVzVTVhsA+QDMRyT6P6kOgrqo2CF4/x2FOEekgIhNEZMKyZcvCHNo5l4/Bg6FuXShVyr7eeSccfrgtNdS+PcycCeeeG3WUzjkXzh9/WM5X585w4omWv5qo3K9Fi6BZM/jrL5uUdOihdn58553Cva7YqGEeO4jMAo5S1eWFOpDIvcB6Ve2Ty+OlgRWqumter9OkSROdMGFCYUJxrsQbPBg6dID167fdvtdeMGSIncCKEhGZqKpNoo4jEfwc5lxiDB4M3bvD3LlQtaqdz8qVs9nbiU6879rVaiT27bt123ffWbrG7NlQJo/krrzOX2GGI+cDqwsWLohINRGpEnxfHlvi6Jds+9SIu3su8HNBj+OcK7ju3bdvgAGULVv0GmDOOZdd7EJy7ly7v3IlbNkCDz5oa0AmOvl+7Fj4v/+DX3+1lA2wtI0yZawHbkeFmR05G/hCRD7CZjwCoSrm1wAGBT1cpYC3VXWEiDwITFDVD4DOInIukAGsAK7egffgnCugedutCGsWLEhtHM45tyNyupDMyrJ0is6dC//669fDuHEwcqTd1q+H+fPhvPNg99237rNypfXC7agwjbB5wW2n4BaKqk4FGuWw/d647+8E7gz7ms65xNhrL8txyK52qCk3zjkXrVgPWHa5XWAWxBVXWK7Xpk1QvrzVGNt/f7j3Xuv9uusu63W74w44+WTYc88dP1aYEhWxyvmVgvtr836Gc64omzULNmzYfnuFCtCrV+rjcc65gli40IpFZ2Zu/1hBLiRXrYIxY+DTT60o9bhxNoxZty507GizIE84wfLMVO38eOihdvvjDzjiCFtHtzDybYQFMxpfA3YL7v8NXKWqMwp3aOdcqs2ZY1duZcrAI4/YUkTz5tmJq1cvaNMm6gidcy53y5fD6adb/mqZMtZbFRP2QvKjj+z89+231pDbZRc47TQr0VOpEvTsuf1zRODuu61xNnUq1KwJ9eoV/v2EGY7sB3RV1c8tEGkBvAQcW/jDO+dSZeFCOOUUWLMGPv8cGjaE22+POirnnAtnzRo480zrhfroI3jvPasDtm6dpVj06bP9heTSpTBqlOV13XUX1K8PGzfaaMAdd1hv11FHWaMujKpVEzt5KUwjrGKsAQagql+ISMXEheCcS7YlS6wBtmwZfPaZNcCccy5dbNxoSfGTJlm+Vt++sGKFJeIvX27LE61cafsuX26PjxwJEyfatmrV4LLLrBF24YV2KwpCzY4UkXuwIUmAK7AZk865NPD333DqqTaz59NPreCgc86li4wMa0B98QW89po1yFassCWJypSxJH1V69m//HLr1XrsMWjSxIYWW7aERo2sMHVRE6YR1g54ABiGrf34VbDNOVfErVxp+ROzZln3/fHH5/8c55wrKrKybImg99+HZ5+14carr7aFuJcsscbZ+PG2b/Xq1lC78ELrDatQIcrIw8m3XaiqK1W1s6o2VtUjVfVmVV2ZiuCcczsulj8xfToMH24J+c45ly5U4eabrferZ0/4739te+XKMG2a5XL99BP07g0zZtisxsqVbZ90aIBBuLUjR8cq3wf3q4rIp0mNyjlXKOvWwVlnwYQJ8Pbb1h3vnHPp5P77bTHurl0tqT7myittebXSpa0X7LbbbK3bv/6ytSPTSZjhyD1UdVXsjqquFJFClCZzziXTxo3QujV8/bXVsGndOuqInHOuYJ54wpYgatfOZj2KWM/YhAmW1/rAA1Y1v3t3G3pcuNB6/MPOciwqwjTCskSktqrOAxCROlhumHOuiNm82fIhxoyBgQPh0kujjsg55wpmwADo0sXOZf36WQNsyxZbjuiFFywhv2PHrcn6lStbD1i6NcAgXCOsOzBeRMYBApwAdEhqVM65AtuyxU5KH38ML74IV10VdUTOOVcww4bBddfZhKLBg23IcfVquOQSq/d1++1w3HG27+67F51SEzsqzLJFI0WkMXB0sOlmVf07uWE55woiM9MaXcOHw5NPQge/THLOpZnPPrMSE0cdZY2xnXeGP/+Es8+G336DV16x4cniJExivgAtgcaqOgKoICJeaci5IiIry64chwyxpTg6d446IuecK5jvvrP81YMPtnI6FYOS8GPGWML9qFHFrwEGIRphwHPAMcDlwf01wLNJi8g5F5oqdOpk+V/33+/LEDnn0s+0aVZOp0YNKyhdtarVAAO7wPz11/Sb9RhWmEbYUaraEdgINjsS2CmpUTnn8qVqU7eff94aX/feG3VEzjlXMLNmWf5XxYowerQVXO3ZEw44wMpOAOxZjOsxhEnM3yIipQlmRIpINSArqVE55/KkalOzn3jChh8ffthmEDnnXLpYuBBOO80mFY0daz1hbdtacdYrroD99486wuQL0wh7ChgO7CkivYCLgLuTGpVzLk89e1rDq0MHa4h5A8w5l06WL7cesOXLrQFWrZo1yL76yuqD3X13yTivhZkdOVhEJgKnYCUqWqvqz0mPzDmXoz59bOixbVsbiiwJJyrnXPERW1Ltjz9g5EhbaPvee+GHH+DNN63UTkkRZnbk/sCfqvosMB04LX4ZI+dc6jzzDHTrZkVYX3kFSoXJ6nTOuSJi40Y47zyYNAmGDoVjj7Xt99wD339fshpgEC4x/10gU0QOAF4EagFvJDUq59x2Xn4ZbrzRpnG/9poVMXSJIyK1RORzEZkpIjNE5KZg+/0islBEfgpureKec6eIzBKRX0XkjOiid67oy8iwRtbnn9uM7mXL4LDDYOlSq3Z/xBFRR5h6oZYtUtUMEbkAeEZVnxaRyckOzDm31euvW/5Xy5ZWDywdl+dIAxnALao6SUQqAxNFZHTwWF9V7RO/s4jUBy4DDgVqAp+JyIGqmpnSqJ1LA1lZVufr/ffhqadg+nTo3dvywHYqwfUWwvSEbRGRy4GrgBHBNv8X4FyKDB1q+V8nnbS1irRLPFVdpKqTgu/XAD8De+fxlPOAIaq6SVX/BGYBXsjauWxU4eabrQf/nntg3DhrgP3nP1aYtUqVqCOMTphG2DVYsdZeqvqniOwLvJbcsJxzAB98AP/3f5Y38cEHUL581BGVDCJSF2gEfB9s6iQiU0Wkv4hUDbbtDcyPe9oCcmm0iUgHEZkgIhOWLVuWrLCdK5Luvx+eftrqGq5YYReTjz1mE4tKeq9+mNmRM4HOcff/BHonMyjnnFWOvvhiaNx422U8XHKJSCUsF/ZmVf1HRJ4HemC1EnsAjwEFWkBFVfsB/QCaNGmiiY3YuaLriSes5ES7djaze8UKOOssmx3pwvWEOedS7IsvLAG/fn2bwr3LLlFHVDKISFmsATZYVYcBqOoSVc1U1SzgJbYOOS7EJirF7BNsc84BAwZAly7Wk79kiRVl3X13b4DF80aYc0XE4MFQt67V/Tr5ZDtZjRpl66i55BMRAV4BflbVx+O214jb7XysVA/AB8BlIrJzkKZRD/ghVfE6V5QNG2brPh58sC3O/ddfsGpV1FEVPXk2wkSktIj0yWufPJ5bTkR+EJEpwXTvB3LYZ2cReSuY4v19kIfhXIkzeLDNfpw71+6rWrf9qFHRxlXCHAdcCZycrRzFoyIyTUSmAicBXQBUdQbwNjATGAl09JmRrqQaPBhq1bKLyOrV4ZJLbM3HX36Bs8+GL78s3mtA7qg8c8JUNVNEjt/B194EnKyqa4Mu/vEi8omqfhe3z7XASlU9QEQuw3LNLt3B4zmXtrp3h/Xrt922YYNtb9MmmphKGlUdj60Kkt3HeTynF9AraUE5lwYeewxuu83KUIDV/QJYvBhuucVmQnpdw5yFqRM2WUQ+AIYC62IbY/kSuVFVBdYGd8sGt+wJqecB9wffvwM8IyISPNe5EiPWA5bdvHmpjcM55wpi40a4/fatDbB4VataMr7LXZhGWDlgOXBy3DYF8myEgQ1nAhOBA4BnVfX7bLv8O8U7KAi7Gtgd+DtEXM4VG7vtZsOP2dWunfpYnHMurPfeg8xcBuFXrkxpKGkpTImKa3b0xYP8iIbBWpPDReQwVZ2ez9O2IyIdgA4Atf2/kitmZs2CdetsHcj4q8kKFaCXD3Q554qwefOgcmVblDu7XXdNfTzpJswC3geKyBgRmR7cbyAidxfkIKq6CvgcaJntoX+neItIGWBXrNct+/P7qWoTVW1SrVq1ghzauSItMxOuvtqKsD7xBNSpY4mtdepAv36eD+acK9qaNs152aFSpaw2mMtbmBIVLwF3AlsAVHUqtl5ankSkWtADhoiUB04Dfsm22wdA2+D7i4Cxng/mSpLHH4evv4ZnnrHFuefMsd6wOXO8AeacK/qOOmpr6YlYz1eFCrD//vC//0UWVtoIkxNWQVV/sBI6/8oI8bwawKAgL6wU8LaqjhCRB4EJqvoBVpPnNRGZBawgROPOueJixgy4+2644AJbmsg559LNjTdaj37jxlaMNSsLLrwQunXzGZFhhGmE/S0i+xPMbBSRi4BF+T0p6DFrlMP2e+O+3whcHDpa54qJLVvgqqvsyvH5520I0jnn0smECTBokDW6hg7189iOCNMI64iteXawiCwE/gR8oMS5QujVCyZNsqrSXsDQOZduZs60Xvzq1eHll70BtqPCzI6cDZwqIhWBUqqawxwI51xYEydCz55w5ZVw/vlRR+OccwWzdi20aAHLlsGbb0KVKlFHlL7CzI7cXUSeAr4CvhCRJ0Vk9+SH5lzxs3GjDUPutRc8+WTU0TjnXMGoQuvW1gA75RS4zDO5CyXM7MghwDLgQmwG4zLgrWQG5Vxxdc891o3/yiu+MLdzLv306AFjxkClSvD221FHk/7C5ITVUNUecfd7ioiv7+hcAY0fb2us/ec/cMYZUUfjnHMFM3Mm3H+/fT9woK304QonTE/YKBG5TERKBbdLgE+THZhzxcnatdC2LdSt62upOefSV+nSlst64YVRR1I8hOkJaw/cDLwW3C8NrBOR/2DrdO+SpNicKzZuvx3+/BO++MK68Z1zLl2sXm3Lq3XsaGV1Xngh6oiKjzCzIyunIhDniqvRo+G556BrV2jePOponHMuvKwsm8k9ahRs2gRvvOFldRIpzHCkc24HrVpl66cdcoiVpXDOuXRy//3w4YdWFf+883w2ZKKFGY50zu2gm2+GRYusKGv58lFH45xz4Q0bZrMhq1e38jrPPedFWRPNG2HOJcn779uSHvfcA02bRh2Nc86FN2uW1TSsWxfmzIEBA6BmzaijKn7CFGvdX0R2Dr5vISKdRaRK0iNzLo0tWwYdOkDDhrZIt3POpZO6deHaa2HJEmjZ0mZ3u8QLkxP2LpApIgdga0jWAt5IalTOpTFVuOEGywd79VXYaaeoI3LOuXAyM+Hvv60UxYwZUKYM9Ovnw5DJEqYRlqWqGcD5wNOq2g2okdywnEtfQ4bAu+/Cgw/C4YdHHY1zzoXXvbv14Pfta5Xx+/SBWrWijqr4CtMI2yIilwNtgRHBtrLJC8m59PXXX1ZL5+ij4dZbo47GOefCe+st6N0bTjrJZkWefDK0bx91VMVbmEbYNcAxQC9V/VNE9mVr4VbnXEAVrrvOZhENGmTd+c45lw6mTIFrroHjjrOc1sxMeOklH4ZMtjDFWmcCnePu/wn0TmZQzqWjV16BTz6Bp56CAw+MOhrnnAvn77+hdWuoWhUuuQRuusnOY/vtF3VkxV++jTAROQ64H6gT7C/YckX+8TgXmDMHunSx7vuOHaOOxjnnwitdGho0gP/8B9q0geOP9/NYqoSpE/YK0AWYCGQmNxzn0k9WFlx9tXXb9+8PpXwdCudcmsjMtB6w996zhbk3brRefT+PpUaYH/NqVf1EVZeq6vLYLemROZcmnn4axo2DJ56AOnWijsY558J57TU44QRYvtyS8t9/3yrkezpF6oRphH0uIv8TkWNEpHHslvTInEsDv/4Kd9wBZ51lSa3OOZcOJkywmY/lytnC3DfeCM2aWVqFS50ww5FHBV+bxG1T4OTEh+Nc+sjIsGU9KlTwWUTOufSxZIkNPe61l/WA3Xgj/POPpVP4rO7UCjM78qRUBOJcunn0UfjhByvOWsPLFzvn0sDmzXDxxTYE+fXXMH68NcR69oRDD406upInzOzIXYH7gObBpnHAg6q6OpmBOVeUTZlixQwvuQQuvTTqaJxzLpwlS2DxYku+r1MHzjwTGjWC226LOrKSKcxwZH9gOnBJcP9KYABwQbKCcq4o27zZhiF32w2efTbqaJxzLrxatWDqVMsFu+oq6xEbORLK+jo4kQiTmL+/qt6nqrOD2wOA1whzJdYDD9hJ7KWXYI89oo7GOefy9913Vgds40ZrgH30kc2OvPNOWyvSRSNMT9gGETleVcfDv8VbN+T3JBGpBbwKVMcS+fup6pPZ9mkBvA/8GWwapqoPho7euRT7/nt45BGbCXnOOVFH43IiIuWAs4ETgJrY+Wo68JGqzogyNueisGgRXHCBNb7Wr7fZkP/5Dxx2GNx9d9TRlWxhGmE3AIOC3DABVgBXh3heBnCLqk4SkcrARBEZHSyDFO8rVT27IEE7F4X16637fu+9oW/fqKNxORGRB7AG2BfA98BSoBxwIPBI0EC7RVWnRhakc0mWlQXffgsrV8KRR8KFF8Lq1fDpp5ZG0b69NcyGD4eddoo62pItzOzIn4AjRGSX4P4/YV5YVRcBi4Lv14jIz8DeQPZGmHNp4a674Lff4LPPYNddo47G5eIHVb0vl8ceF5E9gdqpDMi5VPr1V1sHsmxZqF4dvvzS8liHDoXDD4fRo+Hll+H226Fp06ijdbk2wkTkClV9XUS6ZtsOgKo+HvYgIlIXaIRdmWZ3jIhMAf4CbvXhAlcUff45PPkkdOoEp5wSdTQuN6r6UfZtIlIKqKSq/6jqUqx3bDu5pVCIyG7AW0BdYA5wiaquFDsZPgm0AtYDV6vqpMS/K+fCUbVhx5tvhg4dYN48G3IsVw6qVYM1a6wX7KCD4L7cLlVcSuWVmF8x+Fo5h1ulsAcQkUrAu8DNOfSiTQLqqOoRwNPAe7m8RgcRmSAiE5YtWxb20M4lxJo1lgN2wAGWD+aKPhF5Q0R2EZGKWD7YTBHpls/TYikU9YGjgY4iUh+4AxijqvWAMcF9gDOBesGtA/B8Et6Kc6H98IMNRXboYMWj69SBadNsMtHAgZaEP2+eFWUtXz7qaB3k0QhT1ReDbz9T1Qfib9iJKF8iUhZrgA1W1WE5HOMfVV0bfP8xUFZEtptvpqr9VLWJqjapVq1amEM7lzC33ALz58OgQVCxYv77uyKhfnDR1xr4BNgXK6+TK1VdFOvJUtU1QCyF4jxgULDboOA1Cba/quY7oIqIeNleF5l//rEhyPXr4dVXrWesbl2rjP/HH1ZS56ab4Nhjo47UxYQpUfF0yG3bCLrqXwF+zm3oUkT2CvZDRJoF8fji4C5ygwfbyUvESlG0auUnrjRTNrgIbA18oKpbsCHGULKlUFQPclwBFmPDlWANtPlxT1sQbHMuEkcdZeVzOnaEtm1h8mRriPXvD7/8AvvtZ5XxXdGRV07YMcCxQLVseWG7AGFWlzoOu/KcJiI/BdvuIkiKVdUXgIuAG0QkA5tGfpmqhj5ROpcMgwdbd/769Vu3jRlj29u0iS4uVyAvYvlbU4AvRaQOEGpSUfYUColbFFRVVUQKfI4SkQ7YkCW1a/u8AJccu+xiOV+PPgrHHAMzZsAdd9jXZcvg7be9N7+oyWt25E5Y7lcZLA8s5h+s8ZSnoK5Ynksaq+ozwDP5h+lc6nTvvm0DDGDDBtvujbCiLbh4/E5VnwKeits+D8h3HdxcUiiWiEgNVV0UDDfGEvsXArXinr5PsG07qtoP6AfQpEkTv9B0SbFhA7z3ng0/HnIIjBhhJSo++wxuuAFatIg6Qpddro0wVR0HjBORgao6N4UxORepefMKtt0VKVcBz4rIb8BIYKSqLg562DPyemIeKRQfAG2BR4Kv78dt7yQiQ4CjgNVxw5bOpdx991kZnTvugDffhLlzoUwZqw3Wu3fU0bmchCnWul5E/gccihU9BEBVT05aVM5FaJ99LBE/Ox9FKvpU9QYAETkYm704MCg0/TnWKPtaVTNzeXpuKRSPAG+LyLXAXLauo/sxVp5iFlai4pqEvyHnCuDUU+H33+Gpp7b25mdkwNq18MEH3pNfFIVphA3GauScDVyPXQl6nQhXbB1xxPaNsAoVoFevaOJxBaeqvwC/AH1FpDw2FHkx8DjQJJfn5JVCsV11uKB3rWNCAnYuAU4/fft8VrBlijydomgKMztyd1V9BdiiquNUtR3gvWCuWJo2DUaOhObNrcZOrNZOv35+AktHIlIB68X/UVVvVNUcG2DOpbMHHrA1IFU9nSLdhGmEbQm+LhKRs0SkEbBbEmNyLhJZWbaobZUqMGwYzJlj2+bM8QZYuhCRc0VkjohMEpFWwAxs8s80EWkbcXjOJdzkydCjByxYYBeNuaVNeDpF0RRmOLJnkFNxC1YfbBegS1Kjci4CL71ki94OGgS77x51NG4H9QBOB2J5YA1UdXawZuQYthZddS7tbdliq3lUqwaPB1NJbrvN6oTF83SKoivPRpiIlAbqqeoIYDUhpng7l44WL7YFbU86Ca7Ms666K+KyVPU3ABH5U1VnA6jq0qAeoXPFxiOPwJQpMHy4zYAEmDABSpe2yvmLFlkPWK9e3ptfVOXZCFPVTBG5HOibonici0SXLlZj54UXrEvfpa1SIlIVS7XICr6PfaJh0i+cSwsrVljZicsug9atbdvXX8OAAXZB6evcpocww5Ffi8gz2AzJdbGNsTXWnEt3I0fCkCFw//1w4IFRR+MKaVdgIlsbXvHnKS+S6oqN3XazRlfNmnY/I8OGIWvVgnvuiTY2F16YRljD4OuDcdsUnyHpioH16+G//7XG1x13RB2NKyxVrRt1DM4l29y5Nmv7iCO2bnvuORuafOcdX5ooneTbCFNVzwNzxVbPnvDnn/D557DzzlFH4woryGMtr6prg/tHY0uwAUxW1TWRBedcAvz6KzRsCH36bE3AX7zYer/OOAMuuCDS8FwB5dsIE5HqwENATVU9U0TqA8cEtcOcS1vTp8P//gdXX+1rqhUjvbG1HR8N7r8JTMdW+5gE3B5RXM4VWmYmtGsH5cvDhRdu3d6tG2zcCE8/7Tmt6SZMoupA4FMgGHnmN+DmJMXjXErEaoLtuqs1xFyxcQpWFT9mlaqeg5WtOC6akJxLjGeegW++gSeftEW6Ab78El5/3UpT1KsXbXyu4MI0wvZQ1beBLABVzQByW3vNubTw8st2MnvsMdhjj6ijcQlUKjhHxdwO/y4xVCmakJwrvD/+gDvvhLPOgiuusG1btlhOa5069phLP2ES89eJyO4EM4uCHIvVSY3KuSSK1QRr0QKuuirqaFyC7SQilWO5X6o6CiAoOF0u0sicK4TffrOirPFldJ5+GmbMgPfft4KsLv2EaYR1BT4A9heRr4FqwEVJjcq5JOra1WZFPv+8508UQy8Bb4nI9ao6D0BE6gDPAy9HGplzhXDmmTBrFpQta/cXLoT77rOesXPOiTY2t+PCzI6cJCInAgdhtXd+VdUt+TzNuSJp1Ch48007eR18cNTRuERT1cdFZD0wXkRiE/XXAo+o6vMRhubcDpk3z2oZtm+/tQEGcOutNhz55JN+MZnOwsyOLAf8FzgeG5L8SkReUNWNyQ7OuUTasAFuuMFrghV3qvoC8IKIVA7ue1kKl5ZUrfH19dfW47X33rZ9zJitBab33z/SEF0hhRmOfBVYgy3eDfB/wGvAxckKyrlk6NkTZs+GsWOhnGcHFUsicgXwhqpm5dT4EpH9gRqqOj710TlXMAMGWO/9M89sbYBt3gydOsF++9mMSJfewjTCDlPV+nH3PxeRmckKyLlkmDEDHn0U2ra1RbpdsbU7MFlEJmLLFy3DEvIPAE4E/ga8H9QVeQsXWv5q8+bWgx/Tty/88guMGGH1wlx6C9MImyQiR6vqdwAichQwIblhOZc4sZpgu+xiVaZd8aWqTwZr3Z6M1QVrAGwAfgaujCXrO1fU3XCD9Xq98gqUCopJzZ8PDz4I551nw5Mu/YVphB0JfCMisZNXbeBXEZmGld9pkLTonEuA/v0tp6J/f68JVhKoaiYwOrg5l5ZuvNGWIDrggK3buna1PLEnnogsLJdgYRphLZMehXNJsmSJLenRvLktT+Scc0WZqs12PO20bbePGmWLc/fsCXXrRhKaS4J8K+ar6lygCnBOcKuiqnNjtyTH51yh3HILrFsHL77o07idc0Xf//0f9Oix7bZNmywZv149K03hio98G2EichMwGNgzuL0uIjcmOzDnCmv0aBg82Jbz8Jpgzrmi7p13rPREfD0wsFzW33+3WZI77xxNbC45xJZUy2MHkanAMaq6LrhfEfg2qlywJk2a6IQJPi/A5W3DBjj8cEtonTrVS1KkOxGZqKpNCrD/zsCFQF3i0i5U9cHER1cwfg5zOfn7b6hfH2rXhu++gzLBb+2cObb9rLNg6NBIQ3Q7KK/zV5icMGHbBbszg235HbQWVmOsOlbktZ+qPpltHwGeBFoB64GrVXVSiJicy1OvXrbg7Zgx3gArod7H1ridCGyKOBbn8nXTTbBqlZ2zysT9Z775ZruYfPzxqCJzyRSmETYA+F5Ehgf3WwOvhHheBnBLsOxRZWCiiIxW1fgaY2cC9YLbUdj6bkeFDd65nMycaTXBrrwSTj456mhcRPZRVZ9U5NLC779bL1f37taDH/PRR7Y4d+/eUKtWdPG55AmzduTjIvIFtmwRwDWqOjnE8xYBi4Lv14jIz8DeQHwj7DzgVbUx0e9EpIqI1Aie61yBxWqCVa4Mjz0WdTQuQt+IyOGqOi3qQJzLT716MHmyfY3ZsAE6d4ZDDrHeMFc8hekJIxgi3OFhQhGpCzQCvs/20N7A/Lj7C4Jt3ghzO2TAABg/3gocVqsWdTQu1WL1C7Fz2zUiMhsbjhS8rqErgqZMgSOOgEMP3Xb7o4/aMmtjxsBOO0UTm0u+UI2wwhCRSsC7wM2q+s8OvkYHoANA7dq1ExidK06WLt1aE+yaa6KOxkXk7KgDcC6skSPhzDPh7bfh4rjVmP/4Ax5+GC67zFMqirtcS1QEs4sKRUTKYg2wwao6LIddFgLxI937BNu2oar9VLWJqjap5t0bLhe33AJr18ILL3hNsJIqrn5hz/h6hrFtUcfnXMw//0CHDjbceO65W7er2jBk2bKeUlES5FUn7FsAEXltR144mPn4CvCzquY2r+MD4CoxRwOrPR/M7YjPPoPXX4fbb7eTmivxthncEZHS2BJszhUJt91mi3QPGLBt7a8PPoCPP4YHHoCaNaOLz6VGXsORO4nI/wHHisgF2R/MpWcr3nHAlcA0Efkp2HYXtvYkqvoC8DFWnmIWVqLCB5FcgW3caIvdHnAA3HVX1NG4KInIndh5pryIxNIfBNgM9IssMOfijB1rq3jccgscFVcPYP16K1Vx2GG2dqQr/vJqhF0PtGHrkkXxFMizEaaq48mnnlgwK7JjvlE6l4eHHoJZs6xCfvnyUUfjoqSqDwMPi8jDqnpn1PE4l5OVK6Fp0+2XJ3roIZg7F8aN275qviuecm2EBY2o8SIyQVXD1AVzLuV+/hkeeQSuuAJOPTXqaFzURKRx8O3QuO//5cWgXVFw4YVwwQXb5q7+9hv8739W37B58+hic6kVZnbkayLSGYj9WowDXlDVLckLy7n8xWqCVarkCazuX7HfhHJAE2AK1iPfAJgAHBNRXK6EWrvWCrHOnQsVKkDFipY+USouI1vVhh/LlbPSFK7kCNMIew4oG3wFy/N6HrguWUE5F8bAgfDVV/Dyy7DnnlFH44oCVT0JQESGAY1jxVpF5DDg/ghDcyXQL7/AaafBkUfCwQdbrz1YT9hee23db9gwGDUKnnpq2+2u+MtrdmRMU1Vtq6pjg9s1QNNkB+ZcXpYts5pgxx/vNcFcjg6Kr5avqtMBnzfrUur66+GOO+C99yAzEzZtgmOPhWef3brP2rVWEb9hQ+shcyVLmEZYpojsH7sjIvux7YLezqXcrbfCmjU2w6hUmN9iV9JMFZGXRaRFcHsJmBp1UK7k+PtvW4qoQwf4+mtbgLtDB8v7evfdrfv17AkLFljDrEzSy6e7oibMv69uwOci8oWIjAPGArckNyzncjd2LLz6qtXZqV8/6mhcEXUNMAO4KbjNJEQJHBHpLyJLRWR63Lb7RWShiPwU3FrFPXaniMwSkV9F5IwkvA+XpmJJ9+vWWTX8ffe1fK+srK2P/fyz5bNec431kLmSJ8wC3mNEpB5wULDpV1XdlNywnMvZxo3Wxb///tC9e9TRuKJKVTcCfYNbQQwEngFezba9r6r2id8gIvWBy7DCsDWBz0TkQFX1kQLH7rtDkyaWu/ryy7DffjaJ6NFHrVGmCp062bbevaOO1kUl7ALem/CufFcEPPww/P67JbF6TTCXnYi8raqXxC3kvY38FvBW1S9FpG7Iw50HDAnOj3+KyCygGcFqI65ky8qyHK9bb7Wk/AYN4NNPrXF2223w1lvWq//cc+Cr8ZVcPgLtirzBg63Xa+5cu3/ssTbjyLkc3BR8TfRC3p1E5CqszMUtqroS2Bv4Lm6fBcG27YhIB6ADQO3atRMcmiuK7rrL8r+++cYW5J47F/r0gVNOsSHKrl1t1mSHDlFH6qKUZyMsWP9xH1Wdn6J4nNvG4MF2klq/fuu2yZNte5s20cXliqa4tWdPBb5U1d8T8LLPAz2wnrUeWC2ydgWMqx/BsklNmjTZrofOFS+DBtkQ4w03QLNm2y5NBHD//bB4sc2aLF06ighdUZFnYn6wrNDHKYrFue10775tAwxgwwbPB0tXGRnw0UeWI/Pzz0k9VG3gRRGZLSJDReRGEWm4Iy+kqktUNVNVs4CXsCFHgIVArbhd9wm2uRJs/Hho3956vJ58ctuq+ADTp9v29u2tgeZKtjCzIyeJiNcFc5GYN69g213R9fvvlhvz0EM2Zf+UU+C66yx3JtFU9T5VPRlLmv8Km+U9cUdeS0RqxN09H4jNnPwAuExEdhaRfYF6wA87HrVLd0uXwvnn20zIoUO3X/9RFTp2hCpV7O/AuTA5YUcBbURkLrAOWwJE80twdS4R9tkH5ucwGO5pNennqqugc2e7geXFnHYa9O9vjbFEEpG7geOASsBk4FasMZbf894EWgB7iMgC4D6gRdCLpsAc4D8AqjpDRN7Gyl9kAB19ZmTJVq2a9dK3agVVq27/+ODB8OWX8NJLlqDvXJhGmNe+cZHIyoI99ti+EVahAvTqFU1Mbsf8+SfMmmXfd+1qQzGHHGLVxJ94IvGNMOACrGH0Ebbe7bdhSuuo6uU5bH4lj/17Af7bWMJlZtp5qm5dq34fL35iUalSVl6nXYEyCl1xlu9wpKrOxfIeTg6+Xx/mec4VVo8eloR/1VVQp47lVtSpA/36eVJ+ImzebEMmPXrY2nVbthTu9ZYuhbfftjIi7dvDySfb5zV5stV3A7jpJlvl4PcgXb5Spa2PJZKqNsaS838ATgOmicj4xB/JObuYaNjQKt/Hi00sis3szsqChQvhzTdTHqIrovLtCROR+4AmWLHWAdhi3q9jXf3OJcWHH9oMorZtYcCA7ZNbXeEsWmQ5WdWrW8mPvn3t5z1mTO41i9avh++/h9mzbcp97NazJ7RsCT/9BJdeavtWr27FKZs3h513hoMOgsqV7Tht2tjnqWo1ks45J/HvL1iw+wTgROz8NZ8Qw5HOFVT//lZ6olMnS5+Il9PEoo0bbbtfSDoINxx5PtAImASgqn+JSOWkRuVKtN9+gyuugMaN4fnnvQGWDN26wbnnwiOP2H1V66W6/npr+MYaWLNn22fxf/9nkyFOPtn2L1PGhl72229r8vExx8CUKVsrg2fXv79VCv/2W6hXD4YPt964gQOT8hYfwRpdTwE/qmoh+/mc296XX9rfzOmn2wVGdj6xyOUnTCNss6qqiCiAiFRMckyuBFuzxmYX7bSTDZF5VfzEU7UFhH/5xRq5ZctaTtaNN8KBB9rPHWCXXSx/ZfNmu7/ffvDZZ7Ztn322X2y4cmWrCp6bFi2st2zgQMsPu/56uPBC+6wT/x410cVandvG3LlwwQX2d/HWWzkvvl2rVs4NLp9Y5GLCNMLeFpEXgSoi0h4rUvhScsNyJZGqLWT7yy8werTlE7nEmzbN8r8OPdRmKHbqZNsrVbJJD2PHWkNr99237YXcaScbwiyMvfdObo233JYrivFZ3S5Rqle34febb7aSEzk59tjtG2E+scjFC7OAdx8ROQ34BzgQuFdVRyc9Mlfi9O5tPTSPPbZ12MslVqdO8OyzNktr332taGqzZtYA7t0bLr98++reaSbWA9Yx+Ppa8NUzcFxCZGTYxcuuu9rfUm6+/NImvjRrZtXx58+3HrBevTwfzG0Vdu3IaUB57ApzWvLCcSXVp5/aWmuXXQZdukQdTfHx5582m/SWW6zcxxlnWOOrZUvLz7rjDrta//JLWL3aEvPTWTCDGxE5TVUbxT10h4hMAu6IJjJXXHTrBh9/DD/+aEP2OVm82HrJ9t/fevVz28+5fEtNiMh12DTvC4CLgO9ExKucuISZPdt6YA4/3HpmPBG/cDIzbXZpq1b2T+DRR62RBTYT8ZZbbCjyp5/gv/+FcuWs4TtxYu4zI9OQiMhxcXeOxUvruELq18/q2p15Zu4Nq4wMu5hcvRreeccbYC5vYXrCugGNVHU5gIjsDnwD9E9mYK5kWLfOEvHBZstV9GkfhbJ2rTVm58yBGjXgnnusZlf2qfNgOV4XX5zyEFPlWqC/iOyKrfKxkgIuuu1cvM8/tyWHWra0khS5ueceGDcOXn3V/hady0uYRthyYE3c/TXBNucKRdUaCNOmWff+fvtFHVH6UYWvvrJerC5dLLn+ssugSRMrQZF97bqSQlUnAkcEjTBUdXXEIbk0NmuWzeStVw+GDMl5JiTABx9Y2Zf//AeuvDK1Mbr0lGsjTES6Bt/OAr4XkfexnLDzgKkpiM0Vc337WuXohx6yq0sX3urV8Npr8MILMGOG5Xt16GA9iQ8/HHV00RORnYELgbpAGQnGuFX1wQjDcmmqYkU4+mh4+mlLyM/J7Nm2ukfjxjZk6VwYefWExQqy/hHcYt5PXjiupBg7Fm67zers3OGp0gXy4YeWQ7dunfV4vfKK9X5VqBB1ZEXK+8BqYCKQ75qRzuUkI8NyVGvUsN763GzcCBddZLOO33nH8iydCyPXRpiqPpDKQFzJMW+ezRw68EAr3OmJ+JbLNWiQLQu0995WPHX//e2xjRttqnvt2nDiiXalfemlcMMN1ghzOdpHVb1/1RVK1642FPn++3kP7XfubGukfvihzT52LqwwsyObiMhwEZkkIlNjtxDP6y8iS0Vkei6PtxCR1SLyU3C7d0fegEsvGzZY79fmzfDee1ZlvaRbvtxqc332mdVHy8qyoY9XX7Xp8PvsY8McgwbZ/nvvbb1f3gDL0zci4mnRboc9/7wNPx5ySN4NsEGD4KWX4M474Wxfp8EVUJjE/MHYDMlpQFYBXnsg8Azwah77fOXLi5QcqtZ7M3GiJbAeeGDUERUNffpYra6X4tahmD7d1nAsXRpat7ZSEiedFFmI6eh44GoR+RMbjhRAvWK+C+Ozz2wZr1atrMRLbqZOtXPaSSfBg55t6HZAmEbYMlX9oKAvrKpfikjdgofkiqvnnrOrxvvus3pVznzyiSXYjxhhxVTLlrXewq++gpEjrYHmCuzMqANw6em336x0y8EH28Sh0qVz3m/1assDq1LF9sttxqRzeQlTvPA+EXlZRC4XkQtitwQd/xgRmSIin4jIobntJCIdRGSCiExYtmxZgg7tUumrr2yNtbPPhnt94HkbpUtbT9c559iMR4ArrrCTupft2DGqOjeonr8Bm9UduzmXp7Vrbd3aDz/MvdCqKrRrZzMi33rL1pF0bkeEabtfAxwMlGXrcKQCwwp57ElAHVVdKyKtgPeAejntqKr9gH4ATZo08RNpmlm40K4s990XXn/dZhA5m93YsydMmWL3//c/y/1StbIdRx0Fe+0VbYzpSkTOBR4DagJLgTrAz0CuF3uuZFO1SUKNG8OkSXmfp554AoYNs1SCE05IWYiuGArTCGuqqgcl+sCq+k/c9x+LyHMisoeq/p3oY7nobNpkXfbr1llZitxq7JREl14KH31kuV+lSlmRx2++gV9+sXITHxQ4CcDF6QEcDXymqo1E5CTgiohjckVYp072d/foo3k3wL7+2srrnH++zZ50rjDCNMK+EZH6qjozkQcWkb2AJaqqItIMGxr1SvzFTOfO8N13Vjunfv2oo4ner7/CnntC1aqWG3fXXVtzvubMsUWB994bjjnGS3cU0hZVXS4ipUSklKp+LiJPRB2UK5qefdZyVm+7Le+/u6VL4ZJLoG5dGDDA/0Zd4YVphB0N/FTQWUYi8ibQAthDRBYA92FDmqjqC9hi4DeISAaWt3GZqvpQYzHy0ku24O2dd9qSHyVZbOjxscfsivvxx6Fp0233qVvXbi4hVolIJeBLYLCILAXWRRyTK4JGj4abbrKczIceyn2/zEwrkrxihRVu9V59lwhhGmE7VPBQVS/P5/FnsBIWrhj6/ntrbJxxBvToEXU00VGFd9+1dR0XLICrr4bbb486qhLhPOzirgvQBtgV8CICDtia//XLL5avWr8+DB6c+0xIsJ7rsWOtB+yII1IXqyvewqRIay4353K0eLH1fO29N7zxRt4ntuLuwQftJL/77jB+vJ3AfSZV8qnqOlXNUtUM4CPgaVX1dIcS7t13rQFVqhQccAA89ZT1aH34Yd6Foz/6CHr1gmuvtQsp5xIlTE/YR1ijS4BywL7Ar/gsI5eDLVssZ2LFCvj2W9htt6gjSr1162DNGpvZeOWV9jO44QavI5QKInI08AiwAkvOfw3YAyglIlep6sgo43PR+fBDK5PTv7+tTDFhgpWZ6NrVSlLkZs4c+ztu2NAq6DuXSPn2hKnq4araIPhaD2gGfJv80Fw6uuUWqwn2yislr8s+NvR4yCG29iNYna8bb/QGWAo9AzwEvAmMBa5T1b2A5sDDUQbmovXww/DMM3Dqqdbw+u03K5nz2GO2VFhONm2ynuysLJtcVL58amN2xV+BKzap6iTgqCTE4tLcq6/alWLXrpbAWpL89hu0bGnlOKpWhTvuiDqiEquMqo5S1aHAYlX9DkBVf4k4LhexX3+1mciPPWbDkDNnQqNG8PffsH59zs/p0sV6zAYNgv33T228rmTI9/pcROIroZQCGgN/JS0il5YmToT//Me6+Xv3jjqa1BoxwpYZKl/eTu4+9Bip+D6NDdke81zWEqx+favF9/jjljLRq5edt6pVg4oVt99/8GBbxPu22+C881IfrysZwvyriE9XjCW5vpuccFw6WrbMGiF77glDhpSMBoiq5b3tvjscfzx06AD33ONJ90XAESLyD5bDWj74HrbmtLoS6qyzrFzOYYfBiy9a0dX27aF79+3rfc2YYX/TzZtbY825ZMn336WqPpCKQFx6ysiAyy6DJUvspFatWtQRJd9vv1me16JFtrxJlSqWa+Kip6oleC6uy0tWFtSqZReJe+4J9epZ2kD22Y5r1tjs7sqVS85FpYtOmOHIA4Fbgbrx+6vqyckLy6WLO+6w2jkDB8KRR0YdTeIsXw7jxtkCvi1a2Il43Tor5vi//9nQ44Nedcq5tHHXXVaUNaehxxhVm1QzaxaMGQM1aqQuPlcyhWnjDwVeAF4GMpMbjksnQ4ZsrQDftm3U0STO00/DvffCccdZ0u6iRfDkk3YCnzfPpqs/+qgvru1cUffPPzZZplcvW6EirwYY2N/+229bXuuJJ6YmRleyhWmEZajq80mPxKWFwYMth2LuXMujOPBAS3QtLr7/3k7AP/1ktYM2brSFtDt3tvUcX38dTjgh6iidc/nZvNmGFb/4AlauzH//b7+1EjvnngvduiU9POeAcCUqPhSR/4pIDRHZLXZLemSuyBk82JJV5861+6owf75dORYXr75q+V41a1pv2EEH2dJLNWrYe/cGmHNFX2xY8bPPbA3b00/Pe/9ly2zGZO3aVo7CF+Z2qRKmJyw20BR/baDAfokPxxVl3btvX09nwwbb3qZNNDEl2po1dgJv3hy++87eV0aGzYL855/8n++ci94998Brr1neZn7LDGVm2t/5smXWG1alSioidM6EmR25byoCcUVfrAcsu3nzUhtHMlWtCnffbbkjQ4bApZfCn3/CN9/YUKRzrmjLzISpU60n7O6789+/Rw8YPdp6zBo1Sn58zsXLtREmIser6vg8Ht8FqK2q05MSmStSNm+2GYEbspe/xLrwiwNV+P13qFTJqmOvXm2zIZ991hJ7S+I6mM6lE1UoXRqGDbP7+Q0rjhy5tbfs2muTHp5z28krJ+xCEflGRO4VkbNEpJmINBeRdiLyGjAC8JW0SoCMDFuGaMMG2GmnbR+rUCH9ixnOnGm9eSLwxhuwcCHceqvVPfvrLxg+HDp2jDpKlwoi0l9ElorI9Lhtu4nIaBH5PfhaNdguIvKUiMwSkaki0ji6yN0PP1gawaJFVlImv/pe8+bZMOThh9uFlueBuSjk2ghT1S7A2cAi4GKgB9AVqAe8qKrNVfXHlETpIpOZCVddZVeWTz0F/fvbrEER+9qvX/rmg6naQuNNmlj5CbB8kIoVrdE5aJAVYW3WLNIwXWoNBFpm23YHMEZV6wFjgvsAZ2Lnw3pAB8BnkUfkjz/g7LPtAqpUPtPNBg+2c1edOjZr8qqr7GLSuSjkea2gqiuAl4KbK2GysmxZjzfftLINN95o29O10RVv9Wq4/nrL+zr1VFsjzjlV/VJE6mbbfB7QIvh+EPAFcHuw/VVVVeA7EakiIjVUdVGKwnVYQn3LlnbB+MkneS8dFpvhHZtgpGqzoPfaq3ic11z6CVOiwpVAqtboGjAA7rvPFrEtLn79FRo3hqFDLefr00+98KrLU/W4htViIPZvfm9gftx+C4JtLkXWr7e6XgsWwIcfWkmZvOQ0w3v9etvuXBR8VSy3HVUrVvjcc9b4uu++qCNKrJo1Yd99bQr7scdGHY1LJ6qqIqIFfZ6IdMCGLKldXGayFAGrVsHatdbDFeZvuSTM8HbpxXvC3HbuvdeWI7rxRnjkkeKRsLpsmeV9bdhgC/N+9pk3wFxoS0SkBkDwdWmwfSFQK26/fYJt21HVfqraRFWbVCsJq9wnmaqlS9SsCZMmwQUX5P+cESNyf8zbxS4q+TbCRKSCiNwjIi8F9+uJyNnJD81F4aGHoGdPq7HzxBPFowE2diwccQS8+KLNoHKugD5ga9HqtsD7cduvCmZJHg2s9nyw1OjTxybPbN4MZcvmv/+4cXDxxdYDXj7bnP7iMMPbpa8wPWEDgE3AMcH9hUDPpEXkIvPEE5YbccUV8MIL+c8yKuoyMqxy9qmnwq67WgPMF+V1eRGRN4FvgYNEZIGIXAs8ApwmIr8Dpwb3AT4GZgOzsMlL/40g5BJnyJCtOar5laEA6yk75xxrgP3wgxVlLS4zvF36C5MTtr+qXioilwOo6nqR4tA/4uK98AJ06WJXiwMGWMHDdHfjjfa+2rWz8hoVK0YdkSvqVPXyXB46JYd9FfAKcin0xRfQtq3VAxs0KP8LxV9+sbVfd9sNRo2CPfawBpc3ulxREaYRtllEymPrRSIi+2M9Y66YGDgQbrjBrhZffz3c1WVRlplpjcguXWzB7f/7v6gjcs4V1owZ0Lo1HHAAvPcelCuX9/7z5tnC3aVK2bJE++yTiiidK5gw/27vA0YCtURkMHAccHUyg3KpM2SILddx2mnw9tvbV8RPJxs3WqX7ZcvsfR14oN2cc+lv1SprSH38sa3xmpelS+2c9s8/lg9Wr15KQnSuwMIs4D1aRCYBRwMC3KSqfyc9Mpd0771n+V8nnBDuyrIo++UXuOwymDLFesAyM9O/R885t7Vn+7jjbGHu/IYgV6+24q3z51sP2BFHpCZO53ZEmNmR5wMZqvqRqo4AMkSkdYjnbbcGW7bHfd21CH3yCVxyCTRtakUO03XZDlUbTj3yyK0FGx9/3BtgzhUHmzfDmWfaih2QfwNs/XpLq5g+3ZZaO+645MfoXGGEmf92n6qujt1R1VXYEGV+BrL9GmzxfN21iIwda3V1Dj/cGmOVK0cd0Y5budKGIJs2tV6ws714inPFgqqVyhk9OtyKFps328Si8eMtt7VlXv99nCsiwjTCctonzDDml8CKPHb5d901Vf0OqBIriOiS5+uv7UrxgANstlCVKlFHlD9V+PNPmD3bvgf4+Wcr1rjbbvaexoyBvX3BGOeKjXvusVUtevSwGZF5ycy0fT7+2OoBXnJJamJ0rrDCNMImiMjjIrJ/cHscmJiAY/u6ayn244/Wtb/PPlYxfvfdo44of9OmWS/XscfC8cdDw4bW83XEEfD007bPQQcVj5Iazjnz4otWQLV9+/zXdVSFTp1sMk7v3vYc59JFmEbYjcBm4K3gtokU18YRkQ4iMkFEJixbtiyVhy42pkyxejl77GG9RtWr5/+cqK1bZ0MKHTvCwoXw00/W2HrsMSvAesUVUUfonEuGUqVsYe7nnst/1Y6777Z6gLffvrWIq3PpIsyw4jrgjiQcu0DrrgH9AJo0aVLgxXNLupkzrdFSqZLlg6VLvZxhw6BxY7jmGvj2W8v3WLbMesPOOy89evKcc+Ft3mxlctq3t3yw/BpgffrYUmsdOsDDD6cmRucSKczsyANFpJ+IjBKRsbFbAo7t666lwO+/wymn2GzBMWOgbt2oIwrvr79sqDGmYkX47jtLvl/kvynOpb2FC60+4Rdf2LnqoIMsVxXyb4C98gp06waXXhqux8y5oijMRP6hwAvAy0Bm2BcO1mBrAewhIguwGZVlAVT1BWzdtVbYumvrgWsKErjL35w51gDLyEi/goXr1lkP3ocf2hXuMcfYtPNSpaxnLDZl3TmXflRtGPH5520911mz4Ndfrbc+zIXiO+9Y71fLlvDqq54T6tJXmEZYhqoWuHxEHmuwxR73ddeSaOFCa4CtWQOffw7160cdUTiq8MYblt+xcKENnZ51ll3xli5tNcD23NOqYTvn0tPw4VYg+rffrEbhKcHKnNWr53+xOGqULUV2zDHw7rvpvcqHc2ES8z8Ukf+KSA0R2S12S3pkboctWWIntWXL4NNPLYcqHfz8sxVXvOIKqFHDSk/88YfN6LzvPpsl1aIFfPBB/kUbnXNF16uvwp13Womcyy+H77+3i69162z1i9x8+y2cf75dVI4Ykb5Fpp2LCdMTFqvQ0i1umwL7JT4cV1jLl1sS/vz51gBr1izqiMIrX95yvfr3t5o/sYZWly52c84VD2vXWo2/0qWt9+vpp+Gii6wm2Jo1OT9n6lRo1Qpq1rRzWzrUOHQuP2FmR+6bikBc4a1aBaefbgmuH31kdbWKsk2b4IknYMIES86tW9dyQzy/w7nirVkz6NvXerlffNGS6n/4wRbezqnnftYsO7dVrGg1DtOhxI5zYYRaYU9EDgPqA/8u8ayqryYrKBfO4ME2RDdvnuVO7bwzzJ1ruRaxHIuiSNUS7rt2teHGc86BDRtsaMEbYM4VbxMn2szGtWutYXXppTaJqF8/q/eVPcdr4ULLAc3IsPzWOnUiCdu5pMi3ESYi92GzHOtjMxrPBMYD3giL0ODBNjto/Xq7Pz9Ye+Cmm6zLvqhasADatbP14A45xIYVTj896qicc6nw0Ue2pFC1ava3P2mSzdzec0/7esgh2+6/fLmdH5YvtxqH2R93Lt2F6Qm7CDgCmKyq14hIdeD15Ibl8tO9+9YGWLz33rMhvqJG1YYcdtnFGmJPPQXXXw9ly0YdmXMuFZ5/3pYXatTIkur32suGHtu1y3n/NWvsgvKPP2DkSGjSJKXhOpcSYRphG1Q1S0QyRGQXYCnbVrp3EZg3r2Dbo5KRYcMMQ4ZYsdhddtla78s5VzJkZNjsx1at4M03rR5YXjZuhNatbehy+HCbFe1ccRR2Ae8qwEvYwt2TgG+TGZTLX25L9tSundo48jJ2rF31duxoFftXrLDt3gBzrmTYuBFWr7a//xEjrEGVXwMsI8PKVowdCwMHWs6oc8VVmNmR/w2+fUFERgK7qOrU5IblcrNpE9xyC/z9tzVmsrK2PlahAvTqFV1sMStXwrXX2gm3bl0rqHj++b6siHMlyfLl1pu1006WA7rrrvk/JyvL1ox87z0rW3HFFcmO0rloheqTEJEGInIu0Bg4QEQuSG5YLid//AHHHgvPPmszC/v3t5lCIva1Xz9o0ya6+DRYWn2XXaxg7EMPWQHWCy7wBphzJcns2Xau+uEHm0AUpvdb1c5rgwbBgw9a/phzxV2Y2ZH9gQbADCDW76LAsCTG5bJ55x3rXSpVyq4SzzvPtrdtm+fTEm7ZMnjrLatJdtppVu9HFV5/HR57zGY4VakC48d7w8u5kuiHH+Dss21Y8bPP4IQTwj2vRw948km4+WZbV9K5kiBMYv7RqpomKw8WP5s2wa23wjPPWIPnrbfCLXCbDKNGWa7GWWfZzKbLLoPDDrOG2fffW3x//22NMG+AOVfyZGbahWGlSvDJJ3DQQXnvH6t1OHeu3T/hBLuY8/OHKynCNMK+FZH6qjoz6dG4bfzxhxUynDjRuukffji6xWo3bYKrrrI8r+bNYfNmK6L4xhvW6Bo0yPI3POneuZIpK8uKLQ8fDlWr5l/VPnutQ7DVM958M9q0CudSKUwj7FWsIbYY2AQIoKraIKmRlXDvvmv1c7IPP0bl669h331tgW2wxuCmTTblvHx5a6A550qerCzo1s1WvXj2WTj44HDPu+uu7WsdbthgPWPeCHMlRZh+i1eAK4GWwDnA2cFXlwSbNlnV+4suspPZ5MnRN8DAhhmWLIH997ceOoChQ23WY1S9c865aG3YYBXwH3/cylDEJufkZ+XK9Kl16FwyhWmELVPVD1T1T1WdG7slPbISaPZsW3T7qaegSxf46qvo8r9iVG2pkZtvhj//tAV0N2ywx9avt1y1iy6KNETnXASWLbM1aocNs0bYk0+GS0eYPBmOPDL3x4tSrUPnki1MI2yyiLwhIpeLyAWxW9IjK2GGDYPGjeH33y2n4vHHo+9hysyEM86wmU6bN8O998LixZabdsstUL++JeO3bh1tnM651MrMtNnRkydbj3iXLuGS6QcMsNIVmzfD/fdbbcN4RaXWoXOpEiYnrDyWCxa/zLKXqEiQTZvgttus96tpU5v9uO++0ca0ZIkl1ZYuDcccY8OhHTrYOo+dO8Pbb1uJinff9fXcnCuJSpeGnj1ht92sUZWfjRvt3PHSS3DyyZZ8v+eecMABlgM2b571gPXq5flgrmQRzWMQX0RKA71V9dbUhZS3Jk2a6IQJE6IOIyH+/NNmP/74o+WBPfpotL1fy5fbSfDZZwtW38e5ZBORiapaLJr86XwOe/dduwC79trwz5kzx1IWJk6EO+6wemBlwlz+O1dM5HX+ynM4UlUzgeOSElUJN3y4rav42282FPnEE9E1wDZssAbg/vtbXseVV9r3zjkX88QTcPHFtp5jZma454wcaflfv/9us7wfftgbYM7FC/Pn8JOIfAAMBdbFNqqqD0fugM2bbfjxySdtKO/tt6MdflS1IccpU6zcRO/eVoDVOefAGlxdu1rKxAUX2OoYpUvn/ZysLBuuvP9+O58MG2ZDj865bYVphJUDlgMnx23znLAdMGeOTeeODT/27g077xxNLF99ZTW/SpWC22+3HLCTT87/ec65kiMry3q/hg+35Pv//S//BtiKFVa4+ZNP7OuLL26fgO+cM/k2wlT1mlQEUty99x5cc431PL37rl1RRmHKFOuJGzUKhgyxnLTLL48mFudc0ZGVBb/+aheG++1n20qVsh77Fi0ssT4/kybBhRfaahrPPQfXX+9LEDmXl3xLVIjIPiIyXESWBrd3RWSfVARXHGzebF35559veVaTJkXTAJs3z9Z0a9TIeuIef9xLSzjnzLhxVhy6VSvrIT/iCLtIA6tsH6YB1r+/zZTMyLCe9htu8AaYc/kJUydsAPABUDO4fRhsc/mYM8dmGPbtCzfeaEv/xK4wU0kVzjnHyl9062YV77t0iW4o1DlXdPz1l81e7NvXCkYPGQKzZtlSZBs35v/8jRuhfXubMXnCCXahedRRyY/bueIgTCOsmqoOUNWM4DYQqJbkuNLS4MFW4b5UKauBc+ih8Msv8M47ltSaykbPpk1WamLdOrsa7dfPhhp697bFdZ1zBSMic0Rkmoj8JCITgm27ichoEfk9+Jp2f12DBlkjrFUrO4edcQbUqmW9YaNH5/3cOXOs5+zll63HbORIqOb/HZwLLUwjbLmIXCEipYPbFViifr5EpKWI/Cois0Tkjhwev1pElgUntZ9E5LqCvoGiYvBgK2g6d671PC1bZleI991nORLJMHWqdfm3amXHWbLE8jqGDIFDDoFOnawBCHZlWqdOcuJwrgQ5SVUbxtX8uQMYo6r1gDHB/bSyaJFdPJ51lpWnadrUeu0PP9wey80nn9gqH3/8Ae+/bzUG80vad85tK0wjrB1wCbAYWARcBOSbrB8Uen0WOBOoD1wuIvVz2PWt4KTWUFVfDh15EdO9u62lGC8ry3rAkuGTT+DUU+2K9YYbYOlSO2k2bmyJ9pUrw6efWh6Ycy5pzgMGBd8PAlpHF0rBqVpP1kcfWQ/W44/D559D+fLWq3VcDlUis7Ks9MRZZ9n5Z8IEOPfclIfuXLGQayNMRHoH3zZT1XNVtZqq7qmqrVU1zDr3zYBZqjpbVTcDQ7ATVrGiCl98YT1gOZkX5ie1A8e8+War13PXXZbv9fzzNg181iwrpjhpEpx+en6v5JwrAAVGichEEekQbKuuqrH+osVA9WhCK7jRo6FBA0ubyMyEtWttCPKTT2xdyDPOsMfirVhhja8HHrBes2+/9fpfzhVGXj1hrUREgDt38LX3BubH3V8QbMvuQhGZKiLviEitHTxWyqlamYfmzeGkkywPLCe1ayf+2H/9ZSfDMmUsl2POHNv+yiuw117W++XDAs4l3PGq2hjr3e8oIs3jH1RbAy7HdeBEpIOITBCRCcuWLUtBqLn76y+47DK7SNu40fJGR42yYci777besKuvtjyveBMnWk/7mDF20TdwoNf/cq6w8mqEjQRWAg1E5B8RWRP/NUHH/xCoq6oNgNFs7dbfRlE6ganCiBFw9NF2pThnDjz9tJ2wsp+QKlSwPIlEWrvWZjmuWAGnnGJDBzNn2mOZmbDHHok9nnPOqOrC4OtSYDjW279ERGoABF+X5vLcfqraRFWbVIswc/3ZZ60UxXvv2ZDitGmWL1qxoq3r+M03dk5p337bC7mXX7ahyawsGD/e6385lyi5NsJUtZuqVgE+UtVdVLVy/NcQr70QiO/Z2ifYFn+M5aq6Kbj7MnBkLrFEfgLLyrIiq40b2/Df0qVWCXrWLEuAv+Yam4FYp46dnOrUsftt2iQuhg0bbImjW26BXXeFE0+0KeWtWsHff9vQZPv2iTuec86ISEURqRz7HjgdmI6V74llXrYF3o8mwnBmz7ZaXtOn22SecuXy3n/DBis90b791vITzZqlJlbnSoI8E/OD5PowDa6c/AjUE5F9RWQn4DLshBX/+jXi7p4L/LyDx0qazEx4803LnbjoIuu6HzjQFt7u0GHbshNt2ljPWFaWfS1sAywz03rd7gjmW5Uvb7kY335rJ9NKleyqtkULqFfPEvXbtSvcMZ1zOaoOjBeRKcAP2MXpSOAR4DQR+R04NbhfZKxYAf/5j/VuATzyiOV8hcnj+vNP6/3q398mHo0c6T3tziVanssWqWqmiGSJyK6qurogL6yqGSLSCfgUKA30V9UZIvIgMEFVPwA6i8i5QAawArh6h95FEmzZYmUnHnoIfv8d6teHN96wtR+TnW+1YoWd+J57zk6Ee+9t6ztWrQr//e/W/UaMsNjmzbNGotfncS45VHU2cEQO25cDp6Q+orxlZVn9r9tug5Ur7WLtpJOgbNncnzN4sDW25s2zc8m6dZZ3+sEH1vvvnEu8MAt4rwWmichoYF1so6rmu5CFqn4MfJxt271x39/Jjif+J8WmTXbyeuQRawA1bGi1ts4/P/fk+0QaPdqme2/caMONjz4K552X+8mzXj27OeccWJ7Xf/9ruVvHHmtJ9A0a5P2cWJ3DWJmdpUstraJPH2+AOZdMYRphw4JbsbZhg80u7N0bFiywvIennrLp2MlMQN28GYYNg112sdyuZs1sSPH6663ul3POFcS4cfDzz9ab3rZtuIvHnOocqto5sGvX5MTpnAvRCFPVQSJSHqitqr+mIKaUWrcOXnjBrvgWL4bjj7fG2GmnJbfx9ddfltjfr58d9/zzrRG26642g8k558JQheHDLYf04outePPll8Puu4d7/vffp7bOoXNuq3yvkUTkHOAnrGQFItJQRD7I80lp4J9/4OGHbbmOW2+1ooRffAFffWX1c5LZALv3Xps92aMHHHmkJcrGlhdyzrmwZs+Gs8+2pdFefNEaZKVLh2uA/fijXfgdfXRq6xw657YKk+V0P1YPZxWAqv4E7Je0iJJs5Uqrj1OnjpV0aNrUauN89pnlYO2oDRts1mTXrpaD8U9cJbX1663OzsqVdv+ww+CmmyypfsQIaNkyNflmzrniYdMmu4g79FD48kvo29dmL4a5eJw40RpuzZpZL9jDD1uPfCrqHDrnthUmJ2yLqq6Wbf+6s5IUT8LEz/SpXdtmF86fD888A2vWQOvW9niTJvm+VL4WL7YyEfvtByefbNPBH3rIGmUff2y5GatWWUOrXTubYXnJJYU/rnOuZBo3znrUL70UHnvMZlDnZ/Jkqw324Yc207pXL7jxRltnFqxmWPw5s1evxNY5dM5tL0wjbIaI/B9QWkTqAZ2Bb5IbVuFkn+kzd67NFhKxnInu3fOfLVQQ99xjCfyPPWb3N2+2WZWnnmpTvC+80Aq65rQYrnPOZTd9OgwZYrOkzznHlkdbtMhqBF54oaVMxJYRys+UKdb7/957UKWK9aB17myTgeK1aeONLudSLcwg2I3AocAm4A1gNXBzEmMqtJxm+gDUqGFL/iSyAQZ2cmvWzIYhAXbayWY2liplRV2HDLGEf1/mwzmXn+eesyXJMjOt0XTttXb+OPhg60mPpTrk1wCbNs0abA0bWu/8Aw9YEem7796+Aeaci0auPWEiUg64HjgAmAYco6oZqQqsMHKb0bNoUeKOsXy5JdR/9JF9f9lllkNx9dVW2f7FF63I4T77JO6YzrnibfFiu4icPNkmDX33HQwdanmrTZtawej8GlDTp1uD6513bN9774UuXaxB55wrWvIajhwEbAG+As4EDqGI94DF1K6d85Trwsz0UbWTW926lkPRv79Vo65e3arp77abNbrKl7d9e/SwshN5Vah2zrl4n34KZ5xh55m//rJhyD33tJITe+6Z93JDM2da42voUFvS7O67rfG1224pC985V0B5DUfWV9UrVPVF4CKgeYpiKrRevRIz02fDBuvp+u9/bTZlgwaWaA9wxRXwww92ovzuO5sWfuyxtm/TpjB2LDzxRELejnOuhNhpp62pFDVrWoPq559t0k/8YtuDB1tDrVQpS8o/9libdf3xx3DnnbbaR48eBWuAVapUCYC5c+fSuHFjGjZsyKGHHsoLL7wAwPr16znrrLM4+OCDOfTQQ7kjtqhtHl599VUOO+wwDj/8cBo1akSfPn3o3r07t99++7/7zJ07l/32249Vq1bxww8/0Lx5cw466CAaNWrEddddx/r16xk4cCCdOnXa7vXr1q3L4YcfTsOGDWnYsCGdO+e7kEvCvfmm/exLl7avb75Z+NeMfRYA8+bN4/TTT+eQQw6hfv36zJkzZ5t9O3fuvM3+uSkJn0VaUtUcb8CkvO5HdTvyyCM1jNdfV61TR1XEvr7+eqin6ebN9nXBAtXy5VVBtWJF1datVV9+WXXJkpyfl5WlOm6c6tNPq44cqZqREe54zrn8YevNRn7+ScQtr3PY6tWqu+2m+u23W7fNm6e6116qP/1k919/XbVCBTs3xd/OPlt12bIC/Vi3UbFiRVVV3bRpk27cuFFVVdesWaN16tTRhQsX6rp163Ts2LH/7nP88cfrxx9/nOvrffzxx9qoUSNduHChqqpu3LhR+/Xrp+vXr9cDDzxQZ86cqaqq5513nr7++uu6ePFirV27tn7zzTf/vsbQoUN18eLFOmDAAO3YseN2x6hTp44uK8ybLqQ33lDdd1/VsWPtf8fYsXb/jTcK97qxz0JV9cQTT9RRo0apqn0e69at+/exH3/8Ua+44opt9s9JSfgsirK8zl95DUceISKxalcClA/ui7XdtEindrZpY8OBO++c94LbmZnWozVihN0aNIDXXrOr0G7dbEbjiSfa6+RFxIYOmqdNf6FzrqjZZRc44QS77bqrnbtWrLCe+Jtusn2+/dZmYGc3ejRcdFHOr9uwYfie+Z122unf7zdt2kRWllUkqlChAieddNK/+zRu3JgFCxbk+joPP/wwffr0oWbNmgDsvPPOtG/fHoC+ffvSsWNHbr31VtasWUObNm249957adu2Lcccc8y/r3FRbm8ohVq02H7bJZfYqEePHpae8sADWx+rXNn+d1x+Ofz99/afyRdfhD/2zJkzycjI4LTTTgO27SHLzMykW7duvPHGGwwfPjzP1ykun0VxlOtwpKqWVtVdgltlVS0T932RboCNHWtDgrvtBnvsYX8QmzZtv99dd8Fee1lXfu/eVjvn+OPtMRH7wzr99PwbYM45lyh169rw4+rVtpB2qVKWW7phA/zyS84NMMj5HLej5s+fT4MGDahVqxa33377v/+8Y1atWsWHH37IKaeckutrTJ8+nSOPPDLHx1q1akXVqlVp27Ytzz33XL775+Wkk076dwisb9++BX5+Yfz6qzWW4+26q6WpJMJvv/1GlSpVuOCCC2jUqBHdunUjMzMTgGeeeYZzzz2XGjVq5Ps6JeGzSFdh6oSllSlTbKZiv35w7rmwcKFdsbRpY8tzjBtn66yVKWONq5YtrXr0GWf47CHnXPSaNoWXXoKMYC765s22uoaq5YxVrmwFp7OrU6dgvSx5qVWrFlOnTuWvv/6idevWXHTRRVSvXh2AjIwMLr/8cjp37sx+++344ikdO3Zkw4YNHHTQQYWK9fPPP2ePPfYo1GvkJa+f6SGH2MV60EEYxGNFcME6AQrzmWRkZPDVV18xefJkateuzaWXXsrAgQM588wzGTp0KF8k6ANPl8+iOCp2i+U8+aStBdm6tV2l9Oljsxrffdd6xObO3XqVct99NvR46aXeAHPOFQ051TnMzISKFW2tyOefT90SQzVr1uSwww7jq6+++ndbhw4dqFevHjfffHOezz300EOZOHFiro+XKlWKUnHrteW3f1HUvbvVcfv8c9iyxb5ee61tT4R99tmHhg0bst9++1GmTBlat27NpEmTmDx5MrNmzeKAAw6gbt26rF+/ngPymDpbEj6LdFXsGmGzZlnhVLC6YP362fpqtWtbUdWpU31RWudc0ZVbncO1a63gdJs2dl6rU8fSJurUsfuJqna/YMECNmzYAMDKlSsZP378vz0kd999N6tXr+aJEAlmd955J926dWPx4sUAbN68mZdffjnX/Tt16sSgQYP4/vvv/902bNgwlixZUoh3k1yXX751+ady5exrr162PRGaNm3KqlWrWLZsGQBjx46lfv36nHXWWSxevJg5c+YwZ84cKlSowKxZs3J9nZLwWaSrYjccefjhlhPWooUlyS9fDuvWQb16luzqnHNFWZg6h8lcYujnn3/mlltuQURQVW699VYOP/xwFixYQK9evTj44INpHJTr79SpE9ddd12Or9OqVSuWLFnCqaeeiqoiIrRr1y7X41avXp0hQ4Zw6623snTpUkqVKkXz5s1p2bIlAAMHDuS99977d//vvvsOsDyk0sHsqwYNGvDqq68m4scQ2uWXJ67RlV3p0qXp06cPp5xySmxm7b8J9QVRUj6LdCQ2ezJ9NGnSRCdMmJDr47NmWaL9PffYMOPs2dC1qyXcP/poCgN1ziWMiExU1SZRx5EI+Z3Dsq99CzbcmMjeLudc6uR1/ip2w5EHHACffWa9YQcfDNdcY42xRx6JOjLnnMtfsocbnXNFR7EbjgSr9ZVP2RTnnCuykjncmGi9evVi6NCh22y7+OKL6Z6o7HQXmn8W6afYDUc654qfkjQc6ZwrXkrUcKRzzjnnXDrwRphzzjnnXAS8Eeacc845FwFvhDnnnHPORcAbYc4555xzEfBGmHPOOedcBLwR5pxzzjkXgbSrEyYiy4C5wK7A6mwP57RtX+DPFISWyGPm9D4Scby8Xregj+V0zILGXVBh3mciYoh/jUQdM799cjtmbs8L+/sfRiL/RsLGkNcxc3qNOqparTCBFRUFPIel4/kLknMOK8jfUJjt2Y9ZFM5fiYoj9hqJPGeG/R8R5ueayPNXTsfcUQWJIbdjFuz8pappeQP6hdy2LoLYCnXMnN5HIo6X1+sW9LGcjlnQuJPxc01EDPGvkahj5rdPbsfM7Xlhf/8T+ftT0J/djh4z2b9HReUW5jNMx/PXjnyGifg7K8jfSk7HLArnr0TFEXuNRJ4zw/6PCPNzTeT5qyA/20T+7HM7ZkHfRzoPR34Ycls6Stb7yOt1d/SxHdkvmRIRQ0FfI8z++e2T2+MF2V5Sf/7pqqh+homQjPeR6L+hHd0v2VL9N5SIn09BHyuqv/spP3+l3XBkQYnIOlWtWJyPWRLeox+z+BwvqmOmo5Ly2ZSEY5aE9+jHLLh07gkLa1gJOGZJeI9+zOJzvKiOmY5KymdTEo5ZEt6jH7OAin1PmHPOOedcUVQSesKcc84554qcYtsIE5HfRCRLRDam6HjNRGSliGwSkY0i8m4qjhscu6yIrBeRJSk63nvBe9woInNFZNckHCPHz09Ehsb9jL9P4PF2FZG1IrIheO0vgu1/isjmYNtvIlI+UccMXr+OiCwI3tMmEWkf99gHIqIicmAhj7Hdz1JEfgiOt0FE/hKROsH28iIyK3i/m0Rk5A4cL8e/BRH5QkQyg2NuEJF7455zoYisifu9SvjvVDrx81dSj1fszl/Ba6f8HFYcz1/B66TsHFZsG2HAU8AVKTzeJqCjqu6M1Q85R0TOSdGx3wGWpuJAInIkcBZQQ1XLYb9DjyXhUNt9fiLSBTgZ2CM49jUJPN4/wP6qWh6oCjQRkWuBAcDOQPng6ysJPCbAGOCz4PdmN2AU2EkAOBbITMAxcvpbGAbsErzfecBbwfb/AWWDn29N4FQROb6Ax8vrb+FjVS0f3B4EEJGdgdeBdsFxDwPWF/CYxY2fv5KgGJ+/IJpzWHE8f0EKz2HFthGmqs9gH06qjjdFVd8Ivl8ELAfqJ/u4ItIEOAH7RU0VAaoGv3g7AbMSfYBcPr+uwCOquibYZ2YCj6eqGrsSr4D9baiqPhg8psBkoHaijikitYA6BCdjVV2nqnODh4cBNyTiODn9LFX1EVXdFNz9AqgeewjYOfhsqwJZwF8FPF5B/xZuBxap6tDgObNUdUtBjlnc+PkruYelmJ2/gtdL6TmsuJ6/gtdP2Tms2DbCohS0vKthLeNkew+4EftlSzpVnQi8D/wBbADWq+ojqTg2sAdwVtDlvkpE2ibyxYNhkQ3A38BUVe0f91h54HTgzQQe8ljsiuv3YDjmFxGpJiK9gGWxP+gUuBr4JPj+tiCmDcDvwFuqOntHXziHv4Uzg27830SkbrCtUbDv38HP4aMdPZ4rPD9/JU1Sz1+Q8nNYsT9/QfLPYd4ISzARqQ58CjymqguTfKwHgFWqOjiZx8l2zLrAScAhQEWgnIg8l6rDY1c3lYGbgZdFRBL14qq6JejergscJCKt4x6eDPyqqs8m6njYVXhF4CFVrYCdOD4CbgLOTeBxciUio7B/gB2DTW2D+xWBQ4FLReTEHXzt7H8LHbEhkUrY8NOoYNcyQC3spF4LaC4it+7QG3KF4uev5B6eJJ6/IOXnsGJ9/gpeP/nnME1Aqf+iegOOBzam8HjlsSuQ91N0vG+AjOCWiXXFzk7yMR/D/pBj918Epqfi8wOWAV3i7m8BDk7SsccAHwbfjwUWAaUTfIzDgYy4+x2BFdhJJPa5avD18ET+LINtLwNrgN3jtk0DXoi7/xvQdweOl+ffQnw8wJPArLjHPgNGJONzTaebn7+ScswScf4KXj+p57DifP4KnpuSc5j3hCVIcEUzHZivquel4piqeqyqllHVMsAtwFJV3S/Jh50B1BGR3YP3fArwc5KPGTMKaA0gIqdjV5a/JuKFReTguBk2VYEmwCQRGRh8X19VE5Fk+i9VnQasE5GWwaaLsT/kUnGfa2Zw7GmJPLaIdAeuBJqq6vK4h+ZjnykiUg3L+RhfwNfO8W9BRI6I2+1WIJa/0heoGfxO7Yx17U8o2DtyheHnr5RI2vkreM2UnsOK6/kreG7qzmHJaoVHfQPmsvXqKgMYkOTj3RAca0Pc7d4Uvt+bgSUpOtYX2Lj7RmA2UDkVnx/WxTw7OO56oGsCj3dh8JobgtcfE2xX7Io19pmOSfD7vBRYF7z2IqButsczgAOT8LPcHHwfe18zgn2rYyeyjcFnXOAeqdz+FuI+uw3AYuCIuOc8Fzy2Efg+Fb/HRfnm56+kHqvYnb+CY6b8HFYcz1/B66TsHOYV851zzjnnIuDDkc4555xzEfBGmHPOOedcBLwR5pxzzjkXAW+EOeecc85FwBthzjnnnHMR8EaYSygRURF5LO7+rSJyf4Je+34RWSgiP4nITBG5PBGv65xzMX4Oc6nkjTCXaJuAC0RkjyS9fl9VbQicB7woImWTdBznXMnk5zCXMt4Ic4mWAfQDumR/QEQGishFcffXBl9biMg4EXlfRGaLyCMi0kZEfhCRaSKyf/bXUtXfscKEVYPX6CYiP4rI1GBNOkSkbrCo7GAR+VlE3hGRCsFjjwRXolNFpE8yfhDOubTk5zCXMt4Ic8nwLNBGRHYtwHOOAK7HFta9Equy3AxbH+zG7DuLSGPgd1VdGiwBUg9oBjQEjhSR5sGuBwHPqeohwD/Af0Vkd+B84FBVbQD03IH36Jwrvvwc5lLCG2Eu4VT1H+BVoHMBnvajqi5S1U3AH2xdnX4aUDduvy4iMgP4HugVbDs9uE0GJgEHYyc0sLW/vg6+fx1bdHU1trTEKyJyAXY16pxzgJ/DXOp4I8wlyxPAtdh6aTEZBL9zIlIK2CnusU1x32fF3c8CysQ91ldVD8XWSXtFRMphC+E+rKoNg9sBqvpKsH/2dblUVTOwK853gLOBkTv2Fp1zxdgT+DnMJZk3wlxSqOoK4G3sJBYzBzgy+P5cYIcTUlX1A2yV+rbAp0A7EakEICJ7i8iewa61ReSY4Pv/A8YH++2qqh9jeR9H7Ggczrniyc9hLhXK5L+LczvsMaBT3P2XgPdFZAp25baukK//IPAGloNxCPCtiACsBa4AMoFfgY4i0h+YCTwP7BrEEbsC7VrIOJxzxZOfw1xSiWr2nk7nigcRqQuMUNXDoo7FOecKys9hxZ8PRzrnnHPORcB7wpxzzjnnIuA9Yc4555xzEfBGmHPOOedcBLwR5pxzzjkXAW+EOeecc85FwBthzjnnnHMR8EaYc84551wE/h8lmvJALEacMQAAAABJRU5ErkJggg==\n",
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\n",
       "text/plain": [
        "<Figure size 720x360 with 2 Axes>"
       ]
@@ -217,7 +203,7 @@
    ],
    "source": [
     "for bm in unique_bms:\n",
-    "    flag = \"count\" in bm\n",
+    "    flag = \"nvbench_static_multimap_count\" == bm\n",
     "    \n",
     "    if flag:\n",
     "        tmpdf = perfdf[perfdf[\"Benchmark\"] == bm]\n",
@@ -233,45 +219,19 @@
   },
   {
    "cell_type": "markdown",
-   "id": "apparent-skiing",
    "metadata": {},
    "source": [
-    "### insert by varying CG sizes"
+    "### find_all"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "varying-stranger",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x360 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x360 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 720x360 with 2 Axes>"
       ]
@@ -283,7 +243,7 @@
     },
     {
      "data": {
-      "image/png": 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FYrFYLBaLD7BOmMVisVgsFosPsE6YxWKxWCwWiw+wTpjFYrFYLBaLD7BOmMVisVgsFosPqHB5wtyh8SuN2ZWwK8/xRuGN2Dlypw8sslgsFvew+mWxVB0q5UzYRW0uItAvMMexQL9ALm5zsY8sslgsFvew+mWxVB285oSJyPsisk9EVhdyzekislxE1ojIX57qe1S/UTgk51tz4GBU/1Ge6sJisVi8gtUvi6Xq4M3HkVOBN4CP8jspIjWBN4FzVXW7iNTzVMeR4ZFc0/Ea3l/+ftaxNGcaAz4eQNs6bWlbpy3t6rSjbZ22tI5oTbXAap7q2mKxWEpFZHgkQ7sO5e3Fb6MoAMkZyZz87slZ+pV9q1+tPiLiY6stFktJ8JoTpqpzRSS6kEuuAb5W1e2u6/d5sv9nznyGT1d9SkpGCgGOAG7udjM74newNG4pX637Cqc6s65tWqOpEbSInOLWIKyBFTeLxVLmjOo3iveXvU9KRgqBjkDuOfkediXsYv2B9fy99G+Opx3PurZGUI18nbMWtVoQ4Bfgw3dhsViKwpeB+a2BABH5EwgHJqhqvrNmJSEyPJKbut3EO0ve4ZbutzDpgklZ55LTk9l8aDPrD6zP2jYc3MD7y9/nWOqxrOuqB1U/IWrZHLQWtVvkidmwwbQWi8VTZNevYd2H8cLZL2Sdc6qTXfG7cujX+oPr+XXrr3y44sOs6/wd/rSo1SJfB61mcM0c/Vn9slh8gy+dMH+gB/A/IASYLyILVHVj7gtFZDgwHKBp06ZudzCq3yh+2fJLnliKYP9gOtbrSMd6HXMcV1V2J+zOI25/bPuDj1ac8A/9xI8WtVvkcM5OanQSMzfOJM2ZlnWdDaa1WCwlpSD9coiDJjWa0KRGE85ucXaOc/Ep8Ww4sCGHfq0/sJ4fN/2YQ5vqV6ufwynr2qAr+xL3Wf2yWMoYUVXvNW4eR85U1Y75nHsECFHV0a7994CfVXVGYW327NlTFy9e7A1zCyUhJYGNBzfmEbeNBzeSmpGa7z3BfsFsu3cbDcIalLG1FkvlQkSWqGpPX9vhCXyhYenOdLYd3pZngLlu/zoOJx/O954gvyBi7o2x+mWxlJLC9KvImTBXwPwpQEMgCVgNLFbNFlRVMr4D3hARfyAQ6A28Wso2vUZ4UDg9GvagR8MeOY5nODOIORLD+gPrGff3OBbuWpgVb5bqTGXI10MY2HogF7a+kJa1W/rCdIvFUkpEpAlmkVF9QIHJqjpBRJ4CbgH2uy59TFV/9I2VBePv8KdVRCtaRbRiYJuBWcdVlQPHD7D+wHpGzRnF39v/ztKvlIwU+k/tz8DWAxnYeiCnND0Ff0elTC1psfiMAmfCROQM4BGgNrAM2AcEY2K5WgBfAi+ranwB938OnA7UAfYCo4EAAFV923XNg8BQwAm8q6qvFWWwr2bC3CEuIY7mE5uTnJ5MoF8gw3sMZ862OazZvwaAtnXaZglanyZ9rKBZLG7i65kwEYkEIlV1qYiEA0uAS4ArgGOq+pK7bZVXDcuuX0F+QYzuP5q/Yv9iTswcUjNSqRlck/NansfA1gM5t+W51Aqp5WuTLZYKQWH6VZgT9iLweubqxVzn/IELAT9V/cqTxhZFeRWwTEbMGsE7S97hth63ZS0G2Hp4KzM3zuSHjT/wV8xfpDnTqB1Sm/Nbnc/A1gM5p8U51Aiu4WPLLZbyi6+dsNyIyHeYFDynUEmcMMhfvxJSEvh166/8sPEHZm2cxf7j+/ETP06LOi1rUNkqopWPLbdYyi8lcsLKK+VZwMCMJk/94FT+venffGMp4lPi+WXzL/yw8Qd+3PQjB5MO4u/wp3+Ua9q/zUCa12ruA8stlvJLeXLCXLGuc4GOwEjgRiAeWAzcr6r5B1m5KM8aVpR+ZTgz+G/Xf/yw8Qd+2PgDq/eZXNxtItpk6VffJn3tLL/Fko2SzoSNLKxRVX3FA7YVm/IsYMUlw5nB/J3z+WGDEbR1B9YB0L5u+6wR5smNT8bP4edjSy0W31JenDARCQP+Asap6tciUh84gIkTG4t5ZHlTPvdlX+HdIzY2tgyt9h4xR2KyZvnnbJuTNcuf/bGlneW3VHVK6oSNdr1sA/QCvnftDwT+U9VrPW2oO1QmJyw3Ww5tOfHYMvYv0p3p1Amtk/XYckCLAVQPqu5rMy2WMqc8OGEiEgDMBH7JbxBa2Grw7FRWDUtISWD2ltnmseWmWRw4fgB/hz/9ovplDSpb1G7hazMtljKnVI8jRWQucIGqJrj2w4FZqtrP45a6QWUVsNwcTT7KL1tOPLY8lHSIAEcAp0efzsDWAxn39zj2Ju7Nc59NrmipjPjaCRNTOuND4JCq3pvteKSqxrle3wf0VtWrCmurKmhYhjODhbsWZs3yZy5OalenXdZjyytnXMnuY7vz3Gs1zFLZKK0TtgHorKoprv0gYKWqtvG4pW5QFQQsN+nOdObvmJ8Vh7H+wHoABMmqLQcmueKwbsNyVAewWCoD5cAJOxX4G1iFWc0N8BhwNdAV8zgyBrg10ykriKqoYfktTgryCyLNmZajhJzVMEtlpLRO2OOYZdjfuA5dAkxX1fGeNNJdqqKA5Wbzoc18uvJTxswdk0PAbHJYS2XF106YJ6nqGpa5OGn6mul8ue7LHOdC/EPYes9Wq2GWSkVh+uUo6mZVHQfcBBx2bUN95YBZDC1rt2T06aO5tcetBDhOFOitFliNI8lHfGeYxWKxFEH1oOoM7jCYGVfM4NYet+ZYSXly45OpX62+D62zWMqWIp0wF8uBGZjZsIMi4n4BR4vXGNVvVNbKyUBHIIrSY3IPpi6fSkVLPWKxWKoeo/uPznLCHOJgTswcrv3mWhJSEnxsmcVSNhTphInIXZiM979iVgbNcv20+JjI8EiGdh2KQxwM6z6MVbevonej3gz9bijXfXOdFTKLxVKuya5hw7sPZ+wZY5m2ehrdJ3dnye4lvjbPYvE67syE3QO0UdUOqtpZVTupamdvG2Zxj1H9RhFdM5pR/UfRMLwhv173K2PPGMvnqz+3QmaxWMo9mRo2+vTRPNHvCf668S+S05Pp814fXlvwmp3Vt1Rq3HHCdgBHvW2IpWREhkey5e4tWYGsfg4/nuj3BH/e8KcVMovFUu7JrWGnNj2VFbet4PxW53PfL/dx0bSLOHD8gI+ttFi8gztO2FbgTxF5VERGZm7eNswTpMSlsKDFAlL2pPjalDLntKjTrJBZLBWYqqxftUNq882V3/D6ea8ze8tsurzdhb9i/vK1WRaLx3HHCduOiQcLBMKzbeWezfdvJnlbMrFjK0eJkOKSKWQTz51ohcxiqWBseXhLldYvEeHOk+5k4bCFhAWGceZHZ/LUn0+R4czwtWkWi8dwu4C3q2YaqnrMqxYVgbs5dpJikljYbKHZEag1oBbh3cIJbRdqtrah+IdXnSKzy+KWcdVXV7H50GZG9RuVY2WlxVLeqWp5wlLiUpjfeH5WWtjqfasT1iUsS7+qtatGYMNATCL/ys+x1GPc+eOdfLjiQ/pF9ePTyz6lcfXGvjbLYnGLwvSrSC9ERDoCHwO1XfsHgOtVdY1HrfQwseNjwQ9wDZoS/kvgyB9H0LRsGeYbBVKtXbUTjplL3ALqBVQ6cesW2Y0lw5dwx4938PRfTzMnZo4VMoulnBIzNsY8p3ACDkjakkTimkQyjp6YBfIL9yO0bU7tCm0XSnDzYBz+7mYfqhiEBYYx9ZKp/K/Z/7h91u10ebsLUy+eysA2A31tmsVSKtzJmD8PeFxV57j2TwfGq2pfr1uXD+6OIhc2X4gz+UQ2eUeIg14beqHHlcR1iRxfd/zEtv44GcdOiJt/Lf8cM2ZZ4hYdjDgKd85S4lJYduoyuv3bjaAGQaV7s17ioxUfMWLWCIL9g5l6yVQubH2hr02yWAqlKs2EFaRfJ205CRHJoV2ZWpa6OzXrWgkUQlqF5Bxgtg0ltE0ofqGFz35XBP3aeHAjV355Jcv3LOfe3vfy3FnPEeRfPm21WKD0ZYtWqGqXoo6VFe44YRsCH2JP2lkogVnHhFQiA36ldeqLea5XVVJ2puRwyjLFLW1fWtZ1jmAHIW1C8syehbYKxRFkRp4bRmwg7p04Gt7WkNaTWnvoXXseK2SWikRVcsKKq18A6UfTc+jW8fVGy5K2JJ2odCkQHBWcZ+Y/tF0oAbVN5Y2Kol8p6Sk89OtDTPxvIt0juzPt8mm0imjla7MslnwprRP2DbAU80gS4Fqgh6pe6lEr3cQdJ2yezCCVujhIxklw1vFA9tNXBxerv7RDaTlGnJlbcmwyWbWzHRDSPITgZsEc/uMwZJiRa++tvcvtaBKskFkqDlXJCcupX0GAmX0viX45U5wc33Q8z+xZ0oakHDNtAfUCCG4eTMKiBMgACRZO3nZyudYvgO83fM/Q74aSmpHK2xe8zZDOQ3xtksWSh9I6YbWAp4FTMW7H38DTqnrY04a6g1uB+YXFc8XHQ3jpF3dmHM/g+IYTI87j645z+PfDpB9Od9kANU6rQacfOuFfvXwvAPhu/XcM/W4oac40K2SWcklVcsIK1a/t26Fx48KvcQPNUJJjk3PM/B+cdZC0PSdm/qt1qUb7ae2p1rZaqfryNjuO7uCar6/hn+3/cGPXG3n9vNcJCwzztVkWSxalcsLKG6V2wkSgTRvo2RN69DA/u3aFsNL90eYXxwFmRqzeVfWIvCWS6idXL7cB/7mF7I3z3qBaYPkWX0vVwTph2ahb1+hWdg1r2LBUjllB+gVQ49QaRA6PpO6guviFlM8V1enOdMb+NZaxc8fSOqI1Xwz6gi4NfBIxY7HkobQzYb8Cg1X1iGu/FjBNVc/xtKHuUGon7OmnYckSWLwYdu8+cX27dnkds9BQt+3aMGIDe97bg6Zm+zz9IbRVKMnbk3EmOqnWsRqRt0RS/9r6WTEY5Yl0Zzpj/hrDM3OfsUJmKVdYJ8zFG2+c0K81a8Dpcprq18+pXz16GMfMTfLVrwAI7xlO+v50kjYn4V/Tn/rX1ifylkjCOpfPmaY52+Yw5OshHEo6xMsDXmZErxHlduBrqTqU1glbpqrdijpWVpTaCcv+fuPijKBlitrixbBnjznncED79jlFrUuXAh2zeY3nkborNc/xwEaBnLTuJPZN20fclDgSFiUgQULdQXVpeEtDavSrUe5EIruQvXLOK9ze8/ZyZ6OlamGdMBfZ9ev4cVixwuhWpoatW3fCMYuMzOuYNWiQb7OF6VefHX048tcR4ibHsf+r/WiqEt47nIa3NKTulXXxDytf4Rb7E/dzw7c38NPmn7i07aW8e9G71A6p7WuzLFWY0jphS4BLVXW7az8K+EZVu3vcUjdwywmrXh0SEvIeDw83MWGFsXv3CYcsU9j27TPn/PygQ4e8jllwsNt9JixPIG5KHHs/2UtGfAYhbUKIHBZJgxsaEFg3MO/9PiK3kL130XvUCqnla7MsVZQq5YSVRr8SE2H58pz6tX79CeetUaMT+pWpYfXqud1n2sE09ny8h7gpcRxfexy/MD/qXVOPhsMbEt6j/BRScaqT1xa8xiO/PUJkeCSfXfYZpzQ9xddmWaoopXXCzgUmA39hlumcBgxX1V+KuO994EJgn6p2LOS6XsB84CpV/bJQY3A/Y77HUIVdu3KK2uLFcMBVh9Hf3zhmK1YU3kYuMo5nsH/GfnZP3k38vHgkQKhzSR0ih0dS68xaReYjKwuskFnKC1XKCfM0CQl5HbONG0/oUpMmsGNHwffno1+qSvz8eHZP3s3+6ftxJjkJ6xZmwi2uqY9/jfIxO7Zo1yKu+uoqYo/EMuaMMTx8ysO2UoilzCl1YL6I1AFOdu0uUNUiK0GLSD/gGPBRQU6YiPhh6lImA++XSycsP1SNaGUXtdmzC7++EBLXJBL3bhx7PtpD+qF0gpsFm9mxoQ0IivT9EvFMIdt6eGu+5xuFN2LnyJ1lbJWlKmGdMA8THw/Llp3QsM8/L/jaIvQr7Uga+z4z4RbHlh/DEeqg3hX1iBxePhYjxafEc+vMW5m2ehpBfkGkZOQtiG41zOJNSjsTJsAQoLmqjhGRpkADVf3PjY6jgZmFOGH3AmlAL9d1FcMJy4/ChCYjw8SYFUFGcgYHvjlA3JQ4jsw5An4QcWEEDYc3pPY5tRE/34nZ0eSj9Jzck82HN+c4HugXyLBuw5h0wSQfWWapClgnzMsUpl+HDkGtokMRVJWEJQnETY5j3+f7yDiWQWiHUBre0pD61/l2MZKq8v6y97lt1m2kO9NznLMaZvE2hemXOwXG3gT6AFe79hOAUv+2ikgj4FLgLTeuHS4ii0Vk8f79+0vbddnTtSvMmHEiYLYA/IL9qH91fbr+0ZWTNp5Ek/ubED8/nlUXrGJBswVse2obyduTy8bmXNQIrsFfN/6FvyPnYwY/8WNU/1E+sclisZQB0dEwapRxxgpBRKjeszptJrehT1wfWk9pjV81Pzbfu5l5Deexdshajvx1BF+kRRIRbu5+M79d9xtCTofTapjFl7jjhPVW1TswjwxxJWn1RAT5a8DDqlq4Z2L6nKyqPVW1Z926dT3QdRmTmgpXXAGdOsG0aWZmrAhCW4XS4vkW9NnRhw5fdiC0fSixY2JZEL2AlResZP+3+3GmOUmJS2FBiwWk7Mk7xe5pGlZvyC3db8lyxAThhi430CAs/xVXFoulEnD22fDMMxAVBY89diIethD8w/xpOKwhPRb2oOfynkQOi+TgrIMsP305/7X9j+0vbid1n1mNWZYa1j+6P8O6D8tyxAIcAQztOtRqmMVnuOOEpblitxRAROpyohpZaegJTBORGGAQ8KaIXOKBdn1DQVn4w8NNPp/MmIurr4aOHeHTTyE9Pf97suEIdFD38rp0+bkLvbf2JurxKI4tP8aaS9ewoKlxyJK3JRM7NtaDb6ZgRvUbleWEKUpwQHARd1gslnJPYfr15ZewahWcfz4895yZGXv44ROrxosgrEsYrd9oTd/dfWn7YVsC6wWy9aGtzG88nzWD17Bh+IYy1bCnT3+aQD8zj5DuTOf+vveXSb8WS36444RNBL4B6onIOOAfYHxpO1bVZqoararRwJfACFX9trTt+oz4eBPAmnuLjzepLa66ygjZ9OlmReW115o8ZB9+6JYzBhASHUKzsc04OfZkOn7fkWqdqpG4LBEUdk/eTdL2JC+/SYgMj2Ro16E4xEHbiLa8tuA1ft78s9f7tVgsXqQw/QIzcPziC1i9Gi6+GF56yThj999/IrdiEfiF+tHg+gZ0+7sbvdb2otGdjTj8+2EOzTwECnHvxZXJbFhkeCQ3dbsJQVCU0X+O9skjUosF3HDCVPVT4CHgWSAOuERVZxR1n4h8jkk90UZEdorIzSJym4jcVlqjKywOBwwebNJZfPWVSfx6442mjNL770NaWpFNADj8HdQZWIfglsGQGeuaDkt6LOHYqmNeMz+TUf1GEV0zmllDZtGpXieu/fpath/d7vV+LRaLj2nf3szir10LgwbBa69Bs2Zw770nKpC4QbV21Wj5SkvqDK4DrowRmqKsvXptmThEo/qNolmtZjzY90E+WfkJU5ZO8XqfFkt+uLM6sgWwU1VTROR0oDMm7cQRr1uXD+VyZVFJUYUffoAxY8wy8ehoE3Nxww0QWHjYXUG13iRQaDamGU0eaFImqyk3HtxIz8k9aV+3PXOHzs2a5rdYPIldHVlO2bwZxo+Hjz4yM/y33GIeVTZuXOStBWlYzTNr0u7jdgQ19H56Hqc6Of/T85kTM4f5N8+ne6RPcpBbKjmlXR35FZAhIi2Bd4AmwGcetK/qIgIXXQSLFsHMmaYw7/Dh0LIlvPUWpBQ8NR8zNgZ15nKgAyCocRBbH9nKstOWcXzTcS+/AWgd0Zr3L36fhbsW8tCvD3m9P4vFUo5o2dLM4m/cCNddB2+/DS1awO23w/bCZ8fz1TA/ODr3KIs6LmLv53u9PivmEAefXPYJ9arVY9D0QRxJPuLV/iyW3LjjhDlVNR24DHhDVR8EIr1rVhVDBC64ABYuhJ9+MqVFRowwYvbGG5CcNy3Fwe8P5iy2C5Bmco21+7Qdx9cdZ3HXxeyatCuv0HmYQe0HcfdJdzNh4QS+XFtkqjeLxVLZaN4cpkyBTZtg6FB47z3joA0fDjEx+d6Sr4ZlgH9tf0LbhLLumnWsvWItqQfy1rT0JHVC6zB90HR2xO9g6HdDbXyYpWxR1UI3YCEmR9hqoJnr2Oqi7vPW1qNHD630OJ2qv/6qeuqpJjw2MlL1tddUjx93u4nkncm64twVOoc5uvys5ZoUm+RFg1VT0lO095TeGj4+XDce2OjVvixVD2Cx+khzPL1VCQ3bvl11xAjVwEBVf3/Vm25S3bzZ7dud6U6NeTZG/wz4U/+p94/u/26/F401vDLvFeUp9KV/X/J6X5aqRWH65c5M2FBMstZxqrpNRJoBH3vFI7QYROCss2DuXPjjDxO4f++9JgD2lVdMkd4iCGoURKcfO9H6ndYcnX+URZ0WETc1zmujvEC/QKYPnk6AXwCDZgwiKc37KzUtFks5pUkTmDQJtm41s/qffWZ07MYbzWxZEYifEPVIFD0W9yAwMpDVF69m/dD1pB91byV5Sbj35Hu5rN1lPPzbw/yz/R+v9WOxZMet2pHliUoV1Foc5s41Afy//25ix44eNUlgcxMefmJZuYukrUmsv3E9R/8+SsRFEbR+pzVBDbwT9PrTpp84/7PzuanrTbx38Xte6cNS9bCB+RWcuDh48UUTM5aSAtdcA19/DcfziVvNpWHOVCcxY2LY/ux2ghoF0faDttT6X9FllErC0eSj9JzSk+Npx1l26zLqVavnlX4sVYvSBuZbygP9+sFvv8E//0C3bvk7YAAJCXkOhTQPoeufXWnxcgsO/XKIRR0Xse9L9xItFpfzWp3H46c9zvvL32fq8qle6cNisVQwIiPNLP62bTByZMEOGOTRMEegg+bPNKf7vO44Qh2sOGsFG+/cSEZi0ZVHikuN4BrMGDyDg8cPMuTrIWQ4Pd+HxZId64RVNE45BX75pdi3iUNoMrIJPZf1JKRZCGsHr2XtkLWkHXIvN1lxePr0pzmz2ZmMmDWClXtXerx9i8VSQalf38yIFRCsXxjVe1en57KeNL63Mbsn7WZx18UcnX/U4yZ2bdCVSedP4retvzF27liPt2+xZKdQJ0xE/ETkpbIyxuJ9qrWrRrd53YgeE83+6ftZ1GkRB3866NE+/Bx+fHbZZ9QMrsmg6YOIT4kv+iaLxVJ1KGENYL8QP1q+2pIuc7rgTHOy7NRlbH10K84UT1TSO8FN3W7ihi43MOavMczeMtujbVss2SnUCVPVDODUMrLFUkY4AhxEj4qm+8Lu+NfyZ9X5q9gwfAPpCZ4Leq0fVp9pg6ax9fBWhn0/zC77tlgsHqPW6bXotbIXkTdFsv257SzpuYSE5XlDMUqKiPDmBW/SoV4Hhnw9hJ3xOz3WtsWSHXceRy4Tke9F5DoRuSxz87pllpKTT1xYfoR3D6fH4h40eagJce/GsbjzYo78dcRjZvSL6se4M8cxY+0M3vjvDY+1a7FYLP7V/WkzpQ2dZnYi7UAaS3stJeaZGJzpnpkVCw0I5cvBX5KcnsyVX15JWobnQzcsFnecsGDgIHAmMNC1XehNoyxuEB5e8LlLLy002352/IL9aPF8C7r93Q38YPkZy9k8cjMZSZ4JSH3wlAcZ2Hog98++n4U7F3qkTYvFUgkoTMNWrHC7mYgLIui1uhd1B9clZlQMy/ouI3F90Wl83KFNnTa8O/Bd5u2YxyO/PeKRNi2W7LhTwHtoPttNZWGcpRDi403tydzb1KkmjcUNN4DT/RFhjVNq0HN5Txre3pCdr+5kSfclxC8qfSyXQxx8eMmHNKreiMEzBnPwuGfjzywWSwUlPw2LjTV1J88916ykdJOAiADaf9ae9tPbk7Q1iSXdlrDjtR0eqRZyZccrubPXnbyy4BW+Xvd1qduzWLJTpBMmIq1F5HcRWe3a7ywiT3jfNEuJuOEGeOEF+OILuOceI2xu4h/mT+tJren8S2cyjmWwtM9Stj25DWdq6ab3a4XUYsbgGexN3Mt131yHUz0bRGuxWCoJTZua1d8pKTBgAOwrXiqdeoPr0Wt1L2qdVYst921h+ZnLSdpW+sTRLw14iV4NezH0u6FsPrS51O1ZLJm48zhyCvAokAagqiuBq7xplKWUPPgg3H+/qTs5blyxb689oDY9V/Wk/pD6xI6NZenJSzm2+lipTOrZsCevnfMaP23+ief+ea5UbVkslkpM+/Ywaxbs2gXnn+92jGsmQQ2C6Ph9R9q834ZjS4+xuPNidk/ZXarFQUH+QcwYPAM/8WPwjMG2IojFY7jjhIWq6n+5jnmvdoTFM7zwAlx3HYwaBZMnF/v2gJoBtPuwHR2+6UDKrhSW9FjC9he2oxlKSlwKC1osIGWPe3FnmdzW8zau7ng1o+aMYs62OcW2yWLxFSLSRETmiMhaEVkjIve4jtcWkV9FZJPrp3dSuVc1+vSBGTNg+fJixbhmIiJEDo2k16pehJ8UzsbhG1l14SpSdpt2SqJhUTWj+PjSj1m+Zzn3/HxPseyxWArCHSfsgIi0ABRARAYBcV61ylJ6HA547z0zkrz9dpOhugTUvaQuvVb3IuLCCLY+vJVl/Zax+YHNJMckEzs2tlhtiQiTB06mdURrrv7qauIS7K+RpcKQDtyvqu2Bk4E7RKQ98Ajwu6q2An537Vs8wQUXwPvvmxjX66+HjOIvFgqOCqbLr11o+XpLjsw5wqKOi9j7+V62jdlWIg27oPUFPHrqo0xZOoWPVnxUbHsslty444TdAbwDtBWRXcC9wG3eNMriIQICYPp0OOkkU6vtr79K1Exg3UA6fNmBdp+0I3FNIvs/2w9O2PPBnmLPhoUFhvHl4C9JSE3gqq+uIt1pJ1Ut5R9VjVPVpa7XCcA6oBFwMfCh67IPgUt8YmBl5frrTYb96dOLHeOaiTiExnc2pufynoS2CWXdNevYM3lPiTVszBlj6B/Vn9tm3sbqfauLbY/Fkh13VkduVdWzgLpAW1U9VVWLN3yw+I5q1WDmTGjeHC66qFhLv7MjItQfUp+IiyJAzDFnqrPYI0mADvU68PYFbzM3di6j/hhVInssFl8hItFAN2AhUF9VM6d09wD1fWVXpeWBB0yM66RJJYpxzSS0dShd/+5KeO9wcK0NcqYXX8P8Hf5MGzSNGsE1GDR9EAkpnksSa6l6uLM6MkJEJgJ/A3+KyAQRifC+aRaPERFhVhxVr26Wfm/dWqJmUuJSODDjgOvBNJABcZPjij2SBLiuy3UM7z6c5/59jpkbZ5bIHoulrBGRMOAr4F5VzZHDRU3kd75TNSIyXEQWi8ji/fv3l4GllYxSxrhmkrY/jcQV2XKIpUHc+8XXsAZhDfj88s/ZdGgTw2cOtxVBLCXGnceR04D9wOXAINfrL7xplMULNGliHLHUVDjnnGIv/QaIGRuTJ++OpitrBq8pkUkTzptAtwbduP6b64k5ElOiNiyWskJEAjAO2KeqmhlkuVdEIl3nI4F8/7BUdbKq9lTVnnVLWDexSuOhGNd8NSxF2fak+znJMjk9+nSeOeMZpq2exluL3yqRPRaLO05YpKqOVdVtru0Z7JR7xaR9e/NoctcuOO88kyyxGBz8/iCamnfEF/9PPLsm7Sq2OcH+wcwYPAOnOhk8YzAp6cWfUbNYygIREeA9YJ2qvpLt1PfADa7XNwDflbVtVQYPxLjmq2EKez/cS8bx4gf+P3zqw1zQ6gLu/fleFu1aVOz7LRZ3nLDZInKViDhc2xXAL942rLR8+ilER5sBVHS02bdgln5/+aWJDSvm0u++O/tyup6eY+uX0o+IiyLYdOcmdr+zu9jmtKjdgqmXTGXx7sXcP/v+Yt9vsZQRpwDXAWeKyHLXdj7wHHC2iGwCznLtlxqrXwWQO8Z1+fJi3Z6fhrX7pB2apqy+eHWxy7U5xMFHl35Ew/CGDJ4xmENJh4p1v8WCqha6AQmYMMY01+Z0HUsA4gu5733M1PzqAs4PAVYCq4B5QJeibFFVevTooUXxySeqoaE562GEhprjFhcffWQ+mMGDVdPTS9VURnKGrrhghc5hju5+d3eJ2rj/l/uVp9Bpq6aVyhZL5QRYrG7oQ0XYitIwq19usH27auPGqvXrq27ZUurm4qbG6RyZoyvOXaHpScXXw4U7F2rAmAC98LMLNcOZUWp7LJWLwvTLndWR4arqUNUA1+ZwHQtX1eqF3DoVOLeQ89uA/qraCRgLlDzaMhePPw7Hj+c8dvy4OW5xcd118NJLJiFiCZd+Z+IIctDhyw7UPrc2G27ZQNzU4uf/evZ/z3JKk1MY9sMw1h9YX2JbLJaKjtUvN2jSBGbPhrQ0U95o795SNdfghga0ebcNh34+xJpBa3CmFK+02kmNTuKVc15h5saZvPjvi6WyxVK1cOdxZIlQ1blAgXOzqjpPVQ+7dhcAjT3V9/bt+R+PjS2Vr1H5uP9+s/x70iR45plSNeUX7EeHrztQ66xabLhpA3s+2VOs+wP8Avhi0BeE+IcwaPogElMTi77JYqmEFKZfSbZazgnatTOPJnfvNgH7xYxxzU3kTZG0fqc1h2YdYs0Va4pdM/eOXndwZYcreeyPx/grpmQ5GS1VD685YcXkZuAnTzXWtGnB5zp3hgkT4OBBT/VWwXn+eZMQ8ckn4Z13StWUX4gfHb/tSM0zarL+hvXs/bx4o9NG1Rvx2eWfsXb/Wkb8OMIu+7ZUSQrTr8hIuPPOEqf7q3yUIsY1PxoOb0irSa04+P1B1l61Fmea+46YiDBl4BRa1W7FVV9dxZ5jxRuIWqomPnfCROQMjBP2cCHXFCvHzrhxEBqa81hICNx8s/l5773QqJFZYPPHH+As3oCncuFwwLvvmhIhI0aUeOl3Jn6hfnT6vhM1TqvBumvXsW968VJhnNX8LEb3H81HKz7ivWXvlcoWi6UiUpB+PfaY+TN9913o2hV69TIps0o5AVTxOf98+OADI+bXXVei8kbZaTSiES0ntOTANwdYd806nOnu/4MIDwrnyyu+5GjyUa756hoynKWzxVIFKChYLHMDWgBBrtenA3cDNYu6z3V9NAUE5rvOdwa2AK3daU/dDMxXNUGsUVGqIuZn9qDW5ctV77xTtWZNE/TaooXq+PGqu0sWU145SExU7dNHNTBQdc6cUjeXlpCmS09dqnP85ui+L/cV6970jHQd8PEADRobpMvilpXaFkvFhyoUmK9auH4dOKA6YYJqx45Gv6pVU73pJtV581SdTjc/0MrISy+ZD2TECI98ENtf2a5zmKNrrlqjGWnFC7b/cPmHylPo478/Xmo7LBWfwvTLHUdqOeAPtAQ2Ai8CPxZ1nxbhhAFNgc1AX3faytzcdcLc4fhx1Y8/Vu3f33wSfn6qF1+sOnOmalqax7qpOBw8qNq+vWr16qrLlpW6ubT4NF3SZ4n+6f+n7v92f7Hu3XdsnzZ6uZG2mNBCjyQdKbUtlopNVXPC3MHpVF2wQPXmm40jBqodOqi++qpx1KokDz5oPoinn/ZIc7EvxBpHbMgadaYXz7Eb9t0w5Sl01sZZHrHFUnEpTL/EnC8YEVmqqt1F5EEgWVVfF5FlqtqtiPs+d82c1QH2AqOBANfs29si8i4mC39m4a50Ve1ZqDFAz549dfHixUVdVmw2bjQJmadONcnkGzWCm24yW3S0x7srv+zcCX37msz68+aZfDylIP1oOisGrODYsmN0+LoDdS6s4/a983bM45T3T8n3XKPwRuwcubNUtlkqDiKyxB19qAh4Q8MSEmDaNPOo8r//IDAQLrsMbrkFTj/dRB1UCVRh6FD48EN4+2249dZSNxn7bCzbHttG/evr0/b9toifuHVfUloSNZ6rQZozLc85q19Vi8L0y50/zTQRuRqTDTqzyF9AUTep6tWqGqkmrUVjVX1PVd9W1bdd54epai1V7erafCqwrVubGPUdO+Crr0wA/zPPGB/knHNMJofUVF9aWEY0bmzKG3lo6bd/DX86/9KZap2rsebyNRz8yf0VEX2b9KVv4755jgf6BXJxm4tLZZfFUpkIDzcO18KFJkb91lvh55/hf/8z2vbssxBX/MwxFQ8RmDLFYzGuAFGPRhE9Jpq9H+1lw/ANecoeFURIQAiD2w/Oc9zqlyU77jhhQ4E+wDhV3SYizYCPvWuW78gcQf74I8TEwOjRsG4dXHGFmR174AFYX9nTWLVrB7NmGdUuQXmj3ATUDKDL7C5U61CN1Zeu5tBs97NKzxg8A4fk/DX1Ez9G9R9VKpsslspK584wcaLJ3PDJJyal1mOPmZ+XXGKyOqSn+9pKL5JZ3qh3b7j6avjzz1I3GT0qmqgno9jz/h423rbRbUfspQEvEeDIOWdh9cuSHXeSta5V1btV9XPX/jZVfd77pvmepk2NE7ZtG/z0E/TrZ9JbtGsHp51mZryzJ1WsVKVGTj7ZLP1etcojS78DagfQ5dcuhLYJZfXFqzn8++GibwIaVm/IjV1uPNGOI4ChXYfSIKxBqeyxWCo7ISEwZAjMmWPCLR54ABYsgIEDjT6NGmW0LZNKpV+hocbbbNkSLr642OWN8iP6qWiaPtaUuClxbLpzE0WF8gBEhkcyrPuwrIGkv8Pf6pclB+7EhJ0CPAVEYQL0BVBVLV2wUAnxVkyYu+zda5yvd9+FTZugenUjdI0awfjxOZ2y0FCzhHzIEJ+ZW3o++cQs+x40yASd+PmVqrnU/amsOHMFSVuS6PRjJ2qdXqvIe+IS4oh6LYo0Zxr+4s+OkTusiFUxbEyYZ0hLM77JlCnmcSXAWWdBmzYmJjZ7MthKoV/ZY1z//RdatChVc6rK1ke2suOFHTS6y6SyMLXdCyYuIY5mE5qRkpGCIMTcG0PTGoUkg7NUOkobE/Ye8ApwKtAL6On6WSWpXx8eegg2bDCz3BddZFLUPPFEJS01cu218PLLZlbsrrtKXXIgsG4gXX7vQnCzYFZduIojfx8p8p7M0SSAohxPO17EHRaLJT8CAszE9o8/mgz8o0cbLXvjjbzZ+CuFfmWPcT3nnFLHuIoIzZ9rTuORjdn1+i62jNxS5IxYZHgkN3W7CUFQlGmrp5XKBkvlwp2ZsIWq2ruM7CkSX8+E5cfhw1C7dv7nRCpJMtiHH4YXXsj/XHh4sePGUvaksPz05aTuSqXzL52p0bdGodfHJcRx8nsnc/D4Qc5odgY/XP1DsfqzVGzsTJj3yMgAf//8z1Ua/Vq4EM48E5KT839DxdQwVWXzvZvZNXEXTR5oQvMXmhc6IxaXEMepH5xKq9qt+Gf7P6y7Yx1NajQpyTuxVEBKOxM2R0ReFJE+ItI9c/OwjRWaWrUgKir/c4WVIKlQPPdcwecSEordXFCDILr+0ZXAyEBWnruS+IWFC2BkeCSx98by9OlPM3PjTH7YYJ0wi8UT+PkVrF9NKouf0Lu3WfZekEdZTA0TEVq+1pKGIxqy46UdbHtsW6EzYpHhkWy5ewtvX/g2GZrB/bPvL1Z/lsqLO05Yb8wjyPHAy67tJW8aVRHJr9QImCwPlYIi4h5KQlDDILrO6UpAvQBWDFhB/KKiR6J3976b9nXbc/fPd5OUZqsZWyyeoCD9atAAjh0re3u8wrnnerQ5EaHV662IHB7J9ue2EzM6psh7omtG8/hpjzNj7Qx+2/qbR+2xVEzcWR15Rj7bmWVhXEViyBATxBoVZfyVxo2hQwcTAHvHHVUkx1gJCGrkcsQiAlg5YCUJSwsfkQb4BTDp/EnEHInh+X+rxCJdi8Xr5Navpk1NdofFi02N7M2bfW1h+UQcQuu3WtPgpgbEjo0lZkxMkfc80PcBWtZuyZ0/3klqhv3HUNUp0gkTkRoi8kpmAW0ReVlECg/gqaIMGWJyizmdJunr8uVmWfibb5qkiaWMCa20BDcJpuucrvjV8GPFWStIWF64I3Z69Olc3fFqnvvnObYc2lJGVloslZvs+hUbC599ZlZQ7t5tioVnrqa05EQcQpspbah/Q31iRscQOy620OuD/YOZeO5ENhzcwKvzXy0jKy3lFXceR74PJABXuLZ44ANvGlVZ8PeHF180YrZkCfToYUqKWPISHBVM1z+64hdmHLFjqwp/BvLSgJcI8Avg7p/vditfj8ViKT5nn21mw5o2hfPPN5n37Z9bXsQhtH2vLfWG1GPbE9vY/vz2Qq8/r9V5XNL2EsbMHcOOozvKyEpLecQdJ6yFqo5W1a2u7WnAJznCKipXX23KMAYEmCSv77/va4tKSHh4/serVfNI8yHNQ+jyRxccwQ5W/G8FiWsSC7y2YXhDnj79aX7c9CM/bLRB+haLt2jWzOjXlVeazPtXXFGB48QK0rCCjhcD8RPaTm1LvavqmVxiLxfuXL16zquoKiNnjyx135aKiztOWJKInJq540reaiOii0nXrmZEedppcPPNFTROLD7eDIMztzVrTJ2niy7yWBehLUPp+kdXxF9YfuZyEtcV7IjdddJddKjbgXt+vscG6VuKRESCRWSQiEwQkRki8pGIPCQiHXxtW3mnWjUzo//ii6YcY4WNE8uuYRkZcMopJr+Qh96Mw99B24/bUndwXbY8sIWdEwou0p0ZpP/l2i/5dcuvHunfUvFwxwm7HZgkIjEiEgu8AdzmXbMqJxERJq6i0sSJtW9vhsaff27qOnmI0NahdPmjCwisOHMFxzfkn5w1e5D+c/8UkkLDUuURkaeBfzF1cBcC7wDTgXTgORH5VUQ6+9DEco+I0a5KEyfmcJiVU8eOwb33eq5ZfwftPm1HnUvrmFxik3YVeG1WkP5Pd5KSXrrScJYKiqq6tQHVgeruXu+trUePHloZ+Owz1ZAQ1UaNVBcu9LU1pSA5WbVdO9WmTVUTEjza9LE1x/Sfuv/ovw3/1cP/Htb5zedrclxynuuu+eoaDRobpJsObvJo/5byA7BYS6EbwAVFnK8H9CxNH+5ulUHDtm5V7dxZVUR1/HhVp9PXFpWCp54yc2OzZnm02YyUDF150UqdwxyNeS6mQP36adNPylPos38/69H+LeWHwvSrwJkwEbnW9XOkiIwEhgHDsu1bSkGliRMLCjKFNHfsMLWbPEi19tXo8kcXNFVZOWAlyduSiR2bd+XRi2e/SKBfIPf8fI8N0rfki6rOyn1MRBwiUt11fp+qlp809uWcShUn9sgjZlb/9ts9+iYcgQ46TO9A7Qtqs+2RbQXq17ktz+XStpcydu5Yth8tPKDfUvko7HFkZrR1eD5bmJftqhLkjhO7805T4qzC0bevEbCJE015EA8S1jGMdp+3w5noBIW49+NI2ZNz2t4G6VvcRUQ+E5HqIlINWA2sFZEHfW1XRSS/OLEtFTFjTFCQeSzphYGkI8hBq9dbmf+0CnHv5tUvOBGkbzPpVz0KdMJU9R3Xy99U9ensG/B72ZhX+cmME7v/fpg0qQLHiT37LDRsCLfc4nFPcv/X+8FV205TNN+EiHeedCcd6nbg7p/utgW+LYXRXlXjgUuAn4BmwHU+tagCkz1ObNcu6NmzgsaJ9e0LI0Z4ZSC5/cXt4Gdea6qy9aGtea6JqhmVFaQ/e8tsj/ZvKd+4E5j/upvHLCXE3x9eegk+/dTMjFXIfGLVq5vVBqtWmaGxh0iJS2HvB3tN+DSAwp539+QZTWYG6ccejbVB+pbCCBCRAIwT9r2qpgH2GXYpyZ1P7LnnKmA+sfHjPT6QzNKvbM3t/XQvSTvyrubODNK/66e7bJB+FaKwmLA+InI/UDczDsy1PUWWX2/xJNdcY+Is/P2hXz/4oKKlxL3oIhg0CMaMgY0bPdJkzNgY1JlTzTVN2XTnpjzX9o/uz5BOQ3j+3+fZfKgirp+3lAHvADGYcIu5IhKFSUBtKSXNmxv9uuIKePRREy9WoeLEvDCQzE+/cMLqgavzXBvkH8Tr573OxoMbeWX+Kx7p31L+KWwmLBAT++VPzniweGCQ902rmmTGiZ16Ktx0UwWME3v9dQgJgeHDTf2TUnLw+4Noat4h9YFvD5B2MO8H8+LZLxLkF8TdP9lM+pYTuAaVoqoTVbWRqp7vWrW0HTjD1/ZVFqpVMxlrXngBvvqqAsaJXXQRDB7ssYFkQfqVuCLRhFnkIjNI/5m/n7FB+lWFgpZNZm5AVFHXlOVWGZZ3u0Namur995uV06edprpnj68tKgZTphjDp0zxSvNHFx3VPwP/1BXnrVBnRt618a/Me0V5Cv123bde6d9S9lD6FBVvAUuBacCNQIPStFearapo2OzZqrVqqdasqfrTT762phjExRmj+/dXzcjwePMZyRm6uNdinVt9riZuSsxzPuZwjIY8E6KXf3G5x/u2+IbC9MudmLDjIvKiiPwoIn9kbt5xCS2ZVOg4sZtvhv79TcRuXJzHm6/eszotX23JoZ8Osf25vKPFO0+6k471OnLPz/fYIH0LAKp6u6p2B54CagFTRWS+iIwXkX4iYkMsPEyFjRNr0MA8jvzrL3jvPY837why0GFGB8RPWDNoDRlJGTnOR9WM4ol+T/DVuq/4ZfMvHu/fUr5wxwn7FFiPWUX0NCaeYpEXbbJko0LGiYnA5MmQnAx33+2VLhre3pB6V9dj26htHJ5zOMe57EH6z/79rFf6t1RMVHW9qr6qqucCZwL/AIMxWfQtHqbCxondfDOcfjo8+KBXBpLBUcG0+7gdiSsS2Xx33vjV+/vcT6varWyQfhXAHScsQlXfA9JU9S9VvQkjXoUiIu+LyD4RyRuBaM6LiEwUkc0islJEuhfT9ipDhYwTa90annwSvvwSvvvO482LCK0ntya0dShrr15LSlxOoeoX1Y8hnYbwwrwXbJC+JQ8iEgp0ABap6l2q2tPXNlVWKmScWBkMJCMuiKDpo02JezeOPR/tyXEuM0h/06FNNki/kuOOE5b57z5ORC4QkW5AbTfumwqcW8j584BWrm04JmbDUgB16uTNJ/bmmxAdbUqgRUebR5fligcfhE6dTLXyeM8vQPMP86fDlx3ISMhg7dVrcabnXAhgg/QtmYjIRa76t0tF5HxgDaYO7ioRucHH5lV6RIwc5M4n9umn5VjDWrWC0aO9NpAEiB4TTY3+Ndh420aOrc45RXhOy3O4rN1lNpN+ZaegYLHMDbgQqAF0BOYAS4CLirrPdW80sLqAc+8AV2fb3wBEFtVmVQlqLYxPP1UNDDR120yUhdlCQ1U/+cTX1uVi4UJj6IgRXusi7qM4ncMc3fLoljznXp3/qvIU+s26b7zWv8X7UPrA/BVAa6AXcAxo7jpeD1hVmraLu1V1DduyRbVTJyMLAQHlXMNSU02RzIYNVY8c8UoXybuT9Z/6/+iCNgs0LT4tx7nYI7Ea8kyIXvbFZV7p21I2FKZfhc6EuYJVW6nqUVVdrapnqGoPVf3eA/5fI2BHtv2drmOWIrjmGpNpP/fkzvHj8PjjvrGpQE46yUznv/km/PuvV7pocF0DIm+JZPuz2zk462COc3eedCed6nWyQfoWp6puVNVFwDZV3QqmZiQnUgFbyoDmzWH+fJPJJndYRbnTsIAAU9IoLs4EtXmBoMgg2k9rT9KmJDYO35hj1r5pjaaM6jeKr9d9bYP0KymFOmGqmgFcXUa2FIiIDBeRxSKyeP/+vLlVqiJ79uR/fHt5nLV+5hmzROqWWyDFO0GmLSe2JKxrGOuuW0dybHLWcX+HP2+c/wbbj263QfpVG4eI1BKRCMDpel1bRGrjXliGxYNUq2Ycrvwodxp20klwzz3w1lteG0jWOr0WzcY2Y9+0fex+a3eOcyP7jLRB+pUYd8TnXxF5Q0ROE5HumZsH+t4FNMm239h1LA+qOllVe6pqz7p163qg64pP06bFO+5TwsLg7bdh3TqzTt0L+AX70eHLDmiGsuaKNThTT8SH9Yvqx7Wdr+WFeS+w6WDeTPuWKkENTCjFYqA6JmfYEtcW7kO7qixRUfkfL5caNnasMdiLA8mmjzSl9vm12XzfZuIXn4ihzR6k//L8l73St8V3uOOEdcWsIhoDvOzaXvJA398D17tWSZ4MHFVVz68FrqSMGwehoTmPicBjj/nGniI57zzzHHXcOFi71itdhLQIoe0HbUn4L4EtD+RcfvXi2S8S7B/MXT/dZYP0qyCqGq2qzVW1WT5bc1/bVxXJT8OCgszxckdYmJkJW7cOnvXOjLo4hHYftSOwQSBrB68l7fCJZ7XntDyHy9tdzjNznyH2SKxX+rf4hiKdMFccWO7NnRQVnwPzgTYislNEbhaR20TkNtclPwJbgc3AFGBEKd5HlWPIELOCOirKOF8NGpgVRt9+65FqQd7h1VchPNyMJr1kZN3L6tL4vsbsen0X+2bsyzreIKwBT5/+NL9s+YVv13/rlb4t5RcR8RORsGz7J7uStPYTETsT5gOyaxhAYCD4+Zmnf+WSzIHk+PFeG0gGRATQYXoHUnalsP6G9TnqTr5yziuICPf9cp9X+rb4iIIi9jM3oD7wHvCTa789cHNR93lrq+oriwrjrbfMCqNRo3xtSSFMnWqMnDTJa11kpGbokj5LdG74XE3ccKIsSFpGmnZ6s5M2fbWpJqbmLRdiKb9Q+tWRLwEPZdvfBvwA/Ao8X5q2i7tZDcufmBjViAjVDh1UExJ8bU0B7N2rWru2at++XilplMmOCTt0DnM09vnYHMfHzx2vPIX+tKki1YGyFKZf7jyOnAr8AjR07W8E7vWMC+h9kpK2Vok+AW69FYYONeELXkprU3quvx7OOgseeQR27vRKF44AB+2/aI8EusqCHDdlQfwd/kw6fxLbj25n/N/jvdK3pdzyPyB71ssjqjoQGACc4huTiqYq6VdUFEybZp743XxzOS1xVK+emdGfN8/EuXqJRnc1ou7gumx9bCtH5h7JOj6yz0haR7S2QfqVCHecsDqqOh1wAqhqOpBR+C3lg9jYZ1m4sAWxsWW3Ks4XfWYiYjJB9OwJ110HGzaUuQlFIwLvvAPp6SaJq5eUNrhJMO0/bU/i6kQ23XkiGP+0qNO4rvN1vDjvRTYe3OiVvi3lEodLuzJ5GMA1Sg3L/5YT5FcBRESeEpFdIrLctZ3vSYOrmn6BGZ89+yxMn25q55ZLrrvOFMb04kBSRGjzbhtCmoew9qq1pO5NBU4E6W8+tJmX5pXXD8hSHNxxwhJdy7oVTCwFcNSrVnmA2NhniY19xvX6mTIRFV/0mZvgYFMaJCgILr0UEhLK3ISiad4cxoyB7783xnqJ2ufUJuqJKPZ8sIe4D06s+Xjh7BdskH7VIzB77JeqzgYQkRpAsBv3TyX/CiCvqmpX1/ajRyyl6uoXmMz6gwcbH+f3331iQuGImFkwLw8k/aubiiDph9NZe81aNMP0M6DFAC5vdznj/h5HzJEYr/RtKTvcccJGYlYythCRf4GPgLu8alUpyRQTp9MkonE6j3tdVHzRZ0E0bWpGkhs2mMeT5dLPuPde6N4d7roLDh8u8vKSEj06mpr/q8mmEZs4ttKUBWkQ1oAxp49h9pbZfLP+G6/1bSlXTAG+EJGsBAgiEgV8Drxb1M2qOhc45D3zTlDV9UsE3n8f2rY1Bb9jy+NiwDIaSIZ1DqPVpFYc+eMIMU/HZB23QfqVB3FnJkBE/IE2gAAbVNVn5aN79uypixcvLvB8bjHJiYOQkJaEhrZBxL+Aza+Qc/lvhw79wsGDs1BNzdujI5SoqCeIivJOtmUwMRwhIXlX2b/8MjzwgJnef+QRr3VfcpYuNUuhhg41Wam9ROreVBZ3W4xfmB89FvfAv7o/6c50ur/TnaMpR1k7Yi3VAqt5rX9L6RGRJVrKItuuldmPAZlf9jHgOVV1q26tiEQDM1W1o2v/KeBGIB6Tf+x+Vc13RCEiwzE1cmnatGmP2AI8C/f0qzVQfJ0qSNsOHfqVQ4d8p1+Qv4Zt3Ai9ekHLlvDPPybDfrkiPR169zbFMNetg1q1vNbV+qHr2fPhHjr/1Jna55jSzc/98xyP/v4oP17zI+e1Os9rfVtKT2H6VaQTJiLBmPQRp2IeSf4NvK2qyYXe6CUKc8KSkraycGGLItsIDe2AiAPV9CK2jKzXrpC4ElOv3hDCwjoTHNyM4OBmhIQ0w9+/NiJSqnZjY59l27bHaNZsfB6hVDUrqqdPh59+ggEDStWVd3joIXjxRfjzT+jf32vdHPn7CMvPWE7dy+qaoH0R/o79m35T+/HYqY8x7n/lMTmRJRNPOGHZ2goHUNViPazPxwmrDxzA6OJYTO3bm4pqpyANK55++eWrUwVtpQ3jrVPnEqpV65RDvwIDG+Fw+JeqXShcw2bOhIED4YYb4IMPzCxZuaKMBpIZxzNYevJSUnan0HNZT4KbBJOakUrntzqT7kxn9YjVBPu781Td4gtK64RNBxKAT1yHrgFqqupgj1rpJqWZCSvNqE7VWaDY7dz5Gjt3TiR/v9SPwMD6OJ3JpKfnfJrh5xdOcHC0S9iis8Qtc9/fv3qhNmV/rwW9t8REOPlk2L0bFi+GZs2K/da9y/Hj0KmTSRC0cqUJavMS21/YztaHt9JyYksa39UYgOu/uZ5pq6exesRqWke09lrfltJRWidMRK4FPlPVfEdTItIC40T9U0gb0WRzwtw9l5vCNMx7+qV59AvM/o4dr7Jz54QC9Ss4uAmqTlJSdpJ9MCriT1BQkyzHLDg4Ood+BQY2QKTwiBd3NOypp+Dpp2HSJBhRHrNJZg4k58yB00/3WjfHNxxnSc8lVOtUja5/dcUR4ODXLb8y4JMBjD1jLE/0e8JrfVtKR2mdsLWq2r6oY2VFUU4Y5C9k3p5Wd6fP9PSjJCfHkJS0jeTkGJKTt2VtSUnbcDoTc7Tp7187m2MWnUPs9u2bwY4dz7v1HjdvNtP60dGm9FnuLNU+59dfzTTdY495NV22OpXVl6zm0M+H6PZ3N6r3rs6eY3to80YbTm58Mj8P+bnUM5MW7+ABJ+we4CZOlCrajwnIbwn0x8xoPaKqBda1ymcmLFJdVT5E5D6gt6peVZQtJRlIlgf9cjpTSUnZka9+JSfHkJqas6CtSFA2xyy7fpn93bsns337uCLfp9MJF10Ev/xiJsxPKW8JRbIPJFes8Opz033T97H2yrU0vq8xLV9pCcDgGYOZuXEm6+5YR3TNaK/1bSk5pXXCPgHeUNUFrv3ewB2qer3HLXUDd5wwcG+E5WlK06eqkpZ2MEvQsjtnZj8G1aLzwhTU748/woUXmizVH31UDqf1b7wRPv0UliyBzp291k3a4TSWdF+COpWeS3sSEBHAxIUTuefne/jqiq+4rN1lXuvbUnI8FBPmB5yJyQsWCSQB6zCJqAstG+2qAHI6UAfYC4x27XfFPI6MAW5VN0qvFXcgWRH0CyAjIymbdsXk0q9teZ4EFER+fR85YgaSx44ZiWjYsOD7fcJvv5m0FV4eSAJsumsTu97YRYevOlD3srrsOLqDtpPaMqDFAL650i40Ko+U1glbhwnKzxSppsAGIB2TZsd7/zHzwV0nDAqPNfAW3upT1Ulq6h6OHv2HtWuvLPL6rl3/pmbNU3McGzsWnnwSJk40ixLLFQcPQrt2Zrpu/nwzqvQS8YvjWXbKMmqdVYtOP3Qigwx6TO7B4aTDrLtjnQ3SL4d4MibM1xRnIFlZ9AsgPT2e5ORtHD26gE2bbivy+pNO2kRoaMus/dWrTWhFly7myV9goEfNKz1lNJB0pjhZdtoyjm84To8lPQhtGZoVpD/rmlmc38qj6eosHqC0TlgBte4NqlqmC4iL44RBwSsHvYm3+yx8BZXgSulGWFgP6tS5mDp1LqZatU6oCpdeambF/vgDTjvNayaWjM8/NysJXnsN7rnHq13tenMXm+7YRLPxzYh6NCorSP/RUx9l/P9sNv3yRlV0wqBy6hcUpWGGwMAGREQMpE6di6lZ83/4+QUzfbpJWzFihIkRK1eU4UAyOTaZxd0WExwVTLd53cgIzLBB+uWYwvTLnQLesUBNYKBrq6mqsZmbRy31AmUtYGXRZ1TUo0RFPYHDkTO4y+EIpVmzcfTqtY7mzZ/D4QgkJmY0ixd3YeHC5mzZci+TJs2hZct0Bg0yK6vLFVddZYrkPv6415MDNby9IfWuqse2J7Zx+M/DnBZ1Gtd3uZ6X5r3EhgPlsdSApSpSGfULCtewpk1H0a7dJ9SocRr79n3OqlUX8u+/dVi9+nL69fuYRx89xJtvwtSpXjezeEREwIQJsGgRvPGGV7sKjgqm3cftOLb8GJvv2UygXyCvn/c6Ww5v4cV/X/Rq3xbP4s5M2D3ALcDXrkOXApNV9XUv25YvxZ0Jq8y4E8ORkrKHgwd/4MCB7zh8+DdUUxCpze+/X8Du3RczceI5hIYWWbWl7IiNhQ4doF8/mDXLq8Fr6QnpLOm1hPQj6fRc3pMjYUdo/UZrG6RfDqmqM2GVnaI0zOlM4fDhORw8+B0HDnxPaupuwI+YmNP4+eeLGTnyYk46qRwt+VaFCy6AuXNhzRpTENOLbH10K9uf207bj9rS4LoGXDHjCn7Y+IMN0i9nlPZx5Eqgj6omuvarAfPLOhYsEytgOSlODEd6+jEOH57NgQPfERc3E4fjEBkZQdSt+z/q1LmYiIiBBAVFutWvVx9ZTJhgMup/9hlcfbV3+nBxbPUxlp60lOq9q9P51868seQN7vn5Hr4c/CWXt7/cq31b3MdTTpiIBAGXA9FAVpIrVR1T2rbdxWpYTtzVMFUnCQlLOHDgW/bu/Y6UlDUABAV1okGDi4mIuJjw8B5uD568pmFlOJB0pjtZcdYKEhYl0OO/HhxqfIi2k9pydvOz+faqb73Wr6V4lNYJWwX0ykzO6kreukhVO3ncUjewApaXkoiJ05nOSy/9Q2zsd1xyyXcEBGwDIDy8tyuO7BJCQ9vmK2heDxjOyIC+fWHbNpOJOiLC831kY89He1h/w3qaPtaUpmObEjIuhHRnep7rGoU3YudI7xTstRSOB52wnzG1b5eQLYOpqr5c2rbdxWpYXkqiYYsWbeHVV7/jnHO+IyrqH8BJUFBjIiIucsWRnY7DkX/0vtc1LHMg+emnJs7Vi6TEpbC422ICagXQfVF3Il6PID41Ps91Vr98R6liwoAPgIUi8pSrTMcC4D0P2mcpJSUZzTkc/tx//+ls2vQqF164heDglURHjwUy2LbtMRYtas9//7Vhy5YHOXLkH1TN/6syKfLr52eyTx8+DPff7/n2c9Hg+gZEDotk+/jtHP35KBe2ujDPNYF+gVzc5mKv22LxOo1V9UpVfUFVX87cfG1UVackGtarVwsGDBjJjTf+xezZe2nbdirh4b3Ys2cqK1eew7//1mXNmqvYu/dz0tOPZt1XJhp2550mk/6995qAfS8SFBlE+8/bc3zjcTYO38hVHfKmqrP6VX5xt3Zkd0zZIoC/VXWZV60qBDuK9CwHD0LPnqYM2pIlUK8eJCfvdMWRfcuRI3NQTSMgoC5BQU1JTFyVo8acV3MYPf44jB9vkrmedZbn289GRlIGy/ouI3l7Mk3/bkqzL5vhzJZcPcQ/hK33bKVBWAOv2mHJHw/OhE0GXlfVVR4wq0RYDfMsd9wBb74JX3wBV1xh8pUdPvwbBw58x8GDP5CWtg8Rf2rWPAOHI5hDh37NUR3Aaxq2ahV0726SM5bBKoLYcbFse2Ib9V6pR9djXUlznijxbPXLt5TqcWR5wwqY51m2zDz9O/lk4+/4ZysHl55+lEOHfiY2djyJiSvzvd9rIpacbPLtZGQYQfNyqv/jm4+zpMcSQtuFMun+SXyw9gMAAhwB3NL9FiZdUN7WxFcdPJAxfxUmd4s/0ArYCqTgyulSljGuVsM8S2oqnHGGSVa/YAF0zFY4SjWD+PiFHDjwHXv2vE9a2oF82/CahmUOJGfPNslcvYg6lVUXruLw74eZ/fxsnot/Dqc6rX6VA6wTZimSjz+G66+H++6DV17Jec7dwsK9e2/xfKBraCgkJeU9Hh4O8XnjHkrL/q/3s+byNdS8rSb9a/cnPjCeWqm1WPvoWjuK9CEecMLKTb5Dq2GeZ/du6NEDwsJMhoiaNXOe95mGhYebNP/5HfeCfqUdTGNxt8U4HU6uu+Q6dlfbDX6w7cFtRIa7t+jK4nlKFBPmWkVkqSJcd53Jov/qq2ZRYnZCQprTrNn4PDl9shMa2p70dM+LSr4OGEBCguf7AupeVpfG9zbmyNtHeOvHt2gZ1xInTkIDylvBTUtxyJbX8JnseQ4zj/naPkvpaNgQvvwSYmLM0z9nrjLtRWuYULfuYIKCmnrWsPwcMPCafgVEBNB+envSd6cz8fOJ3DjnRlL8Uvhv139e6c9SegoLzJ8PICIfl5EtFh/z8ssmi/6wYbAy15PHgpIrioRQs+b/SEnZxZIl3Vi9+jISEpaXndFeoPnzzQnrEUbD5Q15+JuHiQ+IZ9SsUb42y+IZOmTfcdWT7OEjWywe5JRTzKLEH3+EMfkkHClYwwIJDGzA/v0zWLSoA3v2fIIzn9XRFYUaJ9cgalQUYfvCGLxgMNH7o7lr1l0kpib62jRLPhTmhAWKyDVAXxG5LPdWVgZayo6AAJg+HWrVgksvhUO56u3mFjGHI5To6FF07fobJ58cQ3T0Uxw+/EeFd8YcgQ5C25v32HJfSy5ecjGTVk5i5d78Y+Is5R8ReVREEoDOIhLv2hKAfcB3PjbP4iFuvx1uuAGefhp++CHv+fw17Cn69NlJhw5f43CEsH79dRXeGUvenQwCfurH498+zo5jOxj3t3cLi1tKRmFO2G3AaeQsWZS55V3Dnw8icq6IbBCRzSLySD7nm4rIHBFZJiIrRcRWHvUxDRqYaf0dO8y0fkZGzvOZImZenwhkDQioSXT06ErhjKXEpXBgxokA3tt+vo2wpDBu/ebWHCsmLRUHVX1WVcOBF1W1umsLV9UIVS2b6tgWryMCb71lFiVeey1s3Jj3mvw0TMRB3bqX0rPn0grvjKXEpbBv6r7MEsK03NWSi5ZexEvzXmLd/nW+Nc6SB3eStd6sqsXOC+aa5t8InA3sBBYBV6vq2mzXTAaWqepbItIe+FFVowtr1wa1lg3vvAO33WYW9zyTT8RMUckV09KOsGvXBHbseJWMjKPUqXMpUVFPEh7etXiGFJZt2kuLSjaM2MCe9/agqSfaX9RqEQ8NeYj3L3qfod2GeqVfS8F4IDC/e2HnVXVpSdsuLlbDvE9srAnUr1/frJgMD897TWEapurkwIHviIl5msTEFYSEtCYqahT16l2Fw+Gf7z35Uk70KzUglcGPDqZH8x78fv3vtiRbGVPaZK0fi8jdIvKla7tLRALcuO8kYLOqblWTWGoakDtbnALVXa9rALvdaNdSBgwfDjfdBOPGwbff5j1f1Aoij82M5aeeAMHB7rdRTA5+fzCHgAH03NSTvlv78tBvD3Eo6VABd1rKMS+7tknAQmAyMMX12q7dr2RERcG0abB+vdGx/PydwjTMYzNjBemXfzEcuWKSn34FpgXy9FdPMydmDtNWT/Na35bi444T9iYmcPVN19YdeMuN+xoBO7Lt73Qdy85TwLUishP4EbjLjXYtZYAITJoEvXqZ1BXr15esnVI7Y/HxRkEzt9RU6NQJ6tTx2gqjvjv7crqenrX1XNUTv2A/nt/5PIcTD/PY7495pV+L91DVM1T1DCAO6K6qPVW1B9AN2OVb6yze4Kyz4LnnTHjFiy+WrI28zlhw8Zyx3PqlavKGpafnH7TmAXLrV//0/tQ4rQY9Yntwjv85jJw9kqPJR4tuyFImuOOE9VLVG1T1D9c2FOjlof6vBqaqamPgfMysWx6bRGS4iCwWkcX79+/3UNeWoggOhq++Mj8vvbR0aW2yO2NRUaNLPjMWEGCele7aBU8+WXKDikFYxzBavNyC9D/SeTXuVSYvmWyXfFdc2mTPlq+qq4F2PrTH4kUeeMBk0X/0UZOIuqSccMaWlcwZy87995sC33feWXAKCw8ifkK7T9oh/sJjXz7GgaMHGP3naK/3a3EPd5ywDBHJynInIs3JVvi2EHYBTbLtNybviPNmYDqAqs4HgoE6uRtS1cmukWvPunXrutG1xVM0aWLKgWzaBDfeWPowhoCAmjRr9lSJnLGkpK3mRZ8+JmBt4kRTa6kMaHh7QyIujqDz1M70Ptqb22fdTobTnT8DSzljpYi8KyKnu7YpgF32WkkRgffeg3bt4OqrTR6x0rVXcmcsS78CA81Acvt2eOqp0hnkJsFNg2k9pTXO5U4mrp/I6/+9zvI9y8ukb0vhuOOEPQjMEZE/ReQv4A/AnarKi4BWItJMRAKBq4Dvc12zHfgfgIi0wzhhdqqrnHHGGfDCC/DNN2Z63xMU1xmLjX2WhQtbnCi2O368KXQ5fLiZ2vcyIkKbd9sQUCeAsd+OZW3sWt5e/LbX+7V4nKHAGuAe17bWdcxSSQkLM9qVng6XXVZw/ufiUFxnLI9+nXKK0a7XXjN148qAeoPqETksknZftqPfrn7cPut2u9q7PKCqRW5AENDZtQW5c4/rvvMxKyS3AI+7jo0BLnK9bg/8C6wAlgMDimqzR48eail7nE7Vq65SFVGtV8/8jIpS/eQTz7SfmnpYt24drXPn1tA5c9BVqy7V+PhlqqoaEzNe//orVOfMQf/6K1RjYsabm6ZNM1EWr73mGSPc4NDvh3SOzNGX+7+sNZ6toXsS9pRZ31UZYLG6qTvlfbMa5ht++MHIxamnqjZt6lkNczozdN++r/W//zrrnDnoggWtNS7uY83ISCtYvw4dMmLaq5dqenrpjXCD9GPpuqDNAv217q9a/aHq+u6Sd8uk36pOYfpla0da3Oa99+CWW3I+kgwNhcmTTU4xT5CWdoSdO19j587XyMg4Smhoe5KStqKanHVNVrHdpo/A+efDP//AunXQuLFnjCiCrY9uZftz2xlz5RgaX9mYjy79qEz6rcp4IEXFdFW9Ilsh7xyoLeBdJbj8cvj665zHPKlhJ1JbPEVi4koCAuqQnh6PSRBgyFEs/LPPTMdvvAF33FF6A9wgYVkCS09eytoOaxl15Sg23LWBiNCIMum7qmILeFs8QnS0yb+Tm6io0sda5CYt7Qhr1lzOkSN/5Hs+S8icV5kg13PPzauuXsKZ5mTZKcs4tP4QQ4YNYcbdM+gf3b9M+q6qeMAJi1TVuIIKeast4F0liIoyoVj5Hfekhqk6Wbfuevbt+4x8fP6cA8lzzjHJzNavN0Uwy4Adr+5gy8gtTLhgAnVvrcvkgZPLpN+qSonzhImhSWHXWKoO+YlXYcdLQ3r6oQIdMACn8zjbtj1GUgOF0aNN0Md3ZVN9xhHgoP3n7QnSIMZ8P4Y7Zt5BWkZamfRtKRmqGud6eRYQqHmLeFuqADt25H/c0xqWnBzDvn2fkp8DBtn0K3mbSfGflgb33ONZIwqh8T2NqXVOLe789U5+/flXFuxcUGZ9W3JSqBPmepb5YxnZYinnNG1avOOlISSkOc2ajc9TbDcTkWCaNRtvEi6OHAkdO5ol317KHZbHvhYhtH6zNW23tKXb1914bcFrZdKvpdQ0Bd4Rka0iMsOVfLqrr42ylA1lpWFF61fgCf1q0QJGjTIJzWbO9KwhBSAOod2H7QiqGcTT3zzNXd/eRXoFKs1UmXBndeRSEfFUXjBLBWbcOBM/kR0RU9rIG+QutpsTJSiosVkAEhBggjp27jSzYmVE/WvrU++aegz9ayhffPwFO44WMMy2lBtUdbSqngl0AP7GrP4umzwnFp+Tn4YFBprjnqZg/XKgmkpS0ibS012DxgcegPbtTVxYYqLnjcmHwPqBtJvajqZxTenzcR/eWuRODnaLp3HHCesNzBeRLa4i26tExObVqYIMGWJ8nago43w1aGB+zp3rvT5zC5nDEUrjxg9QvXov1q+/nrVrryQt7dCJ3GETJsDSsikDKCK0fqs1gU0CeeCLB3j4m4fLpF9LyRGRJ0TkJ2A20BJ4AJPD0FIFyK5hYMZvwcEmu743yE+/oqOfomnTx9mz50MWL+7C0aP/+iR3GEDEeRE0uqcRl/13Gd+++S1xCXFF32TxLAUtm8zcgKj8tqLu89Zml3eXL0aPNsu+v/jCu/3ExIzXOXPIWt7tdKZrTMyz+uefAfrvvw314MHZqocPq9avr9qjR5kt+VZVPTL/iP7h94c+0fEJ/WnjT2XWb1UCD6WoAJYC/wGjgdMpRsodT21Ww8oPq1erBgWpDhxo0vB4i9z6pap65Mg/On9+M50zx6FbtjymGRkpqsOGqfr5qS5b5j1jcpGRnKH/dPxHvwn9Roe9O6zM+q1KFKZfbq2OFJFTgVaq+oGI1AXCVHWbxz1CN7Ari8oXaWlw6qkmo/6qVdAod3VQD5KUtDVP0d2EhGWsWzeE48fX0ajR3TRf0gO/q28wM2J33+09Y3KxZewWdjy5g/eHvM/kqZMJ9vdegfGqSGlXR+ZqqzpwCnAqMBjYp6qneqJtd7AaVr547TW47z4zQ3bLLd7rJz/9Sk9PYPPme9mz533CwrrTruEbVOt6MTRrBvPmgZ+f9wzKRuK6RBZ0W8CyRsvoMbsHZ7Q4o0z6rSqUeHWk6+bRwMPAo65DAcAnnjPPUpEJCICPP4aUFBg6FJxeTMCcW8AAwsO70aPHEho1uptduyaypPnzJFzfB554wsSIlRHNH2uO9launn41E76YUGb9WoqHiHQEhgA3AFdiSqkVvAzXUum5+2743/+MI7Z5s/f6yU+//P3Dadv2PTp0+Jrk5FiWbD6TnZPPQ//7zzyeLCOqtatGy9da0nNrT7554BtSM1KLvsniEdyJCbsUuAhIBFDV3UC4N42yVCxat4aXXzYFct94o+z79/MLoVWrCXTu/Avp6UdYOnQxsZckofeW3UyY+Al9ZvRBAoVaD9di8x4vqrmlNDwHVAcmAu1U9QxVLZtK8JZyicMBU6eaAeW115ZJFbQ81K17Kb16raZmzTPYXPMjVr4bQcqLD8Pu3WVmQ9Nbm5I+IJ2Lvr+IyR/YvGFlhTtOWKrrmaYCiEg175pkqYjceitccAE8/DCsXesbG2rXHkCvXquoU/cStt2UzvJ+35D0w5Qy6z+4STDNJjej9a7WfHXLV7jzqN9Stqjqhar6vKrOU1Wb3M0CmGIbb70FCxfCs8/6xoagoAZ06jSLVq3e5GjLRBZNOMa+iYPKrH8Rof/n/UmqmUStR2oRsyumzPquyrjjhE0XkXeAmiJyC/AbUHb/2SwVAhF4911TLPfaayHVR7PZAQG1ad/+C9q2ep9jrRws9r+VPTHvlJlD1OaaNhy45AC9Z/Zm5vtlk/PHUjSZq7oL2nxtn8X3XHUVXHMNPP00LFrkGxtEhEaNbqdnr+WESCPWnjufdb+eRXp6fJn0H1A7gFYftSLyUCQ/DrEpQsuCIp0wVX0J+BL4CmgNPKmqr3vbMEvFo0EDmDIFli0r01XWeRARGjQaSq/qMwjbqKyPuY01awaTlnawTPof+PFA9jTYQ/rIdA7vOlwmfVqK5EJgIPCzaxvi2n7EJqS2uHjjDYiMNAPJMkrXlS+hoW3odvYGon6sw16/31n8X2eOHPm7TPpue0Fbdl+/m/Z/tefnV38ukz6rMu7MhAGswiQ2nOt6bbHkyyWXwE03wfPPm7raviT4lMvoum4YzScLBw98x6JFnTh40PuiEhQWROMPGlMtsRqzB822jyXLAXqiPNHZqvqQqq5ybY8AA3xtn6V8UKsWfPghbNwIDz3kW1scwdVoNvBrut0FHD7K8uX92br1UZxO7z9mGPzOYLY024LzMSdHNh3xen9VGXdWRw7D5NW5DBgELBCRm7xtmKXi8tprptj39ddDfNnMoheIPPsCTX+vS/dXW+PvX5tVq85j48Y7ycg47tV+Tz33VJYPXU79BfVZ8rxNyF6OEBE5JdtOX9wfjFqqAGeeaSqhvfkm/PSTj4057TRq9LmZnlfGExl4Cdu3P8fSpb1JTFzj1W6DgoJoPrU5TnXy1yV/4Uz34rL3Ko474vMg0E1Vb1TVG4AemJQVFku+hIfDRx9BbCzce6+PjalVC157jfDv19Jj0c00bnwvu3dPYsmSHiQkeNc5uvHVG1nSZglHRh0hYWXZ1LS0FMnNwJsiEiMiscCbgB1UWnIwbpwpR3vTTXDggI+NeeEF/EMjaDNyNx07fENKyi4WL+7Bzp0TUPWec3RGvzP47/b/qLG2BsseXea1fqo67jhhB4Hs/0ESXMcslgI55RR45BH44AP4+msfG3PVVTBgAH6PjqZlyAN07vwr6ekJLF16MrGx41DN8Eq39cLqUWNiDeKD4llw2QIykrzTj8V9VHWJqnYBugCdVbWrqpZNnStLhSE4GD75BA4dguHDwacRBbVrwyuvwMKF1Pkyjl69VlGr1lls3nwvK1eeS0rKLq91ffuY2/m9++8cffkoh+fa+FZvUKATJiIjRWQksBlYKCJPuRK3LgA2lpWBlorL6NHQvbsRsT17fGiIiHm2kJYG99xD7dpn0avXSurUuZxt255g2bJ+JCVt9UrXQ88ayte3fE3AlgDW3bfOK31Y3EdEgkTkGuAO4B4ReVJEbJ4wSx66dIFnnoFvvjFxYj5lyBCTUfaRRwg86KRTpx9o3fptjh79l0WLOrFv33SvdBsZHkn9F+uzu9Zull65lLTDNquLpylsJizctW0BvsWVJwz4DvBJySJLxSIw0IwmExPNtL5PR5MtWsCTT8JXX8EPP7hSWXxOu3afkpi4hsWLuxAX936eIPrSOmd+Dj/ue/A+pvedzoF3DnDgO18/26jyfAdcDKRjElBnbhZLHkaOhP79TVb9bb78rydiEpmlpMC99yIiNGx4Kz17LiMkpBVr117JunXXkZ5+NMdtnhhc3nb6bUwfPh3dp6wZtsYuNPI0BRWVLK+bLX5b8Zg40RT5fvNNHxuSkqLaoYNq06aqCQlZh5OSYnXZstN1zhx01apLNCVln6rmX3S3pNz57Z36TuQ7+metPzV5Z3Kp26tq4LkC3qs90U5pNqthFYuYGNXq1VVPOUU1Pd3Hxowda8R01qysQxkZabpt21M6Z46fzpvXVA8f/lNVPatf87bP06vOukrnMEd3TdlV6vaqGoXplzurI3uKyDcistQmN7SUhDvugAED4P77YcMGHxoSGGjqsW3fniORWXBwU7p0+Z0WLV7i4MEfWbSoE+vW3Uhs7DMAxMY+Q2xs6dJojz1nLG9e+yYpiSmsvW4tmmFHkz5inoh0Ku5NIvK+iOwTkdXZjtUWkV9FZJPrZy3PmmopD0RFmfxh//4LL7zgY2MefBDatoURI7ISmTkc/kRHj6Z7939xOAJZvvwMli3rR0zMWMAz+tWnSR/C7ghjafOlbLx7I4nr7eSxp3AnMP9T4APgckyyw8zNYnELh8ME6IeEwHXXmdAsn3HKKXDLLSaPxrITK35EHDRpcj89ephU2Xv3fojTadJYOJ3HSy1kNYNr8sC1D/Daua9xdM5Rtr+4vVRvw1JiTgWWiMgG14BylZuDyqnAubmOPQL8rqqtgN9d+5ZKyLXXwuDBJqJhmS8XCgYFmYFkbCyMGZPjVPXqvenZcznh4b04evRvVJMAz+gXwHMDnuOtq98i0S+RdVevw5li01Z4AnecsP2q+r2qblNXwkM1SQ8tFrdp2NBox6JFJtjVpzz3HEREmIKXGTlXLB48OCvfEiGeELIhnYZw/JLj/Nv5X7aN2kb8fz5OolY1OQ9ohUnQOpATmfQLRVXnAodyHb4YyAzZ/hC4xGNWWsoVIvD221C3romRT0ryoTH9+pkg25dfhpU5xw87d04kMXF1nls8oV8RoRE8fNnDjBs4jmPLj7H1Ue8sZqpquOOEjRaRd0XkahG5LHPzumWWSsegQWYmbNw4WLDAh4bUrg2vvmo8wrffzjqclLSVbdseyxpB5sbpPM62bY+VONhVRJh0wSRevOBFjtU6xtqr15KekF6itiwlI9sgMgmz2ChzKwn1VTXO9XoPUN8DJlrKKbVrw9SpsG6dSb/jU154weRAvPVWcJoZqUz9ypzBz01p9Qvgpm43of9Tfu77Mztf3cnBn222qtLijhM2FOiKmYrPfBR5oTuNi8i5rmn/zSKS76+tiFwhImtFZI2IfOam3ZYKyuuvQ+PGxhk7dsyHhlx9NZx9Njz6KOzeDUBISHOaNRuPwxGa7y0igTRrNp6QkOYl7rZDvQ4MP2M4j174KMkxyWy6Y1OJ27IUHxG5SEQ2YVZ4/wXEAKXOi+4Kvi3QmROR4SKyWEQW79+/v7TdWXzEgAFw110wcSL8+qsPDYmIMLnDFiyAyZOBovUL/IiOHlMq/XKIg7cueIsJZ07gaNRR1t+wntS93i+jVJlxxwnrpao9VfUGVR3q2orMMC0ifsAkzPR/e+BqEWmf65pWwKPAKaraAbi32O/AUqGoUcPk3NmyxQTq+4zMJd+u3GGZREU9SlTUE/kImQPVNByOwFIv0X6y/5Mc6XSEn877ib0f72Xvp3tL1Z6lWIwFTgY2qmoz4H+Y3IclYa+IRAK4fu4r6EJVnezS0Z5169YtYXeW8sDzz5vY+BtvNMlcfca115oaS488AnFmQrZg/fIHMjhyZA5paaVLutq1QVeG9x3OfeffR1p8GutvXI867UKjkuKOEzYvt/PkJicBm1V1q6qmAtMwMRTZuQWYpKqHAVS1QBGzVB7694cHHjADuB9+8KEhLVrAqFHw5Zcwc2bW4dxC5nCEEhX1JHXrXs6WLQ+wceNtOJ0lX10QFhjGhHMn8FL3l0jsnMjG2zeStNWXQSZVijRVPQg4RMShqnOAniVs63vgBtfrGzA5yCyVnJAQ+PRT2LcPbr/dh/kPMweSyclw331Zh/PTr2bNxtC27YccPfoPS5f2ISlpS6m6HnPGGJJaJPHNZd9w6OdD7Jy4s1TtVWXcccJOBpaXYDVRI2BHtv2drmPZaQ20FpF/RWSBiORefWSppIwdazJSDxtmxMxnPPAAtG9v8mgknlh2nSlk5vUTNGs2mvbtv6Bp00eJi5vMqlXnk5Z2pMTdXtr2Uga0GcDd59yNOpS116zFmWZXG5UBR0QkDJgLfCoiE3AjWauIfA7MB9qIyE4RuRl4Djjb9XjzLNe+pQrQvTs8/TRMn24cMp/RujU8/jh88UWOauO59Ssq6lEaNLieLl1+Iy1tP0uW9ObIkX9K3G2N4Bq8POBlJraaSEK/BLY+vJWEZbY+bokoKIFY5gZE5be5cd8g4N1s+9cBb+S6ZibwDRAANMM4bTXzaWs4sBhY3LRpUw+kTrOUB1atUg0KUr3oIlWn04eG/P23SYD4wAN5Th0/viXPsd2739c///TXhQvb6fHjW0vc7aaDmzRobJA+NvIxncMc3fLYFk3enazzm8/X5Dib0DU7eC5ZazXM4NMfM3t1NxDhibbd3Wyy1spBerpJ4Fq9umpsrA8NSU5WbdNGNTpaNTExx6n89CsxcaMuWNBK//wzUPfs+aTE3TqdTj1j6hnaZFQT/Tvyb13QZoEmbk60+pUPhemXOzNhWsBWFLuAJtn2G7uOZWcn8L2qpqnqNkxNylZ5DLDxFJWSjh3h2Wfh++/hvfd8aMipp5opuVdfhRUrcpzKL4g1MnIonTvPJjV1D0uX9ubo0fkl6rZl7ZY8cuojjK8+HudgJ9uf3c6G2zaQHJNM7FibBcYbqGqiqjpVNR2YBbyu5vGkxVIs/Pzgo4/M4sQbbshapFj2ZOYOi4nJkzssP/0KDW1F9+4LqF69D+vWXcu2bU+VKM5VRJh0/iT2BOxh1u2zSNqYxKqBq6x+FRN3nLBZmBmrWZiEhFtxbzXRIqCViDQTkUDgKkwMRXa+BU4HEJE6mMeTNvlIFeKee0xs6b33wubNPjTk+efNGvR8coflR61aZ9C9+3z8/KqzfPkZ7N07rUTdPnzKw7So1YL7et9HULMgDn1/CJyw54M9pOxJKVGblryIyMki8qeIfC0i3VyZ71djguttGISlRDRvDhMmwJ9/msWKPqN/fxg61OQOW7WqyMsDAmrTpcts6te/gdjYp1m37loyMpKL3W27uu24v8/9POd8Dr1RSVqXZPWrmBTphKlqJ1Xt7PrZChNwX+TQ3zXSvBP4BVgHTFfVNSIyRkQucl32C3BQRNYCc4AHPTEqbTxvHiM2bCAupex+CXzRZ2XA4TC5dwICTNqKdF+lzcrMHbZwoRlVukFoaBvXiPIk1q27mpiYZ4o9ogwJCOH1815n5bGVzO47O+u4M91pR5Oe5Q1gPPA58AcwTFUbAP2A0qUS9zBWvyoWQ4fCJZeY0KyV7kRLe4sXX4SaNXPkDisMhyOQtm0/oFmz8ezb9xkrVpxFamrx06c80e8JmtZoyoj6I1Ax+udMs/rlLu7MhOVAVZcCvd289kdVba2qLVR1nOvYk6r6veu1qupIVW3vcvJKNp2Qi12pqbwbF0f0ggVcu2YNyxMSOJiWxrH0dFKdzlKnGCioz/f27KH5woVWzIpJkybw5psm5c1zvgxtvuYaOOusHLnDiiIwsA5duvxK/frXEhMzivXrb8DpLN53f16r8wjXcJ6NepYjoUfMwTTY+s5WOjzVoZhvwlIA/qo6W1VnAHtUdQGAqq73sV15yNSSZi79WhIfz/7UVI6mp5OckYHT6le5QsSs9K5Vy2SNSC7+hJJniIgwM2Hz58OUKW7dIiJERT1K+/bTOXZsCUuXnkxiYvH+JKoFVkOPKOuD1zOzm2uVebrVL3eRohwSERmZbdcBdMcEsp7jTcMKomfPnrp48eJCr5E//yyynUARghwOghyOrNdZPx0OgkTMz1yvC7p2VExMVtt+gEOEi2rXZmSTJrSpVo2a/v74iZTuzWej8bx5XBQRwajoaCKDgjzWri+5+mqTLWL+fOhZ0qQBpWXzZhOsdtFFZumTm6gqsbHPEBPzJDVqnEbHjt8QEBDh9v0vXPICozuMpvvW7oybNg6AdEkn5rwYhs0aVuy3UdkQkSWqWuLfChFZqqrdc7/Ob9/bFKVh7uiXv4hbupSfnuW+NsjhYHQ++nVB7drc27gxrUNDqeXvT7CfX+nffDYqm4bNmgUXXmjyH770ko+MUIX//Q+WLoX166FBA7dvjY9fyKpVF+F0ptCx41fUqvU/t++dcv4U3q31LquiVvHJhE+ofbw2TpxsG7CNm3+5uSTvpFJRmH6544SNzrabjskw/ZWq+sTfL6kTJkAtf39GNmlCitNJqtNJiqr5met1qmrOn67zBd3nLjX8/KgdEEAtf39q+fsX+TrzZ7ifH5LLgZM//yRQBIcIQ+vXrxRCdvgwdO4M1aoZDQktKPGztwkKgtR8skCHh0N84fUe9+79nPXrhxIc3IROnWYSGtrGrS7/afgPnzT7hHcGvMNTXzxF/3X9AXDUctDvUL9iv4XKhgecsAxMKgoBQoDM2i4CBKtqQOmtdI+SOGGZ+vVAkyZu6VLmNYXpWea1RUdAGkIcDvd0y9+fWtle1/T3x9+R96FLZdSw22830Qy//w5nnOEjI8LCcqTbycIN/UpKimHVqgtJStpAq1Zv0bChewPAfxr+w/bk7QwdMZReW3oxdtpYBEGChX7H+iF+npuAqIiUygkrbxTXCQsUwU+EoQ0aMCoqigYe/iNXVdJVCZw7N+tYAGaa96xatTindm0UOJSWxuH0dA6np+f7Oq2Q78EPskQtU+x+zpaqOXPkemlEBOObN6eFz7yX0vP77+aJ4B13wBtv+MiIwmYs3fh7OXp0HqtXX4JqOh06fE2tWqe71e2tP9zKlKVTUJQQZwgffvkh9bbUo+sfXanRt4Z7tldSSuuElSeK44R5W78AMlTx/+uvrP0AjJ6cXasWF0REoFCgbmW+TiwiBinczy+Pc/bVgQNZ5/0APxGurluX8c2b0zA42OPvsyxITIRu3cwjyZUrTYhWmVNK/UpPP8qaNVdw+PBsmjR5iObNn0Wk6MilEbNGMHnJZDI0Az/x44XdL9D9ne40vq8xLV9pWZx3UOko7UxYa+ABIBqTWwcAVT3Tgza6jbtOWFmIl6f6VFWOO51FOmrZXy9KKDgxXoPAQFoEB9M8JIQWISE0Dw7O+lk/MDDPrFp5Y+RIEyP/009wri/WrZVSxMAU0zUjys20bv0OkZFDi7wnLiGOZhOakZKRgiCsunIVxwYeI/1IOt0XdCekRYi776DSUdWcsIqkXwCpTidH3NCt7K/XHs+/0LQAnapVy6ldISG0CA6maXAwgfnMqpUn/vsP+vaFq66CTz7xgQEe0C+nM53Nm+9m9+63qFPnMtq1+xg/v8IH99n1C2DqxVPp+0Ffdk3cRatJrWg0Ineu9qpDaZ2wFcDbwBI4MXOtqks8aaS7uOOENZ43j4vr1CkT8fJVn9lHy5kj1341a9IjLIx9aWlsTU5mS1ISO1NSciR1C3U4sgQtt5MWXYTAlVUMR3KyiQk7eNCstq5Tx2td5Y8HRAwgLe0Ia9cO5vDh32ja9FGaNXumyBHliFkjeHvx24gIZzY7k697fc2KU1YQUDeA7vO7E1CrzJ6alSuqkhNWFfQL8mqYiHBSeDhtQ0PZk5bG1qQktiYnk5xtls0BNA0OzjGwzO6k1Qwo/O+jrDTs6afhqadg2jS48kqvdZM/HtIvVWXnzgls2TKS8PAedOz4PUFBkYXek6lfdULrkJyezPyh83EOd3Jw1kE6/dCJiPPdj5OtTJTWCVuiqj28YlkJcMcJqwq4O3JNcTqJcTlkW5OS2JKcbH66BC4pm8AJ0CQoKF9xax4SQsS//5ZZDMeKFdCrFwwcaIL1y3TyzkMiBuB0prFp0x3ExU2hbt1BtG37EX5+Bc9oxSXEceoHp3LXSXdx3y/3cd/J9/FkyJOsOGsFNU6pQedfOuMILN8zAd6gKjlhVQV3NMypyp7UVLZk06zsP/en5azhWtvfv8BBZqOgIPz/+qtMNCw93eSA3rDBDCQbN/Z4FwXjQf0COHDgB9auvZqAgNp06vQDYWFdCrw2U79mDJ7B+Z+eT3hQOPOvmk/MOTEkbUqi2z/dCOsSVmwbKjqldcKeAvZhygtlrVtWVZ/Uj7cCZvDEyFVdAre1ACdtb1rBRaodmBiOa+vVY1zz5l4RshdegIcfNnnEbrihyMs9h4dFzIwoX2HLlgcJD+9Fp07fExhYv8j77vnpHib+N5GpF0/lnNXnsP669TS4sQFt3m9T7h8pexrrhFU+PKFhCenpbM01sMz8GZOcTHq2v9dAEVKz7WfGod1Uvz5PNmvmcQ3btAm6doU+fWD2bJMTsUzwsH4BJCQsY9WqgWRkHKV9+y+IiDi/yHv+3f4vZ3x4Bmc0O4Ov+3/Nij4rEBG6L+xOUMOKvQCjuJTWCduWz2FV1bz1EMoAK2BlR2JGRtYjgS1JSdy/ZUu+1wWIcF39+vSpXp2+NWrQNjQUhwechIwMk03/v/9MLtW4OGjaFMaNgyFDSt18wVSvDvnF3DkccOgQ1ChZkPz+/d+ybt0QAgLq0qnTTMLCOhZ6fVpGGud+ei7/bv+Xv278i3rv1iP26ViaPdOMqMejSmRDRcU6YZbiku50sjMlJcfA8vkdO/K9NtTh4ImoKPrWqEGv8HBCPZSOY/Jkkzu1Vi04csTH+gXwyy8wYECJmk1J2cWqVQM5dmwFLVu+RuPGdxV5z5QlUxg+czgP9HmAJ+s9ybJTlxHaJpRuc7vhV82zKU/KM1VudaTFO+QXw9EtLIzq/v4sTUjgoCvdfU1/f06uXt04ZdWrc1L16lT398+/0SJ47TW4776cx0JDjbh5Vchy8+efcPbZZvvhB1M4rgQkJCxxjSiP0aHDDGrXLjzd3sHjB+k1pRfJ6cksumURR0ccZd+n+2j3eTvqX1X0bFplwTphFk+QXcP8MRrWIjiYdGBzUhJgZsi6hoXRp0YN+rp0LCo4uESzz598kreupE/069gxOOUUiI01VUHauJc6Jzfp6cdYt24IBw9+T6NGd9Kixas4HIVr+50/3smkRZP4+NKPOW/Heay6aBURF0bQ8euOVSZ1RYmcMBE5VVX/KaTR6kBTVV3tGTPdwwqY7ygshkNV2ZSUxPz4eOYdPcr8+HhWJyainFjt1LdGjSzHrEVIiFuiFh1tdCM3UVGmXm2Zkjmsve++UhWKS07eyapVF5KYuJpWrV6nUaPbc5xPStqao/Duqr2r6PNeHzrW68gfV//BhnM3EL8ovkqlrrBOmMUTFKZhh9LSWJBNvxbGx2el3ogMDDQOmcsx6x4eTpAbzxfLlX7FxppA25o1TXmS2rVL1IxqBlu2PMTOna9Qu/b5tG8/DX//8KzzufUrLSONsz8+mwU7F/DPTf/Q4OsGbL5rM43vbUzLV6tG6oqSOmGvYsoT/YxZGbkfCAZaAmcAUcD9qrrIG0YXhBUw31HcGI6j6en8Fx/PPJewLYiPJ95VHLtuQAB9MmfLatSgZwGPAByO/MMYRNwqj+Z57r4bXn8d3n0Xbi55Juj09ATWrr2aQ4dm0bjxvbRo8RIifsTGPsu2bY/RrNl4oqIezbr+q7VfMWjGIG7seiPvnPIOy/osq1KpK6wTZvEExdGwdKeTVYmJOQaWW101iQJF6BEenqVffapXp2E+7ZU7/fr3XxPjcdppJgdQEatJC2P37nfYuPEOqlVrT6dOMwkOblqgfu1P3E+vKb1Id6azePhiEp5IYNeEXbR6oxWN7qj8qStK/DhSRGoDlwOnAJFAEqYY96zCZsm8iRWwiotTlbWZohYfz/yjR9ngegTgL0LXsLCs6f++NWrQJCiIgG/mkzE3Aj6KhsMnRM4nI0kwy54uuADmzIHffoN+Jc9mb0aUD7Bz52tERFxIWFgPdux4EafzOA5HKFFRT+QQstFzRjNm7hgmnDuBYbWHsfTkpVUmdYV1wizlgT0pKWa2LD6e+fHxLIqPz6qaEhUUdGK2v0YNOlerRsi3C4x+fRwNh07oV9Om+c+QlQkffgg33uiRjNiHDv3KmjWD8PMLpU6dy9izZ2qB+rV8z3JOef8Uujboyu9DfmfT4E1VJnWFjQmzlFsOZnsEMC8+nv/i4znuGiI2DAxkd2qqKZblFPgmEia3BKeDZ5+FRx7xkdFHjkDv3iZI/7//oFmzUjW3a9ebbNp0p2vvxN9jbiFzqpPLvriMmRtn8su1v9BjZ48qk7rCOmGW8kiq08myY8eY79KveUePsstV8izE4TApgDIw+vVrPXi9FST7c+WVJoeYz3joIXjxRXjzTVNrqRQkJq5l2bJTSU8/nON4fo7YjDUzuOLLK7i52828fcbbLO+/vEqkrrBOmKXCkO50sjLbI4DP9u07cVKB4374v9GKOhvqsGiuf9nm38nOxo3GEWvUCObNMyuSSkhs7LPExDyNakqec7mFLCElgT7v9SHuWByLbllE6KxQ1l+3nvo31KftB20rbeoK64RZKgo7kpOz9GvCrl0nTqjZwn9pTMIHjfno5WCuu85HRmZkwCWXmEeSv/xiin6XEKNfY1FNynMuP0fsiT+eYNzf43jjvDcY1mgYS3ovqfSpK6wTZqmw5FfMGIBkB9XXRvDqpXUZ3KQ24SVcfVkqfvvN1FU67zz49tsSrZhMStrKwoUtiryud+8tWcGuWw5todeUXjSq3oj5N89n//j9lT51hXXCLBWRAvULYF04w1rV5dFT6tI8xAdxnfHxpr7S7t1mxWSrVsVuoiT65VQnl0y7hB83/chv1/9Gz6M9WXbqMkJahdB1blf8w3yg5V6mMP2qvM8vLJWOQBGCRbgoIoKz/esSH32Em2PXUvfff7l41So+3rOHI4UkmPU4Z50FEyfCzJnw6KNFX58PISHNadZsPA5H/nXZREJo1mx8jtVGLWq34ItBX7B2/1qu/+Z6mj7ZlPrX1mfbE9vY+/neEtlhsVi8S6AIIQ4H19Srx/31mhJSTXnXfystFi6k2+LFjIuNZX1iYtkZVL26SbfjcJjSJEeOFLuJkuiXQxx8ctkntI5ozaDpgzgQdYD2X7Tn2IpjrLtmHZpRsSaGSkuRTpiIhIrIKBGZ4tpvJSIXet80i8WQKV7DIiPZdvLJfNepE7PPasfHyX3h7q40WdGQpQnHuH79eurNm8d5K1fyXlwcB1yxGV5lxAizvfiiSe1fAqKiHiUq6ol8haxatbY0aTIyz/GzW5zNS2e/xDfrv+H/7Z15WJXV1sB/m1FxngcQcE4UBWcbNMustLTMW7eszDIbNLPSumZ2m6xu6Zd1tcEsh7S6WZqWlVYeRxzAIQecUEFlUlBERcazvj/2gRA5cIBzQHH/nud9OGe/+91rvWefs1jvHtaasm4KbWe3pdYNtdg3Yh9nNpwplR4Gg8H55Ldfh3v0YGFQEFODWhDVrytNX+hB9fktccty45UjR2gXHk77LVv495Ej7Dx3DpfPVDVvDosXw+HDOsmlLdZjSSjKfrm7V6V+/bsvKa/pXZOl/1xKjuQw+NvBVOlXhdYftSb5p2QOjS88KHhlxZGRsDnodEW9bO9jgbdcppHBkA9fL6884zWzTZuLtpU/+IDi/YdrE/Vsa4Z815ONoZ0Z5+fH/rQ0Ru7fT+OwMG7esYNPYmNJyLh0vZXTmD5dr6l44gm9BbwUFDRkSlWldu2bOXduO3/91Y/MzKRLrhnXcxwPd3qYf6/+N8uOLKPDkg5U8a/C7rt2c+HQpeszDAZD+VKU/WraFP5YUBXPH5tx7pHO7Gzbi/+2akVDLy/eiomhU0QEbbZs4V+HDhGRmuo6h6x3b/jkE51bafz4UjVR0H65ufnQpMmTKOXBtm09OXXqj0uuaV2vNd/e8y27T+zmkaWP0PTppvg+68vx6ceJnRl7Sf1Ki4gUeQARtr/b85X9Vdx1rjq6dOkiBkMuVqvIs8+KgMj77+eWWWVbaqpMOnRI2m7aJFgsoiwWuX7bNpl+7JgcvXDB+YokJ4u0aiXSoIFIdHSpm4mOflssFiQ6+m0REUlM/FbWrKkiGzc2l3Pn9lxS/0LWBek2q5tUf7u67ErcJecPnJd1ddfJprabJDM5s9R6XG7k2qHKcBgbZsjPunUiVaqI9Owpcv68LkvMyJDPYmOl/44d4m6xCBaLBISFyXMHD8qGlBTJsVqdr8i4cdqQzppV6iYK2q8LF6Jly5YOYrG4y/HjnxR6zfsb3hdeQ95a85ZYs62y886dYnGzSNLypFLrcblRlP1yJHdkGHAzsEFEOiulWgLfiEh3VzqH9jCLWg0FsVrh/vvhu+9g4UJ44IG/z4kIkWlp/HDyJN+fPMku25qL7jVqMLRBA+5p4MRFsfv2Qc+eOojZhg1QvXRbrgtGnE5N3cyuXYOxWi/Qvv13l6Q6ik2NpevnXfHx9CH88XDcItx06IprK0/oCrMw31CZWbIE7rkH7rhDzw7m32d0KiuLZUlJ/JCUxMpTp8gUoamXF3fXr8/QBg24oXZt3J2xKzo7W68N++MP+P13uPHGUjVT0H7lD0zt6zuWli2nXZTqSER4aMlDLNy1kKX/XMoA3wHs6F25QlcUab/seWe5B3ALsAYdMX8hEA3cWNx1rjrMU6ShMC5cEOnTR8TTU+SPP+zXO3D+vLwTHS1dwsMF2xNmSHi4vBUdLXvPnRMREd8NG+SpffskLj295IqsWCHi5iYyeLBITk6p7qUwLlyIkS1bOtqeKGdccj7saJh4vekl/eb3k6ycLIn/Kl4sWCRyeKRYXfHUXM5gRsIMlZwZM/RA1OOP6xH+wjiTlSULExJkyK5dUnXNGsFikQbr18vj+/bJiuRkyczJKZv9SkkRueYakbp1RQ4dKtsN5cNqzZaDB58TiwX566/bJSvrzEXn0zLTpMtnXaTG2zVkz4k9kh6bLmF+YbLBd4OkHy/FfVxmFGW/HApRoZSqB/REpwHcJCKXLlApJ8xTpMEeKSlw/fVw9CisWwedOhVdP/rCBRYnJfH9yZNsTE0FIMjHh8i0NDwAd6V4tHFjJgcG0sSBNE15fPQRPPus3jH59tulvp+CZGeftSXP/anQ5Llzts/h0WWP8lzP5/i/W/+P6NejiX4tmsA3Awl8JdBpelQEZiTMcDXw8svwzjvwxhsweXLRdc/n5PBrcjI/JCXxc3Iy53JyqOPhwens7LLZr6goHQOxcWPYuLFMMRALEhc3i4MHR1O1aluCg3+iatW/A10fTz1O11ldqeFdgy0jt+B5wLPShK4oU5wwpdTdwCoROWN7Xxs9Evajk/V0CGPADEVx/Dj06qVjEYaF6QS6jhCbkcGSkyf5ISmJ1fm2arujUyqVyJiJwJNP6oTfX30FDz5Ymlux03QOhw69xPHj06hT51bat/8fHh5/J/F+9tdn+WjLR8wdPJeHOz3Mvof3kbggkXZft6PR/Y2cpkd5Y5www9WAiM4oNH9+ydLTpufksPL0aX44eZL5iX+HqXHDZr8aNeLV5s0dd8YsFujfXx/LlpUqBqI9Tp/+kz17hqKUJx06/EitWtfmndtwdAN95/Wlb/O+LH9gOWd+O8OuO3dRb2A9OizpgHK/MoNRlzVO2L9zHTAAEUkB/u2g4NuUUvuVUlFKKbtJZpRS9yilRClVKYysoeLw84PffoMLF3Qc1eRkx67z9fZmjJ8flpCQi8pzgAwRPomPp8vWrTgycoxSOsl3nz4wciRs2lTi+7DftDutWk2lbdvZpKT8ybZt13LhwuG881P7T+Wm5jfxxM9PsCV2y9+hKx4xoSsMhssdpbTzdeuterP18uWOXVfF3Z1B9eszr127i8qtQKYInyYk0DE8nPM5OY412Levziv5yy/w0kslu4liqFPnZjp33oSHR2127OhLYuLCvHPX+V/HzAEzWXloJRP/mEi9AfVo/V8duiLqhSin6nG54IgTVlidYscFlVLuwEzgdiAIuF8pFVRIvRrAs8BmB3QxGIqlfXtYuhSOHIFBg7RDVlo80aNh3koRn5lJp4gIPo2N5Vxx8XS8vOCHH3Rao7vugmPHSq9EITRp8hgdO/5OZmYC27b1ICVlvdbX3ZPvhn5H0xpNuft/d5OQmaBDVwRUYdfgXaRFpTlVD4PB4Fw8PWHRIr2c4t57dXraUrcFeCpFXQ8PkrKz8Q0L47moKA6mOWAHnngCxoyBadNgzpzSK1EIPj5t6dx5E7VqXcfevQ9y5MhkRHTO4Me7PM7obqOZunEqC3YuwPdpX/zG+RH7YSzHZxx3qh6XA444YRFKqf9TSrW0Hf8HbHXguu5AlIgcFpFM4FtgcCH13gT+A6Q7rLXBUAy9e+udkhs36t2Sjj4A5pIbYPHxpk053qsXp66/ntlt2+KhFE8dPIjvxo2MPXiw6AjX9erpiNRpadobdHI07Dp1brQ9Udblr79uJiFhvhbrU4+l/1xKakYqQ/43hJxaOQQvDwaBXQN3kXWqHLMKGAyGElOjhh4Fa9QIBg7Uy7RKQn77dbRnT5Kuu451ISHcXq8eM2JjabNlC7f+9RfLkpLIKWp0/4MP4JZbtEO2bl3ZbqoAnp516djxN5o0GUlMzFtERt5HTo52Dj+49QP6BPRh5LKRRMRF0HJqS+oNqkfUs1EkL3dweuNKwd6K/dwDqAa8C0TYjneAag5cNxSYne/9Q8CMAnU6Az/YXq8Gutppa1SufH9/f+dvXTBUWj76SO84euop+zuOCuK7YYM8vX+/xBeyu8hqtUpYSooM27NHvFavFiwWuXn7dvnhxAnJsrcbcvlyvWNyyBCn7pjMJTPzlGzffpNYLMihQxPFatUyvt/zvfAa8siPj4jVapXTa0/Laq/Vsq3PNsnJcL4ergSzO9JwFXLggEj9+iItWogkJDh2TVH2S0QkPj1d3jhyRHw3bBAsFvEPC5O3o6PlREZG4Q2eOiXSpo1W5PDhUt6JfaxWqxw9Ok0sFiUREV0lPT1OREROnDshAR8EiO80X4k/Gy/Z57IlvHO4rK2+VlK3pzpdD1dSlP1ymaEpzglDj8KtBgKlGCcs/2EMmKGkvPii/qZPmeLcdhMzMuTt6GjxDwsTLBbxCwuTN48ckYTCjNm0aVqJyZOdq4SNnJxM2bdvlFgsyK5dQyQ7W4fbeHXVq8JryIebPhQRkYQFCTp0xcNXVugK44QZrlY2bRKpWlWkSxeRs2ed125WTo78cOKE3LR9u2CxiNfq1fJgZKRsTEm51Dbs3y9Su7ZIhw4iqa5xgE6eXCZr1lSTsDA/SU3dJiIi2+O3i88UH7n2i2slPSv9ig1dUSYnDGgDzAJWAqtyDweu6wWsyPd+IjAx3/taQBI67lg0ejoyrjhHzBgwQ0nJyREZNkx/2+fMcX772Var/HjypNyyY4dgsYjn6tVy/549su706b+NmdUq8uijWolvvnG+EpL7RPmBWCxuEh7eWdLTj0uONUcGfzNY3F93lz8O6QBqR147IhYscuTNIy7RwxUYJ8xwNfPTT3ow/bbbRDJdkAgj8tw5GXPggNRYu1awWCQ0PFxmx8XJ+ezsvyv9/ruIu7vInXeK5C93ImfP7pCwsGayZo2PnDixREREvtv9nfAa8tjSx8RqtcrZv87K2uprJTw0XLLOZrlED2dTVifsL+Ap9BqvLrmHA9d5AIeB5oCXrZ32RdQ3I2EGl5GRIdKvn7Yhv/7qOjn7z5+XcQcPSi2bMeu4ZYt8Fhsr57KzRdLTRa6/Xuco2bzZZTokJf0sa9dWlw0bmkpq6lZJTU+V9jPbS93/1JVDpw6J1WqVyAcjxYJFjs88LhtbbJT0+Mv7qdI4YYarnc8/1/+xH3nE8aUVJSU1K0s+Pn5c2m/eLFgsUmfdOnn+4EE5mJtPKTei7IsvukYBEUlPj5eIiO5isSiJifmPWK1WmfTnJOE1ZMZmHag66ZcksbhZZOedO+XCsQuXvQ0rqxO2tbg6RVw7ADgAHAIm2creAAYVUtc4YQaXcuaMSEiISLVqIuHhrpV1LjtbZsXGSqctWwSLRWqtXSvPHjgg+44dEwkMFGnSROT4cZfJP3t2p4SFBdieKH+QqOQoqfNuHenwcQc5m3FWctJzZFvvbWJxs4hFWWT/0/tdposzME6YwSDy2mv6v/akSa6VY7VaZc3p03Lv7t3iYVv7ettff8myEyck+6mntBLz5rlMfnZ2muzefZ9YLMjevSMkK/uC3Pn1neL+urtYjlhEROT4zONiwSKbgzeLxe3ytmFF2S9HgrW+BpwAlgAZueUicqrIC12ECXRoKAvx8TqY64ULOphry5aulScibExNZWZsLItOniRLhH6enox+7TXuOHMGj9WrwcfHJbIzMxPZvfsuUlM30bz5OxzI6sxtX9/O4LaD+f7e70mLTCMiOAIElLeiZ3RPvBuXILJ2OWKCtRoMOpjrqFE6ltjHH8NTT7leZnxGBp/Hx/NZXBxxmZkEenvz5C+/8Ninn1J/6VK49triGykFIkJ09OvExLxOrVq9CWg9l+vnDeTE+RNEjIogsHYg+5/YT/yseABUFUXPI5enDStrxPwjhRSLiLQopNzlGANmKCv79sF110HdutoRa9CgfOQmZmYyOz6eT+PiOJ6RQbPERJ6MjmbkuHF03rqVQfXqlTzFSDHk5KSzf/+jnDjxDY0aPcyK0x147vcXef3G17nvf/cRPzsebBEr6vSvQ6cVxeR6qiCME2YwaLKzdejBX3/VoQjvuqt85GZZrSxNSmJmXByrU1LwzsrivrAwRt9/P0OSklxivwASE79h374ReHv7UbPZDK5bcD/+tfwJezSMY2OPkfB5go5K6wZNHm9C20/bOlW+MyiTE3a5YQyYwRmEhcHNN0PHjrBqFVSrVn6ys61WfkpO5uOwMP6oVQsvq5VMN7ey5XsrAhEhJuYtoqNfpWbN6/n0WFM+3/Edb/3wFtftug4aJsCJxgD4veBHy/daotwur/QgxgkzGP7m/Hm46SbYuRP+/NNlg1F22XP+PB/v3s38U6c4V7UqblYrys2t5CneHOTMmU3s3j0YqzWDC3Ve4vbFr3BX4F2MfWIski5QPRXO1QQ36LK9CzU61nCabGdQ1rRFKKU6KKXuVUo9nHs4V0WDoXy59lr49luIiID77tNPl+WFh5sbdzdowO+DBrF38WKeXLIEj+xsstEpkmbFx9N80yae3r+f+IyMYtsrDqUUgYGTCQr6H+fORTCi4Ra65tRnyh1TOPLIf+F/98N935Cjcjg+7Th77t1DzvkSRrc1GAzlRrVq8PPP0KwZ3HmnHt0vT9pXq8bMHj2IU4qZH36Ib1JSXoq3z5xsvwBq1epJly5bqFLFH6+kyXzZ9y4WH1nMnB5z4IGF8NNguH4tYhXWd1vP2e1nnSK3PCjWCVNK/Rv4r+3oC7wHDHKxXgaDyxk8WK+rWL5c59su90Fhpbjmvff4cOtWYocOpVlCAvB3vspPY2O5bvFip4lr2PBeQkLWYLVe4D89z9OrQQ6vtF1MahbwyDzc//ktmT6ZJC1JYvv120k/apJYGAyXKw0a6Dy5Hh4612RcXPnrUOO223j6xhuJue8+HrUlurRiy7cbF0fPH390mqwqVQIIDd1A3bq3EZCzmHfr+vJV33lsuGOurjDpbdRtv+Kd7c3267dz4vsTTpPtShwZCRsK3AwkiMgIoBM6xpfBcMXzxBPwyivwxRfw+usVoECVKrBkCReqVCHsmWdolJyMR1YWympF3NzI8PBgxvHjXChp3iU71KzZnS5dtuBZswavhGTSsx68EQlZXhmkPzabpjtWE/xTMBcOX2Brt62cCTNJv4tDKRWtlNqllNqhlDLzjIZyo0ULnWP71CkYMABSUytAibFjmT1wIF9Mncr9f/yBZ2Ym7tnZuIkQX7cuo/bv53BZEvjmw8OjBsHBS/Hze54ewbFMD4EPorM5ch6okkH6+Pepv24t1TtVJ/IfkUS/Ho1YL+8lV444YRdEZ9bMVkrVRO+UbOZatQyG8uONN2DECO2EzZpVAQo0asSgt96iztmz/DRpEqOXLiV26FB+e/FFWsTH80xUFM03beL9o0c564R508TEhVhzzqIUPNMarqsP43fCuWw4Ff8h59rPovPGzrjXcGdH3x0kzEtwwk1WevqKSEhlWbdmuHLo0kUv0N+zB4YMgczMclZAKUY/+yxrg4P58r33mPLllxy/914OP/AAjy9fzvyEBNps3szDe/ey1wn5c5Vyx9OzPkp50aEm/F8neCsSNiZDFXdIyX6funNX0mh4I6JfiybyvsjLenmFI7sjPwZeBv4JvACcA3bYRsXKHbOo1eAKsrL09OSKFfDjj3qdRXnit2gRk+fP54mff77k3NrTp3krJobfT5+mrocHz/r58YyvL3U8PUss58KFw2zefGlcjqhzMPMQPBoIwbWgR49DeKQ1Y8+9e0hZlUKz8c1o8W4LlHvFLNi/nBfmK6Wi0TEOkxypb2yYwRXMnw/Dh8MDD8BXX4GbQyu+nYPfokU88McfvD17Nh5W60Xn4tLTmXbsGJ/GxXHBauWeBg2Y5O9PSI3SLZ4vaMOsAplW+G8UNPSGhwLATUH37lEkfezJ4RcPUz2kOh2WdqBKsyplus/S4rTdkUqpQKCmiOx0km4lxhgwg6s4dw769tVPlC+9BHPmwNGj4O8PU6bAsGEuFK6KcG5sv9EtqalMiYlhWXIyNdzdGePry3N+fjTw8iqRqJiYd4iJeQurNS2vLEe0MZsbDW2b3sHkW5ehlMKaZSXquSjiZsZRd0Bdgr4JwqOmR2nusExc5k7YEeA0IMBnIlLkeKqxYQZX8c478PLLMHAg7N59edmvpMxMph8/zn9jY0nNyWFg3bpMCgigV62Sr24qaMOsoh2vTclgOal4/6ZX6NjmDQCSlycTeX8kbj5udPixA7V6lv9qqjI7YUqpjkAgOhURACLivBXDJcAYMIMrOXECgoP13/z4+OipSpcZsqKMWHY2uLvnvd157hxvx8Tw3cmTVHFz44mmTRnfrBm+JdgSXpgjli1ueCgru8/APgbz3oCv8fHUgWRjP40l6pkoqrauSodlHfBp5ZoAs/a4zJ0wXxGJVUo1BH4HnhGRtQXqjAJGAfj7+3eJiYmpAE0NlR0R6N8f/vjj4vIKtV/Hj4Ovb97blKwsZsbF8cGxYyRnZ3NT7dq8EhDAjbVro4pqpwAFbZiI3tSUlg3fxjfkX/1/p2PjjgCcjzzPrkG7yDiWQdvP29L44calus3SUtZgrV8CHYE96I0PoIO1PupULR3EOGEGV+PrW/hOo4AAiI52kdCaNeGsnW3Vt9wCCxdeElV2f1oa78TEsCAxEXelGNG4MS/5+9O8alWHROY3Ym5uPvj7T6JKFX927XucnJx0fj7py3P9V9OybisATltOs2foHgDaL2pPnZvqlP5+S8jl7ITlx5Zh5JyITLVXx9gwgysJCNAjYIWVV4j9atgQvvlGBzbLx7nsbD6Lj2fqsWMkZGZybc2aTAoI4Pa6dR12xgrasKZNnyT25AokYw/rk91p3Wom93V6AoCs5Cz2/GMPKZYUmk1oRot3ym95RVnjhPUUka4iMlxERtiOCnHADIbyID6+8PLCDJvTSE3Vj3IFj9mzYe1a6NwZNm686JK2Pj7MbdeOgz168GiTJsxJSKD15s0M37uXfQ4sgA0ImEhAwCu2168QGPgyjRs/SO9eUXj6dGVo41iWr2vHb/vmAVCnbx26bOmCV2Mv/ur/F7Efxzr/c7jCUEpVU0rVyH0N9Ad2V6xWhquZY8cKL68Q+xUZCfXq6QfJt9+GfOvFqnt48EKzZhzp0YOZrVtzPCODgbt20WXrVhafPInVgVm6gjasVatp9O75Fw18J9GzrhXPxCd57/fBZOVk4VnPk44rOtL06aYce/8YuwbvIju1HANE2sERJ2yjUirI5ZoYDJcJ/v6FlzeriD3Bjz2mw/t7ekLv3vDRR5cENGtetSqftGnD4Z49Gevnx6KTJwkKD+e+PXv469y5IpsPCJhIjx6HCAiYmFfm7e3LTT23UMd3Cm2qW8k5/giz1gwhx5pD1ZZV6byxM3Vvq8vB0Qc58PQBrFnWIiRUehoB65VSfwFbgOUi8lsF61Qqqlevnvf66NGj9O/fn3bt2hEUFER0gSGUsWPHXlTfHvPnz6dDhw4EBwcTGhrK1KlTmTRpEi+99FJenZiYGFq0aEFKSgpbtmyhd+/etG3bltDQUEaOHElaWhpz585lzJgxl7QfGBhIcHAwISEhhISEMHbsWLu6LFq0iPbt2+Pm5kZlGoks2G/e3v2BdkAQEJ13rkmTCui3zZsZc8MNOiL2pEkwaBCcPn1Rv/Xs0oV9//kPB3v04Mu2bTmbk8M9e/YQHB7OwsREIrZto2fPnoSEhNC1a1e2bNlyka4FbZhS7rRv/RZdu0Tg5lGf7p7LmPmrH3EpB3DzdKPNzDa0/rg1p347xbZe27hwyDnhM0qNvczeuQfQBzgD7Ad2AruAncVd56qjS5cujqUtNxhKyYIFIj4+lz7W+fuL7NpVQUqdOiVy551akfvuE0lNtVv1REaGvHzokNRYu1awWOTOnTtl05kzpRObukcWrmwoFgvyxa+N5OSZfSIiYs22StSEKLFgke19t0tmUmap2ncUIEIqyOY4+3CGDVuwQCQgQEQp/XfBgjI3KdWqVct73adPH1m5cqWIiJw9e1bOnz+fdy48PFwefPDBi+oXxi+//CKhoaESGxsrIiLp6ekya9YsSUtLkzZt2khkZKSIiAwePFgWLFggCQkJ4u/vL2FhYXltLFq0SBISEmTOnDkyevToS2QEBATIyZMnHbq/yMhI2bdvn/Tp00fCw8MdusYVpMely7be2yQ9Pt0p7RXst5deWmmzX2cFzufZL0/PcOnRo4L6zWoVmTFDxNNTJDBQApo0sdtv2VarfJ2QIB22bBEsFqnavbuM/eorycjJkeXLl0ufPn0c/mxycjJk6ca75I9VyA8r3GT9/ul5506tOiXr6q6TdXXXyalVpxxuszQUZb8cccKi0BHymwMBuUdx17nqME6YoTwo+E/uhRdEGjYU8fYWmT5dJCenApTKyRF55x0RNzeRa64R2bOnyOqnMjPljSNHpO66dYLFIv127JDVp0+L1WotodhsWbD+bvntD+Sn391k68Gpeefi58XLaq/VsrHlRjm351ypbssRjBP2N4U9JPj4lN0Ry/3nvGfPHrnuuusKrZOdnS033nijxMXFFfvP/IYbbpA///yz0HPLly+Xvn37yvLly+Wmm24SEZHJkyfL5MmTC63vDCcsl4p2wvY9tU8sbhbZ//R+p7RXWL8VtF/Tp2dL7do3CsSJu3s1Keojc2m/bdok0qyZBICcnDZNO2d2yLFaZcmJE1KzZ09h8mRpFhYmw//7X7n3n/+0r7wdIg4vkIW/eorFgixe11uysvRDbFpUmmxut1lWe6yW458cL3G7jlKU/XJkYf5GEenlkmG4UmAWtRoqisREPTu4fLlOEzJnjh7iL3csFvjnP3UW388/h/vvL7L6uexsPo2LY+qxYyRmZXFdzZrsS0vjH/Xr82rz5g4n2l17cAEH9o+gVfVs0r17cVOXZXh51efMxjPsvns31jQrQd8GUW9APWfc5UVcKQvzHaE4GzZuHOzYYf/6TZugsJR83t7Qs2fh14SEwPTpRetVvXp1zp07x48//sjs2bPx8vLiyJEj9OvXj3fffRd3d3c+/PBDrFYrzz33XF59e9StW5cjR45Qy04IgnvuuYe1a9eyfv162rZty5AhQxg+fDiDBw++pO7cuXOJiIhgxowZF5UHBgZSo0YN3G27h4cPH85zzz1X5H3eeOONTJ06la5dnft1OjjuIOd2FD39b82wcnbLWb3FzQ1q9KiBm5f9VUHVQ6rTenrrItt0tN9ycqwo9RzPP1+dxo3PMWcO3Hbbpe25vN+Skghs1owa6em416kDfn4MHzHCbr9FRkZyY//+nMnOJjMnh/qffsqEbt2YfuwYd9Wv73Cy8KTzsXy56nq6Vo/mvLUGPUIW07BeP7JTs4l8IJJTy0/R9OmmtJreCjdP5wZZK+vC/O1Kqa+VUvcrpYbkHk7V0GC4AmjUCH76SeebXLtWh7JYsqQCFOnbF7Zvh9BQHZlxzJjC/yvbqO7hwXh/f4707MmM1q05mpFBcnY2nyUkELBpE0/t2+dQot3erR/k9t6HWJHcDPcLG1m1IYDEkz9Sq1ctuoR3oWqrquy6YxdHpx6luIc7Q+mx11VOypVMdnY269atY+rUqYSHh3P48GHmzp1LXFwcixYt4plnnnGKnNGjR9OtWzfatm1bpnYsFgs7duxgx44dxTpgFU1GTIaOJgcgtvdOorh+Gzv2GZ57DqpWhfr14fbbYexYKGlGoTL3W/360KgRlgkT2JGSwg7guTvusFv9008/5ZMPPyQjIYF/v/ce1vff56XDh4nPymJWCZKF16/mywsDo9jBw5zNPMvunbewbc8oVLVsgpcG02xCM+I+jmPnbTvJOpVVunsrBY5EXawKZKB3/eQiQIXECTMYKhKl4KmntB80bJhOE/LYY3qUwYH1rs6jaVNYtQomToRp0yA8HBYtsr+rAKjq7s5oX18eb9IE77VrESBLhM8SEvgiMZERjRrxWjEjY741/Xn9riheW/kQLXO+o8qeu4mvfz8drvmE0HWh7Buxj8MTDnN+13nafNYG9yrudtsyFE5xI1aBgVBYmLGAAFi9uuzy/fz8CAkJoUWLFgDcddddbNq0icaNGxMVFUWrVjpkSVpaGq1atSIqKqrQdtq3b8/WrVu5qUBoglzc3NxwyxfWPbd+YSMqVwLFjVhlxGewucXmi5yw7NPZBH0bhHdjx2P82cPRfktPTyMtrRXPPhvFhx/Cn3/qCDghIbqdcuu3F1+Em2/WhrRLF5g7VxvUAsybN48PP/wQgH8/8gj/9+yzbO7cmR7btpED5IjwWXw8XyYk8GjjxkWOjLm7ufN833n8tHcgK3Y+yO3qc9aeXkGXjj/Q8r2uVGtfjf2j9rO1+1aCfwqmWrtqJbunUlDkSJhSyh1Ilr9DU5gQFQYDcM01OmLExInw5Zd6UGrz5nJWwtMTpk7VieP27tVhLFasKPYyrwL5THKdsVkJCQSHh3O+mGThXu5evH37//Dw/Zxvj7lz6uQ3bNh0DamZqwn6XxCBrweSOD+Rv/r+RUaC8570DZopU3Twzfz4+OhyZ9CtWzdSUlI4efIkAKtWrSIoKIiBAweSkJBAdHQ00dHR+Pj42HXAACZOnMiECRNISNC5RzMzM5k9e7bd+mPGjGHevHlszvdDWrx4MYmJic65sQom+s1Lk0lLjhDzpnMC95ak3w4dimL6dG0uTp2CHj20KbFay7nfbr0Vtm2Ddu3gnnvghRd0Drl8NG3alDVr1uTdU+vWreles+ZFdaxAhgifxMfTfevWYsXe2e5eRvbbw8yYAJLOH2Xrth4cOfIqDR+qS8jqEHLO5bCt5zaSf0ku/h7KSJFOmIjkANe5XAuD4QrEy0uHvlm9WifNve46nQzcCTm2S8aQIbB1qx4du/12eO01KMaRyo8n4KUUTby8SM7Oxn/jRv595AhJxWQCHh46kif6hTMlqglHUxP4669+REWNpdmkBgQtCuLcX+fY1m0bZ7fbCeJoKBXDhuno5wEBemQ2IMC50dDd3d2ZOnUqN998M8HBwYgIjz/+eInbGTBgAGPGjKFfv360b9+ezp07k5qaard+o0aN+Pbbbxk/fjxt27alXbt2rFixghq2HINz587Fz88v7zh+/DgAffv2zQtR8fDDD9ttf8mSJfj5+bFx40YGDhzIrbfeWuJ7KgupG1ORzAJOWKZwJuyMU9ovTb/17w+7duk0RxMmQL9+EBxczv02aBAPt2oFo0fD//2fDuqaL1r2559/zgsvvECnTp14+eWXmTXr0qxgnoC77YjNzOSe3bvZUoTOAK3rtWbO/bv56dzd/J5gJSbmTcK3dsc9+JheXtGyfJZXOLIw/xPAF1gE5EWAFJO2yGDIIyVF25Cvv4Zrr9UJdG2zAuVHWpqeK50/X1vXhQv1+otCUKtX46VUXqT9yQEBNPb2ZuOZM/zn6FGWJidT1c2Nx5o04QU/PwKLiMKfnJbMw0vuo7n8yVA/qFKlJe3azcftcDC7B+8mKymLa+ZfQ8OhDUt9a1fTwnyDobwR0SP6zz6rB9g/+wzuvbcCFPnmGxg5Uq/t+PZbve7DDoXZMKUUHx0/zsdxcaRkZ3Nj7dq81KwZtxYRhV9E+GDTByzdNoHxbRU1PN1o0fwtmtYdx/4RBzn5/UkaDW9E28/a4uZdugX7ZV2YXwVIBm4C7rQd9lfRGQxXIbVra59n4UKdADwkBObNuySuqmvx8dHrKmbNgjVr9PSknTlSXy8vRjZpwuEePZjZpg2NbWsoetWqxY/BwUR268Y/Gzbks7g4Wm3ezLDISLuBX+v51GPZ/Suo2WQi43ZA3NljbN9+AydqvkXIpiCqh1Qn8h+RRL9+6XSMwWCoeJTSa1t37IC2bXVs1eHDdSD8cuX++/X61rp19bDcu+9eFGU/P4XZsEZeXkxp0YKjPXsytWVLDqalcfuuXYRGRPB1YiLZhbSllOL5Xs/z2oA/GLerNmFJwuHDL7HzwI0EzvUk8LVAEuclsuOmHWQmFj07UBocSuB9OWGeIg2XOzEx8PDDegfl0KH6qbJu3XJWYutWLTw2Vg/xjx5ddJJdOxxPT2f68eN8Fh/PuZwcbqtbl5eaNaOPnWS7S/Yu4cllD/N482z6NUzHx6c9bVvOJe6FaiTOS6TB0AY0f7c5Ox74lS5L73B4QbIZCbt8mTJlCosWLbqo7B//+AeTJk2qEH1Gjx7Nhg0bLip79tlnGTFiRIXoc7lir99efHESb70Fb72lp7q/+kovtXA1F/Wb1QrHjvFsSgoj7rxTP9HWKXmu2kyrlYWJibx37Bj70tIIrFKFF/z8eLRJE3zcL900dOzMMYYuuoeaWeG8eE0VvN0VLVq8h+emoex/+ACe9T3psLQDXo292HrXzw7bsCLtl70AYrkH4AcsAU7Yjh8Av+Kus117GzrSfhTwr0LOPw9EoiPx/4kDQWBNsFbDlUB2to6r6uEh4usr8scfFaBEcrLIwIE6muc//yly9mypmzqVmSlToqOl4fr1gsUi3SMi5IcTJyS7kGCLe0/ulWtmXCO9PnKTFZaasnq1hxw+/JrETD0kFmWRNX5LxPK/BhLx0fMOy8cEazUYypUNG0SaN9exoV95RSTTtUkxLsVqFfnoIx1lv3lzka1bS91UjtUqP548Kb22bhUsFqm/fr28fuSIJBVyU+lZ6TJq2Sip/zYy+9d6YrEg27ffJCcjdktYszBZ47NGNo54TywWHLZhRdkvR6Yj5wDLgKa24ydbWXGenzswE7gdncTq/kJyUG4HuopIR+B74D0H9DEYLnvc3eFf/9KzgdWr65H18eOdF8vJIerWhWXL9La5776D7t31LspSUMfTk5cDAoju2ZNPWrcmOSuLe/bsIWjLFmbHxZGRb5j/mvrXsGXkFpo2upuhG1I5cKEpMTGvcfLG+6g963espzzhyU85W+N7Du1+01l3azAYnMi11+rpyYcf1qNi118PBw+WowJKwTPP6CmFrCyt0OzZpVrj4aYUg+vXZ0NoKGtDQuhRowb/jo4mYONGxh08yNH09Ly63h7efHbnZ7xz6+c8HZHK3GP1SDmzkb1pvfBbuQv31qdJn9MNlt3J2dYzy2zDHHHCGojIHBHJth1zgQYOXNcdiBKRwyKSCXwLXBRIREQsIpJme7sJPepmMFQaOnfWO7CfekqH8+reXa8ZKzfc3ODll2HlSkhKgm7d9ILXUlLV3Z0nfX3Z36MH/wsKorq7O48fOEDzTZt47+hRzti2htbwrsGifyzilRv/w5NbjjP7mB9nz+0mpeXb8NVD0HcV+B/jWMLbxMS846y7NRgMTqRmTZ0ZZNEi7YCFhOgkHeW6iqlnT21Ee/eGxx+HRx/Vm5BKgVKKG2rX5ueOHdnVtStDGjRgZlwcLTdv5uG9e9mdb93ryM4jWT9iA6uSfBgRnsN5mnIo4Qmypt0Dw76CLluhSkaZbZgjTliyUupBpZS77XgQvVC/OHyBY/neH7eV2eMx4FcH2jUYrih8fHSU/Z9+gvh4HZfwo4/srjd1DTffrKPsd+qkF78+84yOq1FK3JXi3oYNiejShd87dqR9tWq8dPgw/hs38q9Dh4jPyEApxYvXvcjKB1fyS+wphm1K51QmUPc0PDMT3AQ80tkXNck4YgbDZczQobBzJ/TqBaNG6ag4SUnlqECDBvDrr/Dqq3rzUa9eUESMOkfoUL0689u141CPHoxu2pQfTp4kOCKCO3buZF1KCiJCN99ubB21lRYNr2fAqv1YToC45cDIL8HXFkajjDbMESfsUeBeIAGIB4YCTl3haHPsugLv2zk/SikVoZSKyA1EZzBcadxxh47J06+f3go+YIB2ysoNX18d1Oy552DGDJ1sUKmLjwJBEItDKUW/unX5vVMnIrp04ba6dXn/2DECN21i1P79HEhL49omzfk4JI3qHnDPJlh78uInaW934ciRl7lw4bBz79dguAw5fdrCxo2BnD5tqWhVSoSfnx5QnzYNfvlFp21zIDa083B3h9df18KPH4fWrS+1X6WwYf5VqjC9dWuO9urF64GBbD57lt47dnDd9u0sTUqink99frznE+71gzf2wlt7IavAA3RZbJhdJ0wp9R/by+4iMkhEGohIQxG5S0SOOtB2LNAs33s/W1lBOf2AScAgESl0xYyIzBKRriLStUEDR2ZCDYbLk8LyT/74Yzkq4Ompd0va42zpA6t2qVGD/7Vvz/7u3Xm0SRPmJyRwzZYtPHQoDZ8WH5IcspBmjYP4dyRM3A1ncwNjZ3jTvPnbVK1a3oHVrlAaNy78n0/jxmVqtnq+vFtHjx6lf//+tGvXjqCgIKKjoy+qO3bs2Ivq22P+/Pl06NCB4OBgQkNDmTp1KpMmTeKll17KqxMTE0OLFi1ISUlhy5Yt9O7dm7Zt2xIaGsrIkSNJS0tj7ty5jBkz5pL2AwMDCQ4OzgvWOnbsWLu6TJgwgWuuuYaOHTty9913k5KSUvyH4mROn7awa9cdZGTEsGvXHU5xxMqz3yZOfInnn4ctW6BGjRhuu60FTz6Zwtq1ruu3RYsW0b59e9zc3IiIiNABqbdtA/SOvl5AeyAYSIdS27B6np68GhhITM+e/LdVK+IzM7lr9246hIfzfWoN3rz5HWq0e541yZ7cuxl25Y+xWxYbZm/FPrALUMA2e3WKOtB5KQ8DzQEv4C+gfYE6ocAhoLWj7ZqdRYbKwt69Ip07682LI0eKzJ4tEhAgopT+u2CBC4XrwajCDyeRkJEhLx86JLXXrRMsFsFiEbc/V4jbwmek+nt1ZM7K2vL7Kg+Jjn7bAXXN7sh8H4ZL+q5atWp5r/v06SMrV64UEZGzZ8/K+fPn886Fh4fLgw8+eFH9wvjll18kNDRUYmNjRUQkPT1dZs2aJWlpadKmTRuJjIwUEZHBgwfLggULJCEhQfz9/SUsLCyvjUWLFklCQoLMmTNHRo8efYmMgIAAOXnypEP3t2LFCsnKyhIRkRdffFFefPFFh65zFqdOrZI1a3zEYiHvWLPGR06dWlWmdiuq3+64Y7DceusCgQTx9PSXJ54Iy7Nf9esvkpkzndNvkZGRsm/fPunTp4+Eh4fnlWeBBIPssH33k0CynWjDsnJyZGFCgnTaskWwWMR3wwZtw36aJfzHT7ovulO+t9R1yIYVZb+KSuD9G3AaqK6USrU5ZJL7V0SKHPMTkWyl1BhgBTqbwJciskcp9YZNoWXo6cfqwCJbzKGjIjKoZG6kwXBlkpt/8rXXdEzCL774e5ouJkavvQDnpaNxmEWL4O67waMo81A8uYET/+Xvz6z4eMYfOoTVzQuaDuF844GMUnB39USmN77PSYpXEsaN09vSSsONNxZeHhJSfGZwG5GRkWRnZ3PLLbcAF4+05OTkMGHCBL7++muWLFlSZDvvvPMOU6dOpWnTpgB4e3vnpdH54IMPGD16NOPHj+fs2bMMGzaMV199leHDh9OrV6+8NoYOHeqQzo7Qv3//vNc9e/bk+++/d1rbAAcPjuPcuR2FnsvOPs3587vRWQ7/xmpN46+/+lGtWgc8PC6Ng1W9egitW093SH5591ta2ln+/HMYw4a9yg8/DOezz/7ut6SkoUyYAA8+qAffy0K7du0KLV8JdAQ62d7Xy3/y5Em9jqwMeLi58UCjRtzfsCErTp3ivWPHiM3MxFq9NXSbx1aVzQMymrvKaMPsTkeKyAQRqQ0sF5GaIlIj/19HGheRX0SkjYi0FJEptrJXbQ4YItJPRBqJSIjtMA6Y4aoiN/9kw4aX7jhKS4MKiXd5773Qpo3ePWAnSn5JqOHhwQvNml1UJm7eZClvvj/vz/Xbt5dZhsF5HDhwgNq1azNkyBBCQ0OZMGECObZcpDNmzGDQoEE0adKk2HZ2795Nly5dCj03YMAA6tSpw/Dhw/n444+LrV8U+XNHfvDBBw5d8+WXX3L77beXWFZpSUvbT0EH7G+stvNlo6L67cKF3VSvfmn9tDT44Qf7ckrTb/k5gB4RuhXoTIH4Vv7+8OSTcOBAidstiFKK2+rVY1VISL5CN3LwItMJNqzIR11brK+SrXIzGAwl5sSJwstjYrQf5MAyDuexZAlMnap3D/z73zq+xjPPgAMG3GGsWVRx9+LRJk2YHBDgvHYrA8WNWBWV+WD16jKLz87OZt26dWzfvh1/f3/uu+8+5s6dy+23386iRYtY7QQZoCOkX7hwgbZt25apHYvFQn07OVILY8qUKXh4eDDMyUPMRY1Y5a4Fs1ovDa3g5uZDcPDP1KljP0+iI1RkvyXbiZeQnAxn7OQnL2m/FSQbWA+EAz7AzUAX218eeujvFG6DBukgjdddV6qsIXZxkg0rcnekiOQAVqVUrVJLMBgMxeLvb/+cry+MGePk+GI1atgvv+suWL8ewsJ0aIt334XAQB2fp4xKeClFVTc3nvYL4EjPnhflrTRcHvj5+RESEkKLFi3w8PDgrrvuYtu2bWzfvp2oqChatWpFYGAgaWlptGrVym477du3Z+vWrXbPu7m54ebm5nB9ZzB37lx+/vlnFi5caDehsyuoU6cvwcE/4+bmc1G5sxwwqNh+q1XLfv0FC6B/f/1sZwsj6BT8qlShN1Af7YQNALaBtmGzZukn2FdegXXr4IYbdFiL778H2+hgaXG2DXMkRMU5YJdS6gul1Ee5R6klGgyGS5gyRccTy0/VqjoszqBBOkBihw7Qp4+OtVqGEF+a1NTCl3bnz9iba7QOHtRBEr/9VisxYACsWlXiiI32koYbSkijRiUrLyHdunUjJSWF3HBAq1atIigoiIEDB5KQkEB0dDTR0dH4+PgQVUSspokTJzJhwgQSEhIAyMzMZPbs2Xbrjxkzhnnz5rE5X9L5xYsXk5iY6JT7+u2333jvvfdYtmwZPgV/bOVAQUfMmQ4YVGy/eXjMw9t7c77SxVStmsiwYX8n6hgyRD/Lvf56mf0gAG6Ni2NXaChp58+TnZXFmptvJujnn/+2YY0awRtvwLFjejt6cjL84x86tMWMGXD+fIllusSG2Vuxn3sAwws7irvOVYfZHWmorCxYYH935IkTIv/5j0iLFtpbathQZOJEkSNHylHBpCSRN98UadRIKxEaqpUsh6RymN2RLif/rrmVK1dKcHCwdOjQQYYPHy4ZGRlF1rfHl19+Ke3bt5egoCBp3769TJs2Le+cxWKRgQMHXlQ/LCxMrr/+emnTpo1cc801MmrUKDl//rzMmTNHqlWrJr6+vnnHsWPHJCAgQDp06CCdOnWSTp06yUMPPWRXl5YtW4qfn19e3SeeeMKRj8XpnDq1SsLCAsq8KzKXy6Xf2rS5Xjw82ghcI9Wrj5Ivvri43+rV8xVvb1+BYwIBUrNmB2nZsvh+W7x4sfj6+oqXl5c0bNhQ+vfvn3fuq6++ytNxwoQJRd9UdrbI4sUi116r7VedOiIvvywSF1fs51FWirJfShx4mlVKVQX8RaTsqwfLSNeuXSUiIqKi1TAYKgSrVQdM/OQT+PlnPRg1YIBetnXbbTqeoctJT4eFC/W6sX37dBTHceP0aFkJAyU6ilJqq4h0dUnj5YyxYYarmago+OwznQ4pOVnvAXrySXjkEahz6QZR1xAWpqPOLlmit28++CC88AIEFUxv7RyKsl/FTkcqpe4EdqBDVqCUClFKLXOqhgaDwSHc3LSztXQpHDmid09u3aqj8bdqpZdv2Vvk7zSqVIHHHtPrw37+WQsePx6aNYMJE/Twv8FgMBRCq1bw/vs66P38+VCvHjz/PDRtCiNGQHh4OShx7bV66+aBAzByJHzzDbRvDwMHgsVSrskxHVkT9ho6GXcKgIjsAExoa4OhgvH3hzffhKNH4bvvoHlzmDhRD0w98IBej+pSW+Lm9rfRCg/XQ3IffAAtWujdSaWNdWW4opgyZUpeqIHcY8qUKRWmz+jRoy/RZ86cORWmz+VKRfdblSraTISFaVPRosVo5s8PoXv3EHx8QvD3D+HTT13cb61awcyZ2oi++SZERMBNN0HXrtoxy8oqvo0yUux0pFJqk4j0VEptF5FQW9lOEenocu0KwQzlGwz22bsXPv0U5s3TW8Pbt9dTlQ895LKZwouJjoYPP9Q7Cc6f14kyX3gBbr21TNvDzXSkwVD5SU3Vuyk//lgPtNeqBcOH6+lKOzFbnUt6ulZg2jS91MLfX4fqGTmyTAa0TNORwB6l1AOAu1KqtVLqv0BYqbUxGAwuo1077QPFxsLs2fppc8wYPdT/xBPlMDgVGKhHw44d03OjkZE611vHjnq7pxMS7hoMhspJzZrw9NOwa5fOrTtggF7/GhQEffvqEf8y7wwviipVtMO1Z49O8tu8uX6IbNYMXnxRB2x0sg1zxAl7Bp0fMwP4GjgDjCu1RIPB4HKqVdPLtiIidLLde+/V6y9CQ3Xkifnz9cLYwEA9qxgYqNfaO406deCll/TCtXnztKFKTy+8bhmShhsMhsqHUjq019df67Vj77yjB9nvu08PTr3yip5BXLjQRTbMzU0vtF29WhvQ22/Xo2P2wlqUwYbZnY5USlUBngRaoZN5fyEiTgy1VjrMUL7BUDpOn9b+0CefFJ7Nw8dHxzh0Sa5KEW3YijpfBGY60mC4usnJgRUrtP1avlyXubldHHPMpTYsOlqPjNmjCBtW2unIeUBXtAN2OzDVATUNBsNlSp06OpLEvn06V2VB0tJg7Fh93ukL+ssxOrnBYKh8uLvr6cmfftID7DVqXBr0NS1Nzx5euOACBQIDXdBo0U5YkIg8KCKfAUOB3i7RwGAwlCtKgS2o9iWcOqXXlTVsqLMXTZsGmze7eB2GoUSEfhaKel1dcoR+FlqmdqvnS1B69OhR+vfvT7t27QgKCiI6OvqiumPHjr2ovj3mz59Phw4dCA4OJjQ0lKlTpzJp0iReeumlvDoxMTG0aNGClJQUtmzZQu/evWnbti2hoaGMHDmStLQ05s6dy5gxYy5pPzAwkODg4LzdfWPHji1Wp2nTpqGUIikpqdi6ziQ0PJyn9+8nPiPDqe1W9n6bPHkyHTt2JCQkhP79+xMXF0dAAKSmLgQ6AsHAtcBfACQmQu3aOlXkSy9pp81ebsvLAntRXIFtRb2vqONyjTZtMFxJBARIITmLRJo2FZk9W2T4cJGWLf8ur1pV5MYbRV55ReS330TOnCmF0MITJemj2EtNxPxcnvr5KfF600t4jbzD600vefrnp8vUbv5I6n369JGVK1eKiMjZs2fl/PnzeefCw8PlwQcfLDby+i+//CKhoaESGxsrIiLp6ekya9YsSUtLkzZt2khkZKSIiAwePFgWLFggCQkJ4u/vL2FhYXltLFq0SBISEmTOnDkyevToS2QEBATIyZMnHb7Ho0ePSv/+/cXf379E1zkDLBbxWr1aqqxZI0/t2ydx6elOabey99uZfMbmww8/zMt00KjRBoFTNhPyi0B3AZEGDUQmTBDp1UvE0/NvExMUJDJqlMj8+TrTiNXqkPiLKaUNK8p+eRThn3VSSuUmklNAVdt7pX03MVuaDIYrlClTYNQoPXyfi48PvPeeXk/x2GO6LD4eNmzQ+bzXr4e339ZR+93coFMnuP76v4+mTYsRWqNG4QtY7SUTv0oZ99s4diTssHs+IzuDbOvFy3OzrdlsT9jOjXNvLPSakMYhTL9tukPyIyMjyc7O5pZbbgEuHmnJyclhwoQJfP311yxZsqTIdt555x2mTp1KU9sXw9vbm8cffxyADz74gNGjRzN+/HjOnj3LsGHDePXVVxk+fDi9evXKa2Po0KEO6ewozz33HO+99x6DBw92arsA4w4eZMe5c0XWybT9y/40Pp7P4uNp5OVFgLc33nbWS4ZUr8701q0dkl9Z+61mvp2H58+fz0u8Pm3atflsWE/gOD4+enN27pqwCxd0CMNc+/Xtt3rNGICv78X2KzjYgYwjLrBhdqcjRcRdRGrajhoi4pHvtXHADIYrmGHDtDEKCNDTkwEBhS9obdIEhg6F6dP1TsuUFPj9d707qU4d+OILvWPJ11fHaH34Yd3O3r2FrCtLTWXhAiEwQHBT+u/CBQWShhuKxdvDm0bVGqHQ/4wUisbVGuPl7uWU9g8cOEDt2rUZMmQIoaGhTJgwgRzb4psZM2YwaNAgmjRpUmw7u3fvpkuXLoWeGzBgAHXq1GH48OF8/PHHxdYvir59++ZNa33wwQd26y1duhRfX186depUYhnORgArEJ+Zyd78T0JloLL2G8CkSZNo1qwZCxcu5I033gAutmHwBdWr336JDataFXr3hpdfhl9+0cstduzQ+btvuEE7Zs88o3eN162rs5FMmQJr1thZV+YCG1bUSJjBYKjEDBtW8l1ENWro+Kv9+un3WVnaqOU+aa5YAV99pc/Vq6fXZeQ+aR44oGMA5f7PiYnRo3G5uhg0joxYxZ+Np8VHLUjPTqeKRxW2PrGVxtUbO0V+dnY269atY/v27fj7+3Pfffcxd+5cbr/9dhYtWsTq1audImf06NFcuHCBtm3blqkdi8VC/fr1i6yTlpbG22+/zcqVK8skqyiKG7FS+T43L6VwV4oRjRszOSCAxt7eZZZfGfstlylTpjBlyhTeeecdZsyYweuvvw5ou9G0qYWnn/6C9evXU69e0e24u+sR/E6dYPRo/aB49Ojf9mv9ev2ACTqlZJcuf9uv667T9i3/DIIzbJgjccIMBoOhUDw9oVs3eO45nYotIUE7W198AYMG6RGxF1/UqdpGjLh4+hP0+0mTKkb3K5kmNZowImQEbsqNESEjnOaAAfj5+RESEkKLFi3w8PDgrrvuYtu2bWzfvp2oqChatWpFYGAgaWlptGrVym477du3Z+vWrXbPu7m54ZZvGq64+mXh0KFDHDlyhE6dOhEYGMjx48fp3LkzCQkJLpFnDy+lqOrmxsgmTTjcowcz27RxigMGlbPfCjJs2DB++OGHvPc7d+5k5MiRLF26lHrFeWCFkDsLMGyYDn2xa5dexP/TTzqfpbs7fPSR3qTUoIFrbJhxwgwGg9NQClq3hkcfhS+/1A5ZQoJ20C6ZnrRx9Gj56lhZmNx7Mtf7X8/kPpOd2m63bt1ISUnhpG0L7apVqwgKCmLgwIEkJCQQHR1NdHQ0Pj4+REVF2W1n4sSJTJgwIc/RyczMZPbs2Xbrjxkzhnnz5rF58+a8ssWLF5OYmFjmewoODubEiRN5uvv5+bFt2zYaN3ae81ocIdWqucT5yqUy9hvAwYMH814vXbqUa665BtA7QYcMGcJXX31FmzZtnCIL9LTkHXfohB/r1+v0b+vW6YCx9lJJlsWGmelIg8HgUho1giFD9BNnTMyl5/39y1+nykCTGk1Y88gap7fr7u7O1KlTufnmm3N3c+YtzC4JAwYMIDExkX79+iEiKKV49NFH7dZv1KgR3377LePHj+fEiRO4ubnRu3dvbrvtNgDmzp3Ljz/+mFd/06ZNgF5b5G5bUd2xY0fmz59fYl3Lg+3durm0/crab//617/Yv38/bm5uBAQE8OmnnwLwxhtvkJyczNNPPw2Ah4cHrgiCXKXK31OSn37qfBtWbALvyw0TbdpguDJZuLDwHZmORLg2EfMNBkNFU1obVtYE3gaDwVBmHN2RaTAYDJcjrrBhZjrSYDCUG6XZkWm4vJkyZQqLFi26qOwf//gHkypox8Xo0aPZsGHDRWXPPvssI0aMqBB9LldMv5UOZ9swl05HKqVuAz4E3IHZIvJugfPewHygC5AM3Cci0UW1aYbyDYarj8t5OrI4O1cQY8MMhquLCpmOVEq5AzPRyb+DgPuVUkEFqj0GnBaRVsAHwH9cpY/BYDA4GwftnMFgMBSKK9eEdQeiROSwiGQC3wIFc0UMBubZXn8P3KxycxIYDAbD5Y8jds5gMBgKxZVOmC9wLN/747ayQuuISDZwBrgk4ppSapRSKkIpFZEbA8VgMBguAxyxc8aGGQyGQrkidkeKyCwR6SoiXRs0aFDR6hgMBkOJMDbMYDAUhiudsFigWb73frayQusopTyAWugF+gaDwXAl4IidMxgMhkJxpRMWDrRWSjVXSnkB/wSWFaizDBhuez0UWCVXWvRYg8FwNeOInTMYDIZCcVmcMBHJVkqNAVagt25/KSJ7lFJvABEisgz4AvhKKRUFnEIbMIPBYLgisGfnKlgtg8FwhXDFpS1SSp0EYtBTl2cKnC6sLBTYXg6qOVNmYffhDHlFtVvSc4XJLKneJcWR+3SGDvnbcJbM4urYk2nvOke//47gzN+IozoUJbOwNgJEpFIspiqhDbsS7Re4xoaV5DfkSHlBmZeD/XKWHrltONNmOvo/wpHP1Zn2qzCZpaUkOtiTWTL7JSJX5AHMcrBMKkC3Msks7D6cIa+odkt6rjCZJdXbFZ+rM3TI34azZBZXx55Me9c5+v131udams+utDJd/T26XA5H+vBKtF+l6UNn/M5K8lspTOblYL+cpUduG860mY7+j3Dkc3Wm/SrJZ+vMz96ezJLexxWxO9IOPzlYdiXiqvsoqt3SnitNPVfiDB1K2oYj9YurY+98Scqv1s//SuVy7UNn4Ir7cPZvqLT1XE15/4ac8fmU9Nzl+t0vd/t1xU1HlhSllIhIuQaALW+ZV8M9GpmVR15FybwSuVr65mqQeTXco5FZcq7kkTBHOXcVyLwa7tHIrDzyKkrmlcjV0jdXg8yr4R6NzBJS6UfCDAaDwWAwGC5HroaRMIPBYDAYDIbLjkrrhCmlrEopUUqVy1CfUuqNXHm2I6085Npk1yjne71Q4F6DXCCj0P5TSqXlk5vjRHlBBe4p21aev8yqlGriLJm29u8uICMs37l0W9mIMsq45LNUSuUUkHu3rbxJ/vpKqYxSyCv0t6CUyi5QfijfNUtd/Z26kjD2y6XyKp39srVd7jasMtovWzvlZsMqrROGjt+xshzlpQIrbQv1+gFVlVLflJPscssIrJSaBFQBQvItSgwr4pLSckn/KaV2AVWBNjbZY5woby/Q29ZuU8BdKbUeOIz+nbgBCjjoRJkAPwBnbXIDgfdBGwHA20kyCvstRAH1bXJzgEW28p3oZQoK6AZ4KaXeL6G8on4LGSKibEdLAKVUPWAQ8LPtmoe5OCn21YixXy6gEtsvqBgbVhntF5SnDXNGbI3L9UB/IaSCZAuwtxzkTLbJ2l0e9wpMssn7J1DP9np/efSfTVZkOdxjV5ustQXKLwDZTpRzh02OsvP9+cn2d4SzP8sC52Jzz6H/IYqtbx/K7esyyhb0P4hsIL2Q84cq6nd6OR/GfrlEXqW3XzZZLrdhV4v9ync/LrFhlXkkrMLI53mPKwdxbwB/ANZykIWITEH/kL8BkmxlbctDto02+YZ7Lc5sOHdaBJ0PMEdEeuc71wT9BL3DiSKfsv3NHVq3KqV6KaWOAlYRudOJsoqiKTptGEBH298kYD5wTkS+LW3DhfwWvPPd69B88vNPm6SXVp6h7Bj75VJcZr+g3G1Ypbdf4HobZpwwJ6OUugEYD8SJyAoXy4pGe9+3uFJOAZlD0UPqo/j7i5dcXvL5e1h9DXCjUsppsWFEJHdYfQh6KP/7fKdj0Yalq7PkoQ0iwHqbXAHWA82AwU6UYxelVKbtZX3b3x9tf5sCTwPVlVIflrLtgr+FcUBN9H1bge9yq9r+PoqeQvBWSpn8ixWAsV8ux2X2C8rdhlVq+2Vr3/U2zNnDoJfTQTkP5wNN0F/ES4YrXSQv2ybvosPFMhPQP+Tc96fyv3dl/9nub1eB94+7SHZmbj/aXgvg7WQZ4wrcX0Rh/Wk7xjnzs7SVpdja7pqvzAqcKvA+sRTyivwt5NcHPYWQ/zuVae+6q+kw9sslMq8K+2Vr36U2rDLbL9u15WLDzEiYk7A90cShO6JKcfWdgYh4iG2BILDLVubqqMEH0Lfb1XbPtYHymj46D7RDK/CFrWy2MxpWSj2eb4dNJ8ATSFBKpdpeh4hIqXba2ENEptvkzbUVhaC/PypfvwI8mlvXWSilotCJZkeJSES+U1noPkUp1Qv9hLephG0X+ltQSo3PVy3/ouSn+Ps7VQ/9eceXRKahbBj7VS64zH7Z2ixXG1ZZ7Zft2vKzYa7ywiv64FJP/IyL5YUXIvNQOd7vTsrpqZlLn2Bbl0f/AQEFynY7Ud7SAm1n2tEj08n3ubxA+0ML+RzKtLDVzmdZsMxqq3tDgfISj4rY+y0UUjY+3zXJ+cpzyuN7fDkfxn65VFals182meVuwyqj/bK1U242zETMNxgMBoPBYKgAzHSkwWAwGAwGQwVgnDCDwWAwGAyGCsA4YQaDwWAwGAwVgHHCDAaDwWAwGCoA44QZDAaDwWAwVADGCTM4FVvKhmn53o9XSr3mpLZfU0rFKqV2KKUilVL3O6Ndg8FgyMXYMEN5Ypwwg7PJAIYopeoXW7N0fCAiIei0GJ8ppTxdJMdgMFydGBtmKDeME2ZwNtnALOC5gieUUnPzJTxFKXXO9vdGpdQapdRSpdRhpdS7SqlhSqktSqldSqmWBdsSkYNAGlDH1sYEpVS4UmqnUup1W1mgUmqfUmqhUmqvUup7pZSP7dy7tifRnUqpqa74IAwGwxWJsWGGcsM4YQZXMBMYppSqVYJrOgFPotN6PAS0EZHu6LQezxSsrJTqDBwUkRNKqf5Aa6A7OnVGF6VUb1vVtsDHItIOSAWetqWVuBtoLyIdgbdKcY8Gg6HyYmyYoVwwTpjB6YhIKjAfGFuCy8JFJF50brNDwEpb+S4gMF+955TOTr8ZmGIr6287tgPbgGvQBg3gmIhssL1eAFyPTnuRDnyhlBqCfho1GAwGwNgwQ/lhnDCDq5gOPAZUy1eWje07p5RyA7zyncufWNaa770V8Mh37gMRaQ/cgzZAVdBJWt8RkRDb0UpEchPkFszLJSKSjX7i/B64A/itdLdoMBgqMdMxNszgYowTZnAJInIK+A5txHKJBrrYXg9CZ5ovbfvLgAhgOLACeFQpVR1AKeWrlGpoq+qvlOple/0AsN5Wr5aI/IJe99GptHoYDIbKibFhhvLAo/gqBkOpmQaMyff+c2CpUuov9JPb+TK2/wbwNXoNRjtgo1IK4BzwIJAD7AdGK6W+BCKBT4BaNj1yn0CfL6MeBoOhcmJsmMGlKJGCI50GQ+VAKRUI/CwiHSpaF4PBYCgpxoZVfsx0pMFgMBgMBkMFYEbCDAaDwWAwGCoAMxJmMBgMBoPBUAEYJ8xgMBgMBoOhAjBOmMFgMBgMBkMFYJwwg8FgMBgMhgrAOGEGg8FgMBgMFYBxwgwGg8FgMBgqgP8H2+rjl1d6U0gAAAAASUVORK5CYII=\n",
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\n",
       "text/plain": [
        "<Figure size 720x360 with 2 Axes>"
       ]
@@ -296,51 +256,35 @@
    ],
    "source": [
     "for bm in unique_bms:\n",
-    "    flag = \"insert_cgsize\" in bm\n",
+    "    flag = \"nvbench_static_multimap_find_all\" == bm\n",
+    "    \n",
     "    if flag:\n",
     "        tmpdf = perfdf[perfdf[\"Benchmark\"] == bm]\n",
     "        \n",
-    "        # Plot INT32 performance\n",
-    "        tmp_int32df = tmpdf[tmpdf[\"Label\"].str.contains('I32')]\n",
-    "        \n",
-    "        randomdf = tmp_int32df[tmp_int32df[\"Distribution\"] == \"RANDOM\"].reset_index(drop=True)\n",
-    "        unique_labels = randomdf[\"Label\"].unique()\n",
-    "        plot_perf(bm + \"_INT32_RANDOM\", randomdf, \"NumReps\", unique_labels, flag, True)\n",
-    "        \n",
-    "        cycledf = tmp_int32df[tmp_int32df[\"Distribution\"] == \"CYCLE\"].reset_index(drop=True)\n",
-    "        unique_labels = cycledf[\"Label\"].unique()\n",
-    "        plot_perf(bm + \"_INT32_CYCLE\", cycledf, \"NumReps\", unique_labels, flag, True)\n",
-    "\n",
-    "        \n",
-    "        # Plot INT64 Performance\n",
-    "        tmp_int64df = tmpdf[tmpdf[\"Label\"].str.contains('I64')]\n",
-    "        \n",
-    "        randomdf = tmp_int64df[tmp_int64df[\"Distribution\"] == \"RANDOM\"].reset_index(drop=True)\n",
+    "        randomdf = tmpdf[tmpdf[\"Distribution\"] == \"RANDOM\"].reset_index(drop=True)\n",
     "        unique_labels = randomdf[\"Label\"].unique()\n",
-    "        plot_perf(bm + \"_INT64_RANDOM\", randomdf, \"NumReps\", unique_labels, flag, True)\n",
+    "        plot_perf(bm + \"_RANDOM\", randomdf, \"NumReps\", unique_labels, flag)\n",
     "        \n",
-    "        cycledf = tmp_int64df[tmp_int64df[\"Distribution\"] == \"CYCLE\"].reset_index(drop=True)\n",
+    "        cycledf = tmpdf[tmpdf[\"Distribution\"] == \"CYCLE\"].reset_index(drop=True)\n",
     "        unique_labels = cycledf[\"Label\"].unique()\n",
-    "        plot_perf(bm + \"_INT64_CYCLE\", cycledf, \"NumReps\", unique_labels, flag, True)"
+    "        plot_perf(bm + \"_CYCLE\", cycledf, \"NumReps\", unique_labels, flag)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "wrong-skiing",
    "metadata": {},
    "source": [
-    "### find_all"
+    "### retrieve"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "peaceful-bernard",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 720x360 with 2 Axes>"
       ]
@@ -352,7 +296,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 720x360 with 2 Axes>"
       ]
@@ -365,7 +309,7 @@
    ],
    "source": [
     "for bm in unique_bms:\n",
-    "    flag = \"find_all\" in bm\n",
+    "    flag = \"retrieve\" in bm\n",
     "    \n",
     "    if flag:\n",
     "        tmpdf = perfdf[perfdf[\"Benchmark\"] == bm]\n",
@@ -381,21 +325,22 @@
   },
   {
    "cell_type": "markdown",
-   "id": "musical-merchandise",
    "metadata": {},
    "source": [
-    "### retrieve"
+    "### count by varying matching rate\n",
+    "<ul>\n",
+    "<li>Fixed num_reps: 8</li>\n",
+    "</ul>"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "bound-pulse",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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//oDTT/dVg84VJvHMuaoJrARGiEhjYCrQX1U3FORFu3fffWXg4sV2FnjSSfDFF5bT4Jxzyejoo+2S0WGHwebNdsLonCscch25EpG9RaSjiFwmIr1E5DARiWbEqzjQDHhSVZsCG4Drsnj93iIyRUSmrFy5Ms8/QPXq8PHHdmA64QRYtCjPL+Gcc6Fp2RKOOsq+f/BB+OyzcONxzhVctp0kETlORD4E3gVOAfYD6gODgFkicquI7JHDay8Flqrqt8H117HO1m5UdZiqtlDVFunp6fn6IerUgY8+gg0boG1bWLYsXy/jnHOh2bTJVg+efDK8/XbY0TjnCiKnEahTgYtVtaWq9lbVQao6UFXbY8np04ETs3uyqq4AfhORyERdW2B2rALPLLIS588/4cQT4a+/4tWSc64oEJEBIvKTiPwoIqNFpHQ82ytTxlIbGjeGTp1gxIh4tuaci6dsO1eqerWqZllBSlW3qepbqvpGLq/fFxglIjOBJsAd+Y40Cq1awbhxVvm4XTtLGHXOubwSkQOAfkALVW0IFAPOiXe7lSvDJ59YikOvXnDvvfFu0TkXD9kmtIvIlTk9UVUfyO3FVXUG0CLvYeVfmzbwxhtw5pm2AueDD6BcuURG4JwrJIoDZURkK1AWSEjCQfnydpLYsydUrJiIFp1zsZbTtGCF4NICuAQ4ILj0IYvcqWRy6qnw8svw1Vc2vL55c9gROedSiar+DtwHLAGWA2tUdUKi2i9Z0o5hkb1TZ8ywKu/OudSQ07Tgrap6K1AVaKaqV6nqVUBzIOlrop99thXrmzABunXzA5NzLnoisidwJlZSZn+gnIicm8XjCrTaOecY7OvSpXDkkVYba9OmmDbhnIuTaEoq7ANsyXB9S3Bb0jv/fNssdexYq+K+Y0fYETnnUsQJwEJVXamqW4E3gSMzPygWq51zU7Uq3HOPFR/1XFLnUkM0RURfAL4TkbHB9Q7A83GLKMb69oV16+DGGy2X4YknvFifcy5XS4DDRaQssAlb7TwlrGAuv9yS3Xv0sLzSDz6w3Smcc8kp15ErVR0K9AJWBZcLVDWuq/5i7YYb4Lrr4Kmn4NprfZsJ51zOgvp8rwPTgFnYsXJYmDF16wbjx9t+hE88EWYkzrncRLv9zQwsqbM4gIhUy65MQ7K64w4bwbr3XthjDxg0KOyInHPJTFUHA4PDjiOjk0+GKVOscDJYqkNa3HaIdc7lVzTb3/QF/gA+AsZjFdvHxzmumBOx/KsePeCmm+Dhh8OOyDnn8q5ePShWDH7/HZo1g8mTw47IOZdZNCNX/YFDVPXveAcTb2lptoJwwwa44gqoUMEK9TnnXKrZvt1WD554Irz+Opx2WtgROeciohlQ/g0oNOtTiheHUaNs1c1FF8Grr4YdkXPO5V21ajZq1bChFU1+8cWwI3LORUQzcrUA+FRE3gV2luOMpkJ7sipVyqq4n3IKnHuuVXA//fSwo3LOubxJT4eJE6FjR0t5KFkSunYNOyrnXDQjV0uwfKuS7KraXiGeQSVC2bK2xUTTplacb+LEsCNyzrm8q1AB3n3XVkKfdFLY0TjnIIqRq6BKOyJSPri+Pt5BJcoee8D771vdmPbt4eOP4fDDw47KOefyplQpuOsu+/7ff61UQ//+lvjunEu8aFYLNhSR6cBPwE8iMlVEGsQ/tMSoXBk++gj228+mCWfMCDsi55zLv7Fj4aqroEsX62g55xIvmmnBYcCVqlpdVasDVwHPxDesxNp3Xxu1qlDBhtXnzAk7Iuecy59u3eChh+DNN20T+8mT4bLL4Kyz7Pa1a8OO0LnCL5rOVTlVnRS5oqqfAuXiFlFIqle3DlZaGpxwAixaFHZEzjmXP/372+rBzz+H446DihVtJOvLL20T6FWrwo7QucItms7VAhG5SURqBJdB2ArCQqdOHZgwwepgtW0Ly5aFHZFzzuVP166WV1q5Mpx3nl0fMwaaN7eCys65+Immc9ULSMd2hX8DqBLcVig1amSbov75pxXn++uvsCNyzrm8++knS3lYvNiquoNNCZ5/vuWZOufiJ5qNm1epaj9VbaaqzVX1ClUt1IPKhx1mZRoWLLBio2sKTQlV51xRUbEi/P33rr0HhwyBli1t4+dKlUINzblCL5rVgh+JSKUM1/cUkQ/jGlUSaNPGCo3OnGmdrWrV7CBVo4ZVeHfOuWRWsyY0aGCb1u/YYakOS5bY1l/nnBN2dM4VbtFMC1ZR1dWRK8Go1d5xiyiJnHoqXHKJnen99huo2hB7797ewXLOJb8XX4T33oNDDrGRq2LFYPNmeOEF2LIl7OicK7yi6VztEJFqkSsiUh3Q+IWUXN5++7+3bdwIN96Y+Ficcy4vDjgAvvkGXnkF+vWDhQtt8/qPPvJN652Lp2j2FrwRmCwinwECHA30jmtUSWTJkrzd7pxzyUTEVghGXHCB5WIdeGB4MTlX2EWz/c0HItIMiGwMc4WqFpk1dNWq2VRgVrc751wqGjhw1/eLF1udP+dc7EST0C5AO6CZqo4HyorIYXGPLEkMHWqbPGc2YEDiY3HOuViaPBlq14bnngs7EucKl2hyrp4AjgC6BdfXAY/HLaIk0707DBtmZ3YisP/+UKYMvPSS79vlnEtthx1mFdwvvhjeeSfsaJwrPKLpXLVS1cuAf2HnasGScY0qyXTvbtvh7NgBv/9uyaFTpsCll9oKQuecS0UlS1rJmebNrYL75MlhR+Rc4RBN52qriBQjWCEoIunAjrhGleTat4ebboIRI2xUyznnUlX58vDuu5ZHesYZvljHuViIpnP1CDAW2FtEhgKTgTviGlUKGDwYTjkF+vaFr78OOxrnnMu/9HT48EO4+mpfRehcLESzWnCUiEwF2mKlGDqo6s9xjyzJFStmhURbtIDOnWHqVNvHyznnUlGNGnDDDfb9vHm2RU6VKmFG5Fzqima14MHAQlV9HPgRODHjdjhF2Z57wtixsGoVdOkCW7eGHZFzzhXM5s1wwglw2mmwfn3Y0TiXmqKZFnwD2C4itYCngQOBl+MaVQpp1AiefRa++MKG1J1zLpWVKgWPPGKLdjp39pNG5/Ijqu1vVHUb0Al4TFWvBvaLb1ip5X//s81QH37YSjQ451wqa9/eFut8+KFVdN9RpJcwOZd30a4W7Ab0AMYHt5WIX0ip6Z574NhjbVPnGTPCjsY55wrmwgutiPKoUfDYY2FH41xqiaZzdQFWRHSoqi4UkZrAi/ENK/WUKAGvvgp77QWdOsE//4QdkXPOFcz118Mzz1hHyzkXvVw7V6o6W1X7qero4PpCVb07/qGlnn32sYJ8v/9uU4Xbt4cdkXPO5Z8IXHQRlCsH69bB+++HHZFzqSGakSuXB61a2RD6hx9aLSznXOoRkUNEZEaGy1oRuSLsuMJ0001WZPTdd8OOxLnk552rOLj4YjvbGzoU3nor7Gicc3mlqnNUtYmqNgGaAxuxYspF1m23QdOmcPbZ8NVXYUfjXHLLsXMlIsVE5L5EBVOYPPootGwJPXrAnDlhR+OcK4C2wHxVXRx2IGGqUMFGrapWhdNPh9mzw47IueSVY+dKVbcDRyUolkKldGnLvypdGjp2tHwF51xKOgcYHXYQyWDvvS3loVQp6NXLN653Lju5bn8DTBeRd4AxwIbIjar6ZtyiKiQOPNBWEJ54Ipx/Prz+uiWIOudSg4iUBNoD12dzf2+gN0C1atUSGFl4ataECRNsJMuPZ85lLZqcq9LA38DxwBnB5fR4BpWbNWvgr7/CjCB6xx1nNbDefNO+OudSyinANFX9I6s7VXWYqrZQ1Rbp6ekJDi08hx5qexHu2GHFkzdsyPUpzhUp0WzcfEEiAonG8uVwySXwySe2cXK9erYyr3nzsCPL2YAB8N13tilqs2Y2kuWcSwnd8CnBbH3/PVx5pY1kvfWW1ftzzkW3cXMdEflERH4MrjcSkUHxD213O3bAKadAgwbwxx/w999w6aVw6ql2PZmJwPDhUL8+nHMOLFoUdkTOudyISDngRMBTILLRqhU8+SS8956tkPYcLOdMNNOCz2D5BlsBVHUmluCZUJ9+al9vvx3KlrXNRM87z/bAGjky0dHkXblyMHasFRbt1Ak2bQo7IudcTlR1g6pWVtU1YceSzHr3hiFD4IUX4Lrrwo7GueQQTeeqrKp+l+m2bfEIJieLFkHjxjYKtHSpjQKNHGm3LU6RBdK1atk+XdOnQ58+fpbnnCscBg2Cyy6z/Kt588KOxrnwRdO5+ktEDgYUQEQ6A8vjGlUWmjeHiRNhyxbYYw84+GDbrf2xx6ywXao47TS45RY7y3viibCjcc65ghOxjtV338EBB1h+abVqkJ5uK6V/+y3sCJ1LrGg6V5cBTwN1ReR34AqgTzyDykrjxnDEEXDmmVa87t57oXZtK9A5ZUpq7eN3001WhO+KK+DLL8OOxjnnCq5YMWjUyCq4f/QR3HijjdJXrQrHHgtr14YdoXOJE83GzQtU9QQgHairqkeFVan4pZegTRvbXqZzZ0tmv/xyGDYM7rgjjIjyJy0NXnzRljJ37myrIJ1zLtVNnQqzZtnJ7pVX2ib2t98OLVrYaL1zRUWupRhEpDIwGKvUriIyGRiiqn/HO7jMSpaEa6+1S0YtW9qIViqpVMkS3Fu1sjO9iRPt53POuVQ1a5aNUt17L7RubWkQ335r9f5mzAg7OucSJ5ppwVeAlcBZQOfg+1fjGVRe9egBFSvaCrxevWDJkrAjik7DhjBihE0NXnll2NE451zB1K5tta/23hs++MAW7Zx2GkyebPc5V1RE07naT1VvU9WFweV2YJ94B5Yfc+bYfn5HHmlnUKmgSxcYOBAefxyefz7saJxzLv+OPBL22svSNSpVsu2/5s+H8eMtsd25oiKaztUEETlHRNKCSxfgw2heXEQWicgsEZkhIlMKFmrumjSxMyRVOPpo+OyzeLcYG3feacPmffrAtGlhR+Occ/kjAuPGwcaNUL26jVodfjh89RVUqRJ2dM4ljmguxZZEZB1QDoisxyvGrg2cVVX3yOG5i4AWqhrVToAtWrTQKVMK3gdbsgTatbMzpjFjrNBoslu50spNpKVZUmjlymFH5Fz8ichUVW0RdhyxEKvjV2GxbZsltpcqZde//damDC+/PNy4nIul7I5h0awWrKCqaapaIrikBbdVyKljFaZq1WwE69RToW7dsKOJTnq6TWmuWAHduqVWaQnnnMusePFdHSuAp5+Gvn3h9dfDi8m5RIlmWrAgFJtWnCoivbN6gIj0FpEpIjJl5cqVMWt4r71sNV6dOjZN+Prrtj9hMmvZ0gqLfvQRdOxopRrS0uzrqFFhR+ecc/n3xBOWk3XeeVZs1LnCLN6dq6NUtRlwCnCZiByT+QGqOkxVW6hqi/T09LgE8d57Vu6gRw+r8J7MevWC44+3vIXFi61juHix7d/lHSznXKoqXRreegv2289SNVJl2zLn8iOunStV/T34+icwFjgsnu1l59RTYehQ65ycdhqsWxdGFNHLam+ujRut4rFzzqWq9HR4913491/bLse5wirXzpWIHCwipYLv24hIPxGpFMXzyolIhcj3wEnAjwWMN19EbK+rESNg0iQrcrdiRRiRRCe7fbhSpX6Xc85lp149+OYbKzTqXGEVzcjVG8B2EakFDAMOBF6O4nn7AJNF5AfgO+BdVf0g35HGwPnnW72VhQvhx1C6edGpVi1vtzvnXCqpW9f2Ily2zDpZuSxady7l5Lr9DbBDVbeJSEfgUVV9VESm5/YkVV0ANC5whDHWrp11ripVsut//ZV89VeGDrUcq40bd91WsqTd7pxzhcXIkZbuUKKEbWTvXGERzcjVVhHpBvQExge3lYhfSPEX6ViNHw81a9rXZNK9u21GXb26XS9VylYNtmwZblzOORdL110HnTrZ9l/jxoUdjXOxE03n6gLgCGCoqi4UkZrAi/ENKzFatrTh6TPPhGefDTua3XXvDosW2XD5vHlQrhx07WqJoM45VxikpcGLL1oB5W7dYHqucyLOpYZoiojOVtV+qjo6uL5QVe+Of2jxt88+luB+4olw8cUwZEhyzv1XrWr7Ds6YAVddFXY0zjkXO2XLwjvvWG3Ca64JOxrnYiOa1YKtReQjEZkrIgtEZKGILEhEcIlQvrwNR/foAYMHw4QJYUeUtdNOs47VE094hWPnXOGy335WPHnMmLAjcS42opkWHA48ABwFtARaBF8LjRIlLLFy/Hg46aSwo8neHXdAq1Zw4YWwoNB0b51zDg45xPJhN22CBx7wLcBcaoumc7VGVd9X1T9V9e/IJe6RJZiIjQ6JwMyZcMop8HeS/ZQlS8Irr1iM55yT/NXmnXMur8aNs1H6gQPDjsS5/IumczVJRO4VkSNEpFnkEvfIQrR4seVitW5tSeVr1ti2DZHKwmGqUQOee852l7/++nBjcc65WOvSBfr3h4cesjQI51JRNHWuWgVfW2S4TYHjYx9OcjjjDJv/b98emjSx4enWrW24ulcvePVVaNMmvPg6dYLLL7eh8zZtLF7nnCss7r8f5s+Hfv3goIOsPqFzqUQ0iZbHtWjRQqdMmRJ2GDu99551XMqUgS+/hMaN4ZNPbEpu4UJLhg/Lv//aDvOLFtkqQq/e7lKRiExV1Ra5PzL5JdvxK9WtXw9HHw2rVsHcuZYW4Vyyye4YFs1qwYoi8oCITAku94tIxfiEmVy+/96Sx3v2tP2wANq2hcMPt6XDYSpdGl57DbZts/owW7eGG49zzsVSZCX3hx96x8qlnmhyrp4D1gFdgstaYEQ8g0oW69ZZjtPjj9uH+59/4NNPoXJluy9stWpZJfevvrIyEs45V5hUrWqrCFVh+PDdtwRzLplF07k6WFUHq+qC4HIrcFC8A0sGp5wCL71kuVYAAwbY3P/YsXDyyeHGFnHOOVYA9c477QzPOecKm2nT7DjXowfs2BF2NM7lLprO1SYROSpyRURaA5viF1LyOP5425bhiCNs9Oqgg+yDvXmzbficLB56CBo2hPPOs13mnXNGREqLSGcReVhExojICyJyjYg0CDs2F73mzeG+++CNN+CGG8KOxrncRdO5ugR4XEQWichi4DGgT3zDSg4itu3MrbfamdOKFTB6tFUTbtcOfv457AhN2bKWf7Vhg+1J6MX3nAMRuRX4Etsb9VvgaeA1YBtwV7DzRKMcnl9JRF4XkV9E5GcROSIhgbssDRgAffrA3XfbFKFzySzq1YIisgeAqq6NVzCpstpm3jw46ijLBfj0U+uEJYPnn4fzz4dbbvEcLJca4rlaUEROU9V3c7h/b6CaqmZ50BGR54EvVPVZESkJlFXV1dm9Xqocv1LZ1q1w+unw+ee2YnvffcOOyBV12R3Dsq1zJSLnqupLInJlptsBUNUHYh5liqhVCz7+2DYaTZaOFdiqxokTbaTtmGPguOPCjsi58GTVsRKRNKC8qq5V1T+BP7N6brAi+hjg/OC1tgC+J0LISpSwUfpp07xj5ZJbTtOC5YKvFbK4hFjhKTk0bAj772+lEG67DVavDjsi8/jjUKeOTQ/+meW/DeeKFhF5WUT2EJFywI/AbBG5Open1QRWAiNEZLqIPBs834WsYsVdJ44ffJBc+a/ORWTbuVLVp4NvP1bVWzNegE8SE17ymznTOlenn54cy4TLl7czu1WrLMHdV9Y4R/0gnaED8D7WcTovl+cUB5oBT6pqU2ADcF3mB4lI70gNwJUrV8Y2apejlSuhc2fo0MEWGTmXTKJJaH80ytuKpGbN4OWX4euv4ayzkmMz5UaN4OGHYcIEuOeesKNxLnQlRKQE1rl6R1W3Ylt45WQpsFRVvw2uv451tnajqsNUtYWqtkhPT49lzC4X6em2z+qXX9q2ZDt2WB7W0qVhR+ZczjlXRwBHAumZ8q72AIrFO7BU0rmzFfO86CIbLXr5ZSgW8m/o4ost/2rQINtConXrcONxLkRPA4uAH4DPRaQ6Vgw5W6q6QkR+E5FDVHUO0BaYHfdIXZ506WILjG680bYmS0uzpPd69azjVatW2BG6oiqnkauSWG5VcXbPt1oLdI5/aKnlwgutDsv48clRokHEOnw1alih0b//Djsi5xJLRI4QEVHVR1T1AFU9VW159BIgmuUefYFRIjITaALcEcdwXT5ddBGUKgV//AFvvgnLl9sswskn+7ZgLjy5lmIQkeqqujgRwRSGpcy//QYHHhh2FLtMnWpFUE8+2fZDTKbVjc7FuRTDk0ArYC7wAfCBqq6IR1tQOI5fqeiBB2zz+lNPha5ddx3jjj3WamN16BBmdK6wy3Mphgw2isi9QAOgdORGVT0+hvEVGpGO1RNP2P6D114bbjyRysb9+1sl9wEDwo3HuURR1UsARKQucAowMiixMAnrbH2pql5yN8UtWwYNGtgIPdjMQZkyNjX4++/hxuaKrmgS2kcBv2ArbG7Fche+j2NMKU/VNlO+7jp4+uncHx9vffva2du118J334UdjXOJpaq/qOqDqtoOOB6YDJyNVW13Ka5VKxg3zo67W7bY7hnt28N778Hhh4cdnSuqoulcVVbV4cBWVf1MVXthByiXDREYMcLKM1xyCbzySvjxPPec1eXq2jV5anI5l0giUhYbgf9eVfvGazrSJVaHDrZS8Jxz4PvvbYucWbPstqZNw47OFVXRdK4iKYHLReQ0EWkK7BXHmAqFSCXho4+2FYTvvRduPHvuaZ28pUstATTKXY+cS1ki0j7YE3WaiJwK/ITtjTpLRHqGHJ6LkRIlrOxMw4bQr5/llnboYFOCQ4aEHZ0rqqLpXN0e5ClcBQwEngU8cycKZcrYcHXjxvDrr2FHY0Pkd9xhO8s/+WTY0TgXd7cBJwH/h23Y3FZVDwcaYccyV0iULw833WQLeL7+2lYNnn++bQU2YULY0bmiKMeEdhEpBtRW1fHAGqJbvuwy2GMPy78qWdKub9sGxaNZRhAnV10FkyZZYvuRR0KTJuHF4lyc7VDVuQAislBVFwCo6p8isi3c0Fw8icBTT8Ehh9iqQecSLceRq2AlTbcExVJoRTpWX34J9evD3LnhxZKWBi+8AFWqWAG+devCi8W5OEsTkT1FpDKwI/h+LxHZi+hG7V0KK1XKFhWVKgX//ON7rbrEiuYA86WIPCYiR4tIs8gl7pEVQunpsGYNnHCC1cMKS5UqMHo0zJ9vyZ+ef+UKqYrAVGAKtrPEtOD6VKwgsisCtm+3jZ47dUqO7clc0RBN56oJtsJmCHB/cLkvjjEVWnXqwIcfWgfrxBPDPZM65hjLR3j5ZVtJ6Fxho6o1VPUgVa2ZxeWgsONziVGsGNxwg80cXHqpn0y6xMg1+0dVPc8qhpo0gXffhZNOsnosn35qeVlhuP56a79vX6sV07BhOHE4Fw9BzmgZVV0fXD8c29YLYLqq+qR4EdG1q5VnGDrUNrbv1y/siFxhl+vIlYjsIyLDReT94Hp9Ebkw/qEVXkcdZatZDj3UVhSGpVgxeOkl69x17QobNoQXi3NxcDdwaYbro4GrgZuAQaFE5EIzZAiceaYt5vnkk7CjcYVdNNOCI4EPgf2D63OBK+IUT5HRrh08/7zVaPn77/A2GN13X+tg/fyz7c1Vo4YlvdeoAaNGhROTczHSFnggw/XVqnoGVp6hdTghubCkpcGLL1qJBh+ld/EWTeeqiqq+BuwAUNVtgO/HFSMbN0Lr1tCzpyVehuGEE2y7iM8/h8WLLSdh8WLo3ds7WC6lpQXHq4hrAdR2qy8fTkguTBUqwPDhsM8+VhZn/fqwI3KFVTSdqw3BUmaFnXkLa+IaVRFStiz06mWr9y6/PLxky+nT/3vbxo1w442Jj8W5GCkpIjtXBarqBICgKHLpbJ/lCj1VmyLs2jW8k1pXuEXTuboSeAc4WES+BF4A+sY1qiLmmmtsU+WnngqvM5NdaYglSxIbh3Mx9AzwqohUi9wgItWx3KtnQ4vKhU7E9n597z1bSehcrEWzWnCaiBwLHAIIMEdVQ8oQKrzuvNM2VL7zTqhZ07aq+ewzq43Vvn38E9+rVbOpwKxudy4VqeoDIrIRmCwi5YKb1wN3qapvAFXEXXKJrSC85x5bXHTuuWFH5AqTaFYLlgb6Yft03QpcFtzmYkgEHn/cRq6++MIS3mfNgpEj4eCDs562i6WhQ22KMqNSpex251KVqj6lqtWAGkANVa3uHSsX8fDD0KaNbWb/3XdhR+MKk2imBV/Aiog+iu0o3wB4MZ5BFVXFitn2OLNnw08/2aqW99+HBx6Abt3im4/VvTsMGwbVq9v1EiVstOzkk+PXpnPxJCLnikgagKquy1zXSkQOFpGjwonOJYMSJWDMGKvzF+aer67wiaZz1VBVL1TVScHlYqyD5eLglVdsc+X77rMNRydMsKTLHTtgxoz4tt29OyxaZJ24776zhPYLL/SKxi5lVQami8hzInKZiHQRkR4iMkREPgPuAf4IOUYXsipVrJhys2BTN09wd7EQTedqWrBCEAARaYXt1eXiYOtWm44bOBDq1YOzzoIffrDbElkLq0kTuPtueOcdS7R3LtWo6sNAMyyBPR2re9UM+B04T1XPUtVfQwzRJQkRO4m88kori+MnlK6gohkIbQ58JSKRdWPVgDkiMgsrGdMobtEVQWeeablX7dvbSpbDD7c6VCVK7DqzSpR+/WwvxCuvtL0IG/h4pUsxqrod+Ci4OJctERvFevBB2yLnmmvCjsilsmhGrtoBNYFjg0vN4LbTgTPiF1rR1KuXjVK1bGkV3A8/HP75B/bc03KyEiktzRLqK1SA//0P/v03se0751wiXX+9pWFcd53tAetcfuXauVLVxUAlrCN1BlBJVRdHLnGOr8gpWRLGjbNVeqtXw2GHwWuvWaV0kcTHs88+1sGaOdMOOM45V1iJwHPPQdOmtojo55/DjsilqmhKMfQHRgF7B5eXRMSLiMZRsWK2z98998DVV0PnzvZhB5umS3Q+wKmn2hThww/bVKVzzhVWZcvCW2/ZFOGvnpHn8imanKsLgVaqugFARO4GvsZKM7gE+vhjq381aBDcdlti2777bpg0CS64wEax9tknse07l18iUgo4C6t1tfOYp6pDworJJbcDD4RffrGZBOfyI5qcK2H3jZq3B7e5BGvb1ord3X47PJvgzTtKl7b9D9eutfpbO3Yktn3nCuBt4ExgG7Ahw8W5bEU6Vs89Z6u3ncuLaEauRgDfisjY4HoHYHjcInLZEoEnnoClS6FPH6ha1UayEqVBA7j/frjsMnj0UejfP3FtO1cAVVU1gZ8UV5j89JMVcj7kELj44rCjcakimoT2B4ALgH+CywWq+lCc43LZKFHCEtwPPRS6dIG//kps+5dcAmecYcuUf/ghsW07l09ficihYQfhUtPdd9tOFZddZluTOReNqAr+q+o0YFp+GhCRYljR0d9V9fT8vIbbXYUKtkz4228t6TKRIqtpGjWy8gzff//fPQmdSwaRWnzYce4CEVkAbMbSGrxGn4tK8eK2c0arVlbU+fvvd20T5lx2osm5Kqj+gC9ojbH994eOHe37L76wsg2JUqWK1eCaPdtzEVxSi9TiOwWoBZwUXPcafS5PKlWy3Sq2bvX6Vy462XaughU2BSIiVYHTgASnXxcdf/0Fp5xiHa3NmxPX7oknWsfqySfh7bcT165z0cpQi+/2jLX5Irfl9nwRWSQis0Rkhoj4ll9F3CGHwJw5cOmlYUfiUkFOI1dfA4jIiwV4/YeAawBfWxYnVarA00/bxqOJ3mR56FDbkufCC2HZssS161we7bZxU5Cq0DzK5x6nqk1UtUXsw3KpZu+97euXX9o2OQDr1yd231eXGnLqXJUUkf8BR4pIp8yX3F5YRE4H/lTVqbk8rreITBGRKStXrsxj+A6ge3fr6IwaZTWwEqVkSXj5Zdi0CXr08PIMLrmIyPUisg5oJCJrg8s64E+sPINz+TJypO25WqsWpKfbSW6/fnYsdA5y7lz1AY5m961vIpdoEtNbA+1FZBHwCnC8iLyU+UGqOkxVW6hqi/T09LxF73a6/npbJnzHHfDJJ4lr95BDrHL7J59YmQbnkoWq3qmqFYB7VXWP4FJBVSur6vXRvAQwQUSmikjvrB7gJ4dF0yWXWKL70qU2ivXLL7BihdUhdA5ANJd5JBG5UFULVNdKRNoAA3NbLdiiRQudMsVTG/Jr2zYbverRI7H7EKrC2Wdb7tXXX0MLn0BxURKRqfGachORZjndH6yCzun5B6jq7yKyN/AR0FdVP8/u8X78Kjr69LEk95desu3Kvv8eypeHatVg+nSr8O6KhuyOYdGsFnxRRPqJyOvBpa+IlIhDjK6AiheHnj2tYzV/PsyYkZh2RWDYMNh3XyvPsH59Ytp1Lhf3B5fHgW+BYcAzwfeP5/ZkVf09+PonMBY4LG6RupQyfz4cd5ztQfjnn1bcuWxZqFsXFi0KOzqXDKLpXD2BJX8+EVyaAU/mpRFV/dRrXCWOqu3ofsopifug77WXncXNmwdXXJGYNp3Liaoep6rHAcuBZkH6QXOgKfB7Ts8VkXIiUiHyPVbG4ceCxjRqFNSoAWlp9nXUqIK+ogtD48a212uLFjZaP2gQ/PMP/PijpUo4F03nqqWq9lTVicHlAqBlvANz+SdiCZf//gunngqrViWm3WOPhRtugOHDYcyYxLTpXBQOUdVZkSuq+iNQL5fn7ANMFpEfgO+Ad1X1g4IEMWoU9O4NixfbCdDixXbdO1ipp29fePFFuO8+W0H4/fe2FVnJkjB3btjRuWQQTedqu4gcHLkiIgex+0bOLgnVr29D1vPnJ7YG1uDBVsm4d29YsiQxbTqXi5ki8qyItAkuzwAzc3qCqi5Q1cbBpYGqDi1oEDfeCBs37n7bxo12u0st1avDpEnWqWrSxMrRtGtnI/gnnmjHXle0RdO5uhqYJCKfishnwETgqviG5WLh2GNtBOuzz+DOOxPTZokSdia+bRucdx5s9264C98FwE/YbhH9gdnBbQmV3cnG4sU2yuxSS7168OqrlnM1ezYMGQKTJ9uU4VlnWR6qK7py3VtQVT8RkdpAZCZ5jqomsBa4K4hu3Wyoul27xLV58MGW4NmjB9x1l5+Zu3Cp6r/Ag8ElNNWqWUcqK4ccArffbgtCihVLbFwudqpUsbI0Z58N//d/duw9//ywo3JhiGpvQVXdrKozg4t3rFLMWWdBuXKwbh2MH5+YNs891/5RDB4M33yTmDady0hEXgu+zhKRmZkviY5n6ND/bnJetqzVqEtPt5ORZs3ggw8Su9OCi61y5awszaBB0L592NG4sCRi42aXJG65Bc48MzEdLBEbvTrwQOtkrV0b/zady6R/8DWyUXPmS0J1725TRdWr2+ejenW7fscd8N13MHq0lTE55RQ44QSYmuPeFi6ZlSgBt91mOVibN9uUoVdvL1py7FyJ8XJohcStt0LTptC1KySi1mHFipZ/tXgxXH55/NtzLiNVXR58ewJQMovNmxOue3crj7Jjh33t3t1uT0uDc86Bn3+GRx6BmTNtmX+3brYoxaWuTz6xEfyTT07cym0Xvhw7V2rl299LUCwuzsqXt1GrvfeG009PTA2sI4+0A8uLL/qScxeaasDTIrJARMYEhZCbhB1UVkqWtGX+8+fbtNI771jidL9+4LvrpKZTT7VRyW++gWOOgd9zrLDmCotopgWniYjXtSok9t0X3nsPtmyBXr0S0+YNN8BRR9l+XAsXJqZN5yJUdbCqHg80AL7AVkAn9aTbHnvYtNK8efY5feIJWyhy222wYUPY0bm8OucceP99G8U/4ggboXSFWzSdq1bA1yIyP0gEnRVGMqiLnXr14N13YcSIxLRXvLhVb09Ls2mQbdsS065zACIySETeByYAtYCBQNVwo4rOfvvBU09Z5e8TToCbb4ZateDpp2Hr1rCjc3nRtq2VxSlf3qaFXeEWTefqZOBg4HgsCTSSHOpS2BFHWELtjh02XRfvD3v16vZP4uuv7ezbuQTqBFQGPgbeBN7OkI+VEurWhTffhK++ss5Vnz7QsKHd5isLU0fTpjBrFjRoYO/bjwXeUMklq1w7V0Hi54HA8cH3G6N5nksN48ZZ2YRrroFXXrFl4c8+a2UbYu2cc6zmy+23wxdfxP71ncuKqjbDktq/A04EZonI5HCjyp8jjoDPP7el/sWKWZmV1q2teKVLDZE6ZiNGWHX3RM0guMTKtZMkIoOBa4Hrg5tKAC/FMyiXOO3bW07H/ffblEP58paT1aAB/Ppr7Nt75BE46CDr0K1eHfvXdy4zEWkIdAd6Al2xTZsnhhpUAYjY53bmTDsRWrwYjj7ayqzMnh12dC5aXbrYVGGvXlaOw0cgC5doRqA6Au2BDQCqugyoEM+gXOKIQOnSNm03fz4ceqhNNVxxha1QirUKFeDll2HZMqtg7AcUlwB3AXsAjwD1VPU4Vb055JgKrHhx29Pu11/tn/Onn9rn96KLfEVaKihf3mYOune3XSz69fPtwgqTaDpXW4KSDAogIuXiG5JLtLfftkvz5jZtt2aNrez79NP/bjQbCy1bWt7Va69ZZeq0NKhRw0s1uPhQ1dNV9W5V/UpVC10aeKTK+/z50L+/lT2pXdtW6a5ZY5+rGjX8c5aMSpaEF16Aq66yFaHffx92RC5Wct1bEHhNRJ4GKonIxUAv4Jn4huUSqXhxqyg8bpwdoCtWtErRInZAjocDDrDX/vtvu754MfTubd9HCis6VxAiMovgpDArqtoogeHEXZUq8MADNgIyaJBt1v7oo1YhPLKy0D9nySctDe67D3r2tJFHsAVG8Tr2usQQjWJeRkROBE4Krk5Q1Y/iEUyLFi10SiJKh7vdXHstLF1qZ7yRD/Q559h2DW+/HZ82a9TIehPb6tUTU9zUJQcRmaqqLeL02tWDby8Lvr4YfO0OoKrXxbK9ZDt+TZ9uRXz//fe/9/nnLHmNH2+7abzzjpXicMktu2NYtH3jWVjxvc+D710hctNN1rlq3NhyrZo3h1dftYNyvHIAlizJ2+3O5VWGbW5OVNVrVHVWcLmOXSeLhVbTpjZqlRX/nCWvEiWsyOiRR8LcuWFH4/IrmtWCF2FLmDsBnYFvRCRBtb1dIpQvb/lVDz1kZ7Q33wwPPggTJlguRzxUq5b17Qf6TpYu9kREWme4ciRFpJxMdp+zSpW8kGWyOvlkmDTJUjNat/Y8rFQVTc7V1UBTVf0bQEQqA18Bz8UzMJdYIrYsuG1bu65qq5DuvdcKGMZ6q5yhQy33I3PC/P7722hZpBaMczFwIfCciFQEBFiF5Y4Well9ztLSbAPho4+2Ug716oUXn8tay5ZWMPbkk6FNG/jhByse61JHNGdvfwMZS0quC25zhZgIPPwwnHSSVYNesCC2r9+9OwwbZiNlIvb1nHNsc9MLL/Szahc7qjpVVRsDjYFGqtpEVaeFHVciZPU5e/55W6H2yy9WxPKOO3wrnWRUu7Z1sG6+2faVjFCFtWu9bEOyyzahXUSuDL5tAhwKvI2tvDkTmKmq58c6mGRLCHVW6PODD6zjkwi33WYHk969bbsckcS068IRz4T2DG2UAs4CapBhtF5Vh8SynVQ7fv3xh60sfO0162QNHw7NmoUdlcvOzz9bR/irr+y9K1/ecmSvucZXFoYpPwntFYLLfOAtdi1pfhtYGOsAXXKqVGlXx2r6dJtOiKdBg6w+z7BhduDwIqMuBt7GTgq3YcWQI5cibZ99bOHK2LGwYgUcdpjlWG7aFHZkLitXXw0vvWTv05o1lic7dizcfXfYkbmsZJtzpaq3JjIQl9zWrIHjj7cz2w8+sBUt8SBiew/++6/V7ClVyg4ePoLlCqCqqrYLO4hk1aEDHHus/fO+6y7boWH4cDjqqLAjcxn98w+cfrrtAZuWBs89Z+Vzjj7a3rvi0WRQu4SJZrVgCxEZKyLTRGRm5JKI4FzyqFjRVhNOnAiXXx7fESURK6p36aWWUH/LLfFryxUJX4nIoWEHkcz23NOS2z/6CLZssX/Yl18enw3cXf4sWGCpEnfdZVuIHXkk7LGHnYiuXx92dC6zaPq6o7AVg7MATzMuwnr2hDlzrPJz3bowYED82hLZVV16yBAbwbrhhvi15wq1o4DzRWQhsBlbMaiFrUJ7LJxwAvz4o+1198gjtmvD009DOx/3C13jxnZye+21tsJz2DBYuNA6xnvsEXZ0LrNoOlcrVfWduEfiUsLtt1sH66qrbO6/devcn5NfaWl2YP/3XzvYly4NV16Z+/Ocy+SUsANIJeXK2Sh11662cveUU6BHD5umr1w57OiKrhtvhLPPthPPE0+0acBzz7WT3AED7NhcoULYUbqIaNYYDBaRZ0Wkm4h0ilziHplLSmlptoz77ruhVav4t1esGIwcCZ07W4fu8cfj36YrXDJUat+ELczZuRG9y94RR9gilkGDbBqqfn14/XVfZBKWY46xlZ3PP297EN56q00R7r8/PPaY5cOm0GLVQi+akasLgLpACXZNCyrwZryCcsmtXDlLoARYvtzOpPbdN37tFS9uB/ctWywPpHRpO6N2Lhoi0h64H9gf+BOoDvwMNAgzrlRQqpSVR+nc2T5zZ58NHTvaSY7ve5d4xx5rl8z23ddqmh15pKVtDBjg5RnCFs2vv6WqtlDVnqp6QXApEtWNXc62bbMVhB06xH/5dokSdtbWrh1cfLEtSXYuSrcBhwNzVbUm0Bb4JtyQUkvjxlbg9+674f33bRRrxAgfxUoWRx8NM2bYasKBA61D7MIVTefqKxGpH/dIXMopXtyK2n37LVxwQfwPtKVK2TLxNm0suX7MmPi25wqNrcH2XWkikqaqk4C4Fi4tjIoXt4KVP/xg01K9etkODgu96mFS2GsveOMNW/V5ySV2m1feD080navDgRkiMicowzDLSzG4iI4dbd7/1VctByDeypSxFUxHHgn/+x+8/Xb823Qpb7WIlAc+B0aJyMNEWURURIqJyHQRGR/XCFNInTpWwPKJJ2w0q2FDW1no27GET8Smb/fe22YWTjjBOsRbtoQdWdETTeeqHVAbOAk4Azg9+OocYB/e88+3ztXYsfFvr1w5ePddS+Ds0sWKmjqXgzOBjcAA4ANs14loj2H9sfwsl0Famo2O/PST5QD1729TUz/7byppbNtmJRvuvdcKws6fH3ZERUs0nSvN5uIcYGdLTz9tSe5t2iSmzT32sE5VgwY2ejZxYmLadalHVTeo6g5V3Qa8CzwaTBPmSESqAqcBz8Y7xlRVrZqd6Lz4opVoadIEhg61FW01algnrEYNGDUq5ECLoNKlrejomDHw66/QtKktDHKJke3GzTsfIDIL60wJUBqoCcxR1ZivtEm1jU9d1jZvtq0aErGa6K+/4LjjrHrxhx/6lh2pJp4bN4vI4cBdwD9YUvuLQBXspLKHquY45ikirwN3YnusDlTV03N6fFE/fv35J/TtawtPRHbPwSxb1opedu8eXnxF2eLF9rtfscKKxJYuHXZEhUd+Nm4GQFUPVdVGwdfawGHA1/EI0hUOZ51lia5r18a/rSpV4OOP4cAD4dRTLbneucBjwB3AaGAicJGq7gscg3WasiUipwN/qurUXB7XW0SmiMiUlStXxijs1LT33pZ7mZ7+38UtGzdaEUwXjurVLU/uk0+sY7VpE8z0zOm4ynMlDFWdBiSgfKRLVVdcYbkX3bolJsl1n33soJGeDiefDNOmxb9NlxKKq+oEVR0DrFDVbwBU9ZcontsaaC8ii4BXgONF5D8FQFR1WFCqpkV6enosY09Zf/2V9e1LliQ2Dre74sWtkwVWqqFlS6vE7+U04iOajZuvzHAZKCIvA8sSEJtLUSecYBWD33vPaq4kwgEHWN5VxYo2ajZrVmLadUkt416omSux5fgvRVWvV9WqqloDOAeYqKrnxji+Qqlataxvr1jRVxQmiyuvtBPRAQPgjDOgiA+6xkU0I1cVMlxKYQmhZ8YzKJf6+vSxEayHHoJnnklMm9WrWwerVCnr4P0SzfiEK8wai8haEVkHNAq+j1w/NOzgCquhQy3HKqNixWD1alvwsmBBGFG5jKpUsTI2jzwCH31kRWK/+irsqAqXXLe/UdUEVC9yhdF991l9laOPTlybBx9sHaxjj7Xq8Z9/DrVqJa59lzxUtViMXudT4NNYvFZREElav/FGmwqsVs02FVa17asaN4YHH7R6TCLhxlqUidgChKOPhosu8k25Yy2a1YJ1gIFADTJ0xlT1+FgHU9RX2xRmqraCMFEf4B9/tLPkcuWsgxXJNXDJJZ6rBRPNj1+5W7LEauJNmmTTUc88YzmTLlyqu1Z43n47nHeeldBwucv3akFgDDAdGARcneHiXNSuvRZatco+2TXWGja04e61a20Ea+nSxLTrnMtetWq2uvfBB2HCBPucJqLwsMtZZARx0SKbcWjSZPftxXbsgA0bPPk9L6LpXG1T1SdV9TtVnRq5xD0yV6h07GgdnE6drA5WIjRtarWvVq6Etm2txotzLlxpaZaPOW2adbY6dbLRrDVrwo7M1awJ06dD3bq2+8XFF8OQIbD//panVbeuldtwuYumczVORC4Vkf1EZK/IJe6RuULliCNgxAj44gtLdk/UGdBhh8H778Pvv1sHy1fFOJcc6teHr7+GQYOswnujRlaLyYXroIPsOH3ddTB8uI1kTZpktcqGDbOdON57L+wok180naue2DTgV8DU4OKJBS7PunWDwYNh5Ei4557Etdu6tW32vGCB1XY58EDflsO5ZFCypNVc+vJLW+V73HFw1VXw779hR1a0lShhI1YVKtiK73r1YOtWS35/4AHbr9DlLJoK7TWzuByUiOBc4TN4sA01H3qojV599ZXtdzVnTnzbPe446NfPtoFYutTaXrwYevf2DpZzYTv8cJuOuvRS++fdvLldd+FZtco6Wb162fUHHrDRxb//tr0KXc6y7VyJSI67tInIHiLSMPYhucJMxIaWW7aEI4+ECy6Ad96xlX09ethO7vGSVa6Ab8vhXHIoVw4ef9w2ZF+1yqb0hw6N7zHBZa9yZRtN/OEHu16vnr0XffrAunUwfrwnuOckp5Grs0TkKxG5WUROE5HDROQYEeklIi8C44EyCYrTFTKXXmoVmzdtgvvvtym7ZctsCDpestt+w7flcC55nHyylVI56yzLxzrmGJg3L+yoip5ixeCmmyyx/aOP7GT4+uttqrB8eSul0b9/2FEmr2w7V6o6ADgdWA6cje0qfyVQG3haVY9R1e8TEqUrVNats7PTwYPtDLV9e1tBeOut8MIL8Ws3u205qlSJX5vOubzbay945RVLGfj5Zys8+tRTPlKSaH36wM03W3J73bq28GD8eCvZMGzYroKxy5ZZErzbJcecK1X9R1WfUdXzVfVkVe0Q7Lk1OVEBusJn82Y7KzrsMDuAzpoFRx1lo1gbNsSv3ay25RCx2lv33ecHbueSTbdudnxo3RouuQROOw2WLw87qqKle3eYOtVyrT7+2EYSS5Sw3NlWrewxDz5ot598Mnz3XbjxJotoVgs6F1NVqkDt2lak7rTTbBTrt9+gQwcrlxAv3bvb2Vb16tapql7dKkSfdZYtL77wwsTV4HLORadqVTtGPPaYlWpo2HD3ApcufLfeaieo06ZZh6t9e5gxI+yowhW3zpWIlBaR70TkBxH5SUR8j0K306OP2uq9fv1g4UL7QJYta8t/46l7dxvS3rHDvl54oSW633yz1eE64QT488/4xuCcy5u0NLjsMltBePDBlgd07rmWVuDCV7asldBYsMC2z/niCxvNKsriOXK1GTheVRsDTYB2InJ4HNtzKeTww+0sp3JlmDzZznTmzYN997WaNyNHJi6WtDQ783rlFZgyxaYrZ81KXPvOuegccoiVb4l8Xg891KaqXHKoUMFWXy9cuKuW4bRptldhUVuUkGvnSkTKishNIvJMcL22iJye2/PUrA+ulgguntXidqpa1ZLaR4yAyy+HPfaw2x97zEo03HJLYvOguna1TZ63bLGVMePGJa5t51x0ihe3keZvvrF/5ieeaCPgzz1nhYG9QHD4KlXatSH3jz/CG29YQvxFF1l9waIgmpGrEdgo1BHB9d+B26N5cREpJiIzgD+Bj1T12/wE6YqWF16wvcZuvdWm7bZuTVzbLVvC99/bGfKZZ9rZlye6O5d8WrSwUZF+/SzNIPKP2wsEJ5cePWy68PLLbbVh7dq2+rCwi6ZzdbCq3gNsBVDVjYBE8+Kqul1VmwBVgcOyKjoqIr1FZIqITFnpG785bCXKc8/tGtU67bT4riLM7IADbATr7LPh2muto+eJ7s4lnzJl4OGHYe+9/3sS5AWCk8e++1oNw/nz7YQ5smpb1VZrF0bRdK62iEgZgik9ETkYG8mKmqquBiYB7bK4b5iqtlDVFunp6Xl5WVeIidi04PDhlpdVJsHlasuWtZyOW26xkbTjj/dEd+eSVXbn5V4gOLlUrQpPPmnTumCpFzVqwA03wD//7P7YX3+Fb7+1Ej2pKJrO1WDgA+BAERkFfAJck9uTRCRdRCoF35cBTgR+yX+orijq1csKCaal2YHyp58S17aIjZ699pqtUmrZEmbOTFz7zrnoZFcguHLlxMbh8qZ+fVvMdNddULOmpYL88gsce6xdLrnE3tsRI8KONO+i2bj5I6ATcD4wGmihqp9G8dr7AZNEZCbwPZZzNT7/obqiSoJJ6IsusmKCkyYltv2zz7alxdu2WaL7228ntn3nXM6yKhCclmZTTj16wNq14cTlclarlp08z5xpZXBuucVy6dq0sZPpadOsttmgQbZKNJVEs1qwI7BNVd8NOkfbRKRDbs9T1Zmq2lRVG6lqQ1WNcwUjV9g9+6zlQ518cuITVZs3t0T3+vWhY0e4805PdHcuWWRVIHjECBt5HjUKmja1KSaXnBo2tBWFr7xiG3gPHgzbt1s+XYUKVuT5mWfCjjJvopoWVNU1kStB/tTguEXkXDaqVbOaWK1bWwHBRHdw9t8fPvvMSjbccIOdEf/7b+Lad85lL3OB4B49bCTk88/tH3Xr1lbgcvv2kAN12UpPt5XaaWm2h+EVV1hH+fHH4euvLQ8rVUTTucrqMcVjHYhz0dhzT9sK43//s8rqie7clCljw9hDhsBLL8Fxx8GKFYmNwTkXvdatbSuWLl3gppvsM+uJ7smpeXMr4LxkiW1LNneu5WP98w/MmQN16thtYInuyTx7EE3naoqIPCAiBweXB4Cp8Q7MueyUKmX1UiZNss7Oxo2JLdUgYgfp11+3XIHDDvN9tJxLZpUq2fTgCy/YZ7VRIzs5c8mlYsVdHeDhw60+1o8/WimHmTNtarB2bXts795Qr56V25g2Lfk6WtF0rvoCW4BXg8tm4LJ4BuVcbtLSbBQLbPi/TRv444/ExnDWWZbovmOHnR2/+WZi23fORU/EtmGZMcP+KZ9zjtWwW7cu7MhcRldeadOA778Pd99t04STJ9tWRxddtGuB04knWmmHu++2Ea+DDoJ77w039oyiWS24QVWvi9SiUtXrVTWB4wTO5ez882H2bNuv8JcEF/to1swS3Rs2tM7W0KHJdwblnNvloIMsD+umm2wEvGlT+O67sKNyGbVrZzMDEyfaSsHIiXRGPXrYvpIrVtgoV/36u2plbd8O11xjKw3DyrGLZrVgHREZJiITRGRi5JKI4JyLxumn24do40YrlTB5cmLb328/a/9//7MDwbnnpm7hO+eKghIlLG/y009te63WreGOOzzZPRVVqWL1EN991xY5gZ1kP/qoTS/ut59NIX74YWK3UotmWnAMMB0YBFyd4eJc0mjZ0laTpKdbJyfR29WUKWMJ7kOHWsJ7mzawfHliY3DO5c3RR8MPP9io8403Qtu28NtvYUflCqpBA6va/9prtrvG6NE2Gvb553b/qlW7L4batMkKmNavb9OQ114La9Zk/drRiqZztU1Vn1TV71R1auRSsGadi72DDrJCc++8Y0nvqomdohOxEg1vvmlJmIcdZpXdnXPJq1Il++c7ciRMnWrJ7mPGhB2VK6jy5a0A9CuvWEdr3Dir+g42Spmebnl3r71msx8//ADPP2/X//zTipoWZKQrms7VOBG5VET2E5G9Ipf8N+lc/FSuDE2a2PeDB1udlEQP9XfsCF9+aZ2to46C/v1t/6y0NPua6AKozrmciUDPnnYyVKeOlW3o1QvWrw87MhcLpUtbB6p4UETqzDOhWzfL6era1Vaely9vMyCNG8Nzz9lsREF244imc9UTmwb8CivBMBWYkv8mnYs/VSvP8MgjdvaS6ByoJk0sSXb//S2GxYstpsWLbf7fO1jJTURKi8h3IvKDiPwkIreGHZOLv1q1LGfzxhttJKtpU1uw4gqXo46yiv7Ll8Oll9pKxCpVdt0vYtOIUwrQ04lmtWDNLC4H5b9J5+JPBO6/37ZPeOstm3f/6y+7b/v2xORk7bsvbNny39s3brSDt0tqm4HjVbUx0ARoJyKHhxuSS4QSJayS+6RJlpdz5JFWyNKT3QufYsVs+q9SJXjggd3vmzHDZhryK5qRK0SkoYh0EZEekUv+m3Qucfr1syW9M2bAMcfAZZfBXnvZflVHHWVJ8PGUXXLs4sXxbdcVjJrIpFCJ4OJFNoqQY4+1wpUdO8L119s/4aVLw47Kxdppp8GyZbbS8N9/Lc/q6acttaNbt/y/bjSlGAYDjwaX44B7gPb5b9K5xOrUCT75BPbYw0avfv7ZRo8uuQTat4/vflXVqmV9e7FiMHas18RKZiJSTERmAH8CH6mqb/1bxOy5p1Vyf+45mx5s1Mg2GHaFR8mS8NFHtpIwPd2mB19+GSZMsIrx+RXNyFVnoC2wQlUvABoDBWjSucTbd1/bSuH55220avx42+j1//4Pnnwyfu0OHQply+5+W6lSVnulUyc46STr7Lnko6rbVbUJUBU4TEQaZn6MiPQWkSkiMmXlypUJj9HFnwhccIElux98MHTuDBdfnNgtt1x81ahhFeF/+83+T3z2mZVzKIhoOlebVHUHsE1E9sDO4g4sWLPOJda8eXbWWaqUFZfr2BEGDLDE88hGoPHQvbslTlavbgfp6tWtmvDChZboPmWKxXXllQWvq+LiQ1VXA5OAdlncNyyye0V6enrCY3OJU7u2lXq5/nr7DDdrZrlZvhK48KhUyVacx0K0GzdXAp7BVgpOA+KcqeJcbNWvb5t7rl9vZyiXXw4PPQR9+tgoUjx17w6LFtkehIsW2fXixaFvX+vY9eplsdSpY9MPO3bENx6XOxFJD457iEgZ4EQgwZsruWRTooTVSJo40Won3XSTrwR2WYtmteClqrpaVZ/CDjA9g+lB51JG1apWkqFjR9sa4aabrFOzapXtLxZWgnl6uiVPTpliy8AvvND2SPzmm3DicTvtB0wSkZnA91jO1fiQY3JJok0bKFfuv7f7SmAXEe1qwUYi0h5oBtQSkU7xDcu52Hv8cdveonNn2+Jg7VqbW3/oIZuug8RvmxPRrJnV13npJVuRdMQRtiH1ihXhxFPUqepMVW2qqo1UtaGqDgk7Jpdcfv8969t9JbCD6FYLPgc8B5wFnBFcTo9zXM7FXPHili8xf76NWI0ZY+UY+vSx+6dOtYTV8SGNT4jYlOGcOba31csv21Th/fdnXS/LORee7FYCA3ToAD/9lLBQXBKKZuTq8CBhs6eqXhBcesU9MucSrFw5m6Y74wyr2rtxYzhxVKhgRQt/+sk2lh040JLeP/wwnHicc/+V1UrgMmVsZHzSJKv63bOn5Vm6oieaztXXIlI/7pE4F7K6dS3XaeBAK8/QvHm4Gy/Xrg3vvmsjadu323YMZ55pS4Wdc+HKaiXwM8/YiPiCBXDVVVYjq04d21/0zz/DjtglUjSdqxewDtYcEZkpIrOCJE/nCp1SpeDee62o3Nq1VugzbKedBj/+aKNZn3xiKx8HDfI6O86FLauVwGDL+e+910rAnH++5XsedJBtJr92bYgBu4SJpnM1HDgPq/ESybc6I55BORe2E06AWbNsVSHAt9+Gu/VFqVKWhzV3rq16HDrUkvJfecWrvDuXrKpWtdGtn36CU0+FIUOsk/XAA7bViiu8oulcrVTVd1R1oaoujlziHplzIdtrL6trs3079OhheU9jxoQb0/77W+mIyZNh771t76tjj4Uffgg3Ludc9g45BF57zUquNG9uU4a1a1sx0m3bwo7OxUM0navpIvKyiHQTkU6RS9wjcy5JFCtmuU+1a0OXLlYfa926cGNq3dr2Onv6aZg920o5XHop/P13uHE557LXvLktTPnkEztRuugiaNjQ9iv0EejCJZrOVRlgM3ASXorBFVG1atlo0aBBtj9h06awfHm4MRUrZhWhf/0VLrvMph/q1LFk/Bdf9G05nEtWxx9vi2fGjrXPaOfO0KqVdbpc4ZBj50pEigF/ZyjB4KUYXJFVogTcdht8+qnlZO27b9gRmT33tH0Kp0+3qctLL7Ul4L4th3PJS8TqYc2aBSNGwB9/2HHlhBNsVNqlthw7V6q6HWidoFicSwlHHw1PPWUHx0WLLFF14cKwo7K6OhMnQpUq/51i8G05nEtOxYrZisK5c223iB9+gMMOs9GsX3w3y5QVzbTgDBF5R0TO85wr53Y3dy58+SU0bmxb14SdNyGSfd7V4sXw1Vfhx+ic+69Spawe1oIFcOutlpvVoIHtN/rbb2FH5/Iqms5VaeBv4Hg858q53Zx0kp1pNm4M551ndW5Wrw43puy25RCxRPhDDrFSDkuWJDYu51zuKlSAm2+2Tlb//nbSVru2rTD86y+b3vd8yuQnmkSnsS1atNApU6aEHYZzebZ9uxX5HDwYrrgC7rsvvFhGjbIcq4zb95QtCw8/bPsrjhxpG1aLWGJtz57QqZNt/5NoIjJVVVskvuXY8+OXi4clS+CWW2whTcmSdqzZunXX/WXL2mKWSAFTl1jZHcNy7VyJSFXgUXblXn0B9FfVmJdU9IOTS3Xff2/b6FSoYEP5++4LM2dajZvt26FjRzjySOvYxNOoUZZjtWSJjWQNHbr7wXfhQnjhBTtgL1wI5ctbmYmePS2nLN7xRXjnyrno/PwztGiR9Z6n1av7HoZhKUjn6iPgZeDF4KZzge6qemKsg/SDkysstm61cg2rV9v3ffrYqNFzz1mi6r33hh2h2bHDSkyMHGkFUtevtwrSPXrYpWbN+LbvnSvnopeWln3OZBJNQhUp2R3Dosm5SlfVEaq6LbiMBNJjHqFzhUiJElYSYdkyKzhatarVyJo+3TZzTZb/wWlpcMwx1ulbscJGs2rWtITagw6CNm2s47V+fdiROueyy6cEq5M1ahRs2ZK4eFz2oulc/S0i54pIseByLpbg7pzLwbZttj3NkUdaDtSpp9pIUffuMG5c2NH9V7lylpT/8cc2xXD77fD773DBBbDPPjZlOHGi/QzOucQbOtRyrDIqU8Y+m2vWwLnnWgds8ODwixwXddF0rnoBXYAVwHKgM3BBPINyrjAoXdq+TpgADz5ouRKVKsGGDZZ/lcyqVbOcrUipie7d4a23oG1bG9m66SaYNy/sKJ0rWrp3t+T16tUtL7J6dXjmGRtdnj3byje0bGnFjqtVg//9D77+2qcMw5BtzpWI3K2q14rI2aqakO1qPWfBFSYrV1rZg4kToUkTO8DNm2fD96o2HTd4sO0LmAo2bbIO1siR8NFH9jO0bm0FEM8+G8aPzzmJPiuec+Vc7M2fD48/bhtDr11rexr26wddu1o9LRc7eU5oF5FZQCNgqqom5PDvBydX2LzxBlx8seUuFS9unZI77oB//oH774dVq6B9+9TqZAEsXWr1d55/3qpIR5aIZxyRi2aJuHeunIuf9ettn9FHH7XVhunplqJwySVwwAFhR1c45KdzdS9wMVAe2AgIoJGvqrpHrIP0g5MrjFatgnfftY7HqafaAQ4sR+LRR62TtXq1DevXqxdqqHmmCt99Z/uhZZX0ntsSce9cORd/qrYp9KOPWr5nsWJW265fv8SUhinMClKK4W1VPTNukWXgBydXFK1ZA++8Y8nkAE8/bXuLNW0ablx5kd0ScZGcE+C9c+VcYi1YAE88YVOGq1fbcaZvX1t8E8kTddHLVykGESkGxHyEyjm3S8WKuzpWGzfa1hfNmkGHDla6IRVkt0Q8p6XjzrnEO+gg20Fi6VI7kduyBXr1snIxN9zg+xjGSo6dK1XdDuwQkYoJise5Iq1sWVuhN2SIbVET6WQl+8q8rJaIly1rtzvnkk+5cpZ/NWuWLbo55hi4+25bDXz22fD55zYa7XsZ5k/xKB6zHpgVVGrfELlRVfvFLSrnirCKFa3UQd++8MgjlieRFpwGbdliyePJJpK0ntfVgs65cInAccfZZfFimzJ89ll4/XX7HK9Ysasw6eLF1iED/2znJpqcq55Z3a6qz8c6GM9ZcO6/Nm/etXz69NOt+vvgwVbeIdV5zpVzyWfjRnj5ZdtlIuMm0RG+l+Eu+d7+JuhEvQZ8o6rPRy7xCNI591+RjtWOHVYgcNIkS0Lt2BFmzAg1NOdcIVS2LFx0UdYdK7ARrLfesoLILmu5dq5E5AxgBvBBcL2JiLwT57icc5mkpdmI1aJFcMstuzpZL7/838eqwsKF8McfiY7SOVdYVK+e9e0idnJXuTKcdho89ZQlyLtdotn+5hbgMGA1gKrOAA6KW0TOuRxVqrSrkzVkiNXOAtvmYsYM63TVrw9HHQV168Ipp9gegc45lxfZLVQZMcL2IO3Tx4oIX3IJHHigLcAZPNg2pi/qe5BG07naqqprMt1WxH9tzoWvUiVLfK9Uya5fd52NZLVrZ7kSS5daMuphh8EZZ/j+YnkhIgeKyCQRmS0iP4lI/7Bjci7RstrLcNgw2yi6bVt46CFbyfzTT3DXXdbxuv12S1+oWtWS38eNsxyuoiaaztVPIvI/oJiI1BaRR4Gv4hyXcy6P3noLjj7avu/XD846C+bMsSnELVtsA2YXtW3AVapaHzgcuExE6occk3MJ1727jZLv2GFfM68SFLGR8muvhcmT7YRu5Eir/D56tG3vVaWKfX3mGVi+PIQfIgTRdK76Ag2AzcDLwBrgijjG5JzLhz33tAKB995rhUg/+cSG50Wsds0778C//4YdZWpQ1eWqOi34fh3wM+C7sTmXi/R0G9l6/XX46y/48EO48EKYOdNGsvbf30a2hgyxIsmFdUQ9286ViJQWkSuAe4AlwBGq2lJVB6lqrodoH1Z3LvFatbKCgLfeanlW55xje/5NmmSdrvR06NLFzijXZJ7sd1kSkRpAU+DbkENxLqWUKgUnnWS1+hYutA7W0KG2if0tt1iOVrVqlrP13nu7Tv4KQ+HSnDZufhXYCnwBnAIsUtUron5hkf2A/VR1mohUAKYCHVR1dnbP8ToxzhXM+vVw+OFwxBFw8cW2afStt0KDBjZNOHYsvP22rSIsVcq+VqwI27bZAS/Rkr3OlYiUBz4Dhqrqm1nc3xvoDVCtWrXmixcvTnCEzqWmP/6wDtW4cTBhgpV1KFvWNq+fNWtX4VKw24cNS87Cpfmpc1VfVc9V1aeBzsAxeWnQh9WdS7zy5W3biipVrHM1ZIgN0T/9tCW6P/00LFtm+Vf33GMdK4Azz4TWrW3Psfnzw/0ZkoWIlADeAEZl1bECUNVhqtpCVVukp6cXuM14nLGXL18egMWLF9OsWTOaNGlCgwYNeOqppwDYuHEjp512GnXr1qVBgwZcd911Ob7eLbfcwgEHHECTJk2oX78+o0eP3u3+t956CxHhl19+2XnbokWLEBEeffTRnbddfvnljBw5EoDzzz+fmjVr0rhxY+rUqUOPHj1YmmFt/5o1a+jRowe1atXi4IMPpkePHqwJhl4jrz1o0KCdj//rr78oUaIEl19+eT5+YwUzejQ0bAjFitnXTL+efIm8hwBLlizhpJNOol69etSvX59Fmap59uvXb7fHZyUZ3sOrr+5Bp05rePNNmDp1ESDUrTuIGTMiHau/gBLA5WzcCAMHplZifE6dq53lw1R1W0Ea8WF15xJnr73gzjvhhx+sE/V//7dr+xyw74880pLeI445xg5cV18NtWrBoYfaFhhFlYgIMBz4WVUfSESbo0ZZTsrixZaHEtlqJFZTIvvttx9ff/01M2bM4Ntvv+Wuu+5i2bJlAAwcOJBffvmF6dOn8+WXX/L+++/n+FoDBgxgxowZvP322/zf//0fWzNUmxw9ejRHHXXUf/5h77333jz88MNsyTgkkcG9997LDz/8wJw5c2jatCnHH3/8zsdeeOGFHHTQQcybN4/58+dTs2ZNLrroop3PrVmzJu++++7O62PGjKFBgwZ5+wXFwOjRtgXUo4/aFNejj9r1WHSwInr06MHVV1/Nzz//zHfffcfee++9874pU6awatWqqF4nmd7DUqXsPdyx4122b4+82hgs3dusWAEVKljyfPfucP/9lgLxzz9R/bgJl9NEQGMRWRt8L0CZ4LoAqqp7RNNAMKz+BnCFqq7N4v6Mw+p5id05FyPXXmuXhQtt2nDs2F0HrfXrLUH+zDOtdlaxYlm/hips2mQHyuwek0JaA+dh+6rOCG67QVXfy+8LXnFFzhX1v/nGtjrKaONGSwZ+5pmsn9OkiS2Hj0bJDJtSbt68mR1BIaKyZcty3HHH7XxMs2bNdhtxyEnt2rUpW7Ysq1atYu+992b9+vVMnjyZSZMmccYZZ3DrrbfufGx6ejqtW7fm+eef5+KLL872NUWEAQMGMHbsWN5//30aNGjA1KlTefXVV3c+5uabb6ZWrVrMnz+fYsWKUbZsWerVq8eUKVNo0aIFr776Kl26dNnZeYylNm3+e1uXLlb+5LbbrAOQ4cemQgU7aenWzRK8O3fe/bmffhp927Nnz2bbtm2ceOKJwO4jWtu3b+fqq6/m5ZdfZuzYsVG/ZrK9hytWTGHFihbAq0AXYFnQttXVmj7dNrXPWDy5enUrQ5PxcsABtpgnLNmOXKlqMVXdI7hUUNXiGb6PtmOV8GF151z+1axpnYDPPrN/CGAdgscft38q++5r/+zHj9995eH779sBba+9YO+9YdCg7LfOSAWqOllVRVUbqWqT4JLvjlU0Mnescrs9P3777TcaNWrEgQceyLXXXsv++++/2/2rV69m3LhxtG3bNqrXmzZtGrVr1945evL222/Trl076tSpQ+XKlZk6depuj7/22mu577772L5reCJbzZo145dffmH27Nk0adKEYhl67MWKFaNJkyb89NNPO28755xzeOWVV/jtt98oVqzYf362RJgzZ9dUe0TFijYVHwtz586lUqVKdOrUiaZNm3L11Vfv/F0+9thjtG/fnv322y9Pr5ls72GzZq9QuvRvQDHA3sOyZeHBBy3NYdw4q+H355+2EvGuuyzPdPZsS5Jv394Kmu69tyXTX3stvPoqzJ2bc2HTWE/Jxy2FNYxhdedc7ETO+o46ys6433/fRrTGjIHnnrNpx0aNrMTDRRdZbZt27Ww66//+zzpn0Y6qFAW5/S5q1LDfXWbVq+dtdCMnBx54IDNnzmTZsmV06NCBzp07s88++wCwbds2unXrRr9+/TjooJw34XjwwQcZMWIEc+fOZdy4cTtvHz16NP3728Lwc845h9GjR9O8efOd9x900EG0atWKl7PasymT7BZbZaddu3bcdNNN7LPPPnTt2jVPz82LnN6LevVs1CoYCARspW7fvvZ9lSoFey+3bdvGF198wfTp06lWrRpdu3Zl5MiRnHLKKYwZM4ZP8/DiyfweduiwDxMmdOWff2zk78kn/5vMnp5unaeTTtp127p1tiJx+nS7TJtmnbLIiV758tC48e4jXA0a2DGtd+9dOV2RKXnIfxJ9PNcHxXxY3TkXjgoVbOqjSxcbSZk82fKyAPr3tynERx6xTliPHjZkX7u2nUlGKsi7nA0duvsBHuyMfejQ2Le1//7707BhQ7744gs6B/NUvXv3pnbt2lxxxRW5Pn/AgAEMHDiQd955hwsvvJD58+ezceNGJk6cyKxZsxARtm/fjohw77337vbcG264gc6dO3Psscfm2Mb06dNp27Yt9evXZ8aMGezYsYO0IHlwx44dzJgxg/r1d9V1LVmyJM2bN+f+++9n9uzZvPNO4rfAvfFGG9kdPtxOSiZPtuuxeg+rVq1KkyZNdnZ+O3TowDfffMO+++7LvHnzqFWrFmCLFGrVqsW8efOyfa1kfg/fe+9+fv3V3sMpU6Lv4FSoYAtzWrfedduWLVZBPtLhmj7dTgQfe8zuL1HCvmYead+40d7P/Hauoikimi9hDKs75+KvVCnb+iIyslWypOWTzJtn04VgZ+j77ut7GuZFdluNxGr5+dKlS9m0aRMAq1atYvLkyRxyyCEADBo0iDVr1vBQHoca27dvT4sWLXj++ed5/fXXOe+881i8eDGLFi3it99+o2bNmnzxxRe7Padu3brUr19/t9GSjFSVRx55hOXLl9OuXTtq1apF06ZNuf3223c+5vbbb6dZs2Y7OxMRV111FXfffTd77bVXnn6OWOnWzTpSfftC6dL2dehQuz0WWrZsyerVq1m5ciUAEydOpH79+px22mmsWLGCRYsWsWjRIsqWLZtjxyqjwv4elixpI1S9etkCg8mTrcbfnDnwyitw5ZXZpzAsWZL/duPWuXLOFQ1HHGGjWL/+atOFAL/9ZnVsatQINbSUk9tWIwXx888/06pVKxo3bsyxxx7LwIEDOfTQQ1m6dClDhw5l9uzZO0s1PJuHpaI333wzDzzwAKNHj6Zjx4673XfWWWf9Z8UZwI033vifpPmrr7565zL+77//nkmTJu1Mwh8+fDhz587l4IMP5uCDD2bu3LkMHz78P6/boEEDevbsGXXs8dCtG/z4I2zfbl9j1bECy1O67777aNu2LYceeiiqmmNiebSK2nuYlgZ16kDXrpazVb161o8ryBq7bIuIhsGLiDqXen780XJMbr8dOnWyM8L+/W114c035/zcZC8imhd+/HIuNUXKoGSeko9m5Dg/RUSdcy5XDRtasvu4cXDIIbZc+uKL4aabwo7MOedyF48p+RA2vHDOFTYtWuzKt3KFw9ChQxkzZsxut5199tnceOONIUXk8srfw+h17x7baXifFnTOhcanBZ1zqcynBZ1zzjnnEsA7V84555xzMeSdK+ecc865GPLOlXPOOedcDHnnyjnnnHMuhrxz5ZxzzjkXQ965cs4555yLoaSqcyUiK4HFQEVgTaa7M99WE1iYoNBi2WZWP1tB28ztNbO7P7vbM7eZ15jzKtrfayziiLxGLH6v0Twu433R/F6jvS1asfqc5CWG7NrM6jWqq2p6QQJLFn78yneb8T5+RdNGQcXyeBLta8SqzWh//9H+X4jlMSyWn5NoY8ipzeiPYaqadBdgWG63ARtCiKvAbWb1sxW0zdxeM7v7c7h9Q15eP1G/11jEEXmNWPxeo3lcxvui+b1Ge1usf7ex/N1n12a8/46S5eLHr7y1Ge/jV37ijsfvNpbHr1i2Ge3vP9r/C7E8hsXyc5KH43m2bebl50jWacFxUd6WiuLxc+T2mtndH20syfK7j0UceXmNWPx+8npfsv7tJ/p3n8qS9T2MhVQ8fuX1sfESxmcomsfH+vefrH//Cf39J9W0YF6IyAZVLedtpn6bReFn9DZdRkXlffE2C0+bReFnjGWbyTpyFY03vc1C02ZR+Bm9TZdRUXlfvM3C02ZR+Blj1mbKjlw555xzziWjVB65cs4555xLOinXuRKRuSKyQ0T+TVB7h4nIKhHZLCL/isgbiWg3aLuEiGwUkT8S1N5bwc/4r4gsFpGKcWgjy/dPRMZk+B1/G+M2K4rIehHZFLz+p8HtC0VkS3DbXBEpE8M2q4vI0uBn2iwiF2e47x0RURGpU8A2/vO7FJHvgvY2icgyEake3F5GROYFP+tmEfkgn21m+XkQkU9FZHvQ7iYRuTnDc84SkXUZ/rZi/neVKhJ9/AraDOUYVhiPX0E7CT2GhXH8Cl6/0B3DEnn8SrnOFfAIcG4C29sMXKaqpbD6F2eIyBkJavt14M9ENCQizYHTgP1UtTT2t3F/HJr6z/snIgOA44EqQdsXxLjNtcDBqloG2BNoISIXAiOAUkCZ4OvwGLb5CfBx8HezFzAB7MMNHAlsj0EbWX0W3gT2CH7WJcCrwe33AiWC3+/+wAkiclQ+2szp8/CeqpYJLkMARKQU8BLQK2i7IbAxH+0WFok+fkF4x7DCePyCxB/Dwjh+QeE8hiXs+JVynStVfQz7hSeqvR9U9eXg++XA30D9eLcrIi2Ao7E/vkQRYM/gD6okMC/WDWTz/l0J3KWq64LHzI5xm6qqkbPnstjfvarqkOA+BaYD1WLRnogcCFQnOMCq6gZVXRzc/SZwSSzayep3qap3qerm4OqnwD6Ru4BSwXu7J7ADWJaPNvP6ebgWWK6qY4LnzFPVrXltt7BI9PEraDPhx7DCevyCxB/DEn38gsJ7DEvk8SvlOldhCnrJ6VhPNt7eAvpif0Bxp6pTgbeB+cAmYKOq3pWItoEqwGnB0PdqEekZ6waCKYpNwF/ATFV9LsN9ZYCTgNExau5I7Azp12Ba5BcRSReRocDKyAc1Ac4H3g++vyaIaRPwK/Cqqi4oyItn8Xk4JRhSnysiNYLbmgaP/Sv4XbxbkDZdwSTwGPYWRef4BXE+hiX4+AVF4BgW7+OXd66iJCL7AB8C96vq73Fu61ZgtaqOimc7mdqsARwH1APKAaVF5IlENY+diVQArgCeFRGJZQOqujUYZq4BHCIiHTLcPR2Yo6qPx6i5ktjv8A5VLYsdDN4F+gPtY9RGjkRkAvaP7bLgpp7B9XJAA6CriBxbgNfP/Hm4DJueKI9NBU0IHlocOBA7WB8IHCMiA/Pbrsu/RB3DiuDxC+J8DEvw8QsK+TEsIccvjVFp+URegKOAfxPYXhnsjOHtBLX3FbAtuGzHhkMXxLnN+7EPaOT608CPiXj/gJXAgAzXtwJ14/izfgKMC76fCCwHisXw9Q8FtmW4fhnwD3ZgiLyvGnw9NJa/y+C2Z4F1QOUMt80CnspwfS7wYD7bzPHzkDEm4GFgXob7PgbGx+u9TYVLoo9f0bxnMW6rUB+/snoPE3kMi/fxK3jdQnsMS9Txy0euchGcffwI/KaqZyaiTVU9UlWLq2px4CrgT1U9KM7N/gRUF5HKwc/cFvg5zm1GTAA6AIjISdhZ4JxYvbiI1M2w4mRPoAUwTURGBt/XV9VYJGcCoKqzgA0i0i646WzsA5qW4X3dHrQ7K1btAojIjcB5QEtV/TvDXb9h7ykiko7lU0zOx+tn+XkQkcYZHjYQiOSIPAjsH/xdlcKG2afktV2Xf4k+hhXB4xfE8RiW6OMXFN5jWEKPX/HoWcfzgu06Hzkb2gaMiHN7lwRtbcpwuTmBP+8VwB8JautTbE77X2ABUCER7x82zLsgaHcjcGWM2zwreN1NQRufBLcrdoYZeV8/iWGbXYENwesuB2pkun8bUCcOv8stwfeRn+mn4LH7YAenf4P3OF+jR9l9HjK8f5uAFUDjDM95IrjvX+DbRPwtJ+sl0cevnN6zBP28her4ld17GM9jWBjHr+D1C90xLJHHL6/Q7pxzzjkXQz4t6JxzzjkXQ965cs4555yLIe9cOeecc87FkHeunHPOOediyDtXzjnnnHMx5J0rFzWxXdDvz3B9oIjcEqPXvkVEfheRGSIyW0S6xeJ1nXMO/PjlEss7Vy4vNgOdRKRKnF7/QVVtApwJPC0iJeLUjnOu6PHjl0sY71y5vNgGDAMGZL5DREaKSOcM19cHX9uIyGci8raILBCRu0Sku4h8JyKzROTgzK+lqr9iRfP2DF7jahH5XkRmBvuWISI1gs1ER4nIzyLyuoiUDe67Kzh7nCki98XjF+GcSzl+/HIJ450rl1ePA91FpGIentMY6INtqnoeVtX3MGz/qL6ZHywizYBfVfXPYCuJ2sBhQBOguYgcEzz0EOAJVa0HrAUuFZHKQEeggao2Am7Px8/onCuc/PjlEsI7Vy5PVHUt8ALQLw9P+15Vl6vqZmA+u3Ycn4Xt8h4xQER+Ar4Fhga3nRRcpgPTgLrYwQpsf6gvg+9fwjbcXINtUzBcRDphZ5DOOefHL5cw3rly+fEQcCG2n1bENoK/JxFJA0pmuG9zhu93ZLi+Ayie4b4HVbUBtpfWcBEpjW2AeqeqNgkutVR1ePD4zHs3qapuw84SXwdOBz7I34/onCukHsKPXy7OvHPl8kxV/wFeww5QEYuA5sH37YF8J3Oq6jvYzuM9gQ+BXiJSHkBEDhCRvYOHVhORI4Lv/wdMDh5XUVXfw3IrGuOccwE/frlEKJ77Q5zL0v3A5RmuPwO8LSI/YGdbGwr4+kOAl7E8h3rA1yICsB44F9tJfQ5wmYg8B8wGngQqBnFEzhqvLGAczrnCx49fLq5ENfPIpHPJT0RqAONVtWHYsTjnXF748avw82lB55xzzrkY8pEr55xzzrkY8pEr55xzzrkY8s6Vc84551wMeefKOeeccy6GvHPlnHPOORdD3rlyzjnnnIsh71w555xzzsXQ/wPcYNCXeiAYAwAAAABJRU5ErkJggg==\n",
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\n",
       "text/plain": [
        "<Figure size 720x360 with 2 Axes>"
       ]
@@ -404,10 +349,37 @@
       "needs_background": "light"
      },
      "output_type": "display_data"
-    },
+    }
+   ],
+   "source": [
+    "for bm in unique_bms:\n",
+    "    flag = \"count_varying_matching_rate\" == bm\n",
+    "    \n",
+    "    if flag:\n",
+    "        tmpdf = perfdf[perfdf[\"Benchmark\"] == bm]\n",
+    "        \n",
+    "        unique_labels = tmpdf[\"Label\"].unique()\n",
+    "        plot_perf(bm + \"_RANDOM\", tmpdf, \"MatchingRate\", unique_labels, flag)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### count by varying num_reps\n",
+    "<ul>\n",
+    "<li>Fixed matching rate: 0.5</li>\n",
+    "</ul>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 720x360 with 2 Axes>"
       ]
@@ -420,18 +392,13 @@
    ],
    "source": [
     "for bm in unique_bms:\n",
-    "    flag = \"retrieve\" in bm\n",
+    "    flag = \"count_varying_num_reps\" == bm\n",
     "    \n",
     "    if flag:\n",
     "        tmpdf = perfdf[perfdf[\"Benchmark\"] == bm]\n",
     "        \n",
-    "        randomdf = tmpdf[tmpdf[\"Distribution\"] == \"RANDOM\"].reset_index(drop=True)\n",
-    "        unique_labels = randomdf[\"Label\"].unique()\n",
-    "        plot_perf(bm + \"_RANDOM\", randomdf, \"NumReps\", unique_labels, flag)\n",
-    "        \n",
-    "        cycledf = tmpdf[tmpdf[\"Distribution\"] == \"CYCLE\"].reset_index(drop=True)\n",
-    "        unique_labels = cycledf[\"Label\"].unique()\n",
-    "        plot_perf(bm + \"_CYCLE\", cycledf, \"NumReps\", unique_labels, flag)"
+    "        unique_labels = tmpdf[\"Label\"].unique()\n",
+    "        plot_perf(bm + \"_RANDOM\", tmpdf, \"NumReps\", unique_labels, flag)"
    ]
   }
  ],
diff --git a/benchmarks/analysis/notebooks/Utils.py b/benchmarks/analysis/notebooks/Utils.py
index 8c5173187..68045a8a4 100644
--- a/benchmarks/analysis/notebooks/Utils.py
+++ b/benchmarks/analysis/notebooks/Utils.py
@@ -28,10 +28,11 @@ def plot_perf(bm, df, xaxis, unique_labels, is_multivalue = False, is_singletype
     if is_multivalue:
         lnum = list(df[xaxis])
         
-        for item in lax:
-            item.set_xscale('log')
-            item.set_xticks(lnum)
-            item.get_xaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter())
+        if xaxis == 'NumReps':
+            for item in lax:
+                item.set_xscale('log')
+                item.set_xticks(lnum)
+                item.get_xaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter())
         
     if not is_singletype:
         num_style = len(df["Distribution"].unique())

From 3cd4336b7f8ed157b15d0f7e33bc883535f0dc8c Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Thu, 6 May 2021 13:24:39 -0400
Subject: [PATCH 084/267] Minor updates to notebook: change file names

---
 benchmarks/analysis/notebooks/StaticMultimap.ipynb | 10 ++++++----
 1 file changed, 6 insertions(+), 4 deletions(-)

diff --git a/benchmarks/analysis/notebooks/StaticMultimap.ipynb b/benchmarks/analysis/notebooks/StaticMultimap.ipynb
index 356cedd29..6d0aaa5f2 100644
--- a/benchmarks/analysis/notebooks/StaticMultimap.ipynb
+++ b/benchmarks/analysis/notebooks/StaticMultimap.ipynb
@@ -330,6 +330,7 @@
     "### count by varying matching rate\n",
     "<ul>\n",
     "<li>Fixed num_reps: 8</li>\n",
+    "<li>Random non-unique input keys</li>\n",
     "</ul>"
    ]
   },
@@ -340,7 +341,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 720x360 with 2 Axes>"
       ]
@@ -359,7 +360,7 @@
     "        tmpdf = perfdf[perfdf[\"Benchmark\"] == bm]\n",
     "        \n",
     "        unique_labels = tmpdf[\"Label\"].unique()\n",
-    "        plot_perf(bm + \"_RANDOM\", tmpdf, \"MatchingRate\", unique_labels, flag)"
+    "        plot_perf(bm, tmpdf, \"MatchingRate\", unique_labels, flag)"
    ]
   },
   {
@@ -369,6 +370,7 @@
     "### count by varying num_reps\n",
     "<ul>\n",
     "<li>Fixed matching rate: 0.5</li>\n",
+    "<li>Random non-unique input keys</li>\n",
     "</ul>"
    ]
   },
@@ -379,7 +381,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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+/z7qiJxz8VBkMiUinUXkVWAScAjWHd8CuASYLSJXiEid5IQZHz16wBNPwEcfWVL2998lf8Y551LJwIGw336eiDmXCYrr0eoGnKyqbVV1sKpeoqrnqephQGvgE2wMRFrp0wceewzef9+SsoL1xJxzLpVdeSX8+y8ccICX3XEu3RWZhKnq+apa6D9xVV2rqhNVdULiQkuco46CRx6Bd96xStT//BN1RM45F07LljB5Mvz1lyViCxdGHZFzrqwqFfWCiJxT3AdV9Zb4h5M8Rx8Na9fC8cfbgP2JE6Fataijcs65krVpA6+8YmtMHnecTTZyzqWfIpMwoHbwuBPQFquHA9ADeCeRQSXLccdZInbiiXDEEfDMM1ClStRROedcyfbay2Z9N2oUdSTOubIqMglT1SsARGQy0EZVlwfPLweeSkp0SXDCCbY+25Ah0LcvPPkkVK4cdVTOOVeyffe1R1W44w5rz2rXLv4zzrnUEabURGPg33zP/wVyExJNRE45Be66y25Jxm5TOudcupg1ywq7HnqoTzZyLp0Udzsy5hHgIxF5FlCgNzA2oVFFYOhQ6xE7+2yrrv/II/bonHOpbvfdYdw4u4js1Quef97HuDqXDkpMM1R1hIi8AnQIdg1S1U8SG1Y0zjrLErELLrAEbPRoyM6OOirnnCtZ374203vQIJsBPmGCD61wLtWF7euZBSyMvV9EGhdVviLdnX++JWIXX2yJ2IMPQlbarQ/gnKuIjj/ebkeefbYtAL7XXlFH5JwrTolJmIicDlwG/AqsAwS7LdkqsaFF56KLrBjiFVfYleR993ki5pxLD6eeCt27Q05O1JE450oSpifsTGAnVf0j0cGkkssusx6xa66xHrG77waRqKNyzrmSxRKw8ePhvfds4W9vv5xLPWGSsPnAX4kOJNWIwNVXWyJ2443WI3bbbd6QOefSx+zZdgFZpQrcfLO3X86lmjBJ2I/AWyIyCVgd25nuFfPDEIHrr7dE7LbbLBG78UZvyJxz6WHECFixAm69FWrWhKuuijoi51x+YZKwn4KtSrBVKCJwyy1WO+zmmy0Ru+YaT8Scc6lPxC4g//nHevarV7cxr8651BCmREWscn5te6p/JzyqFCNi1ajXrIHrrrNE7Moro47KOedKJgL33muzJlesiDoa51x+YWZH7oIVbG0QPP8dOE5Vv0hwbClFBO65x3rErrrKErFLL406KuecK1l2Nowdu2GW97JlUKdOtDE558ItWzQKOEdVc1Q1BzgXuD+xYaWmrCwYNcoW/h4+3HrFnHMuHcQSsG++gaZNbVUQ51y0wowJq6mqU2JPVPUtEamZwJhSWlYWPPSQ9YhdeKH1iJ17btRROedcODk5sMsuVti1WjU48sioI3Ku4go1O1JELsVuSQIcA8xJXEipLzsbxoyxROy886yO2JlnRh2Vc86VrFo1eO456NrV1pqsXt0W/nbOJV+Y25EnAA2BZ4Jtc2BQIoNKB5UqwaOPQp8+tubkPfdEHZFzzoVTqxZMmgS77QZHHAHTptn+RYtgyZJIQ3OuQikxCVPVpap6hqq2CbazVHVpMoJLdZUrW0XqHj1g6FA48UTIzbVblrm5MG5c1BE651zh6taFV1+FIUNg3TrYc09o0QK23x66dYP586OO0LnMV2ISJiKviUi9fM/ri8irCY0qjVSpAk89Ba1b21ixefNA1R4HD/ZEzDmXuho0sLphffrACSfA66/Dr79C+/Z2u3LduqgjdC6zhbkdubmq/hl7EvSCbZGwiNJQ1aqwtJC+wZUr4eKLkx+Pc86FNXasLfj9+utwwAE2e/LSS63C/muvRR2dc5ktTBK2XkQax56ISA6giQspPRXVdf/TT8mNwznnSuOnn6BVK1uSrVYtS8h+/dX2zZsXdXTOZbYwSdjFwFQReUREHgHeAS5MbFjpp3Hj0u13zrlUkJcHL71kpSteeMEG5vfpA5MnQ9u2UUfnXGYLMzD/FaAN8ATwJLCHqpY4JkxEthORKSLylYh8ISKbFHEQkU4i8peIzAq24WX5EqlgxAioUWPjfSJeVd85l9qOOgoWL4aTT7bnw4bB++9b6Yo2baKNzblMF2ZgvgBdgTaq+gJQQ0TahTj2WuBcVd0Z2AsYKiItCnnfu6q6W7Cl7YqMAwZYNf2cHEu+Gja0/ZMmwfr10cbmnHNFqVYNpkyBzTaDfv3gmWegd2/rHXPOJVaY25H3AO2B/sHz5cDdJX1IVReq6szg5+XAV8A2ZYwzLQwYAHPnWtK1eDHccgs8+6wPznfOpbb69eH66+Hrr2HWLEvEmjSxtmxOhS7N7VxihUnC9lTVocAq+G92ZJXSnEREcoHdgQ8Lebm9iHwqIi+LSMvSHDfVnXkmnHKKrTE5ZkzU0TjnXOmceaaVq/j556gjcS4zhUnC1ohINsGMSBFpCIS+wSYitYAJwFmquqzAyzOBHFVtDdwJTCziGINFZLqITP/tt9/CnjpyInDnnTbt++ST4d13o47IOefCGzIEVqyw25OrVkUdjXOZJ0wSdgfwLLCFiIwApgLXhDm4iFTGErBxqvpMwddVdZmq/h38/BJQWUQ2L+R9o1Q1T1XzGsYGW6WJypWtmOv221tD9sMPUUfknHPhtGwJjzwCH39sCZl6cSLn4irM7MhxwAXAtcBCoJeqPlXS54IB/Q8CX6nqLUW8Z6vgfQSD/bOAP8KHnx7q14cXX7QGrEcP+PPPqCNyzrlwevWCyy6zIRV33hl1NM5llkolvUFEmgBzVPVuEekEdBGRhfmr6BdhH+BYYLaIzAr2XQQ0BlDV+4AjgP8TkbXAP0A/1cy81mraFCZMgC5doG9fmzVZqcTfvnPORW/4cPjlF9hjj6gjcS6zhEkDJgB5IrIj8ADwAvAY0K24D6nqVEBKeM9dwF3hQk1/nTrBfffBSSfBWWfBXRXmmzuX3kRkO2AssBU2JnaUqt4uIpcDJwOxwaoXBUMrMkpWlpXgiVm1ykpbOOfKJ9SyRaq6FugD3K6qZwONEhtW5jrxRDjvPLj7bk/CnEsjxdU9vDVfrcOMS8AKuuYamzG5cmXUkTiX/sLOjuwPHAe8GOyrnLiQMt9118Fhh9n071deiToa51xJKmLdw6K0aQOffgonnOAD9Z0rrzBJ2CCsWOsIVZ0jItsDjyY2rMyWnQ3jxsGuu9r4sC++iDoi51xYhdQ9PE1EPhORh0SkfnSRJUfXrnDttfDEE7bot3Ou7MLMjvxSVc9Q1fHB8zmqel3iQ8tstWrZYrk1asChh0IalT9zrsIqpO7hvUATYDds9vjNRXwuLWsdFuWCC2zNyWHDvDffufII0xPmEmS77eC552DRIqshtnp11BE554pSWN1DVf1VVdep6nrgfqDQdXXTudZhYUTgoYegbVu/gHSuPDwJi1i7dlZ/5733rKq+j7FwLvUUVfdQRPJPUuoNfJ7s2KJSsya8/z4ce2zUkTiXvopNwkQkW0T8rn+CHXUUXHmlVaa+9tqoo3HOFSJW93B/EZkVbN2AG0Rktoh8BnQGzo40yiTLzrbHxx+HAQNswW/nXHjF1glT1XUisoeISKYWUU0Vl1wCX38NF18MO+0Ehx8edUTOuZhi6h5mfEmKMBYtgsces7Zr+PCoo3EufYQp1voJ8JyIPAWsiO0sbC1IV3Yi8OCDMGeOde/n5EBeXtRROedcyc48Ez75xJY3at0aevaMOiLn0kOYMWENsPUc9wd6BNuhiQyqoqpWDZ59FrbYwuqILVgQdUTOOVcyEVsNJC8PjjkGvvwy6oicSw8l9oSp6qBkBOLMllta6Yq997ZE7N13bQCsc86lsurV7SIyL8/asBYtSv6McxVdiT1hItJMRN4Qkc+D561E5JLEh1Zx7bqrDXT99FO7qvTBrs65dLDttjB7Nvzvf1FH4lx6CHM78n7gQmANgKp+BvRLZFAOuneHW26BiRNtsL5zzqWDWBm0GTPgppuijcW5VBdmYH4NVf3IyuT8Z22C4nH5nHEGfPWVrTW5005w/PFRR+Scc+E8/DDcfTc0bmxleJxzmwrTE/a7iDQBFEBEjsCW53AJJgJ33gkHHACDB8M770QdkXPOhXPLLTa2ddAgG1rhnNtUmCRsKDASaC4iPwNnAUMSGZTboHJleOop2GEH6NMHfvgh6oicc65kVarAhAlQvz706gW//x51RM6lnjALeP+oqgcCDYHmqtpBVeclPjQXU78+vPiiLWl06KHw559RR+SccyXbaiubMblwofWMOec2FmZ25GYicgfwLvCWiNwuIpslPjSX3447wjPPWE/YUUfBWh+V51zGGjcOcnMhK8sex42LOqKya9vWhlJceWXUkTiXesLcjnwc+A04HDgi+PmJRAblCtexoxVEfO01G7TvC0k5l3nGjbMxoPPm2b/xefPseTonYu3aQaVKtrzRS77Qk3P/CVUxX1WvUtU5wXY1UC/BcbkinHACnH8+3Hsv3HVX1NE45+Lt4oth5cqN961cmRmlas47z8a2fvxx1JE4lxrCJGFTRKSfiGQF21HApEQH5op27bW2NtuZZ1qF/Uy4ZeGcMz/9VLr96eS222ycWO/e8OuvUUfjXPTCJGGnAI8Bq4PtceAcEVkuIssSGZwrXHY29OhhPy9enDm3LJxzVlerMHXrpv8QhM03twLUS5bAEUfAN9/A2LHwyis+ztVVTGFmR9ZW1SxVrRxsWcG+2qpaJxlBuk1dddWmDXKm3LJwriIbMQJq1Nh4X3a2zYo++mhYsSKSsOJmt93goYdg6lTYfXd4+WW44gorSP3111FH51xyhamY71JQJt+ycK4iGzDAHi++2P49N24MV18NCxbARRfZKhrPPgvbbx9tnOWRlWXrTL7zzobvMXIk9O8PM2daoWrnKoIwtyNdCirqlkVR+51z6WPAAJg7F9avt8djjoFhw2DSJBt6kJcHr78edZRl9+ijcP31loCtXm23JU8+GZYuhS+/jDo655LHk7A0VdgtC4CuXZMfi3MuOQ45xGYWNmoEBx8MN9+cnuPEVq60MW4AJ55oS7P9/jvUrr3pzFDnMlmYYq1NRKRq8HMnETlDROolPDJXrAEDYNQoyMmxrvvGjaFFCxg9GqZPjzo651yi7LgjfPCBLQV03nnWS5Zuicuhh9rtx/Xr7Tv8/jt07w5//GFjxpyrKML0hE0A1onIjsCDwPbYbEkXsfy3LObNg7fe8unfzlUEtWvD00/bWLHx42GffawNSBeDB9utx86dbYB+x4528dipk62X61xFESYJW6+qa4HewG2qejbQKLFhubJo2NAG7P7xh03//vffqCNyziWKiA3ef+EFmDPHxolNmRJ1VOHUqAFvvAGnnAKzZ9vSRn36wOOPw+TJUUfnXPKEScLWiEh/YCDwYrDPr1VS1O67w4MP2tXl2WdHHY1zLtG6d7dxYg0bQpcuVhA1HcaJValiJTdGjrQevUcegcMPh222iToy55InTBI2CGgPjFDVOSKyPfBoYsNy5dG/vy1tdM898MADUUfjnEu0pk3hww+tiPPZZ8Nxx8E//0QdVenUqAFPPQUtW1oSuX591BE5l3hhirV+qapnqOr44PkcVb0u8aG58rj2WjjoIBg61AbxOucyW+3aMGECXHmllYDo0CE96wauXw+DBtmAfecyXZjZkfuIyGsi8q2I/Cgic0Tkx2QE58ouO9sG7G67rXXx//JL1BE55xItKwsuvRSefx6+/97Gib39dtRRlU5WliWUt95qSaVzmSzM7cgHgVuADkBbIC94dCmuQQNbp23ZMkvEVq+OOiLnXDL06AEffQSbbWY1uO64Iz3GicXcdJMN1j/hBEsmnctUYZKwv1T1ZVVdrKp/xLaER+biYtddYcwYmDbNbk2mU0PsnCu7nXaycWLdu8OZZ9otvlWroo4qnKpVbXxYdrbN9E638W3OhRUmCZsiIjeKSHsRaRPbEh6Zi5vDD7ep7A8+CPfdF3U0zrlkqVPHytZcfrldjO27L8yfH3VU4eTk2IzJefPgiy+ijsa5xAizgPeewWNevn0K7F/ch0RkO2AssBWwHhilqrcXeI8AtwPdgJXA8ao6M1zorjSuuAJmzYIzzoBddrHG2DmX+bKy4LLLrBL9scfaOLGnnoL99os6spJ172410OrVizoS5xIjzOzIzoVsxSZggbXAuaq6M7AXMFREWhR4zyFA02AbDNxbyvhdSNnZNmNqhx2sez9droadc/HRs6fdnqxXz8aJ3X13egxPqFfP4rztNivs6lwmCTM7sq6I3CIi04PtZhGpW9LnVHVhrFdLVZcDXwEFy/D1BMaqmQbUExGvxp8g9erZQP1//rGljXychXMVy84724D9rl3htNNs8ex0GCe2dClcfz0ceSQsXx51NM7FT5gxYQ8By4Gjgm0Z8HBpTiIiucDuwIcFXtoGyN8ns4BNEzUXRzvvbD1iM2bAkCHpcSXsnIufunXhueeslMXDD9u6jQsWRB1V8Ro0sCWNvvsOTjrJ2y2XOcIkYU1U9TJV/THYrgB2CHsCEamFLQJ+lqouK/hyIR/Z5J+XiAyO9cT99ttvYU/tinDYYTZGbOxYm7runKtYsrKsqOszz8CXX9o4seHDITfXXsvNhXHjoo5yYx07wogR8OSTdivVuUwQJgn7R0Q6xJ6IyD5AqBtZIlIZS8DGqeozhbxlAbBdvufbApuUFVXVUaqap6p5DRs2DHNqV4JLLoFeveDcc+HNN6OOxrnEE5FqInKEiNwuIk+JyFgRuUBEWkYdW1R697ZxYiJw1VU2E1HVHgcPTr1E7IIL4NBDbVm2RYuijsa58guThP0fcLeIzBWRecBdwJCSPhTMfHwQ+EpVbynibc8Dx4nZC6tJtjBk7K4csrKsJ2ynneCoo2Du3Kgjci5xRORy4D1sHdwPgZHAk9gEouuCVUFaRRdhdFq0gMqVN92/cqWVtkklWVlWamPSJNhqq6ijca78SixRoaqzgNYiUid4XvCWYlH2AY4FZovIrGDfRUDj4Dj3AS9h5Sm+x0pUDCpF7K6cate2gfpt21qv2Pvv2yK6zmWgj1X18iJeu0VEtiBomyqiosaEpeLakw0awP7B/Pzp06FNG0vOnEtHRSZhInKMqj4qIucU2A9AMb1bsdenUviYr/zvUWBo6Ghd3DVtamtMdu9uM6Uee8xuTTiXSVR1UsF9IpIF1FLVZaq6GFhc1OeLqnsoIg2AJ4BcYC5wlKoujf83SKzGje0WZEHbbpv8WMKaMQPatYOrr4aLLoo6GufKprjrh5rBY+1CtloJjssl0SGHwDXX2Oyjm26KOhrnEkdEHhOROiJSE/gS+EZEzg/x0aLqHg4D3lDVpsAbwfO0M2JE4b3gNWrAX38lP54w2rSBfv1slueUKVFH41zZFJmEqerI4MfXVfWK/BvW2LgM8r//WQ2eYcPg1Vejjsa5hGkRDKnohQ2HaIwNmyhWMXUPewJjgreNCY6bdgYMgFGjbKkgEXs87TT44Qfo3BlScVK6iMXcrBn07w8LfTSxS0Nh7qTfGXKfS2MiVjOoZUu7uvz++6gjci4hKgeztnsBz6nqGgopi1OcAnUPt4xNJgoet4hrtEk0YIBN0Fm/3h7vvBOefx6+/tqWOUvF8WG1asHTT1sB1379YO3aqCNyrnSKGxPWHtgbaFhgXFgdIDvRgZXXd9/Bu+/CllvCQQcVPvvHbaxmzY0H6k+bZo2ccxlkJDZ261PgHRHJwQpQh1Kw7qGEHEApIoOxpdlo3Dh9xv8fcghMnmxjRjt0gNdft56nVNKypfWI/fabLc/mXDopriesCjb2qxIbjwdbBhyR+NDKRtUWqe7QAd56C667zsowfP111JGlhx12gCeegK++goEDvTK1ywwi0l5ERFXvUNVtVLVbMDHoJ6BzyGMUVvfw19hSa8FjoYP707nWYawtXbXKfv7kk6gj2tSAAXDWWdajv2ZN1NE4F15xY8LeDsZ/7VVgTNgtqvpdEmMslfHjYepU6wkbO9Z6w4YNszEDnlCEc+CBcOONVk37mmuijsa5uBgIzBCRx0XkeBHZCmyGtqqWeBOrmLqHzwfHjp3juTjHnRJ2393a1WrVoFMn+zkVvfGGXXR73UOXLsKMCVspIjeKyEsi8mZsS3hkZTRunA0yr1PHFqh+7TVba2zJEuvdceGcfbZdXV56Kbz4YtTROFc+qjpEVdsAlwP1gdEi8oGIXCMi+4lISTeyYnUP9xeRWcHWDbgO6CIi3wFdgucZqVkzeO89aNTIhni8/HLUEW0qN9fa+iOPhNWro47GuZKFScLGAV8D2wNXYOMpPk5gTOWyapUVIQVbX+ygg+CEE6B6dXvNhSMC999vV8ADBsA330QdkXPlp6pfq+qtqtoV2B+YChyJDbIv7nNTVVVUtZWq7hZsL6nqH6p6gKo2DR6XJON7RGW77eCdd6B5c1uD9oknoo5oY02awOjRVsT1nHNKfLtzkQuThG2mqg8Ca4JblCdgdXJS0qGHwr332gyf00+3NRIfecRuT6bi7J5UVr06PPssVK0KPXumbr0g50pLRGoALbFK+qeral7UMaWLLbawulzt29swj/vvjzqijfXqBeedB/fcY8NTnEtlYZKw2DDHhSLSXUR2xxbaTklDhsCyZdCxI4wcaVOX69Sxuje9e1tRQhde48bw1FNWsuLYYy25dS7diMhhwfq3M4PbiF9g6+DOFpGBJXzcFVC3Lrzyis2eHDwYbrgh6og2ds01NokgVceuORdT4tqRwNUiUhc4F6sPVgc4O6FRlUP16jaN+pln4O23rUTFJ5/A1lvbP8zeve19a9Z42YqwOnaE226znsUrrrDNuTRzFXAQUBeYArRS1R+DNSPfYEPBVRdSjRrWUz5woI3DXbrU2thUWPascmVLEn0tXJfqik3CgsGqTVX1ReAvQk7ljlrlytC3r235XX75hp+PO84aizvugM03T2p4aWnoUJg5E6680m73/v679ZKNGGFjxpxLcetV9VsAEZmjqj8CqOpiEfESn2VUpQo8+ijUq2flgP78E+66KzXqddUMFt778ku7LXnllamRIDqXX7G3I1V1HXBYkmJJGlUr8Pf00xseXfFErGp2VpYVRVS1BX8HD7YZqc6luCwRqS8imwHrg58bBAtwhxmW4YqQnW3jry68EO67D445Bv79N+qoNpg40Rb5TrWxa85BuMbnfRG5S0T2FZE2sS3hkSWQiA3YnzEDtt3WpjMfdZT17riiXXHFpmPCVq6Eiy+OJh7nSqEuMAOYjg2pmBk8n4EVoXblIGK3Iq+/Hh5/3AbHr1wZdVRm2DA4+GAr4j1zZtTROLexMGPC9g4er8y3T7Hp3Wlt111taZ4bb7R10nzdseIVNbvUZ526VKequVHHUBFccIHdmhwyBLp2hRdesEH8UcrKslumu+8OffpYbN99BzvuaOWLttwy2vhcxVZiT5iqdi5kS/sELKZyZbjoIvjhB9hqK+vpuewy+PXXqCNLPUUteZdGS+G5CkpEsoN1H2PP9wqKtO4nIt4TFkeDB9sYrGnToHNnWFzoQk7JtfnmNlZt3jxbSWWvveDHH6F1a/jss6ijcxVZiUmYiGwpIg+KyMvB8xYicmLiQ0uu2CyaWbOsS71FC2tIfKmjDUaMKHy2UZ8+yY/FuVK6Hjg13/PxwPnApcAlkUSUwfr2heeftzV79903NXrLJ06Eo4+22fInn2xjxK64wlYHcS4qYcaEjQZeBbYOnn8LnJWgeCLXpo0lYs2a2T/Y3r1h4cKoo0oNAwbAqFFWc03EesC23x4eeMAr6ruUdwCQf83HP1W1B1a2Yp9oQspsXbvC5MmwaJHV7Pr222jjeekluPZaKz69dKn1gA0aZEsx/fNPtLG5iitMEra5qj4JrAcIFrtdl9CoIta8uRX5u+kmePVVW57De8TMgAG2OO769da1/9Zb1qj16mVFcp1LUVkFFur+H9gC3kCtwj/iyqtDB2sjVq2ynz/5JLpYatTYsOpH377QrRvMmWOzOyuFGR3tXAKEScJWBNO6FWwsBVYzLKNlZ8O558Knn9oalCJ2tfTzz1FHlloaN4Ynn7SBrscf7xX1Xcqqkn/sl6pOBggKUVeLLKoKYPfd7aK2WjXo1AnefTeaOI49Fi691Ap1X3+9zYY/6CA4/HAv3O2iEyYJOwd4HmgiIu8BY4HTExpVCmnWDNq1s5+vuMLqio0e7T1j+XXubDNMn33WCjY6l4LuB54Qkf+mkYhIDjY2zCtIJVizZnbbr1EjKxfx8svJj+Gii+xieocd7C5H3bo2Vi0nJ/mxOBcTZnbkTKAjVqriFKClqlbI+SQnnQStWtk4gu7dYcGCqCNKHWedZWPoLrnElgtxLpWo6i3YxeRUEflDRH4H3gFeUNWbo42uYthuO3jnHRvucdhh8MQTyT1/tWp2oThpko1XmzjRbktec43F5VwUwsyOrAacga29dgUwNNhX4ey4o41vuOMOW5eyZUurgxMzb54N/ox6AGoURGy2UatW0L+/lfxwLpWo6n2q2hjIAbZX1RxVvTfquCqSLbaAKVOgfXtrJ048EXJzrZZXbm5yVt9o1cpuTbZvbxONBg6Epk0Tf17nChPmduRYoCW2ePddQAvgkUQGlcqysmwh69mz7R9xkyY2xuDEE2GPPSxB69jRZlWuWBF1tMlVo4YtnC5SMb+/S10icoyIZAGo6t+qurzA601EpEM00VUsdetab3mrVvDQQ3bxGtUyaHXqWAyNGsG6dT6m1SVfmCRsJ1U9UVWnBNtgoFmiA0t1O+xgDUmLFjYe6tVXYfhwG+swb54lJBdcEHWUybfDDlZf7fPPLTH1sXMuRWwGfCIiD4nIUBE5SkSOE5ErReRt4AbASzQnSY0aViaioKiWQVu5Erp0sRIWziVTmCTsk2BGJAAisifwXuJCSj/33w/bbANnnmn/kH/7zQZ+PvKIXV1VNAcfbOMsnngCbrml5Pc7l2iqejvQBhuI3xCrG9YG+Bk4VlUPV9XvIgyxwpk/v/D9URR2rV7desOGD7chJ84lS5jqKHsCx4lI7J9GY+ArEZmNldlplbDo0sSyZfD++zY+7JxzrODr+PGwerXdqszOjjrC5Pvf/2D6dOsN3H132D9jFrpy6UpV1wGvBZuLWOPGdtegoO22S34sIjByJMyYYWPVZs3yNSVdcoTpCesKbI/NkOwY/NwNOBTokbjQ0keXLrYe2eDB8NFHtoBt796Ql2czcioiEXj4YZsJddRRhTe2zrmKq6hl0Jo2jWZsVq1aVvPwzz+tKHVFvIvhki9MiYp5QD0s4eoB1FPVebEtwfGlhauugttvh6FD4auv4JBDLAm55RYbE1VRl8SoXdumhK9ZY+tLVtTfg3NuU4Utg3boofDGG3DKKdEkYq1a2ULf33/vhbldcoQpUXEmMA7YItgeFZEKU6w1jKZNrRt7883h0UehShWYORP23BNuuAH22qvilmxo1sxmO82cCUOG+EB959wGBZdBe/55qzX4wANwwgnR9EadcIJNLGrcuOT3OldeYcaEnQjsqaorAETkeuADrGSFCzRqZBX1C9ptN1siIy/PkpFu3ZIeWuQOPRQuu8x+P23bwmmnRR2Rq6hEpCpwOJBLvvZPVa+MKia3gYjdWahSxQbJr1kDY8Ykd21HEbs1+e+/cOWV1l5ttVXyzu8qljBjwoSNF+xeF+xzIRx8sA1Qz821ZOSqqypmLZrhw+37n312dGvHOQc8B/QE1gIr8m0uhVx6qZWLeOwxW4ljzZrkx/Djjzak5OijfXyYS5ww1xcPAx+KyLPB817AgwmLKAPtsIOtm3bKKXD55ZaM7L571FElV1aW3apt2xaOPNJu326zTdRRuQpoW1XtGnUQrmTDhlmP2LnnWhL2xBP2PFmaN4e777bbk1ddZW23c/EWZmD+LcAgYAmwFBikqrclOK6MU6OGzaCcPn1DAvbHH9HGlGx169pA/b//hsMPtxIeziXZ+yKya9RBuHDOOQfuvNPWeezTB1atSu75Bw2yZY2uvBJefz2553YVQ5jbkajqTFW9Q1VvV9VPEh1UphLZkIC9/DJsv71Nia5IWra0MR4ffghnnBF1NK6iEJHZIvIZ0AGYKSLfiMhn+fa7FHXaaXDffbbwdq9eyZ9lfffdsPPOcPLJ0dwWdZkticMdXX677WbTofv2tdpi112X3MGnUTr8cLvVcN11NmHh5JOjjshVAIdGHYAru1NOgcqV4aSTbDjH889DzZrJOXfNmjBhAqxdazE4F09F9oQFs4hcgjRqBG++aVd5N99sBV8XL446quS5+mo46CD7/h9+GHU0LtPlq2t4df46h7F9UcfnSnbCCTak4623bJb58uUlfiRumjeHXXaxnz/xe0Eujoq7HfkBgIg8UpYDBwvlLhaRz4t4vZOI/CUis4JteFnOk86qVLHxDqNHw7RpdnVXUWRn29JO22xjPWO/+tLJLjla5n8iItnAHhHF4krpmGOs1M9770HXrvDXX8k9/8MPwx57wKuvJve8LnMVl4RVEZGBwN4i0qfgFuLYo7Elj4rzrqruFmwVtk7PwIHw5Zdw4on2vKIs8dOggQ3UX7LEZkz6eAuXKCJyoYgsB1qJyLJgWw4sxspWuDTRr5/NlPzoI+tNX7o0eefu29fGtR5zjFfUd/FRXBI2BNiLjZcsim0ljq9Q1XewGZUuhO23t4H7c+ZYt/fgwRVj9mDr1lYd+913bSq6c4mgqteqam3gRlWtE2y1VXUzVb0w6vhc6Rx+uI3T+uQTOPDA5M00r1EDnnrKJgf072/jxJwrjyKTMFWdqqr/B1ygqoMKbCfE6fztReRTEXlZRFqW/PbM17gxnHkm3H8/7LcfzJ8fdUSJd/TRVsT1zjvhkTLd/HaueCLSRkTaAE/Ffs6/RR2fK73DDrPSFV98AfvvD7/9lpzzNm8OI0fahePwCjeIxsVbmPl4j4jIGcB+wfO3gftUtbw3j2YCOar6t4h0AyYCTQt7o4gMBgYDNM7wBb2ys23Q+h572G3KPfawrvfOnaOOLLFuuMGuagcPtu7+Nv7foouvm4PHakAe8Cm28kcr4EOsdIVLM926wQsvWELWqZMt/p2MJYYGDLD1cNu2Tfy5XGYLUyfsHmzg6j3B1ga4t7wnVtVlqvp38PNLQGUR2byI945S1TxVzWvYsGF5T50Weve2MQ+bbWZJWKarVMm+5+ab23f//feoI3KZRFU7q2pnYB7QJmhP9gB2B76PNjpXHl26wEsv2ULgnTrBL78k57w332xtFVTMpehcfIRJwtqq6kBVfTPYBgHlzv9FZCsRkeDndkEsFayGfPGaN7dE7Pbb7fkPP1i1+Uy1xRbwzDM2U7JfPx9v4RKiuarOjj1R1c+B3Ur6UGGzvUXkchH5Od8M726JCdmVpHNneOUVGyzfsWNyh3Hcdx8ccIBPLHJlEyYJWyciTWJPRGQHNl7Qu1AiMh4rc7GTiCwQkRNFZIiIDAnecgTwuYh8CtwB9FNVLf1XyGy1a0PVqpaQdO8Oe+0F330XdVSJ07Yt3Huv3Va46KKoo3EZ6CsReSAokdNRRO4HvgrxudEUPtv71nwzvF+Ka6SuVPbdFyZPtnqLHTtaz1gy1K1rtcsuuSQ553OZJcyYsPOBKSLyIzaGIgdbS7JYqtq/hNfvAu4KE6Sz23V33WU9RHl5thj23nvbQPZ58yx5OfxwS9jS3aBB8PHHcOONNiaub9+oI3IZZBDwf8CZwfN3CDG8QlXfEZHcBMbl4qB9e1vj8aCDLBF7801o0qTkz5VH//7w9ts2rnXffa2iv3NhSZjOp6B6/k5YEva1qkZWPCEvL0+nT58e1ekjN2+eLWQ7c6Ytp9Gnjy1/9NJLVrjwjTegXr2ooyy/f/+1WwyzZlkh2119yeUKTURmqGpexDHkAi+q6i7B88uB44FlwHTgXFUtsWpVRW/DkiFWuqJ6dUvEmjVL7PlWrbK7FPPn27kzfP6YK6Xi2q+wC3ivVtXPVPXTKBMwBzk5MHWqDWDfemursXXeeZZ8tWpl6zFmgipV4Omnrav/wANhu+0gKwtyc61itnOlISJPBo+zg4W7N9rKeNh7gSbYmLKFbJiBWdj5B4vIdBGZ/luyailUYLvvDlOm2MVcx47wVZgbzuVQrZrVD1u3ztpi58IKlYS51LJsmY0Rmz7dkpUlS6yROf30zFr6qFEjW9x78WJYsABUrSdw8GBPxFypxW4/Hsqmxad7lOWAqvqrqq5T1fXA/UC7Yt5b4WZ4R61VKxurpWqJ2OzZJX6kXJo2tclTg0ocrOPcBsUmYWK2S1YwLpxKlWxKdJUq9nzoUOsKf/PNDfsyxZgxm+5buRIuvjj5sbj0paoLgx8PAKoUsoh3qYlIo3xPewOFrpProtOihY3Xqlx5w/CGRNpsM3t84w0bIuJcSYpNwoLZihOTE4oLa7PNLOm65RZ7fsMNsMMOcP75sO22duWXKX76qXT7nStBLjBSRH4QkSdF5HQR2a2kDxU22xu4IXZ7E+gMnJ3IwF3Z7LSTJWI1alhl/auusmENiRresH49XHihFXRN1gxNl75KHJgvIncDo1X14+SEVDwf1GrmzYODD7aEbNddrUbOv//CwoW2EPg992RGr1hubuELmufkeANXkcR7YL6IVAdOBs4DtlHV7HgduyTehkVj7lxo127T5Y1q1IBRoyxpipcffrBVP5o3t+WNMqEtdmVX3oH5nYFpwZXjZ/mu/FyEcnLg88/timvXXW1Q6IIFVqvmtdfgzz+jjjA+RoywRrKg/sUWQHGucCJyiYi8DEwGdsSSsG2jjcolQ25u4clQIoY3NGkCDz1kxbYvuMBmTE6cWPgFpavYwvSE5RS2v6zjKMrLryJLtmwZ1Kljg/fnzoUdd4w6ovIZN84ayZ9+stutYJMRpkzxtdsqinj1hInITGAtMAlbB3eaqq4q73FLw9uw6GRlFT5cQyQxSw8NHgz332/rWeblwQcfwJFHWs3H7KT1vbqolasnLEi2tgP2D35eGeZzLjp16tjjZZdZl3i6DxCNja1Yv94SsWnToGFDW7z3m2+ijs6lE1Vtgw3O/wjoAswWkanRRuWSpaj6XYmq67Vkia1tuWCBLTQ+Z47dwbi33Ksvu0xRYjIlIpcB/wMuDHZVBh5NZFAuPoYMsW7xHj3g1lszZ8D+1lvb8iQiVhn755+jjsilCxHZBTgGGAj0BRYAb0YalEuaooY3xHM8WMyyZdZOTZhgvV7z5tnM9quugocfjv/5XHoK06PVGzgMWAGgqr8AtRMZlIuP7bazwq69esE551jNrX//jTqq+Gja1CYjLF1qExSWLIk6IpcmrgfqYOvV7qyqnVV1eMQxuSQZMMAG4efk2EXcttvaduut8S+yunKllcaoWdMmA+y+Oxx9NNSvbwmacxAuCfs3KFWhACJSM7EhuXiqWdMG7V98MYwfn1mLf7dpY4Ndv/vOevtWrow6IpfqVLW7ql6vqu+r6pqo43HJl394w/z5MGOG3TE49FBbdzJettwSttnGbkM2bAiXX27t1bHHWg++cxAuCXtSREYC9UTkZOB1rDq0SxNZWXD11fDtt9Cype1btCjamOJl//1t4P4HH8BRR8Ea/2/VFaKo5YrKuWyRywBbbLFhfckePewWYjyIwJ13wkknwbBhttTcLrtY5f5KleJzDpf+wgzMvwl4GpgANAOGq+qdiQ7Mxd8229jj6NHW4KT7gP2YI46wumiTJlmNtETMcnJpL7Zc0SvBNiDYXsLaN1eBNWxotyObN4fDDrOhDvHQsaNdIKrCc8/BccfBwIFwxx12h8K5sPn4bKA6dksywStwuUQ74IANA/ZvugnOOsuu2tLZkCE27mL4cLuyvemmqCNyqSRWUkdE9lHVffK9NExE3gOujCYylyo239xuR3bpAj17wrPP2gzs8tpxR7j++g3P162D1q2t/XUuzOzIk7Dp3H2AI7DCrSckOjCXOJk6YP+SS+C00+Dmm+HGG6OOxqWomiLSIfZERPYGfJyrA2wFktdftwLYvXvDiy/G/xzZ2XD22VCtmk0omuoFUiq0MGPCzgd2V9XjVXUgsAdWssKlsdiA/UsugQcfhLfeijqi8hOB22+Hvn2tSrVPA3eFOBG4W0Tmishc4B7ALyrdfxo0sFVHWreGPn3sNmKinH66ze7+8MPEncOltjBJ2AJgeb7ny4H5iQnHJVNWltWsmT17w2ydv/+ONqbyysqCsWPtlsLJJ8Pzz0cdkUslqjpDVVsDrYDWqrqbqs6MOi6XWurXtwH6u+9uY06ffTYx57nlFmjUyG57fvVVYs7hUluRSZiInCMi5wA/Ax+KyOVB4dZpwPfJCtAl3i672OPUqbD99uk/YL9KFSuQ2KaN9Yq9+27UEblUISJVReRo4DTgTBEZLiJeJ8xtol49S8Ty8mzm9YQJ8T/HllvaOapUsR6x+d69UeEU1xNWO9h+ACYS1AkDngMWJjYsF4WcHCtcmAkV9mvXttmSOTn2fT7zIgTOPAf0xNaPXJFvc24TdevCq6/aGrV9+yZmRuMOO9hszL/+glNOif/xXWorcnakql6RzEBc9GID9o87zgbsf/GFlX6oUiXqyMqmYUNrQPfZB7p2hffes54+V6Ftq6pdow7CpY86dawdOeQQ6N/fSuD07Rvfc7RubYlYTk58j+tSX5jZkXki8qyIzPTihpkvf4X9Bx+EMWOijqh8cnKsAV21ysa9LV4cdUQuYu+LyK5RB+HSS+3a8PLLsPfetvTQ+PHxP0f79rYu7tq1VuTVC09XDGHqhI3DZkjOBrwMZgUQq7B/4IGw3362b+3a9K3y3LKlTTU/8EC7mp0yxa5uXYXUATheROYAqwEBVFVbRRuWS3W1a9t42UMPhWOOsXpfxxwT//O8+SaccYbNmBw71tpjl7nC/Lf6m6r6HLMKqFMne5w3zxKY22+3+jnjxsGff9oMxP33T49Cr3vvDU8/bdWwe/WyxrRataijchE4JOoAXPqqVcvGmvboYcM21q+3x3g66CAYMcLuRjRsaDMo06GNdWUTJse+TEQeEJH+ItIntiU8MpcysrPtKrBHD1vWY948qFHDatz0729XhOmgWzerHTZlyoYrWVexqOq8oHr+P9hko9jmXCg1a1rP+v77w/HH2zJw8XbhhXDmmXDbbRtX23eZJ0wSNgjYDeiKrb3WA1uHzVUQ225rxQsrVYKVK22swrBh8MknMGdOeq2BduyxVlF/wgQYOjS9Z4C60hORw0TkO2AO8DYwF3g50qBc2qlRA154we4QnHACPPRQfI8vYj1gRx9tQ0N++SW+x3epI8ztyNaq6gNZK7hPPrF6Ofvvb43C1lvDlVfC//0fTJwI/fpFHWF455xjA/Svv97q9Fzh84ArkquAvYDXVXV3EekM9I84JpeGqle3avq9esGJJ1rP+sknx+/4WVnWc//tt9beuswUJgmbJiItVPXLhEfjUlalSra+5FVXWc2c/fe3/atWpeeA/WuvtQW/r7zSxl2cdlrUEbkkWaOqf4hIlohkqeoUEfEbPq5MYolY794weLCNEYtnra8qVTYU077vPth5Z+jYMX7Hd9EL899nB2Cgzyaq2PbZBxYtslo2hx1m+37+Gc49Fy66KNrYykIERo6E33+3mUgNG8a/9o9LSX+KSC3gHWCciCzGCrc6VybVqtmyRocfDkOGWI/YqafG9xyrVtnEqF9+gXfesbpiLjOEScK8sKGjcmWrjdOnj9Wz2WoreOYZuxK87DJLYgYPjjrK0qlUCR5/3JYLOfZYW7i3S5eoo3IJ1hMblH82MACoC1wZaUQu7VWrZu3hkUfaWNP16+Pbu16t2qaFp3fYIX7Hd9EJMzBfi9hcBdOhA/zwgy1ou8sudkX24482pfqUU2ysVbrNOKxe3Rb5bt7cbil8/HHUEblEUtUVqrpeVdcCk4A7VfWPqONy6a9qVSuD07OnzRy/4474Hr9xY0vEVq+2NvfXX+N7fBeNMEnYJODF4PEN4Ed8NlGFVbu29RqdfrqNT6hTx5KYM86w9SbPOy/qCEuvXj1r3Bo2tGKuX38ddUQu3kRkLxF5S0SeEZHdReRz4HPgVxHx3n4XF1WqwJNP2gXdmWdamxhPLVpYnbLY0BCX/kpMwlR1V1VtFTw2BdoBUxMfmksXlSrZeIVRoywZS0eNGsHkyVYT7eCDYcGCqCNycXYXcA0wHngTOElVtwL2A66NMjCXWapUgSeesDFi55wDAwZAbq7NdszNtWLX5dG+PXz3HQwcGI9oXdRKvSCCqs4E2iYgFpfmTj7ZFshev956yj76KOqISqdpU1sfbulS2GsvW9A8Xg2ni1wlVZ2sqk8Bi1R1GoCqer+ni7vYGNp27eCxx6zAtao9Dh5c/vakUSN7fOcdq1OWbsNA3AZhFvA+J992nog8BvyWhNhcmlq82LrMO3ZMr0KuAG3aWAL588/WGxbPhtNFKv+6t/8UeM3HuLq4q1zZbhsWtHKlLUkUD7NmWS0xLzydvsL0hNXOt1XFxob1TGRQLr1ttZUtPtumDRx1lK2Dlk4NRGHJVjwbTheJ1iKyTESWA62Cn2PPvRi1S4j58wvf/9NP8Tn+GWfY6iUjR9osdZd+SixRoaplqicuIg9hyxstVtVdCnldgNuBbsBK4PjgVqfLAA0bwhtv2C3KSy6xmTzxni2UKEU1kPFqOF3yqWp21DG4iqdxY+tJL2jbbeN3jmuusbsPV11l7e7pp8fv2C7xwtyObCYio0Rksoi8GdtCHHs0xdcYOwRoGmyDgXvDBOzSR7VqMHasLXPUJ42WfG/cuPD98Ww4nXOZb8QIW2eyoEqVYOHC+JwjVni6Z0+YNi297jq4cLcjnwI+AS4Bzs+3FUtV3wGWFPOWnsBYNdOAeiLSKEQ8Lo2I2G28Tp3s+b33wpcpvgBWUQ1n3brwT8HRRM45V4QBA2zWeE6OtYU5OTZj8tdfYc89Yfbs+JynUiWbkfnII3aeyZOhf38r7HrTTbB8eXzO4+IvTBK2VlXvVdWPVHVGbIvDubcB8t8xXxDscxnqr7+sy7x9e2skUlVhDefJJ8MXX9jVpidizrmwBgyAuXNt1vjcuXDzzfDuu7B2rVXAj1dbWLWqzea+9FLo0cNmqp96qo3P3W8/+Pvv+JzHxVeYJOwFETlVRBqJSIPYFodzSyH7Cu1IFZHBIjJdRKb/9ptPzExXdetad3luLnTrZr1iqapgwzlqFDz4ILz+uidizrnyadPGkqNYW3j//fE57tKlNvZ2yy2tzdppJysem5sLDz0Un3O4+AqThA3Ebj++D8wItulxOPcCYLt8z7cFfinsjao6SlXzVDWvYcOGcTi1i0rjxjB1qlWmP/VUWwA8XQwa5ImYcy4+ttvO2sIuXawEzrBhdtFXHh9/DHvsYZOisrPt2HPnwtFHw5QpcQnbxVmYivnbF7LFY+nQ54HjxOwF/KWqcRqq6FJZ7dowcSKcfTbsuGPU0ZSOJ2IVk4g8JCKLg+WOYvsaiMhrIvJd8Fg/yhhd+qlTB154wdbevf566Nu3fG3KZptZWYwdd7Sl2P7+28bjfvIJbL553MJ2cVRkEiYiHYr7oIjUEZFNSk/ke3088AGwk4gsEJETRWSIiAwJ3vIStg7l98D9wKmljt6lrexsuOUW+L//s+evvVb4VO5U5IlYhTSaTWd7DwPeCJZzeyN47lypVKpkQzNuugkmTID997eSE2XRpo0ldjfeCK1aWY9Yo0Z2K/LEE+Mbt4uP4uqEHS4iNwCvYLcgfwOqATsCnYEcoMibSarav7gTq6oCQ0sbsMs8q1ZZYrNmDTz3nC0ZlOoGDbLHE0+0ROy556B69Whjcomjqu+ISG6B3T2BTsHPY4C3gP8lLyqXKURsaEZuLhxzjLWBkybBzjuX/jjPPGNFsu+5x8aGff+9JWVt2thKINv49LeUUmRPmKqeDXQHFgJHAlcB52B1vUaq6n6q+nFSonQZrVo16wmrVcu6zh9/POqIwhk0yK4wvUeswtoyNoQieNwi4nhcmjv8cHjrLVixAvbeu2zjuHJybALUpEnWuzZ/vq0vefLJNhtz7tx4R+3Ko9gxYaq6VFXvV9XjVfVgVe2lqheq6tRkBegqhp13ttlCbdtafZsrrkiPooPHH++JmCuZz/B2Ye25p7WFjRrBwQfDmDGlP4YItGwJ++67oebhmWfCsmV2oeuJWOoIMzvSuaTYfHNLZo47buOFb1Xhu+/g229TMzHzRKzC+jVWYDp4LHIkj8/wdqWRmwvvv29J1PHHw/Dh5W/72rSxNsoTsdTiSZhLKVWrwujRcNdddjU3cSLsuit07gwHHmiDTafHo0BKnHkiViE9j5XwIXh8LsJYXIapVw9eftmGPVx1lY0VW726fMfMn4j17l3+khiu/EpcwNu5ZBOx2ZPLlsGRR1pj9MYblow9+SQceih8/bXtTyXHH2+PJ5zgg/UzTTDbuxOwuYgsAC4DrgOeFJETgZ+wsbPOxU2VKjYTe8cdbfm3n36yC9PNNiv7MWOJ2Pr1VmHfRSvMAt41RORSEbk/eN5URA5NfGiuops40ZY4qlwZOnSwgaZ9+0LHjrZOWirK3yN22GHeI5YpVLW/qjZS1cqquq2qPqiqf6jqAaraNHgsbq1c58pEBC66CMaPt2Ks7dvbjMfyaNMG8vLs53vuSZ/yQJkoTB78MLAaaB88XwBcnbCInAssXAjt2sFHH9mVYI8ecNpptibawhQu6xtLxN54wxMx51x89OtnbcqSJVbCYmocpsctWmQ9bJ06eSIWlTBJWBNVvQFYA6Cq/1D4uo/OxdXee1s16S22sEGqZ50Fn39uPWJ77x11dMXzRMw5F2/77GPlJxo0gAMOsN6x8thqKysP9OefnohFJUwS9q+IVCdYXFtEmmA9Y84lVIcO0KIFdO9utXMOOcQG7m+7LeyyC9xwA6xdG3WURfNEzDkXbzvuCB98YKUsjj4aRowo38zJvDxPxKIUJgm7DKuav52IjMOW57ggoVE5h42FePJJS2CuucZmCB1yiI0VGz8e/vc/2G8/+OGHqCMtmidizrl422wzS5wGDIBLLrHJQP/+W/bjxRKxv/6C996LX5yuZKIhUmgR2QzYC7sNOU1Vf090YEXJy8vT6alYo8Al3fjxtvbk2rVw661w0kmWuKWiMWNsqvkBB8Dzz/usydISkRmqmhd1HPHgbZiLF1UrbH3FFbbm5IQJ5Zs1vmSJ3eoEWLfOZqm78iuu/QozO7I3sFZVJ6nqi8BaEekV5xidK7X+/WH2bOuWHzwYbr456oiKNnAgPPyw94g55+JHBC6/3C7y3n3XxsrOmVP248USsNdft5qMP/0UlzBdMULdjlTVv2JPVPVP7Balc5HbbjvrRr/nHuuSh9RNcDwRc84lwnHHweTJNmt8zz2tZyw31+qA5ebCuHGlO17durbYd6dOnoglWpgkrLD3eJFXlzKysuy2ZIMGsGaN1REbPBj+/jvqyDbliZhzLhE6dbIB+7HesXnz7HblvHnWHpYmEWvb1i5ulyzxRCzRwiRh00XkFhFpIiI7iMitwIxEB+ZcWaja2IgHHoDWra1RSjWeiDnnEqF5c6uyX9DKlVYPrDTatrXetVgitrjIlVFdeYRJwk4H/gWeAJ4CVgFDExmUc2VVpQpcdx28/bYty9GhA1x6qfWQpRJPxJxzifDzz4XvL0tvVrt2loj16AGbb16+uFzhSkzCVHWFqg5T1TxV3UNVL1TVFckIzrmy2ndf+PRTS3YmTEjNemIFE7GHHy7fOA7nnGvcuPD9jRqV7Xjt2sHtt1u7NG8ezJ9f9tjcpsLMjmwmIqNEZLKIvBnbkhGcc+VRp47V6Jo2zUpC/P23LYZbnsKG8ZY/ETvppPKN43DOuREjoEaNTfcvXWrlccpq/Xro1ctuTXoiFj9hbkc+BXwCXAKcn29zLi3UqWOPDz9siU7XrvDLL9HGlN/AgTapYP36jfeXZRyHc65iGzAARo2CnBwbpJ+TA7fdZquP9OxpxV3XrSv9cbOyYORI+P13T8TiKUwStlZV71XVj1R1RmxLeGTOxdlpp8G991o9nV13haeeijqiDZYsKXy/z0pyzpXWgAEwd65d2M2dC2eeaQt+n3ii9ZR17w5//FH647ZrZ7Mmf/8dOnf2RCwewiRhL4jIqSLSSEQaxLaER+ZcnInAkCEwaxY0aQJHHQXXXx91VKaocRxF7XfOudKoVs1mjY8aBVOm2FJFM2eW/jixwfq//QbnnBP/OCuaMEnYQOz24/tYaYoZgK+54dJWs2a2PtqVV1oiBmXrno+nosZx9OiR/Ficc5nr5JPtbsDatbDPPlZtv7T23NMSuVGj4h9fRVNi0VVV3T4ZgTiXTJUrW+kKsIHwffpYcnb11VC1qu374w+7eqxVK/HxDBhgjxdfbLcgt9nGkrK77rLFeocPtzEZzjlXXu3awYwZ0K8fHH88fPihjRsrrMZYUdq0scdVq2yoR9261kO2fr21pxdcALVrJyL6zBKqWReRXUTkKBE5LrYlOjDnkmXNGth6a7jpJitQ+Mgj9rjjjjatu3//so2fKK384zjmz4fPPrNFv6+4whrLlSsTH4NzrmLYYgtLms4/38bKduxYdI2x4nz7LYwebYP2r7kGxo6FH36Agw9OzdJAqSZMiYrLgDuDrTNwA3BYguNyLmmqVLFG6MUXbe21446DnXe2wac//wwNG9qVXbJLW1StaiU1broJnn7aap8tWJDcGJxzmatSJbjhBpuk9Pnn1rv19tulO8Yff9jY1exsOPts2HJLK62zbh289FJi4s4kYXrCjgAOABap6iCgNVA1oVE5F4Hu3a03qkkTeP11WLbMylvcdpslP2UZxFpeInDuuZYgfved9dB9+GHy43DOZa4jjoCPPoL69eGAA+DWW8NfdH78MfTuDa++aoP199nHevEPOcRec8ULk4T9o6rrgbUiUgdYDOyQ2LCci8bixTb+6uOPNywIPnaslbSYOze6uLp1s3Uwq1e32waPPRZdLM65zLPzzpaIHXaYzXrs398KXJckJwdmz4a99rLB+rVrWy/+7Nn2mite2AW86wH3YzMjZwIfJTIo56Kyxx52Rbfttvb8mWdsXNakSdawRKllS2sk99zTeuwuuWTTAq/OOVdWderYMm/XXWe3KPfay3rgi9Ozp73nlltgl11swP/bb9tFY7Vq3kaVJMzakaeq6p+qeh/QBRgY3JZ0LuOccII1HhdcAN98YwP2W7SwsRO9e8OFF0a72Pbmm1uxxJNOsrIWRxwR7mrVOefCEIH//c8uRhctsnpixS13VK2atUmTJtn42diYsCuvhGOPtaWOli1LWvhpJ+zsyFYichjQBthRRPokNiznolG/vtXQWb7cljcaOtSqTM+dC8ccY1eIRx8dbYxVqlh9nttug+eegw4dvLK+cy6+DjzQerWaNi15uaMdd7T1b3/80XrF3nnH2s277rLB+e3bw/ffJzf+dCFawug7EXkIaAV8AcQ6FlVVT0hwbIXKy8vT6dO9VqyLxptvWpd9Xp5d3a1da2PHovLqq9C3r90qnTjRGrtMJCIzVDUv6jjiwdswl05idcAefNDKTowbZ7ULw5oyBY480hK4J5+ELl0SF2uqKq79CtMTtpeq5qnqQFUdFGyRJGDORW3//S0BAxg2zAazPvFE8stXxBx8MEybZolhp05W48w55+KlvMsdde5sE51ycmyik9tYmCTsAxFpkfBInEszgwdbfZx+/Wx5oahuCTZvbmUrOnSwGmfDhkW/DJNzLrOUZ7mj7be3W5vdutnzV1+1HjYXLgkbgyVi34jIZyIyW0Q+S3RgzqW63XazXqhbbrErxJYtix/AmkgNGsArr9gC5ddfb5MIli+PJhbnXGZq1856wfbe25Y7OvVU+PffcJ/NzrbHOXOsJmPHjvDLLwkLNW2EScIeAo4FugI9gEODR+cqvFiV6C++sLEOu+5q+6O4PVm5slX+jw2G3XvvaGubOecyT8OG1pN1wQVlW+5o++1tbNgXX9itzWnTEhdrOgiThP2kqs+r6hxVnRfbEh6Zc2kkN9dqim2/vSVgvXvbYtxRdLkPHQovv2xV/tu2halTkx+Dcy5zVapkPe75lzu65BJrB7Oy7HHcuKI/36fPhjpiHTva2pMVVZgk7GsReUxE+otIn9iW8MicS1P//gv16tlitq1awVtvJT+GLl1snFiDBjaZ4OGHkx+Dcy6zxZY7ys62uoXz5tlF6Lx5Nma2uERs111twH6HDrZSSUUVJgmrDqwGDsJuQ8ZuSZZIRLoGY8m+F5FhhbzeSUT+EpFZwTa8NME7l4qqVrUru8mTbYB8585WXPWvv5IbR7Nm1tXfqZMVoT33XB+w75yLr513tp6xglautLsBxdlsM7u1ef759nzaNFiyJP4xprJCfnUbiEg28Luqnl/aAwefvRursr8A+FhEnlfVLwu89V1VDZXUOZdOunSx9dMuvxwefzyacWL169v4sHPOsQkEX30F48dD3brJj8U5l5kWLCh8/7wQA5diCdzKlVYUtnZtK0LdsmX84ktlxfaEqeo6rEp+WbQDvlfVH1X1X+BxoGcZj+VcWqpRA264Ab7+2m5RrlkDZ50F8+cnL4ZKleCOO+C++2x5kfbt4Ycfknd+51xma9y48P3VqxedoBVUowY8+6wtw7bXXtHNNE+2MLcjZ4nI8yJybCnHhG0D5P+vZkGwr6D2IvKpiLwsIoXmviIyWESmi8j03377LcSpnUstNWrY4yefwP3323qUd92V3NuDp5xit0gXLbKp5hdfHH4grXPOFWXEiA1tXEzlyrZ4d8uW1uaFuROw994wfbrVPuzVy44bVSHsZAmThDUA/gD2p3RjwqSQfQV/nTOBHFVtDdwJTCzsQKo6Kqjan9ewYcMQp3YuNbVrZ7OJ9t4bTj/dBqV+/rkNTB0+HA46CAYNsgGridC5sw2krVbNJg6UZiCtc84VZsAAq6ifk2MLgOfk2GSgL7+0MhSDB9talD/+WPKxtt3W1p48+mi7g5DpSlw7sswHFmkPXK6qBwfPLwRQ1WuL+cxcIE9Vfy/qPb7umssEqpbwnHUWbLMNLF1qBQx79rRxWzfcYD1lhx+emPM3blz4LdGcnNSsLZbKa0cG7dZyYB2wtqQ4vQ1zFYmqLXt03nlWbX/ECLsAjRVvLe5za9daj9r339tjTk5yYo63cq0dKSLbisizIrJYRH4VkQkism2I834MNBWR7UWkCtAP2Ogur4hsJSIS/NwuiOePEMd2Lq2JwDHHWMLVqpUtcHvddVCzphV/feYZG0yfqNuVRY3TiGrppQzQWVV3S9VE0bmoiNiSR198YT3xZ58N++5rbV9Jn6tc2ZKxY4+1HrV33klOzMkU5nbkw1jytDU2puuFYF+xVHUtcBrwKvAV8KSqfiEiQ0RkSPC2I4DPReRT4A6gnyaqa865FNSwIXz2mXW933AD7Lcf9O1r9b1EbImPRChuIG1pql8751wY224LL7wAjz4K335ry75dc03Ji3qL2DqVm20GBxxgVfozSZgkrKGqPqyqa4NtNBBqYJaqvqSqzVS1iaqOCPbdp6r3BT/fpaotVbW1qu6lqu+X+Zs4l6Y239x6oC66yAbLT5pkA/d/+QWWLUvMOYsaSLtmjQ2KvfnmkhtH9x8FJovIDBEZXNgbfHKRc5ZQDRhgY8V69bL2rl07m7BUnGbNrPj0QQfZepVDhlhR7J9/tpqMEybAP/8k4xvEX5gk7HcROUZEsoPtGPyWoXNxc/LJcOmlVsz16qttMOquu9qtyBtvTMw5ixpI+9VX1ht33nmw++6Z2f2fAPuoahvgEGCoiOxX8A0+uci5DbbYAp54wkpSLFpky6uVtMxb3bpWtmLYMFtE/PrrrZ189VUYOdJmeL/3XtK+QtyEScJOAI4CFgELsVuIJyQyKOcqkr59oV8/m8q9zz429mGbbew25fXX23u+/NISo3h2ogwYYIPw16+3xwEDoEkTePFFmDjR6vV07GjjMRYtit95M42q/hI8LgaexWokOudK0KuXtW3HHmu3Jnff3daULEp2Nlx7rY2ffeABe++wYVZ6Z8wYW0bp33+TFn5cFJmEiUjQ/LOnqh6mqg1VdQtV7eULeDsXPyK2+O2cOdbAvPee3ZJs2XLD2K233oJbb4UddrBesz//TGw8PXta43jRRXbFutNOcOedNlvJbSAiNUWkduxnbHm3z6ONyrn0Ub++9cK/8opVzd9nHxu8v2JF0Z95+mk47TQbR9u+vfXqH3QQ7LgjvPlm8mKPh+J6wrqJSGXgwmQF41xFVq+e3Qps0mTT10491eqJHXKI3bLcYQdLyhKpRg0bO/b557DnnnDGGXbboLgr1QpoS2BqMLnoI2CSqr4ScUzOpZ2DD7a25tRT4bbbbNZ4UQnVqlVQq5b1nu25pxWi3nNPu0hMt7FhxSVhrwC/A61EZJmILM//mKT4nHOBnXeGJ5+08RB7771hFqMqrF6duPM2a2bjLp580m6H7r03nHhifG+NpqtgWbbWwdYyNgHJOVd6tWtbfcS337ZbjwccYIVe//pr4/cdeig8+KBduL75ptVc/OknWwA87DJJqaLIJExVz1fVutiVXR1VrZ3/MYkxOufy2X13G7cVGy/22mvWezZyZOJmNIpYLbOvvrKxaWPH2i3KkSOTu/SScy7z7bcffPopnH++JVstW1qbF9Ozp7V57drBLbdYD9r69ZacHXWUvefnn9NjfFixA/NFJBuomaRYnHOlEKs4Xa+ezW4cMsTKS4wdm7jEqHZtm7E5a5bdLhgyxMZkeAF451w8Va9uY76mTbNxYz16WIHr33+3tm/8eBugP3eu3Q14+22rQ7bllvb8yCNt9uQrKT44oNgkTFXXAStFpG6S4nHOlVK7djB1qg3mr1sXBg6ELl0Se86WLWHKFCu8+NNPFsP//R8sWZLY8zrnKpa2bWHGDLj8chsS0aKFPYrYGNk777QJTS1abPhMbLKTqr3nsMNs6aNUFKZExSpgtog8KCJ3xLZEB+acC08EunWzHqmnnrJxFGC3JydPtsYoEeccMAC++cbWghs1ym5RPvyw3Rpwzrl4qFIFLrvMkrGcHCvr06ePjR/LzYWsLHscN27DZ7p1s9uUN9xgF4yxC8dUEyYJmwRcCrwDzMi3OedSTFaW1crp18+eP/GEzTraZ5/ETd2uWxduv90mDDRrBiecYGvDffppYs7nnKuYdt3VZmffcIP1/J9+OsybZxeZ8+bZxWf+RKxKFRtX9u23MHQo7LWX7f/558RcmJZFiUmYqo4BngSmqeqY2Jb40Jxz5dW3rw2enz/fZhodcMCGEhMzZ9oYi7w8u4VZ3qSpdWt491146CFr9Nq0gbPO2nRmk3POlVWlSpZYFbbwxMqVVnm/oEaNbAB/9epWwqJDBxv8P2tWwsMtUYlJmIj0AGZhJSsQkd1E5PkEx+Wci4PKle3q8LvvrPbO559bTZ233oKuXS0Bu+ceu8I88EB4v5yrt2ZlwaBBdovylFPgjjtsssC4calz5emcS38LFxa+f9684otKV6liidrXX8Mee1hdsj8iXIgxzO3Iy7FlOP4EUNVZwPYJi8g5F3fVqsGZZ8IPP9iYsYsvti79WbNsxuN559mV4qWXxud8DRpYcvfRR7Dddtbj1rmzldUoagyHc86FFVtNpDDNm8P99xdePzE7G046yXrrTzvNxrI2bWoXqlEIk4StVdWCNxT8mta5NFSrljU4H3wAW28NEybYgNUDD7QGa+rU+J4vL8/ONXKkTRoYNqz4MRzOORfGiBG2qkd+1avbEIgGDaxt2WEHu7j8++9NP1+/vo1lnTULjj/eljyC5K+TGyYJ+1xEjgayRaSpiNwJlPOmhXMuKllZVktnu+1svcrLLoMff4STT7bZlEuXxvd82dnWINavv+lrRY3hcM654gwYYL1YOTk2Uzsnx3q/br0VPvzQilg3bw7nnmuvXXFF4SV0dtnFEjURS8B22gn697dxtMkQJgk7HWgJrAYeA/4CzkpgTM65BBs61Op6qVoSNnWq9Yh17bohWTr9dGu45syJzzljyywV9NNP8Tm+c65iGTDAirWuX2+PAwbYfhHr3X/jDSv2uu++VmescWMbevHLL4Ufr04dWzx84kRL4EaMsHUqE6nIJExEqonIWcANwE9Ae1Vtq6qXqGqCw3LOJdKFF9qtwmbN7EqwZUurSB1bGmT9eitueMUV1qXfqROMHl14t35YRY3hKG5sh3POlceee1pSNXs29OplPWXbb28Th374YeP31qhhydpXX1mR10susbZxWbBatqqV+rn2WhgzBlasKH98xfWEjQHygNnAIcBN5T+dcy4VZGfDTTdZL9djj9n4rGuvtVuVYI8vv2xXl1dfbb1YgwZZAwa2LFJpC7IWNoajRg3b75xzibTLLrbCx3ffWS3D0aPtInTAAEvQ8svNhaefhtdfh6OPth6y1autd+30023IxjPP2OcLfrbUVLXQDZid7+dKwMyi3pvMbY899lDnXHKtX6/63nuqP/9szydMUM3JUb30UtXvvgt/nEcftc+J2OOjj4b7HDBdU6D9iceWqm1YzZo1VVV17ty52qZNG23durW2aNFC77333v/ec/TRR2uzZs20ZcuWOmjQIP3333+jCjeuYt9dVXXevHnapUsXbd68ue688846Z86cjd572mmnbfR+l55++UX1/PNVa9VSBdUePVQ/+KDo9599tmpWlupZZ6n+9Zfte/BB1bZtSz5Xce1XcUnYzOKeR7WlagPmXEXy9tuqBx9syRSoduigev/9qmvWJOZ8noRtrKzJbHFiicXq1at11apVqqq6fPlyzcnJ0Z+D7HvSpEm6fv16Xb9+vfbr10/vueee8p+4lB57TLVlS/sPsWVLe15e+ZOqjh076uTJk1XVvv+KFSv+e+3jjz/WY445xpOwDPLHH6pXXKHaoIG1ZZ06qU6ebBee+bVpo9qtm/2ba9XKXl+7VnXLLVXnzi3+HMW1X8XdjmwtIsuCbTnQKvaziCwrZweccy6N7bcfvPKKzSC67jr4/XerAZadba9//bXdsizMunU2K9KVzbhxNts0UaU+qlSpQtWqVQFYvXo16/Pdd+7WrRsigojQrl07FixYEJ+ThjR+vM2mvfNOGzB95532fPz4+Bz/yy+/ZO3atXTp0gWAWrVqUSO4h75u3TrOP/98brjhhviczKWEBg1g+HD7d3TLLVY/7KCDbOHwZ57ZMOwiVqn/o4/gmmts8L+IvablKNolWp5PRyAvL0+nT58edRjOuXxUYfFiK32xapXVIKtRA447zpZE2mknK39x2WVWM2zFCth5Z0vgDj645OOLyAxVzUv8N0m8ktqws84qfjmVadMKL0JZteqGtfEK2m03WzGhOLVq1eLvYObF/Pnz6d69O99//z033ngjQ4cO3ei9a9asYc899+T2229n3333Lf7ApdSp06b7jjrKKpu3aGGrQOQvd7J0qVU8X7DALgaOOGLjz771VsnnjH33iRMn8sADD1ClShXmzJnDgQceyHXXXUd2dja3334769ev5+yzz97od+Uyy+rV8Mgj1jb98IO1U//7n82ofP99+7t46aU2q7tBAxsv9sMPGxKywhTXfoUpUeGcc8USsQQMrDds5EhbS/L6622qd/v2to7lzJkwY4b1hF19tSVpH30UbezpprAErLj9ZbHddtvx2Wef8f333zNmzBh+/fXXjV4/9dRT2W+//eKegJXkm29swfj86tYtuuRAaa1du5Z3332Xm266iY8//pgff/yR0aNH88svv/DUU09x+umnx+dELmVVrWoV9b/+2npYK1e2Yq733mtt13HHbeiF/uMPWz7pscfKccKi7lOm6uZjwpxLH7/8onrjjarNm6vWrKn6+++qX36pOn26vX7nnar9+pV8HHxM2H9ycmzsSsEtJ6dchy1ynNPxxx+vTz311H/PL7/8cu3Zs6euW7eufCcsg5YtVd98c+N9b75p+8sj9t0/+OAD7dix43/7x44dq6eeeqq++OKLuuWWW2pOTo7m5OSoiGiTJk3Kd1KXFtavV33xRdW99y78312Yf3vFtV/eE+acS5hGjaw44uOPW9XqzTazXrJYx0rbtlaPzIWX6FIfCxYs4J9//gFg6dKlvPfee+y0004APPDAA7z66quMHz+erKzk//dx8cVw4okwZYrd3p4yxZ7Ha9WFtm3bsnTpUn777TcA3nzzTVq0aEH37t1ZtGgRc+fOZe7cudSoUYPv/S9uhSAC3bsXv6RbeQpOVyr7R51zLpwddrBbRj//bAuHxwbwv/kmtGoVbWzpJlYV/OKLrfFv3NgSsNj+8vrqq68499xzERFUlfPOO49dd90VgCFDhpCTk0P79u0B6NOnD8OHD4/PiUPo398eTz/dCmruvLN999j+8srOzuamm27igAMOiPVacvLJJ8fn4C6txZZGmjdv09fKU3DaB+Y755Lisstg0iRbNLdpU5t5dOml8PbbNuC6OBVpYL5zLjXFZibnn91do4atYVncRVBx7Zf3hDnnkuLyy23w/uDBNph1n33g1VdLTsCccy4VJKIX2pMw51xSiFiZgVNPjToS55wrmwED4nfrH7xEhXPOOedcJDwJc84555yLgCdhzjnnnHMR8CTMOeeccy4CnoQ555xzzkXAkzDnnHPOuQh4Euacc845FwFPwpxzzjnnIpB2yxaJyG/APKAu8FeBlwvbtz0wJwmhxfOchX2PeJyvuOOW9rXCzlnauEsrzPeMRwz5jxGvc5b0nqLOWdTnwv79DyOe/0bCxlDcOQs7Ro6qNixPYKmilG1YOrZfkJg2rDT/hsLsL3jOVGi/4hVH7BjxbDPD/h8R5vcaz/arsHOWVWliKOqcpWu/VDUtN2BUyH0rIoitXOcs7HvE43zFHbe0rxV2ztLGnYjfazxiyH+MeJ2zpPcUdc6iPhf27388//6U9ndX1nMm+u9Rqmxh/gzTsf0qy59hPP6dlebfSmHnTIX2K15xxI4RzzYz7P8RYX6v8Wy/SvO7jefvvqhzlvZ7pPPtyBdC7ktHifoexR23rK+V5X2JFI8YSnuMMO8v6T1FvV6a/RX195+uUvXPMB4S8T3i/W+orO9LtGT/G4rH76e0r6Xq3/2kt19pdzuytERkharWzORzVoTv6OfMnPNFdc50VFH+bCrCOSvCd/Rzll4694SF9UwFOGdF+I5+zsw5X1TnTEcV5c+mIpyzInxHP2cpZXxPmHPOOedcKqoIPWHOOeeccyknY5MwEflWRNaLyKokna+diCwVkdUiskpEJiTjvMG5K4vIShH5NUnnmxh8x1UiMk9E6ibgHIX++YnIUyLyb3DuD+N4vroi8reI/BMc+61g/0fBn+k/IvKLiOTE65zB8XNEZEFwjtUicnK+114QERWRZuU8xya/y6K+l4hUF5Hvg9/BahF5pQznK/Tfgoi8JSLrgnP+IyLD833mcBFZnu/vVdz/TqUTb78Ser6Ma7+CYye9DcvE9is4TtLasIxNwoA7gGOSeL7VwFBVrYrVD+khIj2SdO6ngcXJOJGI7AF0BxqpajXs79DNCTjVJn9+InI2sD+wWXDuQXE83zKgiapWB+oDeSJyInbfv06w/yfgiTieE+AN4PXg700DYDJYIwC0B9bF4RyF/Vso6nvdCFQOfr9bAweKSIdSnq+4fwsvqWr1YLsSQESqAo8CJwTn3QVYWcpzZhpvvxIgg9sviKYNy8T2C5LYhmVsEqaqd2F/OMk636eq+ljw80LgD6BFos8rInnAvthf1GQRoH7wF68K8H28T1DEn985wHWqujx4z5dxPJ+qauxKvAb2b0NV9TpVXR3sfwvYMl7nFJFtgByCxlhVV6jqvODlZ4D/i8d5CvtdFvO9FKga/NnWB9YDv5TyfKX9t/A/YKGqPhV85ntVXVOac2Yab78Se1oyrP0KjpfUNixT26/g+ElrwzI2CYtSkHk3xDLjRJsInI79ZUs4VZ0BPAf8APwDrFTV65JxbmBzoHvQ5f6niAyM58GD2yL/AL8Dn6nqQwXecjzwchxP2QG74vouuB3ztYg0FJGrgd9i/6CT4Hg2fK8Lgpj+Ab4DnlDVH8t64EL+LRwSdON/KyK5wb7dg/f+HvweJpX1fK78vP1KmIS2X5D0Nizj2y9IfBvmSViciciWwKvAzar6c4LPdQXwp6qOS+R5CpwzF+gM7AzUBKqJyD3JOj1QD6gNnAU8ICISr4Or6pqgezsX2ElEev13YpHJ2H8UQ+N1PuwqvCZwjarWwBqOSdh3OyyO5ylSId9rYPC8JtAS6CsiHct47IL/FoYC1YFa2O2nycFbKwHbAXsHj/uJyHll+kKuXLz9SuzpSWD7BUlvwzK6/QqOn/g2rKSS+um8YZn6qiSerzp2BfJcks73PrA22NZhXbE/JvicNwPf5Hs+Evg8GX9+wG/AWfmerwGaJ+jcbwIvBD+PApZjYznieY5dgbX5ng8FlmCNSOzPVYPHXeP5uyzqewGzgfvyPf8WuLUM5yv230L+eIDbge/zvfY68GIi/lzTafP2KyHnrBDtV3D8hLZhmdx+BZ9NShvmPWFxElzRfA7MV9WeyTinqu6tqpVUtRJwLrBYVXdI8Gm/AHJEZLPgOx8AfJXgc8ZMBnoDiMhB2JXlN/E4sIg0zzfDpj6wBzBTRC7Grq7aquof8ThXjKrOBlaISNdg15HYP+SsfH+u64AWwXvjppjvNR84QExDbMzH1FIeu9B/CyLSOt/bzgNi41duBbYO/k5Vxbr2p5f2O7my8/YrKRLWfgXHTGoblqntV3D85LVhicrCo96AeWy4uloLPJzg8/1fcK5/8m3Dk/h9zwJ+TdK53sLuu68CfgRqJ+PPD+ti/jE470rgnDie7/DgmP8Ex38j2P9vcP7Yn+kXcf6efYEVwbEXArkFXl8LNEvA77LQ74UNcJ0f/A5WU4YeqaL+LeT7s/sHWAS0zveZe4LXVgEfJuPvcSpv3n4l9FwZ134F50x6G5aJ7VdwnKS1YV4x3znnnHMuAn470jnnnHMuAp6EOeecc85FwJMw55xzzrkIeBLmnHPOORcBT8Kcc8455yLgSZiLKxFREbk53/PzROTyOB37chH5WURmiciXItI/Hsd1zrkYb8NcMnkS5uJtNdBHRDZP0PFvVdXdgJ7ASBGpnKDzOOcqJm/DXNJ4EubibS22nMTZBV8QkdEickS+538Hj51E5G0ReTJYFPU6ERkgIh+JyGwRaVLwWKr6HVaYsH5wjPNF5GMR+SxYkw4RyQ0WlR0T7H9aRGoEr10XXIl+JiI3JeIX4ZxLS96GuaTxJMwlwt3AABGpW4rPtAbOxNYjOxarstwOeAA4veCbRaQN8J2qLg6WAGkKtAN2A/YQkf2Ct+4EjFLVVsAy4FQRaYAtH9Iy2H91Gb6jcy5zeRvmksKTMBd3qroMGAucUYqPfayqC1V1NfADG1annw3k5nvf2SLyDfAhcHmw76Bg+wSYCTTHGjSwtb/eC35+FFt0dRm2tMQDItIHuxp1zjnA2zCXPJ6EuUS5DTgRWy8tZi3B37lggdQq+V5bne/n9fmerwcq5XvtVlXdCVuzbKyIVMMWwr1WVXcLth1V9cHg/QXX5VJVXYtdcU4AegGvlOkbOucy2W14G+YSzJMwlxCqugR4EmvEYuYCewQ/9wTKPCBVVZ/BVqkfCLwKnCAitQBEZBsR2SJ4a2MRaR/83B+YGryvrqq+hC0cvFtZ43DOZSZvw1wyVCr5Lc6V2c3Aafme3w88JyIfAW8AK8p5/CuBx4Cdg+0Duzjlb+AYYB3wFTBQREYC3wH3AnWDOGJXoJsMwHXOObwNcwkmqgV7Op3LDCKSC7yoqrtEHYtzzpWWt2GZz29HOuecc85FwHvCnHPOOeci4D1hzjnnnHMR8CTMOeeccy4CnoQ555xzzkXAkzDnnHPOuQh4Euacc845FwFPwpxzzjnnIvD/cbKv2j1JUFEAAAAASUVORK5CYII=\n",
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\n",
       "text/plain": [
        "<Figure size 720x360 with 2 Axes>"
       ]
@@ -398,7 +400,7 @@
     "        tmpdf = perfdf[perfdf[\"Benchmark\"] == bm]\n",
     "        \n",
     "        unique_labels = tmpdf[\"Label\"].unique()\n",
-    "        plot_perf(bm + \"_RANDOM\", tmpdf, \"NumReps\", unique_labels, flag)"
+    "        plot_perf(bm, tmpdf, \"NumReps\", unique_labels, flag)"
    ]
   }
  ],

From 7e89eff9b12637faec877dc8232e64bda42d6306 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Thu, 6 May 2021 13:44:19 -0400
Subject: [PATCH 085/267] Per-block shared memory buffer with CG-level parallel
 retrieval

---
 include/cuco/detail/static_multimap.inl       |  2 +-
 .../cuco/detail/static_multimap_kernels.cuh   | 50 ++++++++++---------
 2 files changed, 27 insertions(+), 25 deletions(-)

diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index 52664a3e4..540143a33 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -144,7 +144,7 @@ OutputIt static_multimap<Key, Value, CGSize, Scope, Allocator>::find_all(InputIt
 {
   auto num_keys          = std::distance(first, last);
   auto const block_size  = 128;
-  auto const buffer_size = CGSize * 2;
+  auto const buffer_size = block_size * 2;
   auto const stride      = 1;
   auto const grid_size   = (CGSize * num_keys + stride * block_size - 1) / (stride * block_size);
   auto view              = get_device_view();
diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap_kernels.cuh
index 18c44c25f..285e468a1 100644
--- a/include/cuco/detail/static_multimap_kernels.cuh
+++ b/include/cuco/detail/static_multimap_kernels.cuh
@@ -595,14 +595,12 @@ __global__ void find_all(InputIt first,
   auto tid     = blockDim.x * blockIdx.x + threadIdx.x;
   auto key_idx = tid / tile_size;
 
-  constexpr uint32_t num_cgs = block_size / tile_size;
-  const uint32_t cg_id       = threadIdx.x / tile_size;
-  const uint32_t lane_id     = tile.thread_rank();
+  const uint32_t lane_id = tile.thread_rank();
 
-  __shared__ cuco::pair_type<Key, Value> output_buffer[num_cgs][buffer_size];
-  __shared__ uint32_t cg_counter[num_cgs];
+  __shared__ uint32_t block_counter;
+  __shared__ cuco::pair_type<Key, Value> output_buffer[buffer_size];
 
-  if (lane_id == 0) { cg_counter[cg_id] = 0; }
+  if (0 == threadIdx.x) { block_counter = 0; }
 
   while (first + key_idx < last) {
     auto key          = *(first + key_idx);
@@ -611,7 +609,7 @@ __global__ void find_all(InputIt first,
     bool running     = true;
     bool found_match = false;
 
-    while (running) {
+    while (__syncthreads_or(running)) {
       pair<Key, Value> arr[2];
       if constexpr (sizeof(Key) == 4) {
         auto const tmp = *reinterpret_cast<uint4 const*>(current_slot);
@@ -634,48 +632,52 @@ __global__ void find_all(InputIt first,
         auto num_first_matches  = __popc(first_exists);
         auto num_second_matches = __popc(second_exists);
 
-        uint32_t output_idx = cg_counter[cg_id];
+        uint32_t output_idx;
+        if (0 == lane_id) {
+          output_idx = atomicAdd_block(&block_counter, (num_first_matches + num_second_matches));
+        }
+        output_idx = tile.shfl(output_idx, 0);
 
         if (first_equals) {
           auto lane_offset = __popc(first_exists & ((1 << lane_id) - 1));
           Key k            = key;
-          output_buffer[cg_id][output_idx + lane_offset] =
+          output_buffer[output_idx + lane_offset] =
             cuco::make_pair<Key, Value>(std::move(k), std::move(arr[0].second));
         }
         if (second_equals) {
           auto lane_offset = __popc(second_exists & ((1 << lane_id) - 1));
           Key k            = key;
-          output_buffer[cg_id][output_idx + num_first_matches + lane_offset] =
+          output_buffer[output_idx + num_first_matches + lane_offset] =
             cuco::make_pair<Key, Value>(std::move(k), std::move(arr[1].second));
         }
-        if (0 == lane_id) { cg_counter[cg_id] += (num_first_matches + num_second_matches); }
       }
       if (tile.any(first_slot_is_empty or second_slot_is_empty)) {
         running = false;
         if ((not found_match) && (lane_id == 0)) {
-          auto output_idx = cg_counter[cg_id]++;
-          output_buffer[cg_id][output_idx] =
+          auto output_idx = atomicAdd_block(&block_counter, 1);
+          output_buffer[output_idx] =
             cuco::make_pair<Key, Value>(key, view.get_empty_key_sentinel());
         }
       }
 
-      tile.sync();
+      __syncthreads();
+      __shared__ uint32_t block_offset;
+      if (0 == threadIdx.x) {
+        block_offset = num_items->fetch_add(block_counter, cuda::std::memory_order_relaxed);
+      }
+      __syncthreads();
+
+      if (threadIdx.x < block_counter) {
+        *(output_begin + block_offset) = output_buffer[threadIdx.x];
+      }
+      __syncthreads();
 
-      flush_output_buffer<tile_size>(
-        tile, cg_counter[cg_id], output_buffer[cg_id], num_items, output_begin);
-      // First lane reset CG-level counter
-      if (lane_id == 0) { cg_counter[cg_id] = 0; }
+      if (0 == threadIdx.x) { block_counter = 0; }
 
       current_slot = view.next_slot(tile, current_slot);
     }  // while running
     key_idx += (gridDim.x * blockDim.x) / tile_size;
   }
-
-  // Final flush of output buffer
-  if (cg_counter[cg_id] > 0) {
-    flush_output_buffer<tile_size>(
-      tile, cg_counter[cg_id], output_buffer[cg_id], num_items, output_begin);
-  }
 }
 
 /**

From 6dbf36f93a689d77832e61132729a8bed2db5cfb Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Fri, 7 May 2021 13:26:14 -0400
Subject: [PATCH 086/267] Flush when next iteration won't fit into cache

---
 include/cuco/detail/static_multimap.inl       |   2 +-
 .../cuco/detail/static_multimap_kernels.cuh   | 193 +++++++-----------
 2 files changed, 77 insertions(+), 118 deletions(-)

diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index 540143a33..f11130fe3 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -144,7 +144,7 @@ OutputIt static_multimap<Key, Value, CGSize, Scope, Allocator>::find_all(InputIt
 {
   auto num_keys          = std::distance(first, last);
   auto const block_size  = 128;
-  auto const buffer_size = block_size * 2;
+  auto const buffer_size = block_size * 3;
   auto const stride      = 1;
   auto const grid_size   = (CGSize * num_keys + stride * block_size - 1) / (stride * block_size);
   auto view              = get_device_view();
diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap_kernels.cuh
index 285e468a1..62a662dc2 100644
--- a/include/cuco/detail/static_multimap_kernels.cuh
+++ b/include/cuco/detail/static_multimap_kernels.cuh
@@ -37,63 +37,19 @@ namespace cg = cooperative_groups;
  * @param num_items Size of the output sequence
  * @param output_begin Beginning of the output sequence of key/value pairs
  */
-template <uint32_t cg_size,
-          typename CG,
-          typename Key,
-          typename Value,
-          typename atomicT,
-          typename OutputIt>
-__inline__ __device__ std::enable_if_t<thrust::is_contiguous_iterator<OutputIt>::value, void>
-flush_output_buffer(CG const& g,
-                    uint32_t const num_outputs,
-                    cuco::pair_type<Key, Value>* output_buffer,
-                    atomicT* num_items,
-                    OutputIt output_begin)
+template <uint32_t block_size, typename Key, typename Value, typename atomicT, typename OutputIt>
+__inline__ __device__ void flush_output_buffer(uint32_t const num_outputs,
+                                               cuco::pair_type<Key, Value>* output_buffer,
+                                               atomicT* num_items,
+                                               OutputIt output_begin)
 {
-  std::size_t offset;
-  const auto lane_id = g.thread_rank();
-  if (0 == lane_id) { offset = num_items->fetch_add(num_outputs, cuda::std::memory_order_relaxed); }
-  offset = g.shfl(offset, 0);
-
-  cg::memcpy_async(g,
-                   output_begin + offset,
-                   output_buffer,
-                   cuda::aligned_size_t<alignof(cuco::pair_type<Key, Value>)>(
-                     sizeof(cuco::pair_type<Key, Value>) * num_outputs));
-}
-
-/**
- * @brief Flushes shared memory buffer into the output sequence.
- *
- * @tparam Key key type
- * @tparam Value value type
- * @tparam atomicT Type of atomic storage
- * @tparam OutputIt Device accessible output iterator whose `value_type` is
- * convertible to the map's `mapped_type`
- * @param output_size Number of valid output in the buffer
- * @param output_buffer Shared memory buffer of the key/value pair sequence
- * @param num_items Size of the output sequence
- * @param output_begin Beginning of the output sequence of key/value pairs
- */
-template <uint32_t cg_size,
-          typename CG,
-          typename Key,
-          typename Value,
-          typename atomicT,
-          typename OutputIt>
-__inline__ __device__ std::enable_if_t<not thrust::is_contiguous_iterator<OutputIt>::value, void>
-flush_output_buffer(CG const& g,
-                    uint32_t const num_outputs,
-                    cuco::pair_type<Key, Value>* output_buffer,
-                    atomicT* num_items,
-                    OutputIt output_begin)
-{
-  std::size_t offset;
-  const auto lane_id = g.thread_rank();
-  if (0 == lane_id) { offset = num_items->fetch_add(num_outputs, cuda::std::memory_order_relaxed); }
-  offset = g.shfl(offset, 0);
+  __shared__ std::size_t offset;
+  if (0 == threadIdx.x) {
+    offset = num_items->fetch_add(num_outputs, cuda::std::memory_order_relaxed);
+  }
+  __syncthreads();
 
-  for (auto index = lane_id; index < num_outputs; index += cg_size) {
+  for (auto index = threadIdx.x; index < num_outputs; index += block_size) {
     *(output_begin + offset + index) = output_buffer[index];
   }
 }
@@ -592,7 +548,7 @@ __global__ void find_all(InputIt first,
                          KeyEqual key_equal)
 {
   auto tile    = cg::tiled_partition<tile_size>(cg::this_thread_block());
-  auto tid     = blockDim.x * blockIdx.x + threadIdx.x;
+  auto tid     = block_size * blockIdx.x + threadIdx.x;
   auto key_idx = tid / tile_size;
 
   const uint32_t lane_id = tile.thread_rank();
@@ -610,73 +566,74 @@ __global__ void find_all(InputIt first,
     bool found_match = false;
 
     while (__syncthreads_or(running)) {
-      pair<Key, Value> arr[2];
-      if constexpr (sizeof(Key) == 4) {
-        auto const tmp = *reinterpret_cast<uint4 const*>(current_slot);
-        memcpy(&arr[0], &tmp, 2 * sizeof(pair<Key, Value>));
-      } else {
-        auto const tmp = *reinterpret_cast<ulonglong4 const*>(current_slot);
-        memcpy(&arr[0], &tmp, 2 * sizeof(pair<Key, Value>));
-      }
-
-      auto const first_slot_is_empty  = (arr[0].first == view.get_empty_key_sentinel());
-      auto const second_slot_is_empty = (arr[1].first == view.get_empty_key_sentinel());
-      auto const first_equals         = (not first_slot_is_empty and key_equal(arr[0].first, key));
-      auto const second_equals        = (not second_slot_is_empty and key_equal(arr[1].first, key));
-      auto const first_exists         = tile.ballot(first_equals);
-      auto const second_exists        = tile.ballot(second_equals);
-
-      if (first_exists or second_exists) {
-        found_match = true;
+      if (running) {
+        pair<Key, Value> arr[2];
+        if constexpr (sizeof(Key) == 4) {
+          auto const tmp = *reinterpret_cast<uint4 const*>(current_slot);
+          memcpy(&arr[0], &tmp, 2 * sizeof(pair<Key, Value>));
+        } else {
+          auto const tmp = *reinterpret_cast<ulonglong4 const*>(current_slot);
+          memcpy(&arr[0], &tmp, 2 * sizeof(pair<Key, Value>));
+        }
 
-        auto num_first_matches  = __popc(first_exists);
-        auto num_second_matches = __popc(second_exists);
+        auto const first_slot_is_empty  = (arr[0].first == view.get_empty_key_sentinel());
+        auto const second_slot_is_empty = (arr[1].first == view.get_empty_key_sentinel());
+        auto const first_equals  = (not first_slot_is_empty and key_equal(arr[0].first, key));
+        auto const second_equals = (not second_slot_is_empty and key_equal(arr[1].first, key));
+        auto const first_exists  = tile.ballot(first_equals);
+        auto const second_exists = tile.ballot(second_equals);
 
-        uint32_t output_idx;
-        if (0 == lane_id) {
-          output_idx = atomicAdd_block(&block_counter, (num_first_matches + num_second_matches));
-        }
-        output_idx = tile.shfl(output_idx, 0);
+        if (first_exists or second_exists) {
+          found_match = true;
 
-        if (first_equals) {
-          auto lane_offset = __popc(first_exists & ((1 << lane_id) - 1));
-          Key k            = key;
-          output_buffer[output_idx + lane_offset] =
-            cuco::make_pair<Key, Value>(std::move(k), std::move(arr[0].second));
-        }
-        if (second_equals) {
-          auto lane_offset = __popc(second_exists & ((1 << lane_id) - 1));
-          Key k            = key;
-          output_buffer[output_idx + num_first_matches + lane_offset] =
-            cuco::make_pair<Key, Value>(std::move(k), std::move(arr[1].second));
+          auto num_first_matches  = __popc(first_exists);
+          auto num_second_matches = __popc(second_exists);
+
+          uint32_t output_idx;
+          if (0 == lane_id) {
+            output_idx = atomicAdd_block(&block_counter, (num_first_matches + num_second_matches));
+          }
+          output_idx = tile.shfl(output_idx, 0);
+
+          if (first_equals) {
+            auto lane_offset = __popc(first_exists & ((1 << lane_id) - 1));
+            Key k            = key;
+            output_buffer[output_idx + lane_offset] =
+              cuco::make_pair<Key, Value>(std::move(k), std::move(arr[0].second));
+          }
+          if (second_equals) {
+            auto lane_offset = __popc(second_exists & ((1 << lane_id) - 1));
+            Key k            = key;
+            output_buffer[output_idx + num_first_matches + lane_offset] =
+              cuco::make_pair<Key, Value>(std::move(k), std::move(arr[1].second));
+          }
         }
-      }
-      if (tile.any(first_slot_is_empty or second_slot_is_empty)) {
-        running = false;
-        if ((not found_match) && (lane_id == 0)) {
-          auto output_idx = atomicAdd_block(&block_counter, 1);
-          output_buffer[output_idx] =
-            cuco::make_pair<Key, Value>(key, view.get_empty_key_sentinel());
+        if (tile.any(first_slot_is_empty or second_slot_is_empty)) {
+          running = false;
+          if ((not found_match) && (lane_id == 0)) {
+            auto output_idx = atomicAdd_block(&block_counter, 1);
+            output_buffer[output_idx] =
+              cuco::make_pair<Key, Value>(key, view.get_empty_key_sentinel());
+          }
         }
-      }
+      }  // if running
 
-      __syncthreads();
-      __shared__ uint32_t block_offset;
-      if (0 == threadIdx.x) {
-        block_offset = num_items->fetch_add(block_counter, cuda::std::memory_order_relaxed);
-      }
       __syncthreads();
 
-      if (threadIdx.x < block_counter) {
-        *(output_begin + block_offset) = output_buffer[threadIdx.x];
-      }
-      __syncthreads();
+      if ((block_counter + 2 * block_size) > buffer_size) {
+        flush_output_buffer<block_size>(block_counter, output_buffer, num_items, output_begin);
 
-      if (0 == threadIdx.x) { block_counter = 0; }
+        if (0 == threadIdx.x) { block_counter = 0; }
+      }
 
       current_slot = view.next_slot(tile, current_slot);
     }  // while running
-    key_idx += (gridDim.x * blockDim.x) / tile_size;
+    key_idx += (gridDim.x * block_size) / tile_size;
+  }
+
+  // Final remainder flush
+  if (block_counter > 0) {
+    flush_output_buffer<block_size>(block_counter, output_buffer, num_items, output_begin);
   }
 }
 
@@ -761,10 +718,9 @@ __global__ void count(
   auto tid     = blockDim.x * blockIdx.x + threadIdx.x;
   auto key_idx = tid / tile_size;
 
-  using BlockReduce = cub::BlockReduce<int, block_size>;
+  typedef cub::BlockReduce<std::size_t, block_size> BlockReduce;
   __shared__ typename BlockReduce::TempStorage temp_storage;
-
-  int thread_counter{0};
+  std::size_t thread_num_items = 0;
 
   while (first + key_idx < last) {
     auto key          = *(first + key_idx);
@@ -784,7 +740,7 @@ __global__ void count(
       auto const second_slot_is_empty = (arr[1].first == view.get_empty_key_sentinel());
       auto const first_equals         = (not first_slot_is_empty and key_equal(arr[0].first, key));
       auto const second_equals        = (not second_slot_is_empty and key_equal(arr[1].first, key));
-      thread_counter += (first_equals + second_equals);
+      thread_num_items += (first_equals + second_equals);
 
       if (tile.any(first_slot_is_empty or second_slot_is_empty)) { break; }
 
@@ -792,8 +748,11 @@ __global__ void count(
     }
     key_idx += (gridDim.x * block_size) / tile_size;
   }
-  auto const block_count = BlockReduce(temp_storage).Sum(thread_counter);
-  if (threadIdx.x == 0) { num_items->fetch_add(block_count, cuda::std::memory_order_relaxed); }
+
+  // compute number of successfully inserted elements for each block
+  // and atomically add to the grand total
+  std::size_t block_num_items = BlockReduce(temp_storage).Sum(thread_num_items);
+  if (threadIdx.x == 0) { *num_items += block_num_items; }
 }
 
 }  // namespace detail

From 07091b84c0a075b2b613d31cd8710e31ca188db0 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Mon, 10 May 2021 16:15:31 -0400
Subject: [PATCH 087/267] Add key_generator header file

---
 benchmarks/key_generator.hpp | 97 ++++++++++++++++++++++++++++++++++++
 1 file changed, 97 insertions(+)
 create mode 100644 benchmarks/key_generator.hpp

diff --git a/benchmarks/key_generator.hpp b/benchmarks/key_generator.hpp
new file mode 100644
index 000000000..98753a97c
--- /dev/null
+++ b/benchmarks/key_generator.hpp
@@ -0,0 +1,97 @@
+/*
+ * Copyright (c) 2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <limits>
+#include <random>
+
+enum class dist_type { GEOMETRIC, GAUSSIAN, UNIFORM };
+
+template <dist_type Dist, std::size_t Multiplicity, typename Key, typename OutputIt>
+static void generate_keys(OutputIt output_begin, OutputIt output_end)
+{
+  auto const num_keys = std::distance(output_begin, output_end);
+
+  std::random_device rd;
+  std::mt19937 gen{rd()};
+
+  switch (Dist) {
+    case dist_type::GAUSSIAN: {
+      auto const max  = std::numeric_limits<Key>::max();
+      auto const mean = max / Multiplicity / 2;
+      auto const dev  = max / Multiplicity / 5;
+
+      std::normal_distribution<> distribution(mean, dev);
+
+      for (auto i = 0; i < num_keys; ++i) {
+        output_begin[i] = std::abs(distribution(gen));
+      }
+      break;
+    }
+    case dist_type::GEOMETRIC: {
+      std::geometric_distribution<Key> distribution{1e-9};
+
+      for (auto i = 0; i < num_keys; ++i) {
+        output_begin[i] = distribution(gen) / Multiplicity;
+      }
+      break;
+    }
+    case dist_type::UNIFORM: {
+      std::uniform_int_distribution<Key> distribution{1, static_cast<Key>(num_keys / Multiplicity)};
+
+      for (auto i = 0; i < num_keys; ++i) {
+        output_begin[i] = distribution(gen);
+      }
+      break;
+    }
+  }
+}
+
+template <dist_type Dist,
+          std::size_t Multiplicity,
+          typename Key,
+          typename InputIt,
+          typename OutputIt>
+static void generate_prob_keys(InputIt input_begin,
+                               InputIt input_end,
+                               OutputIt output_begin,
+                               double const matching_rate)
+{
+  auto const num_keys = std::distance(input_begin, input_end);
+
+  std::random_device rd;
+  std::mt19937 gen{rd()};
+
+  auto const max = std::numeric_limits<Key>::max();
+
+  std::uniform_real_distribution<double> rate_dist(0.0, 1.0);
+  std::uniform_int_distribution<std::size_t> idx_dist{0, num_keys - 1};
+  std::uniform_int_distribution<Key> non_match_dist{max / Multiplicity, max};
+
+  for (auto i = 0; i < num_keys; ++i) {
+    auto const tmp_rate = rate_dist(gen);
+
+    // If match, randomly sample from the input keys
+    if (tmp_rate <= matching_rate) {
+      output_begin[i] = input_begin[idx_dist(gen)];
+    }
+    // Otherwise, randomly draw from [Max/Multiplicity, Max]
+    else {
+      output_begin[i] = non_match_dist(gen);
+    }
+  }
+}

From 57b4f92278c0813080a923592813294e6558781e Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Mon, 10 May 2021 17:03:42 -0400
Subject: [PATCH 088/267] Add distribution map: mapping string to enum

---
 benchmarks/key_generator.hpp | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/benchmarks/key_generator.hpp b/benchmarks/key_generator.hpp
index 98753a97c..749ce69cf 100644
--- a/benchmarks/key_generator.hpp
+++ b/benchmarks/key_generator.hpp
@@ -18,9 +18,15 @@
 
 #include <limits>
 #include <random>
+#include <unordered_map>
 
 enum class dist_type { GEOMETRIC, GAUSSIAN, UNIFORM };
 
+static std::unordered_map<std::string, dist_type> const dist_map = {
+  {"GAUSSIAN", dist_type::GAUSSIAN},
+  {"GEOMETRIC", dist_type::GEOMETRIC},
+  {"UNIFORM", dist_type::UNIFORM}};
+
 template <dist_type Dist, std::size_t Multiplicity, typename Key, typename OutputIt>
 static void generate_keys(OutputIt output_begin, OutputIt output_end)
 {

From 7b0727ae652a763fec978384eed925a8b8d7fdff Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Mon, 10 May 2021 20:46:55 -0400
Subject: [PATCH 089/267] A comprehensive multimap benchmark

---
 .../static_multimap/static_multimap_bench.cu  | 442 ++++++++----------
 benchmarks/key_generator.hpp                  |  30 +-
 2 files changed, 205 insertions(+), 267 deletions(-)

diff --git a/benchmarks/hash_table/static_multimap/static_multimap_bench.cu b/benchmarks/hash_table/static_multimap/static_multimap_bench.cu
index 2b174fe7e..813ca57d5 100644
--- a/benchmarks/hash_table/static_multimap/static_multimap_bench.cu
+++ b/benchmarks/hash_table/static_multimap/static_multimap_bench.cu
@@ -14,54 +14,59 @@
  * limitations under the License.
  */
 
-#include <nvbench/nvbench.cuh>
-
-#include <thrust/device_vector.h>
 #include <random>
 
-#include "cuco/static_multimap.cuh"
+#include <thrust/device_vector.h>
+#include <nvbench/nvbench.cuh>
 
-/**
- * @brief Generates input keys with a specific distribution.
- *
- */
-template <typename Key, typename OutputIt>
-static void generate_keys(OutputIt output_begin,
-                          OutputIt output_end,
-                          size_t const num_reps,
-                          const std::string& dist)
-{
-  auto num_keys = std::distance(output_begin, output_end);
-
-  if (dist == "RANDOM") {
-    std::random_device rd;
-    std::mt19937 gen{rd()};
-    std::uniform_int_distribution<Key> distribution{1, static_cast<Key>(num_keys / num_reps)};
-    for (auto i = 0; i < num_keys; ++i) {
-      output_begin[i] = distribution(gen);
-    }
-  } else if (dist == "CYCLE") {
-    for (auto i = 0; i < num_keys; ++i) {
-      output_begin[i] = (i % (num_keys / num_reps)) + 1;
+#include <cuco/static_multimap.cuh>
+#include <key_generator.hpp>
+
+NVBENCH_DECLARE_ENUM_TYPE_STRINGS(
+  // Enum type:
+  dist_type,
+  // Callable to generate input strings:
+  // Short identifier used for tables, command-line args, etc.
+  // Used when context is available to figure out the enum type.
+  [](dist_type d) {
+    switch (d) {
+      case dist_type::GAUSSIAN: return "GAUSSIAN";
+      case dist_type::GEOMETRIC: return "GEOMETRIC";
+      case dist_type::UNIFORM: return "UNIFORM";
+      default: return "ERROR";
     }
-  }
-}
+  },
+  // Callable to generate descriptions:
+  // If non-empty, these are used in `--list` to describe values.
+  // Used when context may not be available to figure out the type from the
+  // input string.
+  // Just use `[](auto) { return std::string{}; }` if you don't want these.
+  [](auto) { return std::string{}; })
 
 /**
- * @brief Helper function to launch insertion benchmark.
- *
+ * @brief A benchmark evaluating multi-value `insert` performance:
+ * - Total number of insertions: 100'000'000
+ * - CG size: 8
  */
-template <typename Key, typename Value, size_t cg_size>
-void launch_nvbench_insert(nvbench::state& state,
-                           std::vector<Key> const& h_keys,
-                           std::size_t const num_keys,
-                           std::size_t const size)
+template <typename Key, typename Value, dist_type Dist, nvbench::int32_t Multiplicity>
+std::enable_if_t<(sizeof(Key) == sizeof(Value)), void> nvbench_static_multimap_insert(
+  nvbench::state& state,
+  nvbench::type_list<Key, Value, nvbench::enum_type<Dist>, nvbench::enum_type<Multiplicity>>)
 {
+  auto const num_keys  = state.get_int64("NumInputs");
+  auto const occupancy = state.get_float64("Occupancy");
+
+  std::size_t const size = num_keys / occupancy;
+  auto const cg_size     = 8;
+
+  std::vector<Key> h_keys(num_keys);
   std::vector<cuco::pair_type<Key, Value>> h_pairs(num_keys);
 
+  generate_keys<Dist, Multiplicity, Key>(h_keys.begin(), h_keys.end());
+
   for (auto i = 0; i < num_keys; ++i) {
     Key key           = h_keys[i];
-    Value val         = i;
+    Value val         = h_keys[i];
     h_pairs[i].first  = key;
     h_pairs[i].second = val;
   }
@@ -69,357 +74,308 @@ void launch_nvbench_insert(nvbench::state& state,
   thrust::device_vector<cuco::pair_type<Key, Value>> d_pairs(h_pairs);
 
   state.add_element_count(num_keys, "NumKeys");
-  state.add_global_memory_writes<Key>(num_keys * 2);
 
   state.exec(nvbench::exec_tag::sync | nvbench::exec_tag::timer,
              [&](nvbench::launch& launch, auto& timer) {
                cuco::static_multimap<Key, Value, cg_size> map{size, -1, -1};
-               auto m_view = map.get_device_mutable_view();
 
+               // Use timers to explicitly mark the target region
                timer.start();
                map.insert(d_pairs.begin(), d_pairs.end(), launch.get_stream());
                timer.stop();
              });
 }
 
-/**
- * @brief A benchmark evaluating multi-value insertion performance by varing number of repetitions
- * per key:
- * - Total number of insertions: 100'000'000
- * - Map occupancy: 0.8
- * - CG size: 8
- * - Number of repetitions per key: 1, ... , 128, 256
- *
- */
-template <typename Key, typename Value>
-std::enable_if_t<(sizeof(Key) == sizeof(Value)), void> nvbench_static_multimap_insert(
-  nvbench::state& state, nvbench::type_list<Key, Value>)
-{
-  std::size_t const num_keys = state.get_int64("NumInputs");
-  auto const occupancy       = state.get_float64("Occupancy");
-  std::size_t const num_reps = state.get_int64("NumReps");
-  auto const dist            = state.get_string("Distribution");
-
-  std::size_t const size    = num_keys / occupancy;
-  std::size_t const cg_size = 8;
-
-  std::vector<Key> h_keys(num_keys);
-
-  generate_keys<Key>(h_keys.begin(), h_keys.end(), num_reps, dist);
-
-  launch_nvbench_insert<Key, Value, cg_size>(state, h_keys, num_keys, size);
-}
-
-template <typename Key, typename Value>
+template <typename Key, typename Value, dist_type Dist, nvbench::int32_t Multiplicity>
 std::enable_if_t<(sizeof(Key) != sizeof(Value)), void> nvbench_static_multimap_insert(
-  nvbench::state& state, nvbench::type_list<Key, Value>)
+  nvbench::state& state,
+  nvbench::type_list<Key, Value, nvbench::enum_type<Dist>, nvbench::enum_type<Multiplicity>>)
 {
   state.skip("Key should be the same type as Value.");
 }
 
 /**
- * @brief A benchmark evaluating multi-value count performance by varing number of repetitions
- * per key:
- * - 100'000'000 keys are inserted
- * - Map occupancy: 0.8
+ * @brief A benchmark evaluating multi-value `count` performance:
+ * - Total number of insertions: 100'000'000
  * - CG size: 8
- * - Number of repetitions per key: 1, ... , 128, 256
- *
  */
-template <typename Key, typename Value>
+template <typename Key, typename Value, dist_type Dist, nvbench::int32_t Multiplicity>
 std::enable_if_t<(sizeof(Key) == sizeof(Value)), void> nvbench_static_multimap_count(
-  nvbench::state& state, nvbench::type_list<Key, Value>)
+  nvbench::state& state,
+  nvbench::type_list<Key, Value, nvbench::enum_type<Dist>, nvbench::enum_type<Multiplicity>>)
 {
-  std::size_t const num_keys = state.get_int64("NumInputs");
-  auto const occupancy       = state.get_float64("Occupancy");
-  std::size_t const num_reps = state.get_int64("NumReps");
-  auto const dist            = state.get_string("Distribution");
+  auto const num_keys      = state.get_int64("NumInputs");
+  auto const occupancy     = state.get_float64("Occupancy");
+  auto const matching_rate = state.get_float64("MatchingRate");
 
   std::size_t const size = num_keys / occupancy;
+  auto const cg_size     = 8;
 
   std::vector<Key> h_keys(num_keys);
-  generate_keys<Key>(h_keys.begin(), h_keys.end(), num_reps, dist);
-
   std::vector<cuco::pair_type<Key, Value>> h_pairs(num_keys);
+
+  generate_keys<Dist, Multiplicity, Key>(h_keys.begin(), h_keys.end());
+
   for (auto i = 0; i < num_keys; ++i) {
     Key key           = h_keys[i];
     Value val         = h_keys[i];
     h_pairs[i].first  = key;
     h_pairs[i].second = val;
-
-    if (dist == "RANDOM") { h_keys[i] = i; }
   }
 
-  // Get an array of unique keys
-  std::set<Key> key_set(h_keys.begin(), h_keys.end());
-  std::vector<Key> h_unique_keys(key_set.begin(), key_set.end());
-  thrust::device_vector<Key> d_unique_keys(h_unique_keys);
-
-  thrust::device_vector<cuco::pair_type<Key, Value>> d_pairs(h_pairs);
-
-  state.add_element_count(num_keys, "NumKeys");
-  state.add_global_memory_writes<Key>(num_keys * 2);
-
-  state.exec(
-    nvbench::exec_tag::sync | nvbench::exec_tag::timer, [&](nvbench::launch& launch, auto& timer) {
-      auto const cg_size = 8;
-      cuco::static_multimap<Key, Value, cg_size> map{size, -1, -1};
-      map.insert(d_pairs.begin(), d_pairs.end());
-
-      // Use timers to explicitly mark the target region
-      timer.start();
-      auto count = map.count(d_unique_keys.begin(), d_unique_keys.end(), launch.get_stream());
-      timer.stop();
-    });
-}
-
-template <typename Key, typename Value>
-std::enable_if_t<(sizeof(Key) != sizeof(Value)), void> nvbench_static_multimap_count(
-  nvbench::state& state, nvbench::type_list<Key, Value>)
-{
-  state.skip("Key should be the same type as Value.");
-}
-
-template <typename Key, typename Value>
-std::enable_if_t<(sizeof(Key) == sizeof(Value)), void> nvbench_static_multimap_count_analysis(
-  nvbench::state& state, nvbench::type_list<Key, Value>)
-{
-  std::size_t const num_keys = state.get_int64("NumInputs");
-  auto const occupancy       = state.get_float64("Occupancy");
-  std::size_t const num_reps = state.get_int64("NumReps");
-  auto const matching_rate   = state.get_float64("MatchingRate");
-
-  std::size_t const size = num_keys / occupancy;
-
-  std::vector<Key> h_keys(num_keys);
-  generate_keys<Key>(h_keys.begin(), h_keys.end(), num_reps, "RANDOM");
-
-  std::vector<cuco::pair_type<Key, Value>> h_pairs(num_keys);
-
-  std::random_device rd;
-  std::mt19937 gen{rd()};
-  auto tmp_max = static_cast<double>(num_keys / num_reps) / matching_rate;
-  std::uniform_int_distribution<Key> distribution{1, static_cast<Key>(tmp_max)};
-
-  for (auto i = 0; i < num_keys; ++i) {
-    Key key           = h_keys[i];
-    h_pairs[i].first  = key;
-    h_pairs[i].second = i;
-
-    h_keys[i] = distribution(gen);
-  }
+  generate_prob_keys<Multiplicity, Key>(h_keys.begin(), h_keys.end(), matching_rate);
 
   thrust::device_vector<Key> d_keys(h_keys);
   thrust::device_vector<cuco::pair_type<Key, Value>> d_pairs(h_pairs);
 
   state.add_element_count(num_keys, "NumKeys");
-  state.add_global_memory_writes<Key>(num_keys * 2);
+
+  cuco::static_multimap<Key, Value, cg_size> map{size, -1, -1};
+  map.insert(d_pairs.begin(), d_pairs.end());
 
   state.exec(nvbench::exec_tag::sync | nvbench::exec_tag::timer,
              [&](nvbench::launch& launch, auto& timer) {
-               auto const cg_size = 8;
-               cuco::static_multimap<Key, Value, cg_size> map{size, -1, -1};
-               map.insert(d_pairs.begin(), d_pairs.end());
-
-               // Use timers to explicitly mark the target region
                timer.start();
                auto count = map.count(d_keys.begin(), d_keys.end(), launch.get_stream());
                timer.stop();
              });
 }
 
-template <typename Key, typename Value>
-std::enable_if_t<(sizeof(Key) != sizeof(Value)), void> nvbench_static_multimap_count_analysis(
-  nvbench::state& state, nvbench::type_list<Key, Value>)
+template <typename Key, typename Value, dist_type Dist, nvbench::int32_t Multiplicity>
+std::enable_if_t<(sizeof(Key) != sizeof(Value)), void> nvbench_static_multimap_count(
+  nvbench::state& state,
+  nvbench::type_list<Key, Value, nvbench::enum_type<Dist>, nvbench::enum_type<Multiplicity>>)
 {
   state.skip("Key should be the same type as Value.");
 }
 
 /**
- * @brief A benchmark evaluating multi-value retrieval performance by varing number of repetitions
- * per key:
- * - 100'000'000 keys are inserted
- * - Map occupancy: 0.8
+ * @brief A benchmark evaluating multi-value `find_all` performance:
+ * - Total number of insertions: 100'000'000
  * - CG size: 8
- * - Number of repetitions per key: 1, ... , 128, 256
- *
  */
-template <typename Key, typename Value>
+template <typename Key, typename Value, dist_type Dist, nvbench::int32_t Multiplicity>
 std::enable_if_t<(sizeof(Key) == sizeof(Value)), void> nvbench_static_multimap_find_all(
-  nvbench::state& state, nvbench::type_list<Key, Value>)
+  nvbench::state& state,
+  nvbench::type_list<Key, Value, nvbench::enum_type<Dist>, nvbench::enum_type<Multiplicity>>)
 {
-  std::size_t const num_keys = state.get_int64("NumInputs");
-  auto const occupancy       = state.get_float64("Occupancy");
-  std::size_t const num_reps = state.get_int64("NumReps");
-  auto const dist            = state.get_string("Distribution");
+  auto const num_keys      = state.get_int64("NumInputs");
+  auto const occupancy     = state.get_float64("Occupancy");
+  auto const matching_rate = state.get_float64("MatchingRate");
 
   std::size_t const size = num_keys / occupancy;
+  auto const cg_size     = 8;
 
   std::vector<Key> h_keys(num_keys);
-  generate_keys<Key>(h_keys.begin(), h_keys.end(), num_reps, dist);
-
   std::vector<cuco::pair_type<Key, Value>> h_pairs(num_keys);
+
+  generate_keys<Dist, Multiplicity, Key>(h_keys.begin(), h_keys.end());
+
   for (auto i = 0; i < num_keys; ++i) {
     Key key           = h_keys[i];
     Value val         = h_keys[i];
     h_pairs[i].first  = key;
     h_pairs[i].second = val;
-
-    if (dist == "RANDOM") { h_keys[i] = i; }
   }
 
-  // Get an array of unique keys
-  std::set<Key> key_set(h_keys.begin(), h_keys.end());
-  std::vector<Key> h_unique_keys(key_set.begin(), key_set.end());
-  thrust::device_vector<Key> d_unique_keys(h_unique_keys);
+  generate_prob_keys<Multiplicity, Key>(h_keys.begin(), h_keys.end(), matching_rate);
 
-  thrust::device_vector<cuco::pair_type<Key, Value>> d_results(2 * num_keys);
+  thrust::device_vector<Key> d_keys(h_keys);
   thrust::device_vector<cuco::pair_type<Key, Value>> d_pairs(h_pairs);
 
+  std::size_t output_size = num_keys * matching_rate * Multiplicity + num_keys;
+  thrust::device_vector<cuco::pair_type<Key, Value>> d_results(output_size);
+
   state.add_element_count(num_keys, "NumKeys");
-  state.add_global_memory_writes<Key>(num_keys * 2);
+
+  cuco::static_multimap<Key, Value, cg_size> map{size, -1, -1};
+  map.insert(d_pairs.begin(), d_pairs.end());
 
   state.exec(
     nvbench::exec_tag::sync | nvbench::exec_tag::timer, [&](nvbench::launch& launch, auto& timer) {
-      auto const cg_size = 8;
-      cuco::static_multimap<Key, Value, cg_size> map{size, -1, -1};
-      map.insert(d_pairs.begin(), d_pairs.end());
-
-      // Use timers to explicitly mark the target region
       timer.start();
-      map.find_all(
-        d_unique_keys.begin(), d_unique_keys.end(), d_results.data().get(), launch.get_stream());
+      map.find_all(d_keys.begin(), d_keys.end(), d_results.data().get(), launch.get_stream());
       timer.stop();
     });
 }
 
-template <typename Key, typename Value>
+template <typename Key, typename Value, dist_type Dist, nvbench::int32_t Multiplicity>
 std::enable_if_t<(sizeof(Key) != sizeof(Value)), void> nvbench_static_multimap_find_all(
-  nvbench::state& state, nvbench::type_list<Key, Value>)
+  nvbench::state& state,
+  nvbench::type_list<Key, Value, nvbench::enum_type<Dist>, nvbench::enum_type<Multiplicity>>)
 {
   state.skip("Key should be the same type as Value.");
 }
 
-/**
- * @brief A benchmark evaluating multi-value retrieval performance (`count` + `find_all`) by varing
- * number of repetitions per key:
- * - 100'000'000 keys are inserted
- * - Map occupancy: 0.8
- * - CG size: 8
- * - Number of repetitions per key: 1, ... , 128, 256
- *
- */
-template <typename Key, typename Value>
+template <typename Key, typename Value, dist_type Dist, nvbench::int32_t Multiplicity>
 std::enable_if_t<(sizeof(Key) == sizeof(Value)), void> nvbench_static_multimap_retrieve(
-  nvbench::state& state, nvbench::type_list<Key, Value>)
+  nvbench::state& state,
+  nvbench::type_list<Key, Value, nvbench::enum_type<Dist>, nvbench::enum_type<Multiplicity>>)
 {
-  std::size_t const num_keys = state.get_int64("NumInputs");
-  auto const occupancy       = state.get_float64("Occupancy");
-  std::size_t const num_reps = state.get_int64("NumReps");
-  auto const dist            = state.get_string("Distribution");
+  auto const num_keys      = state.get_int64("NumInputs");
+  auto const occupancy     = state.get_float64("Occupancy");
+  auto const matching_rate = state.get_float64("MatchingRate");
 
   std::size_t const size = num_keys / occupancy;
+  auto const cg_size     = 8;
 
   std::vector<Key> h_keys(num_keys);
-  generate_keys<Key>(h_keys.begin(), h_keys.end(), num_reps, dist);
-
   std::vector<cuco::pair_type<Key, Value>> h_pairs(num_keys);
+
+  generate_keys<Dist, Multiplicity, Key>(h_keys.begin(), h_keys.end());
+
   for (auto i = 0; i < num_keys; ++i) {
     Key key           = h_keys[i];
     Value val         = h_keys[i];
     h_pairs[i].first  = key;
     h_pairs[i].second = val;
-
-    if (dist == "RANDOM") { h_keys[i] = i; }
   }
 
-  // Get an array of unique keys
-  std::set<Key> key_set(h_keys.begin(), h_keys.end());
-  std::vector<Key> h_unique_keys(key_set.begin(), key_set.end());
-  thrust::device_vector<Key> d_unique_keys(h_unique_keys);
+  generate_prob_keys<Multiplicity, Key>(h_keys.begin(), h_keys.end(), matching_rate);
 
-  thrust::device_vector<cuco::pair_type<Key, Value>> d_results(2 * num_keys);
+  thrust::device_vector<Key> d_keys(h_keys);
   thrust::device_vector<cuco::pair_type<Key, Value>> d_pairs(h_pairs);
 
+  std::size_t output_size = num_keys * matching_rate * Multiplicity + num_keys;
+  thrust::device_vector<cuco::pair_type<Key, Value>> d_results(output_size);
+
   state.add_element_count(num_keys, "NumKeys");
-  state.add_global_memory_writes<Key>(num_keys * 2);
+
+  cuco::static_multimap<Key, Value, cg_size> map{size, -1, -1};
+  map.insert(d_pairs.begin(), d_pairs.end());
 
   state.exec(
     nvbench::exec_tag::sync | nvbench::exec_tag::timer, [&](nvbench::launch& launch, auto& timer) {
-      auto const cg_size = 8;
-      cuco::static_multimap<Key, Value, cg_size> map{size, -1, -1};
-      map.insert(d_pairs.begin(), d_pairs.end());
-
-      // Use timers to explicitly mark the target region
       timer.start();
-      auto count = map.count(d_unique_keys.begin(), d_unique_keys.end(), launch.get_stream());
-      map.find_all(
-        d_unique_keys.begin(), d_unique_keys.end(), d_results.data().get(), launch.get_stream());
+      auto count = map.count(d_keys.begin(), d_keys.end(), launch.get_stream());
+      map.find_all(d_keys.begin(), d_keys.end(), d_results.data().get(), launch.get_stream());
       timer.stop();
     });
 }
 
-template <typename Key, typename Value>
+template <typename Key, typename Value, dist_type Dist, nvbench::int32_t Multiplicity>
 std::enable_if_t<(sizeof(Key) != sizeof(Value)), void> nvbench_static_multimap_retrieve(
-  nvbench::state& state, nvbench::type_list<Key, Value>)
+  nvbench::state& state,
+  nvbench::type_list<Key, Value, nvbench::enum_type<Dist>, nvbench::enum_type<Multiplicity>>)
 {
   state.skip("Key should be the same type as Value.");
 }
 
+/**
+ * @brief A benchmark evaluating multi-value retrieve (`count` + `find_all`) performance:
+ * - Total number of insertions: 100'000'000
+ * - CG size: 8
+ */
+
 using key_type   = nvbench::type_list<nvbench::int32_t, nvbench::int64_t>;
 using value_type = nvbench::type_list<nvbench::int32_t, nvbench::int64_t>;
+using d_type =
+  nvbench::enum_type_list<dist_type::GAUSSIAN, dist_type::GEOMETRIC, dist_type::UNIFORM>;
+
+using multiplicity = nvbench::enum_type_list<1, 2, 4, 8, 16, 32, 64, 128, 256>;
 
-NVBENCH_BENCH_TYPES(nvbench_static_multimap_insert, NVBENCH_TYPE_AXES(key_type, value_type))
-  .set_type_axes_names({"Key", "Value"})
+NVBENCH_BENCH_TYPES(nvbench_static_multimap_insert,
+                    NVBENCH_TYPE_AXES(key_type, value_type, d_type, multiplicity))
+  .set_name("staic_multimap_insert_multiplicity")
+  .set_type_axes_names({"Key", "Value", "Distribution", "Multiplicity"})
+  .set_max_noise(3)                            // Custom noise: 3%. By default: 0.5%.
+  .add_int64_axis("NumInputs", {100'000'000})  // Total number of key/value pairs: 100'000'000
+  .add_float64_axis("Occupancy", {0.8});
+
+NVBENCH_BENCH_TYPES(nvbench_static_multimap_insert,
+                    NVBENCH_TYPE_AXES(key_type, value_type, d_type, nvbench::enum_type_list<8>))
+  .set_name("staic_multimap_insert_occupancy")
+  .set_type_axes_names({"Key", "Value", "Distribution", "Multiplicity"})
+  .set_max_noise(3)                            // Custom noise: 3%. By default: 0.5%.
+  .add_int64_axis("NumInputs", {100'000'000})  // Total number of key/value pairs: 100'000'000
+  .add_float64_axis("Occupancy", nvbench::range(0.1, 1., 0.1));
+
+NVBENCH_BENCH_TYPES(nvbench_static_multimap_count,
+                    NVBENCH_TYPE_AXES(key_type, value_type, d_type, multiplicity))
+  .set_name("staic_multimap_count_multiplicity")
+  .set_type_axes_names({"Key", "Value", "Distribution", "Multiplicity"})
+  .set_timeout(100)                            // Custom timeout: 100 s. Default is 15 s.
   .set_max_noise(3)                            // Custom noise: 3%. By default: 0.5%.
   .add_int64_axis("NumInputs", {100'000'000})  // Total number of key/value pairs: 100'000'000
   .add_float64_axis("Occupancy", {0.8})
-  .add_string_axis("Distribution", {"CYCLE", "RANDOM"})
-  .add_int64_power_of_two_axis("NumReps", nvbench::range(0, 8, 1));
+  .add_float64_axis("MatchingRate", {0.5});
+
+NVBENCH_BENCH_TYPES(nvbench_static_multimap_count,
+                    NVBENCH_TYPE_AXES(key_type, value_type, d_type, nvbench::enum_type_list<8>))
+  .set_name("staic_multimap_count_occupancy")
+  .set_type_axes_names({"Key", "Value", "Distribution", "Multiplicity"})
+  .set_timeout(100)                            // Custom timeout: 100 s. Default is 15 s.
+  .set_max_noise(3)                            // Custom noise: 3%. By default: 0.5%.
+  .add_int64_axis("NumInputs", {100'000'000})  // Total number of key/value pairs: 100'000'000
+  .add_float64_axis("Occupancy", nvbench::range(0.1, 1., 0.1))
+  .add_float64_axis("MatchingRate", {0.5});
 
-NVBENCH_BENCH_TYPES(nvbench_static_multimap_count, NVBENCH_TYPE_AXES(key_type, value_type))
-  .set_type_axes_names({"Key", "Value"})
+NVBENCH_BENCH_TYPES(nvbench_static_multimap_count,
+                    NVBENCH_TYPE_AXES(key_type, value_type, d_type, nvbench::enum_type_list<8>))
+  .set_name("staic_multimap_count_matching_rate")
+  .set_type_axes_names({"Key", "Value", "Distribution", "Multiplicity"})
   .set_timeout(100)                            // Custom timeout: 100 s. Default is 15 s.
   .set_max_noise(3)                            // Custom noise: 3%. By default: 0.5%.
   .add_int64_axis("NumInputs", {100'000'000})  // Total number of key/value pairs: 100'000'000
   .add_float64_axis("Occupancy", {0.8})
-  .add_string_axis("Distribution", {"CYCLE", "RANDOM"})
-  .add_int64_power_of_two_axis("NumReps", nvbench::range(0, 8, 1));
+  .add_float64_axis("MatchingRate", {0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1});
 
-NVBENCH_BENCH_TYPES(nvbench_static_multimap_find_all, NVBENCH_TYPE_AXES(key_type, value_type))
-  .set_type_axes_names({"Key", "Value"})
+NVBENCH_BENCH_TYPES(nvbench_static_multimap_find_all,
+                    NVBENCH_TYPE_AXES(key_type, value_type, d_type, multiplicity))
+  .set_name("staic_multimap_find_all_multiplicity")
+  .set_type_axes_names({"Key", "Value", "Distribution", "Multiplicity"})
   .set_timeout(100)                            // Custom timeout: 100 s. Default is 15 s.
   .set_max_noise(3)                            // Custom noise: 3%. By default: 0.5%.
   .add_int64_axis("NumInputs", {100'000'000})  // Total number of key/value pairs: 100'000'000
   .add_float64_axis("Occupancy", {0.8})
-  .add_string_axis("Distribution", {"CYCLE", "RANDOM"})
-  .add_int64_power_of_two_axis("NumReps", nvbench::range(0, 8, 1));
+  .add_float64_axis("MatchingRate", {0.5});
 
-NVBENCH_BENCH_TYPES(nvbench_static_multimap_retrieve, NVBENCH_TYPE_AXES(key_type, value_type))
-  .set_type_axes_names({"Key", "Value"})
+NVBENCH_BENCH_TYPES(nvbench_static_multimap_find_all,
+                    NVBENCH_TYPE_AXES(key_type, value_type, d_type, nvbench::enum_type_list<8>))
+  .set_name("staic_multimap_find_all_occupancy")
+  .set_type_axes_names({"Key", "Value", "Distribution", "Multiplicity"})
+  .set_timeout(100)                            // Custom timeout: 100 s. Default is 15 s.
+  .set_max_noise(3)                            // Custom noise: 3%. By default: 0.5%.
+  .add_int64_axis("NumInputs", {100'000'000})  // Total number of key/value pairs: 100'000'000
+  .add_float64_axis("Occupancy", nvbench::range(0.1, 1., 0.1))
+  .add_float64_axis("MatchingRate", {0.5});
+
+NVBENCH_BENCH_TYPES(nvbench_static_multimap_find_all,
+                    NVBENCH_TYPE_AXES(key_type, value_type, d_type, nvbench::enum_type_list<8>))
+  .set_name("staic_multimap_find_all_matching_rate")
+  .set_type_axes_names({"Key", "Value", "Distribution", "Multiplicity"})
   .set_timeout(100)                            // Custom timeout: 100 s. Default is 15 s.
   .set_max_noise(3)                            // Custom noise: 3%. By default: 0.5%.
   .add_int64_axis("NumInputs", {100'000'000})  // Total number of key/value pairs: 100'000'000
   .add_float64_axis("Occupancy", {0.8})
-  .add_string_axis("Distribution", {"CYCLE", "RANDOM"})
-  .add_int64_power_of_two_axis("NumReps", nvbench::range(0, 8, 1));
+  .add_float64_axis("MatchingRate", {0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1});
 
-NVBENCH_BENCH_TYPES(nvbench_static_multimap_count_analysis, NVBENCH_TYPE_AXES(key_type, value_type))
-  .set_name("count_varying_matching_rate")
-  .set_type_axes_names({"Key", "Value"})
+NVBENCH_BENCH_TYPES(nvbench_static_multimap_retrieve,
+                    NVBENCH_TYPE_AXES(key_type, value_type, d_type, multiplicity))
+  .set_name("staic_multimap_retrieve_multiplicity")
+  .set_type_axes_names({"Key", "Value", "Distribution", "Multiplicity"})
   .set_timeout(100)                            // Custom timeout: 100 s. Default is 15 s.
   .set_max_noise(3)                            // Custom noise: 3%. By default: 0.5%.
   .add_int64_axis("NumInputs", {100'000'000})  // Total number of key/value pairs: 100'000'000
   .add_float64_axis("Occupancy", {0.8})
-  .add_float64_axis("MatchingRate", {0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1})
-  .add_int64_power_of_two_axis("NumReps", {3});
+  .add_float64_axis("MatchingRate", {0.5});
+
+NVBENCH_BENCH_TYPES(nvbench_static_multimap_retrieve,
+                    NVBENCH_TYPE_AXES(key_type, value_type, d_type, nvbench::enum_type_list<8>))
+  .set_name("staic_multimap_retrieve_occupancy")
+  .set_type_axes_names({"Key", "Value", "Distribution", "Multiplicity"})
+  .set_timeout(100)                            // Custom timeout: 100 s. Default is 15 s.
+  .set_max_noise(3)                            // Custom noise: 3%. By default: 0.5%.
+  .add_int64_axis("NumInputs", {100'000'000})  // Total number of key/value pairs: 100'000'000
+  .add_float64_axis("Occupancy", nvbench::range(0.1, 1., 0.1))
+  .add_float64_axis("MatchingRate", {0.5});
 
-NVBENCH_BENCH_TYPES(nvbench_static_multimap_count_analysis, NVBENCH_TYPE_AXES(key_type, value_type))
-  .set_name("count_varying_num_reps")
-  .set_type_axes_names({"Key", "Value"})
+NVBENCH_BENCH_TYPES(nvbench_static_multimap_retrieve,
+                    NVBENCH_TYPE_AXES(key_type, value_type, d_type, nvbench::enum_type_list<8>))
+  .set_name("staic_multimap_retrieve_matching_rate")
+  .set_type_axes_names({"Key", "Value", "Distribution", "Multiplicity"})
   .set_timeout(100)                            // Custom timeout: 100 s. Default is 15 s.
   .set_max_noise(3)                            // Custom noise: 3%. By default: 0.5%.
   .add_int64_axis("NumInputs", {100'000'000})  // Total number of key/value pairs: 100'000'000
   .add_float64_axis("Occupancy", {0.8})
-  .add_float64_axis("MatchingRate", {0.5})
-  .add_int64_power_of_two_axis("NumReps", nvbench::range(0, 8, 1));
+  .add_float64_axis("MatchingRate", {0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1});
diff --git a/benchmarks/key_generator.hpp b/benchmarks/key_generator.hpp
index 749ce69cf..6606b4983 100644
--- a/benchmarks/key_generator.hpp
+++ b/benchmarks/key_generator.hpp
@@ -18,15 +18,9 @@
 
 #include <limits>
 #include <random>
-#include <unordered_map>
 
 enum class dist_type { GEOMETRIC, GAUSSIAN, UNIFORM };
 
-static std::unordered_map<std::string, dist_type> const dist_map = {
-  {"GAUSSIAN", dist_type::GAUSSIAN},
-  {"GEOMETRIC", dist_type::GEOMETRIC},
-  {"UNIFORM", dist_type::UNIFORM}};
-
 template <dist_type Dist, std::size_t Multiplicity, typename Key, typename OutputIt>
 static void generate_keys(OutputIt output_begin, OutputIt output_end)
 {
@@ -64,20 +58,15 @@ static void generate_keys(OutputIt output_begin, OutputIt output_end)
       }
       break;
     }
-  }
+  }  // switch
 }
 
-template <dist_type Dist,
-          std::size_t Multiplicity,
-          typename Key,
-          typename InputIt,
-          typename OutputIt>
-static void generate_prob_keys(InputIt input_begin,
-                               InputIt input_end,
+template <std::size_t Multiplicity, typename Key, typename OutputIt>
+static void generate_prob_keys(OutputIt output_end,
                                OutputIt output_begin,
                                double const matching_rate)
 {
-  auto const num_keys = std::distance(input_begin, input_end);
+  auto const num_keys = std::distance(output_begin, output_end);
 
   std::random_device rd;
   std::mt19937 gen{rd()};
@@ -85,19 +74,12 @@ static void generate_prob_keys(InputIt input_begin,
   auto const max = std::numeric_limits<Key>::max();
 
   std::uniform_real_distribution<double> rate_dist(0.0, 1.0);
-  std::uniform_int_distribution<std::size_t> idx_dist{0, num_keys - 1};
   std::uniform_int_distribution<Key> non_match_dist{max / Multiplicity, max};
 
   for (auto i = 0; i < num_keys; ++i) {
     auto const tmp_rate = rate_dist(gen);
 
-    // If match, randomly sample from the input keys
-    if (tmp_rate <= matching_rate) {
-      output_begin[i] = input_begin[idx_dist(gen)];
-    }
-    // Otherwise, randomly draw from [Max/Multiplicity, Max]
-    else {
-      output_begin[i] = non_match_dist(gen);
-    }
+    // If non-match, randomly sample from [Max/Multiplicity, Max]
+    if (tmp_rate > matching_rate) { output_begin[i] = non_match_dist(gen); }
   }
 }

From 542cb3cb173a6244cb22bc2fa16a3238decb2b52 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Tue, 11 May 2021 11:34:16 -0400
Subject: [PATCH 090/267] Fix a silly bug in key generator: from begin to end,
 not the opposite!

---
 benchmarks/key_generator.hpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/benchmarks/key_generator.hpp b/benchmarks/key_generator.hpp
index 6606b4983..5c862261d 100644
--- a/benchmarks/key_generator.hpp
+++ b/benchmarks/key_generator.hpp
@@ -62,8 +62,8 @@ static void generate_keys(OutputIt output_begin, OutputIt output_end)
 }
 
 template <std::size_t Multiplicity, typename Key, typename OutputIt>
-static void generate_prob_keys(OutputIt output_end,
-                               OutputIt output_begin,
+static void generate_prob_keys(OutputIt output_begin,
+                               OutputIt output_end,
                                double const matching_rate)
 {
   auto const num_keys = std::distance(output_begin, output_end);

From e7bf44b05667aa47a597b32bb2ec5d496c6d849b Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Tue, 11 May 2021 16:06:01 -0400
Subject: [PATCH 091/267] Update benchmark: solid control of key
 multiplicities; binomial instead of normal

---
 .../static_multimap/static_multimap_bench.cu  | 22 ++++---
 benchmarks/key_generator.hpp                  | 65 ++++++++++++-------
 2 files changed, 56 insertions(+), 31 deletions(-)

diff --git a/benchmarks/hash_table/static_multimap/static_multimap_bench.cu b/benchmarks/hash_table/static_multimap/static_multimap_bench.cu
index 813ca57d5..a516d2a0b 100644
--- a/benchmarks/hash_table/static_multimap/static_multimap_bench.cu
+++ b/benchmarks/hash_table/static_multimap/static_multimap_bench.cu
@@ -30,7 +30,7 @@ NVBENCH_DECLARE_ENUM_TYPE_STRINGS(
   // Used when context is available to figure out the enum type.
   [](dist_type d) {
     switch (d) {
-      case dist_type::GAUSSIAN: return "GAUSSIAN";
+      case dist_type::BINOMIAL: return "BINOMIAL";
       case dist_type::GEOMETRIC: return "GEOMETRIC";
       case dist_type::UNIFORM: return "UNIFORM";
       default: return "ERROR";
@@ -60,9 +60,10 @@ std::enable_if_t<(sizeof(Key) == sizeof(Value)), void> nvbench_static_multimap_i
   auto const cg_size     = 8;
 
   std::vector<Key> h_keys(num_keys);
+  std::vector<Key> unique_keys;
   std::vector<cuco::pair_type<Key, Value>> h_pairs(num_keys);
 
-  generate_keys<Dist, Multiplicity, Key>(h_keys.begin(), h_keys.end());
+  generate_keys<Dist, Multiplicity, Key>(unique_keys, h_keys.begin(), h_keys.end());
 
   for (auto i = 0; i < num_keys; ++i) {
     Key key           = h_keys[i];
@@ -112,9 +113,10 @@ std::enable_if_t<(sizeof(Key) == sizeof(Value)), void> nvbench_static_multimap_c
   auto const cg_size     = 8;
 
   std::vector<Key> h_keys(num_keys);
+  std::vector<Key> unique_keys;
   std::vector<cuco::pair_type<Key, Value>> h_pairs(num_keys);
 
-  generate_keys<Dist, Multiplicity, Key>(h_keys.begin(), h_keys.end());
+  generate_keys<Dist, Multiplicity, Key>(unique_keys, h_keys.begin(), h_keys.end());
 
   for (auto i = 0; i < num_keys; ++i) {
     Key key           = h_keys[i];
@@ -123,7 +125,7 @@ std::enable_if_t<(sizeof(Key) == sizeof(Value)), void> nvbench_static_multimap_c
     h_pairs[i].second = val;
   }
 
-  generate_prob_keys<Multiplicity, Key>(h_keys.begin(), h_keys.end(), matching_rate);
+  generate_prob_keys<Multiplicity, Key>(unique_keys, matching_rate, h_keys.begin(), h_keys.end());
 
   thrust::device_vector<Key> d_keys(h_keys);
   thrust::device_vector<cuco::pair_type<Key, Value>> d_pairs(h_pairs);
@@ -167,9 +169,10 @@ std::enable_if_t<(sizeof(Key) == sizeof(Value)), void> nvbench_static_multimap_f
   auto const cg_size     = 8;
 
   std::vector<Key> h_keys(num_keys);
+  std::vector<Key> unique_keys;
   std::vector<cuco::pair_type<Key, Value>> h_pairs(num_keys);
 
-  generate_keys<Dist, Multiplicity, Key>(h_keys.begin(), h_keys.end());
+  generate_keys<Dist, Multiplicity, Key>(unique_keys, h_keys.begin(), h_keys.end());
 
   for (auto i = 0; i < num_keys; ++i) {
     Key key           = h_keys[i];
@@ -178,7 +181,7 @@ std::enable_if_t<(sizeof(Key) == sizeof(Value)), void> nvbench_static_multimap_f
     h_pairs[i].second = val;
   }
 
-  generate_prob_keys<Multiplicity, Key>(h_keys.begin(), h_keys.end(), matching_rate);
+  generate_prob_keys<Multiplicity, Key>(unique_keys, matching_rate, h_keys.begin(), h_keys.end());
 
   thrust::device_vector<Key> d_keys(h_keys);
   thrust::device_vector<cuco::pair_type<Key, Value>> d_pairs(h_pairs);
@@ -220,9 +223,10 @@ std::enable_if_t<(sizeof(Key) == sizeof(Value)), void> nvbench_static_multimap_r
   auto const cg_size     = 8;
 
   std::vector<Key> h_keys(num_keys);
+  std::vector<Key> unique_keys;
   std::vector<cuco::pair_type<Key, Value>> h_pairs(num_keys);
 
-  generate_keys<Dist, Multiplicity, Key>(h_keys.begin(), h_keys.end());
+  generate_keys<Dist, Multiplicity, Key>(unique_keys, h_keys.begin(), h_keys.end());
 
   for (auto i = 0; i < num_keys; ++i) {
     Key key           = h_keys[i];
@@ -231,7 +235,7 @@ std::enable_if_t<(sizeof(Key) == sizeof(Value)), void> nvbench_static_multimap_r
     h_pairs[i].second = val;
   }
 
-  generate_prob_keys<Multiplicity, Key>(h_keys.begin(), h_keys.end(), matching_rate);
+  generate_prob_keys<Multiplicity, Key>(unique_keys, matching_rate, h_keys.begin(), h_keys.end());
 
   thrust::device_vector<Key> d_keys(h_keys);
   thrust::device_vector<cuco::pair_type<Key, Value>> d_pairs(h_pairs);
@@ -270,7 +274,7 @@ std::enable_if_t<(sizeof(Key) != sizeof(Value)), void> nvbench_static_multimap_r
 using key_type   = nvbench::type_list<nvbench::int32_t, nvbench::int64_t>;
 using value_type = nvbench::type_list<nvbench::int32_t, nvbench::int64_t>;
 using d_type =
-  nvbench::enum_type_list<dist_type::GAUSSIAN, dist_type::GEOMETRIC, dist_type::UNIFORM>;
+  nvbench::enum_type_list<dist_type::BINOMIAL, dist_type::GEOMETRIC, dist_type::UNIFORM>;
 
 using multiplicity = nvbench::enum_type_list<1, 2, 4, 8, 16, 32, 64, 128, 256>;
 
diff --git a/benchmarks/key_generator.hpp b/benchmarks/key_generator.hpp
index 5c862261d..15ca0eda6 100644
--- a/benchmarks/key_generator.hpp
+++ b/benchmarks/key_generator.hpp
@@ -18,68 +18,89 @@
 
 #include <limits>
 #include <random>
+#include <unordered_set>
+#include <vector>
 
-enum class dist_type { GEOMETRIC, GAUSSIAN, UNIFORM };
+enum class dist_type { BINOMIAL, GEOMETRIC, UNIFORM };
 
 template <dist_type Dist, std::size_t Multiplicity, typename Key, typename OutputIt>
-static void generate_keys(OutputIt output_begin, OutputIt output_end)
+static void generate_keys(std::vector<Key>& unique_keys, OutputIt output_begin, OutputIt output_end)
 {
   auto const num_keys = std::distance(output_begin, output_end);
+  auto const size     = num_keys * 2;
 
   std::random_device rd;
   std::mt19937 gen{rd()};
 
+  auto const max = std::numeric_limits<Key>::max();
+
+  std::unordered_set<Key> set;
+
   switch (Dist) {
-    case dist_type::GAUSSIAN: {
-      auto const max  = std::numeric_limits<Key>::max();
-      auto const mean = max / Multiplicity / 2;
-      auto const dev  = max / Multiplicity / 5;
+    case dist_type::BINOMIAL: {
+      auto const t = max;
+      auto const p = 0.5;
 
-      std::normal_distribution<> distribution(mean, dev);
+      std::binomial_distribution<Key> distribution{t, p};
 
-      for (auto i = 0; i < num_keys; ++i) {
-        output_begin[i] = std::abs(distribution(gen));
+      while (set.size() < size) {
+        set.insert(distribution(gen));
       }
       break;
     }
     case dist_type::GEOMETRIC: {
       std::geometric_distribution<Key> distribution{1e-9};
 
-      for (auto i = 0; i < num_keys; ++i) {
-        output_begin[i] = distribution(gen) / Multiplicity;
+      while (set.size() < size) {
+        set.insert(distribution(gen));
       }
       break;
     }
     case dist_type::UNIFORM: {
-      std::uniform_int_distribution<Key> distribution{1, static_cast<Key>(num_keys / Multiplicity)};
+      std::uniform_int_distribution<Key> distribution{0, max};
 
-      for (auto i = 0; i < num_keys; ++i) {
-        output_begin[i] = distribution(gen);
+      while (set.size() < size) {
+        set.insert(distribution(gen));
       }
       break;
     }
   }  // switch
+
+  unique_keys.resize(set.size());
+  std::copy(set.begin(), set.end(), unique_keys.begin());
+  std::random_shuffle(unique_keys.begin(), unique_keys.end());
+  auto it = unique_keys.begin();
+
+  for (auto i = 0; i < num_keys / Multiplicity; ++i) {
+    for (auto j = 0; j < Multiplicity; ++j) {
+      output_begin[i * Multiplicity + j] = *it;
+    }
+    ++it;
+  }
+
+  std::random_shuffle(output_begin, output_end);
 }
 
 template <std::size_t Multiplicity, typename Key, typename OutputIt>
-static void generate_prob_keys(OutputIt output_begin,
-                               OutputIt output_end,
-                               double const matching_rate)
+static void generate_prob_keys(std::vector<Key> const& unique_keys,
+                               double const matching_rate,
+                               OutputIt output_begin,
+                               OutputIt output_end)
 {
   auto const num_keys = std::distance(output_begin, output_end);
 
   std::random_device rd;
   std::mt19937 gen{rd()};
 
-  auto const max = std::numeric_limits<Key>::max();
-
   std::uniform_real_distribution<double> rate_dist(0.0, 1.0);
-  std::uniform_int_distribution<Key> non_match_dist{max / Multiplicity, max};
+  std::uniform_int_distribution<std::size_t> idx_dist{num_keys / Multiplicity,
+                                                      static_cast<std::size_t>(num_keys * 2 - 1)};
 
   for (auto i = 0; i < num_keys; ++i) {
     auto const tmp_rate = rate_dist(gen);
 
-    // If non-match, randomly sample from [Max/Multiplicity, Max]
-    if (tmp_rate > matching_rate) { output_begin[i] = non_match_dist(gen); }
+    if (tmp_rate > matching_rate) { output_begin[i] = unique_keys[idx_dist(gen)]; }
   }
+
+  std::random_shuffle(output_begin, output_end);
 }

From d67b14560a3fd66fcd6dc36f77d52fe4ccd52e82 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Wed, 12 May 2021 11:22:16 -0400
Subject: [PATCH 092/267] Fix sync bug in the very last block in find_all

---
 include/cuco/detail/static_multimap_kernels.cuh | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap_kernels.cuh
index 62a662dc2..94f82e807 100644
--- a/include/cuco/detail/static_multimap_kernels.cuh
+++ b/include/cuco/detail/static_multimap_kernels.cuh
@@ -558,15 +558,15 @@ __global__ void find_all(InputIt first,
 
   if (0 == threadIdx.x) { block_counter = 0; }
 
-  while (first + key_idx < last) {
-    auto key          = *(first + key_idx);
-    auto current_slot = view.initial_slot(tile, key, hash);
-
-    bool running     = true;
+  while (__syncthreads_or(first + key_idx < last)) {
+    bool running     = ((first + key_idx) < last);
     bool found_match = false;
 
     while (__syncthreads_or(running)) {
       if (running) {
+        auto key          = *(first + key_idx);
+        auto current_slot = view.initial_slot(tile, key, hash);
+
         pair<Key, Value> arr[2];
         if constexpr (sizeof(Key) == 4) {
           auto const tmp = *reinterpret_cast<uint4 const*>(current_slot);

From e806f2698040b92caa0470dac734fb0b5be9bc5b Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Wed, 12 May 2021 16:29:54 -0400
Subject: [PATCH 093/267] Fix the last block sync bug in find_all

---
 include/cuco/detail/static_multimap_kernels.cuh | 10 ++++++----
 1 file changed, 6 insertions(+), 4 deletions(-)

diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap_kernels.cuh
index 94f82e807..7e36f7e02 100644
--- a/include/cuco/detail/static_multimap_kernels.cuh
+++ b/include/cuco/detail/static_multimap_kernels.cuh
@@ -559,14 +559,16 @@ __global__ void find_all(InputIt first,
   if (0 == threadIdx.x) { block_counter = 0; }
 
   while (__syncthreads_or(first + key_idx < last)) {
+    auto key = *(first + key_idx);
+    typename viewT::iterator current_slot;
+
     bool running     = ((first + key_idx) < last);
     bool found_match = false;
 
+    if (running) { current_slot = view.initial_slot(tile, key, hash); }
+
     while (__syncthreads_or(running)) {
       if (running) {
-        auto key          = *(first + key_idx);
-        auto current_slot = view.initial_slot(tile, key, hash);
-
         pair<Key, Value> arr[2];
         if constexpr (sizeof(Key) == 4) {
           auto const tmp = *reinterpret_cast<uint4 const*>(current_slot);
@@ -626,7 +628,7 @@ __global__ void find_all(InputIt first,
         if (0 == threadIdx.x) { block_counter = 0; }
       }
 
-      current_slot = view.next_slot(tile, current_slot);
+      if (running) { current_slot = view.next_slot(tile, current_slot); }
     }  // while running
     key_idx += (gridDim.x * block_size) / tile_size;
   }

From 02126977a12ef0ab3fdbec1dde19e082364b956c Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Wed, 12 May 2021 16:32:07 -0400
Subject: [PATCH 094/267] Update benchmarks: revert back to gaussian due to the
 incredibly high overhead with binomial sampling

---
 .../static_multimap/static_multimap_bench.cu  | 50 ++++++++------
 benchmarks/key_generator.hpp                  | 67 +++++++------------
 2 files changed, 55 insertions(+), 62 deletions(-)

diff --git a/benchmarks/hash_table/static_multimap/static_multimap_bench.cu b/benchmarks/hash_table/static_multimap/static_multimap_bench.cu
index a516d2a0b..64593aafc 100644
--- a/benchmarks/hash_table/static_multimap/static_multimap_bench.cu
+++ b/benchmarks/hash_table/static_multimap/static_multimap_bench.cu
@@ -30,7 +30,7 @@ NVBENCH_DECLARE_ENUM_TYPE_STRINGS(
   // Used when context is available to figure out the enum type.
   [](dist_type d) {
     switch (d) {
-      case dist_type::BINOMIAL: return "BINOMIAL";
+      case dist_type::GAUSSIAN: return "GAUSSIAN";
       case dist_type::GEOMETRIC: return "GEOMETRIC";
       case dist_type::UNIFORM: return "UNIFORM";
       default: return "ERROR";
@@ -60,10 +60,9 @@ std::enable_if_t<(sizeof(Key) == sizeof(Value)), void> nvbench_static_multimap_i
   auto const cg_size     = 8;
 
   std::vector<Key> h_keys(num_keys);
-  std::vector<Key> unique_keys;
   std::vector<cuco::pair_type<Key, Value>> h_pairs(num_keys);
 
-  generate_keys<Dist, Multiplicity, Key>(unique_keys, h_keys.begin(), h_keys.end());
+  generate_keys<Dist, Multiplicity, Key>(h_keys.begin(), h_keys.end());
 
   for (auto i = 0; i < num_keys; ++i) {
     Key key           = h_keys[i];
@@ -113,10 +112,9 @@ std::enable_if_t<(sizeof(Key) == sizeof(Value)), void> nvbench_static_multimap_c
   auto const cg_size     = 8;
 
   std::vector<Key> h_keys(num_keys);
-  std::vector<Key> unique_keys;
   std::vector<cuco::pair_type<Key, Value>> h_pairs(num_keys);
 
-  generate_keys<Dist, Multiplicity, Key>(unique_keys, h_keys.begin(), h_keys.end());
+  generate_keys<Dist, Multiplicity, Key>(h_keys.begin(), h_keys.end());
 
   for (auto i = 0; i < num_keys; ++i) {
     Key key           = h_keys[i];
@@ -125,7 +123,7 @@ std::enable_if_t<(sizeof(Key) == sizeof(Value)), void> nvbench_static_multimap_c
     h_pairs[i].second = val;
   }
 
-  generate_prob_keys<Multiplicity, Key>(unique_keys, matching_rate, h_keys.begin(), h_keys.end());
+  generate_prob_keys<Key>(matching_rate, h_keys.begin(), h_keys.end());
 
   thrust::device_vector<Key> d_keys(h_keys);
   thrust::device_vector<cuco::pair_type<Key, Value>> d_pairs(h_pairs);
@@ -169,10 +167,9 @@ std::enable_if_t<(sizeof(Key) == sizeof(Value)), void> nvbench_static_multimap_f
   auto const cg_size     = 8;
 
   std::vector<Key> h_keys(num_keys);
-  std::vector<Key> unique_keys;
   std::vector<cuco::pair_type<Key, Value>> h_pairs(num_keys);
 
-  generate_keys<Dist, Multiplicity, Key>(unique_keys, h_keys.begin(), h_keys.end());
+  generate_keys<Dist, Multiplicity, Key>(h_keys.begin(), h_keys.end());
 
   for (auto i = 0; i < num_keys; ++i) {
     Key key           = h_keys[i];
@@ -181,7 +178,7 @@ std::enable_if_t<(sizeof(Key) == sizeof(Value)), void> nvbench_static_multimap_f
     h_pairs[i].second = val;
   }
 
-  generate_prob_keys<Multiplicity, Key>(unique_keys, matching_rate, h_keys.begin(), h_keys.end());
+  generate_prob_keys<Key>(matching_rate, h_keys.begin(), h_keys.end());
 
   thrust::device_vector<Key> d_keys(h_keys);
   thrust::device_vector<cuco::pair_type<Key, Value>> d_pairs(h_pairs);
@@ -223,10 +220,9 @@ std::enable_if_t<(sizeof(Key) == sizeof(Value)), void> nvbench_static_multimap_r
   auto const cg_size     = 8;
 
   std::vector<Key> h_keys(num_keys);
-  std::vector<Key> unique_keys;
   std::vector<cuco::pair_type<Key, Value>> h_pairs(num_keys);
 
-  generate_keys<Dist, Multiplicity, Key>(unique_keys, h_keys.begin(), h_keys.end());
+  generate_keys<Dist, Multiplicity, Key>(h_keys.begin(), h_keys.end());
 
   for (auto i = 0; i < num_keys; ++i) {
     Key key           = h_keys[i];
@@ -235,7 +231,7 @@ std::enable_if_t<(sizeof(Key) == sizeof(Value)), void> nvbench_static_multimap_r
     h_pairs[i].second = val;
   }
 
-  generate_prob_keys<Multiplicity, Key>(unique_keys, matching_rate, h_keys.begin(), h_keys.end());
+  generate_prob_keys<Key>(matching_rate, h_keys.begin(), h_keys.end());
 
   thrust::device_vector<Key> d_keys(h_keys);
   thrust::device_vector<cuco::pair_type<Key, Value>> d_pairs(h_pairs);
@@ -274,13 +270,16 @@ std::enable_if_t<(sizeof(Key) != sizeof(Value)), void> nvbench_static_multimap_r
 using key_type   = nvbench::type_list<nvbench::int32_t, nvbench::int64_t>;
 using value_type = nvbench::type_list<nvbench::int32_t, nvbench::int64_t>;
 using d_type =
-  nvbench::enum_type_list<dist_type::BINOMIAL, dist_type::GEOMETRIC, dist_type::UNIFORM>;
+  nvbench::enum_type_list<dist_type::GAUSSIAN, dist_type::GEOMETRIC, dist_type::UNIFORM>;
 
 using multiplicity = nvbench::enum_type_list<1, 2, 4, 8, 16, 32, 64, 128, 256>;
 
 NVBENCH_BENCH_TYPES(nvbench_static_multimap_insert,
-                    NVBENCH_TYPE_AXES(key_type, value_type, d_type, multiplicity))
-  .set_name("staic_multimap_insert_multiplicity")
+                    NVBENCH_TYPE_AXES(key_type,
+                                      value_type,
+                                      nvbench::enum_type_list<dist_type::UNIFORM>,
+                                      multiplicity))
+  .set_name("staic_multimap_insert_uniform_multiplicity")
   .set_type_axes_names({"Key", "Value", "Distribution", "Multiplicity"})
   .set_max_noise(3)                            // Custom noise: 3%. By default: 0.5%.
   .add_int64_axis("NumInputs", {100'000'000})  // Total number of key/value pairs: 100'000'000
@@ -295,8 +294,11 @@ NVBENCH_BENCH_TYPES(nvbench_static_multimap_insert,
   .add_float64_axis("Occupancy", nvbench::range(0.1, 1., 0.1));
 
 NVBENCH_BENCH_TYPES(nvbench_static_multimap_count,
-                    NVBENCH_TYPE_AXES(key_type, value_type, d_type, multiplicity))
-  .set_name("staic_multimap_count_multiplicity")
+                    NVBENCH_TYPE_AXES(key_type,
+                                      value_type,
+                                      nvbench::enum_type_list<dist_type::UNIFORM>,
+                                      multiplicity))
+  .set_name("staic_multimap_count_uniform_multiplicity")
   .set_type_axes_names({"Key", "Value", "Distribution", "Multiplicity"})
   .set_timeout(100)                            // Custom timeout: 100 s. Default is 15 s.
   .set_max_noise(3)                            // Custom noise: 3%. By default: 0.5%.
@@ -325,8 +327,11 @@ NVBENCH_BENCH_TYPES(nvbench_static_multimap_count,
   .add_float64_axis("MatchingRate", {0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1});
 
 NVBENCH_BENCH_TYPES(nvbench_static_multimap_find_all,
-                    NVBENCH_TYPE_AXES(key_type, value_type, d_type, multiplicity))
-  .set_name("staic_multimap_find_all_multiplicity")
+                    NVBENCH_TYPE_AXES(key_type,
+                                      value_type,
+                                      nvbench::enum_type_list<dist_type::UNIFORM>,
+                                      multiplicity))
+  .set_name("staic_multimap_find_all_uniform_multiplicity")
   .set_type_axes_names({"Key", "Value", "Distribution", "Multiplicity"})
   .set_timeout(100)                            // Custom timeout: 100 s. Default is 15 s.
   .set_max_noise(3)                            // Custom noise: 3%. By default: 0.5%.
@@ -355,8 +360,11 @@ NVBENCH_BENCH_TYPES(nvbench_static_multimap_find_all,
   .add_float64_axis("MatchingRate", {0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1});
 
 NVBENCH_BENCH_TYPES(nvbench_static_multimap_retrieve,
-                    NVBENCH_TYPE_AXES(key_type, value_type, d_type, multiplicity))
-  .set_name("staic_multimap_retrieve_multiplicity")
+                    NVBENCH_TYPE_AXES(key_type,
+                                      value_type,
+                                      nvbench::enum_type_list<dist_type::UNIFORM>,
+                                      multiplicity))
+  .set_name("staic_multimap_retrieve_uniform_multiplicity")
   .set_type_axes_names({"Key", "Value", "Distribution", "Multiplicity"})
   .set_timeout(100)                            // Custom timeout: 100 s. Default is 15 s.
   .set_max_noise(3)                            // Custom noise: 3%. By default: 0.5%.
diff --git a/benchmarks/key_generator.hpp b/benchmarks/key_generator.hpp
index 15ca0eda6..0ab3a7478 100644
--- a/benchmarks/key_generator.hpp
+++ b/benchmarks/key_generator.hpp
@@ -19,87 +19,72 @@
 #include <limits>
 #include <random>
 #include <unordered_set>
-#include <vector>
 
-enum class dist_type { BINOMIAL, GEOMETRIC, UNIFORM };
+enum class dist_type { GAUSSIAN, GEOMETRIC, UNIFORM };
 
 template <dist_type Dist, std::size_t Multiplicity, typename Key, typename OutputIt>
-static void generate_keys(std::vector<Key>& unique_keys, OutputIt output_begin, OutputIt output_end)
+static void generate_keys(OutputIt output_begin, OutputIt output_end)
 {
   auto const num_keys = std::distance(output_begin, output_end);
-  auto const size     = num_keys * 2;
 
   std::random_device rd;
   std::mt19937 gen{rd()};
 
-  auto const max = std::numeric_limits<Key>::max();
-
-  std::unordered_set<Key> set;
-
   switch (Dist) {
-    case dist_type::BINOMIAL: {
-      auto const t = max;
-      auto const p = 0.5;
-
-      std::binomial_distribution<Key> distribution{t, p};
-
-      while (set.size() < size) {
-        set.insert(distribution(gen));
+    case dist_type::GAUSSIAN: {
+      auto const mean = static_cast<double>(num_keys / 2);
+      auto const dev  = static_cast<double>(num_keys / 5);
+
+      std::normal_distribution<> distribution{mean, dev};
+
+      for (auto i = 0; i < num_keys; ++i) {
+        auto k = distribution(gen);
+        while (k >= num_keys) {
+          k = distribution(gen);
+        }
+        output_begin[i] = k;
       }
       break;
     }
     case dist_type::GEOMETRIC: {
+      auto const max   = std::numeric_limits<Key>::max();
+      auto const coeff = static_cast<double>(num_keys) / static_cast<double>(max);
       std::geometric_distribution<Key> distribution{1e-9};
 
-      while (set.size() < size) {
-        set.insert(distribution(gen));
+      for (auto i = 0; i < num_keys; ++i) {
+        output_begin[i] = distribution(gen) * coeff;
       }
       break;
     }
     case dist_type::UNIFORM: {
-      std::uniform_int_distribution<Key> distribution{0, max};
+      std::uniform_int_distribution<Key> distribution{1, static_cast<Key>(num_keys / Multiplicity)};
 
-      while (set.size() < size) {
-        set.insert(distribution(gen));
+      for (auto i = 0; i < num_keys; ++i) {
+        output_begin[i] = distribution(gen);
       }
       break;
     }
   }  // switch
-
-  unique_keys.resize(set.size());
-  std::copy(set.begin(), set.end(), unique_keys.begin());
-  std::random_shuffle(unique_keys.begin(), unique_keys.end());
-  auto it = unique_keys.begin();
-
-  for (auto i = 0; i < num_keys / Multiplicity; ++i) {
-    for (auto j = 0; j < Multiplicity; ++j) {
-      output_begin[i * Multiplicity + j] = *it;
-    }
-    ++it;
-  }
-
-  std::random_shuffle(output_begin, output_end);
 }
 
-template <std::size_t Multiplicity, typename Key, typename OutputIt>
-static void generate_prob_keys(std::vector<Key> const& unique_keys,
-                               double const matching_rate,
+template <typename Key, typename OutputIt>
+static void generate_prob_keys(double const matching_rate,
                                OutputIt output_begin,
                                OutputIt output_end)
 {
   auto const num_keys = std::distance(output_begin, output_end);
+  auto const max      = std::numeric_limits<Key>::max();
 
   std::random_device rd;
   std::mt19937 gen{rd()};
 
   std::uniform_real_distribution<double> rate_dist(0.0, 1.0);
-  std::uniform_int_distribution<std::size_t> idx_dist{num_keys / Multiplicity,
-                                                      static_cast<std::size_t>(num_keys * 2 - 1)};
+  std::uniform_int_distribution<Key> non_match_dist{static_cast<Key>(num_keys), max};
 
   for (auto i = 0; i < num_keys; ++i) {
     auto const tmp_rate = rate_dist(gen);
 
-    if (tmp_rate > matching_rate) { output_begin[i] = unique_keys[idx_dist(gen)]; }
+    if (tmp_rate > matching_rate) { output_begin[i] = non_match_dist(gen); }
   }
 
   std::random_shuffle(output_begin, output_end);

From 1f88244e65cf03bf5604ec2305eb68c686eb4f58 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Thu, 13 May 2021 13:40:41 -0400
Subject: [PATCH 095/267] Minor cleanups

---
 include/cuco/static_multimap.cuh | 22 ++++++++++------------
 1 file changed, 10 insertions(+), 12 deletions(-)

diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index 73fef6bac..774dad7ee 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -398,12 +398,11 @@ class static_multimap {
     template <typename CG, typename Hash>
     __device__ iterator initial_slot(CG g, Key const& k, Hash hash) noexcept
     {
-      auto const cg_size    = g.size();
-      step_size_            = (hash(k + 1) % (capacity_ / (cg_size * 2) - 1) + 1) * cg_size * 2;
-      auto const h          = hash(k);
-      auto const slot_index = h % (capacity_ / (cg_size * 2)) * cg_size * 2 + g.thread_rank() * 2;
-      auto const p          = begin_slot() + slot_index;
-      return p;
+      auto const cg_size = g.size();
+      step_size_         = (hash(k + 1) % (capacity_ / (cg_size * 2) - 1) + 1) * cg_size * 2;
+      auto const slot_index =
+        hash(k) % (capacity_ / (cg_size * 2)) * cg_size * 2 + g.thread_rank() * 2;
+      return begin_slot() + slot_index;
     }
 
     /**
@@ -421,12 +420,11 @@ class static_multimap {
     template <typename CG, typename Hash>
     __device__ const_iterator initial_slot(CG g, Key const& k, Hash hash) const noexcept
     {
-      auto const cg_size    = g.size();
-      step_size_            = (hash(k + 1) % (capacity_ / (cg_size * 2) - 1) + 1) * cg_size * 2;
-      auto const h          = hash(k);
-      auto const slot_index = h % (capacity_ / (cg_size * 2)) * cg_size * 2 + g.thread_rank() * 2;
-      auto const p          = begin_slot() + slot_index;
-      return p;
+      auto const cg_size = g.size();
+      step_size_         = (hash(k + 1) % (capacity_ / (cg_size * 2) - 1) + 1) * cg_size * 2;
+      auto const slot_index =
+        hash(k) % (capacity_ / (cg_size * 2)) * cg_size * 2 + g.thread_rank() * 2;
+      return begin_slot() + slot_index;
     }
 
     /**

From 26c0effcee2d5b42b23cde101297450aa9b6c7b4 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Thu, 13 May 2021 13:42:26 -0400
Subject: [PATCH 096/267] Use relaxed atomic add

---
 include/cuco/detail/static_multimap_kernels.cuh | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap_kernels.cuh
index 7e36f7e02..51d75cd74 100644
--- a/include/cuco/detail/static_multimap_kernels.cuh
+++ b/include/cuco/detail/static_multimap_kernels.cuh
@@ -717,7 +717,7 @@ __global__ void count(
   InputIt first, InputIt last, atomicT* num_items, viewT view, Hash hash, KeyEqual key_equal)
 {
   auto tile    = cg::tiled_partition<tile_size>(cg::this_thread_block());
-  auto tid     = blockDim.x * blockIdx.x + threadIdx.x;
+  auto tid     = block_size * blockIdx.x + threadIdx.x;
   auto key_idx = tid / tile_size;
 
   typedef cub::BlockReduce<std::size_t, block_size> BlockReduce;
@@ -742,6 +742,7 @@ __global__ void count(
       auto const second_slot_is_empty = (arr[1].first == view.get_empty_key_sentinel());
       auto const first_equals         = (not first_slot_is_empty and key_equal(arr[0].first, key));
       auto const second_equals        = (not second_slot_is_empty and key_equal(arr[1].first, key));
+
       thread_num_items += (first_equals + second_equals);
 
       if (tile.any(first_slot_is_empty or second_slot_is_empty)) { break; }
@@ -754,7 +755,7 @@ __global__ void count(
   // compute number of successfully inserted elements for each block
   // and atomically add to the grand total
   std::size_t block_num_items = BlockReduce(temp_storage).Sum(thread_num_items);
-  if (threadIdx.x == 0) { *num_items += block_num_items; }
+  if (threadIdx.x == 0) { num_items->fetch_add(block_num_items, cuda::std::memory_order_relaxed); }
 }
 
 }  // namespace detail

From e1dc700e1703974778afecc52a126512760c8893 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Thu, 13 May 2021 13:47:10 -0400
Subject: [PATCH 097/267] Update benchmark: use count to determine output size

---
 .../static_multimap/static_multimap_bench.cu  | 22 ++++++++++---------
 1 file changed, 12 insertions(+), 10 deletions(-)

diff --git a/benchmarks/hash_table/static_multimap/static_multimap_bench.cu b/benchmarks/hash_table/static_multimap/static_multimap_bench.cu
index 64593aafc..e53efcee6 100644
--- a/benchmarks/hash_table/static_multimap/static_multimap_bench.cu
+++ b/benchmarks/hash_table/static_multimap/static_multimap_bench.cu
@@ -183,14 +183,15 @@ std::enable_if_t<(sizeof(Key) == sizeof(Value)), void> nvbench_static_multimap_f
   thrust::device_vector<Key> d_keys(h_keys);
   thrust::device_vector<cuco::pair_type<Key, Value>> d_pairs(h_pairs);
 
-  std::size_t output_size = num_keys * matching_rate * Multiplicity + num_keys;
-  thrust::device_vector<cuco::pair_type<Key, Value>> d_results(output_size);
-
   state.add_element_count(num_keys, "NumKeys");
 
   cuco::static_multimap<Key, Value, cg_size> map{size, -1, -1};
   map.insert(d_pairs.begin(), d_pairs.end());
 
+  auto num_matches        = map.count(d_keys.begin(), d_keys.end());
+  std::size_t output_size = num_matches + num_keys;
+  thrust::device_vector<cuco::pair_type<Key, Value>> d_results(output_size);
+
   state.exec(
     nvbench::exec_tag::sync | nvbench::exec_tag::timer, [&](nvbench::launch& launch, auto& timer) {
       timer.start();
@@ -236,14 +237,15 @@ std::enable_if_t<(sizeof(Key) == sizeof(Value)), void> nvbench_static_multimap_r
   thrust::device_vector<Key> d_keys(h_keys);
   thrust::device_vector<cuco::pair_type<Key, Value>> d_pairs(h_pairs);
 
-  std::size_t output_size = num_keys * matching_rate * Multiplicity + num_keys;
-  thrust::device_vector<cuco::pair_type<Key, Value>> d_results(output_size);
-
   state.add_element_count(num_keys, "NumKeys");
 
   cuco::static_multimap<Key, Value, cg_size> map{size, -1, -1};
   map.insert(d_pairs.begin(), d_pairs.end());
 
+  auto num_matches        = map.count(d_keys.begin(), d_keys.end());
+  std::size_t output_size = num_matches + num_keys;
+  thrust::device_vector<cuco::pair_type<Key, Value>> d_results(output_size);
+
   state.exec(
     nvbench::exec_tag::sync | nvbench::exec_tag::timer, [&](nvbench::launch& launch, auto& timer) {
       timer.start();
@@ -291,7 +293,7 @@ NVBENCH_BENCH_TYPES(nvbench_static_multimap_insert,
   .set_type_axes_names({"Key", "Value", "Distribution", "Multiplicity"})
   .set_max_noise(3)                            // Custom noise: 3%. By default: 0.5%.
   .add_int64_axis("NumInputs", {100'000'000})  // Total number of key/value pairs: 100'000'000
-  .add_float64_axis("Occupancy", nvbench::range(0.1, 1., 0.1));
+  .add_float64_axis("Occupancy", nvbench::range(0.1, 0.9, 0.1));
 
 NVBENCH_BENCH_TYPES(nvbench_static_multimap_count,
                     NVBENCH_TYPE_AXES(key_type,
@@ -313,7 +315,7 @@ NVBENCH_BENCH_TYPES(nvbench_static_multimap_count,
   .set_timeout(100)                            // Custom timeout: 100 s. Default is 15 s.
   .set_max_noise(3)                            // Custom noise: 3%. By default: 0.5%.
   .add_int64_axis("NumInputs", {100'000'000})  // Total number of key/value pairs: 100'000'000
-  .add_float64_axis("Occupancy", nvbench::range(0.1, 1., 0.1))
+  .add_float64_axis("Occupancy", nvbench::range(0.1, 0.9, 0.1))
   .add_float64_axis("MatchingRate", {0.5});
 
 NVBENCH_BENCH_TYPES(nvbench_static_multimap_count,
@@ -346,7 +348,7 @@ NVBENCH_BENCH_TYPES(nvbench_static_multimap_find_all,
   .set_timeout(100)                            // Custom timeout: 100 s. Default is 15 s.
   .set_max_noise(3)                            // Custom noise: 3%. By default: 0.5%.
   .add_int64_axis("NumInputs", {100'000'000})  // Total number of key/value pairs: 100'000'000
-  .add_float64_axis("Occupancy", nvbench::range(0.1, 1., 0.1))
+  .add_float64_axis("Occupancy", nvbench::range(0.1, 0.9, 0.1))
   .add_float64_axis("MatchingRate", {0.5});
 
 NVBENCH_BENCH_TYPES(nvbench_static_multimap_find_all,
@@ -379,7 +381,7 @@ NVBENCH_BENCH_TYPES(nvbench_static_multimap_retrieve,
   .set_timeout(100)                            // Custom timeout: 100 s. Default is 15 s.
   .set_max_noise(3)                            // Custom noise: 3%. By default: 0.5%.
   .add_int64_axis("NumInputs", {100'000'000})  // Total number of key/value pairs: 100'000'000
-  .add_float64_axis("Occupancy", nvbench::range(0.1, 1., 0.1))
+  .add_float64_axis("Occupancy", nvbench::range(0.1, 0.9, 0.1))
   .add_float64_axis("MatchingRate", {0.5});
 
 NVBENCH_BENCH_TYPES(nvbench_static_multimap_retrieve,

From 827e8fc79147a4c04e124715344b914e00c6e99a Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Thu, 13 May 2021 15:42:02 -0400
Subject: [PATCH 098/267] Set geometric sampling range to [0, INT32_MAX] to
 avoid the left biased issue with INT64 variables

---
 benchmarks/key_generator.hpp | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/benchmarks/key_generator.hpp b/benchmarks/key_generator.hpp
index 0ab3a7478..3dca16d64 100644
--- a/benchmarks/key_generator.hpp
+++ b/benchmarks/key_generator.hpp
@@ -47,8 +47,9 @@ static void generate_keys(OutputIt output_begin, OutputIt output_end)
       break;
     }
     case dist_type::GEOMETRIC: {
-      auto const max   = std::numeric_limits<Key>::max();
+      auto const max   = std::numeric_limits<int32_t>::max();
       auto const coeff = static_cast<double>(num_keys) / static_cast<double>(max);
+      // Random sampling in range [0, INT32_MAX]
       std::geometric_distribution<Key> distribution{1e-9};
 
       for (auto i = 0; i < num_keys; ++i) {

From 8c58ac786312af6ddc81d9506cd9a1062b7021f6 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Fri, 14 May 2021 18:06:31 -0400
Subject: [PATCH 099/267] Update notebooks for the comprehensive benchmark

---
 .../analysis/notebooks/StaticMultimap.ipynb   | 334 +++++++++++++-----
 benchmarks/analysis/notebooks/Utils.py        | 105 +++---
 2 files changed, 315 insertions(+), 124 deletions(-)

diff --git a/benchmarks/analysis/notebooks/StaticMultimap.ipynb b/benchmarks/analysis/notebooks/StaticMultimap.ipynb
index 6d0aaa5f2..8e3b6c05c 100644
--- a/benchmarks/analysis/notebooks/StaticMultimap.ipynb
+++ b/benchmarks/analysis/notebooks/StaticMultimap.ipynb
@@ -38,8 +38,8 @@
     "# Specify the data path\n",
     "datafile = '../data/static-multimap-data.csv'\n",
     "\n",
-    "output_keys = ['Benchmark', 'Label', 'Distribution', 'MatchingRate', 'NumReps', 'NumInputs', \\\n",
-    "               'Occupancy', 'GPU Time (sec)', 'Elem/s (elem/sec)', 'Bandwidth (GB/s)']"
+    "output_keys = ['Benchmark', 'Label', 'Distribution', 'MatchingRate', 'Multiplicity', \\\n",
+    "               'NumInputs', 'Occupancy', 'GPU Time (sec)', 'Elem/s (elem/sec)']"
    ]
   },
   {
@@ -66,8 +66,6 @@
     "perfdf['Label'] = perfdf[\"Key\"]\n",
     "perfdf.loc[perfdf['Distribution'].notnull(), 'Label'] += \"_\" + perfdf['Distribution']\n",
     "\n",
-    "perfdf[\"Bandwidth (GB/s)\"] = perfdf[\"GlobalMem BW (bytes/sec)\"] / (1000 * 1000 * 1000)\n",
-    "\n",
     "# Trim data frame for visualization\n",
     "perfdf = perfdf[output_keys]"
    ]
@@ -95,12 +93,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "nvbench_static_multimap_insert\n",
-      "nvbench_static_multimap_count\n",
-      "nvbench_static_multimap_find_all\n",
-      "nvbench_static_multimap_retrieve\n",
-      "count_varying_matching_rate\n",
-      "count_varying_num_reps\n"
+      "staic_multimap_insert_uniform_multiplicity\n",
+      "staic_multimap_insert_occupancy\n",
+      "staic_multimap_count_uniform_multiplicity\n",
+      "staic_multimap_count_occupancy\n",
+      "staic_multimap_count_matching_rate\n",
+      "staic_multimap_find_all_uniform_multiplicity\n",
+      "staic_multimap_find_all_occupancy\n",
+      "staic_multimap_find_all_matching_rate\n",
+      "staic_multimap_retrieve_uniform_multiplicity\n",
+      "staic_multimap_retrieve_occupancy\n",
+      "staic_multimap_retrieve_matching_rate\n"
      ]
     }
    ],
@@ -115,7 +118,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### insert "
+    "### `insert` performance by varying key muliplicities\n",
+    "<ul>\n",
+    "<li>100'000'000 insertions</li>\n",
+    "<li>Fixed matching rate: 0.5</li>\n",
+    "<li>Fixed occupancy: 0.8</li>\n",
+    "<li>UNIFORM distribution</li>\n",
+    "</ul>"
    ]
   },
   {
@@ -125,21 +134,49 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
-       "<Figure size 720x360 with 2 Axes>"
+       "<Figure size 360x360 with 1 Axes>"
       ]
      },
      "metadata": {
       "needs_background": "light"
      },
      "output_type": "display_data"
-    },
+    }
+   ],
+   "source": [
+    "for bm in unique_bms:\n",
+    "    flag = \"staic_multimap_insert_uniform_multiplicity\" == bm\n",
+    "    \n",
+    "    if flag:\n",
+    "        tmpdf = perfdf[perfdf[\"Benchmark\"] == bm]\n",
+    "        \n",
+    "        unique_labels = tmpdf[\"Label\"].unique()\n",
+    "        plot_single_perf(bm, tmpdf, \"Multiplicity\", unique_labels)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### `insert` performance by varying occupancies\n",
+    "<ul>\n",
+    "<li>100'000'000 insertions</li>\n",
+    "<li>Fixed matching rate: 0.5</li>\n",
+    "</ul>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<Figure size 720x360 with 2 Axes>"
+       "<Figure size 1080x360 with 3 Axes>"
       ]
      },
      "metadata": {
@@ -150,49 +187,80 @@
    ],
    "source": [
     "for bm in unique_bms:\n",
-    "    flag = \"nvbench_static_multimap_insert\" == bm\n",
+    "    flag = \"staic_multimap_insert_occupancy\" == bm\n",
     "    \n",
     "    if flag:\n",
     "        tmpdf = perfdf[perfdf[\"Benchmark\"] == bm]\n",
     "        \n",
-    "        randomdf = tmpdf[tmpdf[\"Distribution\"] == \"RANDOM\"].reset_index(drop=True)\n",
-    "        unique_labels = randomdf[\"Label\"].unique()\n",
-    "        plot_perf(bm + \"_RANDOM\", randomdf, \"NumReps\", unique_labels, flag)\n",
-    "        \n",
-    "        cycledf = tmpdf[tmpdf[\"Distribution\"] == \"CYCLE\"].reset_index(drop=True)\n",
-    "        unique_labels = cycledf[\"Label\"].unique()\n",
-    "        plot_perf(bm + \"_CYCLE\", cycledf, \"NumReps\", unique_labels, flag)"
+    "        unique_labels = tmpdf[\"Label\"].unique()\n",
+    "        plot_dual_perf(bm, tmpdf, \"Occupancy\", unique_labels)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### count"
+    "### `count` performance by varying key muliplicities\n",
+    "<ul>\n",
+    "<li>100'000'000 key/value pairs inserted</li>\n",
+    "<li>100'000'000 probing keys</li>\n",
+    "<li>Fixed matching rate: 0.5</li>\n",
+    "<li>Fixed occupancy: 0.8</li>\n",
+    "<li>UNIFORM distribution</li>\n",
+    "</ul>"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<Figure size 720x360 with 2 Axes>"
+       "<Figure size 360x360 with 1 Axes>"
       ]
      },
      "metadata": {
       "needs_background": "light"
      },
      "output_type": "display_data"
-    },
+    }
+   ],
+   "source": [
+    "for bm in unique_bms:\n",
+    "    flag = \"staic_multimap_count_uniform_multiplicity\" == bm\n",
+    "    \n",
+    "    if flag:\n",
+    "        tmpdf = perfdf[perfdf[\"Benchmark\"] == bm]\n",
+    "        \n",
+    "        unique_labels = tmpdf[\"Label\"].unique()\n",
+    "        plot_single_perf(bm, tmpdf, \"Multiplicity\", unique_labels)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### `count` performance by varying occupancies\n",
+    "<ul>\n",
+    "<li>100'000'000 key/value pairs inserted</li>\n",
+    "<li>100'000'000 probing keys</li>\n",
+    "<li>Fixed matching rate: 0.5</li>\n",
+    "</ul>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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6dHD1MjnInc9OV/Me8DgQm8pxbhk1Ctq1S/nr7rshIiLxcyIi7PaUnjNqVNpi2L9/P/Xq1aN8+fI88cQTlClTJtH+S5cuMW3aNLp06ZLiObZu3UrDhg1T3H/hwgX++usvbr75ZgYMGMAsN7OYBx54gLvvvpv27dszduzY+F7K22+/nZ9++omgoCAee+wx1q9fn+zzf/jhB7p06cL1119PkSJFWLduXaL9Tz75JG+//TYxDg6ZSe49/OQTu++VVyB/fnjpJejc2X7Pnx/GjLH7jx278rlpsWvXLgoVKkTv3r1p0KABY8aMif9dfPTRR/To0YPSpUunep6M/P4f+P0A45eNZ/aW2Zy4cMJjvYDg3SRQktmWtH812WOMMduBN4A/gN+AjUB0shcRGSYia0RkTXh4eHriVUp5yQsvwJNP2uqgY8fCvHn2pvvYsSDJtQJKKeVLUVHw4INwlUTCR9z57JT8E0VuBo4aY9amcpzHPjdFRqZt+7UoX748mzZtIiQkhK+++oojR44k2j9ixAjatGnDDTfc4Nb5jh8/TlBQENdffz3jx9selp9//pn27duTJ08e+vTpw9y5c+OTDUnmj1Tctptuuondu3dz7733smPHDho0aEB4eDjlypVj586dvP766wQEBNCxY0f++uuvK84za9Ys+vfvD0D//v2vSD4qVapE06ZNmTlzpluvzdd27oSCBRNvK1jQc/dSoqOjCQ4OZvz48axevZrdu3czZcoUDh48yDfffMODDz6Y5nNmtPe/ZbOWmM2G4xeO06N6D4/1AoJ3q4OGkbj3rhyQ9J9FiscYYz4HPgcQkddcx17BGDMJmATQuHFj9wbxKqV8qlkzWLDAVgfdtAmqVrXDhdq3dzoypVSWV7gwlHV1tiUoxEEyw7d8wJ3PTilpBfQQkW5ALqCAiEw3xtyR8KC0fG56772rX7BiRTsENKnrroPFi92M2k1lypShdu3aBAcH07dvXwBeeuklwsPDmThx4lWfW7t2bdatW0f9+vUpWrQoGzZsYPz48Zw7dw6wH8aXLVtGxYoVAZsoLFq0iE6dOlG0aFFOnjxJsWK2JP+JEyfifwYoUqQIAwcOZODAgdx8880sXbqUPn36kDNnTrp27UrXrl0pWbIkP/zwAx07dox/3vHjx1m4cCFbtmxBRIiJiUFErhje+PTTT9O3b99EQxd96WrvY82atvcv4d/yRYvs/RSAYsXS9++gXLlyNGjQgMqVKwPQq1cvVq5cSalSpQgJCaFq1aoAREREULVqVUJCQpI9T0Z+/3vd24vpfadTvFZxWpRrce2/zGR4sydwNVBNRCqJSA6gPzAvyTHzgMGuKqHNgdPGmEMAIlLC9b0C0Bvw8FRTpZQvFSwIt94Kq1fboaGaACql/MKJE7BlC1y6lLg6qDPLQ7jz2SlZxpinjDHljDEVXc9bmDQB9LSxY68c0p8nj93uCWFhYVy4YNdJO3nyJMuWLaN69eoATJ48mQULFjBr1qz4uVcpefzxxxk7dizbt1+ulxPhGsd65swZ/v77b/bt20doaCihoaF8/PHH8b0y7dq1Y9q0aYCdhzZ9+nTau/6ALVy4MP48Z8+e5b///qNChQqsW7cufmhgbGwsmzZt4rrrrksU07fffsvgwYPZu3cvoaGh7N+/n0qVKvH3338nOq5GjRrUqlWLn3/2v2Kvzzxjh/4uWmT/+yxaZB8/84xnzt+kSRNOnjxJXI/1woULqVWrFt27d+fw4cPx71eePHlSTAAhY7//e7LtgeKQ/b/sBAZ4dk0tryWBroIuI4EF2CpVc4wxW0VkuIgMdx02H9gNhACfASMSnOI7EdkG/AQ8YIxxplazUsoj5s6FAQPg7FmnI1FKqQTOnYP69f1izRp3PjuJSCkRCQMeBZ4VkTARKeBEvIMGwaRJtudPxH6fNMlu94Tt27fTrFkz6tevT9u2bRk9ejR169YFYPjw4Rw5coQWLVoQFBTEyy+/nOJ56taty/vvv8/gwYOpUaMGrVq1Yvv27QwcOJDvv/+eDh06xBegAejZsyfz5s0jMjKS5557jpCQEOrXr0+DBg2oWrUqd9xhc+u1a9fSuHFj6tWrR4sWLbjnnnto0qQJR48e5ZZbbqFOnTrUq1ePbNmyMXLkyEQxzZo1i1tvTVzzsE+fPskO/XzmmWeuWvjGKQMG2IT/wQchVy77fexYu90TAgMDGT9+PB07dqRu3boYY7j33nvTfJ6M/P4vCl3Edbdcx6EDh9L8ulMj7pZBzQgaN25s1qxZ43QYSqlk9OgBO3bArl1pe56IrDXGNPZOVL6hbZNSfuyPP+DGG+2Y9RtvdPtp2jYppbxt9pbZREZHcmfQnWl6njvtkzfnBCqlFACxsfD339C7t9ORKKVUEkuX2kXuWnh2vo1SSqVX/zr9vXZuTQKVUl63dSucPAluFm9TSinfCQ6GBg1sbXuVoX355Ze8//77iba1atWKjz/+2KGIlC9ltvd//aH15M2Rl+uLXu+V82sSqJTyun/+sd8dKm6mlFLJi4yEVatg+PDUj1V+b+jQoQwdOtTpMJRDMtv7//ifj3Pk3BE23b/JK+f3ZnVQpZQC4K67IDTUlhNXSim/ERBgyxVfQ7EJpZTylsjoSJbtW0b7it4rpa49gUopr4urGqeUUn4le3bo1MnpKJRSKpF/DvzDhegLtK/kvSRQewKVUl61e7ctF711q9ORKKVUEtOmwYoVTkehlFKJLNyzEEFoe11br11Dk0CllFctXgyzZ9veQKWU8hsxMXZhsy+/dDoSpZRKZPHexTQo3YDCuQt77RqaBCqlvGrpUihWDGrWdDoSpZRKYMsWOH1aK1alR6lS9g5f0q9SpdJ12nz58gGwd+9eGjVqRFBQELVr12bChAnxxwwaNIjq1atTp04d7rrrLi5dunTVc/722280bdqUGjVqEBQURL9+/di3bx8AQ4YMoVKlSgQFBREUFETLli3jn/fDDz9Qr149atSoQd26dfnhhx/i9w0ZMoQ8efJw9uzZ+G0PP/wwIsKxY8cAu+B53HmDgoIYN24ct956K0FBQVStWpWCBQvG71u+fDnt2rWjevXq1K9fnyZNmrBhw4b4c1esWDH+vIcPH6Z///5UqVKFWrVq0a1bN3aldSHe9KpYMfn3P50FAOLef4B9+/Zx4403UrNmTWrVqkVoaGiiYx988MFEx6cko73/4ePDifwk0rvvvzEm03w1atTIKKX8S+XKxvTqde3PB9YYP2hf0vOlbZNSfuiDD4wBY0JDr+np2jYZ+/tL6Ssd8ubNa4wxJjIy0ly8eNEYY8zZs2fNddddZw4cOGCMMeaXX34xsbGxJjY21vTv39988sknKZ5v8+bNpmrVqmbbtm3x23788UezZMkSY4wxd955p/nmm2+ueN6GDRtMlSpVzO7du40xxuzevdtUqVLFbNy4Mf55devWNdOmTTPGGBMTE2Pq1q1rypYta8LDwxO9luQsWrTIdO/ePdG2tm3bmtWrVxtjjPniiy9Mp06d4vddd911Jjw83MTGxprmzZubTz/9NH7f+vXrzdKlS1O8llek9D576P03xv4+fv/9d2OM/Tdw/vz5+H2rV682d9xxx1V/x8ZkzfffnfZJC8MopbwmLMzOCRw50ulIlFIqieBgKF9eq1ZdzahRkKAnIk3atUt+e1AQvPeeW6fIkSNH/M+RkZHExsbGP+7WrVv8z02bNiUsLCzF87zxxhs8/fTT1EwwJKVHjx6pXn/8+PE8/fTTVKpUCYBKlSrx1FNP8dZbbzFt2jQABgwYwNdff80dd9zB4sWLadWqFb/++qtbry81LVq04K233rpi+6JFi8iePTvDEyxtEhQU5JFrXiG59/H222HEiJT3xzl2DPr2Tbxt8WK3L71t2zaio6Pp3LkzkLiHMCYmhjFjxjBz5kzmzp171fNktPd//r/z+Tv6b7q06OLV91+HgyqlvObIEahXT0dbKaX80MaN2jhlAPv376devXqUL1+eJ554gjJlyiTaf+nSJaZNm0aXLl1SPMfWrVtp2LDhVa8zZsyY+GF5gwYNin9eo0aNEh3XuHFjtiaodFatWjXCw8M5efIks2bNon///omOv3DhQqLhgF9//bVbrxvsEMZevXpdsX3Lli1XxJUZ7dq1i0KFCtG7d28aNGjAmDFjiImJAeCjjz6iR48elC5dOtXzZLT3/7vt3zF7y2yvv//aE6iU8ppGjeznLKWU8jtbt8KZM05H4d9S67G7WsWvNPT4XE358uXZtGkTBw8epFevXvTt25eSJUvG7x8xYgRt2rThhhtucOt8x48fp2PHjkRERDBs2DBGjx4NwFtvvUXfJL1WxhgkyWtMblvv3r2ZPXs2q1atYuLEiYn25c6dO9G8LncMGjSI8+fPExMTw7p169L0XI9L7X1Mbn/c76dYsXT9O4iOjiY4OJj169dToUIF+vXrx5QpU+jatSvffPMNi6/h3P7+/seYGDZ9son8kp83sr3h1fdfewKVUl6TYOSOXxGRLiKyU0RCROTJZPaLiHzg2r9JRBom2PeIiGwVkS0iMktEcvk2eqWUR2TLBkWKOB2FclOZMmWoXbs2wcHB8dteeuklwsPDeeedd6763Nq1a8d/mC5atCgbNmxg2LBhnDt3LtXnrVmzJtG2devWUatWrUTb+vfvz3PPPUfnzp0JCEj/R+sZM2awZ88eBg4cyAMPPJBsXGvXrk33dfxduXLlaNCgAZUrVyZbtmz06tWLdevWsX79ekJCQqhatSoVK1YkIiKCqlWrpniejPT+n754GnrD18Ffe/391yRQKeUVx47Zz1ezZjkdSWIiEgh8DHQFagEDRKRWksO6AtVcX8OAT13PLQs8BDQ2xtQBAoH+KKUylpdfhueeczqKjC9Bj5xb29MoLCyMCxcuAHDy5EmWLVtG9erVAZg8eTILFixg1qxZqX7wfvzxxxk7dizbt2+P3xYREZHq9UePHs3rr78eX5EyNDSU1157jcceeyzRcRUqVGDs2LGMiJsn5wHZs2fn1VdfZeXKlYniBujQoQORkZF89tln8dtWr17NkiVLPHZ9t1x3XfLVQT00z7ZJkyacPHmS8PBwABYuXEitWrXo3r07hw8fJjQ0lNDQUPLkyUNISEiK58lI7/+pi6cIDAikbZW2Xn//dTioUsor/v7bVl8vX97pSK7QFAgxxuwGEJHZQE9gW4JjegJTXRW2VopIIRGJm3iQDcgtIpeAPMBB34WulPKIqVOhbl2no8j4Dh/26um3b9/OY489hohgjGH06NHUdb1vw4cP57rrrqNFixaAHZL3/PPPJ3ueunXr8v777zN48GDOnj1L0aJFqVChAi+99FL8MWPGjOHVV1+Nf/zPP/8QFBTEG2+8wS233MKlS5fInj07b775ZrJFOO67775krx03JyxOly5dGDdunFuvP3fu3Dz22GOMHz+ezz//PH67iDB37lxGjRrFuHHjyJUrFxUrVuQ9NwvueEyS5Ro8LTAwkPHjx9OxY8e4arbce++9aT5PRnr/o2OjaVi6IXmy54HsePX9F/sZJ3No3LixSdptq5RyxmOPwccf20QwZ85rP4+IrDXGNPZUXCLSF+hijLnH9fh/QDNjzMgEx/wMjDPG/O16/BfwhDFmjYg8DIwFLgC/G2MGpXZNbZuU8iOHDkGZMvD22/Doo9d8Gk+3TU7Qtkkp/xNrYgmQ9A3WdKd90uGgSimvWLoUmjVLXwLoJclVMkh6NyzZY0SkMLaXsBJQBsgrInckexGRYSKyRkTWxA1lUUr5gbg5ZW4WElFKKV9KbwLo9nV8chWlVJZy9iysW+e31dfDgISDVMtx5ZDOlI7pBOwxxoQbYy4B3wMtk7uIMWaSMaaxMaZx8eLFPRa8Uiqdli6FvHmhQQOnI1Ee9uWXXyYqxx8UFJRsYQ2VOWX09/+JP57gxmk34qtRmjonUCnlcVFR8NRTcMstTkeSrNVANRGpBBzAFnYZmOSYecBI13zBZsBpY8whEdkHNBeRPNjhoB0BHUulVEZSoAD07m2rg6pMZejQoQwdOtTpMJRDMvr7/9t/v1E8T/ErlqDwFm0BlVIeV7QoJJhf7VeMMdEiMhJYgK3u+YUxZquIDHftnwDMB7oBIUAEMNS1b5WIfAusA6KB9cAk378KpdQ1e+01pyNQSqlEjkUcY9ORTbza3ncfnjQJVEp53Nq1ULMm5MnjdCTJM8bMxyZ6CbdNSPCzAZIdQ2KMeQF4wasBKqW849IlyJ7d6SiUUiqRxaGLAehQqYPPrqlzApVSHnXxIrRsCSlU6lZKKec8+yxcfz3ExDgdiVJKxVu0ZxF5s+elcRnfFRzWnkCllEetXm3nBPppURilVFYWHAzFi0NgoNORKKVUvKZlm1I0T1GyB/pupIL2BCqlPGrpUvu9VStn41BKqUQiIuxdKl0awmMaTGyAvCRXfDWYmL7Kq/ny5QNg7969NGrUiKCgIGrXrs2ECfGj9hk0aBDVq1enTp063HXXXVy6dCnF87344ouMHz8+0baKFSty7NgxwC6+/dhjj8XvGz9+PC+++OIVzx0yZAiVKlWKrzz5wQcfAHD69GkGDx5MlSpVqFKlCoMHD+b06dMAhIaGkjt3boKCgqhVqxaDBw+Oj3Xx4sWISKKFwNevX4+IXBGvP2r9Retk3//WX7RO13nj3n+Affv2ceONN1KzZk1q1apFaJIF6h988MFExycnI7z/dwbdycvtX/bp+69JoFLKo5YuhTp1bHEYpZTyG6tWQXS0JoEe1KJcC3IE5ki0LUdgDlqWS3blnDQrXbo0y5cvZ8OGDaxatYpx48Zx8KBd0WfQoEHs2LGDzZs3c+HCBSZPnnzN18mZMyfff/99fFJwNW+99RYbNmxgw4YNPPTQQwDcfffdVK5cmf/++4///vuPSpUqcc8998Q/p0qVKmzYsIHNmzcTFhbGnDlz4vfVrVuXr7/+Ov7x7NmzqV+//jW/Fl9qUqYJo5qNwrxg4r9GNRtF07JNPXaNwYMHM2bMGLZv384///xDiRIl4vetWbOGU6dOpfsaTr//X834iuMRxwHfvv86HFQp5THR0bB8OQwe7HQkSimVRHAwiOgwhTQY9dsoNhzekOL+yOhIomOjE22Ljo1m/eH1tJvSLtnnBJUK4r0u77l1/Rw5LieYkZGRxMbGxj/u1q1b/M9NmzYlLCzMrXMmJ1u2bAwbNox3332XsWPHpum5ISEhrF27NlEi9/zzz1O1alX+++8/AhMMPQ4MDKRp06YcOHAgfluFChU4c+YMR44coUSJEvz222+JXpvTknsfb699OyOajGBks5HU+KgGK8NWkjNbTiKjI1lzaA3jO9terGMRx+g7p2+i5y4estjta2/bto3o6Gg6d+4MJO4hjImJYcyYMcycOZO5c+em/YUl4PT7v3bPWqq8VoXjLx/36fvv1Z5AEekiIjtFJEREnkxmv4jIB679m0SkYYJ9j4jIVhHZIiKzRCSXN2NVSqVfQAD89Rc8+KDTkSilVBKtW8OLL0KhQk5HkmnkzJaTknlLIth1zQShVN5SV/QOpsf+/fupV68e5cuX54knnqBMmTKJ9l+6dIlp06bRpUuXdF3ngQceYMaMGfHD+FIyZsyY+OGAmzdvZtu2bQQFBV3xYT8oKIitW7cmeu7FixdZtWrVFbH27duXb775huXLl9OwYUNy5syZrtfiK6XzlaZk3pLsP7MfgP1n9lMyb0kK5irokfPv2rWLQoUK0bt3bxo0aMCYMWOIcRV1+uijj+jRowelS5f2yLWcfP8v1bhEhQMVWLlipU/ff6/1BIpIIPAx0BkIA1aLyDxjzLYEh3UFqrm+mgGfAs1EpCzwEFDLGHNBROZgF3Se4q14lVLpFxAATT03CkQppTynQwf7pdzmTo/dobOHqPxBZS5GXyRXtlysvW8tpfKV8lgM5cuXZ9OmTRw8eJBevXrRt29fSpYsGb9/xIgRtGnThhuuMsw3pcW3E24vUKAAgwcP5oMPPiB37twpnuutt96ib9/LvVu7d+9O9vzGmPjt//33H0FBQfz777/07duXevXqJTr29ttvp1+/fuzYsYMBAwawfPnyFK/va1frucuTPQ+r711N7U9q8+5N79JpWie2jtga//4Xy1MsTT1/SUVHRxMcHMz69eupUKEC/fr1Y8qUKXTt2pVvvvmGxYvdO7c/v/8Xoi9wvMZxCv1ViFmzZvn0/fdmT2BTIMQYs9sYEwXMBnomOaYnMNVYK4FCIhKX0mcDcotINiAPcNCLsSqlPODzz+GPP5yOQimlkjh0CDZuhATDCZVnlM5fmqFBQwmQAIYGDfVoAphQmTJlqF27NsHBwfHbXnrpJcLDw3nnnXeu+tyiRYty8uTJRNvOnj1LoSS9wqNGjeLzzz/n/PnzbsdVu3Zt1q9fn2ioamxsLBs3bqRmzZrA5TlhISEhrFy5knnz5iU6R6lSpciePTt//PEHHTt2dPva/qB0/tLcWf9OOk7tyJ317/To+1+uXDkaNGhA5cqVyZYtG7169WLdunWsX7+ekJAQqlatSsWKFYmIiKBq1aopnsef3//jEcchPxTLX8zn7783k8CywP4Ej8Nc21I9xhhzABgP7AMOAaeNMb97MValVDrFxsITT8DMmU5HopRSScycCUFBcOSI05FkSs+1eY7WFVrzXNvnPHresLAwLly4AMDJkydZtmwZ1atXB2Dy5MksWLCAWbNmERBw9Y+zbdq0Yd68eZw9exaA77//nvr16ycawgdQpEgRbr/99kTVOlNTtWpVGjRowKuvvhq/7dVXX6Vhw4ZXJCalS5dm3LhxvP7661ec5+WXX+aNN964IqaM4PFWj9O0bFMeb/W4R8/bpEkTTp48SXh4OAALFy6kVq1adO/encOHDxMaGkpoaCh58uQhJCQkxfP48/t/POI4RXMXZfzr433+/nszCUyu79W4c4yIFMb2ElYCygB5ReSOZC8iMkxE1ojImrh/JEop39u+HY4f1/UBlVJ+KDgYqlYFD80fUomVzl+aJUOWeLwXcPv27TRr1oz69evTtm1bRo8eTd26dQEYPnw4R44coUWLFgQFBfHyyy+neJ569eoxcuRIWrduTVBQEBMmTEixmuhjjz3mVpXIhD7//HN27dpF1apVqVKlCrt27UoxkejVqxcRERGJejQBWrZsSa9evdJ0XX9ROn9pfrvjN4+//4GBgYwfP56OHTtSt25djDHce++9aT6PP7//VYtWZUbvGbRu1drn778YkzQv89CJRVoALxpjbnI9fgrAGPN6gmMmAouNMbNcj3cC7YDWQBdjzN2u7YOB5saYEVe7ZuPGjc2aNWu88GqUUqn59FMYMQJCQqBKFc+dV0TWGmMae+6Mvqdtk1IOio21C8T37AlffOGx02rbpJTyV+60T97sCVwNVBORSiKSA1vYZV6SY+YBg11VQptjh30ewg4DbS4iecTOquwIbPdirEqpdFq6FMqUgcqVnY5EKaUS2L4dTpzIMOsDulFZvYaIrBCRSBEZnWB7eRFZJCLbXdXVH/Zt5EqptFgSuoSvNnx1xTIrvuK16qDGmGgRGQksAAKBL4wxW0VkuGv/BGA+0A0IASKAoa59q0TkW2AdEA2sByZ5K1alVPrt2mU/Y6VQhEsppZwRN+wuA4xVd7Oy+glsBfVeSZ4eDTxmjFknIvmBtSLyR5LnZmpffvkl77//fqJtrVq14uOPP3YoIuVLGe39n7RuEgv3LGRwfWcWV/bqYvHGmPnYRC/htgkJfjbAAyk89wXgBW/Gp5TynDVrIA0FtZRSyjcGDoRKlTLKMIX4yuoAIhJXWT0+kTPGHAWOikj3hE90jaQ65Pr5rIhsxxbgyzJJ4NChQxk6dKjTYSiHZKT33xjDwj0LaV+xfYpLWHibVxeLV0plHSKQL5/TUSilVBIFCsBNN2WUYQruVFZPlYhUBBoAqzwTllLKk3Ye38nhc4dpX7G9YzFoEqiUSrdnn4VRo5yOQimlkjhwAF55BcLCnI7EXe5UVr/6CUTyAd8Bo4wxZ5LZr1XVlXLYwj0LAehQqYNjMWgSqJRKt6+/hj17nI5CKaWSWLgQnn/eFobJGMKA8gkelwMOuvtkEcmOTQBnGGO+T+4YY8wkY0xjY0zj4sWLpytYpdS12R6+nfIFylO5sHPD1DUJVEqly8GDdlmIDFBzQSmV1QQHQ6FCUKeO05G4y53K6slyVVP/HNhujHnHizEqpdLpw24fsmXEFsfmA4KXC8MopTK/DFR4TymV1QQHQ6tWEJAx7nm7U1ldREoBa4ACQKyIjAJqAfWA/wGbRWSD65RPu4r0KaX8TIGcBRy9viaBSql0CQ6GvHmhQQOnI1FKqQSOHoUdO2DIEKcjSRM3Kqsfxg4TTepvkp9TqJTyI5+v+5z5IfOZ0XsGubLlciwOTQKVUulSqhQMGADZtDVRSvmT7dshV64Ms0i8UiprmLdrHluPbnU0AQRNApVS6fTss05HoJRSyWjbFk6fhsBApyNRSikAYmJjWBK6hNtq3eZ0KFoYRil17c6fh9hYp6NQSqkU5MihSaBSym+sP7ye05GnHV0aIo4mgUqpa/bii1CuHERHOx2JUkolcOYMNGsGv/3mdCRKKRVv0Z5FALSr2M7ZQNAkUCmVDkuXQpUqOh9QKeVnVqyAf/7RXkCllF8pmqcot9W6jdL5SzsdiiaBSqlrc+4crF2rS0MopfxQcLBNAFu0cDoSpZSKd1eDu5hz2xynwwA0CVRKXaOVKyEmRgvvKaX8UHAwNGwI+fI5HYlSSgFwNvIsUTFRTocRT5NApdQ1WbrUrr/csqXTkSilVAKRkbBqld6hUkr5lQ//+ZAibxThbORZp0MBdIkIpdQ16tIFCheGAgWcjkQppRI4eRK6dYObbnI6EqWUirdwz0KqFKlC/pz5nQ4F0J5ApdQ1atkSHnnE6SiujYh0EZGdIhIiIk8ms19E5APX/k0i0tC1vbqIbEjwdUZERvn8BSilUlaqFHz/Pdx4o9ORKKUUAJHRkSzbv4z2Fds7HUo8TQKVUmm2dy/8/TdcuuR0JGknIoHAx0BXoBYwQERqJTmsK1DN9TUM+BTAGLPTGBNkjAkCGgERwFwfha6Ucsfp005HoJRSiaw6sIqL0Rc1CVRKZWwzZ9rpNqdOOR3JNWkKhBhjdhtjooDZQM8kx/QEphprJVBIRJLWc+4I/GeM2ev9kJVSbomJgQoV4JlnnI5EKaXiLdyzkAAJoG3Ftk6HEk/nBCql0iw4GGrVguLFnY7kmpQF9id4HAY0c+OYssChBNv6A7NSuoiIDMP2IlKhQoV0hKuUctumTXah+FpJO/eVUso5t9a4ldL5SlMoVyGnQ4mnPYFKqTSJiYFlyzJ04T1JZptJyzEikgPoAXyT0kWMMZOMMY2NMY2LZ9BsWakMJzjYfs/ADZRSKvOpX6o+9zW+z+kwEtEkUCmVJnE32jPwIvFhQPkEj8sBB9N4TFdgnTHmiFciVEpdm+BguO46OyRUKaX8wM5jO/ll1y9ERkc6HUoimgQqpdJk6VL7PQPfaF8NVBORSq4evf7AvCTHzAMGu6qENgdOG2MSDgUdwFWGgiqlHGCMbaAycOOklMp8pm6cSs/ZPf1qoXjQOYFKqTS67z5o1gzKl0/9WH9kjIkWkZHAAiAQ+MIYs1VEhrv2TwDmA92AEGwF0KFxzxeRPEBnwL/GdSiV1cXEwBtvQKVKTkeilFLxFoYupGnZpn6zPmAcTQKVUmmSKxc0b+50FOljjJmPTfQSbpuQ4GcDPJDCcyOAol4NUCmVdtmywZAhTkehlFLxzkaeZfWB1TzR6gmnQ7mCDgdVSrlt9254/HHYt8/pSJRSKonFi2HXLqejUEqpeMH7gokxMXSo1MHpUK6gSaBSym1//glvvQWR/jW3WSml4K674MknnY5CKaXiLdu3jByBOWhZvqXToVzBq0mgiHQRkZ0iEiIiV7TMrqILH7j2bxKRhq7t1UVkQ4KvMyIyypuxKqVSFxwMJUtC1apOR6KUUgkcOAB79mhRGKWUX3mlwytsHbGV3NlzOx3KFbw2J1BEAoGPsQUUwoDVIjLPGLMtwWFdgWqur2bAp0AzY8xOICjBeQ4Ac70Vq1LKPUuX2qUhJLlV9JRSyim6PqBSyg8FSABVi/jnnXNv9gQ2BUKMMbuNMVHAbKBnkmN6AlONtRIoJCKlkxzTEfjPGLPXi7EqpVKxd6+dC5iB1wdUSmVWwcGQLx8EBTkdiVJKAbBwz0Lu++k+jkUcczqUZHkzCSwL7E/wOMy1La3H9EfX41LKcbt3Q5EimgQqpfzQ339Dy5a2QqhSSvmBH3f8yLRN08ifw7+WhojjzdYyuQFjJi3HuBZy7gE8leJFRIYBwwAqVKiQ9iiVUm5p3x7Cw3UoqFLKD/31F5w44XQUSikVb1HoIlpVaEXObDmdDiVZ3uwJDAMSLiddDjiYxmO6AuuMMUdSuogxZpIxprExpnHx4sXTGbJS6moCAjQJVEr5oWLF4PrrnY5CKaUACD8fzuajm+lQ0f+WhojjzSRwNVBNRCq5evT6A/OSHDMPGOyqEtocOG2MOZRg/wB0KKhSjjtyBGrUgAULnI5EKaWSmDIF3nvP6SiUUire4tDFALSv1N7ZQK7Ca0mgMSYaGAksALYDc4wxW0VkuIgMdx02H9gNhACfASPini8iebCVRb/3VoxKKfcEB8POnVCokNORKKVUEhMnwrffOh2FUkrFuxB9gVrFa9GodCOnQ0mRV2dQG2PmYxO9hNsmJPjZAA+k8NwIoKg341NKuSc4GPLkgYYNnY5EKaUSOH8e1qyB0aOdjkQppeINrj+YwfUHOx3GVXl1sXilVOawdCm0aAHZszsdiVJKJbBqFURH6/qASim/ERMbg+3n8m+aBCqlrurUKdi4UZeGUEr5oeBgW62qZUunI1FKKQBmb5lN2XfKsveUfy9xrkmgUuqqzp2DwYPhxhudjkQppZI4eRKaNtUJy0opv7EodBEXoi9QrkA5p0O5Kl1VVSl1VeXK2eJ7Sinld957D2JjnY5CKaXiLQpdRNvr2hIYEOh0KFelPYFKqavavx8ywNB2pVRWFZC5PsqISBcR2SkiISLyZDL7a4jIChGJFJHRaXmuUsq79p7ay+6Tu2lf0X+XhoiTuVpOpZRHRURAlSrw8stOR6KUUkl88AG0agUXLjgdiceISCDwMdAVqAUMEJFaSQ47ATwEjL+G5yqlvGhR6CIAOlTy30Xi42gSqJRK0apVcOkSNGnidCRKKZXEn39CeDjkzu10JJ7UFAgxxuw2xkQBs4GeCQ8wxhw1xqwGLqX1uUop76pTog6PtXiM2iVqOx1KqnROoFIqRUuX2sJ7rVo5HYlSSiUQGwt//w233up0JJ5WFtif4HEY0MwHz1VKeUDjMo1pXKax02G4RXsClVIpWroUgoKgYEGnI1FKqQS2bbOVQTPf+oCSzDZ3Z2W79VwRGSYia0RkTXh4eJqCU0ql7FjEMVYfWE1MbIzTobhFk0ClVLKiomDFisz4GUspleEFB9vvmW8B0zCgfILH5YCDnnyuMWaSMaaxMaZx8eLFrzlQpVRic7fPpenkpvx74l+nQ3GLW8NBRaQxcANQBrgAbAH+NMac8GJsSimHTZ8OlSs7HUXKtG1SKosqXx7uuAMqVXI6khRdY/u0GqgmIpWAA0B/YKCbl0zPc5VS6bQodBGl85WmetHqTofilqv2BIrIEBFZBzwF5AZ2AkeB1sAfIvKViFTwfphKKV/LkQN697bDQf2Ntk1KZXE33wzTptlJy34mPe2TMSYaGAksALYDc4wxW0VkuIgMd52/lIiEAY8Cz4pImIgUSOm53n21SikAYwwL9yykfaX2iB+2S8lJrScwL9DKGJNs/WURCQKqAfs8HJdSymHffQfVqkG9ek5Hkixtm5TKqs6dg4sXoVgxpyNJSbraJ2PMfGB+km0TEvx8GDvU063nKqW8b8exHRw5f4QOFf1/aYg4V+0JNMZ8nFIj5tq/wRjzl+fDUko5KSYG7r4bPvrI6UiSp22TUlnY999D8eKwY4fTkSRL2yelsp6FexYC0L6S/y8SH8etwjCuoQuFEjwuLCJfeC0qpZSjtmyB06f9v+aCtk1KZUHBwVC4MFx/vdORXJW2T0plHUOChrBw8EIqFfLfecpJuVsdtJ4x5lTcA2PMSaCBVyJSSjlu6VL73d+TQLRtUirrCQ62i5cG+H2Bc22flMoi8ubIm6HmA4L7SWCAiBSOeyAiRdCF5pXKtIKDoUIF++XntG1SKis5cgR27swoa9do+6RUFrDr+C6eW/gch84ecjqUNHE3CXwbWC4ir4jIy8By4E3vhaWUctLKlRmiFxCusW0SkS4islNEQkTkyWT2i4h84Nq/SUQaJthXSES+FZEdIrJdRFp49BUppVL299/2e8ZIAvWzk1JZwPx/5/Nq8KtEx0Y7HUqauHVHyhgzVUTWAB0AAXobY7Z5NTKllGO2b4czZ5yOInXX0jaJSCDwMdAZu7jyahGZl+R5XbHV+6oBzYBPXd8B3gd+M8b0FZEcQB5Pvial1FU0bw4TJ0KjRk5Hkir97KRU1rBwz0KqFqlK+YLlnQ4lTdIyoL4IcN4Y8yEQ7lqMVCmVCeXNC6VLOx2F29LaNjUFQowxu40xUcBsoGeSY3oCU421EigkIqVFpADQBvgcwBgTlXDOj1LKy8qWhWHD7EKmnlKggF1vMOlXgQKeOLt+dlIqE4uOjWbJ3iUZammIOO5WB30BeAK78ClAdmC6t4JSSjln3DgYP97pKNxzjW1TWWB/gsdhrm3uHFMZCAe+FJH1IjJZRPJeY/hKqbQ4examTIGjRz1/3rRsd5N+dlIq81t/aD1nIs9kqKUh4rjbE3gr0AM4D2CMOQjk91ZQSinnTJxo5wRmENfSNiVXusu4eUw2oCHwqTGmgeu6V8wpBBCRYSKyRkTWhIeHpxKSUipVy5bB0KGwaZPTkbhLPzsplcmFngolf478tKvYzulQ0szdJDDKGGNwfVDSO99KZU7790NoaIYpCgPX1jaFAQkH7pcDDrp5TBgQZoxZ5dr+LTYpvIIxZpIxprExpnHx4sXdCEspdVXBwRAYaOcFZgz62UmpTO622rdx4okTlMpXyulQ0szdUsVzRGQidl7MvcBdwGfeC0sp5YTgYPs9YxTeA66tbVoNVHPNzTkA9AcGJjlmHjBSRGZjC8KcNsYcAhCR/SJS3RizE+gIaKEHpXxh6VJbECZfPqcjcZd+dlIqC8gW4J2VX8q9U44DZw9csb1s/rKEPRqW7vO71RNojBmPveP9HVAdeN41yVkplYksXWprIdSr53Qk7rmWtskYEw2MBBYA24E5xpitIjJcRIa7DpsP7AZCsB/aRiQ4xYPADBHZBAQBr3nuFSmlknXxIvzzT4a6Q6WfnZTK3FbsX0H9CfXZdMQ7Q9R7VO9BjsDERbByBOagZ/WkteyujVupq2sIw0JjzB8iUh2oLiLZjTGXPBKFUsovBARAt252xFVGcK1tkzFmPjbRS7htQoKfDfBACs/dADROb+xKqTTYuBGioryTBObNC+fPX7k9f/qm7+lnJ6Uyt7/2/MWmI5somz9pbTnPeK7Nc3y54ctE2wIlkOfaPueR87s7J3ApkFNEygJ/AkOBKak9SRdkVipj+eQTmDXLe+efMQMqVrTJZsWK9nE6XVPbpJTKYJo1g4MHoXNnz5/71Vft961bwZjLX+lfLFXbJ6UysUWhi6hfsj5F8xT1yvlL5y/N0KChBIhN17IHZGdo0FCPzT90NwkUY0wE0Bv40BhzK1Drqk+4vCBzV9exA0Qk6XMSLsg8DLsgc5y4BZlrAPWxw7aUUl4SG+vd88+YYZf32rvXfr7au9c+TmcimOa2SSmVQZUuDXnyeP6806dDw4ZQy+NNh7ZPSmVSkdGRLN+/nPYVvbs0xJiWY4g19gNatoBsHusFhDQkga6euEHAL65tqQ0l1QWZlcpARo+2N9tN0sUSPOSZZyAiIvG2iAi7PR2upW1SSmUkMTEwYAD89Zfnz719O6xdC3fc4flza/ukVKa1MmwlF6Mv0qGSdxeJX75/OQCCeLQXENxvjB7GLnY611VAoTKwKJXnJLfYcjM3jikLRHN5Qeb6wFrgYWNMMoP2lVKesGQJFCwIktwKeR6wb1/atrvpWtompVRGsnEjzJ4NPXp4/twzZtjx6f37e/7c2j4plWnExMbw0K8Pcf6STUWORRyjYsGKzNoyi81HN/P0DU975boT106kYsGKiIhHewHB/eqgS40xPYwxb7ge7zbGPJTK03RBZqUyiDNnYMMG764PWKFC2ra74xrbJqVURuKttWtiY20S2KmTHWrqYdo+KZV5iAiL9y4mW0A22lVsR99afXmh3Qscv3CcjUc2euWa28K3EbwvmPub3M/uh3d7fC3CqyaBIjJJROqmsC+viNwlIoNSeLouyKxUBrF8uf085M3q6088ceW2PHlg7Ni0nyudbZNSKiMJDraVpMqV8+x5ly+H0FCPDwXV9kmpzCdAAnih7QtsObqFfrX70b5ie/rW6suGwxt4ro1ne+jiTFo7Kb4YjDek1hP4CfCcqzrnNyLyiYh8ISLBwHIgPzZBS078gswikgO7IPO8JMfMAwa7qoQ2x7UgszHmMLDfVVIZdEFmpbxq6VLIlg2aN/feNS65iqKXLm2HnF53HUyaBIOu7aNQetompVRGYYxtoLwxTGH6dHsn6tZbPX1mbZ+UyoT61urL2aizjPt7HBXfr8ijCx6lXcV21ClRx+PXunDpAl9t/Io+tfpQPK93OrmuOifQtR7W7SKSD7suVmngArDdGLMzledGi0jcgsyBwBdxCzK79k/ArtPVDbsgcwS2fHKcuAWZc2AXbfZOGqyUolkzePJJu1yWt0yZAo0awZo16T9XetompVQGcvKk7QVs7+EKfJGRMGcO9OoF+fJ59NTaPimVOQVIAE+0eoJhPw2jcqHK/LDjBxbeudAr15qzdQ6nLp7ivkb3eeX84GZhGGPMOWBxWk+uCzIrlTH07Gm/vGXjRli/Hj76yLPnvda2SSmVQRQpAv/84/nz/vqrTTC9UxUU0PZJqcxo7aG1RMZEUixvMRqXbeyVXkCwBWGqF61O2+vaeuX84P4SEUqpTCgmBjZvhl27vLc0BNhewBw5vFWATymVaXmrYZo+HYoX987i80qpTGnZvmV8uOpDbqpyE6sPrPbaXMBNRzaxImwF9zW6D/FWyXY0CVQqy5o82Y6yat4cqle3hVtiYjx/nago+3mrRw8oWtTz51dKZWJ16sDzz3v2nKdOwU8/2bUHs+myfUop9+w7vY9axWsx57Y5rLh7hfd6AddMJGdgTu4MutMr54+TpiRQRLw4Y0gp5SvffgvjxsG8edCyJdSoAStXwiuveP5av/4Kx47BkCGeP3ccbZuUykSKFLHVo0Rg2zbbMInY7Z7w7bf27pQXh4ImpO2TUpnDgLoD2Dh8IwVyFqBZuaRLn3vG+ajzTN88ndtq30aR3B5q81LgVhIoIi1FZBuw3fW4voh84tXIlFJe88478N579ib78uV2mawpU+ycvbgqnp4yZQqUKgU33eTZ84K2TUplSidP2mGgM2fax2vX2scnT3rm/DNmwPXXQ2Pvlh3Q9kmpzGH1gdVM3TgVYwyBAYFevdbsLbM5E3nGqwVh4rjbE/gucBNwHMAYsxHw4rLSSilvCg2FoCCYNg0iIqBjR6hcGaKj4exZz13n6FH4+Wf43/+8NupK2yalMqvgYMifH+rX99w59+2DxYttL6AX59q4aPukVAYXGR3JkB+H8MzCZzh/6bzXrzdx7URqF69Nq/KtvH4ttz+WGWP2J5mc6IXZQ0opX2jYEObPh3fftctv9ehhh4MWKQKFCnnuOjNn2sTyTi8Oa9e2SalM6MAB22PXqRMEevDO+6xZ9vs1LlCaVto+KZWxvbL0FbaFb2P+wPnky+HZ5WSSWndoHasPruaDLh94tSBMHHeTwP0i0hIwrnX7HsI1vEEplfE8+6xN/J56yq4R+MMP8OijdupNgAfLRU2ZAk2aQO3anjtnEto2KZUZFS8Oo0fDwIGeO6cxdvhDy5Z26IP3afukVAa27tA6xv09jjvr30nXal29fr2JayaSO1tu/lf/f16/Frg/HHQ4dj2/skAYEEQK6/sppfxfvnwwd64dFTVgAHzwgf36nwfbnQ0b7PqA3iwIg7ZNSmVOOXLAc89BlSqeO+emTbB1q88KwqDtk1IZVkxsDEN/HErxvMV556Z3vH69s5FnmbllJv3r9KdQrkJevx64v1j8McA3YyeUUl515Ai0bQt9+sCPP3rvOl9+6f21AbVtUiqTOXTIDv9MbihU4cLpO/f06XZy8u23p+88btL2SamMKzAgkJfbvUyOwBxer9IJMGPzDM5FnfNJQZg4biWBIlIJeBComPA5xpge3glLKeUtI0fCuXPwyCPeu0ZUlJ3O06uX56q6J0fbJqUyEWNgxAjInt322FWv7rlzx8TYScrduvlswVJtn5TKmGJiYwgMCKRnjZ4+uZ4xholrJ1K/ZH2alm3qk2uC+3MCfwA+B34CYr0WjVLKq7791n699hrUrOm96/zyCxw/7vWhoKBtk1KZx5w5doLym296NgEEO/b94EFfDgUFbZ+UynCiY6NpO6UtA+oMYGTTkT655uqDq9lweAOfdPvEJwVh4ribBF40xnzg1UiUUl51/Dg88AA0agRjxnj3WlOmQOnS0Lmzd6+Dtk1KZQ7h4XaYQpMm3hmmMH06FCgAN9/s+XOn7JraJxHpArwPBAKTjTHjkuwX1/5uQAQwxBizzrXvEeAewACbgaHGmIvpehVKZSHjl49n+f7ljGo2ymfXnLhmInmz52VQPd+OHnc3CXxfRF4Afgci4zbGNTpKKf8XHg5lysAXX3htzT7Azjn85Rd47DHvXsdF2yalMoMvvoAzZ+xkYk83HBER8N13cNttkDu3Z899dWlun0QkEPgY6IwtJrNaROYZY7YlOKwrUM311Qz4FGgmImWxFUhrGWMuiMgcoD8wxaOvSqlManv4dl5Y/AJ9avbhttq3+eSapy6eYtaWWdxR7w4K5Czgk2vGcbelrQv8D+jA5SENxvVYKZUB1KgB69Z5f33kGTPs9BsfDAUFbZuUyhwefxy6dvXOejI//QRnz/p6KChcW/vUFAgxxuwGEJHZQE8gYRLYE5hqjDHAShEpJCKlXfuyAblF5BKQBzjoqRejVGYWExvDXfPuIl+OfHzc7WOfXXf6pulciL7g04IwcdxNAm8FKhtjorwZjFLK806dgnHj4JlnIH9+717LGHsjv1kz7845TEDbJqUyshMnbIJ23XVQr553rjF9OpQrZ8si+9a1tE9lgf0JHodhe/tSO6asMWaNiIwH9gEXgN+NMb8nvYCIDAOGAVSoUCENoSmVeS3fv5x/DvzD1F5TKZmvpE+uGVcQpnGZxjQq08gn10zI3XUCNwKFvBiHUspLHnsMxo+HXbu8f63162HLFp/1AoK2TUplbKNGQYMGdiioN4SHw2+/2UXnA9z9yOMx19I+JTdWw7hzjIgUxvYSVgLKAHlF5IruT2PMJGNMY2NM4+LFi6cxPKUypxuuu4GtI7YysO5An11zRdgKthzd4kgvILjfE1gS2CEiq0k8rl3LHCvlx37/3U61efJJWxDG26ZMgZw5oV8/71/LRdsmpTKqX36BadPsovAFvDQXZs4ciI52YigoXFv7FAaUT/C4HFcO6UzpmE7AHmNMOICIfA+0BKZf6wtQKrOLNbGsObiGpmWbUqNYDZ9ee8KaCeTPkZ/+dby4oPJVuJsEvuDVKJRSHnf2LNx7r620/oIP/gdHRl5eGzC9azqngbZNSmVEp07BsGFQpw48+6z3rjN9uh1mWreu966Rsmtpn1YD1VxrDB7AFnZJ2jUxDxjpmi/YDDhtjDkkIvuA5iKSBzsctCOw5pqjVyoL+GT1Jzz464Msu2sZLcu39Nl1T1w4wZytc7irgZ2H6AS3kkBjzBJvB6KU8qxnn4X9++HvvyFXLu9f7+ef7fQeHw4F1bZJqYxq9Gg4fNiuC5gjh3euERICK1fCG2945/ypuJb2yRgTLSIjgQXYJSK+MMZsFZHhrv0TgPnY5SFCsEtEDHXtWyUi3wLrgGhgPTDJE69Fqcxoz8k9PPnnk9xU5SZalGvh02tP3TiVyJhIx4aCQipJoIj8bYxpLSJnSTwmXQBjjPFtLVOllNseftgW2mvpoxtbU6bYJSh8sDZgutumdK7DFQqcBWKAaGNMY8+8KqWyiNhYOz/v8cftuoDeMmOGLYc8YID3rpGM9LZPxpj52EQv4bYJCX42wAMpPPcFdISEUqkyxnDPT/cQIAF8dstnPl2kPa4gTPNyzalfqr7PrptUaj2BYwCMMV6uKaiU8pRLl+wyW5Ur29FWvnD4MPz6q12EPjDQJ5e85rYpPetwJdjf3hhz7BpjVyprCwiASZNsOWFvMcYOBW3XDsqXT/VwD9PPTkr5uUlrJ7Fwz0Im3jyR8gV920Ys3buUHcd28GXPL3163aRSK5Xlu4UylFIe8cQT0LOnrYXgK3FrA955p88umZ62KX4dLlfp9rh1uBKKX4fLGLMSSLgOl1LqWr31Fqxda3/25p33f/6xw0GdKQijn52U8nPZArLRq0Yv7m14r8+vPXHtRArmLMjttW/3+bUTSi0J9F3fqFIq3VasgPfes0tiZXO37FM6GWOHgjZvbhek95H0tE3JrrGVhmMM8LuIrHWtt6WUcseiRXYI6IwZ3r/W9Om2VHGfPt6/1pX0s5NSfu7uhnfz/e3f+3QYKED4+XC+2/4dd9a/kzzZ8/j02kml9jGxkojMS2mnlmFXyn9cvAh33WVHPvmyDsLatXZtwAkTUj/Wg9LTNl3zOlyu762MMQdFpATwh4jsMMYsveIiuiCzUpedPw/33ANVq8Krr3r3WpcuwezZ0KMHFCzo3WslTz87KeWnZm2exaXYS/yv3v98ngACTNkwhaiYKO5r7FxBmDipJYHhwNu+CEQplT4vvQQ7dsCCBZDfhzNRpkyx1Ud9uDYgpK9tSs86XBhj4r4fFZG52OGlVySBxphJuCrzNW7c2IuTn5TKAJ55BnbvhiVLII+X737//jscO+bUUFDQz05K+aWDZw9y/y/3U79Ufe6odwfi4077WBPLpHWTaF2hNbWK1/LptZOTWhJ4VkuwK+X/zp+3o5/uugtuvNF3142MhJkz4dZboVAh312X9LVN6VmHKy8QYIw56/r5RuDla4xDqazhn3/ggw/ggQegTRvvX2/6dChSBLp08f61kqefnZTyM8YYhv88nKiYKD7v8TkBktqMOM9btGcRISdCeLHtiz6/dnJSSwJD03NyLcOulG/kzQsbN/qsMme8n36Ckyd9uzagS+i1PjE963ABJYG5riEk2YCZxpjfrjUWpbKEoCAYP9435YrPnLFrDw4d6r31B1MX6tSFlVLJm7l5Jj/t+om3b3ybqkWqOhLDxLUTKZq7KH1qOTJX+QpXTQKNMb2v9cRahl0p3wgOtkVZihTx/bWnTIGyZaFjR99eNz1tk+v517QOlzFmN+Dcoj5KZTRRUTYZe/RR31xv7lw7Qdq5oaDpbp+UUp51+uJpHvrtIZqXa87DzR52JIbD5w4zd8dcHmr6ELmy5XIkhqS82ReqZdiV8rJNm6BDB3jZgQGJhw7ZtQEHD/Z9D6RSKgNYtcoWglm/3nfXnD4dKlWCFi18d02llF8rmKsgX/b8ki96fEFggDMfWL5c/yXRsdEMa+Q/RcW9mQRqGXalvCg62o54KlIERo3y/fWnT4fYWEeGgiql/F1kpJ2kbAxUruybax48CAsX2l5AB6r+KaX8z4VLFwDoUb0HNYvXdCSGWBPLZ+s+o33F9lQvVt2RGJJz1eGgItIwySYDHDPG7E/u+KRPT2ablmFXykPGj4d16+Dbb6FoUd9eO25twJYt4frrfXttSHfbpJTytldegW3bYP583y3TMHu2vTM1aJBvrpcCbZ+U8g/h58NpOKkhr7Z/lTuD7nQsjj/++4M9p/bwesfXHYshOakVhkmuxHEREckBDDDGbLjKc7UMu1Jesn07vPAC9O3rzFrIa9bYz3eTJvn+2i7paZuUUt60fj2MGwd33gldu/ruutOnQ5MmUN3xO+3aPinlBx7+7WGOnDtCw9JJ78v41oS1Eyiepzi31rzV0TiSSq0wTPvktotIY+AD4Gq1nrUMu1JeEhVli8F89JEz1//yS7s24O23O3P9dLZNSilvmjoVSpSAd9/13TW3brXJ5/vv++6aKdD2SSnn/bjjR2ZtmcVL7V6ibsm6jsVx8OxBftr5E6NbjiZHoGMVi5OVWk9gsowxa0QkXyrHaBl2pbykfn275rITLl6EWbOgd2/fjfJylzttk1LKy955x1YDLVzYd9ecMcNWqOrXz3fXTCNtn5TyjZMXTjL8l+HUL1mfp1o/5Wgsn6/7nBgTw70N73U0juRcUxIoIiW5cn7fFbQMu1KeFRJib3SPHQsFCjgTw7x5cOqULUrjb9xtm5RSXrBrF+TMCdddB+XLp368p8TG2iTwxhuhZEnfXTeNtH1Syjf+2vMXJy+cZP7A+WQPzO5YHDGxMXy27jM6V+5MlSJVHIsjJakVhvmQKxusIkBLwJmFNpTKomJj4e677aLwTz7pXBI4ZYr9fNc+2QFPvqFtk1J+JjoaBg60d4h27vTtujF//w379sHr/lF0QdsnpZzVt1ZfWldoTal8pRyN49eQX9l/Zj/v3uTDofFpkFpP4Jokjw1wHHjUGHPUOyEppZIzYQIsXQqff24XaHfCwYOwYAE89ZTjawNq26SUPxk/HtauhW++8X3jMH065M0LPZMuRewYbZ+U8oE9J/dwLupc/OOzkWfZfmw7Xap2oWwBhz4oJTBx7URK5StFj+o9nA4lWakVhvlKRBoAVYCtxpjtvglLKZVQaCg8/rgd7eTkMMxp02yP5J3OVVoGtG1Syq/ElSvu08eWLPalixdhzhw7STlvXt9eOwXaPinlfcYY6n5al6J5ilIgpx0adeDMAU5ePMn1Ra9n58idjsa37/Q+5v87n6daP+XokNSruepi8SLyHPA10Af4RUT8b1ajUlnAqFF27eNJk5xbAzlubcDWraFaNWdiiKNtk1J+IibGLgqfLx98/LHvrz9/Ppw+bReI9xPaPinlfSLCiCYjuLnazWy+fzPv3vQuJy+epFKhSjzS/BGnw2PyuskYY/yyIEyc1IaD9geCjDERIlIU+A34zPthKaUSevdd2LLF1ltwyj//wI4dMHmyczEkoG2TUv4gKgrq1oWRI50pyjJ9OpQqBR06+P7aKdP2SSkfGN1yNDU+qsGIJiO4Z949VChYgaiYKIYGOVu5Ljo2ms/Xf06Xql24rpCDH9xSkVoSeNEYEwFgjDkuIlftOVRKedb585AnD1SqZL+cNGUK5M4Nt93mbBwu2jYp5Q9y57ZDFJxw4gT88gs88ABku6Zi596i7ZNSPlAibwmGBg2l3ZR2nLx4ksZlGjMkaAg5s+V0NK6fd/3MwbMH+bT7p47GkZrUWs0qIjLP9bMkeYwxxj9nOiqVCRgD/fvbz1hff+3cMFC4vDZgnz7OVSVNQtsmpZwUGwv33w/33guNGzsTw7ff2p5IPxoK6qLtk1I+8kTrJ5iwZgJP3/A0UzZMcbwXEGxBmLL5y9KtWjenQ7mq1JLApKW2xnsrEKVUYjNmwM8/23WXnUwAAX74wU67GTLE2TgS0LZJKSd98ontAWze3LkkcPp0qFkTGjRw5vop0/ZJKS+bvmk6RXMXpWu1rjzQ9AFe//t1Puz6oeO9gHtO7mFByAKeb/s82QL8aoTCFVKrDrrEV4EoldVdugQ//ghr1kDhwjBuHLRoAQ895HRkdihohQrOrg2YkLZNSjlozx67WOlNNzl3Zyg0FIKDYexY5++SJaHtk1LeE2tieXbhs7z+9+v0rN6TrtW6MrrlaPae3usXvYCfrfsMEeGehvc4HUqqUqsO2lNEHkjweJWI7HZ9+bgOtFKZ1+nT0LIlvPce5M8PH35o11z2g/X4OHAA/vjDLgsR4CczW7RtUsohxsA999jGwMlyxTNn2u8DBzpz/avQ9kkp7zgfdZ7bvrmN1/9+nXsb3suc2+YAdm7g132/drwX8FLMJb5Y/wU3X38z5QqUczQWd6T2ke5xYF6CxzmBJkA74H4vxaRUlvP661C7tr2xfffdtur6rbfa9Zed5i9rAyahbZNSvlKkiE32RGzyt3AhnD0LQUHOxGOMbZhuuAEqVnQmhqvT9kkpDzsTeYY2U9rww44fePemd5l480RyBOZwOqxEftz5I0fOH+G+Rvc5HYpbUhusmsMYsz/B47+NMceB4yLiH6uyKpUJzJ17ufhLqVKwbRvkymUrrh8/DkWLOhNX3NqAN9wAVao4E0MKtG1SyldOnrSNAdhx6zNnwuDBzg0NWL/erlfziPNrgaVA2yelPCx/jvy0LNeSl9u9TPfruzsdTrImrJlAhYIVuKnKTU6H4pbUWvDCCR8YY0YmeFjc8+EolTUFBto1j+++237WKlzYJoTGODscdOVK2LnTrwrCxNG2SSlf2rfPLlaaPbsdFuDkPLzp0yFHDr9ZryYZ2j4p5SHfbvuWHcd2ICJ82O1Dv00AQ06E8NeevxjWcBiBAQ7P43FTakngKhG5Yql7EbkP+Mc7ISmVtURHQ6FC8MwzsH07nDtnt7/3HrRqZfc5ZcoUu06hH37W0rZJKV9q2BD+97/LPYJOiY6269V0727vlvknbZ+USidjDC8veZnbvrmN14JfczqcVE1aO4lsAdm4q8FdTofittSGgz4C/CAiA4F1rm2NsOPbe3kxLqWyhKNHoV8/WLECype3ax8/+yxs3Hi5IItTLlyA2bOhb19brMbPaNuklLfFxsIrr9ify5RxfsFSsPMRDx/2x7UBE9L2Sal0uHDpAnfNu4vZW2YzuP5gJt08yemQrioyOpIvN3xJj+o9KJ2/tNPhuC21JSKOAi1FpANQ27X5F2PMQq9HplQmFxtrK6zv2AFffWVvsgcH2yUi2raFm2+2I56c8sMPcOaMXw4F1bZJKW87cwZuvx0WLLCPV660wwKcNn26HR7RzX8XYdb2SalrdyziGDfPvJl/DvzDuI7jeLzV44jTN59S8f327zkWcSzDFISJ49Yqhq6GSxsvpTwgbjRVQAC8+y4ULHh5reM2beyXP5gyBa67ziak/krbJqW8JE8e21hNmgSPPw55k6ln4uvhmOfPw/ff22UhcuXy7bWvgbZPSqVdvhz5yJ8zP9/3+55eNXo5HY5bJq6dSOXClelUuZPToaSJn6z6pVTWcPGiXWLr9dft43btLieA/mT/fv9bG1Ap5WVxSd/Ro5AtG/z2G9x77+XqoEm/TpzwbXw//mgTQf8eCppuItJFRHaKSIiIPJnMfhGRD1z7N4lIwwT7ConItyKyQ0S2i0gL30av1LX59d9fOXXxFLmy5eL3O37PMAngjmM7WLJ3CcMaDiNAMtYHpowVrVIZ2L59dqmFL76AyEino7m6adPsZzw/WxtQKeUtZ8/CgAFw330wYYLd5m9DsKZPhwoVoHVrpyPxGhEJBD4GugK1gAEiUivJYV2Baq6vYcCnCfa9D/xmjKkB1Ae2ez1opdLBGMMbf79B95ndeXnJywB+P/wzoYlrJpI9IDtDGwx1OpQ0c2s4qFIqff76C/r3h6goO9euZ0+nI0pZ3NqAbdtC5cpOR6OU8rqtW6FPH/j3Xxg3DsaMcTqiKx05Ar//bmPL3MMTmgIhxpjdACIyG+gJbEtwTE9gqjHGACtdvX+lgfNAG2AIgDEmCojyYexKpUlkdCTDfh7G1I1T6Ve7H2M7jHU6pDS5cOkCX238it41e1Mibwmnw0mzTN2SKuUPDh601cxLlIDVq/07AQRbqfTff/2zIIxSysP++guaNoVTp+zPTzzhn0nW119DTEymHwoKlAUSLjQf5trmzjGVgXDgSxFZLyKTdXF65a/Cz4fTaVonpm6cyottX2RWn1nkzp7b6bDS5Ntt33Ly4skMVxAmjh+29EplDtHR9nuZMraWwapVcP31zsbkji+/tDUg+vZ1OhLvSc+cG9f+QNeHrJ99F7VSXhAUZO9MrV9vJyn7qxkzbKy1a6d6aAaX3Di4pIszpnRMNqAh8KkxpgG2ZzC59m2YiKwRkTXh4eHpjVepaxIdG82Rc0eY3Wc2L7R7IUMNAY0zce1Eri96Pe0qtnM6lGuiSaBSXrBzp/28Mm+efdytG+TL52hIbomIsDfc+/bNGPFeCw/MuQF4GJ1rozKqvXthxAi4dAmKFoWZM6G0H69ttWsX/PNPVugFBNurVz7B43LAQTePCQPCjDGrXNu/xSaFiRhjJhljGhtjGhcvXtxjgSvljhX7VxAdG03p/KXZOmIr/er0czqka7Ll6BaW7V/GsIbDMmQCC5oEKuVxc+dCkya2wF6BAk5HkzZz59r6EEMz3vzmtIifc+OaMxM35yah+Dk3xpiVQNycG0SkHNAdmOzLoJXyiPnzbUniGTPsXMCMYMYMW6RmwACnI/GF1UA1EakkIjmA/sC8JMfMAwa7Riw0B04bYw4ZYw4D+0Wkuuu4jiSeS6iUY4wxvLviXVp/2Zr3Vr4HQPbA7M4GlUbl3imHvCTIS0LdT+sCMPqP0ZR7p5zDkV0bTQKV8pCYGHj6aejdG2rWhLVr/Xt0VXKmTIFKlWwV00wsPXNuAN4DHgdivRSfUp4XEwPPPmsnKFeoAOvW2eEK/s4YWxW0Y0c7tj6TM8ZEAyOBBdjRBnOMMVtFZLiIDHcdNh/YDYQAnwEjEpziQWCGiGwCgoDXfBW7UimJionivp/v49HfH6Vn9Z7c3/h+p0O6Jj2q9yBHYI5E23IE5qBndT8v9pACrQ6qlIf8/LNd/2/YMPjgA8iZ0+mI0mbfPlsX4oUX/LMuhAdd85wbEbkZOGqMWSsi7a56EZFh2KGkVKhQ4RrCVMqD7r8fPvsM7roLPvoIcmeQAgwrV8Lu3fD8805H4jPGmPnYRC/htgkJfjbAAyk8dwPQ2JvxKZWS27+5nRMXEq8feinmEv+e+JdD5w7xdOuneaXDKxluPb04z7V5ji/Wf5FoW6AE8lzb5xyKKH28mgSKSBfsmjWBwGRjzLgk+8W1vxsQAQwxxqxLsD8QWAMcMMbc7M1YlbpW58/bQio9esDixXZphYxo6lR7033wYKcj8br0zLnpC/QQkW5ALqCAiEw3xlwxWckYMwmYBNC4ceOkSaZSvjVyJDRvbpPAjGT6dJuw3nqr05EopVJx8uJJKheuzO21b4/f9uayNzkWcYypvabyv/r/czC69CuWpxjlC5Qn5GQIYHsBhwYNpVS+Ug5Hdm28lopr8QWVFXz1FVSsCNu22SkrGTUBjFsbsF07Oxw0k0vPnJunjDHljDEVXc9bmFwCqJTjjIF337UFYADq1cs4CWCBArZBFYFPPoELF6BgwYw3yVqpLOaFti/wx+4/aHtdWyoVqkTzcs3ZeGQjS4cszfAJ4MXoi/SZ04eQkyFkC7B9aBm5FxC8OydQiy+oTCsqyn62GjIE6taFYsWcjih9li2D//7L9AVhAI/MuVHKv505A7fdBo8+CocO2QYrIzl7Nm3blVJ+oXWF1lQoWIGuM7pS4+Ma3PfTfbSv2J7m5Zs7HVq6nI86z80zb+anXT/xSbdPuLfhvQRIQIbuBQTvDgdNrrBCMzeOKQsc4nLxhfzeC1GptDtwwH6+WrECxoyB116DbBl8du2UKXZJiD59nI7EN9Iz5ybBMYuBxV4IT6lrt3mz/Y+8eze89RY89pjtUVNKKS+KuBTBuyveZc3BNURciuDO+nfyy7+/sPjOxU6Hli6nL56m+8zurAhbwZSeU7gz6E4OnT3Egv8WZOheQPBuEqjFF1SGFhsLCxbA6tVQvrxN/PLls0VfNm2COXPstozu/PnLryVvXqejUUpdswsXoHNnW9lp0aJMX+ZXKeUfjDF0+KoDqw6soleNXhw5d4St4VvpWKkjtUvUdjq8a3Y84jg3Tb+JjUc28nXfr+lbqy8ApfOX5r+H/nM4uvTz5nDQ9BRfaIUtvhCKHUbaQUSmJ3cRXfRUeUNEhK1I/swzdiTV3LlQrZqd+/fyy3b5h8yQAM6YAdddZ0dZzZ9vHyul/FyRIpfnzCX8KlsWZs2yyz9oAqiU8iJjDPP/nU9kdCQiwvNtn+fvoX8zt99c3uz8JmsPruW5Nhm3p+zwucO0+6odW45u4Yd+P8QngJmJN5NALb6gMqzx46FoUVizBp56CvLnh+hoW1chZ06oXj31c/i7GTPschbHj9vHR47Yx5oIKuXnTp60hV+MscM+Gza0QxROnoT27aFUxp2jwrykHxOUUv5mZdhK2kxpQ/eZ3Zm2aRoA3ap1o1WFVoCdGxg6KjTD9gLuO72PG768gT0n9zB/0Hy6X9/d6ZC8wmtJoBZfUBnZd9/BqFHw/ffQrJm9uf7QQ7BzJxw+7HR0nvHMM7bHM6GICLtdKeXnIiPtZN6GDW0imBnK+q5eDf37p7xQaX4tEaCUk3Yd30XfOX1p8XkLQk6EMKH7BIYEDUn22AoFM+YUrZATIdzw5Q2Enw/n9//9TodKHZwOyWu8Ws5Ciy+ojCo6Gm6/3RbWq1QJfv0VOnSwFdczS42FvXuT375vn2/jUEpdg/LlITwcGjWyk3orV3Y6ovTZvRtuvhlKlrQLxJcs6XRESqkkhvwwhM1HN/NSu5d4tMWj5MuRz+mQPGpb+DY6Te1EVEwUC+9cSMPSDZ0Oyau8ORxUqQwlNBS+/db+3L8/5MplH//7L9x0E3z2GdSunTk+m2zblvLNdq2vpJSfiY2F336ziVKcVq3g99/hn38yfgJ4/Dh07QqXLtk7bpmhkVUqEzgbeZZXlrzCsYhjAEzuMZmQB0N4vu3zmS4BXHdoHW2+bAPA0qFLM30CCF7uCVQqI1ixwvbwffedrf7ZrRuMHg2LF8NLL9kRSps324qgf/zhdLTpt3s3dOpkR1ZFRsLFi5f35ckDY8c6F5tSKoHjx+HLL+HTT+1/3CeegHHj7L65c52NzVMuXoSePe3QhD//hBo1nI5IqSzvUswlJq+bzItLXuTo+aOUK1COoQ2GUqt4LadD84rl+5fTbUY3CuYqyF+D/6JqkapOh+QT2hOosqw1a6BFC2jZ0iZ3o0fDli02Ecqd2257+20oUMD2DO7cmfE/nxw4YKueRkbC33/D5Mm2OqiI/T5pEgwa5HSUSilGjIBy5exipOXKwezZtjQxQOHCyVcHLVzY2ZjTKjYWBg+GZctg6lRo3drpiJTK8r7f/j11Pq3DiPkjqFGsBivvXsnQBkOdDstrFu5ZyI3TbqRE3hIEDw3OMgkgaE+gymJOn4YzZ+x0mnz54Ngx+PBDGDLEPk4oIMAuudW5syOhetzRo7YH8Phx+OsvqFPHfmnSp5QfiIiwC5Peeqt9nC0bDB0K998PdesmPvbECd/H5w2PPw7ffGMXtb/9dqejUUoBMzfPJHtAdn4a8BPdq3VHMkshhGT8susX+szpQ7Wi1fjjf39QKl8Grqx8DTQJVFnCnj22gvrnn8ONN9q5fjVqwK5dmafQy9WcOmXnNe7da6cWNWnidERKKcBOOp4wwQ77PHkS1q+HoCDbYGVmH35oh1o88AA89pjT0SiVZW0L38bTfz3N6x1fp2bxmnx2y2cUyFmAwIBAp0Pzqm+3fcvA7wZSr2Q9FtyxgKJ5ijodks9pEqgytVWr7E3muXNtz16/fvDII5f3Z4UE8Nw5O89x61a7BFebNk5HpJQiNBTuu88Wd8mWDXr3tkNA69d3OjLv+/FHePhh6NED3n8/azTESjngnRXvxBd1SahUvlL0rdWXFxa9wBcbviBfjnxsP7admsVrUjh3BhtWfg2mbpzK0B+H0qJcC34Z+AsFcxV0OiRHaBKoMp3oaJvwBQTATz/BwoV21NHIkVC2rNPR+dbFi9Crl02G58yBLl2cjkglq0gR2wuUVOHCmWfon7Jjsvftg8aNoXhxO0n35ZfhnnugdGmno/ONVatgwAA7HGHWLAjM3L0NSjlp+qbplMlfhpblW8ZvC94XzJajW3jyzyeJjo3mwaYP8mybZymWp5iDkfrOp6s/ZcT8EXSq3Ikf+v1A3hx5nQ7JMZoEqgwlNtbOaStQAHLmTLzv1Clb6OSDD+Cjj+xN5jFj4KmnIG8W/D9+6ZLt+fzrL7umdJ8+TkekUnTyJBhz5XbtIcn4jIHly+GTT+z8t2rVbAWqvHlt2eGs9B7/9x/ccotNeH/6yVbhUkp5zfNtn+elJS/xVOuniI6NJjAgkJmfzqRZ2WbkCMzBqx1epXLhDL7ETBqMXz6eMX+M4Zbrb2HObXPIlS2X0yE5KstVBzXGft5KWBZfZQzffAPXXw/Vq0OpUraaZ1SUrZz+8MO22MuYMVC1KhR1De0uWDBrJoAxMbbYzbx5NiG+806nI1Jue/11O345JsbpSJS7ihRJvlpnvnx2fl/r1vDLL3a453ffXU78slICeOyYXQswJsauBViihNMRKZXp9azek+jYaO6edzfVP6rOC4teIG+OvMzpO4eZfWZmmQTQGMOLi19kzB9j6Fe7H9/d/l2WTwAhi/UE/vWXnX8et97ugAHwzjtZM0nIaBYuhFGjbJX01q3h4EEYNgwefdQWOtm3zy7j8Mgj0KCB09E6yxhbUHDmTJtPPPCA0xEpt0VF2QIh//57eQHws2ftoo7KfyXsyd22zS52XrSoTfICAuCzz+wfnKz6x+bCBbsW4L599g/x9dc7HZFSmVqsieWv3X8xef1kdhzbwZajW2herjnTN0/nk26fEBCQdfqAjDGM+WMMb694myFBQ5h8y+RMX/TGXVnmX8GWLfZv8Msv22GD//0H58/b3hLl/957zy5iXqMGTJ8O994LEyfanz/5xNZYmDpVE0BjbA/pZ5/ZYbBPPul0RCpNcuSwScQ339jubrBd3D//7GxcKnnG2JK7YO+2BAVB7dq2cYqzbp2d85dVE8CYGPjf/2DFCttgt2rldERKZVoRlyIAm/jcNe8u/tz9Jw80eYDqRatTrUg1SuQtQZeqWac4QKyJZcQvI3h7xduMbDKSz3t8rglgAlmmJ/Djjy8XI4uNtYtlT55sP1+FhkLFik5HqFKyc6ddS3jbNrtsFtgbyefPQ7Fi9r0rU8bREP3GK6/Y3u2RI23SrDKgbNmgb1/7JWKr+cStE7d+ve0tbNbM2Rizquho2LDBvgctW9p5BdWq2X1ffQXNm8Mbb1xuqCBrDflMzpgxdgjsO+/Yf9NKKY+6FHOJX/79hcnrJrPh8Ab2PLyH7IHZ+W3Qb1QtUpWc2XLyw44fuPXrW5k/cH6mXvcvoejYaO768S6mbZrGE62e4PWOr2eZ1+6uLJME7t5tE0CAP/+0UxNuvtmO2tm9W5NAf3HyJCxdCosW2ferQwe7uPuZM3Z01euvQ7t20LSp7c09fRoqVHA6av/w7rvwwgu2d1urrmcwhQsn/4YVLmzHQMd59VX4/nubgDzyiC39mi3LNOPOWLTIjkdftsxWtoyIgBtusA1V7twwbZodi37qlL4XSb3/vm2YHnrIjudXSnnMvtP7+GT1J0zZMIUj549QJn8ZhgYN5WL0RbIHZqd2idrxx/as3pPvbv8uy/QCRsVEMfC7gXy3/Tteaf8Kz9zwjCaAycgyw0EbNLDLMQHUqgVPPGFHp2zdam/avvGGnbagfO/CBTtXs1Ejm+j16mVHU23ZYvc3agSrV9vPWLlz21FyCxbYKSZPPw25dG4vn31m50f27Wt/zkLD/TOHEyfs0MKkX0mXh5gyxX6wPnwYbrvNVkH68ktHQs50jLF3lqZOtXdT4owfb+8+nTkDd99tk/KZMy/v79fPftcEMLHvv798o+Kdd/SulFIecOHSBU5csH8XQk6EMH75eJqXa85PA35i76i9vNrhVfLnvHIOuYjQu2bvLJEIXbh0gVu/vpXvtn/HOze+w7Ntns0Sr/taiEmuLHkG1bhxY7NmzZpk94WF2WWJRo6EQYNg/347SqVYMTus8N9/7bDQbNnssWXL6t8sdxnj/u/q3Dn4+297cz1/fnj2Wfv8ypXhuuugfXv71azZlUtAbNxoO0JWroRy5S6/l1nd7NkwcCDcdJNdgzlHDqcj8iwRWWuMaex0HOlxtbbpmsTE2NKv774L3brZyZ+XLtl153RYQ9rWXfztN3vnZNkyOHLEbitUyP4hyJvXFjMpUsRW+vTE9bKCFSvsMI6gIFsIJpMuBaFtk/KVDYc3MHndZKZvms5dDe7inZveIdbEcuTcEUrnzyJrjCaj3DvlOHD2QLL7Jt48kWGNhvk4Iv/hTvuUZW5dlisHwcHw0kt2XnqxYra4yIgRttckbiTPpUs2ASlUCIYPh8GD7TID6kqLF8Mzz9i/96VK2boITz6Z/Nq/H35o1wVevdpOq8meHXr3tvtEICQk9TWD69e39TLUZT/9ZGsu3HCDnXaT2RJAlYLAQLj1VvsVG2u3ffON/cfQu7ftFm7RwtkYnZTcuosnTtihBk89ZRO+r76CSpXs3b/166FzZzvMtlUrW9wlrkFyZ7x5Vkz0UvLvv3YtwLJl7Y2KTJoAKuULUzZM4aN/PmLtobXkDMxJn1p96F3TfngKkIAsnQAC9Kjeg8/Xf05UTFSi7Z0qdcrSCaC7skwSCHbk1LRpye8rVMh+N8b2Nn36qZ3G8OSTtqro6NG2MqWy1q2D22+3a9AtXmz/7t9/Pxw9akf/LFoEa9faJCUgwBZ3Adv72r69/ayVsFheagmgutJff9kRgQ0a6LrLWVrc2N+2beHxx2HCBPj2W3s365FH7D+SrDI+OCoKDh2yP+/fbyt/rVljk+MdO+z28eOhYUM4ftwmgcOG2Tt+Kv3Cw23PNNi1AIsXdzYepfzMq0tf5fC5w1dsL56nOC+0ewFjDOsOraNRmUYALNm7hKiYKD7o8gGD6g2iSO4ivg7Zrz3X5jm+3JB4SkSOgBxM653Ch32VSJZKAt2RI4edIzh0qE1iJkyw0z/69bNJ4IkTdphiVq32Heftt22C3KePTeAOHLA9fB98YL8CAuxcvvBwW3znww91eK0nrVhh50RWq2Y/axUo4HREynFly9q5a888Y3u53nvProlz++12f3S0c/PW0jtc0hg7J+/AgctfNWvaRPfQIVvl68ABexcqrgfw229tElymDFSpYod1PP20PU/u3JfPnVUSZG+LiLDVvMLCbCGduKqpSql4f+z+g0K5CtG5cuf4bQv3LGRl2Ery5cgXv67f2mFraVi6IR93+5jc2XLrnLYUBEgAVQpXYWv4VgCyBWTjnob3UCpfKYcjyxiyzJzA9Dh92s5fCwiwPYKTJ9vPE8OH2yIz/uT8eVtQpUQJe5M7veLm+x08CHPm2Eqq//1n/8bHxsIvv0CnTjYReeYZu2TWSy/ZG+86jNY7NmywFVKLF7dDnEtl8rZO591co9hYmxiVL28n49asaYeKPvSQnSDtyzlsIlcOz0y4/fBhmzyEhV1O8urUsZNdIyPtP/azZxM/99FH7d2oiAhbEals2ctfw4ZdntztThwqfWJibI/zDz/Y5DturH8mp22TSquFexZy/y/3s3XEVrIFZGPfqX3UnVCXiEsRRMdG06JcC+5peA+3176dfDmuMg85izsTeYa3l7/N2yve5mL0RQBiTAy5s+Vm98O7NQlE5wR6TMJkpk8fe+N54kTbu9Wmjf1M1aePc/HF+egjW9SuYkX7+adhQ7s2b9GiqT/33DlbVCQuyYv7eu01u+TAoUP2pnr+/PameokStphL3Pp8XbvaYYk1a9oEWXumvGPHDrjxRvv7/fPPzJ8AqnQICLAJINj/4O3b23HuH35oE6GlS6F168Rd9Gm525ywItSRI/ZuWUTE5a/cuRMvDP7WW3b7iRM2yUs4vr5OHTs8M062bLYS58CBdujFAw/Y3sRy5RIne2DHQc+fnzi2YcOuTACV9zz2GMyda3ufs0gCqNS1aFmuJTkCcvDUn0/x1o1vsSh0EZHRkYxsMpJ7Gt6TaFkHdaXI6Egmrp3IK0tf4VjEMW6rdRuvdniV91a+x8S1ExkaNFQTwLQwxmSar0aNGhlfOXrUmDfeMKZyZWMGD768PSzs8s/nzxvz6afG9O9vzIMPGrNhg/fi+fVXYypVMubff+3jyEhjRo0ypnt3+zg62pgFC4z55BNjRo825tZbjalXz5g337T7Dx++XJe+bFlj2rQxZsgQYxYtsvujouxrjo21jzdsMKZ4cWOmTjXm3Dlj1q41pmVLY5580nuvMavbvdu+NyVKGLNzp9PR+A6wxvhB+5KeL1+2TVd14IAxTz99+T/7pk12+5w59j8vGHPPPcYMHGjMvfdeft4DDxhTo4YxFSoYU6yYMXnyGFOnzuX9LVteucBFkyaX9yfcnj+/PddDD9nHxhgza5YxP/xgzJo1xhw6ZExMTPpeZ+HCV8YDdrvyrHfesb/bUaOcjsTntG1S7jhw5oD5bO1nptfsXibv2LyGFzG5Xs1lIqMjTa2Pa5n5u+Y7HaLfi4mNMdM3TjeV3qtkeBHTfkp780/YP/H7D545aCq/X9kcOnvIwSj9izvtkw4HTafYWDtKqWBBW2CuUSPo0gXuvBPefBNKl7ZTcvbutT11H3xweVmpaxUZaae1nD5tb/ZXrmyLBJYpY4ennjplrxcSYpdT2LnT3hTPndvWTciZ0z6nShW7xvGgQfYT0o4ddgipu+vuLV9ul3hYvty+zgcesCO0dIqN5x08aCuAnjxpC/HUq+d0RL6jQ668QMQO24sbwnDHHba6aFSU/c+cJ4/tdVu82O5/9VXYtMluj/sqW9YODwDbE3fqlN2eO7f9XqSIrbIZd71z52zjkrAKlA7PzNi+/db+gevd284XyGKNv7ZNKjmxJpatR7dSt2RdAHrO7sm8nfMoX6A83at1p3u17rz+9+uUzFeSg2cPsuLuFTrnLwXGGBb8t4An/3ySjUc2ElQqiHEdx3FjlRv1d5YKd9onTQI96MgR+OQTu+TUoUP2s9CTT8LDD9skcdUqWzl7yRK4eNEmcmCL+oFdo3jHDpvcxSV55cvDxx/b/W3a2HNEJaiE27GjHRbYqpWtdH7woN1erJhN8kJD7eezhg3hn3/s57bSpbPc3+oM7dgx+29k3z77Xjdr5nREvqUftLwgafIVG2sbBW8lZanNCVQZz/Lldi3Ahg1tqeKExXayCG2bVJwzkWf4478/+Pnfn/n13185cv4IYY+EUbZAWdYfWk9gQCB1S9SNT1wW7llIx6kd+XXQr3Sp2sXh6P3T6gOreeLPJ1gUuohKhSoxtsNY+tXpR4DoB1h36JxAHytZ0hZFefZZ2yOYLZutWxB3s/y112y1zITFZCpVsvPwwC5fsXixTRgLFLBfhQtfPrZHD7u0QsL9cUtYtW1re/fefttuz5ULtm6104Dirte0qdd/BcrDTp+2i8Dv3m2L72S1BNBbRKQL8D4QCEw2xoxLsl9c+7sBEcAQY8w6EckFLAVyYtvPb40xL/g0eG/w9l2hwoWTn2+YsIFTGceuXfYPUoUKdi3ALJgAKhUTG0NgQCC/7PqFW7++lUuxlyiUqxBdqnbh5mo3UzCXLSjRoHSDK57bvmJ7/vjfH3Ss1NHXYfu9Xcd38czCZ/h227cUz1OcD7p8wH2N7yNHoC6E7GmaBHpB9ux2TcJevWzPX/78dvuQIbYWwxNP2JoIBQokLtoyf75NHFPq4R49OuVrjhoFzZvDc8/ZIm3//Qdjx9qK8e4O71T+5fx56N7djsL78UdbEVSln4gEAh8DnYEwYLWIzDPGbEtwWFegmuurGfCp63sk0MEYc05EsgN/i8ivxpiVPn0R6eXrpEwXU8/YChS4sjor2CEtxYr5Pp5M7lpvUiXYHwisAQ4YY272WeAZ0JLQJRw9f/SK7QVzFeTGKjcm2hYVE0Xw3mB+3vUzv/z7C4+2eJThjYfTsHRDHmn+CN2v707L8i3JFpD6R2sRoVPlTh57HZnBobOHeHnJy3y27jNyZcvFC21f4LEWj5E/Z36nQ8u0NAn0kiFDbLJ30032sTG2N6d6dTtENDnZs1/79UqUsPP/PvzQTt8pVcoOL9XEIWOZMePyUhu5ctn5n19/fXn9ZeURTYEQY8xuABGZDfQEEiaBPYGprsnVK0WkkIiUNsYcAs65jsnu+sp44xk1KVNpkVwCCPZOlfKodN6kivMwsB3QOt2peOi3hxCEakUvr2u599ReDp49SNijYQBEx0Yz4LsBLAhZwNmos+QIzEH7iu0pX8BWXy6dvzRvdH7Dkfgzg9MXT/PW8rd4d+W7RMVEcX/j+3m2zbOUzFfS6dAyPa8mgVl5yNUtt9hCMTVq2CGcoaG2HsKPP3rvmiVKwCuveO/8yrtmzLCV7SMi7OOLFyFHjsRzQJVHlAX2J3gcRuIPUCkdUxY45PqQthaoCnxsjFnlxViVUllLum5SiUg5oDswFnjUx7FnOI+3fJwJaycwp+8cRARjDO2/ak+9kvUY8/sY3rrxLbIFZCMyOpL+dfrTvVp3OlbuqGv4eUBkdCSfrP6EscFjOX7hOAPqDOCV9q9QpUgVp0PLMryWBGb1IVcids2+4cNhxQqboLVokbZluFTW8swzlxPAOFFRdvugQc7ElEkl978waW9eiscYY2KAIBEpBMwVkTrGmC1XXERkGDAMoELc5F2lMhq9C+Vr6bpJBbwHPA7oGDo39K/Tn5eXvsxby99i1/Fd/LjzR45FHEMQImMibRl9EeYNmOd0qJlGTGwMMzbP4PlFz7P39F46V+7MuE7jaFi6odOhZTne7AnUIVfYYjG9ejkdhcoI9u5Nfvu+fb6NIwsIA8oneFwOOJjWY4wxp0RkMdAFuCIJNMZMAiaBrcCX7qiV8rUVK+zwBOVL13yTSkRuBo4aY9aKSLsUL5CFb1BFxUSx7tA6lu1bxvKw5XzZ80ueb/M8Ty98mjORZ8iTPQ+D6g7i3ZvepXje4k6Hm6kYY/g15Fee/PNJNh/dTKPSjZjcY7LOjXSQN5NAHXKllBv27btcQTY5WexvtC+sBqqJSCXgANAfGJjkmHnASNfNq2bAaddQq+LAJVcCmBvoBOhkEJW5nD4NTz8Nn35q14tUvpSem1R9gR4i0g3IBRQQkenGmDsSPjktN6je+PsN/j3x7xXb82bPyzs3vUNgQGAyz/IfcT15K/av4PE/H2f1gdVExkQCULlwZfad3kf/Ov15ccmLPNX6Kd5f9T6f3fIZubNrxdtrUe6dchw4e+CK7cXzFKdm8Zos3buUKoWrMLvPbG6rfZsu9+Awb/720z3kyhgThG3cmopInWQvIjJMRNaIyJrw8PD0xKuUT0VFwbhxULOmXf7h9tuvrLSeJ4+t8qo8xxgTDYwEFmCLJ8wxxmwVkeEiMtx12HxgNxACfAaMcG0vDSwSkU3YZPIPY8zPPn0BSnnT3Ll2XaFPP4WHHrJrDeVPYWRhSttVesTfpBKRHNibVEnHIs4DBovVHNdNKmPMU8aYcsaYiq7nLUyaAKbV2kNr2Xd6H83LNY//ijEx/LnnT79brNsYw85jO/li/RfcM+8ean5ck6+3fg1A7uy5iYqJYkSTEXx727cceuwQ/z30H3VK1CEwIJAX277IE38+wROtntAEMB16VO9xxVIOghAeEc7OYzv5uNvHbHtgm6735ye82ROoQ66USsGff8LIkbBzJ9x6K7z3nu3xi6sOum+ffTx2rM4H9AZjzHxsopdw24QEPxvggWSetwm4ctEnpTK6sDB48EH44QeoX99+b9LE7jtzxsnIshRjTLSIxN2kCgS+iLtJ5do/Adt2dcPepIoAhnornufaPEfnaZ2Z228ueXPkxRjD5+s/5/k2z3vlQ3ynqZ3YcvSKj3oUzFWQHQ/sSJR4Xoy+yOmLpymZryTHIo5R8+OaHIs4BkCR3EVoWb4lRXIXASCoVBCr7kl5QFn/Ov3ZeXwn9za818OvKGt5rs1zfLnhy0TbDIYxLcfwfNvntaCOn/FmEqhDrpRKIiwMHn0UvvkGqlSxa0N27Xp5/6BBmvQppXwoJgYmTICnnoLoaHjzTbvwbHrWLFLpcq03qZIcvxhYnN5Y6pasyw3X3cCnaz6lQsEKPP3X0xw6d4iZm2fya8ivFMpViKdveJoSeUuwLXwbO47toGDOghTKVYiCuez3IrmLuJ0wNijVgNL5S/NW57fit32w6gN2Hd9FeEQ4y/cvZ9m+ZSzbv4y1h9bSt1ZfZvSeQdHcRRlYZyB1S9alVflWVC9WPU1JamBAIC+3fznNvx912f7T+5m5eSZ5sufhYvRFAAIkgDvr3cmbnd90ODqVHK8lgem8m1Ua+Mo1LzAAO1xLh1ypDCsqCt5/H156yX7meuUVGD3argWolFKO2LzZFn5ZuRJuvNEOAa1c2emolJ95vs3zdJ7WmU+6fcKJCye4ruB1hJ4OZcORDZy6eIrRLUcD8N2273h+8fNXPD98TDjF8hTjzWVv8tXGrxIniTkL8WG3D8kWkI3l+5dTtWhVJqyZwG01b6NsgbLsPbWXL9Z/we//+53O0zqz6cgmcgTmoHGZxjzc7OH4Bd1FhPe7vu/T34uCUxdP8e22b5mxeQZLQpdgMDQq3YgzkWeIjo0mZ2BOXuv0mtNhqhR4dZ1AHXKlFCxcaId+bt8OPXrYoZ+VKjkdlVIqy7pwAV591fb6FS4M06fDwIG6hpFKVlxv4OcbPqd0/tJsGr4p2YIwI5qMoEf1HpyOPM2pi6c4dfEUpy+eplCuQgCUzV+WmsVqcjryNOER4fx74l/OR53nk+6fADB53eT4oYQ9v+4JQK5suehWtRv1Stbjrc5vkTd7XhqVaUSubHoH1SmR0ZH8GvIr0zdN5+ddPxMZE8n1Ra/npXYvMbDuQKoUqcKIX0Ywce1EhgYNpVS+Uk6HrFIgNg/LHBo3bmzWrFnjdBhKAXDggO3tmz3bJn0ffAA33+x0VBmPiKw1xjR2Oo700LZJ+Y2//rIL2IaEwNCh8NZbULSo01FlSFmpbdp8ZDP1JtRjdp/Z9KvTzyuxHI84ztHzR9lzag/9vunHax1f46UlL/HX4L+oX6q+V66p3BNrYlm+fznTN01nztY5nLx4kuJ5ijOgzgDuqHcHjcs0TjRf89DZQ7T+sjXL7lqmSaBD3GmfvNoTqFRWdOkSfPghvPCC/fnFF+Hxx6+s/KmUUj5z7Ji9K/XVV1C1qk0GO3RwOiqVQdQtWZdldy2jebnmXrtG0TxFKZqnKDWL12R44+G8ufxN2lVspwmgg7aHb2f6punM2DyDvaf3kid7HnrV6MUdde+gU+VOZA9Mfu5w6fyl+e+h/3wcrUorTQKV8qAlS+CBB2xV9W7dbO9flSpOR6WUyrKMsWWHH3kETp2y5YefeUbvSqk0a1m+pc+uNabVGKZsnMLzba+cY6i869DZQ8zeMpvpm6ez7tA6AiSAzpU782qHV+lVo5dW+MxENAlUygMOHYIxY+xnreuugx9/hFtu0Sk2SikH/fcf3H8//PEHtGgBkyZBnWSX3FXKr5TIW4JDjx0iW4B+TPWFs5FnmbtjLtM3TeevPX8Ra2JpXKYx7970Lv3r9NchnZmU/u9SKh2io+Hjj+H55+HiRXjuOXjySbvIu1JKOeLSJXjnHTsWPUcO+OQTuO8+CNDFmVXGoQlg+pV7pxwHzh64YnvZ/GXZ8/Ae/tj9B9M3TeeHHT9wIfoCFQtV5OnWTzOo3iBqFKvhQMTKl/R/mFLX6O+/7dDPTZvgppvsPMBq1ZyOSimVpf3zD9x7r22Yeve2Y9LLlnU6KqWUA3pU78Hn6z8nKiYqflu2gGwUyV2EMu+U4VjEMYrkLsKd9e/kjnp30LJ8y0QFXlTmprcFlUqjI0dgyBC44QY7xeb77+HXXzUBVEo56OxZePhhaN4cjh+HuXPhu+80AVQqC3uuzXMIiZO66Nhodh3fRfuK7fmx/48ceuwQn978Ka0qtNIEMIvRnkClrmLGDFtDYd8+KF8e2rWz8/0iIuCpp+y+vHmdjlIplaUUKGCTvuSMHAljx9pjlFJZ0pnIM/y5+0/m/zvfrukYY7cLQruK7Zjbby4FcxV0NkjlOE0ClUrBjBkwbJhN+MAmglOn2roK334L1as7G59SKotKKQEEOy5dKZWlGGPYcWwH8/+dzy///kLwvmCiY6MpkLMAHSt15NeQX4mOjSZXtlzM7DNTE0AFaBKoVIqefvpyApjQmTOaACqlHBIS4nQESik/EHEpgkV7FjH/3/nMD5lP6KlQAOqUqMOjzR+lW7VutCzfkuyB2Rnxywgmrp3I0KChWulTxdMkUKkEjIHNm2HWLNvzl5z9+30bk1IqCzPGLjz6/fd2jt+mTU5HpJRyyO6Tu23S9+98FoUu4mL0RfJkz0Onyp14stWTdK3WlQoFK1zxvOfaPMeC/xbwXNvnHIha+StNApXCLqc1a5b92rYNAgMhVy677ENSFa5sX5VSynOMgXXrbNL33Xewa5dddLRVK3j3Xbvwu1Iq04uKiSJ4b3B8b9+OYzsAqFakGvc1uo9u1brR5ro25MqW66rnKZ2/NP899J8vQlYZiCaBKss6cADmzLGJ3+rVdtsNN9h1/267DX7/PfGcQLDr/40d60y8SqlMLDYWVqywSd/338PevfZuVPv2MGoU9OoFpUvbYzUJVCrDutrafWGPhnHgzAF+DfmVX/79hT93/8m5qHPkCMxBu4rtGN5oON2qdaNaUS1HrtJPk0CVpRw/bou6zJoFS5faG+4NG8Jbb0G/frYCaJxBg+z3uOqgFSrYBDBuu1JKpUt0NCxZYhO/uXPh8GG7uHvnzvDCC9CjBxQteuXz8udPvjhM/vzej1kplS4prd1XMm9JgiYEsfHIRgDKFyjPoLqD6F6tOx0qdSBvDi1FrjxLk0CV6Z09a5d1mDXL9u5FR9vCLi+8AP37X73Iy6BBmvQppTwoMhL+/NMmfj/+CCdO2CEGXbtCnz7QvXvqyzucOeObWJVSHvfsDc/y+frPE22Ljo1m45GNtK7Qmjc6vUG3at2oXby2rtunvEqTQJUpXbxoF3CfNQt+/hkuXLC9fI88AgMGQFCQnWKjlFJed/48/PabHeb58882iStQAG65xSZ+N91kE0GlVKZzMfoiaw+uZUXYCpbvX86KsBWJegEDJIBOlTrx9W1fUyhXIecCVVmOJoEq04iOhoULbeL3/ff2c1bx4nDXXTbxa9ECAgKcjlIplemktHh7rlzQrZu9I3Xhgh3aedttNvHr0AFy5vR9rEoprwo7E8aK/ZcTvnWH1nEp9hIAVQpXoVPlTtQqVosXl7xIVEwUOQNz8tWtX2kCqHxOk0CVocyYkXiO3iuvQKVKNvH75hsID7efx3r3tolfhw6QTf+VK6W8KaXF2y9etMVe7rrLJn433KANklKZSFRMFBsOb4hP+JbvX07YmTAAcmXLRZMyTXik+SO0LN+S5uWaUzJfyfjn7j+zX9fuU47Sv0Yqw5gxI3G1zr174c47bXGXXLnsyKoBA+zUmlxXr5aslFLpd+KELexyNWFhOgRBqUzi8LnDrNi/Ij7hW3toLRej7VpSFQpWoFX5VrQs35IW5VpQv1R9cgTmSPFcunafcpomgcrvXbwIGzfCgw8mXq4BbAJYtOj/27v32CrrPI/j729bqsV2ClJ2LNCKCBS8g5QR1nG8ZoXdeIuTMTLLDKLGqBOv2TFO2N3JhsQ1Ozujq6NBV81EN7qJl9WNqzPRWXVUkMuqqAUVqoJ21AKCWhHafveP33PKaek5PaXn8pxzPq/kSZ/bOc/v10O/nO/z/C7Q3q6B8UQkx3btgpdeCu3O//hHeP31EITSUQIoEmuppmyYUDuBpy5+KjTt3PoKr255lfYv2gGorqxmduNsrpxzJfOa5jFv0jwmfmfisK6rufuk0JQESqx0d0NbG7z2Wpi7b/VqePPNsD+V7duVAIpIDnR1wcsvh4Tv+edhzRro6QnTOMyfD7/8ZZjH7/vfL3RJReQADTZlg2F81vUZJ644EYDG2kbmN83nqtarmNc0j9mNs4ecoF0k7pQESsG4w+bNIdFLJH3r1u172ldfD3PmwI03wty54Ungx/vfrKO5Ob/lFpES9e23sGpVSPiefx5WroS9e0M/vrlz4aabQkfjefOgpqbQpRWRYdrbs5f2L9rZ2LmRjds2sqFzA+s/W98vAUz4yfE/4YwjzmB+03ya65s1XYOUHCWBkjcdHf0TvjVrwlM8CH34Zs2CSy8N37VaW2Hq1P4tqbq6+vcJhDCq+vLl+a2HiJSI7u4QiBLNO19+OYziaQazZ8O114ak7+STobY29fto8naRWOns6uyX6G3ctpGNnRvZtGMT3b37mhaNHz2eloYWZjTM4L1t79HjPYyqGMVlsy/jzr++s4A1EMk9JYEyIgNH61y+PEyu/sUX4btVcrPOxFO8yko45pgwgmdra0j6jj4aRo1Kf63EpO2DXU9EpE+qKRtqa8NALomk78UX4auvwrFjjw13mU47DU45BcaOzfx6mrxdJGtS9dGbWDeRrddv7dve07OHTds37UvyokRv47aNbP9me9951ZXVTD10KkeNP4rzZ5zPjIYZtDS00DKuhbE14e+848sOptw+hZ7uHqoqqjRYi5QFJYFywFKN1nnjjfDnP+87b9o0+MEPQsLX2hqe+B3ovMiLFinpE5EhpJqy4auv4MTQx4eWFvjxj8OTvlNPDZOKikjBDdZHr6qiiqmHTuWGZ2/oS/jad7TT4z195xxWexgzGmbww6N+SMu4lr5Eb/KYyVRWVKa9ZmNdI0tOWKIpG6SsKAmUtPbsCU/wtmwJI51v2bJveeaZcDxZTw/s3Bme0LW2hj59w7mhLiKSsV27QjD66KOwJNbTefDB8LRvwoT8lFFEhtTd2037jnbaOtsYe/DYfk02E8df+PAFVn28iunjpjPrsFlcdPRFfYne9HHTqT+4fkRl0JQNUm5ymgSa2dnAbUAlcK+73zLguEXHFwJdwE/dfZ2ZNQG/Aw4DeoEV7n5bLstaKlI1zxxMdzd88sngCV5i+9NP9x8BfcwYaGraPwFM2L0bbr45q9USySrFpjxL1Tyzri51U8rkO1DJCV7y+s6d/V9TWQkThximXU0JRArmm73f8O62d2nrbKPt87bws7ONd7e92+/J3yGjDqFrbxeOU2mVLJi6gDsW3kFTfRMVlptpVzRlg5SbnCWBZlYJ3AmcBWwFVpvZk+7+TtJpC4Bp0fI94K7oZzdwQ/Slqw5Ya2Z/GPDaAzKcJCkb8nm9wZpnLl0a+uVNnrx/ktfRAb29/d+jri4keE1NcPzxMGnSvu2mprCdGB9h8uRwjYE0WqfEWSxj04EkScV0vVTNM7/8Eh5/fPAkr6Nj/ztQ48aFADNlSmhj3twclqam8LOxMSSCGsVPJGsy7aOXbMc3O2jrbGND54Z+yV77jnac8HddYRVMGTuFmQ0zWTh1ITPHz2Rmw0xmNMyga28XU26fwu7u3VRXVnPPOfeoiaZIluXySeBc4H133wxgZg8D5wLJX5bOBX7n7g6sNLMxZtbo7h1AB4C7f2lmbcDEAa8dtsGSpMsvD+u5SMyGul5PTzjW1QVff71vPdW+obZXrw6jmSf79lu4/fawPnr0vmTurLP6J3eJBK9+GK0pli/XaJ1SlGIXm9ImSdnS2xsCwu7d6a/3zDMhkOzZE5bhrg92LJ0LLgg/a2r2JXNnn71vPZHkNTUdeGdiETlgg/XRq66s5pzp5/Dxro/3e6rX9nkbn379ad+5B1UeREtDC60TWll83OK+ZG/auGkp59qrP7heffREciyXSeBEYEvS9lbCnfShzplI9CULwMwmA7OAVSMt0C9+0T9hgbB9zTXhe9HevaGJZHf38NcHO/bii+E718DrLV4Ml1ySujllOtXVcMgh4btQYklsp/uutW1b6JuXzRvkGq1TilTsYlNav/51CFAjXTINOAsWZF62UaNCUEosydvJ6+msXRuCx7hx2QtQmrJBJGuWnbKM+/7vvn779vbs5cH1D3LX2rv69tUfVM/M8TNZOG0hMxtm9iV7mQzMkuq66qMnkju5TAIH+9/ch3OOmdUCjwLXuvugbZTM7HLgcoDmIdohphovYNu2MD9dOlVV4TtNVVXm6wMTwITeXrjuuv2TuKG2a2rC+6aSqnnm4YfDoYemr9+B0midUoRiF5vSuv76fesHH5x6qakJiVS6cxLLNdekvt6rr6ZP6BLrVVWZJ23pzps9O7P3GA5N2SBFLG59lhvrGjlvxnk88vYjffsm1E3gvBnn9Uv2Dqs9LKsTqquPnkhu5TIJ3Ao0JW1PAj7J9BwzG0X4kvWQuz+W6iLuvgJYATBnzpyBX+T6aW4ePEmaMAFWrkyd0FVUHNgN6nRJ2S237L9/pNQ8UyQjsYtNaX3xRUjcqquz96QsXRJ40knZuYaIDFss+ywDt555K49veJw9PXuoqaphzeVr1ERTpMjlZoilYDUwzcyOMLNq4CLgyQHnPAkstuAkYKe7d0R3uf4daHP3f81WgZYv379LyejRcOutobtJYyM0NITRL2tr4aCDRjbGQKrr5SopW7QIVqwISaZZ+LlihZ7UiQwQu9iUVn19CEbFPNhJqmaYap4pMlBfn2V33wMk+iwn6+uz7O4rgb4+y+6+DkKfZSDRZ3nEmsc0s3TWUiqsQn30REpEzpJAd+8GrgaeJQSi/3T3t83sCjO7IjrtaWAz8D5wD3BltP8vgb8FTjez16Nl4UjLlO8kqRBJ2aJF8MEHocnpBx8oARQZKI6xKe9JUr6vt2tXGOlz4KJmmyIDpeqPPKxzctFnedkpy5g8ZrL66ImUiJzOE+juTxO+TCXvuztp3YGrBnndnxi8T86I5bsPm/rMicRP7GJTvpMhJV8icZXzPssH2l9ZffRESksum4OKiIiISOZy3mfZ3Ve4+xx3nzN+/PisFVxEiouSQBEREZF4KK4+yyJStHLaHFREREREMuPu3WaW6LNcCdyX6LMcHb+b0JR9IaHPchewJHp5os/yejN7Pdp3c9T8XUSkHyWBIiIiIjERuz7LIlKS1BxURERERESkjCgJFBERERERKSNKAkVERERERMqIkkAREREREZEyYqF/cWkws8+BDwtdjhQagM5CFyKHVL/iFuf6He7uRT2ZlWJTQal+xS3O9VNsyr04f/7ZUMr1K+W6QfzrN2R8KqkkMM7MbI27zyl0OXJF9StupV4/Sa3UP3vVr7iVev0kvVL//Eu5fqVcNyiN+qk5qIiIiIiISBlREigiIiIiIlJGlATmz4pCFyDHVL/iVur1k9RK/bNX/YpbqddP0iv1z7+U61fKdYMSqJ/6BIqIiIiIiJQRPQkUEREREREpI0oCs8jMzjazjWb2vpndNMjxRWb2ZrS8YmbHF6KcB2qo+iWd12pmPWZ2YT7LN1KZ1M/MTjWz183sbTN7Id9lHIkM/n3Wm9lTZvZGVL8lhSin5IbiU995ik8xpPhUvhSb+s5TbIqhko5N7q4lCwtQCWwCpgDVwBvAUQPOmQ+MjdYXAKsKXe5s1i/pvOeBp4ELC13uLH9+Y4B3gOZo+y8KXe4s1+9m4J+j9fHAdqC60GXXkrfPX/Eppovik+JTqS6KTf3OU2yK2VLqsUlPArNnLvC+u2929z3Aw8C5ySe4+yvuviPaXAlMynMZR2LI+kV+BjwKfJbPwmVBJvW7GHjM3T8CcPdiqmMm9XOgzswMqCUEsu78FlNyRPEpUHyKJ8Wn8qXYFCg2xVNJxyYlgdkzEdiStL012pfKUuB/clqi7BqyfmY2ETgfuDuP5cqWTD6/6cBYM/tfM1trZovzVrqRy6R+dwAzgU+A9cA17t6bn+JJjik+KT7FmeJT+VJsUmyKs5KOTVWFLkAJsUH2DTr0qpmdRghkJ+e0RNmVSf1+A/zc3XvCDZGikkn9qoATgTOAGuBVM1vp7u/munBZkEn9/gp4HTgdOBL4g5m95O67clw2yT3FJ8WnOFN8Kl+KTYpNcVbSsUlJYPZsBZqSticR7gr0Y2bHAfcCC9x9W57Klg2Z1G8O8HAUxBqAhWbW7e5P5KWEI5NJ/bYCne7+NfC1mb0IHA8UQyDLpH5LgFs8NGx/38zagRnAa/kpouSQ4pPiU5wpPpUvxSbFpjgr7dhU6E6JpbIQEurNwBHs6zx69IBzmoH3gfmFLm8u6jfg/Acors7NmXx+M4HnonNHA28BxxS67Fms313AP0br3wU+BhoKXXYtefv8FZ9iuig+KT6V6qLYtN/5ik0xWko9NulJYJa4e7eZXQ08SxhN6D53f9vMroiO3w38PTAO+G10x6fb3ecUqszDkWH9ilYm9XP3NjN7BngT6AXudfe3ClfqzGX4+f0T8ICZrSc0gfi5u3cWrNCSNYpPik9xpvhUvhSbFJvirNRjk0WZq4iIiIiIiJQBjQ4qIiIiIiJSRpQEioiIiIiIlBElgSIiIiIiImVESaCIiIiIiEgZURIoIiIiIiJSRpQESlaZ2SQz+y8ze8/MNpnZbWZWXehyiUh5U2wSkThSbJJCURIoWWNhAp/HgCfcfRowHagFlhe0YCJS1hSbRCSOFJukkJQESjadDux29/sB3L0HuA64xMwOMbN/MbP1Zvammf0MwMxazewVM3vDzF4zszoz+6mZ3ZF4UzP7bzM7NVr/ysx+ZWbrzOw5Mxsf7b/MzFZH7/OomY2O9j9gZrdH19hsZhcmve/fReV5w8xuMbMjzWxd0vFpZrY25781Eck1xSYRiSPFJikYJYGSTUcD/f743X0X8BFwKXAEMMvdjwMeipo7PAJc4+7HA2cC3wxxjUOAde4+G3gB+Ido/2Pu3hq9TxuwNOk1jcDJwN8AtwCY2QLgPOB70WtudfdNwE4zOyF63RLggeH8AkQklhSbRCSOFJukYJQESjYZ4Cn2nwLc7e7dAO6+HWgBOtx9dbRvV+J4Gr2EAAjwICFIARxjZi+Z2XpgESGwJjzh7r3u/g7w3WjfmcD97t6VVB6Ae4ElZlYJ/Aj4jwzqLSLxptgkInGk2CQFoyRQsultYE7yDjP7DtDE4IEuVfDrpv+/zYPTXDPx+geAq939WOCXA17z7YBrprv2o8ACwt2vte6+Lc21RaQ4KDaJSBwpNknBKAmUbHoOGG1miwGiu0K/IgSa3wNXmFlVdOxQYAMwwcxao3110fEPgBPMrMLMmoC5SdeoABLt0y8G/hSt1wEdZjaKcEdrKL8ntLlPtIE/FMDddwPPAncB9w/3FyAisaTYJCJxpNgkBVNV6AJI6XB3N7Pzgd+a2TJC4HkauBnoIYx69aaZ7QXucfc7zOxHwL+ZWQ2hXfuZwMtAO7AeeAtYl3SZr4Gjo47HOwlNDwCWAauAD6PX1Q1R1meiNuxrzGxPUjkBHgIuIAQ8ESlyik0iEkeKTVJI5j7Yk12ReDKzr9y9NsfXuBGod/dlubyOiJQOxSYRiSPFJklFTwJFkpjZ48CRhGGbRURiQbFJROJIsal46UmgiIiIiIhIGdHAMCIiIiIiImVESaCIiIiIiEgZURIoIiIiIiJSRpQEioiIiIiIlBElgSIiIiIiImVESaCIiIiIiEgZ+X92PUfXVMGA8gAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<Figure size 720x360 with 2 Axes>"
+       "<Figure size 1080x360 with 3 Axes>"
       ]
      },
      "metadata": {
@@ -203,49 +271,80 @@
    ],
    "source": [
     "for bm in unique_bms:\n",
-    "    flag = \"nvbench_static_multimap_count\" == bm\n",
+    "    flag = \"staic_multimap_count_occupancy\" == bm\n",
     "    \n",
     "    if flag:\n",
     "        tmpdf = perfdf[perfdf[\"Benchmark\"] == bm]\n",
     "        \n",
-    "        randomdf = tmpdf[tmpdf[\"Distribution\"] == \"RANDOM\"].reset_index(drop=True)\n",
-    "        unique_labels = randomdf[\"Label\"].unique()\n",
-    "        plot_perf(bm + \"_RANDOM\", randomdf, \"NumReps\", unique_labels, flag)\n",
-    "        \n",
-    "        cycledf = tmpdf[tmpdf[\"Distribution\"] == \"CYCLE\"].reset_index(drop=True)\n",
-    "        unique_labels = cycledf[\"Label\"].unique()\n",
-    "        plot_perf(bm + \"_CYCLE\", cycledf, \"NumReps\", unique_labels, flag)"
+    "        unique_labels = tmpdf[\"Label\"].unique()\n",
+    "        plot_dual_perf(bm, tmpdf, \"Occupancy\", unique_labels)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### find_all"
+    "### `count` performance by varying matching rates\n",
+    "<ul>\n",
+    "<li>100'000'000 key/value pairs inserted</li>\n",
+    "<li>100'000'000 probing keys</li>\n",
+    "<li>Fixed matching rate: 0.5</li>\n",
+    "</ul>"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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48MP0hw6dcy4R1a+/9/v//Q8OOcRLyziX12U4LCgiJ4vIB8BE4AygEtAQuB2YJyKDRKR0bMLM3MknWw2sL7+Erl1h5854R+Scc9mzaxdMngw9e8Ijj8Q7GudcbmTWc9UOuERV/1PoQEQKAmcBpwDjoxRbtnTpAk8+CZdfbomiL7xgeVnOOZcXFCxoswl794brr7c80ocfttUonHN5S4aNK1W9IZPHdgFvRyOg3LjsMvjjD7jrLqhY0brYnXMuryhSBMaNg0qVYMgQO5+9/LJfKDqX12TYuBKR6zJ7oao+Gvlwcu+OO+yE9NBDcNBBMHBgvCNyzrnwUlKsYXXIIVCmjDesnMuLMhsWLBV8rQccCUwI7rcHPotmULkhYstJ/PGHda1XqGDd7M45l1eIwA1pxg6mTbPE90qV4heTcy68zIYFBwGIyBSgmapuCu7fDbwek+hyqEAB60r/80+48EIoV87qyDjnXF6zZQt07w5Fi1pV93r14h2Rcy4rYVIlqwE70tzfAdSISjQRVKQIvPWWLS1x7rnwxRfxjsg557KveHGYOBG2brWaWF99Fe+InHNZCdO4ehn4RkTuFpG7gK+Bl6IbVmSUKgWTJtkq9GedBQsWxDsi55zLvubN7QLxgAOgdWt47714R+Scy0yWjStVHQxcCPwFbAD6qur9UY4rYipWtK70IkWsivvK/xSWcM65xFe7tjWwGjWykg3OucQVdvmbucCa1OeLSLX06l8lqpo1rYF1wgnWwPr8cyhfPt5ROedc9lSsCJ98YvlXAGvX2rnMZxQ6l1iy7LkSkQHA78CHwHtYxfY81yndpAm8+66tQH/mmfDPP/GOyDnnsq9ECZu0s349tGhhhZN90WfnEkuYnKurgXqq2khVm6jqYaraJNqBRcPxx8Orr8LMmVbRfceOrF/jnHOJ6MAD4fzz4Zln7Hy2dWu8I3LOpQrTuFoF/B3tQGKlQwcYMcKGCfv2hT0Jsfy0c85ljwg88AAMG2aL159yipWfcc7FX5icq6XAJyIyEdieujFRK7SHcdFFVmT01lutyOiQIZ6z4JzLm/r3h4MPhh494MYb4dln4x2Rcy5M42plcCsc3JLCzTdbA+uxx2yZnFtuiXdEzjmXM126QOXK0KBBvCNxzkGIxlWaSu2l7K4mRSq4CDzyiM22ufVWm4Vz0UXxjso553LmmGPs67Zt0LUrXHstnHRSXENyLt8KM1uwsYjMAeYDC0Rklog0in5o0ZeSAs8/b+UZ+vWzvAXnnMvL/voLliyx89rrr4OqTeKZONF6651z0RcmoX0EcJ2qVlfV6sBAYGR0w4qdwoXhjTdsSnO3bvBZwi5J7ZxzWatUCaZPhyOPtB6sGjVsbcLHH4e6deHuu63B5ZyLnjCNqxKqOi31jqp+ApSIWkRxULKkXdVVr26zCb//Pt4ROedczh14IHz4oS1av3Kl9cx/8AH8+KP1Zr35ZrwjdC65hWlcLRWRO0SkRnC7HVgW7cBirXx5mDLFGlqnnQbLku4dOufyk99+s9SH666ztVXBJu/cfju88EJ8Y3Mu2YVpXF0IVADeDG7lgb7RDCojO3da2YTmzW19rZtuimxdl2rVrIG1fTuceqrnJzjn8q6//7aeq0cesVmEqpZXetBBsGFDvKNzLrmFmS34F3BVDGLJ0gUXwO+/w6OPQqlS8OSTNhvm66+hWLHIHKNhQxsibNMGzjgDpk2D0qUjs2/nnIuVRo2sEfXtt5Z/9e670LGjbT/vvHhH51xyCzNb8EMROSDN/bIi8kFUo0rHvHm2YOl778GJJ0KzZjByJBxyCLzySmSP1aqV5SV89x2cc471ZDnnXF5SqJAlsbdvD4MHw+bNtoj9ggW+MoVz0RZmWLC8qm5IvRP0ZFWMWkQZ+PZbaNvWVoPfsgX69IGff7ZFmL/9NvLHO/NMK9MwdSqccIIlu6ek2MybMWMifzznnIu0Ll0skX3NGhg/3qq5d+4MgwbZxalzLjrCVGjfIyLVVHUlgIhUB2I+kbdqVZg/3/IGVq2yobupU+G446BJlJaR7t3bTkxjx+7dtmKFzbwBW27COecSWdOmMHz43vv9+9vwYP/+lvpQpUrcQnMuaYXpuboNmC4iL4vIy8BnQMwXi2ndGnbsgHvvtV6kDz+0fILXXoOTT47ecWfM+O+2LVvgttuid0znnIuW1Np+U6Z4w8q5aMmycaWqk4FmwKvAa0BzVY15zlWBAvD++/DVVzbb5dRTLd+qRAkrlBet0gkrV2Zvu3POJbrixS13Fazm1RdfxDce55JNmIR2AU4Hmqnqu0BxEWkZ4nX1RGRumttGEbkmN8FWqQKTJlmu1dy5sGgRfPqpzRT8J0orHlarlr3tzjmXV+zcab3wZ57pxZOdi6Qww4JPAq2A7sH9TcATWb1IVX9U1cNV9XCgObAFeCuHce6jfHnrtRKBI46w2S+HHWb5WJGu3zJ4sF3lpZWSAvfcE9njOOdcrBUqZCMCJUpY8eSff453RM4lhzCNq6NU9UpgG/w7W7BwNo/TBvhZVVdk83WhFChgXwcPthINkRyy69EDRoywPC8RK8q3Zw8sXhy5YzjnXLzUqGH5Vzt2wCmnwOrV8Y7IubwvTONqp4gUIJghKCIVgOxWSekGjMvma7LttNOsYvvJJ9uMwkjp0QOWL7dG1bp1cNFF1pCbPDlyx3DOxZ6IPC8if4jI/DTbDgzq+y0JvpaNZ4yx0LCh9WCtXQvjon6mdi75hWlcPY4N51UUkcHAdOD+sAcQkcJAB+D1DB7vJyIzRWTm2rVrw+42XUceaVdg69ZZ5fZffsnV7jI0bJgNQ/bsGb1jOOdiYhSWU5rWzcDHqnoo8HFwP+m1bGl5V9ddF+9InMv7wswWHAPcCDwArAE6qmq6DaUMnAHMVtXfM9j/CFVtoaotKlSokI3dpq9lS2tgrV1rRUd37Mj1Lv+jWDGr4L59O3TrZkmhzrm8R1U/A/ZfofRs4MXg+xeBjrGMKZ5q1rT0h/nzoVcvX53CuZwKM1uwNrBMVZ8A5gOnpF0OJ4TuxGBIMK2jjrIG1qBBVtMlGurVswrHM2Z4zSvnksxBqroGIPia4YoUkex5TyRz58Lo0XD++bBrV7yjcS7vCTMsOB7YLSJ1gGeBmsDYzF9iRKQ4cArwZo4jzKGjj7b6V2ANrWgkaXbrBpddBg89ZGseOufyl0j3vCeKnj1hyBCrgXXppTYT2zkXXpjG1R5V3QV0Aoaq6rVApTA7V9UtqlpOVf/OTZC5sXEjdO9uFd7XrIn8/ocMsXIQvXvb0jjOuTzvdxGpBBB8/SPO8cTFNdfAHXfYGqs33ugNLOeyI+xswe5AbyC1f6ZQ9EKKrNKl4Z13LPG8dWv47bfI7r9oUcu/2r0bzjsvOjlezrmYmgD0Cb7vA7wTx1jiatAgW4NwzhzPLXUuO8I0rvpiRUQHq+oyEakJjI5uWJF13HE2zXjVKmtg/Z5uan3O1a5tV3fffAM33RTZfTvnokdExgFfAvVE5BcRuQh4EMstXYKlNTwYzxjjSQSGDoWJEy1/dU92i/A4l0+FmS24UFWvUtVxwf1lqprnTjbHH28niBUrrCEUaZ07w1VXwWOPWZ6Ccy7xqWp3Va2kqoVUtYqqPqeq61W1jaoeGnzdfzZhvpKSAkWKwF9/2Xn01VfjHZFzia9gvAOIpRNPtO7tQw+Nzv4fegi+/BIuvBAOPxxq1YrOcZxzLtaKFoWCBS3ZvUwZOH3/6mDOuX+FGRZMKnXrWlf3kiXQrp3Vw4qUwoXhtdds/+eeC9u2RW7fzjkXT8WKwYQJVkC5UycrQ+OcS1+mjSsRKSAiD8UqmFj69VeYNs0Kja5bF7n91qgBL74Is2fDwIGR269zzsVbmTK27FfVqnDmmfDdd/GOyLnElGnjSlV3A81FRGIUT8ycdBK8+64twNy2LaxfH7l9d+gA118PTz7p+QnOueRSsSJ8+KGlPpQsGe9onEtMYYYF5wDviEgvEemUeot2YLHQtq11c//wg33/ZwTTVu+/H445Bi6+2BpwzjmXLKpVg08+sZnSqpbs7pzbK0zj6kBgPdAaaB/czopmULF0yinWwCpfHgpFsHpXoULwyis2y+bcc2Hr1sjt2znnEsXVV1u5m0j2/juX14UpxdA3nduFsQguVk491ZbIKVUKNm+O3FVY1arw8su20vxVV0Vmn845l0g6dYKff7YJQps2xTsa5xJDmIWb64rIxyIyP7jfRERuj35osSVi3dudOllv1oYNkdnvGWfALbfAs8/aQqjOOZdMTjrJZknPmgUdO/osaecg3LDgSOAWYCeAqn4PdItmUPEiYl3c8+ZZb1akGlj33AMnnGALoC5cGJl9OudcoujQAV54AaZOhQsusOXAZsyAjz6CLVviHZ1zsRemiGhxVf1mvwmDu6IUT9y1awfjx1sP1mmn2XBhmTK522fBgjBunM2uOfdcWyanRImIhOuccwmhVy8bFhSBevWgeHFb2/WHH2D4cOiWlJfkzqUvTONqnYjUBhRARLoAa6IaVZyddZY1sDp3hh494KijLOm9UCHo3h0uv9waTNlRuTKMHWs9YldcAaNG2UnIOeeSxcUX2wzChx+2lTCOOMJGAtq2tYvL+vXjHaFzsRFmWPBK4Bmgvoj8ClwDXBbNoBJB+/aWR/DLLzB/Pjz6KNx9t60b2Lt3zvbZti3ceSe89JJ1oTvnXDL54ANb9qtyZWjRAh54AJo0sSXBXnop3tE5FzthZgsuVdW2QAWgvqoep6oroh9a/O3YYTMIx42zGX+tWsH778P06TB3bs72eccd0KYNXHmlXdE551yy+Osva1gde6z1+t92m61YUamS18Jy+UuY2YLlRORx4HPgExEZKiLloh9a/H35JZx9tvVcXXONfV+ggC1Y+uWXOdtngQIwZgwccIDlX/nUZedcsjj5ZMtTXb8ennvOeusvvhieespmYTuXX4QZFnwFWAt0BroE3+eLRV0OOQQWLbJu7eeft7UIr77ath1ySM73e9BBVmB0yRKbQagauZidcy5eqlaF/v2t5+q55+C886yQ8k8/2TChc/lFmLTsA1X13jT37xORjlGKJ6H06mUrwI8fDz172tDgww9DuXJWvyo3TjwR7r3Xus1PPNEaWc45l9cNGmRLf40ebWUYBg2CokVtyRzn8oswjatpItINeC243wWYGL2QEsdBB8E770C/flZhfft2KFsW/vnHur0PPjh3+7/5Zvj8c+sNa9nSZtY451xed9ppdtvf/PnWu5Xb8jbOJTrRLMakRGQTUALYHWwqAGwOvldVLR2pYFq0aKEzZ86M1O4iRhWWLYPCha1uy/ff21pakbBunU1RLlrUKhz7ScflJyIyS1WTYsAoUc9fiWLjRqhZE5o1g4kT7XzqXF6X0TkszGzBUqqaoqqFgltKsK1UJBtWiUzEphdXqWKNq9SG1fvvw99/527f5cvDq6/C8uVw0UWef+WcS06lS8Mjj1jV9n79/FznkluYhHaXjl9+sXW0unWzpR5y49hjrR7M+PFWydg555LRBRdYDtaLL1rdQOeSlTeucqhKFXj8cZg8GW66Kff7GzjQCpcOHAjffpv7/TnnXCK64w7o29fWXJ2YL7J3XX6UzUVcXFqXXmoJmo88Ao0a2Qkjp1JSbEmcZs2s/tWcOZY875xzyUQEnnnGZmKfemq8o3EuOsIUEa0tIkWC708SkatE5ICoR5ZHDBlihfIuvdRqueTGgQda/tXq1bbP6tWt0VWjhhUedc65ZFCoEFx7rX394w9YsCDeETkXWWGGBccDu0WkDvAcUBMYG9Wo8pCCBW0NwpEjoU6d3O/vqKOga1eYPRtWrrSkzxUrLAHUG1jOuWTTtatVb1+5Mt6ROBc5YRpXe1R1F3AO8JiqXgtUim5YeUvZstCnj32/cKHVwcqNzz7777YtW6zgqHPOJZPHH4fNm6FdO9iwId7ROBcZYRpXO0WkO9AHeC/YVih6IeVdf/5plYl794Y9e3K+n1Wr0t/uV3bOuWRz2GHw1luweDF06gQ7dsQ7IudyL0zjqi/QChisqstEpCYwOrph5U0HHmjTi996K3fTjDNaJsKXj3DOJaPWrW0twmnTbDahc3ldlrMFVXUhcFWa+8uAB6MZVF529dUwb56tG9iokeUTZNfgwZZjtWXL3m2FC9t255xLRr16WY9/u3bxjsS53AszW/BYEflQRBaLyFIRWSYiS2MRXF4kAk8+aVXcL7jASjVkV48eMGKEzRYEW1U+JQVatYpoqM45l1D69IEKFWxo8JNP4h2NczkXZljwOeBR4DjgSKBF8NVloEgRq7Z+3XVQt27O9tGjhy2Jowo//mj77NkTdu2KaKjOOZdwBg+2cjTvvx/vSJzLmTCNq79V9X1V/UNV16feoh5ZHlexop0gChe2xZm3bs35vqpXh6efhi+/tOFG55xLZtdfD02aWEHl2bPjHY1z2RemcTVNRB4SkVYi0iz1FvXIksSWLXD00XDxxblbqLRbN5uFeN99MH165OJzzrlEU6oUvPcelCsHZ55ptf6cy0vCNK6OwoYC7wceCW4PRzOoZFK8OFx4IYwdCw/mchrA8OFWrb1nT68H45xLbpUrw6RJ1uvfrVvuLk6di7UwswVPjkUgyeyWWyyx/bbboGFDOPvsnO2nVClrpB17LFxxhVVsF4lsrM45lygaNYIJE6BMGT/XubwlzGzBMiLyqIjMDG6PiEiZWASXLESshkvz5paoPm9ezvd11FFWQ2vcOBjt1cacc0nuhBOgaVP7ftKk3BVodi5WwgwLPg9sAs4LbhuBF6IZVDIqVgzeecfyBw4+OHf7uuUWOP54uPJKWOpFMZxz+cBHH9n58/bb4x2Jc1kL07iqrap3qerS4DYIqBXtwJJR5crw6qtWx2Xnzpwv81CggPVapaRYT9jOnZGN0znnEk2bNlZc+YEH4Jln4h2Nc5kL07jaKiLHpd4RkWOBUIUFROQAEXlDRH4QkUUi4mUwscbQ6adbz1NOkzSrVbMTzFdfeXkG50SkqIh0EZGhIvK6iLwkIjeKSKN4x+YiQwSeeMJ6r664wmYTOpeowjSuLgeeEJHlIrICGA5cFnL/Q4HJqlofaAosylmYyaVQIau2/uyzMGxYzvfTtatVNB48GD7/PHLxOZeXiMjdwAxsDdSvgWeA14BdwIPBChNN4hehi5SCBeGVV+CII+D8862GoHOJKMxswblAUxEpHdzfGGbHwfNPAC4IXrcD8PXOA/fcAwsWwLXXQv36cOqpOdvPsGFW96pnT/juOzjggIiG6Vxe8K2q3p3BY4+KSEXAlz1PEiVLWq/VN99A+fLxjsa59GXYcyUiPYOv14nIdcDFwMVp7melFrAWeEFE5ojIsyJSIiJRJ4GUFHj5ZWjcGM47z5a4yYlSpawkw6+/wuWXey0Yl/+o6sT9t4lISpoLwj9UdWbsI3PRcvDB0KGDff/JJ/Dnn3ENx7n/yGxYMLUhVCqdW8kQ+y4INAOeUtUjgM3Azfs/SUT6pZZ5WLt2bXZiz/NKlrQaLnXr5jy5Haw8w6BB1l3+8suRi8+5vERExopI6eAibiHwo4jcEO+4XPSsWwdnnQXnnAMrV8KLL1otwL//jndkLr8TzaKrQ0SOVdUZWW1L53UHA1+pao3g/vHAzap6ZkavadGihc6cmf8uMFX3Fsjbs8d6tbJr925o3drW4Zo7F2rXjmiIzkWFiMxS1RYR2tdcVT1cRHoAzYGbgFmqGpN8q/x6/oq3V16B7t0tl7VjR9i+3XJQX3wR2rePd3Qu2WV0Dgvzbzy9lOss07BV9TdglYjUCza1wa4m3X5ErIHVv7/lYOVEgQLWa1WwoJdncPlWIREpBHQE3lHVnYAPlCe5pk2hRAk759WsafUEJ0+2yT5//RXv6Fx+lWFCe1A24Rigwn45VqWBAiH3PwAYIyKFgaVA35wGmuxEoHBhGDLElnzo1y/7+0gtz9C1qyXMe4kGl888AywHvgM+E5HqWNFjl8ReeQUuuwy2bIH//Q/atoVTToGTT7a0iz594h2hy48y67kqjOVWFWTffKuNQJcwO1fVuaraQlWbqGpHVfXriEz87397618dfbQt+ly9Otx/P+zaFW4f550HF1xgr/nss6iG61xCEJFWIiKq+riqHqKq7dTyHVYCOV4bVUSuFZEFIjJfRMaJSNHIRe0iZft2y199/HHLt2rb1raXLAnbtsU3Npd/hcm5qq6qK2IRjOcsWM5Uy5ZQtCjMmGE9WtdeCw0awPDh4faxaRM0a2Ynne++g7JloxuzczkViZwrEXkaaAksBiZjtfV+y+U+DwGmAw1VdauIvAZMUtVRGb3Gz1/xMWMG9O4Nc+ZA6dJ7t7VrB/PnQ9Wq8Y3PJbfc5FxtEZGHRGSSiExNvUUhRgeMGgWXXmoNojVroEkTGD/eyi38/nu4fZQqZVdwa9ZYd7mXZ3DJTFUvU9VmwN1AWWCUiHwpIveLyAkiEjaNYX8FgWIiUhAoDqyOTMQuko45xhpSzZpZj/3tt8NJJ1mCe/Hi8Y7O5VdhGldjgB+AmsAgLKfh2yjGlK8tWGAzXn76yYYIwQqDNmgAS5aE38+RR1re1Wuv2awZ55Kdqv6gqkNU9XSgNdbzdC5WtT27+/oVeBgbWlwD/K2qU/Z/Xn4uJZMoRGxI8LnnrDTDzp0wcqT14HfunLsyN87lVJYV2oFyqvqciFytqp8Cn4rIp9EOLL+qWxe+/NISMlVhxAgrkPfDD9kvr3DjjfDBBzYL8bjjoE6d6MTsXCIRkeJAQ6xy+6Qc7qMscDZ2UbkBeF1Eeqrq6LTPU9URwAiwYcHcxO1yTgROPNFuqQoVspUrrrzSzqOp5W6ci4UwPVepk/rXiMiZInIEUCWKMeVrV11lS9qMGWNXYBMmwK23WqHQSpWyt6/U8gyFC9s6XF6ewSUjEekQrH06W0TaAQuwNVDniUhO54q1BZap6tqgpMOb2Oxpl0f06AG33WZruHpxZRdrYRpX94lIGWAgcD3wLJDDakwuKw0aWINqxAgoVgy++grKlYNZs+CXX7K/v6pVbV/ffgt33x3xcJ1LBPcCpwKXYgs2t1HVo4Em2DkrJ1YCR4tIcRERrE6fLzyfx9xzj5W36RJqfrtzkZNp4ypIBD1UVf9W1fmqerKqNlfVCTGKL19q1Qo+/dRyBdats4WZt26Fc8/NWf5Aly5w4YXwwAO2X+eSzB5VXayq32K9TUvB1hQEQhYx2Zeqfg28AcwG5mHnyhERitfFSEoKXHONJbZv3Ji9vFXnciPTxpWq7gY6xCgWt58CBSxPoH59m0X49deWQ5UTQ4dazlXPnl612CWdFBEpKyLlgD3B9weKyIGE651Pl6repar1VbWxqvZS1e2RC9nF2nnnWS7rH3/EOxKXH4Q58XwhIsNF5HgRaZZ6i3pkbh+dO1vNlpyulVWypOVx/fablXrw8gwuiZQBZgEzsRUkZgf3Z2GFj53jvvusnM0551gNQOeiKcxswdQkznvSbFNsqrOLoYYN7evnn0OZMlYDKzuOPNKWxLnlFjjjDOjrixG5JJC6OLxzmWnRAl56yXqw+vWz0QCfQeiiJcvGlarmePkIF3k7dkCvXrZA88yZVgMrO264wYYWBwyw8gyHHhqVMJ2LmSA3tJiq/hPcPxpbvgtgjqpuiltwLqGce64lud95JxxxhOVjORcNWQ4LishBIvKciLwf3G8oIhdFPzSXnsKFrfr6ihW25MOePdl7fdryDD16eHkGlxT+D7gizf1xwA3AHcDtcYnIJazbb7fGVefO8Y7EJbMwOVejgA+AysH9xcA1UYrHhXDMMfDoo/Duu/Dgg9l/fZUqVsH422/hrrsiH59zMdYGeDTN/Q2q2h4rz3BsfEJyiUoEBg2yMjW7d8OqVfGOyCWjMI2r8qr6GrAHQFV3AbujGpXLUv/+Vhj09ttteDC7OneGiy6yxtknn0Q8POdiKSU4L6W6CUBtVfqS8QnJ5QUDBtjF6po18Y7EJZswjavNwRRnhX/zGf6OalQuSyJWHPSJJ2zB0px47DErz9Crly2x41weVVhE/p0VmLoGYFD8uGjconIJr18/O/edfbbVEnQuUsI0rq4DJgC1RWQG8BIwIKpRuVBKlIDLL7dCeb/8Atu2Ze/1JUta/paXZ3B53EjgVRGplrpBRKpjuVcj4xaVS3iHH24lambOtNnTfg50kZJl40pVZwMnYiUZLgUaqer30Q7Mhffnn9Z7dfXV2X9tixZW/+WNN6yBVaOGNdZq1LCTjnOJTlUfxS4Ap4vIehFZB3wGvKuqj8Q3OpfoOna01StefdXOhc5FQpalGESkKDYT5zhsaPBzEXlaVbPZT+Ki5cAD9+ZPHX109utX3XCD1X8ZmeYaf8UK6zIHm1XoXCJT1aeBp0WkJCBefsFlx403WuX2Nm3iHYlLFmGGBV8CGgHDsJXmGwK+xniCufdeOzFcfjnMnp2916akwN/pZNFt2WKryjuXyESkp4ikAKjqP/s3rESktogcF5/oXF4gAo88Ysnt4EuEudwLU6G9nqo2TXN/moh8F62AXM4ULAjjxtnwYOfOMGuW9WiFtXp1+ttXroxMfM5FUTlgjoikLnmzFktkr4OlNKwDbo5feC4veewx+N//4JtvrGyNczkRpudqTjBDEAAROQqYEb2QXE5VqADjx8Oxx0KhQtl7bbVq2dvuXKJQ1aFAMyyBvQJW96oZ8CvQS1U7q+qSOIbo8pC2beGff6BDB9i8Od7RuLwqTM/VUUBvEUntw6gGLBKReVgpmWyucOeiqWVLGD3avt+92yqyhzF4sOVYbdmyd1uxYrbduUSnqruBD4ObcznWuDG88gq0b29lat54w1InnMuOML8ypwM1se71E4Pv2wFnAe2jF5rLjV9/tZmAkyaFe36PHlY3q3r1vduOP96T2Z1z+U+7dpaD9dZbVqjZuewKU4phBXAA1pBqDxygqitSb1GOz+VQ2bJWs6VnT1i2LNxrevSA5cvtdVddBVOmwMcfRzVM55xLSFdfbSth+OL2LifCLNx8NTAGqBjcRouIFxFNcMWLW/6VqiW4Z7f68AMPQL16VtYhvZmEzjmXzERg2LC9pW22b49vPC5vCTMseBFwlKreqap3AkcDl0Q3LBcJtWvDyy/DnDlw5ZXZqz5cvLjVvlq9OmfFSZ2LNREpIiLni8itInJn6i3ecbm879137WJz+fJ4R+LyijCNK2HfhZp3B9tcHnDWWXDHHVb7alM2yyq2bAm33govvghvvx2V8JyLpHeAs4FdwOY0N+dypW5d2LDBktw3box3NC4vEM2iO0NErgP6AG8FmzoCo1T1sUgH06JFC505c2akd5vv7d4NO3bY7L/s2rHDqr7/8gvMnw8VK0Y+Ppd/icgsVW0RoX3NV9XGkdhXTvj5K7l99BGcfrrd3nkn/Exsl9wyOoeFSWh/FOgL/An8BfSNRsPKRU+BAtaw2rjRhgfXrQv/2sKFbXjw77/hsst8YVOX0L4QkcPiHYRLTm3bWg7WxIm2XI5zmQlT5yp18eZsLqriEs3PP8Nzz8HixTB5cvgrr8aNrd7VDTdYDlfv3tGN07nsSK25h53P+orIUmA7lr7gtfhcxFx+OSxcCHv22IWmeIKMy4CXRstHjjgCnnjCurfvuit7r732Wqt7NWAArFoVnficy6HUmntnYEvenBrc91p8LuKGDoUhQ2yJsbZtbTWMgw+GO++EnTvjHZ1LFBk2rkSkSCwDcbFx0UVw8cXWEzVhQvjXFSgAo0ZZ/lbfvnbl5lwiSFNz7760NfhSt8U7PpdcUlJgyRI45RT48Uebjf3pp/Dtt3DFFfGOziWKzHquvgQQkZdjFIuLkWHDoHlzGDgQdu0K/7pateDRR62w6JNPRi8+53KoUdo7IlIAaB6nWFwSGzbMii5v2QLnngsHHQSvvWa1BVevjnd0LhFk1rgqLCJ9gGNEpNP+t1gF6CKvaFE7CUydCgVDZd3tdcklcMYZltC5eHF04nMuO0TkFhHZBDQRkY3BbRPwB1aewbmIWrTIyjK8+ablsrZpY4s8N2wIP/0U7+hcIsiscXUZVjD0APYufZN6Oyvqkbmoql4dqla14b233go/C1AEnn3WGmi9e2ev58u5aFDVB1S1FPCQqpYObqVUtZyq3hLv+FzyadAApk+HE0+0sgw//ghHHQXz5vlyOc5k2G+hqtOB6SIyU1Wfi2FMLoZeecW6t594wvIF9uzJegX4ypVtWLB7d/jf/6zQqHPxIiLNgm9fT/P9v4LZzs5FzIABcOyxdpF6/vmWjzpgAHTpApUqxTs6lwjCzBZ8WUSuEpE3gtsAESkU9chcTHTrBmeeaUvc1K5tieu1alnjKbPerG7doGtXuPtumDs3VtE6l65HgtsTwNfACGBk8P3jcYzLJalDD4X334fXX4cDDrCF7i+9FJ5+Gv78E957L94RungL07h6EksKfTK4NQOeimZQLnZSUqBXL2tIbdwIa9ZYYubTT8PjWfxbeuIJKFfOXu+Lmrp4UdWTVfVkYAXQTFVbqGpz4AjAM2BcVDRvDh98YKtYrF5tF5qFCsG990KHDlauweVfYRpXR6pqH1WdGtz6AkdGOzAXO8OH2/DeP/9Anz520hgzxrZlVnKhXDkrSjp/vtV4cS7O6qvqvNQ7qjofODx+4bj86IEHoFMnuO46qw/oZWvypzBzxXaLSG1V/RlARGqx70LOGRKR5cCm4Pm7IrWGmIusH36wvKsqVeCQQyxp/bDDrCdr0yYoUybj17ZrZzMIH3rIrtaOPTZ2cTu3n0Ui8iwwGqvY3hNYFN+QXH5TtKj1/g8cCI89ZkWXX345Z2u7urwrTOPqBmBasKSEANWxtQbDOllVs7GanYu1Ro3gs8/gvPP2bvv0UyhbFkqVyvr1jzxiVd/79LH8q5Iloxaqc5npC1wOXB3c/wxPYXBxkJJiw4LVqllNrI0bvXGV34RZuPlj4FDgquBWT1WnRTswFzu33moJ7RMmWFG8q66yui1XXpn1zEGwBtioUbB0qa0/6Fw8qOo2VR2iqucEtyGqui3ecbn869prLW3ioIOsbM2vv8Y7IhcrodYWVNXtqvq9qn6nqtlJXVZgiojMEpF+OQvRRdupp1rtqvvvtzyqSZNs1uAHH9hyN2GccILlGDz9tL3OuVgRkdeCr/NE5Pv9b/GOz+VvxYvb15tusnzWmTPjG4+LDdGw1SNzsnORyqq6WkQqAh8CA1T1s/2e0w/oB1CtWrXmK1asiFo8LrxRo2wNwbvuslkwYWzbZiePDRvsaq1s2SgG6JKCiMzKbS6miFRS1TUiUj29x4M1BqOuRYsWOtP/c7oMLFpkq1usXWslHNq1i3dELhIyOodl2nMlpmpOD6qqq4OvfwBvAS3Tec6IYOp0iwoVKuT0UC7CLrjAKrDfc48tkxNG0aKWuPnHH9C/f1TDc+5fqrom+LYNUDidxZudi7sGDeCrr6B+fZv8M3JkvCNy0ZRp40qtW+vtnOxYREqISKnU74FTgfk52ZeLjyeegMMPz16eQLNmVpZh7Fi7OnMuhmoAz4jIzyLyWlDw+PA4x+Tcvw4+2CYLnXKK5WOtWZP1a1zeFGa24FcicqSqfpvNfR8EvCUiqccZq6qTsxugi5+SJeHbby3/KjtuuQXefRcuvxyOP95OKM5Fm6reCSAixYBLsJnOjwHZ/A12LnpKlrTJQwsW7F0qZ/fu7J9nXWILk9B+MtbA+jlIEJ0XJklUVZeqatPg1khVB+c+XBdrqX/wY8dayYUwChaEl16yVeIvuST8otDO5YaI3C4i7wNTgDrA9UCV+Ebl3H8VKmSjAgBPPQWnn27lGlzyCNO4OgOoBbQG2gNnBV9dPjJxItx4o3Vph1G/Pjz4oK2x9fzz0Y3NuUAnoBzwEfAmMCFNPlZCGDMGatSwEic1ath9l78VKwaffGK9/F6qIXmEqXO1AqgKtA6+3xLmdS65PP001KljK8CvXRvuNQMGwMknwzXXwPLl0YzOOVDVZlhS+zfAKcA8EZke36j2GjMG+vWDFSusN3fFCrvvDaz87YIL7CJ06VJo1cqGC13el2UjSUTuAm4Cbgk2FcKWl3D5SKlS8OqrsH69LdQcZr2slBR44QVbTueCC3yNLRddItIYW/KmD9AV+AUIOdc1+m67zYr0prVli213+dtpp9kqGbt2wXHHwTpf0yTPC9MDdQ7QAdgM/5ZXCLEoiks2hx9uSzp88AFMmRLuNdWrw9ChNpw4dGhUw3Pu/4DSwONAA1U9OTXJPRGsXJn+9hUrYOfO2MbiEs8RR8CXX8LDD0P58vGOxuVWmMbVjqAkg8K/ZRVcPnXZZfD555aAGdYFF0D79jaLcOHCqIXm8jlVPVNV/09Vv1DVhGuuVKuW8WP16lndox07YhePSzzVq8NFF9n3n34KDz3kE4LyqjCNq9dE5BngABG5BEsW9fJn+ZSIdVsDzJljw4RhXjNypA0t9u7tV+kusjJa9ia3y9+IyAEi8oaI/CAii0SkVW7iHDx471IoqYoVs2Wjype3/Kvata2+3DZfETHfGzfOJhH17x9+GTKXOMIktD8MvAGMB+oCd6rqsGgH5hLbX3/BiSdar1SYK6uDDrKk+FmzbA1D5yIodQbz5ODWI7hNws5dOTUUmKyq9YGmwKLcBNmjB4wYYb0TIvZ15EgrcfL11zB5sm3r3x9q1bIh+P1ztFz+8eSTcMMN9rVzZ/9dyGtCrS0oIgdjS9co8K2q/haNYHxtrrxl2DC46irLERg4MNxrevWyK7KvvoIWuVpRziWDSKwtmGZfM1T12Ky2hdxXaeA7oJaGXIA1EucvVZuWf++9MG0aVKhgf1tXXGE9vy7/GT7czrMtW8Kbb1qB5rlzrQF+wQX2O+LiJ0drCwYvvBib2twJ6IIVFL0w8iG6vKZ/fzjnHLj5ZrvyDmPYMKvY3rs3bN0a3fhcvlNCRI5LvSMixwA5zRGtBawFXhCROSLybHr5piLST0RmisjMtWFrlGRCxMqXTJ0K06fbclI332w9WvfcY4uiu/ylf39rVNWqZaMFkydD48a2EPRhh8H3OR74dtGUZc+ViPwIHKOq64P75YAvVLVepIPxnqu856+/7B+Aql1NHXBA1q+ZMsWmHl93Xfiq7y45RbjnqjnwPFAm2LQBuFBVZ+dgXy2Ar4BjVfVrERkKbFTVOzJ6TbTOX998A/fdZz0WpUtb/bhrr4Vy5SJ+KJfALr/ccvYGDIDff4ejjrJh5TFjrLfTxUeOe66wWjGb0tzfBKyKVGAubytb1upf9ehha2aFceqpNswxZEj4iu/OZUVVZ6lqU6AJ0FRVD89JwyrwC/CLqqb2yb4BNItEnNnVsqWtRTdnji34O3iw9WTdeKP9k3X5w3vvWQOrf3/r3Rw1ykYAvvkGNm3K8uUuxjJsXInIdSJyHfAr8LWI3B0UFP0K+ClWAbrE17KlnfALFgw/E/B//4OKFaFNGxsK8aVAXG6JSBEROR/oD1wtIneKSI7qXAV5patEJLWHvg0Q10Iihx8Ob7wB8+dDhw7W61uzpq2AsHp1PCNzsVC0qDWinn/ezrl9+0K3bvZYwYLxjc39V2Y9V6WC28/A2wR1roB3gIRar8slhrlzrV7PrFlZP/ftty1/JHWKsS8F4iLgHeBsYBdW9Dj1llMDgDFBOYfDgYSY59qokS2kvmgRnHeeJTzXrGm9wStWxDs6Fy09e8Ldd9towccf26zrCRPsMf/cE0+o2YKx4jlXedv69VZluFAhmD0bypTJ+Lk1aqR/Qqhe3dchzE8inHM1X1UbR2JfORGv89fSpbZI+qhRlvvYp48V7K1dO+ahuCjats16qubMsWHBWbPs4rR2bXj9devZcrGXm9mCLUTkLRGZHYnCfC55lSsHr7xijaZLLsm8/lVGS4FktN25EL4QkcPiHUSs1apl9bN++gkuvRRGj7Ye5N694ccfrTe4Rg1b69OH3/OuokWtx//tt23W4PDhtsjzu+/aYxs3Wj2sH36Id6QOws8WvAGYB/y79K6qRrwj0nuuksP//Z9NH3/ySUvATE9GPVcVK3qSbn4S4Z6rhUAdYBmwHRBAVbVJJPaflUQ5f61ebbXnnn7aejtSUvat8F28uDXGevSIX4wu8r79Fs44w4qNPvqoNbRF4h1V8svNbMG1qjpBVZep6orUWxRidEnihhvsj/yjjzLuvUpvKRARy8OaPj3qIbrkdAZwKHAqVrE9tXJ7vlK5sv1zXb7cCo/uv3TKli1w221xCc1F0ZFHwrx5cPzxdlF79tkQgdJrLofCNK7uCgrodReRTqm3qEfm8qyUFHjtNcsDyOjKKb2lQJ54whJzzzoLvvsutjG7vC/Nhd9WbALOvwvO50cVK2Y8Rd8ToJNTpUrw/vvw2GNWT/CKK+IdUf4VpnHVF5spczp2FZh6RehchkqWtEbWypXWS5VeD1aPHnZ1vWePfb38cjshlC5tRUaXLIl11C4vE5EOIrIEGxb8FFgOvB/XoOKsWrX0txcoYBdACTSfyUVISgpcfbUNE6YWaV6/3lfEiLUwjaumqtpCVfuoat/g5svfuFBefRVuvx2efTbc86tVgw8/tKGMU06BX3+NbnwuqdwLHA0sVtWaWG2qGfENKb7SG34vUgSqVIGuXaF1axtKcsnnsMPsfKpqa7oeeaQvlRNLYRpXX4lIw6hH4pLSwIHWSLrqqvB/2PXq2fpZf/5pr123LroxuqSxM1imK0VEUlR1Gtbrnm+lN/z+3HPw88824eT77618ylVX2VJWLvmI2Oe7bp01sB57zEYLXHSFaVwdB8wVkR+DMgzzvBSDCyslBV5+2dYc7NoV/vkn3OuaN7cpxkuXQrt2vryDC2WDiJQEPsOKfw7FCorma/sPv/foYcOCl18Oixdb8d4nnoC6da2Hef8EeJf3nX669VCedpqtS3n66T4rO9rCNK5Ox2fguFw46CCrrfPjj3DvveFfd+KJlhQ/ezZ07GjTyp3LxNnAFuBaYDK2uoSfqzJRrpz1YM2aBfXrW326o46Cr76Kd2Qu0ipUgHfesRIdy5dbsWcXPWEaV5rBzbnQWre2ddFuvz17r2vf3ipPT50K3bvDrnzfD+EyoqqbVXWPqu4CJgLDgmFCl4XDD4fPPrOLoDVroFUruOAC+O23eEfmIknE6l8tWAAHHmhrwd5/P2zOzSJRLl1hGlcTgfeCrx8DS8nnM3BcznTqZHV3tm7NXiX2nj1h6FCrTHzJJZ4v4PYlIkeLyCci8qaIHCEi84H5wO8icnq848srROD8863C90032fqFdetazaywC7K7vCG112raNLvgbdYMEqD+bVLJsnGlqoepapPg66FAS8DLPLocO/tsy6PasiX8a666yhYtHTUKrr/ep5C7fQzHFlUeB0wFLlbVg4ETgAfiGVheVKqUrVW4YIEVpBw4EJo0sVm8LrmceqqNCmzZYr2VDzzgOXeREqbnah+qOhs4MgqxuHzi+uth4UIYMAC+/trKNSxenPXr7rzTGllDhlhXtnOBgqo6RVVfB35T1a8AVNVXWcuFQw+FiRNtYsnOnfaPuFMnX1g92Zx0ks0a7dQJbr0VLrss3hElh4JZPUFErktzNwVoBnhRfZdjp55qRe4eewwmTYLjjrNG0xln2Gylghn8VopYw+qvv6wr+8ADM1670OUraQeK9y+V6H2cuXTWWdC2rQ0PDh4MDRrAjTfa0OH+NbRc3lS2LLzyCpx5pvVSguW3pp6LN22y2YVVq1qdNJe1MD1XpdLcimC5V2dHMyiX/FatgkMOsT/a++6zq+FVqyy3KjMpKVanp317uPJKGDcuJuG6xNZURDaKyCagSfB96v3D4h1cMiha1Ho1fvzRZu7ec481ssaP9yH6ZCECvXvb5AawC9eePa0nq2pVuyiuWjXrc7QzYXKuBqW5DVbVMarqk+Jdjm3aBB98YAs7H3OM1dwpVsxO2C+9lPXrCxWyocQTTrCTwaRJ0Y/ZJS5VLaCqpVW1lKoWDL5Pve8TziOoShW7oPnkEyhTBrp0sUK/CxfGOzIXSar2WY8da+fkMWOs5uDnn1spB7+ozVqWjSsRqSsiI0RkiohMTb3FIjiXnLZvtwbVoYfaWoJ16lhOR5Ei4YuFFisGEyZYF3bnzvZH75yLjRNPtPpzw4dbjawmTaw45d9/xzsyFwkicPPNUKKE1cfq0MFyXqtVs/UKhw2Ld4SJL8yw4OvAHOB24IY0N+dypHx5a1C98cbebX372h9wmzbh91O6tC2TU7265YXMmRP5WJ1z6StY0IbmlyyBiy6y4aK6deGFF2xVhho1bBi/Rg3r+XB5y8aNULiwJbv36mU5d//8Y8PBv/wS7+gSX5jG1S5VfUpVv1HVWam3qEfmktqwYTZb8Jpr7GS8bJkVL5w9O3tXvxUq2BTxMmVsaYcwsw6dc5FTvjw884zVSapTBy680AqQrlhhw0srVtgSO97AylvKlbNly+bPtxI4ixbZ+XbCBCvXMHy4jUK49IVpXL0rIleISCUROTD1FvXIXFJr1cpOxmXKWP7GeefZyXfePEuc3LAh/L6qVrUGlqrlf/hVlXOx16wZTJ9u/5T3L/S7ZQvcdlt84nI5k5JiJW+6drW8q7/+gocestzYgw+2i+M6dSwHa8eOeEebeESzmOohIsvS2ayqWivSwbRo0UJnepnYfO3ddy2H6rjj4OOPbew/rNmzrWZLlSq2lEf58lEL00WIiMxS1RbxjiMS/PxlUlIynkHoMwvzno8+srI5y5fDEUdYCY5Gjez8fNdd8MUXlos1aZJtz28yOodlWedKVWtGJyTn/qt9e3jrLeuOzk7DCuzK+d13bcX3M86wysOlSkUlTOdcBqpVs6HA/RUpYqUc6tWLfUwu59q2tVt629u0sUlJTz0FtWvb9iVLoGbNjOsV5hcZDguKyHGZvVBESotI48iH5PK7M8+EY4+170ePhj//DP/aE0+E116z5Pazz4ZtXjTEuZgaPPi/xUULFbIZwk2a2DJW/neZHEQs1/Xtt60W2o4d1uhq0MAmNeTnpXQyy7nqLCJfiMidInKmiLQUkRNE5EIReRlbzLlYjOJ0+dCqVbZQc5s2sH59+Ne1b28JmNOmQbduVmnYORcbPXrAiBE2i1fEvr7wgtVJ6tIFBg2yRtZUL+iTdAoVsslKJUtaDcJGjaxWVn5sZGXYuFLVa4EzgTXAucC9wHXAocAzqnqCqn4bkyhdvlS1KrzzDvzwA7RuDWuzsehSz57w+OP2+osv/m+CrXMuenr0sBydPXvsa48ecNBBNmllyhTb3qaN/QPOzt+1S2wiVlJn1ix4800r5dCjh33m+U2mswVV9S9VHamqF6jqaaraUVVvUdXpsQrQ5W+nnmp5VIsXWwPrjz/Cv3bAALtKfvFFGDjQk2mdSwSnnGKzgm+/3dazq1fP1hT1C6DkkZIC55wDc+daovvpp9v2ESNsyaT88FmHKcWQKyJSQETmiMh70T6WS05t28LEiTaskN0roDvusEWhH3vMckGcc/FXrBjcey989x0cdpgN/594IixYEO/IXCSlpNjkIhG7uH3uORsabtbMRhWS+YI36o0r4GpgUQyO45JY69Y2C6VnT7sf9o9SBIYMsQrDd9wBTzwRvRidc9nToIHVuXv+eVuf8PDDbYHoLVviHZmLNBEr2/Dyy7B5sy0A3qIFfPVVvCOLjqg2rkSkCpa39Ww0j+Pyh8qV7evnn0PLllbRPYyUFLti6tAB+ve3+le+LIdziUHElr/64QfLz3ngAWjc2Ja2csmlQAG7QF60yCY5bNiwt2TD1q17L5pXr4aHH7aG9kcf5c0erjALNxcXkTtEZGRw/1AROSvk/h8DbgTywQiri6VFi+Dkk+2PMIxChexKKSXFZh76shzOJZYKFfbO8i1c2IaTunULfxHl8o6CBW2JpMWLrfcK4IorrATP/ffbUPHixVYb7ZprbCgxr836DtNz9QKwHWgV3P8FuC+rFwUNsD+yWodQRPqJyEwRmbnWp424EI4/Hj74AH791Sqy//pruNcNGuTLcjiX6E46yXKx7rnH6ifVr29FKvNDEnR+U6DA3u+PO86WLrvtNltlo3t3qwA/ezb89puVdMhLwjSuaqvq/4CdAKq6FQhTO/tYoIOILAdeAVqLyOj9n6SqI1S1haq2qFChQvjIXb527LHWwPrtNzsZh5lFuHJl+tvTqybtnIufIkUsR3LePDjySOvVOOYYa3S55HTRRdZzWbUqrFtnebZPP229mP3728odeUmYxtUOESkGKICI1MZ6sjIVlGyooqo1gG7AVFXtmZtgnUvrmGNs9mDr1nBgiKXEq1VLf3uJErBzZ2Rjc87l3qGH2qLso0fDsmXQvDlcfz3880+8I3PRUKyYLX32888wdKjNIAX4+mv49FN48EEbLswLwjSu7gImA1VFZAzwMZZH5VzcHX00PPOMjeGvXp15L1R6y3IULGgzV9q1g7//jm6szrnsE7FE90WL4MIL4ZFHrPL3u+/GOzIXaS1bwqZNdtF81VU2m3TTJpgwwS6gb7nF6qI1bgx33pnYyyhl2bhS1Q+BTsAFwDighap+kp2DqOonqho2Cd65bFOFTp1siHD58vSfk96yHKNG2TTwTz6xoUYfInQuMR14oP39Tp9uC7J36GB/87/8Eu/IXKQUKADjxlndsw4dbDi4Xj0rQrpkiZ2fhw61Gd8vv2zDxwBvvGFlHhIpL080izmOInIONqT3d3D/AOAkVX070sG0aNFCZ86cGendunxi5kyr/ly6tDWWatYM/9qPP4bOna1b+r33bPjBRZ+IzFLVFvGOIxL8/BU7O3bAo49a0nuBAnDffdb4uuMOy62sVs16qnv0iHekLif++ccmM6xfb2kfhx323+ds22aLRavahfKqVXDwwTYrPPVCu1Ch6Mea0TksTONqrqoevt+2Oap6RGRD9JOTy73Zs62ie8mS1sCqVSv8axcutOHBtWttWY727aMWpgt448rlxrJl1rsxefLeKuCpihe3ni5vYCW/v/+2ZXbefNO+btliw4pDh9rvxLZtduEcDRmdw8LkXKX3nIK5D8m5yGvWzHqhNm+2tQWzo2FDqxbcsKFd/QwbFpUQnXMRUrOm/TMtX/6/hSa9zEr+UaaMlW54/XWbafj22zb7EODbb+3349xzbcgxo9za7dst1yu1cZZbYRpXM0XkURGpLSK1RGQIkGntKufi6YgjbGbJqFHZf+3BB1uPV/v2duVz7bWwe3ekI3TORYqIDR+lJ6PyKy55FSsGZ58NTZrY/TJloE8fy9U7/3wrVtuunQ0jppo2zVbsuOceqwxfrVruSz+EaVwNAHYArwKvA9uAK3N3WOeiq3Fj+yPasQMuuwx+/DH8a0uUsJXbr7nGFnzu3Nl6wpxziSmjMivFi8Nff8U2FpdY6tWDJ5+0YtMzZthF86+/2v8HsAK1Z51la9BOnw5Tp1oP1sUX79sAy64wswU3q+rNQaHP5kH9Kv9X4/KEVatsHP7kk23tsrAKFLA/tscftynfJ51kBUudc4knozIrW7faMH9eK0DpIi8lxWojPvywFaMtWtS2P/+8DQN2724Fa1etsvSSrl1zVxU+zNqCdUVkhIhMEZGpqbecH9K52Kld27p8d++2BtKQIXDaaVZP5ZZbLHk9MwMG2Il54UKrqbVwYUzCds5lQ0ZlVr791ob6O3WynBu/QHL769XLGlIPPghly0KlSra9QoXc1T4MM1vwO+BpLM/q3+yTrNYMzAmfbeOiZdEia1Bt3QoPPWTfjx5tK65//XXWFd5nzbKu461brSesdevYxJ3sfLagi7adO623YtAg69167DH7hyphFnFzSW/RIhvZWLTIGldg5/mmTeHZZ+GEEzJ/fW5mC+5S1adU9RtVnZV6y8F7cC5uKla0k2mtWrZA6LHH2lh7q1Z2xZuV5s1tJmGVKtbzlZNkeedc7BUqZL3Uc+daxe8+feCMM7xgsDMNGlhj++ijYfhw+39wzDH2v+H443O+3zCNq3dF5AoRqSQiB6becn5I52Jv9mxrIC1aZOPqqvZ9p06W5BhG9er23JNOgr59rWBhFh2/zrkEUb8+fP65lViZPt2W0Bk+PLGqerv4+N//LL929mz73bjzTnjhhdz1boapV9Un+HpDmm0KZKM8o3PxVbmyLQaaatw4u1o5/XQ45JDw+ylTxuqgXHaZVYVetgyee27vMgwuuYhIAWAm8Ksv4ZX3paRA//5WauXSSy2n8pVX7G+4Xr14R+fiRcRGJE47LXL7DDNbsGY6N29YuTylUSMbErzlFqvW2769rbg+aZIND2Sn1EKhQjYWP3gwjBkDp54Kf/4ZvdhdXF0NLIp3EC6yqleH99+34f2FCy2/5oEHLD/LuUgIMyyIiDQWkfNEpHfqLdqBORdpr70GCxZY3lTz5lb76vzz4cMPbYx96dLw+xKBW2+1qbpffWWvT9sz5vI+EakCnAk8G+9YXOSJWP7VwoV2sXXrrTbRZc6ceEfmkkGYUgx3AcOC28nA/4AOUY7LuYirWBEmToTvv7fyCitWWM/TpEnwyy8wf37299m9u804XLvWEiK//DLycbu4eQy4EfCsnCR28MG2bMr48Vaq4cgj9/ZwO5dTYXquugBtgN9UtS/QFPAME5dnVa5sw4QFg4zD00+3XqsOwSXDrFnZS1Q//nhrVJUpYyUa3ngj8jG72BKRs4A/spoZLSL9RGSmiMxcm1XRNJfQOnWyXqzeva3mUdOmltzsXE6EaVxtVdU9wC4RKQ38gSezuyRTpox9nT8fjjrKisr980/419etaw2sZs2sWOFDD/lMwjzuWKCDiCwHXgFai8jo/Z+kqiOC1StaVEhdT8PlWWXLWsXuKVNs6azjj7cE+E2b4h2Zy2vCLtx8ADASKyQ6G/gmmkE5Fy+NGlli6/jxVufkp5/Cv7ZCBfj4YzjvPLjxRrjiCti1K3qxuugJlvmqoqo1gG7AVFXtGeewXIyccgrMmwdXX23r0jVuDJMnxzsql5eEmS14hapuUNWngVOAPsHwoHNJRwRuuMFOpKtXW/7F+++Hf33Rolbm4aab4Omnbajx2WdtxfWUFPs6Zky0onfORUrJklbNfcYMW8z9jDMsAX79+nhH5vKCsLMFm4hIB6AZUEdEOkU3LOfi65RTYOZMawzNyuZ6BCkplrMxYgR88AH062fJ86r2tV8/b2DlJar6ide4yr9atbIZhLffbrODGza0BHgf9neZCTNb8HngeaAz0D64+YnGJb2aNeGLL2yKNlj13uzkXlxyCZQv/9+T8JYtcNttkYvTORddRYrAvffaBVfVqjb036mTVXj3XmmXnjAV2o9W1YZRj8S5BFSsmH3dsgXatbMFnt9+2xLYw8hoAtnKlREJzzkXQ02bWl27IUPsAuntt/c+ltorDdCjR1zCcwkkzLDglyLijSuXrxUvbkMCa9daHtZ774V7XbVq6W/3iWXO5U0FC1peZnp/w94r7VKFaVy9iDWwfhSR70Vknoh8H+3AnEs0rVvbsECdOlbR+Z57sl70dfBga5ilJQJ//GElG379NXrxOueiZ82a9LevWOGzhF24xtXzQC/gdPbmW7WPZlDOJarq1a2wYK9eVhMrq1XTe/SwxPbq1e251avbauuDB1vvV/36MHSon4ydy2sy6pUGSxt45hmv8p6fhWlcrVTVCaq6TFVXpN6iHplzCapYMXjxRRg92hpMS5fCDz9k/PwePWD5cuvlWr7cpnPfequtc3jccXDNNVa49NtvY/QGnHO5ll6vdLFicO21NmR42WW2WPyjj2ZvYXiXHMI0rn4QkbEi0l1EOqXeoh6ZcwlMBAoXtu8vvdQWfH3nnezto1YtW9fw9ddtTbOjjoIrr4QNGyIernMuwtLrlR450hpTX31la47Wrw8DB9pj994Lf/0V76hdrIRpXBUDtgOn4qUYnPuP55+HevWgY0e4666s87DSEoEuXWDRIhgwwAqP1q9vhUi9jo5ziW3/XunUWYIi0KYNTJ1q5VxatYI777RG1s03w++/xzNqFwuZNq5EpACwTlX77ne7MEbxOZfwqlaFzz+HCy6wJPezz4aNG7O3j9KlLffq229tf+efD6edBkuWRCVk51yMtGoF774Lc+daOZf//c9qYl11lZdkSWaZNq5UdTdWld05l4miRa0H64kn7Kq0YJgKculo1syGFIYPh6+/hsMOswbb9u2Rjdc5F1tNm8Irr1h+Zvfu8NRTULs2XHSRX0QlozDDgnNFZIKI9PKcK+cyJmKLNX/5pSW6btoEEyZYwcFWrSwv64EHrBZOZgoUsNyrH36Ac86xocYmTWxRaOdc3la3rl2I/fyzJb2PHWupAN26wfde5ChphGlcHQisB1rjOVfOZalAAfv6wAM2RDhsGAwaBA89ZMN+7drB7t1Z76dSJcu9+uADe37btpbT4fkazuV91arZuWH5citKOmmS9W516GC91y5vy7JxlU6+ledcORfCqadaLtWyZfDYY9b79MYbNi170qTs7Wf+fEuIfeMNS55/+unsJc475xLTQQfZQu8rVlgKwIwZ1tPdpo31VvvElrwpzMLNVUTkLRH5Q0R+F5HxIlIlFsE5l5fNnm01rZ5+2qZlN25sQ4bt22f/yrRoUev9+v57aN4cLr8cjjnGkmSdc3lf2bJwxx3WyHr4YVi40HqrUxPiUxtZY8b4YtF5QZhhwReACUBl4BDg3WCbcy4ThxwCP/5odbCmT7dci1q1LJeqYsWc7bNePWuojR5tPWLNm8N111l+l3Mu7ytZ0mpjLVtmSe+//25DhYcfDv37wyWXWANMde9i0d7ASjxhGlcVVPUFVd0V3EYBvuysc1k4+2yrX/X009YI+vhjmDbNvr72miWwrsjBWgcilnv1ww92Yn3sMWjQAN5804cQnEsWRYtawvvixfDSS7Bzp81G3rp13+f5YtGJKUzjap2I9BSRAsGtJ5bg7pzLRNGilow+apQlpx9yiNW4mTDBuvvfecdmCd1+O/zzT/b3X7asXdl+8QWULw+dO9uQ47JlPnTgXLIoVGjvWqYZ8XpZiSdM4+pC4DzgN2AN0CXY5pzLQoMGll81c6Y1gubMsWVuBg2yIcNOnWyNsrp17bGcOPpo2/+jj8Inn1iDrW9fHzpwLpmkpFiF9/QULGgzD9d7t0fCyLBxJSL/F3x7lKp2UNUKqlpRVTv6ws3OZU+1alCzpg3ppd02ZowlubdqZflUAH//nf39FyxoC8YuWmTf79y57+M+dOBc3pfeYtGFCkGVKlbxvXJlSzeYMsVnE8dbZj1X7USkEHBLrIJxLj86+mgYP95Omtu3W37WeefZ8F52Va3635yMVDnJ73LOJY70Fot+4QVYutRmDl92GXz4oS2dVbOmFSBevjzeUedPmTWuJgPrgCYislFENqX9GqP4nMtXVK18w8SJNqR4yy3ZX6ewWrWMHzv+eMsB27w5V2E65+Iko8Wimza19Ul//RVefdXSA+691xpZbdtaQeKMLrxc5GXYuFLVG1S1DDBRVUuraqm0X7PasYgUFZFvROQ7EVkgIoMiGrlzSahoUat1s3gxdO1qxQXr1rWlMsJKb+igWDHb3++/Wz5WpUqWh/X11z7D0LlkUrSo9Xx/8IE1vgYNsvPH+efbsOGVV1oNPhddmSa0i0gBoEQO970daK2qTYHDgdNF5Ogc7su5fOWQQ+DFF63xc845dvUJsGZN1q9Nb+hg5EhbNPbHH+GzzyyRfswYG5I87DBb/3Dt2ui+J+dcbFWrZis7/Pyz1cdr1w6ee85SD444wpPgoynTxpWq7ga2iEiZ7O5YTeoE80LBza+RncuGli2t3EJKijV+GjSwhlFWPVkZDR2I7B0aXLMGnnkGSpSwQqSHHAJdusD774db+9A5lzekpNhyOmPG2N/9E0/YNk+Cj54wpRi2AfNE5DkReTz1FmbnQV2sucAfwIeq+nU6z+knIjNFZOZav3R2LkMlS9oCr1OmQMOGcOONOZtZmKp06b1Dg/PmWfXnTz+1q9saNWx4MidJ9c65xFW2LFxxBcyaZeVfPAk+OkSzSLgQkT7pbVfVF0MfROQA4C1ggKpmWAqtRYsWOnPmzLC7dS5fWr3ayiqMGgUVKtgaZOXLR2bfO3ZYkdPnnrOcDVVo3Rouush6zIoWjcxxUonILFVtEdm9xoefv1xetW3b3r/7Dz+0v/s2bezvfts2y9taudKGGQcP3tsT7jI+h2XZuApeXAyopqo/5iKAu4DNqvpwRs/xk5Nz4c2aZUN4t99u95csgUMPte9XrrSTZMmScOaZ9jW7Vq2yBtzzz9uV7AEH2En1oossXyMSvHHlXGJZudL+7l94If0erOLFLafTG1gmo3NYlsOCItIemIuVZkBEDheRCSFeVyHosUptnLUFfshe2M65jDRvvrdhtXCh5WOdfTZcf701fqZNs6T4mjWtcnt2Va1qQ4OpybBnnAHPPgvNmtntiSfgr7/2Pt+X3HEu70ubBJ/eAvNbtsDVV8Mff8Q+trwkTM7V3UBLYAOAqs4FaoZ4XSVgmoh8D3yL5Vy9l6MonXOZqlXLuus//BAeeQTOPReGD4dJk2yWYNeu1r2fE6nJsGPH2pDksGE2bNC/v5V06NHD6nFdcokvueNcskidRJOe9evhoIPsIuvmm2HqVCuA7PYKk3P1taoeJSJzVPWIYNv3qtok0sF4t7pzudO3LyxYYGsNVq4MP/1keVInnwwDB8JZZ0XuWLNnW47GmDEZJ9ZXr555cqwPCzqXuGrUSH9lh4MPhgEDbHLNjBmwa5cNF550Epx6qiXH16u373JfySrHw4LAfBE5HyggIoeKyDDgi4hH6JzLtYIFoVcva/gMGrQ3Af2nn+Ddd+HPPyN3rNShwcxqb61cGbnjOediK72CxMWLw8MPw623WrrBn39aMvyFF9p55pprLEWhenW4+GJ47bXInnfyijCNqwFAI6wo6Fjgb+CaKMbknMuh9u2tN6l+fUs8B/jmGxvOGzHCrjjPOcfWMszpMOH+ihWzE2l6MluKxzmX2NIrSLx/MnupUnbeGTbMihQvW2b181q2hDfesJSE8uXhqKMsh3P69P8uLJ+MMmxcBcvXXAP8D1gJtFLVI1X1dlWN0GnZORdJZ50FjRtbsvuDD8JNN9m2kSOtN2vAAPjqKysWOn68vWb79twXD8zoCnfw4Nzt1zkXXxkVJM5IjRqWb/nGG7BuHXzxhdXOKlgQ7r/fihiXKwcdO1qB5PQKIifD5JgMc65E5FVgJ/A5cAawXFWviWYwnrPgXO6p2uy+SZOsBMP551s3fardu20m4dFH2+MPPwyPP27P69nTGmc5MWaM1d/KTj0cz7lyLv/YsMGS36dM2bv2IdiEnNNOs3yttWttaHHLlr2vS+TyDxmew1Q13RswL833BYHZGT03UrfmzZurcy62Jk9WbddOtUABVVBt2lT10UdV9+yJ/rGBmRrl80qsbpE4f40erVq9uqqIfR09Ote71BIlSqiq6vLly7VZs2batGlTbdiwoT711FP/Puf888/XunXraqNGjbRv3766Y8eODPf3wgsvaPny5bVp06Zar149ffTRR/d5fPbs2Qro5MmT99kO6HXXXffv/YceekjvuusuVVW96667tHLlytq0aVOtU6eOnnPOObpgwYJ/n7t9+3a9+uqrtVatWlqnTh3t0KGDrlq1ap999+zZ89/7O3fu1PLly+uZZ56ZjZ9U5Iwdq9qokWpKin0dOzb3+0z9HFVVV6xYoaeccorWr19fGzRooMuWLdvnuf3799/n+elJhM+xTZsOeu+9q7RDB9WSJW3f0FPtElEVdiqUVzhTq1fPxg8rhjI6h2WWc/XvqKiq7opQI885l2BOOw0mTrS8rMcfhyJFrDhp6kyfyZNh48b4xpgfjBljwynRKmdRqVIlvvjiC+bOncvXX3/Ngw8+yOrVqwHo0aMHP/zwA/PmzWPr1q08++yzme6ra9euzJ07lxkzZjB48GBWrVr172Pjxo3juOOOY9y4cfu8pkiRIrz55pusW7cu3X1ee+21zJ07lyVLltC1a1dat25N6pJot956K5s2bWLx4sUsWbKEjh070qlTp9SLf0qUKMH8+fPZunUrAB9++CGHHHJIzn5QuTRunPXgDhtmeY3Dhtn9/X4cudK7d29uuOEGFi1axDfffEPFNAWpZs6cyYYNG0LtJ96fY48eHZkwoRNvv62sXw/FipUA5gNbg719CNjnuGKFpTm8/LIt27N1a3pHTxwFM3msqYiknlIFKBbcF2xd5tJRj845FzMVK1pO1oABe09cv/1mFd6LFLECpT17Wtd9oUL/fb2qva5IEShQILax5wXXXANz52b8+Fdf/bdW0JYtNjFh5Mj0X3P44fDYY+GOX7hw4X+/3759O3vSJNq1a9fu3+9btmzJL7/8Emqf5cqVo06dOqxZs4aqVauiqrzxxht8+OGHHH/88Wzbto2iwZTVggUL0q9fP4YMGcLgLJLxunbtysSJExk7diyXXHIJL7zwAsuWLaNA8IvVt29fnn/+eaZOnUqbNm0AOOOMM5g4cSJdunRh3LhxdO/enc8//zzcDyebTjrpv9vOO8/W7Lv3XkvyHjRo72OlStm6oN27Wx5Sly77vjY7RX4XLlzIrl27OOWUUwAomWb5hd27d3PDDTcwduxY3nrrrdD7TJTPMSUFSpc+g40bJwJdgHFAd+BzChWy3/UdO2zfKSlQu7alMaS9HXpo+uenWMuw50pVC6hq6eBWSlULpvneG1bOJbFixezrQQfZ7J6+fa1A6VlnWf2sjz/e9/nvv29V4Q880Bppt9+eP2YERVJGRRgjWZxx1apVNGnShKpVq3LTTTdRuXLlfR7fuXMnL7/8Mqeffnqo/a1cuZJt27bRpImVPZwxYwY1a9akdu3anHTSSUyaNGmf51955ZWMGTOGv0OsON6sWTN++OEHfvrpJ6pVq0bp0vv+22nRogULFiz49363bt145ZVX2LZtG99//z1HHXVUqPcQaT/+CGXK7LutTBnrGY6ExYsXc8ABB9CpUyeOOOIIbrjhBnbv3g3A8OHD6dChA5UqVcrWPhPpc7z55m4UKPAKsA34HjiKlBRbjmfzZli0CF5/3WYeNmliq1MMHmyzEhs1ghIlbPv551sC/YQJsHRp1pN2Ip1En1nPlXMunxOBVq3sNmSIJaGOHm2lHsCGE996C95+G156CU4/3brvL73UrtTD9qrkB1n9LDIq2Fi9es6WL0pP1apV+f7771m9ejUdO3akS5cuHHTQQf8+fsUVV3DCCSdw/PHHZ7qfV199lWnTpvHjjz8ycuTIf3s1xo0bR7du3QBr7Lz88st06tTp39eVLl2a3r178/jjj1MstQWfgdQhP1VF0qlGuf/2Jk2asHz5csaNG7dPT1w0ZPZ5NGhgvVYnn7x327Rp1iMMVpYgN5/nrl27+Pzzz5kzZw7VqlWja9eujBo1ijPOOIPXX3+dT7Kx80T8HG+5pQkjRy5n48ZxrF/fjoMO2rsSBNi5p379fXv/tm2zRtf8+XtvM2bsOxRbogQ0bPjfnq5KlWz1iX799ibRpw7JQ86T6MPUuXLOOQoXtno2r74KqeksM2ZYXa3162045PnnbS3DsWNtXcOQqR+O2JazqFy5Mo0aNdpn2GzQoEGsXbuWRx99NMvXd+3alQULFvD5558zcOBAfvvtN3bv3s348eO55557qFGjBgMGDOD9999n06ZN+7z2mmuu4bnnnmPz5s2ZHmPOnDk0aNCAOnXqsGLFiv/sZ/bs2TRs2HCfbR06dOD666+ne/fuWb6HaLntNhvKnTbNem+nTbP7t90Wmf1XqVKFI444glq1alGwYEE6duzI7NmzmTNnDj/99BN16tShRo0abNmyhTp16mS6r0T9HC+4oAOq1/P999155ZW955uMFC1qPee9esH//Z9d9K1YYStHfPGFzTS86CIbnp040VarOO0022+5cvZY2tmJYPdz85l548o5l2P3329d8VddZSejKVNse/nyVrD011/jG19eEqZgY2788ssv/yZ8//XXX8yYMYN69eoB8Oyzz/LBBx8wbtw4UlLC/1to1aoVvXr1YujQoXz00Uc0bdqUVatWsXz5clasWEHnzp15++2393nNgQceyHnnncdzzz2X4X7Hjx/PlClT6N69OyVKlKBPnz5cd911/w5/vfTSS2zZsoXWrVvv87oLL7yQO++8k8MOOyz0e4i07t2tQTxggP3THzDA7keqvXfkkUfy119//ZskPnXqVBo2bMiZZ57Jb7/9xvLly1m+fDnFixfnp59+CrXPZP0cS5e2XvdLLoGhQy2d4fff7TZ1qk3gOffcjIfec7PChA8LOudypUULuwL87ru9V3+rVtkJrEaNuIaW5/ToEb1aPosWLWLgwIGICKrK9ddf/+8/r8suu4zq1avTqlUrADp16sSdd94Zar833XQTzZo1Y/HixZxzzjn7PNa5c2eeeuopevXqtc/2gQMHMnz48H22DRkyhNGjR7N582YaN27M1KlTqVChAgAPPPAA119/PXXr1iUlJYX69evz1ltv/WeYqUqVKlx99dXhfyhR0r175BpT+ytQoAAPP/wwbdq0SS0BwiWXXJLr/eanz7FiRbulDt1+8EH6Q/K5WWEiy4WbY8mL8DmX98yfbyep++6DTp0soffqq212YVb/n72IqHMu3lLLoOSkcGluFm52zrkMNW5sswXffRfq1YPLLrNu+DvuiHdkzjmXtWgMyfuwoHMu11q0gPfei3cULpJeeOEFhg4dus+2Y489lieeeCJOEbmc8M8xnEgPyfuwoHMubhJ1WFBEqgIvAQcDe4ARqjo0s9f4+cu5/Cejc5j3XDnn3H/tAgaq6mwRKQXMEpEPVXVhvANzziU+z7lyzrn9qOoaVZ0dfL8JWETqImfOOZcFb1w551wmRKQGcATwdZxDcc7lEd64cs65DIhISWA8cI2qbkzn8X4iMlNEZqYWdXTOOW9cOedcOkSkENawGqOqb6b3HFUdoaotVLVFaqFE55zzxpVzzu1HrGT0c8AiVc16sT3nnEvDG1fOOfdfxwK9gNYiMje4tYt3UM65vCGh6lyJyFpgBVAG+Hu/h/ffVhNYFqPQInnM9N5bbo+Z1T4zejyj7fsfM7sxZ1fYn2sk4kjdRyR+rmGel/axMD/XsNvCitTfSXZiyOiY6e2juqomxXian79yfMxon7/CHCO3Ink+CbuPSB0z7M8/7P+FSJ7DIvl3EjaGzI4Z/hymqgl3wwr2ZboN2ByHuHJ9zPTeW26PmdU+M3o8k+2bs7P/WP1cIxFH6j4i8XMN87y0j4X5uYbdFumfbSR/9hkdM9q/R4ly8/NX9o4Z7fNXTuKOxs82kuevSB4z7M8/7P+FSJ7DIvl3ko3zeYbHzM77SNRhwXdDbsuLovE+stpnRo+HjSVRfvaRiCM7+4jEzye7jyXq736sf/Z5WaJ+hpGQF89f2X1utMTjbyjM8yP980/U3/+Y/vwTalgwO0Rks6qW8GPm/WPmh/fox3Rp5ZfPxY+ZPMfMD+8xksdM1J6rMNKdGu3HzJPHzA/v0Y/p0sovn4sfM3mOmR/eY8SOmWd7rpxzzjnnElFe7rlyzjnnnEs4ea5xJSKLRWSPiGyL0fFaishfIrJdRLaJyPhYHDc4diER2SIiv8foeG8H73GbiKwQkTJROEa6n5+IvC4iO4JjR3QNNxEpIyL/iMjWYP+fBNu/CT7XrSKyWkSqR/CY1UXkl2D/20XkkjSPvSsiKiJ1c3mM//wsM3pPIlJMRH4K3v92EZmcw2Om+/cgIp+IyO7guFtF5M40r+ksIpvS/G5F/Pcqr4j1+Ss4ZlzOYcl4/gqOE9NzWDzOX8H+k+4cFsvzV55rXAGPAz1jeLztwJWqWgSrf9FeRNrH6NhvAH/E4kAi0hw4E6ikqkWx341HonCo/3x+InIt0BooFxy7b4SPuRGorarFgLJACxG5CBtbLx1sXwm8GsFjfgx8FPzeHAhMAfvjBloBuyNwjPT+FjJ6Tw8BhYKfb2WgrYgcl4NjZvb3MElViwW3ewBEpAgwGrgwOHZjYEsOjpssYn3+gvidw5Lx/AWxP4fF4/wFyXkOi9n5K881rlR1OPYDj9XxvlPVscH3a4D1QMNoH1dEWgDHY798sSJA2eAXqjDwU6QPkMHndx3woKpuCp6zMMLHVFVNvXoujv3eq6o+qKrbg+2fAAdF4ngicghQneAEq6qbVXVF8PCbwOWROE56P8tM3pMCRYLPtiywB1idg2Nm9+/hJmCNqr4evOYnVd2Z3eMmi1ifv4JjxvwclqznL4j9OSzW5y9I3nNYLM9fea5xFU9BK7kC1pKNtreBAdgvUNSp6izgHeBnYCuwRVUfjMWxgfLAmUHX9wYR6RPpAwRDFFuBdcD3qvr8fk+5AHg/Qoc7DrtCWhIMi/wgIhVE5D5gbeofagxcwN73dGMQ01ZgCfCqqi7Nzc7T+Xs4I+hSXywiNYJtRwTPXRf8LCbm5pgud2J4Dnub/HP+giifw2J8/oJ8cA6L9vnLG1chichBwAfAI6r6a5SPNQjYoKpjonmc/Y5ZAzgZaACUAIqKyJOxOjxwAFAKuAZ4VkQkkgdQ1Z1BN3MNoJ6IdPz34CJTsH8CV0bocIWxn+H9qlocOxlMxN5bhwgdI1PpvKc+wf0SQCOgq4icmIv97//3cCVQDCiJDQVNCZ5aEKgKHBN8PUFErs/pcV3Oxeoclg/PXxDlc1iMz1+Q5OewmJy/sirhnog3rFW9LYbHK4ZdMbwTo+N9AewKbrux7tClUT7mI8CPae4/A8yPxecHrAWuSXN/J1A/iu91KvBu8P0IYBOWKxGp/R8G7Epz/0rgT+zEkPq5avD1sEj+LDN6T8A84Ok09xcDQ3J4zEz/HtLGBAwFfkrz2EfAe9H6bPPCLdbnrzCfWYSPldTnr/Q+w1iew6J9/gr2m7TnsFidv7znKgvB1cd8YJWqnh2LY6rqMapaUFULAgOBP1S1VpQPuwCoLiLlgvfcBlgU5WOmmgKcAyAip2JXgT9GauciUj/NjJOyQHNgtojchl0NHamq6yN1PFWdB2wWkdODTedif6ApaT7X3UDD4LkRk8l7WgW0EVMBy6eYnoP9p/v3ICJN0zzteiA1R2QIUDn4vSqCdbPPzO5xXc7F+hyWD89fEMVzWKzPX5C857CYnr+i0bKO5g1bdT71amgX8EKUj3d5cKytaW53xvD9XgP8HqNjfYKNaW8DlgKlYvH5Yd28S4PjbgGui/AxOwf73Roc4+Ng+44ghtTPdUEEj9kV2Bzsdw1QY7/HdwF1o/CzTPc9YUmhq4L3v50c9h5l9PeQ5vPbCvwGNE3zmieDx7YBX8fidzlRb7E+f2X2mcXo/SbV+SujzzCa57B4nL+C/SfdOSyW5y+v0O6cc845F0E+LOicc845F0HeuHLOOeeciyBvXDnnnHPORZA3rpxzzjnnIsgbV84555xzEeSNKxea2Croj6S5f72I3B2hfd8tIr+KyFwRWSgi3SOxX+ecAz9/udjyxpXLju1AJxEpH6X9D1HVw4GzgWdEpFCUjuOcy3/8/OVixhtXLjt2YcsSXLv/AyIySkS6pLn/T/D1JBH5VEReCxbEfFBEeojINyIyT0Rq778vVV2CFc0rG+zjBhH5VkS+D9YtQ0RqBIuJvhhsf0NEigePPRhcPX4vIg9H4wfhnMtz/PzlYsYbVy67ngB6iEiZbLymKXA1tl5VL6yqb0vgWWDA/k8WkWbAElX9I1hK4lCgJXA40FxETgieWg8YoapNgI3AFSJyILYMRaNg+305eI/OueTk5y8XE964ctmiqhuBl4CrsvGyb1V1japuB35m74rj87BV3lNdKyI/Al8DdwfbTg1uc4DZQH3sZAW2PtSM4PvR2IKbG7FlCp4VkU7YFaRzzvn5y8WMN65cTjwGXIStp5VqF8HvU7A4ZuE0j21P8/2eNPf3AAXTPDZEVetha1q9JCJFsQVQH1DVw4NbHVV9Lnj+/ms3qaruwq4SxwMdgck5eofOuWT1GH7+clHmjSuXbar6J/AadoJKtRxbrR0soTPHyZyq+ia28ngf4APgQhEpCSAih4hIxeCp1USkVfB9d2B68LwyqjoJWzT28JzG4ZxLPn7+crFQMOunOJeuR4D+ae6PBN4RkW+Aj7HV1HPjHmAs0CC4fWkXlPwD9MRWUl8E9BGRZ4AlwFNAmSCO1KvG/ySvOufyPT9/uagS1f17Jp1LfCJSA3hPVRvHOxbnnMsOP38lPx8WdM4555yLIO+5cs4555yLIO+5cs4555yLIG9cOeecc85FkDeunHPOOeciyBtXzjnnnHMR5I0r55xzzrkI8saVc84551wE/T8h1gqaD9ajhgAAAABJRU5ErkJggg==\n",
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\n",
       "text/plain": [
-       "<Figure size 720x360 with 2 Axes>"
+       "<Figure size 1080x360 with 3 Axes>"
       ]
      },
      "metadata": {
       "needs_background": "light"
      },
      "output_type": "display_data"
-    },
+    }
+   ],
+   "source": [
+    "for bm in unique_bms:\n",
+    "    flag = \"staic_multimap_count_matching_rate\" == bm\n",
+    "    \n",
+    "    if flag:\n",
+    "        tmpdf = perfdf[perfdf[\"Benchmark\"] == bm]\n",
+    "        \n",
+    "        unique_labels = tmpdf[\"Label\"].unique()\n",
+    "        plot_dual_perf(bm, tmpdf, \"MatchingRate\", unique_labels)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### `find_all` performance by varying key muliplicities\n",
+    "<ul>\n",
+    "<li>100'000'000 key/value pairs inserted</li>\n",
+    "<li>100'000'000 probing keys</li>\n",
+    "<li>Fixed matching rate: 0.5</li>\n",
+    "<li>Fixed occupancy: 0.8</li>\n",
+    "<li>UNIFORM distribution</li>\n",
+    "</ul>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<Figure size 720x360 with 2 Axes>"
+       "<Figure size 360x360 with 1 Axes>"
       ]
      },
      "metadata": {
@@ -256,49 +355,78 @@
    ],
    "source": [
     "for bm in unique_bms:\n",
-    "    flag = \"nvbench_static_multimap_find_all\" == bm\n",
+    "    flag = \"staic_multimap_find_all_uniform_multiplicity\" == bm\n",
     "    \n",
     "    if flag:\n",
     "        tmpdf = perfdf[perfdf[\"Benchmark\"] == bm]\n",
     "        \n",
-    "        randomdf = tmpdf[tmpdf[\"Distribution\"] == \"RANDOM\"].reset_index(drop=True)\n",
-    "        unique_labels = randomdf[\"Label\"].unique()\n",
-    "        plot_perf(bm + \"_RANDOM\", randomdf, \"NumReps\", unique_labels, flag)\n",
-    "        \n",
-    "        cycledf = tmpdf[tmpdf[\"Distribution\"] == \"CYCLE\"].reset_index(drop=True)\n",
-    "        unique_labels = cycledf[\"Label\"].unique()\n",
-    "        plot_perf(bm + \"_CYCLE\", cycledf, \"NumReps\", unique_labels, flag)"
+    "        unique_labels = tmpdf[\"Label\"].unique()\n",
+    "        plot_single_perf(bm, tmpdf, \"Multiplicity\", unique_labels)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### retrieve"
+    "### `find_all` performance by varying occupancies\n",
+    "<ul>\n",
+    "<li>100'000'000 key/value pairs inserted</li>\n",
+    "<li>100'000'000 probing keys</li>\n",
+    "<li>Fixed matching rate: 0.5</li>\n",
+    "</ul>"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<Figure size 720x360 with 2 Axes>"
+       "<Figure size 1080x360 with 3 Axes>"
       ]
      },
      "metadata": {
       "needs_background": "light"
      },
      "output_type": "display_data"
-    },
+    }
+   ],
+   "source": [
+    "for bm in unique_bms:\n",
+    "    flag = \"staic_multimap_find_all_occupancy\" == bm\n",
+    "    \n",
+    "    if flag:\n",
+    "        tmpdf = perfdf[perfdf[\"Benchmark\"] == bm]\n",
+    "        \n",
+    "        unique_labels = tmpdf[\"Label\"].unique()\n",
+    "        plot_dual_perf(bm, tmpdf, \"Occupancy\", unique_labels)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### `find_all` performance by varying matching rates\n",
+    "<ul>\n",
+    "<li>100'000'000 key/value pairs inserted</li>\n",
+    "<li>100'000'000 probing keys</li>\n",
+    "<li>Fixed matching rate: 0.5</li>\n",
+    "</ul>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
     {
      "data": {
-      "image/png": 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L1Q5Gt9xixesPP2yXBCtUiDoy55wrWE6OLb9VvTqsWbNtXVUiFZhcicgthb1RVfsU9ryINADezvPQPsC9qvpkcQJ0ziXOjBlWqD5ihM2oeeklaNAg6qicc65g69dDnz42c/m772DHHa3GSiS6mAprxVAtuLUErgX2DG7XYLVXhVLVP1W1mao2A1oA64D3Sxuwcy72srPhySehcWNrq/Dss/DVV55YOeeSV966qrvvtqbFq1fbc1EmVlD4ZcEHAERkONBcVVcH9+8HBhdzP62Av1S1gEUynHNR+fVXuOIKGDvWFit9/nnvrO6cS24LF8K550ZbV1WYME1E6wAb89zfCNQr5n4uBN4s5nucc3GUmQn33w/Nm9vU5IwMW/LBEyvnXLLatMk+7rYbVK1qpQvjxiVXYgXhmoi+AfwoIu8DCpwNvB52ByJSEWgH3FnA81cBVwHU8aO6cwnxww9w+eXw++9w0UV2SbBGjaijcs45mDABXnkFli2DE0+0mX+qVlf10ktWT7XzzvD551FHWrAiR65UtSdwGbAcWAFcqqoPF2MfrYEJqrqwgO33V9WWqtqyhh/dnYurNWvgppvgyCNh1SobqcrI8MTKOZccXn8dTj8datWCU0+1mqqmTTfXVTVvbgXsyS7s8jc/A/NzXy8idfLrf1WADvglQeciN2IEXHWV9YC59lro1ctm1TjnXDJYuxZuvhm++cYaF69ZY2uWTp0Ke+5py2+dcELUUYYTZvmbLsB9wEIgGxDs8mCTEO+tgjUavbp0YTrniiMjA7p3h9mz7aC09952wNp/f5sFeOyxUUfonHNb+uknm6HcKOhHsMMOdr9xY5g7N3USKwg3cnUj0EBVlxZ346q6Dti12FE550osI8NGqNats/tz59qtbVt45x3Yfvto43POufxUqwaLF0PPnjYTsGFDW8+0X7/NLRZSRZjZgnOAlfEOxDkXG927b06s8po0yRMr51zyOvhgWyT+7ruttQLAvHnw2GPWaT2VhBm5+hsYLSKfAJm5DxbVod05F41ZBXSTmx22StI55xJM1ZbZWrLEZgJ+8gn88os1Nb7jDjj55KgjLJ4wydXs4FYxuDnnkthOO8HKfMaavdOJcy4ZqcKNN8Izz9jH3r3h++9h6VIbwUrF2cxFJld5OrVXs7u6Ju5ROedK5NVXLbEqX96WtMlVpYrVMTjnXLLJzLRRqltugccft6Vrjjkm6qhKp8iaKxFpLCITgV+B30RkvIgcGP/QnHPF8fnncOWVcNJJVgRat64dpOrWhf79oWPHqCN0zrnNcnKs/cL229vxKzexSgdhLgv2B25R1VEAInI88CJwZPzCcs4Vx4QJ0L69TWF+913rX3XJJVFH5Zxz+cvJsZPBP/+EkSPTb7JNmNmCVXMTKwBVHQ1UjVtEzrlimTnTOhrvsgt89pk3BnXOJbfsbLjsMlviplUrqJiG1dyhZguKyD3YGoMAnYAZ8QvJORfWsmVw2mmwYQN88YUtGeGcc8kqK8vaKmRkwAMPwL33Rh1RfIQZuboMqAG8F9x2Ay6NZ1DOuaKtXw/t2sGMGfDBB3CgV0KWiIjcLCK/icivIvKmiGwvItVFZISITAs+7hJ1nM6lg65dLbHq2TN9EysIN1twOfDfBMTinAspO9tWiv/uO1vY9Ljjoo4oNYnIntjxrZGqrheRd4ALgUbASFXtJSJ3AHcAt0cYqnNp4frrYb/97GM6CzNbcISI7Jzn/i4i8nlco3LOFUjVpiy/9571gzn//KgjSnnbAZVFZDugCjAPOBMIekTzGnBWNKE5l/o2brQZzKplI7GCcJcFd1PVFbl3gpGs3eMWkXOuUH36wNNPw003WZLlSk5V/wEexxolzwdWqupwYA9VnR+8Zj5+zHOuRDZutBPAK66Ar7+OOprECZNc5YjIv72dRaQuoPELyTlXkLfespqF9u1t1MqVTlBLdSawN1ALqCoinYrx/qtEZJyIjFu8eHG8wnQuJWVm2gLMH34IffuWrfKFMLMFuwPfishXwf1jgaviF5JzLj+jR1vvqqOPhjfegHJhTo1cUU4CZqjqYgAReQ/r4bdQRGqq6nwRqQksyu/Nqtof6wVIy5Yt/aTTucCGDXDOOdYepl8/uPrqqCNKrDAF7cNEpDlwOCDAzaq6JO6ROef+9dtvcNZZsO++dhaYbg33IjQbOFxEqgDrgVbAOGAtcAnQK/j4YWQROpeCxo+HL7+EF1+0S4JlTZHJlYgIcBqwj6r2EJE6InKoqv4Y//Ccc//8A61bQ+XKdhZYvXrUEaUPVR0rIkOACUAWMBEbidoBeEdELscSsPOii9K51KFqS9gcdRRMnw61a0cdUTTCXFh4DjgC6BDcXw08G7eInHP/WrkS2rSB5cstsapbN+qI0o+q3qeqDVW1saperKqZqrpUVVup6n7Bx2VRx+lcslu7Fk45xWpDoewmVhAuuTpMVa8HNsC/swXTsFm9c8ll40YrBv39d1svsFmzqCNyzrn8rVljI+xffmnrBpZ1YQraN4lIeYIZgiJSA/BvnXNxpAqXX24Lmr76qp0NOudcMlq92hKrH36AQYPggguijih6YUaungbeB3YXkZ7At8DDcY3KuTKue3cYOBAefNBmCDrnXDLasAFOPRXGjrXVIjyxMmFmC2aIyHhsFo0AZ6nqlLhH5lwZ9fzz8L//wVVXWZLlnHPJqlIlOOkk6NYNzj476miSR5jZgvtifWCeFZHjgZNFZH7eru3Oudj48EO44QY44wx49lmbdeOcc8lm2TJYsAAaNYIePaKOJvmEuSz4LpAtIvWBl7BOxoPiGpVzZdAPP0CHDtCihc222S5MRaRzziXY0qU2WnXaaXZZ0G0rzOE7R1WzROQc4ClVfUZEJsY7MOfKkmnToG1bqFULhg6FqlWjjsg558z06TBzJjRuDOXLW2L155/e0LgwYWcLdgD+A7QNHqsQv5CcK1sWLbIzQIBhw2B3XyLYOZcEVq+GTp2sWL1RI+u6XqGC9bMaOtSSLJe/MJcFL8WaiPZU1RkisjcwML5hOVc2rF1r9VXz59vBqn79qCNyzjlz4422IsTs2da/6rzzrKHxlVd6YlWUMLMFfwf+m+f+DGy9LedcKWRl2bTl8ePh/ffhsMOijsg558y6dTBkCMyYARWDtuFPPw2HHmozml3hwoxclZiI7CwiQ0TkDxGZIiJHxHN/zqUKVbjuOvjkE5sV2K5d1BE559xm69ZBuXKw445w772wZAlUqQInnmgzBV3h4ppcAU8Bw1S1IdAU8P5YzgE9e9pq8XfdBddcE3U0zjm3pV13hTp1oHNna2b82Wf2+MCBlmC5whV6WTBY9qaXqnYr7oZFZEfgWKAzgKpuBDaWIEbn0sqrr8I998DFF8NDD0UdjXPObUvEVofo2hWaNLERrOuvhw8+gG+/jTq65FfoyJWqZgMtRErUynAfYDEwQEQmishLIuITzF2ZNnz45mLQl17yJqHOueS0aBH07g316sHhh9tI+667wrhxsPfeUUeX/MK0YpgIfCgig4G1uQ+q6nshtt0c6KKqY0XkKeAO4J68LxKRq4CrAOrUqVOM0J1LDRkZtozNrFmWTO21F7z77uYiUeecSzZdu1pt1dix0LRp1NGknjA1V9WBpcCJWJ+rtsAZId43F5irqmOD+0OwZGsLqtpfVVuqassaNWqEi9q5FJGRYWsEzppl91XtjPDjj6ONyznnCtO7N7z3nidWJRWmFcOlJdmwqi4QkTki0kBV/8QWfv69JNtyLlV1726zbvLasMEe79gxmpicc64gf/0FdetCjRrQpk3U0aSuIkeuRGR/ERkpIr8G95uIyN0ht98FyBCRSUAz4OESR+pcCpo9u3iPO+dcVJYsgeOOg8svjzqS1BfmsuCLwJ3AJgBVnQRcGGbjqvpzcMmviaqeparLSx6qc6mnoCvdXl7onEsmOTnwn//A4sVw001RR5P6whS0V1HVH7eaMJgVp3icSxsLFtglQBGrtcpVpYr1uXLOuWTx+OPWy+rZZ+Hgg6OOJvWFGblaIiL7AgogIu2B+XGNyrkUl5NjPWI2bYJevayGQcQ+9u/v9VbOueTx/ffW0Lh9e7j22qijSQ9hRq6uB/oDDUXkH2AG4P8anCvEE09YT6t+/eDqq+G226KOyDnn8lelCrRqZb2svPdebISZLfg3cFLQALScqq6Of1jOpa4JE+DOO+Gss6wNg3POJSNVS6aaNYPPP486mvQSZrbgriLyNPANMFpEnhKRXeMfmnOpZ80a6NABdt/dO7A755Lbk0/a2qabNkUdSfoJU3P1FraMzblA++Dzt+MZlHOp6qabYNo0eOMNWyqirFi1yi6Fnn++fQ9++y3qiJxzhfnxR7j9dpt4s12YAiFXLKE6tKvqg6o6I7g9BOwc57icSzmDB8PLL9slwRNOiDqaxFm6FA47DMaMgbPPhp13tq/fu9A7l5xWrIALLoBatWDAAB9hj4cw+eooEbkQeCe43x74JH4hOZd6Zs2yBZkPOwzuvz/qaBKrTx84+mgrhs114onWM6dNGyhfPrrYnHNbUoXLLoO5c+Hbb2GXXaKOKD2FGbm6GhgEZAa3t4BbRGS1iKyKZ3DOpYKsLGutkJMDgwZBhQpRR5RYw4fbiNXDD8Mjj9hjxx4L5crZJVLnXPKYNg1GjLAWMYcdFnU06SvMbMFqiQjEuVTVsyd89x0MHAj77BN1NIk1fTrMnw/nnAOZmVZzBfb5ypWw447Rxuec29L++1tN5F57RR1JegszcuWcK8C330KPHnDxxWWvMWhmJhx6KCxcCFWrwjffwNtv2wjeQw/BIYdYTYdzLnorV8Jrr9llwTp1vM4q3jy5cq6EVqywhKpePejbN+po4i8rC956y75mVahUCd580xahvvJKaNvWaqwaNLDLDgMGRB2xcw7s7/XKK21B5j/+iDqassEnYDpXAqrWeX3ePLskmM6Xv1autJ5dTz9tidT++9vXveeecOqp9ppeveDGG2HcOButat7cz4ydSxb9+tls5l694IADoo6mbCgyuQrWFZyrqpkicjzQBHhdVVfENzTnkteAAfDOO1bEfeihUUcTPxMmwPHHw+rVcNxxNkJ3+ulWrL61mjVt9Mo5lzx+/hluvhlat4Zu3aKOpuwIc1nwXSBbROoDLwN7Y7MHnSuT/vwTunSxXk7puGbgTz/BRx/Z5wcdBJ062WOjR1vylF9i5ZxLPps2wYUXWkPj117zv91ECnNZMEdVs0TkbOBJVX1GRCbGOzDnklFmJlx0EWy/vXVhT5ceTtnZ1vSzd28r0j/gAEukKlSA556LOjrnXElUqGB96KpVgxo1oo6mbAmTx24SkQ7AJcDQ4LEy1snHOdO9u10qe+UVqzlKB0OHQsOG1qtqzhxbxuaHH7xmyrlUtnSpfWzTBo45JtpYyqIwydWlwBFAT1WdISJ7AwPjG5ZzyWf4cBvZufZaOPPMqKMpnXnzYNEi+7xiRahe3dooTJ9uawOmc4G+c+lu8mSbxfzOO0W+1MVJkcmVqv6uqv9V1TeD+zNUtVf8Q3MueSxaZMu5NGpkCVYyW7PGOqWfcAKccYYdYFXtuV9+gUsusQPvo4/aYyefbCNV55/vC7g6l+rWrLG/5R12sEkoLhphZgseBdwP1A1eL4CqahnrRe3KKlW49FLrazViBFSuHHVEBduwAVq1su7Ld9wBy5ZZB/nBgy3+L76whp/XXGMjcOCX/5xLJ9dfb5NuvvgC9tgj6mjKrjDnqS8DNwPjgez4huNc8nnmGfj0U/t40EFRR1O4QYNg550tmcrKsoLWU0+1Ngm77GJ9bq66yhdrdS4dvfoqvP463HefLZ7uohMmuVqpqp/FPRLnktAvv1hvmDPOsDPCZPfNN9C+vTXzPP98+OADaNoUTjkFLrjA2io459LTsmV2mf+ee6KOxIUpaB8lIo+JyBEi0jz3FvfInIvYunXQoYP1iHnlldS4fFajBrz7Lhx9tF3OzL3NnZs+sxudc/m75RYYNix9WsSksjAjV4cFH1vmeUwBH3R0ae2WW2DKFKuzSoUeMWvXWq3F55/D4Ydbi4Wdd4annrJaLC9udS49detmE1jatPFGocmiyORKVU9IRCDOJZP33oMXXrCD1kknRR1NOP37WyPQiy6yburHHmvrAtaubYmWH3SdSz8DB8Ljj9tC6m3aRB2NyxVmtuBOwH3AscFDXwE9VHVlPANzLipz58IVV0DLlvDQQ1FHU7Tly61AvUsXOOIIG7XatMl63eywgy207JxLP3/8YTN/jzkG7r8/6mhcXmEuC74C/AqcH9y/GBgAnBOvoJyLSna2FX1v3Ggz7ypWjDqigm3aZO0W3nwTJk60adeHH27PVagAzb0y0rm0snKlzQj8+WeoU8dmBW+/vR0DvEddcgnz49hXVc/Nc/8BEfk5TvE4F6leveCrr2DAANhvv6ijKdi8eTb779tv4YYbvLWCc+nun39shOrQQ21G4BtvWE3oU0/5ZJVkFCa5Wi8iR6vqt/BvU9H1YTYuIjOB1Vh/rCxVbVn4O5yLzg8/WH+YCy+0LubJavRoS6zWrLHRtQ4doo4oeiKyPXAGcAxQCztG/Qp8oqq/RRmbc7Fw//3WXqVXsD7KZZfBvffa6NV//xtpaC4fYZKra4HXgtorAZYBnYuxjxNUdUkJYnMuYVautELw2rXh+eeTu+3Ck0/aWoCjRtlyPGWdiNwPtAVGA2OBRcD2wP5AryDxulVVJ0UVo3Ol9dln8PXXMGuWNQjed19Lrvr0gdWroVq1qCN0eYWZLfgz0FREdgzur4p3UM4l2vXXw+zZdvDaeeeoo9nWihXWd6tWLau5KF/eD6Z5/KSq9xfwXB8R2R2ok8B4nIu5qlWtSejtt8Pvv8OMGdZiRcRqLF1yKTC5EpFOqjpQRG7Z6nEAVLVPiO0rMFxEFHhBVfuXJljn4uGNNyAjA3r0gCOPjDqabf38s3Vdr1kzeZO/KKnqJ1s/JiLlgB1UdZWqLsJGs/IlIjsDLwGNsWPWZcCfwNtAPWAmcL6qLo917M6FdfHF0Lkz/PabtYmpWNFaxZx1lhW1u+RSWOebqsHHavncdgi5/aNUtTnQGrheRI7d+gUicpWIjBORcYsXLw4fuXMxMH06XHedFYredVfU0WxrwABrr7BhAzzySHJfroyaiAwSkR1FpCrwO/CniHQL8dangGGq2hBoCkwB7gBGqup+wMjgvnORueACaxJcsSKMHAkNG9qJ11NPRR2Zy4+oauEvEDlKVb8r6rEid2R1EWtU9fGCXtOyZUsdN25ccTbrXIlt2gRHHQXTptkagnWS6MLRhg3Wt+qll6BVKytc3333qKOKPREZH6uJLiLys6o2E5GOQAvgdmC8qjYp5D07Ar8A+2ieg6GI/Akcr6rzRaQmMFpVGxS2fz9+uXhRtQXYv//eGhwvXQp77w2HHeYnXFEr6BgWpqD9GWDrjjn5Pbb1DqsC5VR1dfD5KUCPkPE6F3f33gs//WSzbZIpsQJL/L77Drp3hwce8LXCQqogIhWAs4C+qropKEkozD7AYmCAiDQFxgM3Anuo6nyAIMHKN7UVkauAqwDqJNsvkUsbmzZZM+Czz7ZF2F3yK6zm6gjgSKDGVnVXOwJhDvV7AO8HNVrbAYNUdVgpYnWu1DIyLGGZNcvuH3+81TMliy++sLqvatVg/HioXDnqiFLKC1h91C/A1yJSFyhqAs522IliF1UdKyJPUYxLgEEdaX+wkauSBO1cUSpWhL59o44iveX+b5g92062e/aEjh1Lvr3Caq4qYrVV27FlvdUqoMh/R6r6t6o2DW4HqmrPkofpXOllZMBVV21OrADGjrXHo5aVZTVfJ58Mjz1mj3liFY6IHCEioqpPq+qeqtomuMQ3GyhqbdS5wFxVHRvcH4IlWwuDy4EEHwssiHcuXlSth9UPP0QdSXrL+79B1T5edVXp/jeEqbmqq6qzCn1RjHjNgounevW2TKxy1a0LM2cmOprNFi60RqCjRsGVV8LTT5ed2T+xqLkSkX7AocBUYBhWnL6gGO//BrhCVf8MakNzJ/MsVdVeInIHUF1VbytsO378crH22ms2Q/Dpp60G08VHaf43lKbmap2IPAYciDXmA0BVTwzxXueSxuzZxXs8EX76yaZSL1tmMwM7d44ullSlqtcAiEhDbGbyq0HT41FYsvWdqmYXsokuQIaIVAT+Bi7FRvXfEZHLsRGw8+L4JTi3jXnz4KabbCbz9ddHHU36+uOP/BMrKN3/hjDJVQbW7+UM4BrgEqwA1LmUUrs2zJmz7eNR1iHvsos1Bv30U2jaNLo40oGq/gH8ATwhIpWxS4LnAX2AAkfHgkbJ+T3fKg5hOlckVbjmGsjMhJdfhnKFFfC4Ytu0CT78EJ57zq4YFKQ0/xvCJFe7qurLInKjqn4FfCUiX5V8l85Fo1GjbZOrKlWscDGepkyxGYnZ2TbbZ9994ZVXrJaifn348UefTh1LIlIFaIR1bv806nicK66PP7Zbnz7JvYB8qpk7F1580W7z59tlv4cfhp12soas69Ztfm1p/zeEyYc3BR/ni8jpInIwULvku3Qu8X78EYYPh5NOsj8oEfvYv3/pZoQU5emnbUbiqlV2FnrKKdaf5tZbYcIEe40nVqUjIu1EZKaITBCRNsBvQF9gsogk8RLczuWvTZvNJ2CudHJybBb2uedabdWDD0KzZpa8/vUX3HmnNZLu3z+2/xvCjFw9FNQv3Ir1t9oRuLnku3QusTZtskLxmjVhyBA7S0mEWbOsR9XEiTa8nJEBa9dag9D+/aFFi8TEUQY8iPXRy62zaqKqfwe9qUYCr0UZnHNhqdoizDvuCJdeGnU0qW35cluHtV8/mDoVdt3VTmqvvhr22Wfb13fsGNsT7UKTKxEpD+ynqkOBlRQ9rdm5pNO7N0yaBO+/n7jECuzM6KyzLLG6+24bYj7mGGjc2IanXczkqOpUABGZoap/A6jqIhHJijY058J7+20rYv/qK2hQ6HoAriDjx1st1Ztvwvr1tnzYG29YP8NEzsIu9LJgMMOmXYJicS7mpk+30aNzzrFEJ5HKl7dRM4ATToCbb7Y1wSpVgu3CjBm7sMqJyC4isiuQE3xeXUSqE670wbnILVoEN9xgl6Tq1486mtSyfr2NUh16KLRsCW+9ZQtdT5xoSwZ16pT49jZhDvHfi0hfbMbg2twHVXVC3KJyLgZyZ9xUrAjPPJP4/Z99Ntx+O/z+u60P2KqVXSrMyIBvvkl8PGlsJ2zZmtzqtbzHJu+a7lLCDTfYJcEBA3y5q7CmTbPLfgMG2GXAAw6wOtf//CexVynyEya5OjL4mHddQAW8z5VLaq+9ZiNFzz9v7Q4SbfBgWLPGFlc96ywbrfrwQyuo9CH/2FHVelHH4FxpDBlix4uHH7ZZza5gWVkwdKhd+hsxwo6r55wD114Lxx2XPBOEikyuVNXrrFzKWbTIihePOsqWMUi03Jk+Z51lo2affGIHhR49YK+9Eh9POgtqQyur6prg/uHY8l0AE1V1dWTBORfCsGE2waVbt6gjSQ75rfN34onw0ks2GWjuXOtb2KMHXHGFTVZKNkUmVyKyB/AwUEtVW4tII+AIVX057tE5V0I332xD7P37J74B31tv2R/8KafY55Uq2QwVFzePYGv/PRrcfxP4FVtRYgJwe0RxORfKiy/CypVeiwmb1/nL7Tk1axZcEjRUyc629VefeQbOOCO5v19hQnsVGAB0D+5PxeqvPLlySWnYMBg0CO69N/FD7NOmWSHl0Ufb7MRKlRK7/zKqFXBInvsrVLWtiAjg1W0uaX39Ney5pzUW3nnnqKNJDt27b9nMEyypqlbNZgKmSlPVMOf0u6nqO0AOgKpmAYWt0+VcZNautWvvDRvCXXclfv/77WfFlUOHWodflxDlguNSrtsB1Fal3yGakJwr3LJlcP75m0dlnClonb81a1InsYJwI1drgynOCv/WM6yMa1TOldB999kq5l9/ndhRo++/hwoV4JBDbNqvS6iKIlItt7ZKVYcDBM2PEzwB27lwbrwRli6Fzz+POpLkMWmSHUdzW9jkFeUasCURZuTqFuAjYF8R+Q54HVtF3rmkMn48PPGEXa8/5pjE7XfCBGjd2pZQUJ/4H4UXgbdF5N/Dr4jUxWqvXowsKucK8PHHMHCgja77gu123Hz6aetTVaXKtifGiVgDNtaKTK6CflbHYS0ZrgYOVNVJ8Q7MueLIyrIlbnbfHR55JHH7/e03K1zfZRd4773kmQZclqhqH+wE8FsRWSoiS4CvgY9VtXe00Tm3peXLbYLLQQdZfVFZt3gxtG1rI3knnWR1qy+/nNg1YOMhzGzB7YHrgKOxS4PfiEg/Vd0Q7+CcC+vJJ60b7+DBiSsMnT7dDgYVK1o/LW+xEB1V7Qf0E5EdAPH2Cy5ZVahgiwh37mzHjrJsxAhr+Ll8uY1c3XCDJVSxXucvCmEuC74OHIgt2twXaAS8Ec+gnCuOv/+2mYHt2tlBK1H69LERsy++sNk+Lhoi0klEygGo6pqtEysR2VdEjo4mOue2tMMO1kqgLC/cvnGj9fTKHfX/8Ufo0iW9Rv7DFLQ3UNW8V4VHicgv8QrIueJQtdmB5ctD376J/eN86inrp5VKM1jS1K7ARBEZjy2DsxgrZK+PlTQsAe6ILjznrI/VeedZF/aWLaOOJjpTp8JFF1mN7DXXQO/e6TmzOszI1cRghiAAInIY8F38QnIuvIwMGD4c/ve/xFyWW7oUOnSAhQtteN8Tq+ip6lNAc6yAvQbW96o58A9wsaqeq6rTIgzRObp2tfKBnJyoI4mGqi2u3Lw5zJhhNarPP5+eiRWEG7k6DPiPiMwO7tcBpojIZKyVTJO4RedcIZYssZGjww+30at4W7kSTj0Vfv3VZgbusUf89+nCUdVsYERwcy6pjBhhS7fcdpvNiCtrVqywY/Rbb8Hxx8Mbb9jyNeksTHJ1WtyjcK4Ebr3V/mj794//KvJr19pyC7/8Ah98kNhWD8651LV6tS2H1bAhPPBA1NEk3vff22XAuXOtncLtt8f/eJ0MwizcPEtEmgK5/06+UVWvuXKRGjECXn/dpjIfdFB897VhA5x9th0k3nwTTj89vvtzzqWPvn1hzhz47jvYvgy1tM3OtmSqRw9rAPrtt3aVoawosuZKRG4EMoDdg9tAEfEmoi4y69ZZIeR++8Hdd8d/fytW2FnXyy/bchXOORdWt242o/iII6KOJHFmz4YTTrAVMy64wNrklKXECsJdFrwcOExV1wKIyCPAGKw1g3MJ98AD1n5h1Kj4nglmByto/t//2cHBF2FObiJSCTgXqEeeY5uq9ogqJld2rVkDmZmw665w4olRR5M4775rl0GzsuzqwsUXRx1RNMLMFhS2XKg5O3jMuYT7+Webunv55VYYGS85OdZF+aKLLMnyxColfAicCWQBa/PcnEu4O++Exo1tIkxZsHatLT3Wvr1dVZg4sewmVhBu5GoAMFZE3g/unwW8HLeInCtAdrYtcbPrrvDoo/HbjyrccotdBrznnrJRfJkmaquqT8BxkfvqK6u1uvFG2GmnqKOJv59/thY1f/4Jd9xhVxfKevf5MAXtfURkNLb8jQCXqurEsDsQkfLAOOAfVT2jpIE698wzMG6cTeetXj1++7n33s0NQsvi7J4U9r2IHKSqk6MOxJVd69bZyPo++6TeYsPFpWrHyttvt5PeESOgVauoo0oOYUauchdvnlDCfdwITAF2LOH7nWPWLCteb9MmvkXljz8ODz1kw9u9e6fXcgzpKrfnHnY8u1RE/gYysZNB78XnEqp7d/jrL6sJrVo16mjiZ+FCuPRS+OwzW3j5lVdgt92ijip5hEquSkpEagOnAz2BW+K5L5e+cpe4AXjuufgmPIcfbjMRE72UjisVHxF3SSE7204Er78+vjWhUfv8c7jkEptJ3bevNVX24+WWCkyuRKSSqmaWcvtPArcB1Uq5HVeGvf22nR098QTUrRufffz5JzRoAEcfbTeXOlR1FoCIvKGqW5TQisgbQBkuq3WJVL68zZbLyoo6ktjJyLDRuNmzbYmxAw+04/GBB1qLicaNo44wORU2W3AM/HtwKjYROQNYpKrji3jdVSIyTkTGLV68uCS7cmls2TIrCj3kEFs1PR7eegsaNYLBg+OzfZcwB+a9E9R7togoFldGrFkDmzbBiy/C9Ok2glOhQtRRxUZGhpVIzJplVxBmz7bE6qST4KefPLEqTGGXBSuKyCXAkSJyztZPqup7RWz7KKCdiLTBVqjfUUQGqmqnrbbTH+gP0LJlSy1W9C7tdetmiyUPHx6bWXuqtoTN8uW2Mv3o0TZd+KijvPN6qhKRO4G7gMoisir3YWAjwbHFuVgbNcqOT7//bgnV+vWWiPTrF3VksdO9uxXob23aNKhcOfHxpJLCkqtrgI7AzkDbrZ5ToNDkSlXvBO4EEJHjga5bJ1bOFWbUKCuSvP12aNq09NubOdN6sCxfDjVrWpK1YQMcfDAMHZq+q7OnO1X9H/A/EflfcNxxLq5+/dUm1vTvb4u5N20K//xjJ4LpZPbs4j3uNiswuVLVb4FvRWScqnpfK5dQuWeB++5rSyiUliqcey5ceKEt+LxwIdSvb2ec998PO/pc1pQlIs2DTwfn+fxfwWxn52Kmb1+46SZbc/SOO+xy4IcfwmWX2TqCe+0VdYSll5MD1arBqlXbPlenTuLjSTVhZgu+ISL/BY4N7n8F9FPVTWF3oqqjgdHFjs6VWQ89ZAesESNiM/z888/WKblrV0uoataEF16w0azBg63Fg0tZvYOP2wMtgV+wy4JNgLFYjz7nYuavvyyx+vZbeOwxS6ratYOGDe2YkurJVWYmdO5sidV2221ZoF+lSvr374qFMMvfPIcVhT4X3JoDz8czKFe2TZpkHdgvucQKJ2Nh6VLYc0+YNw9+/NEe69QJDjgg/YbyyxpVPUFVTwBmAc1VtaWqtgAOBqZHG51LR02b2ky5Fi3grruskeayZfDbb5ZgpbIVK+C002yiT69eMGCAzdIWsY/9+0PHjlFHmfzCjFwdoqp5K16+FJFf4hWQK9uys+1y4M47W0PPWDn0UKuxOvVUWLwYZsywEbGBA72jcBppmLc7u6r+KiLNIozHpakrr4RjjoHdd7e+eJMmWXH7pZdCjRpRR1dyc+ZA69YwdaodG3OTqE5eLV1sYZKrbBHZV1X/AhCRfdhyIWfnYua552DsWPvDjmW33x12sLOuSZPsYPj55/DGGzbF+PLLY7cfF6kpIvISMBCbdNMJWx3CuZh6/HGrRxo71j7fbTc7KYxXu5hEmDzZEqvVq2HYMDjxxKgjSm1hkqtuwKhgSQkB6gKXxjUqVybNmWND7KeeChddFNtt33mnJVZdutiw98sv2yXH11+3xMulhUuBa7EltwC+xksYXIy99JLd7rorfWqPvvzSasiqVYNvvoEmvmBUqYVZuHmkiOwHNMCSqz9i0LnduS2o2pIROTnw/POxXUrh88+thuu666w2wpdpSE+qugF4Irg5F3PjxsENN8DJJ0OPHlFHExuDBlnx+v77W4PQVC/GTxZhF27OBCbFORZXhg0ZAh9/bEPse+8d222fdJI19rv8ck+s0pGIvKOq5+dZwHkLvnCzi4UlS6ydyx57WEISi6bGUVK1mY633w7HHQcffGC1ri424rpws3OFyV2zatYsKFcO6tWzpW5i5a+/YPvtbZbg1VfHbrsu6eT+1vgCzi5uNm6EffaxhCSW9aBRyM62Y+2zz1rvv1dfhUqVoo4qvRTaikGMDxK6mMu7ZhXY5cAFC2yR5lhYtsyKM884w87QXPpS1fnBp62Aiqo6K+8tythcelCFWrWsNqlly6ijKZ316+G88yyx6trVjsWeWMVeocmVqirwQWJCcWVJfmtWbdhgj5fWxo22zM2sWfDMM34psAypB7wgIn+JyDsi0sVbMbjS+uADaNvWmhCn+rFk6VJrPfPBB1Z/+thjdtXAxV6Yb+sPInJI3CNxZUq81qxStcL1UaNsRs/R3pu7zFDVe1X1RKAx8C0203l8tFG5VDZ1KvznP7ZcVqqP7syYAUceCRMm2KoU//1v1BGltzA1VycA14jITGAtNmNQvUjUlcZee+WfSJV2zaqXX7Zb9+5w8cWl25ZLLSJyN3AUsAMwEegKfBNpUC5lrVkD55xjSdW771r9ZqoaPx5OP91G9b/4wk86EyFMctU67lG4MueAA7ZNrmKxZtV559msnttuK912XEo6B8gCPsHWQP0haM/gXLGowhVXwJQpMHx4ai9U/NlndlzcbTcYPTr1l+dJFUVeFgwKQvcCTgw+Xxfmfc4V5K23rPfUaafFbs2q6dOtUHOnnWyVeq8jKHtUtTlW1P4jcDIwWUS+jTYql4rmzYOvv4aHH07t5bFeecXqxfbfH8aM8cQqkYocuRKR+7CV5hsAA4AK2PISR8U3NJeOfv/dzgiPOgo++ggqVCj9NufPhxNOgMMPt1oCVzaJSGPgGOA47Jg1B78s6Epgzz1tOZjq1aOOpGRUrcnp/ffDKadYH8Fq1aKOqmwJc35/NtAOq7dCVecB/mNyxbZ6tTXhq1oV3nknNonVunXQrh0sXw5331367bmU9giwI/A0cICqnqCq90Yck0sh8+ZZUpKVBbvumpqzAzdtsoWl778fLrkEhg71xCoKYWquNqqqiogCiEjVOMfk0pAqXHYZTJtmBZW1apV+mzk5dvAYPx4+/BCaNi39Nl3qUtXTS/peESkPjAP+UdUzRKQ68DbW3mEmcL6qLo9FnC455bZwmTTJ1jatXz/qiIpvzRo4/3yrs7rnHnjggdRMENNBmOTqHRF5AdhZRK4ELgNejG9YLt08+aQNTT/6KBx/fGy22bOnbbN3b6srcGVTQcve5Ao5s/lGYAo28gVwBzBSVXuJyB3B/dtLG6tLXrfeanVJ77yTmonVwoU2I3DiRHjhBWvS7KITZuHmx0XkZGAVsD9wr6qOiHtkLm188w1062arrnftGrvtXnCBjV7dfHPstulSUu6yN9cHH98IPnbEJuAUSkRqA6cDPYFbgofPBI4PPn8NGI0nV2lr4EDo29cSrPPOizqa4ps61SYILVxoo/hn+EJQkQu7tuBkoDJ2djg5fuG4dLNggQ1T77MPDBgQmyHq2bOtT9b++8N995V+ey615S5xIyJHqWreiTZ3iMh3QI8iNvEkcBtb1pLukbusjqrOF5HdYxiySyKrV1tDzeOOg169oo6m+MaMsZH7cuWsefKhh0YdkYMQBe0icgU2tfkcoD3Wsf2yeAfmUt+mTTa6tHKlNeHbaafSb/Ovv6B5c7jXy5TdtqqKyL/tEUXkSKDQGlEROQNYpKol6uQuIleJyDgRGbd48eKSbMJFrFo1GDHC1jXdLuxwQ4QyMmyR+3LlYPfdLSncZRf4/ntPrJJJmF+lbsDBqroUQER2Bb4HXolnYC713XWX9YoZOBAOOqj021uxYvNCzP/5T+m359LO5cArIpKbxq/AakQLcxTQTkTaANsDO4rIQGChiNQMRq1qAovye7Oq9gf6A7Rs2dKXCE8hOTk20tOqFbRoEXU04eQueJ+7LuvixZZk3XJLataJpbMwrRjmAqvz3F+N9Y9xrkDvvguPP27r/JW0MWhemzbZ5cW//oL33oP99iv9Nl16UdXxqtoUaAI0VdVmqjqhiPfcqaq1VbUecCHwpap2Aj4CLgledgnwYRxDdxF46CE46SSrCU0V+S14n5MDjzwSTTyuYAWOXIlIbmHnP8BYEfkQq7k6E7tM6Fy+/vwTLr0UDjsM+vSJzTZvvtmG7l95xYbBnduaiFQCzsXaJ2wnQYGfqhZVc5WfXthM6cuB2UAKljm7gnz2mfWBuvji1Fpnb9as/B8v7YL3LvYKuyyYW9z5V3DL5WdwrkBr11qj0EqVrFt6rFaSb9vW6gsuvTQ223Np6UNgJTAeyCzum1V1NDYrkKAMIoUXPnEF+ftv62PVpAn065c6faBUrT5s9eptn0vltQ/TVYHJlao+kMhAXOpTtc7Av/9ui53utVfpt7l0qXVKPvVUuzlXiNqqelrUQbjktWmTnfyBlS5UqRJtPGGp2uj96tVWdJ+Vtfm5WCx472IvzGzBliLyvohMEJFJubdEBOdSy7PPwptvwoMPWi1DaU2eDPvua0WczoXwvYjEYOqES1cVKljPvYwMO7akAlUrWH/qKbjpJmtpE6sF7138hJktmIHNGJwM5MQ3HJeqxoyxA0DbtnDnnaXf3oIFNjOwatXYdXR3ae9ooLOIzMAuCwqgITu0uzS3fLm1LLjooqgjCU/VGps++STceKPVsIpAp05RR+aKEia5WqyqHxV3wyKyPfA1UCnYzxBV9ZaPaWjRIutqvNde8NprNjW4NNavh7POgiVLbCbPnnvGJEyX/lpHHYBLTmPGWFnB4MGpU16gaitaPPGENTl94onUqQ9z4ZKr+0TkJWAkeYpEVfW9It6XCZyoqmtEpALwrYh8pqo/lDxcl2yys6FDB6uNGjPGzgxLQ9WK1seOtZYLzZvHJk6X/vJ0at8d61nlHAsX2oLMNWqkTpNNVbjtNhupuuEGG7nyxCq1hEmuLgUaAhXYfFlQgUKTK1VVYE1wt0Jw8yZ7aeaee+DLL60OoFmz2GzzsMMsqTr77Nhsz5UNItIO6A3Uwpp+1sUWYz4wyrhcdLKybJWIZctic/KXCKpw++3WJ/D66+Hppz2xSkVhkqumqlqiIlERKY9Ni64PPKuqY0uyHZecPvoI/vc/myHYuXPJtrFqlZ1Z1qljo2BVqvhCzK7EHgQOB75Q1YNF5ASgQ8QxuQjdcQd89RW8/nrsTv7iSdVifuwxuPZaeOYZT6xSVZjqmB9EpFFJNq6q2araDKgNHCoijbd+ja/NlZqmT7claFq0sDOr4tq40Ya769Sx1dz/7/9gjz1sfSznSmhT0J+qnIiUU9VRQLOIY3IRUbW2BTfcYM1Ck52qLRn26KNwzTXQt68nVqkszMjV0cAlpZmBo6orRGQ0cBrw61bP+dpcKWbdOusVU748DBkC25eguuWOO2DmTEvS1qyxJC0z03pkHXlkzEN2ZcMKEdkBm0iTISKLgKwi3uPSRGamnZxVrAiHH27Hp169LGlJdqq2tE2vXnD11dbWprQTg1y0wiRXJWrKJyI1sDPJFSJSGTgJ8BWQUpyqrRc4eTJ8+qmtzl5cGzbYMjZTpljfmTPOsPWxnnrKareuuCLmYbuy4UxgPXAz0BHYCSjJ0jcuxXz8sR039t7bVomYPt0uqV1xRfKP/qjC3XdbicVVV8Fzz3lilQ7C/Ai1gFtRagKjgoajPwEjVHVoSQN1yeHFF63dwr332uW8kli50pKqatVsG3/+aSNgp5wC//wT23hd2aGqa1U1R1WzgE+AZ4LLhC6NzZ5tM4w/+siK1vff38oObrtt20WOk42qHUsfftgSweef98QqXYT5MX4CDA0+jgT+Bj4r6k2qOklVD1bVJqrauISLp7ok8tNP0KWLJUT33lvy7dSoYYnVzz9Dw4bw9tvQqpWdfR52WMzCdWWEiBwuIqNF5D0ROVhEfsXKDxaKiC+Hk+YGDoQLL7RjxyOPWAuXxx6zS4MfFbtDY2Ldfz889BBcfjm88IInVumkyMuCW88UFJHmwNVxi8glpaVLrVdMzZp2MCvNQWDNGmuO16GDrYm1775WxPn44zByZOxidmVGX+Au7DLgl0BrVf1BRBoCbwLDogzOxdfy5VCrli1pc+edlmjdfLPVby5bFnV0Bbv/fujRAy67zJaw8cQqvYSpudqCqk4QkUPiEYxLTtnZtnbVggXw3Xe2kHJJrVplHZJXrYKXXrK6iMces75Wo0dDoxLNS3Vl3HaqOhxARHrkNipW1T8k2QtuXKm1amWXAPfbz5bKGjDAji8ff2zrCCajBx6w26WXWqmFJ1bpp8jkSkRuyXO3HNAc8J4JZUiPHvD55zZs3bJlybeTm1iNGwfvvGOfp8pSFC6p5V3zdP1Wz6XAXDFXGqecYsemBQssWRk40DqaX3QRNGgQdXTb6tHDRq06d7YTTE+s0lOYkatqeT7PLRR9Nz7huGTz6ad2MOjc2ZqFltTWiZV3X3cx1FREVmFtYioHnxPc92Vw0tiUKdbH6tVXbeT7gw+sFUPPntCuXcTB5eOhh+C++6xHoCdW6S1MzdUDiQjEJZ8ZM2z19aZNre9Kaa6w3HKLJ1YuPlS1fNQxuMSbM8dO2DZutNvFFyd3s9CePW25sIsvtlY05f23Nq2FuSy4P9AVqJf39ap6YvzCclHbsMEK2HNy4N13bVma0ujVC847zy8DOudKb+lSO5asXGkjVvvuG3VEhXv4Yetl1amT1YR5YpX+wlwWHAz0A14CsuMbjksWXbrAhAk2lbmkB66VK20W4H33wW67eWLlnCu9tWut8fDff8OwYXDwwVFHVLhevaz7eseOdvnSE6uyIUxylaWqz8c9Ehe5jAw7CMyaZffbtYO2bUu2rZUrrR/WuHHQpg0cdVTs4nTOlV2rVlmCNWiQzQ5MZo88Yu0hLrrImi97YlV2hEmuPhaR64D3sbUFAVDVJO4g4oorI8OWXsjb0XjECHu8Y8fibStvYjV4sCdWzrnSy8mxjuY1a9qo+nbFbiSUWI8+amuoXnihJ1ZlUZi5CpcA3YDvgfHBbVw8g3KJ1737tktFrF9vjxfH1onVWWfFLETnXBl2223WeDgrK/kTq8cfh9tvt8TqjTeSP14Xe2FmC+6diEBctHIvBW5t9uzib+evvzyxcs7FzmOPQe/e1nYh2UeAeve25qXnn++JVVlW4I9dRI5W1W8LeX5HoI6q/hqXyFzCrF8PlSpBZua2z9WpE24bGzdaf5kmTazQdIcdYhujc65sevVVG7W68EJ46qnStYSJtz59bGmv886zkgpPrMquwi4Lnisi34vIvSJyuogcKiLHishlIvIGtphz5QTF6eJk0ya44ILNyVFeVapYb5airFwJxx5rxZvgiZVzLjaGDoUrroCTT7a6pWRrupmRAfXqWVzVq8Ott1oLG0+sXIE/flW9WUR2AdoD5wE1saUlpgAvFDaq5VJDTo6txv7xx/Dcc7DjjlZjNXu2jVj17Fl0MfvKldZiYfx4mxXjnHOxsssutnbgkCHbnvxFbetJQMuX2yXLM86AChWijc1FT1STZ+mtli1b6rhxXiufCKpw4422cPJDDxW/cB22TKyGDIEzz4x9nC69ich4VS3FipXJw49fsbNyJey0U9RRFK5evfxrVevWhZkzEx2Ni0pBx7AkG2R1ifLAA5ZY3XIL3HVX8d+fnQ2tW3ti5ZyLrVmz4MAD7fiUzGI1CcilJ0+uyqCnnrLk6rLLbMpwSQpEy5e3S4qeWDnnYmXxYjjlFGsSmswNQpcvh+0LWBI87CQgl9685K6Mee01uOkmOOcceOGF4idWK1fCb7/BkUdacuWcc7GwZg2cfrqN/IwYAQcdFHVE+fvzT1u9YtMmqwPbuHHzc2EnAbn0V+TIlYhUEZF7ROTF4P5+InJG/ENzsfbBB5YQnXSSLR1R3NksK1faWWXr1nbm5pxzsZCTA+eea53X334bjj466ojy9/nncNhhdvwbPRpeecVqrETsY//+xV/RwqWnMP9eB2Bd2Y8I7s/FFnMeGq+gXOx9+aW1XGjZEt5/3/paFUduYjVxojUI3WWX+MTpnCt7ypWz3lAXXmijQslGFZ54wpqDNm5sC9rXrWtJoCdTLj9hkqt9VfUCEekAoKrrRZK5jZvb2o8/Wl3U/vvDp58Wvw/V1omV11g552JBFWbMgH32sX5WySgzE665xpqZnn02vP669/JzRQtT0L5RRCoDCiAi+5JnAWeX3H7/3S7j7b67DWlXr178bTz3nCdWzrnYe/hhGwn6NUnX+Vi4EE480RKre++1CTyeWLkwwoxc3QcMA/YSkQzgKKBzPINysTFzpnU2rlTJCkRr1SrZdm6/3UauWrSIaXjOuTLsxRfh7ruhUydo1CjqaLY1caKdTC5ZAu+8Y5ctnQuryJErVR0BnIMlVG8CLVV1dHzDcqW1YIElVuvXw/DhNuxelD/+sGLSceNgxQqrf5g92+ohPLFyzsXK++/bpbbWra0oPNmWtRkyxOqpVOHbbz2xcsUXZrbg2UCWqn6iqkOBLBE5K+6RuRJbvtw6p8+bZzVWjRsX/vqNG6FDBzjhBDuonHee9Wp57z1ru+Ccc7EyaZIdbw491EoNkmmpmJwcuP9+OwY2bWonms2bRx2VS0VhzhfuU9WVuXdUdQV2qdAlobVrbW2rP/6w1guHH170ex591IrWZ8yAl16y+qy1a+3MrXXruIfsnCtDDjzQLgcOHQpVq0YdzWZr18L551uD5c6dYdQo2GOPqKNyqSpMzVV+CZg3H01CGzdar5gffrAzwpNPDve+116zy4Hr1lkyNXGiPXbNNTZTprhtG5xzbmt//21NN2vXtuQqmcyaZfVVkydDnz7WaNnnxLvSCDNyNU5E+ojIviKyj4g8gfW9ckkkO9sKQz//3ApFzzkn/HvXrIFdd908PD9kiPXEys62LsTOOVcaCxfapJh27ayOKZl8+y0ccohNAPrkE7j5Zk+sXOmFSa66ABuBt7HmoRuA64t6k4jsJSKjRGSKiPwmIjeWLlRXEFW49lobrXr8cVszsDhatoRnn4Vq1WDMGDsAZmTYAcenHTvniuu77+yY1Lmz9YVq3Rrmz4fnn0+uxOXll63Vws47w9ixcNppUUfk0kWRl/dUdS1wRwm2nQXcqqoTRKQaMF5ERqjq7yXYlivEnXfaaFX37nDrrcV775tv2mzCr76yAviTToLx423q8aefxide51z6evRRO1m7/nqoXBluuMFGx4cOtaVjkkFWFnTtaovYn3yylUX4qhMulsLMFtxfRPqLyHAR+TL3VtT7VHW+qk4IPl8NTAH2LH3ILq9HHrHbtdfCgw+Gf58qPPQQXHSRFb3//DMcfLAtk7PbbrbGl7dfcM4Vx7x58L//2Qj4bbfB3LmwerXNPs7Ojjo6s3w5tGljidVNN9lJpCdWLtbCFKYPBvoBLwEl+vMQkXrAwcDYkrzf5a9/f7jjDpvW3Ldv+OH2jRvhqqusaP3ii23Uq1Kl4o96OedcXiNH2khQbsPiO++0k7R//oHPPoO2baON748/rOxh5ky7JFjcEgrnwgpTc5Wlqs+r6o+qOj73FnYHIrID8C5wk6quyuf5q0RknIiMW7x4cTFCL9vefttm851+uiVJxWnCt2CBna098IC912cDOudioVo1q6264QbYsMFqmc4/H5YuteeiNGyYjdKvWGEj9J5YuXgK8y/5YxG5TkRqikj13FuYjYtIBSyxylDV9/J7jar2V9WWqtqyRo0axQi97Bo2zEacjj7aaqPCNuGbP98uB9apY2dw996bXMWlzrnU9n//B99/b/3yJk60x6ZOtfsXXxxNTKrWXuH006FePWsMevTR0cTiyo4wydUlQDfge6wFw3hgXFFvEhEBXgamqGqf0gTpNvvuO2uz0LgxfPwxVKkS7n3ffw9Nmlg9BJRsAWfnnCvIu+9Cq1ZWs1mtmhWMt25tRew9exa9UkQ8ZGbaCNWtt8LZZ9vxs06dxMfhyp4wswX3LuG2jwIuBiaLyM/BY3epqs9BK6FffrGzr732stGrnXYK97533oH//Mfe1759fGN0zpU9/frZpJrDD7d1A6tXtw7n69fbjOSdd058TAsW2InomDG2pM099yTfGoYufYXqtC4ijYFGwPa5j6nq64W9R1W/BfyiU4xMm2brBVarBiNG2BI1RVG1mYR33glHHWXL4ey2W9xDdc6VMSecYMlVnz6wffBf4tRTExtDRoa1o5k92y5PZmZa3dfgwX5S6RIvTCuG+4BngtsJwKNAuzjH5fKYO9dm4GRnW2IVdlj7jz/sbK1DB/jiC0+snHOx888/drlPFRo0gOee25xYJVpGhs2AnjXL4pk/31oudO/uiZWLRphB0vZAK2CBql4KNAV8flmcZWRY8aUI7LOPLR8xbBg0bFj0e3OXrDngAFtnMCMjuoOec8msoJUkgok7I0RkWvDROyHl8eOPtoJDr17w119RR2NJ1Lp1Wz6mau1qnItCmORqvarmAFkisiOwCNgnvmGVbXnPwsCSJVUbiSrKzJnQvLmtDwjWY8ZnBDpXoNyVJA4ADgeuF5FG2KoUI1V1P2AkJVulIi0NGgTHHmsnbGPGQP36UUe0+Vi5tdmzExuHc7nCLty8M/AiNlNwAvBjPIMq6/I7C8vMtMcL8+OPNjNn7lxbiNk5V7hCVpI4E3gteNlrwFmRBJhkevaEjh3tOPPjj9HMAMwrJ8fqvAriMwNdVIpMrlT1OlVdoar9gJOBS4LLgy5OSnIW9t57cPzxULWqnU2ecEJcQnMubW21ksQeqjofLAED8p1CUtaaIDdvDldfbbWfUddwzphhx7lbb7UR+sqVt3y+ShVLBp2LQqiJqSLSRETaAc2B+iJyTnzDKruGDCn4Ml5BZ2E//2xFm02b2sruYeqynHObFbWSREHKQhPkmTNh4ED7vHVra7tQsWJ08aja0jVNmlij0gED4KefbBmvunXt+Fm3rtVbdewYXZyubCuyFYOIvAI0AX4DcoKHFci347ormexsuPtuKxCtX98u7W3YsPn5ws7CmjWzZWzat9/27M05V7gCVpJYKCI1VXW+iNTEak3LnG++sV5RqnDGGdH0q8prwQK44gr45BMbtRowwBIpsETKkymXLMKMXB0enJldoqqXBjdflSmGli2z5qC9elkh+6+/2nIRhZ2FrVoF551no1ZgS0t4YuVc8RSyksRH2OoUBB8/THRsUXv5Zeu4Xr26dTaPOrEaMsRqvEaOhKeesvYyuYmVc8kmTHI1Jpg94+Jg8mSb0vzll/DCC3arVMkSqZkzrWBz5swtE6s5c2xtrPffh99+iypy59JC7koSJ4rIz8GtDdALOFlEpmG1pr2iDDLRuna1EaITTrB2Lg0aRBfL8uV2/DvvPGtLM3Ei/Pe/3m3dJbcwHdpfwxKsBUAm1nVdVbVJXCMrA955By691Jax+eorOOKIot8zfjy0bQtr18Jnn1lzUedcyRSxkkSrRMaSTP7v/+DGG+Hxx2G7UOt4xMfnn9vagIsWQY8ettpElPE4F1aYX9NXCNYIZHPNlSuF7Gy46y549FE48kgb7q5Zs+j3TZhg/WVq1LDZOgceGP9YnXNlw7Rp1nX9+ONtBl6U/fHWroVu3eD556FRI1ukvnnz6OJxrrjCJFezVfWjuEdSRixbZsvRDB8O11xjtQNbz7zJzoahQ2H0aEukLr7YFl0+6CCbBn3bbXZm6ZxzsfDFF3bZbdddrVlxlKND330Hl1wCf/9tSd5DD/kKEy71hLlq/YeIDBKRDiJyTu4t7pGloUmToGVLS5pefNHOyrZOrDIzoU0bO6DUrGmzBhs0gLfeggoVrGGeJ1bOuVhQhWefhdNOg9q1bUQ8qsQqMxPuuMNG57Oz7Tj5+OOeWLnUFObPqDJWa3VKnse8FUMxvfUWXH65zbj56is4/PD8X/fyy1bE/sMP1qX9wgth/Xob5Wrf3usNnHOxkZUFXbpY36q2bW3ZrWrVoonll19shH7yZLjySujdO7pYnIuFQkeuRKQ8sCRPCwZvxVBMWVl2Ga9DBzj4YCtILyixAvjoI7j+ejvIHHWUFXQ+/7w1EB0/PnFxO+fSW/nydgJ3xx028ziKZCYrC/73P5sxvXixlUP07++JlUt9hY6DqGq2iHgZYQktXWojT198AddeC08+WXRn40qVbGSrfXtbXuKTT+CUU+xMrlKlhITtnEsjn31mo1MLFlgLl7ZtoVYt2H9/a8IZVUuDadOstmrMGKv3ev55XxPVpY8wf1Y/i8hHInKx11yF9/PPVl/19dfWEPS554pOrDIzbYRr5EjrMfP773DqqTZcX6GCLW/jnHNhPf+8jYSfe67Va/71F5x4InTubM9HkVip2vGwWTMrnh80CN5+2xMrl17CVPBUB5YCJ+Z5zGuuCvHmm1ZfVb26LR9x6KGFv37NGmvNMGYMfP+9vefdd2HTJlvE+Y8/bAQryqnRzrnUsn493HOPzb7bf38b/f7oIxsRbxJRl8K5c61v1YgRduL48suw557RxOJcPBWZXKnqpYkIJB1kZVn9Qu/ecMwxMHgw7LFH4e8ZNszaK8yZY8WlWVk2e6dLF7s8ePrpdvNLgs654pgyxS7/7babLbj8+edWbnDFFfDAA4mNRdVGqK6/3k4a+/Wzpb78hNGlqyIHhUWktoi8LyKLRGShiLwrIrUTEVwqWbLEpjP37g033GB1VoUlVqtWWb1B69a2KPO331rPq9z1ARs2tKTrnHM8sXLOFd9uu8G8eXb8yMy0ms+337bRozBNi0sjIwPq1bPLjnvtBYcdBp062dqAkybZsc0TK5fOwlwWHAAMAs4L7ncKHvOFVwITJ8LZZ1vB6IABm+sZClOpkh1k7r4bunf3Xi7OudjYuNFGv5991mYmd+tms/CqVrW1SHv0sMtx8ZKRYaNS69bZ/blz7XbhhTBwoM1SdC7dhSlnrKGqA1Q1K7i9CtSIc1wpY9Aga5mQnW21UoUlVvPm2RnbqlWWXP34Izz4oCdWzrnSU7WaqsaN4ZZboH59a3OwaJGNHjVqZMXs99wDJ50Uvzi6d9+cWOU1ZownVq7sCDNytUREOgFvBvc7YAXuZVpu/6onnrCOwu+8U/BlQFV45RVbyiEz0+oeTj7ZZgA651xprV8PZ5wBX35pJQWffmolB2CTY+bPtz5SDRrEt8xg+nSbhJOf2bPjt1/nkk2YkavLgPOBBcB8oH3wWJm1eLH1nnriCSs8L6y+6q+/7Czxiits6vGkSZZYOedcaW3YYB8rV4a6deGZZ+wYk5tY5apZ02YIxiuxmjHDZkg3bFhwLVWdOvHZt3PJqMDkSkQeCT49TFXbqWoNVd1dVc9S1QLOTdJT3uLMmjXhgAOsZcKrr8LTTxc+AnXTTfDTTzY75ssvYb/9EhS0cy5tZWbCo4/a5b5p0+yxV16xyTSJHBGfM8eW5tp/fztO3nCDJXhVqmz5uipVoGfPxMXlXNQKuyzYRkTuBu4EBiconqSzdXHmggV2Ztajh832y8+vv9oagrVrW1FpuXL2uXPOlYYqvPeelST8/bddCoxivdF586yeq39/i+mqq6xXX27Pqp13ttqr2bNtxKpnT+jYMfFxOheVwi4LDgOWAE1EZJWIrM77MUHxRS6/4kxV67q+tcxMuP9+aN7c+l2BHVg8sXLOlVZOjpUjtG9vI0HDh8PHH8PeeycuhoULrVh+331tNL5zZ6uzevbZLZuBduwIM2dazDNnemLlyp4Cz3lUtRvQTUQ+VNUzExhT0lizJnxx5tixVnPw229w0UVWj+Wcc6W1YoWNBJUrZ82J27e3Y00iR6yWLIHHHoO+fe0k8j//sTYy++yTuBicSyWFFrSLSHmgakk2LCKvBI1Hfy1RZBHK7ROz774FvyZvcebbb8MRR8DKldZPJiMDanizCudcKaxfDw8/bHVVo0fbY/fea+1cEpVYLVtmSdTee1tydc451vn9lVc8sXKuMIUmV6qaDawTkZ1KsO1XgdNKElRUcnLgrbesYP2GG2zmy333FVycuX693T/lFOja1UatTj898XE759KHqp2wNWxoZQknn5z4mXYrV9oSOXvvbce6Nm3s+PbGGz4px7kwwpz/bAAmi8gIYG3ug6r638LepKpfi0i90oWXOCNGwO23W7f1gw6yhZJbt7Yzt7Fjrd1CVhbstJMdbEaNsqVuxo6FXXaxmTvOOVdaZ55ptVRNm8Jrr8Hxxydu36tX22y/xx+H5ctt5YkHHrBjonMuvDDJ1SfBLS2NG2fF5yNHWp+Y11+3mqny5a2HzIkn2hISEyfaSNV111kz0KwsG63KyYn6K3DOpQpVW8lh6lQ48EA7tojY7Lv/+z+rqzr7bEuwOndOXEfzdeusFOLRR62+6owzLKlq3jwx+3cu3RSZXKnqayJSGaijqn/GOgARuQq4CqBOAse+p02zIffBg22B0yeftH4teZvsDR4Mu+9us2JWrrTpxuPGQcWKtjZXQa0YnHNua8uWWdKycqUtZPzII1ZPdcQRNgHmuecsobr00sTFtH49vPAC9OplMwFPPdXazBx6aOJicC4dFZlciUhb4HGgIrC3iDQDeqhqu1gEoKr9gf4ALVu21FhsszDz59vB48UXbU2/e+6xEagdd9z2tePHw3HH2ZlllSrWV+bhh+1jfmtnOedcQbp2tVUann3WRrAGDrQTulGj4Lzz7FiTKJmZ1k7m4Ydt1OzEE22ZnKOOSlwMzqWzMJcF7wcOBUYDqOrPIpLAziqxsXKlzXZ54gmbDXjNNZZY5bdsjSp8/bVdKpw2DW6+2VaUHzvWhukPP9xmzTjnXBg5OVakPnOmnax16mSLvjdubMnNO+/Eb98ZGZsbeu61ly3HNWKEdVc/5hh7PpF1Xc6VBWHWFsxS1ZVbPVbkCJOIvAmMARqIyFwRubwkAZbWhg3Qp4+1VejZE9q1s6nEfftum1ht2mQHmpYt7WAzf75NeX7lFXsut0no6tU2Q9A558JQtWNI1aCxzSWX2PJZw4bFt24zd4WJWbMshtmz7XhWqZIlWF995YmVc/EQJrn6VUQuAsqLyH4i8gzwfVFvUtUOqlpTVSuoam1VfbnU0RZDdrbNtGnQwArQW7Swy3xvvgn16+f/ngkT7Ixy3Tpb1mHOHFtD8J13YNddrf5qwgTrjJyoQlPnXOorX97qmfr1s/unnGIJVr9+0LZtfPaZlWXd1PMrYdi40UawClpk2TlXOmEuC3YBugOZwCDgc+CheAZVGqrWRuHOO22NvxYt7EytVattXzt9Ojz1lC102qePFZl+/bXVHZQL0s4mTWx2z7JlNoqVX22Wc84VpXdvq20aP95KC0aPtlnIX30Vu33k5MB331m/viFDYNGi/F83Z07s9umc21aBI1cisr2I3AQ8CswGjlDVQ1T1blXdkKgAi+P77+HYY+1McMMGq3H48cctE6vcqdBnn20rub/wgp3ZaXCh85hjNidWeVWv7omVc67k9t8fJk+2k7g//rDmoL/8YnVQpaFqx7lbb7Vmo8ceCwMGWIF8QStFJLopqXNlTWEjV68Bm4BvgNbAAcBNCYip2H7/3VZk//BD6xXz/PO29laFCtu+tlcve2316vbx+uuhZs3Ex+ycK3t22QX+W2j75XBUYdIkG6F6+22YMcOOd61b28Sdtm1hhx0211zlvTSYu8KEcy5+Cqu5aqSqnVT1BaA9cGyCYipURgbUq2ejS7Vr29nZQQfZdOaHHrJLfddcszmxWrHCDjZjx9r99u0t+Zozx17viZVzLlnkPb7Vq2f38/rjD2vu2aiRtXV47DEbERswwC4BfvghdOhgiRVAx46Qnb0DdesCzKJixRbstlszHn74QPrlFoABHTt2pEGDBjRu3JjLLruMTZs2FRrnZ599RsuWLTnggANo2LAhXbt2pX///lxwwQX/vmbVqlXsu+++zJgxg6lTp9KmTRvq16/PAQccwPnnn8/ChQsZPXo0Z5xxxjbbP/7442nQoAHNmjWjWbNmtG/fvkTfz9J4802bzVm+vH18883Sb3OH3B8MMHv2bE455RQOOOAAGjVqxMyZM7d4bZcuXbZ4fUHKws8iJalqvjdgQmH343Fr0aKFFmbgQNUqVVTtvG3zrXVr1cWLt3ztX3+p/ve/qlWr2mvuvbfQTTvnIgCM0zgfVxJ1K+r4VZT8jm9Vqqg+8YTq//6n2rSpPSaievzxqv36qS5aVPR2q1atqqqqmZmZumHDBlVVXb16tdatW1f/+ecfVVX95JNPNCcnR3NycvTCCy/U5557rsDtTZ48WffZZx+dMmWKqqpu2rRJn332Wc3JydEjjzxSR4wYoaqqN954oz700EO6fv16rV+/vn700Uf/buPLL7/UyZMn66hRo/T000/fZh/HHXec/vTTT2G+bXExaJDq3nurfvml6saN9nHvve3x0sj9Waja1zh8+HBVtZ/H2rVr/33up59+0k6dOm3x+vyUhZ9FsivoGFbYZcGmIrIq+FyAysF9sZxME16B1L17/jNffv/duqznuvpqaxJavrydxd18Mxx8cOLidM65rd10E/z8c8HP//CDtXvJa906O36B1XzWr291VKo2kjJliq0uEUbFihX//TwzM5OcPD0g2rRp8+/nhx56KHPnzi1wO48++ijdu3enYcOGAGy33XZcd911ADz//PNcdNFFvPrqq4wcOZLx48czcOBAjjjiCNrmmRZ5wgknADB69OhwwcdBfi0ozj/fljh78EGoVs1GCXNVqwbdutn/lCVL7CpIXsX5Un7//XeysrI4+eSTgS1HtLKzs+nWrRuDBg3i/fffL3Q76fKzSEcFXhZU1fKqumNwq6aq2+X5PJLS7lmzCn78/fet/QLYMPkdd1jDvtdf98TKOZccFi60JOqrr+zjwoWbn9s6scrrsMPsOLbnnrb8VknNmTOHJk2asNdee3H77bdTq1atLZ7ftGkTb7zxBqeddlqB2/j1119p0aJFvs81adKEU089lVatWvH0009TsWLFQl9fmI4dO/57Kapbt27Ffn9p/Pkn7LTTlo/ttJM1fI2FqVOnsvPOO3POOedw8MEH061bN7KDf2B9+/alXbt21AxRs1IWfhapKkwrhqSgageVjRu3fa58eeuYPnQonH66zZpxzrlkcsghNqKem0RlZtqJ4dFH24lgQerWtUQsFvbaay8mTZrEvHnzOOuss2jfvj175OmmfN1113HsscdyzDHHlHgf119/PZ999tm/IyIllZGRQcuWLUu1jcIUNlBzwAE2apX3Sxg1Crp0sc932614I1Vby8rK4ptvvmHixInUqVOHCy64gFdffZXWrVszePDgmI0ipcrPIh2FaSKaFL75xobDK1fe9rldd7VCztatEx+Xc86FkV9Zw/r1tsbfzJlw1lm23mle8ZrZV6tWLQ488EC++eabfx974IEHWLx4MX369Cn0vQceeCDjx48v8Ply5cpRLk8/m6Jen4y6d7cZ56NGWWf9UaPsfvfusdl+7dq1Ofjgg9lnn33YbrvtOOuss5gwYQITJ05k+vTp1K9fn3r16rFu3TrqF9T1mrLxs0hVKZNcTZ9uHYVffHFzj5aqVW2V+XPOsWVt8utP5ZxzyWD27IKfmzrVShteeslGqkTsY//+NuMvFubOncv69esBWL58Od999x0NGjQA4KWXXuLzzz/nzTff3OKfcX66devGww8/zNSpUwHIyckpNCG76KKL+P777/nkk0/+fWzYsGFMnjy5tF9S3HToYEltly6W8HbpYvc7dIjN9g855BCWL1/O4sWLAfjyyy9p1KgRp59+OgsWLGDmzJnMnDmTKlWqMH369AK3UxZ+FqkqZdKRgw6yOoULLrCh9MxMWLPGLhU2aRJ1dM45V7iCGnfmJlNgidTMmdZpfebM2CVWAFOmTOGwww6jadOmHHfccXTt2pWDDjoIgGuuuYaFCxdyxBFH0KxZM3r06FHgdpo0acKTTz5Jhw4dOOCAA2jcuDHz588v8PWVK1dm6NChPPPMM+y33340atSIV199ld133x2AkSNHUrt27X9vY8aMAbas8znppJNi940IqUMHW+UjO9s+xiqxAihfvjyPP/44rVq14qCDDkJVufLKK4u9nbLys0hFormtyZNAy5Ytddy4cfk+p2qjVJUrQ48eNnPjueds3b+ff7b7zrnUIiLjVTUtijkKO35BwQ09Yzk65ZxLrIKOYSkzciVia2U1aGC1VS1aWNO8r7/2xMo5l/w6drREKl6X/ZxzySNlRq6cc+mnLI1cpZoBAwbw1FNPbfHYUUcdxbPPPhtRRGWX/yySV0HHME+unHOR8eTKOZfKUv6yoHPOOedcKvDkyjnnnHMuhjy5cs4555yLIU+unHPOOediyJMr55xzzrkY8uTKOeeccy6GPLlyzjnnnIuhpOpzJSKLgVnATsDKrZ7e+rG9gRkJCi2W+8zvayvtPovaZkHPF/T41vssbszFFfb7Gos4crcRi+9rmNflfS7M9zXsY2HF6u+kODEUtM/8tlFXVWuUJrBk4cevEu8z3sevMPsorVgeT8JuI1b7DPv9D/t/IZbHsFj+nYSNobB9hj+GqWrS3YD+RT0GrI0grlLvM7+vrbT7LGqbBT1fyONri7P9RH1fYxFH7jZi8X0N87q8z4X5voZ9LNbf21h+7wvaZ7x/j5Ll5sev4u0z3sevksQdj+9tLI9fsdxn2O9/2P8LsTyGxfLvpBjH8wL3WZyvI1kvC34c8rFUFI+vo6htFvR82FiS5XsfiziKs41YfH+K+1yy/u4n+nufypL1ZxgLqXj8Ku5r4yWKv6Ewr4/19z9Zf/8T+v1PqsuCxSEia1W1qu8z9fdZFr5G36fLq6z8XHyf6bPPsvA1xnKfyTpyFcZ7vs+02WdZ+Bp9ny6vsvJz8X2mzz7LwtcYs32m7MiVc84551wySuWRK+ecc865pJNyyZWITBWRHBHZkKD9HSoiy0UkU0Q2iMi7idhvsO8KIrJORBYmaH8fBF/jBhGZJSI7xWEf+f78RGSwiGwM9j02xvvcSUTWiMj6YPujg8d/DH6u60VknojUjeE+64rI3GD7mSJyZZ7nPhYRFZH9S7mPbb6XBX1NIlJZRKYHX3+miAwr4T7z/XsQkdEikh3sd72I3JvnPeeKyOo8v1sx/71KFYk+fgX7jOQYlo7Hr2A/CT2GRXH8CrafdsewRB6/Ui65Ap4GOiVwf5nA9apaCet/0VZE2iZo30OARYnYkYi0AE4Haqrq9tjvRu847Gqbn5+I3AycCOwa7PvSGO9zFbCvqlYGdgFaisjl2LX1HYPHZwNvx3CfI4Evgt+b6sBwsD9u4AggOwb7yO9voaCv6TGgQvD9rQWcJCJHl2Cfhf09fKqqlYNbDwARqQQMBC4L9t0YWFeC/aaLRB+/ILpjWDoevyDxx7Aojl+QnsewhB2/Ui65UtW+2Dc8Ufv7RVUHBZ/PB5YCjeK9XxFpCRyD/fIligC7BL9QFYHpsd5BAT+/W4Beqro6eM3vMd6nqmru2XMV7PdeVbWXqmYGj48G9ojF/kRkT6AuwQFWVdeq6qzg6feAa2Oxn/y+l4V8TQpUCn62uwA5wLwS7LO4fw+3A/NVdXDwnumquqm4+00XiT5+BftM+DEsXY9fkPhjWKKPX5C+x7BEHr9SLrmKUpAl18Ay2Xj7AOiC/QLFnaqOBz4E/gLWA+tUtVci9g3sBpweDH2vEJFLYr2D4BLFemAJMElVX9nqJZ2Bz2K0u6OxM6RpwWWRP0Skhog8BCzO/UNNgM5s/ppuC2JaD0wD3lbVv0uz8Xz+HloHQ+pTRaRe8NjBwWuXBN+LT0qzT1c6CTyGfUDZOX5BnI9hCT5+QRk4hsX7+OXJVUgisgfwOdBbVf+J874eAFaoakY897PVPusBJwAHAFWB7UXkuUTtHtgZqAbcBLwkIhLLHajqpmCYuR7QQETO+nfnIsOxfwLXx2h3FbHv4cOqWgU7GHyCfW3tYrSPQuXzNV0S3K8KHAhcICLHlWL7W/89XA9UBnbALgUND166HbAXcGTw8VgR6VrS/bqSS9QxrAwevyDOx7AEH78gzY9hCTl+FdXCPRlvWFa9IYH7q4ydMXyYoP19D2QFt2xsOPTvOO+zN/BnnvsvAL8m4ucHLAZuynN/E9Awjl/rl8DHwef9gdVYrUSstn8QkJXn/vXAMuzAkPtz1eDjQbH8Xhb0NQGTgX557k8FnijhPgv9e8gbE/AUMD3Pc18AQ+P1s02FW6KPX2F+ZjHeV1ofv/L7GSbyGBbv41ew3bQ9hiXq+OUjV0UIzj5+Beao6pmJ2KeqHqmq26nqdsCtwCJV3SfOu/0NqCsiuwZfcytgSpz3mWs4cDaAiJyCnQX+GauNi0jDPDNOdgFaABNEpDt2NnSIqi6N1f5UdTKwVkROCx46D/sDLZfn55oNNApeGzOFfE1zgFZiamD1FN+WYPv5/j2ISNM8L+sK5NaIPAHUCn6vKmHD7OOKu19Xcok+hpXB4xfE8RiW6OMXpO8xLKHHr3hk1vG8YavO554NZQED4ry/a4N9rc9zuzeBX+9NwMIE7Ws0dk17A/A3UC0RPz9smPfvYL/rgFtivM9zg+2uD/YxMnh8YxBD7s/1txju8wJgbbDd+UC9rZ7PAvaPw/cy368JKwqdE3z9mZRw9Kigv4c8P7/1wAKgaZ73PBc8twEYm4jf5WS9Jfr4VdjPLEFfb1odvwr6GcbzGBbF8SvYftodwxJ5/PIO7c4555xzMeSXBZ1zzjnnYsiTK+ecc865GPLkyjnnnHMuhjy5cs4555yLIU+unHPOOediyJMrF5rYKui989zvKiL3x2jb94vIPyLys4j8LiIdYrFd55wDP365xPLkyhVHJnCOiOwWp+0/oarNgDOBF0SkQpz245wre/z45RLGkytXHFnYsgQ3b/2EiLwqIu3z3F8TfDxeRL4SkXeCBTF7iUhHEflRRCaLyL5bb0tVp2FN83YJttFNRH4SkUnBumWISL1gMdHXgseHiEiV4LlewdnjJBF5PB7fCOdcyvHjl0sYT65ccT0LdBSRnYrxnqbAjdh6VRdjXX0PBV4Cumz9YhFpDkxT1UXBUhL7AYcCzYAWInJs8NIGQH9VbQKsAq4TkerYMhQHBo8/VIKv0TmXnvz45RLCkytXLKq6Cngd+G8x3vaTqs5X1UzgLzavOD4ZW+U9180i8icwFrg/eOyU4DYRmAA0xA5WYOtDfRd8PhBbcHMVtkzBSyJyDnYG6ZxzfvxyCePJlSuJJ4HLsfW0cmUR/D4Fi2NWzPNcZp7Pc/LczwG2y/PcE6raAFvT6nUR2R5bAPV/qtosuNVX1ZeD12+9dpOqahZ2lvgucBYwrERfoXMuXT2JH79cnHly5YpNVZcB72AHqFwzsdXawQo6S1zMqarvYSuPXwJ8DlwmIjsAiMieIrJ78NI6InJE8HkH4NvgdTup6qfYorHNShqHcy79+PHLJcJ2Rb/EuXz1Bm7Ic/9F4EMR+REYia2mXho9gEHAAcFtjJ1QsgbohK2kPgW4REReAKYBzwM7BXHknjVuU7zqnCvz/Pjl4kpUtx6ZdC75iUg9YKiqNo46FuecKw4/fqU/vyzonHPOORdDPnLlnHPOORdDPnLlnHPOORdDnlw555xzzsWQJ1fOOeecczHkyZVzzjnnXAx5cuWcc845F0OeXDnnnHPOxdD/A9/CkPS3csxeAAAAAElFTkSuQmCC\n",
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\n",
       "text/plain": [
-       "<Figure size 720x360 with 2 Axes>"
+       "<Figure size 1080x360 with 3 Axes>"
       ]
      },
      "metadata": {
@@ -309,41 +437,80 @@
    ],
    "source": [
     "for bm in unique_bms:\n",
-    "    flag = \"retrieve\" in bm\n",
+    "    flag = \"staic_multimap_find_all_matching_rate\" == bm\n",
     "    \n",
     "    if flag:\n",
     "        tmpdf = perfdf[perfdf[\"Benchmark\"] == bm]\n",
     "        \n",
-    "        randomdf = tmpdf[tmpdf[\"Distribution\"] == \"RANDOM\"].reset_index(drop=True)\n",
-    "        unique_labels = randomdf[\"Label\"].unique()\n",
-    "        plot_perf(bm + \"_RANDOM\", randomdf, \"NumReps\", unique_labels, flag)\n",
+    "        unique_labels = tmpdf[\"Label\"].unique()\n",
+    "        plot_dual_perf(bm, tmpdf, \"MatchingRate\", unique_labels)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### `retrieve` (`find_all`  + `count`) performance by varying key muliplicities\n",
+    "<ul>\n",
+    "<li>100'000'000 key/value pairs inserted</li>\n",
+    "<li>100'000'000 probing keys</li>\n",
+    "<li>Fixed matching rate: 0.5</li>\n",
+    "<li>Fixed occupancy: 0.8</li>\n",
+    "<li>UNIFORM distribution</li>\n",
+    "</ul>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for bm in unique_bms:\n",
+    "    flag = \"staic_multimap_retrieve_uniform_multiplicity\" == bm\n",
+    "    \n",
+    "    if flag:\n",
+    "        tmpdf = perfdf[perfdf[\"Benchmark\"] == bm]\n",
     "        \n",
-    "        cycledf = tmpdf[tmpdf[\"Distribution\"] == \"CYCLE\"].reset_index(drop=True)\n",
-    "        unique_labels = cycledf[\"Label\"].unique()\n",
-    "        plot_perf(bm + \"_CYCLE\", cycledf, \"NumReps\", unique_labels, flag)"
+    "        unique_labels = tmpdf[\"Label\"].unique()\n",
+    "        plot_single_perf(bm, tmpdf, \"Multiplicity\", unique_labels)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### count by varying matching rate\n",
+    "### `retrieve` (`find_all`  + `count`) performance by varying occupancies\n",
     "<ul>\n",
-    "<li>Fixed num_reps: 8</li>\n",
-    "<li>Random non-unique input keys</li>\n",
+    "<li>100'000'000 key/value pairs inserted</li>\n",
+    "<li>100'000'000 probing keys</li>\n",
+    "<li>Fixed matching rate: 0.5</li>\n",
     "</ul>"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<Figure size 720x360 with 2 Axes>"
+       "<Figure size 1080x360 with 3 Axes>"
       ]
      },
      "metadata": {
@@ -354,36 +521,37 @@
    ],
    "source": [
     "for bm in unique_bms:\n",
-    "    flag = \"count_varying_matching_rate\" == bm\n",
+    "    flag = \"staic_multimap_retrieve_occupancy\" == bm\n",
     "    \n",
     "    if flag:\n",
     "        tmpdf = perfdf[perfdf[\"Benchmark\"] == bm]\n",
     "        \n",
     "        unique_labels = tmpdf[\"Label\"].unique()\n",
-    "        plot_perf(bm, tmpdf, \"MatchingRate\", unique_labels, flag)"
+    "        plot_dual_perf(bm, tmpdf, \"Occupancy\", unique_labels)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### count by varying num_reps\n",
+    "### `retrevie` (`find_all`  + `count`) performance by varying matching rates\n",
     "<ul>\n",
+    "<li>100'000'000 key/value pairs inserted</li>\n",
+    "<li>100'000'000 probing keys</li>\n",
     "<li>Fixed matching rate: 0.5</li>\n",
-    "<li>Random non-unique input keys</li>\n",
     "</ul>"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<Figure size 720x360 with 2 Axes>"
+       "<Figure size 1080x360 with 3 Axes>"
       ]
      },
      "metadata": {
@@ -394,13 +562,13 @@
    ],
    "source": [
     "for bm in unique_bms:\n",
-    "    flag = \"count_varying_num_reps\" == bm\n",
+    "    flag = \"staic_multimap_retrieve_matching_rate\" == bm\n",
     "    \n",
     "    if flag:\n",
     "        tmpdf = perfdf[perfdf[\"Benchmark\"] == bm]\n",
     "        \n",
     "        unique_labels = tmpdf[\"Label\"].unique()\n",
-    "        plot_perf(bm, tmpdf, \"NumReps\", unique_labels, flag)"
+    "        plot_dual_perf(bm, tmpdf, \"MatchingRate\", unique_labels)"
    ]
   }
  ],
diff --git a/benchmarks/analysis/notebooks/Utils.py b/benchmarks/analysis/notebooks/Utils.py
index 68045a8a4..3e6a001e8 100644
--- a/benchmarks/analysis/notebooks/Utils.py
+++ b/benchmarks/analysis/notebooks/Utils.py
@@ -7,70 +7,93 @@
 colors = ['b','r','g','m','y','c']
 styles = ['o','s','v','^','D',">"]
 
-def plot_perf(bm, df, xaxis, unique_labels, is_multivalue = False, is_singletype = False):
-    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5))
+def plot_single_perf(bm, df, xaxis, unique_labels):
+    fig = fig = plt.figure(1,figsize=(5, 5))
     fig.suptitle(bm)
     
-    marker_handles = []
+    ax = fig.gca()
+    ax.set_xlabel(xaxis)
+    ax.set_ylabel('GPU Time (sec)')
     
-    # Frist sub-figure shows performance (#ops/s)
-    ax1.set_xlabel(xaxis)
-    ax1.set_ylabel('Performance (number of operations per second)')
-
-    # Second sub-figure shows bandwidth
-    ax2.set_xlabel(xaxis)
-    ax2.set_ylabel('Bandwidth (GB/s)')
+    ax.set_xscale('log')
+    ax.set_xticks(list(df[xaxis]))
+    ax.get_xaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter())
     
-    # Custom x-axis for multi-value test cases:
-    # - power-of-two ticks
-    # - log-scale
-    lax = [ax1, ax2]
-    if is_multivalue:
-        lnum = list(df[xaxis])
-        
-        if xaxis == 'NumReps':
-            for item in lax:
-                item.set_xscale('log')
-                item.set_xticks(lnum)
-                item.get_xaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter())
+    marker_handles = []
         
-    if not is_singletype:
-        num_style = len(df["Distribution"].unique())
-    else:
-        num_style = len(df["CGSize"].unique())
+    num_style = len(df["Distribution"].unique())
 
     # Iterate over labels and label indices
     for lindex, lbl in enumerate(unique_labels):
         tmpdf = df.loc[df['Label'] == lbl]
 
         x = tmpdf[xaxis]
-        perf = tmpdf["Elem/s (elem/sec)"]
-        bw   = tmpdf['Bandwidth (GB/s)']
+        perf = tmpdf["GPU Time (sec)"]
 
         # Get style & type index
         sid = lindex % num_style
         tid = int(lindex / num_style)
 
-        if (not tid) or is_singletype:
-            ax1.plot(x, perf, color=colors[sid])
-            ax1.scatter(x, perf, color=colors[sid], marker=styles[sid])
+        if not tid:
+            ax.plot(x, perf, color=colors[sid])
+            ax.scatter(x, perf, color=colors[sid], marker=styles[sid])
+
+            # Add legend
+            marker_handles.append(ax.plot([], [], c=colors[sid], marker=styles[sid], \
+                                          label=lbl)[0])
+        else:
+            ax.plot(x, perf, color=colors[sid], linestyle="--")
+            ax.scatter(x, perf, color=colors[sid], marker=styles[sid], facecolors='none')
+
+            # Add legend
+            marker_handles.append(ax.plot([], [], c=colors[sid], marker=styles[sid], \
+                                          mfc='none', linestyle="--", label=lbl)[0])
+
+    leg = plt.legend(handles = marker_handles, loc="upper left", ncol=2, frameon=False)
+    plt.savefig(bm + '.eps')
+
+def plot_dual_perf(bm, df, xaxis, unique_labels):
+    fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(15, 5))
+    fig.suptitle(bm)
+
+    marker_handles = []
+
+    lax = [ax1, ax2, ax3]
+
+    for item in lax:
+        item.set_xlabel(xaxis)
+        item.set_ylabel("GPU Time (sec)")
+
+    num_style = len(df["Distribution"].unique())
+
+    # Iterate over labels and label indices
+    for lindex, lbl in enumerate(unique_labels):
+        tmpdf = df.loc[df['Label'] == lbl]
+
+        x = tmpdf[xaxis]
+        perf = tmpdf["GPU Time (sec)"]
+
+        # Get style & type index
+        sid = lindex % num_style
+        tid = int(lindex / num_style)
 
-            ax2.plot(x, bw, color=colors[sid])
-            ax2.scatter(x, bw, color=colors[sid], marker=styles[sid])
+        # INT32
+        if not tid:
+            lax[sid].plot(x, perf, color=colors[sid])
+            lax[sid].scatter(x, perf, color=colors[sid], marker=styles[sid])
 
             # Add legend
-            marker_handles.append(ax1.plot([], [], c=colors[sid], marker=styles[sid], \
+            marker_handles.append(lax[sid].plot([], [], c=colors[sid], marker=styles[sid], \
                                           label=lbl)[0])
+        # INT64
         else:
-            ax1.plot(x, perf, color=colors[sid], linestyle="--")
-            ax1.scatter(x, perf, color=colors[sid], marker=styles[sid], facecolors='none')
 
-            ax2.plot(x, bw, color=colors[sid], linestyle="--")
-            ax2.scatter(x, bw, color=colors[sid], marker=styles[sid], facecolors='none')
+            lax[sid].plot(x, perf, color=colors[sid], linestyle="--")
+            lax[sid].scatter(x, perf, color=colors[sid], marker=styles[sid], facecolors='none')
 
             # Add legend
-            marker_handles.append(ax1.plot([], [], c=colors[sid], marker=styles[sid], \
+            marker_handles.append(lax[sid].plot([], [], c=colors[sid], marker=styles[sid], \
                                           mfc='none', linestyle="--", label=lbl)[0])
     
-    leg = plt.legend(handles = marker_handles, loc="lower left", ncol=2, frameon=False)
-    plt.savefig(bm+'.eps')
\ No newline at end of file
+    leg = plt.legend(handles = marker_handles, loc="upper left", ncol=2, frameon=False)
+    plt.savefig(bm + '.eps')
\ No newline at end of file

From 67a98809fb774ddd8b8848349fe8232b45fdf346 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Fri, 21 May 2021 11:52:30 -0400
Subject: [PATCH 100/267] Minor code cleanups: remove debugging code path

---
 include/cuco/detail/static_multimap_kernels.cuh | 5 -----
 1 file changed, 5 deletions(-)

diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap_kernels.cuh
index 51d75cd74..3eb3ea69f 100644
--- a/include/cuco/detail/static_multimap_kernels.cuh
+++ b/include/cuco/detail/static_multimap_kernels.cuh
@@ -157,11 +157,6 @@ __global__ void insert(InputIt first, InputIt last, viewT view, Hash hash, KeyEq
     view.insert(tile, insert_pair, hash, key_equal);
     it += (gridDim.x * blockDim.x) / tile_size;
   }
-  if (threadIdx.x == 0 && blockIdx.x == 0) {
-    for (auto i = 0; i < view.get_capacity(); i++) {
-      auto slot = view.get_slots() + i;
-    }
-  }
 }
 
 /**

From 7577d3be639e2e1701e0549a7cc9e0c2f73f2e5f Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Fri, 21 May 2021 12:02:10 -0400
Subject: [PATCH 101/267] Use template integer block size

---
 .../cuco/detail/static_multimap_kernels.cuh   | 32 +++++++++----------
 1 file changed, 16 insertions(+), 16 deletions(-)

diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap_kernels.cuh
index 3eb3ea69f..388c6f5a2 100644
--- a/include/cuco/detail/static_multimap_kernels.cuh
+++ b/include/cuco/detail/static_multimap_kernels.cuh
@@ -77,11 +77,11 @@ template <typename atomic_key_type,
           typename pair_atomic_type>
 __global__ void initialize(pair_atomic_type* const slots, Key k, Value v, std::size_t size)
 {
-  auto tid = threadIdx.x + blockIdx.x * blockDim.x;
+  auto tid = threadIdx.x + blockIdx.x * block_size;
   while (tid < size) {
     new (&slots[tid].first) atomic_key_type{k};
     new (&slots[tid].second) atomic_mapped_type{v};
-    tid += gridDim.x * blockDim.x;
+    tid += gridDim.x * block_size;
   }
 }
 
@@ -106,13 +106,13 @@ __global__ void initialize(pair_atomic_type* const slots, Key k, Value v, std::s
 template <uint32_t block_size, typename InputIt, typename viewT, typename Hash, typename KeyEqual>
 __global__ void insert(InputIt first, InputIt last, viewT view, Hash hash, KeyEqual key_equal)
 {
-  auto tid = blockDim.x * blockIdx.x + threadIdx.x;
+  auto tid = block_size * blockIdx.x + threadIdx.x;
   auto it  = first + tid;
 
   while (it < last) {
     typename viewT::value_type const insert_pair{*it};
     view.insert(insert_pair, hash, key_equal);
-    it += gridDim.x * blockDim.x;
+    it += gridDim.x * block_size;
   }
 }
 
@@ -155,7 +155,7 @@ __global__ void insert(InputIt first, InputIt last, viewT view, Hash hash, KeyEq
     // force conversion to value_type
     typename viewT::value_type const insert_pair{*it};
     view.insert(tile, insert_pair, hash, key_equal);
-    it += (gridDim.x * blockDim.x) / tile_size;
+    it += (gridDim.x * block_size) / tile_size;
   }
 }
 
@@ -190,7 +190,7 @@ template <uint32_t block_size,
 __global__ void find(
   InputIt first, InputIt last, OutputIt output_begin, viewT view, Hash hash, KeyEqual key_equal)
 {
-  auto tid     = blockDim.x * blockIdx.x + threadIdx.x;
+  auto tid     = block_size * blockIdx.x + threadIdx.x;
   auto key_idx = tid;
   __shared__ Value writeBuffer[block_size];
 
@@ -208,7 +208,7 @@ __global__ void find(
     writeBuffer[threadIdx.x] = found->second.load(cuda::std::memory_order_relaxed);
     __syncthreads();
     *(output_begin + key_idx) = writeBuffer[threadIdx.x];
-    key_idx += gridDim.x * blockDim.x;
+    key_idx += gridDim.x * block_size;
   }
 }
 
@@ -250,7 +250,7 @@ __global__ void find(
   InputIt first, InputIt last, OutputIt output_begin, viewT view, Hash hash, KeyEqual key_equal)
 {
   auto tile    = cg::tiled_partition<tile_size>(cg::this_thread_block());
-  auto tid     = blockDim.x * blockIdx.x + threadIdx.x;
+  auto tid     = block_size * blockIdx.x + threadIdx.x;
   auto key_idx = tid / tile_size;
   __shared__ Value writeBuffer[block_size];
 
@@ -272,7 +272,7 @@ __global__ void find(
     if (tile.thread_rank() == 0) {
       *(output_begin + key_idx) = writeBuffer[threadIdx.x / tile_size];
     }
-    key_idx += (gridDim.x * blockDim.x) / tile_size;
+    key_idx += (gridDim.x * block_size) / tile_size;
   }
 }
 
@@ -305,7 +305,7 @@ template <uint32_t block_size,
 __global__ void contains(
   InputIt first, InputIt last, OutputIt output_begin, viewT view, Hash hash, KeyEqual key_equal)
 {
-  auto tid     = blockDim.x * blockIdx.x + threadIdx.x;
+  auto tid     = block_size * blockIdx.x + threadIdx.x;
   auto key_idx = tid;
   __shared__ bool writeBuffer[block_size];
 
@@ -322,7 +322,7 @@ __global__ void contains(
     writeBuffer[threadIdx.x] = view.contains(key, hash, key_equal);
     __syncthreads();
     *(output_begin + key_idx) = writeBuffer[threadIdx.x];
-    key_idx += gridDim.x * blockDim.x;
+    key_idx += gridDim.x * block_size;
   }
 }
 
@@ -362,7 +362,7 @@ __global__ void contains(
   InputIt first, InputIt last, OutputIt output_begin, viewT view, Hash hash, KeyEqual key_equal)
 {
   auto tile    = cg::tiled_partition<tile_size>(cg::this_thread_block());
-  auto tid     = blockDim.x * blockIdx.x + threadIdx.x;
+  auto tid     = block_size * blockIdx.x + threadIdx.x;
   auto key_idx = tid / tile_size;
   __shared__ bool writeBuffer[block_size];
 
@@ -382,7 +382,7 @@ __global__ void contains(
     if (tile.thread_rank() == 0) {
       *(output_begin + key_idx) = writeBuffer[threadIdx.x / tile_size];
     }
-    key_idx += (gridDim.x * blockDim.x) / tile_size;
+    key_idx += (gridDim.x * block_size) / tile_size;
   }
 }
 
@@ -434,7 +434,7 @@ __global__ void find_all(InputIt first,
                          Hash hash,
                          KeyEqual key_equal)
 {
-  auto tid     = blockDim.x * blockIdx.x + threadIdx.x;
+  auto tid     = block_size * blockIdx.x + threadIdx.x;
   auto key_idx = tid;
 
   constexpr uint32_t step = 1;
@@ -666,12 +666,12 @@ __global__ void count(
   __shared__ typename BlockReduce::TempStorage temp_storage;
   std::size_t thread_num_items = 0;
 
-  auto tid = blockDim.x * blockIdx.x + threadIdx.x;
+  auto tid = block_size * blockIdx.x + threadIdx.x;
   auto it  = first + tid;
 
   while (it < last) {
     thread_num_items += view.count(*it, hash, key_equal);
-    it += gridDim.x * blockDim.x;
+    it += gridDim.x * block_size;
   }
 
   // compute number of successfully inserted elements for each block

From 94697b05402972486583102277e2482e7a3cdd3d Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Fri, 21 May 2021 12:19:59 -0400
Subject: [PATCH 102/267] Remove view::count functions

---
 include/cuco/detail/static_multimap.inl       | 72 -------------------
 .../cuco/detail/static_multimap_kernels.cuh   |  4 +-
 include/cuco/static_multimap.cuh              | 70 ------------------
 3 files changed, 2 insertions(+), 144 deletions(-)

diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index f11130fe3..e559934cf 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -502,76 +502,4 @@ __device__ bool static_multimap<Key, Value, CGSize, Scope, Allocator>::device_vi
   }
 }
 
-template <typename Key,
-          typename Value,
-          std::size_t CGSize,
-          cuda::thread_scope Scope,
-          typename Allocator>
-template <typename Hash, typename KeyEqual>
-__device__ size_t static_multimap<Key, Value, CGSize, Scope, Allocator>::device_view::count(
-  Key const& k, Hash hash, KeyEqual key_equal) noexcept
-{
-  auto found   = this->find_all(k, hash, key_equal);
-  size_t count = 0;
-  while (found != this->end()) {
-    ++found;
-    ++count;
-  }
-  return count;
-}
-
-template <typename Key,
-          typename Value,
-          std::size_t CGSize,
-          cuda::thread_scope Scope,
-          typename Allocator>
-template <typename Hash, typename KeyEqual>
-__device__ size_t static_multimap<Key, Value, CGSize, Scope, Allocator>::device_view::count(
-  Key const& k, Hash hash, KeyEqual key_equal) const noexcept
-{
-  auto found   = this->find_all(k, hash, key_equal);
-  size_t count = 0;
-  while (found != this->end()) {
-    ++found;
-    ++count;
-  }
-  return count;
-}
-
-template <typename Key,
-          typename Value,
-          std::size_t CGSize,
-          cuda::thread_scope Scope,
-          typename Allocator>
-template <typename CG, typename Hash, typename KeyEqual>
-__device__ size_t static_multimap<Key, Value, CGSize, Scope, Allocator>::device_view::count(
-  CG g, Key const& k, Hash hash, KeyEqual key_equal) noexcept
-{
-  auto found   = this->find_all(g, k, hash, key_equal);
-  size_t count = 0;
-  while (found != this->end()) {
-    ++found;
-    ++count;
-  }
-  return count;
-}
-
-template <typename Key,
-          typename Value,
-          std::size_t CGSize,
-          cuda::thread_scope Scope,
-          typename Allocator>
-template <typename CG, typename Hash, typename KeyEqual>
-__device__ size_t static_multimap<Key, Value, CGSize, Scope, Allocator>::device_view::count(
-  CG g, Key const& k, Hash hash, KeyEqual key_equal) const noexcept
-{
-  auto found   = this->find_all(g, k, hash, key_equal);
-  size_t count = 0;
-  while (found != this->end()) {
-    ++found;
-    ++count;
-  }
-  return count;
-}
-
 }  // namespace cuco
diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap_kernels.cuh
index 388c6f5a2..48c40eb0b 100644
--- a/include/cuco/detail/static_multimap_kernels.cuh
+++ b/include/cuco/detail/static_multimap_kernels.cuh
@@ -77,11 +77,11 @@ template <typename atomic_key_type,
           typename pair_atomic_type>
 __global__ void initialize(pair_atomic_type* const slots, Key k, Value v, std::size_t size)
 {
-  auto tid = threadIdx.x + blockIdx.x * block_size;
+  auto tid = threadIdx.x + blockIdx.x * blockDim.x;
   while (tid < size) {
     new (&slots[tid].first) atomic_key_type{k};
     new (&slots[tid].second) atomic_mapped_type{v};
-    tid += gridDim.x * block_size;
+    tid += gridDim.x * blockDim.x;
   }
 }
 
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index 774dad7ee..35ce7a345 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -914,76 +914,6 @@ class static_multimap {
                              Key const& k,
                              Hash hash          = Hash{},
                              KeyEqual key_equal = KeyEqual{}) noexcept;
-
-    /**
-     * @brief Counts the number of matching key/value pairs contained in the multimap.
-     *
-     * @tparam Hash Unary callable type
-     * @tparam KeyEqual Binary callable type
-     * @param k The key to count for
-     * @param hash The unary callable used to hash the key
-     * @param key_equal The binary callable used to compare two keys
-     * @return The total number of the matching key/value pairs
-     * in the multimap
-     */
-    template <typename Hash     = cuco::detail::MurmurHash3_32<key_type>,
-              typename KeyEqual = thrust::equal_to<key_type>>
-    __device__ size_t count(Key const& k,
-                            Hash hash          = Hash{},
-                            KeyEqual key_equal = KeyEqual{}) noexcept;
-
-    /**
-     * @brief Counts the number of matching key/value pairs contained in the multimap.
-     *
-     * @tparam Hash Unary callable type
-     * @tparam KeyEqual Binary callable type
-     * @param k The key to count for
-     * @param hash The unary callable used to hash the key
-     * @param key_equal The binary callable used to compare two keys
-     * @return The total number of the matching key/value pairs
-     * in the multimap
-     */
-    template <typename Hash     = cuco::detail::MurmurHash3_32<key_type>,
-              typename KeyEqual = thrust::equal_to<key_type>>
-    __device__ size_t count(Key const& k,
-                            Hash hash          = Hash{},
-                            KeyEqual key_equal = KeyEqual{}) const noexcept;
-
-    /**
-     * @brief Counts the number of matching key/value pairs contained in the multimap.
-     *
-     * @tparam CG Cooperative Group type
-     * @tparam Hash Unary callable type
-     * @tparam KeyEqual Binary callable type
-     * @param k The key to count for
-     * @param hash The unary callable used to hash the key
-     * @param key_equal The binary callable used to compare two keys
-     * @return The total number of the matching key/value pairs
-     * in the multimap
-     */
-    template <typename CG,
-              typename Hash     = cuco::detail::MurmurHash3_32<key_type>,
-              typename KeyEqual = thrust::equal_to<key_type>>
-    __device__ size_t
-    count(CG g, Key const& k, Hash hash = Hash{}, KeyEqual key_equal = KeyEqual{}) noexcept;
-
-    /**
-     * @brief Counts the number of matching key/value pairs contained in the multimap.
-     *
-     * @tparam CG Cooperative Group type
-     * @tparam Hash Unary callable type
-     * @tparam KeyEqual Binary callable type
-     * @param k The key to count for
-     * @param hash The unary callable used to hash the key
-     * @param key_equal The binary callable used to compare two keys
-     * @return The total number of the matching key/value pairs
-     * in the multimap
-     */
-    template <typename CG,
-              typename Hash     = cuco::detail::MurmurHash3_32<key_type>,
-              typename KeyEqual = thrust::equal_to<key_type>>
-    __device__ size_t
-    count(CG g, Key const& k, Hash hash = Hash{}, KeyEqual key_equal = KeyEqual{}) const noexcept;
   };  // class device_view
 
   /**

From 22e6b5e5e5fd0c0f48f2d24ad47ae2c4702768ad Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Fri, 21 May 2021 15:45:18 -0400
Subject: [PATCH 103/267] Cleanups: remove unused stuff

---
 benchmarks/key_generator.hpp                  |   1 -
 include/cuco/detail/static_multimap.inl       |   2 +-
 .../cuco/detail/static_multimap_kernels.cuh   | 200 ------------------
 3 files changed, 1 insertion(+), 202 deletions(-)

diff --git a/benchmarks/key_generator.hpp b/benchmarks/key_generator.hpp
index 3dca16d64..29b7a5d5d 100644
--- a/benchmarks/key_generator.hpp
+++ b/benchmarks/key_generator.hpp
@@ -18,7 +18,6 @@
 
 #include <limits>
 #include <random>
-#include <unordered_set>
 
 enum class dist_type { GAUSSIAN, GEOMETRIC, UNIFORM };
 
diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index e559934cf..5d1bb4324 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -276,7 +276,7 @@ __device__ void static_multimap<Key, Value, CGSize, Scope, Allocator>::device_mu
         using cuda::std::memory_order_relaxed;
         auto expected_key    = this->get_empty_key_sentinel();
         auto expected_value  = this->get_empty_value_sentinel();
-        auto insert_location = current_slot + !(first_slot_is_empty);
+        auto insert_location = first_slot_is_empty ? current_slot : current_slot + 1;
         auto& slot_key       = insert_location->first;
         auto& slot_value     = insert_location->second;
         bool key_success =
diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap_kernels.cuh
index 48c40eb0b..8a2560c5a 100644
--- a/include/cuco/detail/static_multimap_kernels.cuh
+++ b/include/cuco/detail/static_multimap_kernels.cuh
@@ -276,56 +276,6 @@ __global__ void find(
   }
 }
 
-/**
- * @brief Indicates whether the keys in the range `[first, last)` are contained in the map.
- *
- * Writes a `bool` to `(output + i)` indicating if the key `*(first + i)` exists in the map.
- *
- * @tparam block_size The size of the thread block
- * @tparam InputIt Device accessible input iterator whose `value_type` is
- * convertible to the map's `key_type`
- * @tparam OutputIt Device accessible output iterator whose `value_type` is
- * convertible to the map's `mapped_type`
- * @tparam viewT Type of device view allowing access of hash map storage
- * @tparam Hash Unary callable type
- * @tparam KeyEqual Binary callable type
- * @param first Beginning of the sequence of keys
- * @param last End of the sequence of keys
- * @param output_begin Beginning of the sequence of booleans for the presence of each key
- * @param view Device view used to access the hash map's slot storage
- * @param hash The unary function to apply to hash each key
- * @param key_equal The binary function to compare two keys for equality
- */
-template <uint32_t block_size,
-          typename InputIt,
-          typename OutputIt,
-          typename viewT,
-          typename Hash,
-          typename KeyEqual>
-__global__ void contains(
-  InputIt first, InputIt last, OutputIt output_begin, viewT view, Hash hash, KeyEqual key_equal)
-{
-  auto tid     = block_size * blockIdx.x + threadIdx.x;
-  auto key_idx = tid;
-  __shared__ bool writeBuffer[block_size];
-
-  while (first + key_idx < last) {
-    auto key = *(first + key_idx);
-
-    /*
-     * The ld.relaxed.gpu instruction used in view.find causes L1 to
-     * flush more frequently, causing increased sector stores from L2 to global memory.
-     * By writing results to shared memory and then synchronizing before writing back
-     * to global, we no longer rely on L1, preventing the increase in sector stores from
-     * L2 to global and improving performance.
-     */
-    writeBuffer[threadIdx.x] = view.contains(key, hash, key_equal);
-    __syncthreads();
-    *(output_begin + key_idx) = writeBuffer[threadIdx.x];
-    key_idx += gridDim.x * block_size;
-  }
-}
-
 /**
  * @brief Indicates whether the keys in the range `[first, last)` are contained in the map.
  *
@@ -386,110 +336,6 @@ __global__ void contains(
   }
 }
 
-/**
- * @brief Finds all the values corresponding to all keys in the range `[first, last)`.
- *
- * If the key `k = *(first + i)` exists in the map, copies `k` and all associated values to
- * unspecified locations in `[output_begin, output_begin + *num_items - 1)`. Else, copies `k` and
- * the empty value sentinel.
- *
- * Behavior is undefined if the total number of matching keys exceeds `std::distance(output_begin,
- * output_begin + *num_items - 1)`. Use `count()` to determine the number of matching keys.
- *
- * @tparam block_size The size of the thread block
- * @tparam buffer_size Size of the output buffer
- * @tparam Key key type
- * @tparam Value The type of the mapped value for the map
- * @tparam InputIt Device accessible input iterator whose `value_type` is
- * convertible to the map's `key_type`
- * @tparam OutputIt Device accessible output iterator whose `value_type` is
- * convertible to the map's `mapped_type`
- * @tparam atomicT Type of atomic storage
- * @tparam viewT Type of device view allowing access of hash map storage
- * @tparam Hash Unary callable type
- * @tparam KeyEqual Binary callable type
- * @param first Beginning of the sequence of keys
- * @param last End of the sequence of keys
- * @param output_begin Beginning of the sequence of values retrieved for each key
- * @param num_items Size of the output sequence
- * @param view Device view used to access the hash map's slot storage
- * @param hash The unary function to apply to hash each key
- * @param key_equal The binary function to compare two keys for equality
- */
-template <uint32_t block_size,
-          uint32_t buffer_size,
-          typename Key,
-          typename Value,
-          typename InputIt,
-          typename OutputIt,
-          typename atomicT,
-          typename viewT,
-          typename Hash,
-          typename KeyEqual>
-__global__ void find_all(InputIt first,
-                         InputIt last,
-                         OutputIt output_begin,
-                         atomicT* num_items,
-                         viewT view,
-                         Hash hash,
-                         KeyEqual key_equal)
-{
-  auto tid     = block_size * blockIdx.x + threadIdx.x;
-  auto key_idx = tid;
-
-  constexpr uint32_t step = 1;
-  auto const end          = view.end();
-
-  __shared__ cuco::pair_type<Key, Value> output_buffer[buffer_size];
-  __shared__ uint32_t block_counter;  // TODO: do we really need uint32_t?
-
-  if (0 == threadIdx.x) { block_counter = 0; }
-
-  if (first + key_idx < last) {
-    auto key   = *(first + key_idx);
-    auto found = view.find_all(key, hash, key_equal);
-
-    bool running     = true;
-    bool found_match = false;
-
-    while (__syncthreads_or(running)) {
-      if (running) {
-        if (found == end) {
-          running = false;
-        } else {
-          found_match = true;
-
-          auto index           = atomicAdd_block(&block_counter, step);
-          output_buffer[index] = cuco::make_pair<Key, Value>(key, (*found).second);
-
-          ++found;
-        }
-
-        if ((not running) && (not found_match)) {
-          auto index           = atomicAdd_block(&block_counter, step);
-          output_buffer[index] = cuco::make_pair<Key, Value>(key, view.get_empty_value_sentinel());
-        }
-      }  // if (running)
-
-      __syncthreads();
-
-      if ((block_counter + block_size) > buffer_size) {
-        flush_output_buffer<Key, Value, atomicT, OutputIt>(
-          block_counter, output_buffer, num_items, output_begin);
-        __syncthreads();
-        if (0 == threadIdx.x) { block_counter = 0; }
-        __syncthreads();
-      }
-    }  // while syncthreads_or
-  }
-
-  // Final flush of output cache
-  if (block_counter > 0) {
-    flush_output_buffer<Key, Value, atomicT, OutputIt>(
-      block_counter, output_buffer, num_items, output_begin);
-  }
-}
-
 /**
  * @brief Finds all the values corresponding to all keys in the range `[first, last)`.
  *
@@ -634,52 +480,6 @@ __global__ void find_all(InputIt first,
   }
 }
 
-/**
- * @brief Counts the occurrences of keys in `[first, last)` contained in the multimap.
- *
- * @tparam block_size The size of the thread block
- * @tparam Value The type of the mapped value for the map
- * @tparam InputIt Device accessible input iterator whose `value_type` is convertible to the map's
- * `key_type`
- * @tparam atomicT Type of atomic storage
- * @tparam viewT Type of device view allowing access of hash map storage
- * @tparam Hash Unary callable
- * @tparam KeyEqual Binary callable
- * @param first Beginning of the sequence of keys to count
- * @param last End of the sequence of keys to count
- * @param num_items The number of all the matches for a sequence of keys
- * @param view Device view used to access the hash map's slot storage
- * @param hash Unary function to apply to hash each key
- * @param key_equal Binary function to compare two keys for equality
- */
-template <uint32_t block_size,
-          typename Value,
-          typename InputIt,
-          typename atomicT,
-          typename viewT,
-          typename Hash,
-          typename KeyEqual>
-__global__ void count(
-  InputIt first, InputIt last, atomicT* num_items, viewT view, Hash hash, KeyEqual key_equal)
-{
-  typedef cub::BlockReduce<std::size_t, block_size> BlockReduce;
-  __shared__ typename BlockReduce::TempStorage temp_storage;
-  std::size_t thread_num_items = 0;
-
-  auto tid = block_size * blockIdx.x + threadIdx.x;
-  auto it  = first + tid;
-
-  while (it < last) {
-    thread_num_items += view.count(*it, hash, key_equal);
-    it += gridDim.x * block_size;
-  }
-
-  // compute number of successfully inserted elements for each block
-  // and atomically add to the grand total
-  std::size_t block_num_items = BlockReduce(temp_storage).Sum(thread_num_items);
-  if (threadIdx.x == 0) { *num_items += block_num_items; }
-}
-
 /**
  * @brief Counts the occurrences of keys in `[first, last)` contained in the multimap.
  *

From 28fb29d64bae8aebba1c51930525f41e2b3c5383 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Tue, 25 May 2021 12:47:49 -0400
Subject: [PATCH 104/267] Remove multimap find functions

---
 include/cuco/detail/static_multimap.inl       | 160 ------------------
 .../cuco/detail/static_multimap_kernels.cuh   | 117 -------------
 include/cuco/static_multimap.cuh              | 122 -------------
 3 files changed, 399 deletions(-)

diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index 5d1bb4324..3526faca1 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -81,30 +81,6 @@ void static_multimap<Key, Value, CGSize, Scope, Allocator>::insert(
   CUCO_CUDA_TRY(cudaDeviceSynchronize());
 }
 
-template <typename Key,
-          typename Value,
-          std::size_t CGSize,
-          cuda::thread_scope Scope,
-          typename Allocator>
-template <typename InputIt, typename OutputIt, typename Hash, typename KeyEqual>
-void static_multimap<Key, Value, CGSize, Scope, Allocator>::find(InputIt first,
-                                                                 InputIt last,
-                                                                 OutputIt output_begin,
-                                                                 cudaStream_t stream,
-                                                                 Hash hash,
-                                                                 KeyEqual key_equal)
-{
-  auto num_keys         = std::distance(first, last);
-  auto const block_size = 128;
-  auto const stride     = 1;
-  auto const grid_size  = (CGSize * num_keys + stride * block_size - 1) / (stride * block_size);
-  auto view             = get_device_view();
-
-  detail::find<block_size, CGSize, Value>
-    <<<grid_size, block_size, 0, stream>>>(first, last, output_begin, view, hash, key_equal);
-  CUCO_CUDA_TRY(cudaDeviceSynchronize());
-}
-
 template <typename Key,
           typename Value,
           std::size_t CGSize,
@@ -312,142 +288,6 @@ __device__ void static_multimap<Key, Value, CGSize, Scope, Allocator>::device_mu
   }
 }
 
-template <typename Key,
-          typename Value,
-          std::size_t CGSize,
-          cuda::thread_scope Scope,
-          typename Allocator>
-template <typename Hash, typename KeyEqual>
-__device__ typename static_multimap<Key, Value, CGSize, Scope, Allocator>::device_view::iterator
-static_multimap<Key, Value, CGSize, Scope, Allocator>::device_view::find(
-  Key const& k, Hash hash, KeyEqual key_equal) noexcept
-{
-  auto current_slot = initial_slot(k, hash);
-
-  while (true) {
-    auto const existing_key = current_slot->first.load(cuda::std::memory_order_relaxed);
-    // Key doesn't exist, return end()
-    if (detail::bitwise_compare(existing_key, this->get_empty_key_sentinel())) {
-      return this->end();
-    }
-
-    // Key exists, return iterator to location
-    if (key_equal(existing_key, k)) { return current_slot; }
-
-    current_slot = next_slot(current_slot);
-  }
-}
-
-template <typename Key,
-          typename Value,
-          std::size_t CGSize,
-          cuda::thread_scope Scope,
-          typename Allocator>
-template <typename Hash, typename KeyEqual>
-__device__
-  typename static_multimap<Key, Value, CGSize, Scope, Allocator>::device_view::const_iterator
-  static_multimap<Key, Value, CGSize, Scope, Allocator>::device_view::find(
-    Key const& k, Hash hash, KeyEqual key_equal) const noexcept
-{
-  auto current_slot = initial_slot(k, hash);
-
-  while (true) {
-    auto const existing_key = current_slot->first.load(cuda::std::memory_order_relaxed);
-    // Key doesn't exist, return end()
-    if (detail::bitwise_compare(existing_key, this->get_empty_key_sentinel())) {
-      return this->end();
-    }
-
-    // Key exists, return iterator to location
-    if (key_equal(existing_key, k)) { return current_slot; }
-
-    current_slot = next_slot(current_slot);
-  }
-}
-
-template <typename Key,
-          typename Value,
-          std::size_t CGSize,
-          cuda::thread_scope Scope,
-          typename Allocator>
-template <typename CG, typename Hash, typename KeyEqual>
-__device__ typename static_multimap<Key, Value, CGSize, Scope, Allocator>::device_view::iterator
-static_multimap<Key, Value, CGSize, Scope, Allocator>::device_view::find(
-  CG g, Key const& k, Hash hash, KeyEqual key_equal) noexcept
-{
-  auto current_slot = initial_slot(g, k, hash);
-
-  while (true) {
-    auto const existing_key = current_slot->first.load(cuda::std::memory_order_relaxed);
-
-    // The user provide `key_equal` can never be used to compare against `empty_key_sentinel` as the
-    // sentinel is not a valid key value. Therefore, first check for the sentinel
-    auto const slot_is_empty =
-      detail::bitwise_compare(existing_key, this->get_empty_key_sentinel());
-
-    // the key we were searching for was found by one of the threads,
-    // so we return an iterator to the entry
-    auto const exists = g.ballot(not slot_is_empty and key_equal(existing_key, k));
-    if (exists) {
-      uint32_t src_lane = __ffs(exists) - 1;
-      // TODO: This shouldn't cast an iterator to an int to shuffle. Instead, get the index of the
-      // current_slot and shuffle that instead.
-      intptr_t res_slot = g.shfl(reinterpret_cast<intptr_t>(current_slot), src_lane);
-      return reinterpret_cast<iterator>(res_slot);
-    }
-
-    // we found an empty slot, meaning that the key we're searching for isn't present
-    if (g.ballot(slot_is_empty)) { return this->end(); }
-
-    // otherwise, all slots in the current window are full with other keys, so we move onto the
-    // next window
-    current_slot = next_slot(g, current_slot);
-  }
-}
-
-template <typename Key,
-          typename Value,
-          std::size_t CGSize,
-          cuda::thread_scope Scope,
-          typename Allocator>
-template <typename CG, typename Hash, typename KeyEqual>
-__device__
-  typename static_multimap<Key, Value, CGSize, Scope, Allocator>::device_view::const_iterator
-  static_multimap<Key, Value, CGSize, Scope, Allocator>::device_view::find(
-    CG g, Key const& k, Hash hash, KeyEqual key_equal) const noexcept
-{
-  auto current_slot = initial_slot(g, k, hash);
-
-  while (true) {
-    auto const existing_key = current_slot->first.load(cuda::std::memory_order_relaxed);
-
-    // The user provide `key_equal` can never be used to compare against `empty_key_sentinel` as the
-    // sentinel is not a valid key value. Therefore, first check for the sentinel
-    auto const slot_is_empty =
-      detail::bitwise_compare(existing_key, this->get_empty_key_sentinel());
-
-    // the key we were searching for was found by one of the threads, so we return an iterator to
-    // the entry
-    auto const exists = g.ballot(not slot_is_empty and key_equal(existing_key, k));
-    if (exists) {
-      uint32_t src_lane = __ffs(exists) - 1;
-      // TODO: This shouldn't cast an iterator to an int to shuffle. Instead, get the index of the
-      // current_slot and shuffle that instead.
-      intptr_t res_slot = g.shfl(reinterpret_cast<intptr_t>(current_slot), src_lane);
-      return reinterpret_cast<const_iterator>(res_slot);
-    }
-
-    // we found an empty slot, meaning that the key we're searching
-    // for isn't in this submap, so we should move onto the next one
-    if (g.ballot(slot_is_empty)) { return this->end(); }
-
-    // otherwise, all slots in the current window are full with other keys,
-    // so we move onto the next window in the current submap
-
-    current_slot = next_slot(g, current_slot);
-  }
-}
-
 template <typename Key,
           typename Value,
           std::size_t CGSize,
diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap_kernels.cuh
index 8a2560c5a..0083347d6 100644
--- a/include/cuco/detail/static_multimap_kernels.cuh
+++ b/include/cuco/detail/static_multimap_kernels.cuh
@@ -159,123 +159,6 @@ __global__ void insert(InputIt first, InputIt last, viewT view, Hash hash, KeyEq
   }
 }
 
-/**
- * @brief Finds the values corresponding to all keys in the range `[first, last)`.
- *
- * If the key `*(first + i)` exists in the map, copies its associated value to `(output_begin + i)`.
- * Else, copies the empty value sentinel.
- * @tparam block_size The size of the thread block
- * @tparam Value The type of the mapped value for the map
- * @tparam InputIt Device accessible input iterator whose `value_type` is
- * convertible to the map's `key_type`
- * @tparam OutputIt Device accessible output iterator whose `value_type` is
- * convertible to the map's `mapped_type`
- * @tparam viewT Type of device view allowing access of hash map storage
- * @tparam Hash Unary callable type
- * @tparam KeyEqual Binary callable type
- * @param first Beginning of the sequence of keys
- * @param last End of the sequence of keys
- * @param output_begin Beginning of the sequence of values retrieved for each key
- * @param view Device view used to access the hash map's slot storage
- * @param hash The unary function to apply to hash each key
- * @param key_equal The binary function to compare two keys for equality
- */
-template <uint32_t block_size,
-          typename Value,
-          typename InputIt,
-          typename OutputIt,
-          typename viewT,
-          typename Hash,
-          typename KeyEqual>
-__global__ void find(
-  InputIt first, InputIt last, OutputIt output_begin, viewT view, Hash hash, KeyEqual key_equal)
-{
-  auto tid     = block_size * blockIdx.x + threadIdx.x;
-  auto key_idx = tid;
-  __shared__ Value writeBuffer[block_size];
-
-  while (first + key_idx < last) {
-    auto key   = *(first + key_idx);
-    auto found = view.find(key, hash, key_equal);
-
-    /*
-     * The ld.relaxed.gpu instruction used in view.find causes L1 to
-     * flush more frequently, causing increased sector stores from L2 to global memory.
-     * By writing results to shared memory and then synchronizing before writing back
-     * to global, we no longer rely on L1, preventing the increase in sector stores from
-     * L2 to global and improving performance.
-     */
-    writeBuffer[threadIdx.x] = found->second.load(cuda::std::memory_order_relaxed);
-    __syncthreads();
-    *(output_begin + key_idx) = writeBuffer[threadIdx.x];
-    key_idx += gridDim.x * block_size;
-  }
-}
-
-/**
- * @brief Finds the values corresponding to all keys in the range `[first, last)`.
- *
- * If the key `*(first + i)` exists in the map, copies its associated value to `(output_begin + i)`.
- * Else, copies the empty value sentinel. Uses the CUDA Cooperative Groups API to leverage groups
- * of multiple threads to find each key. This provides a significant boost in throughput compared
- * to the non Cooperative Group `find` at moderate to high load factors.
- *
- * @tparam block_size The size of the thread block
- * @tparam tile_size The number of threads in the Cooperative Groups used to perform
- * inserts
- * @tparam Value The type of the mapped value for the map
- * @tparam InputIt Device accessible input iterator whose `value_type` is
- * convertible to the map's `key_type`
- * @tparam OutputIt Device accessible output iterator whose `value_type` is
- * convertible to the map's `mapped_type`
- * @tparam viewT Type of device view allowing access of hash map storage
- * @tparam Hash Unary callable type
- * @tparam KeyEqual Binary callable type
- * @param first Beginning of the sequence of keys
- * @param last End of the sequence of keys
- * @param output_begin Beginning of the sequence of values retrieved for each key
- * @param view Device view used to access the hash map's slot storage
- * @param hash The unary function to apply to hash each key
- * @param key_equal The binary function to compare two keys for equality
- */
-template <uint32_t block_size,
-          uint32_t tile_size,
-          typename Value,
-          typename InputIt,
-          typename OutputIt,
-          typename viewT,
-          typename Hash,
-          typename KeyEqual>
-__global__ void find(
-  InputIt first, InputIt last, OutputIt output_begin, viewT view, Hash hash, KeyEqual key_equal)
-{
-  auto tile    = cg::tiled_partition<tile_size>(cg::this_thread_block());
-  auto tid     = block_size * blockIdx.x + threadIdx.x;
-  auto key_idx = tid / tile_size;
-  __shared__ Value writeBuffer[block_size];
-
-  while (first + key_idx < last) {
-    auto key   = *(first + key_idx);
-    auto found = view.find(tile, key, hash, key_equal);
-
-    /*
-     * The ld.relaxed.gpu instruction used in view.find causes L1 to
-     * flush more frequently, causing increased sector stores from L2 to global memory.
-     * By writing results to shared memory and then synchronizing before writing back
-     * to global, we no longer rely on L1, preventing the increase in sector stores from
-     * L2 to global and improving performance.
-     */
-    if (tile.thread_rank() == 0) {
-      writeBuffer[threadIdx.x / tile_size] = found->second.load(cuda::std::memory_order_relaxed);
-    }
-    __syncthreads();
-    if (tile.thread_rank() == 0) {
-      *(output_begin + key_idx) = writeBuffer[threadIdx.x / tile_size];
-    }
-    key_idx += (gridDim.x * block_size) / tile_size;
-  }
-}
-
 /**
  * @brief Indicates whether the keys in the range `[first, last)` are contained in the map.
  *
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index 35ce7a345..5591c7403 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -200,35 +200,6 @@ class static_multimap {
               Hash hash           = Hash{},
               KeyEqual key_equal  = KeyEqual{});
 
-  /**
-   * @brief Finds the values corresponding to all keys in the range `[first, last)`.
-   *
-   * If the key `*(first + i)` exists in the map, copies its associated value to `(output_begin +
-   * i)`. Else, copies the empty value sentinel.
-   *
-   * @tparam InputIt Device accessible input iterator whose `value_type` is
-   * convertible to the map's `key_type`
-   * @tparam OutputIt Device accessible output iterator whose `value_type` is
-   * convertible to the map's `mapped_type`
-   * @tparam Hash Unary callable type
-   * @tparam KeyEqual Binary callable type
-   * @param first Beginning of the sequence of keys
-   * @param last End of the sequence of keys
-   * @param output_begin Beginning of the sequence of values retrieved for each key
-   * @param hash The unary function to apply to hash each key
-   * @param key_equal The binary function to compare two keys for equality
-   */
-  template <typename InputIt,
-            typename OutputIt,
-            typename Hash     = cuco::detail::MurmurHash3_32<key_type>,
-            typename KeyEqual = thrust::equal_to<key_type>>
-  void find(InputIt first,
-            InputIt last,
-            OutputIt output_begin,
-            cudaStream_t stream = 0,
-            Hash hash           = Hash{},
-            KeyEqual key_equal  = KeyEqual{});
-
   /**
    * @brief Indicates whether the keys in the range `[first, last)` are contained in the map.
    *
@@ -772,99 +743,6 @@ class static_multimap {
                          source_device_view.get_empty_value_sentinel());
     }
 
-    /**
-     * @brief Finds the value corresponding to the key `k`.
-     *
-     * Returns an iterator to the pair whose key is equivalent to `k`.
-     * If no such pair exists, returns `end()`.
-     *
-     * @tparam Hash Unary callable type
-     * @tparam KeyEqual Binary callable type
-     * @param k The key to search for
-     * @param hash The unary callable used to hash the key
-     * @param key_equal The binary callable used to compare two keys
-     * for equality
-     * @return An iterator to the position at which the key/value pair
-     * containing `k` was inserted
-     */
-    template <typename Hash     = cuco::detail::MurmurHash3_32<key_type>,
-              typename KeyEqual = thrust::equal_to<key_type>>
-    __device__ iterator find(Key const& k,
-                             Hash hash          = Hash{},
-                             KeyEqual key_equal = KeyEqual{}) noexcept;
-
-    /** @brief Finds the value corresponding to the key `k`.
-     *
-     * Returns a const_iterator to the pair whose key is equivalent to `k`.
-     * If no such pair exists, returns `end()`.
-     *
-     * @tparam Hash Unary callable type
-     * @tparam KeyEqual Binary callable type
-     * @param k The key to search for
-     * @param hash The unary callable used to hash the key
-     * @param key_equal The binary callable used to compare two keys
-     * for equality
-     * @return An iterator to the position at which the key/value pair
-     * containing `k` was inserted
-     */
-    template <typename Hash     = cuco::detail::MurmurHash3_32<key_type>,
-              typename KeyEqual = thrust::equal_to<key_type>>
-    __device__ const_iterator find(Key const& k,
-                                   Hash hash          = Hash{},
-                                   KeyEqual key_equal = KeyEqual{}) const noexcept;
-
-    /**
-     * @brief Finds the value corresponding to the key `k`.
-     *
-     * Returns an iterator to the pair whose key is equivalent to `k`.
-     * If no such pair exists, returns `end()`. Uses the CUDA Cooperative Groups API to
-     * to leverage multiple threads to perform a single find. This provides a
-     * significant boost in throughput compared to the non Cooperative Group
-     * `find` at moderate to high load factors.
-     *
-     * @tparam CG Cooperative Group type
-     * @tparam Hash Unary callable type
-     * @tparam KeyEqual Binary callable type
-     * @param g The Cooperative Group used to perform the find
-     * @param k The key to search for
-     * @param hash The unary callable used to hash the key
-     * @param key_equal The binary callable used to compare two keys
-     * for equality
-     * @return An iterator to the position at which the key/value pair
-     * containing `k` was inserted
-     */
-    template <typename CG,
-              typename Hash     = cuco::detail::MurmurHash3_32<key_type>,
-              typename KeyEqual = thrust::equal_to<key_type>>
-    __device__ iterator
-    find(CG g, Key const& k, Hash hash = Hash{}, KeyEqual key_equal = KeyEqual{}) noexcept;
-
-    /**
-     * @brief Finds the value corresponding to the key `k`.
-     *
-     * Returns a const_iterator to the pair whose key is equivalent to `k`.
-     * If no such pair exists, returns `end()`. Uses the CUDA Cooperative Groups API to
-     * to leverage multiple threads to perform a single find. This provides a
-     * significant boost in throughput compared to the non Cooperative Group
-     * `find` at moderate to high load factors.
-     *
-     * @tparam CG Cooperative Group type
-     * @tparam Hash Unary callable type
-     * @tparam KeyEqual Binary callable type
-     * @param g The Cooperative Group used to perform the find
-     * @param k The key to search for
-     * @param hash The unary callable used to hash the key
-     * @param key_equal The binary callable used to compare two keys
-     * for equality
-     * @return An iterator to the position at which the key/value pair
-     * containing `k` was inserted
-     */
-    template <typename CG,
-              typename Hash     = cuco::detail::MurmurHash3_32<key_type>,
-              typename KeyEqual = thrust::equal_to<key_type>>
-    __device__ const_iterator
-    find(CG g, Key const& k, Hash hash = Hash{}, KeyEqual key_equal = KeyEqual{}) const noexcept;
-
     /**
      * @brief Indicates whether the key `k` was inserted into the map.
      *

From f6c38378974505565ed7f4a159d94759ba7a593a Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Wed, 26 May 2021 11:44:28 -0400
Subject: [PATCH 105/267] Update multimap example: replace find with count and
 find_all

---
 examples/static_multimap/static_multimap_example.cu | 13 +++++++++----
 1 file changed, 9 insertions(+), 4 deletions(-)

diff --git a/examples/static_multimap/static_multimap_example.cu b/examples/static_multimap/static_multimap_example.cu
index 00ab44f89..4b0292e06 100644
--- a/examples/static_multimap/static_multimap_example.cu
+++ b/examples/static_multimap/static_multimap_example.cu
@@ -47,11 +47,16 @@ int main(void)
   // Sequence of keys {0, 1, 2, ...}
   thrust::device_vector<int> keys_to_find(50'000);
   thrust::sequence(keys_to_find.begin(), keys_to_find.end(), 0);
-  thrust::device_vector<int> found_values(50'000);
 
-  // Finds all keys {0, 1, 2, ...} and stores associated values into `found_values`
-  // If a key `keys_to_find[i]` doesn't exist, `found_values[i] == empty_value_sentinel`
-  map.find(keys_to_find.begin(), keys_to_find.end(), found_values.begin());
+  // Counts number of matches for all keys {0, 1, 2, ... 50'000}
+  auto num_matches = map.count(keys_to_find.begin(), keys_to_find.end());
+
+  std::size_t output_size = num_matches + 25'000;
+  thrust::device_vector<cuco::pair_type<int, int>> d_results(output_size);
+
+  // Finds all keys {0, 1, 2, ...} and stores associated key/value pairs into `d_results`
+  // If a key `keys_to_find[i]` doesn't exist, `d_results[i].second == empty_value_sentinel`
+  map.find_all(keys_to_find.begin(), keys_to_find.end(), d_results.data().get());
 
   return 0;
 }

From 0518095bda86165ef82d7ca8edf8972c38050e0f Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Wed, 26 May 2021 11:46:45 -0400
Subject: [PATCH 106/267] Remove naive insert global function

---
 .../cuco/detail/static_multimap_kernels.cuh   | 31 -------------------
 1 file changed, 31 deletions(-)

diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap_kernels.cuh
index 0083347d6..c2c16a8d3 100644
--- a/include/cuco/detail/static_multimap_kernels.cuh
+++ b/include/cuco/detail/static_multimap_kernels.cuh
@@ -85,37 +85,6 @@ __global__ void initialize(pair_atomic_type* const slots, Key k, Value v, std::s
   }
 }
 
-/**
- * @brief Inserts all key/value pairs in the range `[first, last)`.
- *
- * If multiple keys in `[first, last)` compare equal, it is unspecified which
- * element is inserted.
- *
- * @tparam block_size
- * @tparam InputIt Device accessible input iterator whose `value_type` is
- * convertible to the map's `value_type`
- * @tparam viewT Type of device view allowing access of hash map storage
- * @tparam Hash Unary callable type
- * @tparam KeyEqual Binary callable type
- * @param first Beginning of the sequence of key/value pairs
- * @param last End of the sequence of key/value pairs
- * @param view Mutable device view used to access the hash map's slot storage
- * @param hash The unary function to apply to hash each key
- * @param key_equal The binary function used to compare two keys for equality
- */
-template <uint32_t block_size, typename InputIt, typename viewT, typename Hash, typename KeyEqual>
-__global__ void insert(InputIt first, InputIt last, viewT view, Hash hash, KeyEqual key_equal)
-{
-  auto tid = block_size * blockIdx.x + threadIdx.x;
-  auto it  = first + tid;
-
-  while (it < last) {
-    typename viewT::value_type const insert_pair{*it};
-    view.insert(insert_pair, hash, key_equal);
-    it += gridDim.x * block_size;
-  }
-}
-
 /**
  * @brief Inserts all key/value pairs in the range `[first, last)`.
  *

From 7bdad3bca60b233e6de82ddf2a83676c5efb46fa Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Wed, 26 May 2021 11:47:11 -0400
Subject: [PATCH 107/267] Update contains to use vector load

---
 include/cuco/detail/static_multimap.inl | 46 ++++++++-----------------
 1 file changed, 15 insertions(+), 31 deletions(-)

diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index 3526faca1..2bde5a90c 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -288,28 +288,6 @@ __device__ void static_multimap<Key, Value, CGSize, Scope, Allocator>::device_mu
   }
 }
 
-template <typename Key,
-          typename Value,
-          std::size_t CGSize,
-          cuda::thread_scope Scope,
-          typename Allocator>
-template <typename Hash, typename KeyEqual>
-__device__ bool static_multimap<Key, Value, CGSize, Scope, Allocator>::device_view::contains(
-  Key const& k, Hash hash, KeyEqual key_equal) noexcept
-{
-  auto current_slot = initial_slot(k, hash);
-
-  while (true) {
-    auto const existing_key = current_slot->first.load(cuda::std::memory_order_relaxed);
-
-    if (detail::bitwise_compare(existing_key, empty_key_sentinel_)) { return false; }
-
-    if (key_equal(existing_key, k)) { return true; }
-
-    current_slot = next_slot(current_slot);
-  }
-}
-
 template <typename Key,
           typename Value,
           std::size_t CGSize,
@@ -322,19 +300,25 @@ __device__ bool static_multimap<Key, Value, CGSize, Scope, Allocator>::device_vi
   auto current_slot = initial_slot(g, k, hash);
 
   while (true) {
-    key_type const existing_key = current_slot->first.load(cuda::std::memory_order_relaxed);
+    pair<Key, Value> arr[2];
+    if constexpr (sizeof(Key) == 4) {
+      auto const tmp = *reinterpret_cast<uint4 const*>(current_slot);
+      memcpy(&arr[0], &tmp, 2 * sizeof(pair<Key, Value>));
+    } else {
+      auto const tmp = *reinterpret_cast<ulonglong4 const*>(current_slot);
+      memcpy(&arr[0], &tmp, 2 * sizeof(pair<Key, Value>));
+    }
 
-    // The user provide `key_equal` can never be used to compare against `empty_key_sentinel` as the
-    // sentinel is not a valid key value. Therefore, first check for the sentinel
-    auto const slot_is_empty =
-      detail::bitwise_compare(existing_key, this->get_empty_key_sentinel());
+    auto const first_slot_is_empty  = (arr[0].first == get_empty_key_sentinel());
+    auto const second_slot_is_empty = (arr[1].first == get_empty_key_sentinel());
+    auto const first_equals         = (not first_slot_is_empty and key_equal(arr[0].first, k));
+    auto const second_equals        = (not second_slot_is_empty and key_equal(arr[1].first, k));
 
-    // the key we were searching for was found by one of the threads, so we return an iterator to
-    // the entry
-    if (g.ballot(not slot_is_empty and key_equal(existing_key, k))) { return true; }
+    // the key we were searching for was found by one of the threads, so we return true
+    if (g.any(first_equals or second_equals)) { return true; }
 
     // we found an empty slot, meaning that the key we're searching for isn't present
-    if (g.ballot(slot_is_empty)) { return false; }
+    if (g.any(first_slot_is_empty or second_slot_is_empty)) { return false; }
 
     // otherwise, all slots in the current window are full with other keys, so we move onto the next
     // window

From 0f90d541e16ce2e881872bb6950d0a074e559a62 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Wed, 26 May 2021 14:26:58 -0400
Subject: [PATCH 108/267] prime.hpp instead of prime.cuh

---
 include/cuco/detail/{prime.cuh => prime.hpp} | 0
 include/cuco/static_multimap.cuh             | 2 +-
 2 files changed, 1 insertion(+), 1 deletion(-)
 rename include/cuco/detail/{prime.cuh => prime.hpp} (100%)

diff --git a/include/cuco/detail/prime.cuh b/include/cuco/detail/prime.hpp
similarity index 100%
rename from include/cuco/detail/prime.cuh
rename to include/cuco/detail/prime.hpp
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index 5591c7403..1512ebbdd 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -37,7 +37,7 @@
 #include <cuco/detail/error.hpp>
 #include <cuco/detail/hash_functions.cuh>
 #include <cuco/detail/pair.cuh>
-#include <cuco/detail/prime.cuh>
+#include <cuco/detail/prime.hpp>
 #include <cuco/detail/static_multimap_kernels.cuh>
 
 namespace cuco {

From 0148769dea574c8514fae509c006baf139b36725 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Thu, 27 May 2021 18:39:37 -0400
Subject: [PATCH 109/267] Add probe sequence header: DoubleHashing class

---
 include/cuco/detail/probe_sequences.cuh | 79 +++++++++++++++++++++++++
 1 file changed, 79 insertions(+)
 create mode 100644 include/cuco/detail/probe_sequences.cuh

diff --git a/include/cuco/detail/probe_sequences.cuh b/include/cuco/detail/probe_sequences.cuh
new file mode 100644
index 000000000..a7c9414c5
--- /dev/null
+++ b/include/cuco/detail/probe_sequences.cuh
@@ -0,0 +1,79 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cuda/std/atomic>
+
+#include <cuco/detail/hash_functions.cuh>
+#include <cuco/detail/pair.cuh>
+
+namespace cuco {
+
+template <typename Key,
+          typename Value,
+          uint32_t CGSize          = 8,
+          typename Hash1           = cuco::detail::MurmurHash3_32<Key>,
+          typename Hash2           = cuco::detail::MurmurHash3_32<Key>,
+          cuda::thread_scope Scope = cuda::thread_scope_device>
+class DoubleHashing {
+ public:
+  using value_type         = cuco::pair_type<Key, Value>;
+  using key_type           = Key;
+  using mapped_type        = Value;
+  using atomic_key_type    = cuda::atomic<key_type, Scope>;
+  using atomic_mapped_type = cuda::atomic<mapped_type, Scope>;
+  using pair_atomic_type   = cuco::pair_type<atomic_key_type, atomic_mapped_type>;
+  using iterator           = pair_atomic_type*;
+  using const_iterator     = pair_atomic_type const*;
+
+  __host__ __device__ static constexpr uint32_t cg_size() noexcept { return CGSize; }
+
+  __host__ __device__ explicit DoubleHashing(iterator slots, std::size_t capacity)
+    : slots_{slots}, capacity_{capacity}
+  {
+  }
+
+  __host__ __device__ std::size_t get_capacity() const noexcept { return capacity_; }
+
+  __device__ iterator get_slots() noexcept { return slots_; }
+  __device__ const_iterator get_slots() const noexcept { return slots_; }
+
+  template <typename CG>
+  __device__ iterator initial_slot(CG const& g, Key const k) noexcept
+  {
+    step_size_ = (hash2(k + 1) % (capacity_ / (cg_size() * 2) - 1) + 1) * cg_size() * 2;
+    std::size_t index =
+      hash1(k) % (capacity_ / (cg_size() * 2)) * cg_size() * 2 + g.thread_rank() * 2;
+    return slots_ + index;
+  }
+
+  template <typename CG>
+  __device__ iterator next_slot(CG const& g, iterator s) noexcept
+  {
+    std::size_t index = s - slots_;
+    return &slots_[(index + step_size_) % capacity_];
+  }
+
+ private:
+  iterator slots_;
+  const std::size_t capacity_;
+  std::size_t step_size_;
+  Hash1 hash1{};
+  Hash2 hash2{};
+};  // class DoubleHashing
+
+}  // namespace cuco

From ea7fe7879cd1bdd353b37951ca96ebcd734aa969 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Thu, 27 May 2021 18:41:51 -0400
Subject: [PATCH 110/267] Passing ProbeSequence as template parameter instead
 of CGSize

---
 .../static_multimap/find_all_bench.cu         |   5 +-
 .../static_multimap/static_multimap_bench.cu  |  12 +-
 include/cuco/detail/static_multimap.inl       | 115 ++++-----
 .../cuco/detail/static_multimap_kernels.cuh   |  27 +-
 include/cuco/static_multimap.cuh              | 238 +++---------------
 5 files changed, 101 insertions(+), 296 deletions(-)

diff --git a/benchmarks/hash_table/static_multimap/find_all_bench.cu b/benchmarks/hash_table/static_multimap/find_all_bench.cu
index 21de4a26d..40582eb38 100644
--- a/benchmarks/hash_table/static_multimap/find_all_bench.cu
+++ b/benchmarks/hash_table/static_multimap/find_all_bench.cu
@@ -78,7 +78,7 @@ std::enable_if_t<(sizeof(Key) == sizeof(Value)), void> nvbench_find_all(
 
   state.exec(
     nvbench::exec_tag::sync | nvbench::exec_tag::timer, [&](nvbench::launch& launch, auto& timer) {
-      cuco::static_multimap<Key, Value, CGSize> map{size, -1, -1};
+      cuco::static_multimap<Key, Value, cuco::DoubleHashing<Key, Value, CGSize>> map{size, -1, -1};
       map.insert(d_pairs.begin(), d_pairs.end());
 
       timer.start();
@@ -89,10 +89,8 @@ std::enable_if_t<(sizeof(Key) == sizeof(Value)), void> nvbench_find_all(
       auto const stride      = 1;
       auto const grid_size = (CGSize * num_keys + stride * block_size - 1) / (stride * block_size);
 
-      using Hash     = cuco::detail::MurmurHash3_32<Key>;
       using KeyEqual = thrust::equal_to<Key>;
 
-      Hash hash;
       KeyEqual key_equal;
 
       using atomic_ctr_type = typename cuco::static_multimap<Key, Value>::atomic_ctr_type;
@@ -110,7 +108,6 @@ std::enable_if_t<(sizeof(Key) == sizeof(Value)), void> nvbench_find_all(
                                                             d_results.data().get(),
                                                             num_items,
                                                             view,
-                                                            hash,
                                                             key_equal);
       CUCO_CUDA_TRY(cudaDeviceSynchronize());
       timer.stop();
diff --git a/benchmarks/hash_table/static_multimap/static_multimap_bench.cu b/benchmarks/hash_table/static_multimap/static_multimap_bench.cu
index e53efcee6..a4a6a0a55 100644
--- a/benchmarks/hash_table/static_multimap/static_multimap_bench.cu
+++ b/benchmarks/hash_table/static_multimap/static_multimap_bench.cu
@@ -57,7 +57,6 @@ std::enable_if_t<(sizeof(Key) == sizeof(Value)), void> nvbench_static_multimap_i
   auto const occupancy = state.get_float64("Occupancy");
 
   std::size_t const size = num_keys / occupancy;
-  auto const cg_size     = 8;
 
   std::vector<Key> h_keys(num_keys);
   std::vector<cuco::pair_type<Key, Value>> h_pairs(num_keys);
@@ -77,7 +76,7 @@ std::enable_if_t<(sizeof(Key) == sizeof(Value)), void> nvbench_static_multimap_i
 
   state.exec(nvbench::exec_tag::sync | nvbench::exec_tag::timer,
              [&](nvbench::launch& launch, auto& timer) {
-               cuco::static_multimap<Key, Value, cg_size> map{size, -1, -1};
+               cuco::static_multimap<Key, Value> map{size, -1, -1};
 
                // Use timers to explicitly mark the target region
                timer.start();
@@ -109,7 +108,6 @@ std::enable_if_t<(sizeof(Key) == sizeof(Value)), void> nvbench_static_multimap_c
   auto const matching_rate = state.get_float64("MatchingRate");
 
   std::size_t const size = num_keys / occupancy;
-  auto const cg_size     = 8;
 
   std::vector<Key> h_keys(num_keys);
   std::vector<cuco::pair_type<Key, Value>> h_pairs(num_keys);
@@ -130,7 +128,7 @@ std::enable_if_t<(sizeof(Key) == sizeof(Value)), void> nvbench_static_multimap_c
 
   state.add_element_count(num_keys, "NumKeys");
 
-  cuco::static_multimap<Key, Value, cg_size> map{size, -1, -1};
+  cuco::static_multimap<Key, Value> map{size, -1, -1};
   map.insert(d_pairs.begin(), d_pairs.end());
 
   state.exec(nvbench::exec_tag::sync | nvbench::exec_tag::timer,
@@ -164,7 +162,6 @@ std::enable_if_t<(sizeof(Key) == sizeof(Value)), void> nvbench_static_multimap_f
   auto const matching_rate = state.get_float64("MatchingRate");
 
   std::size_t const size = num_keys / occupancy;
-  auto const cg_size     = 8;
 
   std::vector<Key> h_keys(num_keys);
   std::vector<cuco::pair_type<Key, Value>> h_pairs(num_keys);
@@ -185,7 +182,7 @@ std::enable_if_t<(sizeof(Key) == sizeof(Value)), void> nvbench_static_multimap_f
 
   state.add_element_count(num_keys, "NumKeys");
 
-  cuco::static_multimap<Key, Value, cg_size> map{size, -1, -1};
+  cuco::static_multimap<Key, Value> map{size, -1, -1};
   map.insert(d_pairs.begin(), d_pairs.end());
 
   auto num_matches        = map.count(d_keys.begin(), d_keys.end());
@@ -218,7 +215,6 @@ std::enable_if_t<(sizeof(Key) == sizeof(Value)), void> nvbench_static_multimap_r
   auto const matching_rate = state.get_float64("MatchingRate");
 
   std::size_t const size = num_keys / occupancy;
-  auto const cg_size     = 8;
 
   std::vector<Key> h_keys(num_keys);
   std::vector<cuco::pair_type<Key, Value>> h_pairs(num_keys);
@@ -239,7 +235,7 @@ std::enable_if_t<(sizeof(Key) == sizeof(Value)), void> nvbench_static_multimap_r
 
   state.add_element_count(num_keys, "NumKeys");
 
-  cuco::static_multimap<Key, Value, cg_size> map{size, -1, -1};
+  cuco::static_multimap<Key, Value> map{size, -1, -1};
   map.insert(d_pairs.begin(), d_pairs.end());
 
   auto num_matches        = map.count(d_keys.begin(), d_keys.end());
diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index 2bde5a90c..594d93793 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -30,14 +30,12 @@ enum class insert_result {
 
 template <typename Key,
           typename Value,
-          std::size_t CGSize,
+          class ProbeSequence,
           cuda::thread_scope Scope,
           typename Allocator>
-static_multimap<Key, Value, CGSize, Scope, Allocator>::static_multimap(std::size_t capacity,
-                                                                       Key empty_key_sentinel,
-                                                                       Value empty_value_sentinel,
-                                                                       Allocator const& alloc)
-  : capacity_{cuco::detail::get_valid_capacity<CGSize>(capacity)},
+static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::static_multimap(
+  std::size_t capacity, Key empty_key_sentinel, Value empty_value_sentinel, Allocator const& alloc)
+  : capacity_{cuco::detail::get_valid_capacity<cg_size()>(capacity)},
     empty_key_sentinel_{empty_key_sentinel},
     empty_value_sentinel_{empty_value_sentinel},
     slot_allocator_{alloc}
@@ -53,76 +51,70 @@ static_multimap<Key, Value, CGSize, Scope, Allocator>::static_multimap(std::size
 
 template <typename Key,
           typename Value,
-          std::size_t CGSize,
+          class ProbeSequence,
           cuda::thread_scope Scope,
           typename Allocator>
-static_multimap<Key, Value, CGSize, Scope, Allocator>::~static_multimap()
+static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::~static_multimap()
 {
   std::allocator_traits<slot_allocator_type>::deallocate(slot_allocator_, slots_, capacity_);
 }
 
 template <typename Key,
           typename Value,
-          std::size_t CGSize,
+          class ProbeSequence,
           cuda::thread_scope Scope,
           typename Allocator>
-template <typename InputIt, typename Hash, typename KeyEqual>
-void static_multimap<Key, Value, CGSize, Scope, Allocator>::insert(
-  InputIt first, InputIt last, cudaStream_t stream, Hash hash, KeyEqual key_equal)
+template <typename InputIt, typename KeyEqual>
+void static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::insert(InputIt first,
+                                                                          InputIt last,
+                                                                          cudaStream_t stream,
+                                                                          KeyEqual key_equal)
 {
   auto num_keys         = std::distance(first, last);
   auto const block_size = 128;
   auto const stride     = 1;
-  auto const grid_size  = (CGSize * num_keys + stride * block_size - 1) / (stride * block_size);
+  auto const grid_size  = (cg_size() * num_keys + stride * block_size - 1) / (stride * block_size);
   auto view             = get_device_mutable_view();
 
-  detail::insert<block_size, CGSize>
-    <<<grid_size, block_size, 0, stream>>>(first, first + num_keys, view, hash, key_equal);
+  detail::insert<block_size, cg_size()>
+    <<<grid_size, block_size, 0, stream>>>(first, first + num_keys, view, key_equal);
   CUCO_CUDA_TRY(cudaDeviceSynchronize());
 }
 
 template <typename Key,
           typename Value,
-          std::size_t CGSize,
+          class ProbeSequence,
           cuda::thread_scope Scope,
           typename Allocator>
-template <typename InputIt, typename OutputIt, typename Hash, typename KeyEqual>
-void static_multimap<Key, Value, CGSize, Scope, Allocator>::contains(InputIt first,
-                                                                     InputIt last,
-                                                                     OutputIt output_begin,
-                                                                     cudaStream_t stream,
-                                                                     Hash hash,
-                                                                     KeyEqual key_equal)
+template <typename InputIt, typename OutputIt, typename KeyEqual>
+void static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::contains(
+  InputIt first, InputIt last, OutputIt output_begin, cudaStream_t stream, KeyEqual key_equal)
 {
   auto num_keys         = std::distance(first, last);
   auto const block_size = 128;
   auto const stride     = 1;
-  auto const grid_size  = (CGSize * num_keys + stride * block_size - 1) / (stride * block_size);
+  auto const grid_size  = (cg_size() * num_keys + stride * block_size - 1) / (stride * block_size);
   auto view             = get_device_view();
 
-  detail::contains<block_size, CGSize>
-    <<<grid_size, block_size, 0, stream>>>(first, last, output_begin, view, hash, key_equal);
+  detail::contains<block_size, cg_size>
+    <<<grid_size, block_size, 0, stream>>>(first, last, output_begin, view, key_equal);
   CUCO_CUDA_TRY(cudaDeviceSynchronize());
 }
 
 template <typename Key,
           typename Value,
-          std::size_t CGSize,
+          class ProbeSequence,
           cuda::thread_scope Scope,
           typename Allocator>
-template <typename InputIt, typename OutputIt, typename Hash, typename KeyEqual>
-OutputIt static_multimap<Key, Value, CGSize, Scope, Allocator>::find_all(InputIt first,
-                                                                         InputIt last,
-                                                                         OutputIt output_begin,
-                                                                         cudaStream_t stream,
-                                                                         Hash hash,
-                                                                         KeyEqual key_equal)
+template <typename InputIt, typename OutputIt, typename KeyEqual>
+OutputIt static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::find_all(
+  InputIt first, InputIt last, OutputIt output_begin, cudaStream_t stream, KeyEqual key_equal)
 {
   auto num_keys          = std::distance(first, last);
   auto const block_size  = 128;
   auto const buffer_size = block_size * 3;
   auto const stride      = 1;
-  auto const grid_size   = (CGSize * num_keys + stride * block_size - 1) / (stride * block_size);
+  auto const grid_size   = (cg_size() * num_keys + stride * block_size - 1) / (stride * block_size);
   auto view              = get_device_view();
 
   atomic_ctr_type* num_items;
@@ -132,9 +124,8 @@ OutputIt static_multimap<Key, Value, CGSize, Scope, Allocator>::find_all(InputIt
   CUCO_CUDA_TRY(cudaGetDevice(&device_id));
   CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_items, sizeof(atomic_ctr_type), device_id));
 
-  detail::find_all<block_size, CGSize, buffer_size, Key, Value>
-    <<<grid_size, block_size, 0, stream>>>(
-      first, last, output_begin, num_items, view, hash, key_equal);
+  detail::find_all<block_size, cg_size(), buffer_size, Key, Value>
+    <<<grid_size, block_size, 0, stream>>>(first, last, output_begin, num_items, view, key_equal);
   CUCO_CUDA_TRY(cudaDeviceSynchronize());
 
   auto output_end = output_begin + *num_items;
@@ -145,17 +136,19 @@ OutputIt static_multimap<Key, Value, CGSize, Scope, Allocator>::find_all(InputIt
 
 template <typename Key,
           typename Value,
-          std::size_t CGSize,
+          class ProbeSequence,
           cuda::thread_scope Scope,
           typename Allocator>
-template <typename InputIt, typename Hash, typename KeyEqual>
-std::size_t static_multimap<Key, Value, CGSize, Scope, Allocator>::count(
-  InputIt first, InputIt last, cudaStream_t stream, Hash hash, KeyEqual key_equal)
+template <typename InputIt, typename KeyEqual>
+std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::count(InputIt first,
+                                                                                InputIt last,
+                                                                                cudaStream_t stream,
+                                                                                KeyEqual key_equal)
 {
   auto num_keys         = std::distance(first, last);
   auto const block_size = 128;
   auto const stride     = 1;
-  auto const grid_size  = (CGSize * num_keys + stride * block_size - 1) / (stride * block_size);
+  auto const grid_size  = (cg_size() * num_keys + stride * block_size - 1) / (stride * block_size);
   auto view             = get_device_view();
 
   atomic_ctr_type* num_items;
@@ -165,8 +158,8 @@ std::size_t static_multimap<Key, Value, CGSize, Scope, Allocator>::count(
   CUCO_CUDA_TRY(cudaGetDevice(&device_id));
   CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_items, sizeof(atomic_ctr_type), device_id));
 
-  detail::count<block_size, CGSize, Key, Value>
-    <<<grid_size, block_size, 0, stream>>>(first, last, num_items, view, hash, key_equal);
+  detail::count<block_size, cg_size(), Key, Value>
+    <<<grid_size, block_size, 0, stream>>>(first, last, num_items, view, key_equal);
   CUCO_CUDA_TRY(cudaDeviceSynchronize());
 
   size_t result = *num_items;
@@ -177,14 +170,15 @@ std::size_t static_multimap<Key, Value, CGSize, Scope, Allocator>::count(
 
 template <typename Key,
           typename Value,
-          std::size_t CGSize,
+          class ProbeSequence,
           cuda::thread_scope Scope,
           typename Allocator>
-template <typename Hash, typename KeyEqual>
-__device__ void static_multimap<Key, Value, CGSize, Scope, Allocator>::device_mutable_view::insert(
-  value_type const& insert_pair, Hash hash, KeyEqual key_equal) noexcept
+template <typename KeyEqual>
+__device__ void
+static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_mutable_view::insert(
+  value_type const& insert_pair, KeyEqual key_equal) noexcept
 {
-  auto current_slot{initial_slot(insert_pair.first, hash)};
+  auto current_slot{initial_slot(insert_pair.first)};
 
   while (true) {
     using cuda::std::memory_order_relaxed;
@@ -218,14 +212,15 @@ __device__ void static_multimap<Key, Value, CGSize, Scope, Allocator>::device_mu
 
 template <typename Key,
           typename Value,
-          std::size_t CGSize,
+          class ProbeSequence,
           cuda::thread_scope Scope,
           typename Allocator>
-template <typename CG, typename Hash, typename KeyEqual>
-__device__ void static_multimap<Key, Value, CGSize, Scope, Allocator>::device_mutable_view::insert(
-  CG g, value_type const& insert_pair, Hash hash, KeyEqual key_equal) noexcept
+template <typename CG, typename KeyEqual>
+__device__ void
+static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_mutable_view::insert(
+  CG g, value_type const& insert_pair, KeyEqual key_equal) noexcept
 {
-  auto current_slot = initial_slot(g, insert_pair.first, hash);
+  auto current_slot = initial_slot(g, insert_pair.first);
   while (true) {
     // key_type const existing_key = current_slot->first.load(cuda::memory_order_relaxed);
     pair<Key, Value> arr[2];
@@ -290,14 +285,14 @@ __device__ void static_multimap<Key, Value, CGSize, Scope, Allocator>::device_mu
 
 template <typename Key,
           typename Value,
-          std::size_t CGSize,
+          class ProbeSequence,
           cuda::thread_scope Scope,
           typename Allocator>
-template <typename CG, typename Hash, typename KeyEqual>
-__device__ bool static_multimap<Key, Value, CGSize, Scope, Allocator>::device_view::contains(
-  CG g, Key const& k, Hash hash, KeyEqual key_equal) noexcept
+template <typename CG, typename KeyEqual>
+__device__ bool static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::contains(
+  CG g, Key const& k, KeyEqual key_equal) noexcept
 {
-  auto current_slot = initial_slot(g, k, hash);
+  auto current_slot = initial_slot(g, k);
 
   while (true) {
     pair<Key, Value> arr[2];
diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap_kernels.cuh
index c2c16a8d3..70a182254 100644
--- a/include/cuco/detail/static_multimap_kernels.cuh
+++ b/include/cuco/detail/static_multimap_kernels.cuh
@@ -100,21 +100,18 @@ __global__ void initialize(pair_atomic_type* const slots, Key k, Value v, std::s
  * @tparam InputIt Device accessible input iterator whose `value_type` is
  * convertible to the map's `value_type`
  * @tparam viewT Type of device view allowing access of hash map storage
- * @tparam Hash Unary callable type
  * @tparam KeyEqual Binary callable type
  * @param first Beginning of the sequence of key/value pairs
  * @param last End of the sequence of key/value pairs
  * @param view Mutable device view used to access the hash map's slot storage
- * @param hash The unary function to apply to hash each key
  * @param key_equal The binary function used to compare two keys for equality
  */
 template <uint32_t block_size,
           uint32_t tile_size,
           typename InputIt,
           typename viewT,
-          typename Hash,
           typename KeyEqual>
-__global__ void insert(InputIt first, InputIt last, viewT view, Hash hash, KeyEqual key_equal)
+__global__ void insert(InputIt first, InputIt last, viewT view, KeyEqual key_equal)
 {
   auto tile = cg::tiled_partition<tile_size>(cg::this_thread_block());
   auto tid  = block_size * blockIdx.x + threadIdx.x;
@@ -123,7 +120,7 @@ __global__ void insert(InputIt first, InputIt last, viewT view, Hash hash, KeyEq
   while (it < last) {
     // force conversion to value_type
     typename viewT::value_type const insert_pair{*it};
-    view.insert(tile, insert_pair, hash, key_equal);
+    view.insert(tile, insert_pair, key_equal);
     it += (gridDim.x * block_size) / tile_size;
   }
 }
@@ -144,13 +141,11 @@ __global__ void insert(InputIt first, InputIt last, viewT view, Hash hash, KeyEq
  * @tparam OutputIt Device accessible output iterator whose `value_type` is
  * convertible to the map's `mapped_type`
  * @tparam viewT Type of device view allowing access of hash map storage
- * @tparam Hash Unary callable type
  * @tparam KeyEqual Binary callable type
  * @param first Beginning of the sequence of keys
  * @param last End of the sequence of keys
  * @param output_begin Beginning of the sequence of booleans for the presence of each key
  * @param view Device view used to access the hash map's slot storage
- * @param hash The unary function to apply to hash each key
  * @param key_equal The binary function to compare two keys for equality
  */
 template <uint32_t block_size,
@@ -158,10 +153,9 @@ template <uint32_t block_size,
           typename InputIt,
           typename OutputIt,
           typename viewT,
-          typename Hash,
           typename KeyEqual>
 __global__ void contains(
-  InputIt first, InputIt last, OutputIt output_begin, viewT view, Hash hash, KeyEqual key_equal)
+  InputIt first, InputIt last, OutputIt output_begin, viewT view, KeyEqual key_equal)
 {
   auto tile    = cg::tiled_partition<tile_size>(cg::this_thread_block());
   auto tid     = block_size * blockIdx.x + threadIdx.x;
@@ -170,7 +164,7 @@ __global__ void contains(
 
   while (first + key_idx < last) {
     auto key   = *(first + key_idx);
-    auto found = view.contains(tile, key, hash, key_equal);
+    auto found = view.contains(tile, key, key_equal);
 
     /*
      * The ld.relaxed.gpu instruction used in view.find causes L1 to
@@ -210,14 +204,12 @@ __global__ void contains(
  * convertible to the map's `mapped_type`
  * @tparam atomicT Type of atomic storage
  * @tparam viewT Type of device view allowing access of hash map storage
- * @tparam Hash Unary callable type
  * @tparam KeyEqual Binary callable type
  * @param first Beginning of the sequence of keys
  * @param last End of the sequence of keys
  * @param output_begin Beginning of the sequence of values retrieved for each key
  * @param num_items Size of the output sequence
  * @param view Device view used to access the hash map's slot storage
- * @param hash The unary function to apply to hash each key
  * @param key_equal The binary function to compare two keys for equality
  */
 
@@ -230,14 +222,12 @@ template <uint32_t block_size,
           typename OutputIt,
           typename atomicT,
           typename viewT,
-          typename Hash,
           typename KeyEqual>
 __global__ void find_all(InputIt first,
                          InputIt last,
                          OutputIt output_begin,
                          atomicT* num_items,
                          viewT view,
-                         Hash hash,
                          KeyEqual key_equal)
 {
   auto tile    = cg::tiled_partition<tile_size>(cg::this_thread_block());
@@ -258,7 +248,7 @@ __global__ void find_all(InputIt first,
     bool running     = ((first + key_idx) < last);
     bool found_match = false;
 
-    if (running) { current_slot = view.initial_slot(tile, key, hash); }
+    if (running) { current_slot = view.initial_slot(tile, key); }
 
     while (__syncthreads_or(running)) {
       if (running) {
@@ -342,13 +332,11 @@ __global__ void find_all(InputIt first,
  * `key_type`
  * @tparam atomicT Type of atomic storage
  * @tparam viewT Type of device view allowing access of hash map storage
- * @tparam Hash Unary callable
  * @tparam KeyEqual Binary callable
  * @param first Beginning of the sequence of keys to count
  * @param last End of the sequence of keys to count
  * @param num_items The number of all the matches for a sequence of keys
  * @param view Device view used to access the hash map's slot storage
- * @param hash Unary function to apply to hash each key
  * @param key_equal Binary function to compare two keys for equality
  */
 template <uint32_t block_size,
@@ -358,10 +346,9 @@ template <uint32_t block_size,
           typename InputIt,
           typename atomicT,
           typename viewT,
-          typename Hash,
           typename KeyEqual>
 __global__ void count(
-  InputIt first, InputIt last, atomicT* num_items, viewT view, Hash hash, KeyEqual key_equal)
+  InputIt first, InputIt last, atomicT* num_items, viewT view, KeyEqual key_equal)
 {
   auto tile    = cg::tiled_partition<tile_size>(cg::this_thread_block());
   auto tid     = block_size * blockIdx.x + threadIdx.x;
@@ -373,7 +360,7 @@ __global__ void count(
 
   while (first + key_idx < last) {
     auto key          = *(first + key_idx);
-    auto current_slot = view.initial_slot(tile, key, hash);
+    auto current_slot = view.initial_slot(tile, key);
 
     while (true) {
       pair<Key, Value> arr[2];
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index 1512ebbdd..3455bb248 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -20,7 +20,6 @@
 #include <thrust/distance.h>
 #include <thrust/functional.h>
 #include <cub/cub.cuh>
-#include <cuda/std/atomic>
 #include <memory>
 
 #include <cuco/allocator.hpp>
@@ -35,9 +34,8 @@
 #endif
 
 #include <cuco/detail/error.hpp>
-#include <cuco/detail/hash_functions.cuh>
-#include <cuco/detail/pair.cuh>
 #include <cuco/detail/prime.hpp>
+#include <cuco/detail/probe_sequences.cuh>
 #include <cuco/detail/static_multimap_kernels.cuh>
 
 namespace cuco {
@@ -112,7 +110,7 @@ namespace cuco {
  */
 template <typename Key,
           typename Value,
-          std::size_t CGSize       = 8,
+          class ProbeSequence      = DoubleHashing<Key, Value>,
           cuda::thread_scope Scope = cuda::thread_scope_device,
           typename Allocator       = cuco::cuda_allocator<char>>
 class static_multimap {
@@ -142,6 +140,8 @@ class static_multimap {
   static_multimap& operator=(static_multimap const&) = delete;
   static_multimap& operator=(static_multimap&&) = delete;
 
+  static constexpr uint32_t cg_size() noexcept { return ProbeSequence::cg_size(); }
+
   /**
    * @brief Construct a fixed-size map with the specified capacity and sentinel values.
    * @brief Construct a statically sized map with the specified number of slots
@@ -184,20 +184,15 @@ class static_multimap {
    *
    * @tparam InputIt Device accessible input iterator whose `value_type` is
    * convertible to the map's `value_type`
-   * @tparam Hash Unary callable type
    * @tparam KeyEqual Binary callable type
    * @param first Beginning of the sequence of key/value pairs
    * @param last End of the sequence of key/value pairs
-   * @param hash The unary function to apply to hash each key
    * @param key_equal The binary function to compare two keys for equality
    */
-  template <typename InputIt,
-            typename Hash     = cuco::detail::MurmurHash3_32<key_type>,
-            typename KeyEqual = thrust::equal_to<key_type>>
+  template <typename InputIt, typename KeyEqual = thrust::equal_to<key_type>>
   void insert(InputIt first,
               InputIt last,
               cudaStream_t stream = 0,
-              Hash hash           = Hash{},
               KeyEqual key_equal  = KeyEqual{});
 
   /**
@@ -209,23 +204,17 @@ class static_multimap {
    * convertible to the map's `key_type`
    * @tparam OutputIt Device accessible output iterator whose `value_type` is
    * convertible to the map's `mapped_type`
-   * @tparam Hash Unary callable type
    * @tparam KeyEqual Binary callable type
    * @param first Beginning of the sequence of keys
    * @param last End of the sequence of keys
    * @param output_begin Beginning of the sequence of booleans for the presence of each key
-   * @param hash The unary function to apply to hash each key
    * @param key_equal The binary function to compare two keys for equality
    */
-  template <typename InputIt,
-            typename OutputIt,
-            typename Hash     = cuco::detail::MurmurHash3_32<key_type>,
-            typename KeyEqual = thrust::equal_to<key_type>>
+  template <typename InputIt, typename OutputIt, typename KeyEqual = thrust::equal_to<key_type>>
   void contains(InputIt first,
                 InputIt last,
                 OutputIt output_begin,
                 cudaStream_t stream = 0,
-                Hash hash           = Hash{},
                 KeyEqual key_equal  = KeyEqual{});
 
   /**
@@ -242,45 +231,34 @@ class static_multimap {
    * convertible to the map's `key_type`
    * @tparam OutputIt Device accessible output iterator whose `value_type` is
    * convertible to the map's `value_type`
-   * @tparam Hash Unary callable type
    * @tparam KeyEqual Binary callable type
    * @param first Beginning of the sequence of keys
    * @param last End of the sequence of keys
    * @param output_begin Beginning of the sequence of key/value pairs retrieved for each key
-   * @param hash The unary function to apply to hash each key
    * @param key_equal The binary function to compare two keys for equality
    * @return The iterator indicating the last valid key/value pairs in the output
    */
-  template <typename InputIt,
-            typename OutputIt,
-            typename Hash     = cuco::detail::MurmurHash3_32<key_type>,
-            typename KeyEqual = thrust::equal_to<key_type>>
+  template <typename InputIt, typename OutputIt, typename KeyEqual = thrust::equal_to<key_type>>
   OutputIt find_all(InputIt first,
                     InputIt last,
                     OutputIt output_begin,
                     cudaStream_t stream = 0,
-                    Hash hash           = Hash{},
                     KeyEqual key_equal  = KeyEqual{});
 
   /**
    * @brief Counts the occurrences of keys in `[first, last)` contained in the multimap.
    *
    * @tparam Input Device accesible input iterator whose `value_type` is convertible to `key_type`
-   * @tparam Hash Unary callable
    * @tparam KeyEqual Binary callable
    * @param first Beginning of the sequence of keys to count
    * @param last End of the sequence of keys to count
-   * @param hash Unary function to apply to hash each key
    * @param key_equal Binary function to compare two keys for equality
    * @return The sum of total occurrences of all keys in `[first,last)`
    */
-  template <typename InputIt,
-            typename Hash     = cuco::detail::MurmurHash3_32<key_type>,
-            typename KeyEqual = thrust::equal_to<key_type>>
+  template <typename InputIt, typename KeyEqual = thrust::equal_to<key_type>>
   std::size_t count(InputIt first,
                     InputIt last,
                     cudaStream_t stream = 0,
-                    Hash hash           = Hash{},
                     KeyEqual key_equal  = KeyEqual{});
 
  private:
@@ -294,19 +272,16 @@ class static_multimap {
     using const_iterator = pair_atomic_type const*;
 
    private:
-    pair_atomic_type* slots_{};     ///< Pointer to flat slots storage
-    std::size_t capacity_{};        ///< Total number of slots
+    ProbeSequence probe_sequence_;
     Key empty_key_sentinel_{};      ///< Key value that represents an empty slot
     Value empty_value_sentinel_{};  ///< Initial Value of empty slot
-    std::size_t step_size_{};
 
    public:
     __host__ __device__ device_view_base(pair_atomic_type* slots,
                                          std::size_t capacity,
                                          Key empty_key_sentinel,
                                          Value empty_value_sentinel) noexcept
-      : slots_{slots},
-        capacity_{capacity},
+      : probe_sequence_{slots, capacity},
         empty_key_sentinel_{empty_key_sentinel},
         empty_value_sentinel_{empty_value_sentinel}
     {
@@ -317,41 +292,16 @@ class static_multimap {
      *
      * @return Slots array
      */
-    __device__ pair_atomic_type* get_slots() noexcept { return slots_; }
+    __device__ pair_atomic_type* get_slots() noexcept { return probe_sequence_.get_slots(); }
 
     /**
      * @brief Gets slots array.
      *
      * @return Slots array
      */
-    __device__ pair_atomic_type const* get_slots() const noexcept { return slots_; }
-
-    /**
-     * @brief Returns the initial slot for a given key `k`
-     *
-     * @tparam Hash Unary callable type
-     * @param k The key to get the slot for
-     * @param hash The unary callable used to hash the key
-     * @return Pointer to the initial slot for `k`
-     */
-    template <typename Hash>
-    __device__ iterator initial_slot(Key const& k, Hash hash) noexcept
-    {
-      return &slots_[hash(k) % capacity_];
-    }
-
-    /**
-     * @brief Returns the initial slot for a given key `k`
-     *
-     * @tparam Hash Unary callable type
-     * @param k The key to get the slot for
-     * @param hash The unary callable used to hash the key
-     * @return Pointer to the initial slot for `k`
-     */
-    template <typename Hash>
-    __device__ const_iterator initial_slot(Key const& k, Hash hash) const noexcept
+    __device__ pair_atomic_type const* get_slots() const noexcept
     {
-      return &slots_[hash(k) % capacity_];
+      return probe_sequence_.get_slots();
     }
 
     /**
@@ -360,20 +310,14 @@ class static_multimap {
      * To be used for Cooperative Group based probing.
      *
      * @tparam CG Cooperative Group type
-     * @tparam Hash Unary callable type
      * @param g the Cooperative Group for which the initial slot is needed
      * @param k The key to get the slot for
-     * @param hash The unary callable used to hash the key
      * @return Pointer to the initial slot for `k`
      */
-    template <typename CG, typename Hash>
-    __device__ iterator initial_slot(CG g, Key const& k, Hash hash) noexcept
+    template <typename CG>
+    __device__ iterator initial_slot(CG const& g, Key const& k) noexcept
     {
-      auto const cg_size = g.size();
-      step_size_         = (hash(k + 1) % (capacity_ / (cg_size * 2) - 1) + 1) * cg_size * 2;
-      auto const slot_index =
-        hash(k) % (capacity_ / (cg_size * 2)) * cg_size * 2 + g.thread_rank() * 2;
-      return begin_slot() + slot_index;
+      return probe_sequence_.initial_slot(g, k);
     }
 
     /**
@@ -382,43 +326,14 @@ class static_multimap {
      * To be used for Cooperative Group based probing.
      *
      * @tparam CG Cooperative Group type
-     * @tparam Hash Unary callable type
      * @param g the Cooperative Group for which the initial slot is needed
      * @param k The key to get the slot for
-     * @param hash The unary callable used to hash the key
      * @return Pointer to the initial slot for `k`
      */
-    template <typename CG, typename Hash>
-    __device__ const_iterator initial_slot(CG g, Key const& k, Hash hash) const noexcept
-    {
-      auto const cg_size = g.size();
-      step_size_         = (hash(k + 1) % (capacity_ / (cg_size * 2) - 1) + 1) * cg_size * 2;
-      auto const slot_index =
-        hash(k) % (capacity_ / (cg_size * 2)) * cg_size * 2 + g.thread_rank() * 2;
-      return begin_slot() + slot_index;
-    }
-
-    /**
-     * @brief Given a slot `s`, returns the next slot.
-     *
-     * If `s` is the last slot, wraps back around to the first slot.
-     *
-     * @param s The slot to advance
-     * @return The next slot after `s`
-     */
-    __device__ iterator next_slot(iterator s) noexcept { return (++s < end()) ? s : begin_slot(); }
-
-    /**
-     * @brief Given a slot `s`, returns the next slot.
-     *
-     * If `s` is the last slot, wraps back around to the first slot.
-     *
-     * @param s The slot to advance
-     * @return The next slot after `s`
-     */
-    __device__ const_iterator next_slot(const_iterator s) const noexcept
+    template <typename CG>
+    __device__ const_iterator initial_slot(CG g, Key const& k) const noexcept
     {
-      return (++s < end()) ? s : begin_slot();
+      return probe_sequence_.initial_slot(g, k);
     }
 
     /**
@@ -433,10 +348,9 @@ class static_multimap {
      * @return The next slot after `s`
      */
     template <typename CG>
-    __device__ iterator next_slot(CG g, iterator s) noexcept
+    __device__ iterator next_slot(CG const& g, iterator s) noexcept
     {
-      uint32_t index = s - slots_;
-      return &slots_[(index + step_size_) % capacity_];
+      return probe_sequence_.next_slot(g, s);
     }
 
     /**
@@ -451,10 +365,9 @@ class static_multimap {
      * @return The next slot after `s`
      */
     template <typename CG>
-    __device__ const_iterator next_slot(CG g, const_iterator s) const noexcept
+    __device__ const_iterator next_slot(CG const& g, const_iterator s) const noexcept
     {
-      uint32_t index = s - slots_;
-      return &slots_[(index + step_size_) % capacity_];
+      return probe_sequence_.next_slot(g, s);
     }
 
    public:
@@ -463,7 +376,10 @@ class static_multimap {
      *
      * @return The maximum number of elements the hash map can hold
      */
-    __host__ __device__ std::size_t get_capacity() const noexcept { return capacity_; }
+    __host__ __device__ std::size_t get_capacity() const noexcept
+    {
+      return probe_sequence_.get_capacity();
+    }
 
     /**
      * @brief Gets the sentinel value used to represent an empty key slot.
@@ -481,70 +397,6 @@ class static_multimap {
     {
       return empty_value_sentinel_;
     }
-
-    /**
-     * @brief Returns iterator to the first slot.
-     *
-     * @note Unlike `std::map::begin()`, the `begin_slot()` iterator does _not_ point to the first
-     * occupied slot. Instead, it refers to the first slot in the array of contiguous slot storage.
-     * Iterating from `begin_slot()` to `end_slot()` will iterate over all slots, including those
-     * both empty and filled.
-     *
-     * There is no `begin()` iterator to avoid confusion as it is not possible to provide an
-     * iterator over only the filled slots.
-     *
-     * @return Iterator to the first slot
-     */
-    __device__ iterator begin_slot() noexcept { return slots_; }
-
-    /**
-     * @brief Returns iterator to the first slot.
-     *
-     * @note Unlike `std::map::begin()`, the `begin_slot()` iterator does _not_ point to the first
-     * occupied slot. Instead, it refers to the first slot in the array of contiguous slot storage.
-     * Iterating from `begin_slot()` to `end_slot()` will iterate over all slots, including those
-     * both empty and filled.
-     *
-     * There is no `begin()` iterator to avoid confusion as it is not possible to provide an
-     * iterator over only the filled slots.
-     *
-     * @return Iterator to the first slot
-     */
-    __device__ const_iterator begin_slot() const noexcept { return slots_; }
-
-    /**
-     * @brief Returns a const_iterator to one past the last slot.
-     *
-     * @return A const_iterator to one past the last slot
-     */
-    __host__ __device__ const_iterator end_slot() const noexcept { return slots_ + capacity_; }
-
-    /**
-     * @brief Returns an iterator to one past the last slot.
-     *
-     * @return An iterator to one past the last slot
-     */
-    __host__ __device__ iterator end_slot() noexcept { return slots_ + capacity_; }
-
-    /**
-     * @brief Returns a const_iterator to one past the last slot.
-     *
-     * `end()` calls `end_slot()` and is provided for convenience for those familiar with checking
-     * an iterator returned from `find()` against the `end()` iterator.
-     *
-     * @return A const_iterator to one past the last slot
-     */
-    __host__ __device__ const_iterator end() const noexcept { return end_slot(); }
-
-    /**
-     * @brief Returns an iterator to one past the last slot.
-     *
-     * `end()` calls `end_slot()` and is provided for convenience for those familiar with checking
-     * an iterator returned from `find()` against the `end()` iterator.
-     *
-     * @return An iterator to one past the last slot
-     */
-    __host__ __device__ iterator end() noexcept { return end_slot(); }
   };
 
  public:
@@ -598,39 +450,29 @@ class static_multimap {
     /**
      * @brief Inserts the specified key/value pair into the map.
      *
-     * @tparam Hash Unary callable type
      * @tparam KeyEqual Binary callable type
      * @param insert_pair The pair to insert
-     * @param hash The unary callable used to hash the key
      * @param key_equal The binary callable used to compare two keys for
      * equality
      * @return void.
      */
-    template <typename Hash     = cuco::detail::MurmurHash3_32<key_type>,
-              typename KeyEqual = thrust::equal_to<key_type>>
-    __device__ void insert(value_type const& insert_pair,
-                           Hash hash          = Hash{},
-                           KeyEqual key_equal = KeyEqual{}) noexcept;
+    template <typename KeyEqual = thrust::equal_to<key_type>>
+    __device__ void insert(value_type const& insert_pair, KeyEqual key_equal = KeyEqual{}) noexcept;
     /**
      * @brief Inserts the specified key/value pair into the map.
      *
      * @tparam Cooperative Group type
-     * @tparam Hash Unary callable type
      * @tparam KeyEqual Binary callable type
      *
      * @param g The Cooperative Group that performs the insert
      * @param insert_pair The pair to insert
-     * @param hash The unary callable used to hash the key
      * @param key_equal The binary callable used to compare two keys for
      * equality
      * @return void.
      */
-    template <typename CG,
-              typename Hash     = cuco::detail::MurmurHash3_32<key_type>,
-              typename KeyEqual = thrust::equal_to<key_type>>
+    template <typename CG, typename KeyEqual = thrust::equal_to<key_type>>
     __device__ void insert(CG g,
                            value_type const& insert_pair,
-                           Hash hash          = Hash{},
                            KeyEqual key_equal = KeyEqual{}) noexcept;
 
   };  // class device mutable view
@@ -749,21 +591,16 @@ class static_multimap {
      * If the key `k` was inserted into the map, find returns
      * true. Otherwise, it returns false.
      *
-     * @tparam Hash Unary callable type
      * @tparam KeyEqual Binary callable type
      * @param k The key to search for
-     * @param hash The unary callable used to hash the key
      * @param key_equal The binary callable used to compare two keys
      * for equality
      * @return A boolean indicating whether the key/value pair
      * containing `k` was inserted
      */
 
-    template <typename Hash     = cuco::detail::MurmurHash3_32<key_type>,
-              typename KeyEqual = thrust::equal_to<key_type>>
-    __device__ bool contains(Key const& k,
-                             Hash hash          = Hash{},
-                             KeyEqual key_equal = KeyEqual{}) noexcept;
+    template <typename KeyEqual = thrust::equal_to<key_type>>
+    __device__ bool contains(Key const& k, KeyEqual key_equal = KeyEqual{}) noexcept;
 
     /**
      * @brief Indicates whether the key `k` was inserted into the map.
@@ -775,23 +612,16 @@ class static_multimap {
      * `contains` at moderate to high load factors.
      *
      * @tparam CG Cooperative Group type
-     * @tparam Hash Unary callable type
      * @tparam KeyEqual Binary callable type
      * @param g The Cooperative Group used to perform the contains operation
      * @param k The key to search for
-     * @param hash The unary callable used to hash the key
      * @param key_equal The binary callable used to compare two keys
      * for equality
      * @return A boolean indicating whether the key/value pair
      * containing `k` was inserted
      */
-    template <typename CG,
-              typename Hash     = cuco::detail::MurmurHash3_32<key_type>,
-              typename KeyEqual = thrust::equal_to<key_type>>
-    __device__ bool contains(CG g,
-                             Key const& k,
-                             Hash hash          = Hash{},
-                             KeyEqual key_equal = KeyEqual{}) noexcept;
+    template <typename CG, typename KeyEqual = thrust::equal_to<key_type>>
+    __device__ bool contains(CG g, Key const& k, KeyEqual key_equal = KeyEqual{}) noexcept;
   };  // class device_view
 
   /**

From 14ca3fd24f251c5e0a6596f64010cd4361ffcc56 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Thu, 27 May 2021 18:57:38 -0400
Subject: [PATCH 111/267] Minor corrections: follow naming convention

---
 include/cuco/detail/probe_sequences.cuh | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/include/cuco/detail/probe_sequences.cuh b/include/cuco/detail/probe_sequences.cuh
index a7c9414c5..0eb35bb46 100644
--- a/include/cuco/detail/probe_sequences.cuh
+++ b/include/cuco/detail/probe_sequences.cuh
@@ -55,9 +55,9 @@ class DoubleHashing {
   template <typename CG>
   __device__ iterator initial_slot(CG const& g, Key const k) noexcept
   {
-    step_size_ = (hash2(k + 1) % (capacity_ / (cg_size() * 2) - 1) + 1) * cg_size() * 2;
+    step_size_ = (hash2_(k + 1) % (capacity_ / (cg_size() * 2) - 1) + 1) * cg_size() * 2;
     std::size_t index =
-      hash1(k) % (capacity_ / (cg_size() * 2)) * cg_size() * 2 + g.thread_rank() * 2;
+      hash1_(k) % (capacity_ / (cg_size() * 2)) * cg_size() * 2 + g.thread_rank() * 2;
     return slots_ + index;
   }
 
@@ -72,8 +72,8 @@ class DoubleHashing {
   iterator slots_;
   const std::size_t capacity_;
   std::size_t step_size_;
-  Hash1 hash1{};
-  Hash2 hash2{};
+  Hash1 hash1_{};
+  Hash2 hash2_{};
 };  // class DoubleHashing
 
 }  // namespace cuco

From f16c15399766f440e5401ed37f1395dba6a18fb0 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Mon, 31 May 2021 12:05:21 -0400
Subject: [PATCH 112/267] Add inner/outer functions for count and retrieve

---
 include/cuco/detail/static_multimap.inl       |  95 ++++-
 .../cuco/detail/static_multimap_kernels.cuh   | 332 ++++++++++++++----
 include/cuco/static_multimap.cuh              |  82 ++++-
 3 files changed, 412 insertions(+), 97 deletions(-)

diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index 594d93793..455f8f158 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -101,13 +101,77 @@ void static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::contains(
   CUCO_CUDA_TRY(cudaDeviceSynchronize());
 }
 
+template <typename Key,
+          typename Value,
+          class ProbeSequence,
+          cuda::thread_scope Scope,
+          typename Allocator>
+template <typename InputIt, typename KeyEqual>
+std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::count_inner(
+  InputIt first, InputIt last, cudaStream_t stream, KeyEqual key_equal)
+{
+  auto num_keys         = std::distance(first, last);
+  auto const block_size = 128;
+  auto const stride     = 1;
+  auto const grid_size  = (cg_size() * num_keys + stride * block_size - 1) / (stride * block_size);
+  auto view             = get_device_view();
+
+  atomic_ctr_type* num_items;
+  CUCO_CUDA_TRY(cudaMallocManaged(&num_items, sizeof(atomic_ctr_type)));
+  *num_items = 0;
+  int device_id;
+  CUCO_CUDA_TRY(cudaGetDevice(&device_id));
+  CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_items, sizeof(atomic_ctr_type), device_id));
+
+  detail::count_inner<block_size, cg_size(), Key, Value>
+    <<<grid_size, block_size, 0, stream>>>(first, last, num_items, view, key_equal);
+  CUCO_CUDA_TRY(cudaDeviceSynchronize());
+
+  size_t result = *num_items;
+  CUCO_CUDA_TRY(cudaFree(num_items));
+
+  return result;
+}
+
+template <typename Key,
+          typename Value,
+          class ProbeSequence,
+          cuda::thread_scope Scope,
+          typename Allocator>
+template <typename InputIt, typename KeyEqual>
+std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::count_outer(
+  InputIt first, InputIt last, cudaStream_t stream, KeyEqual key_equal)
+{
+  auto num_keys         = std::distance(first, last);
+  auto const block_size = 128;
+  auto const stride     = 1;
+  auto const grid_size  = (cg_size() * num_keys + stride * block_size - 1) / (stride * block_size);
+  auto view             = get_device_view();
+
+  atomic_ctr_type* num_items;
+  CUCO_CUDA_TRY(cudaMallocManaged(&num_items, sizeof(atomic_ctr_type)));
+  *num_items = 0;
+  int device_id;
+  CUCO_CUDA_TRY(cudaGetDevice(&device_id));
+  CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_items, sizeof(atomic_ctr_type), device_id));
+
+  detail::count_outer<block_size, cg_size(), Key, Value>
+    <<<grid_size, block_size, 0, stream>>>(first, last, num_items, view, key_equal);
+  CUCO_CUDA_TRY(cudaDeviceSynchronize());
+
+  size_t result = *num_items;
+  CUCO_CUDA_TRY(cudaFree(num_items));
+
+  return result;
+}
+
 template <typename Key,
           typename Value,
           class ProbeSequence,
           cuda::thread_scope Scope,
           typename Allocator>
 template <typename InputIt, typename OutputIt, typename KeyEqual>
-OutputIt static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::find_all(
+OutputIt static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::retrieve_inner(
   InputIt first, InputIt last, OutputIt output_begin, cudaStream_t stream, KeyEqual key_equal)
 {
   auto num_keys          = std::distance(first, last);
@@ -124,7 +188,7 @@ OutputIt static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::find_all(
   CUCO_CUDA_TRY(cudaGetDevice(&device_id));
   CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_items, sizeof(atomic_ctr_type), device_id));
 
-  detail::find_all<block_size, cg_size(), buffer_size, Key, Value>
+  detail::retrieve_inner<block_size, cg_size(), buffer_size, Key, Value>
     <<<grid_size, block_size, 0, stream>>>(first, last, output_begin, num_items, view, key_equal);
   CUCO_CUDA_TRY(cudaDeviceSynchronize());
 
@@ -139,17 +203,16 @@ template <typename Key,
           class ProbeSequence,
           cuda::thread_scope Scope,
           typename Allocator>
-template <typename InputIt, typename KeyEqual>
-std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::count(InputIt first,
-                                                                                InputIt last,
-                                                                                cudaStream_t stream,
-                                                                                KeyEqual key_equal)
+template <typename InputIt, typename OutputIt, typename KeyEqual>
+OutputIt static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::retrieve_outer(
+  InputIt first, InputIt last, OutputIt output_begin, cudaStream_t stream, KeyEqual key_equal)
 {
-  auto num_keys         = std::distance(first, last);
-  auto const block_size = 128;
-  auto const stride     = 1;
-  auto const grid_size  = (cg_size() * num_keys + stride * block_size - 1) / (stride * block_size);
-  auto view             = get_device_view();
+  auto num_keys          = std::distance(first, last);
+  auto const block_size  = 128;
+  auto const buffer_size = block_size * 3;
+  auto const stride      = 1;
+  auto const grid_size   = (cg_size() * num_keys + stride * block_size - 1) / (stride * block_size);
+  auto view              = get_device_view();
 
   atomic_ctr_type* num_items;
   CUCO_CUDA_TRY(cudaMallocManaged(&num_items, sizeof(atomic_ctr_type)));
@@ -158,14 +221,14 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::count(
   CUCO_CUDA_TRY(cudaGetDevice(&device_id));
   CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_items, sizeof(atomic_ctr_type), device_id));
 
-  detail::count<block_size, cg_size(), Key, Value>
-    <<<grid_size, block_size, 0, stream>>>(first, last, num_items, view, key_equal);
+  detail::retrieve_outer<block_size, cg_size(), buffer_size, Key, Value>
+    <<<grid_size, block_size, 0, stream>>>(first, last, output_begin, num_items, view, key_equal);
   CUCO_CUDA_TRY(cudaDeviceSynchronize());
 
-  size_t result = *num_items;
+  auto output_end = output_begin + *num_items;
   CUCO_CUDA_TRY(cudaFree(num_items));
 
-  return result;
+  return output_end;
 }
 
 template <typename Key,
diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap_kernels.cuh
index 70a182254..9723381f4 100644
--- a/include/cuco/detail/static_multimap_kernels.cuh
+++ b/include/cuco/detail/static_multimap_kernels.cuh
@@ -134,8 +134,7 @@ __global__ void insert(InputIt first, InputIt last, viewT view, KeyEqual key_equ
  * to the non Cooperative Group `contains` at moderate to high load factors.
  *
  * @tparam block_size The size of the thread block
- * @tparam tile_size The number of threads in the Cooperative Groups used to perform
- * inserts
+ * @tparam tile_size The number of threads in the Cooperative Groups
  * @tparam InputIt Device accessible input iterator whose `value_type` is
  * convertible to the map's `key_type`
  * @tparam OutputIt Device accessible output iterator whose `value_type` is
@@ -182,19 +181,169 @@ __global__ void contains(
   }
 }
 
+/**
+ * @brief Counts the occurrences of keys in `[first, last)` contained in the multimap.
+ *
+ * @tparam block_size The size of the thread block
+ * @tparam tile_size The number of threads in the Cooperative Groups used to perform counts
+ * @tparam Key key type
+ * @tparam Value The type of the mapped value for the map
+ * @tparam InputIt Device accessible input iterator whose `value_type` is convertible to the map's
+ * `key_type`
+ * @tparam atomicT Type of atomic storage
+ * @tparam viewT Type of device view allowing access of hash map storage
+ * @tparam KeyEqual Binary callable
+ * @param first Beginning of the sequence of keys to count
+ * @param last End of the sequence of keys to count
+ * @param num_items The number of all the matches for a sequence of keys
+ * @param view Device view used to access the hash map's slot storage
+ * @param key_equal Binary function to compare two keys for equality
+ */
+template <uint32_t block_size,
+          uint32_t tile_size,
+          typename Key,
+          typename Value,
+          typename InputIt,
+          typename atomicT,
+          typename viewT,
+          typename KeyEqual>
+__global__ void count_inner(
+  InputIt first, InputIt last, atomicT* num_items, viewT view, KeyEqual key_equal)
+{
+  auto tile    = cg::tiled_partition<tile_size>(cg::this_thread_block());
+  auto tid     = block_size * blockIdx.x + threadIdx.x;
+  auto key_idx = tid / tile_size;
+
+  typedef cub::BlockReduce<std::size_t, block_size> BlockReduce;
+  __shared__ typename BlockReduce::TempStorage temp_storage;
+  std::size_t thread_num_items = 0;
+
+  while (first + key_idx < last) {
+    auto key          = *(first + key_idx);
+    auto current_slot = view.initial_slot(tile, key);
+
+    while (true) {
+      pair<Key, Value> arr[2];
+      if constexpr (sizeof(Key) == 4) {
+        auto const tmp = *reinterpret_cast<uint4 const*>(current_slot);
+        memcpy(&arr[0], &tmp, 2 * sizeof(pair<Key, Value>));
+      } else {
+        auto const tmp = *reinterpret_cast<ulonglong4 const*>(current_slot);
+        memcpy(&arr[0], &tmp, 2 * sizeof(pair<Key, Value>));
+      }
+
+      auto const first_slot_is_empty  = (arr[0].first == view.get_empty_key_sentinel());
+      auto const second_slot_is_empty = (arr[1].first == view.get_empty_key_sentinel());
+      auto const first_equals         = (not first_slot_is_empty and key_equal(arr[0].first, key));
+      auto const second_equals        = (not second_slot_is_empty and key_equal(arr[1].first, key));
+
+      thread_num_items += (first_equals + second_equals);
+
+      if (tile.any(first_slot_is_empty or second_slot_is_empty)) { break; }
+
+      current_slot = view.next_slot(tile, current_slot);
+    }
+    key_idx += (gridDim.x * block_size) / tile_size;
+  }
+
+  // compute number of successfully inserted elements for each block
+  // and atomically add to the grand total
+  std::size_t block_num_items = BlockReduce(temp_storage).Sum(thread_num_items);
+  if (threadIdx.x == 0) { num_items->fetch_add(block_num_items, cuda::std::memory_order_relaxed); }
+}
+
+/**
+ * @brief Counts the occurrences of keys in `[first, last)` contained in the multimap. If no matches
+ * can be found for a given key, the corresponding occurrence is 1.
+ *
+ * @tparam block_size The size of the thread block
+ * @tparam tile_size The number of threads in the Cooperative Groups used to perform counts
+ * @tparam Key key type
+ * @tparam Value The type of the mapped value for the map
+ * @tparam InputIt Device accessible input iterator whose `value_type` is convertible to the map's
+ * `key_type`
+ * @tparam atomicT Type of atomic storage
+ * @tparam viewT Type of device view allowing access of hash map storage
+ * @tparam KeyEqual Binary callable
+ * @param first Beginning of the sequence of keys to count
+ * @param last End of the sequence of keys to count
+ * @param num_items The number of all the matches for a sequence of keys
+ * @param view Device view used to access the hash map's slot storage
+ * @param key_equal Binary function to compare two keys for equality
+ */
+template <uint32_t block_size,
+          uint32_t tile_size,
+          typename Key,
+          typename Value,
+          typename InputIt,
+          typename atomicT,
+          typename viewT,
+          typename KeyEqual>
+__global__ void count_outer(
+  InputIt first, InputIt last, atomicT* num_items, viewT view, KeyEqual key_equal)
+{
+  auto tile    = cg::tiled_partition<tile_size>(cg::this_thread_block());
+  auto tid     = block_size * blockIdx.x + threadIdx.x;
+  auto key_idx = tid / tile_size;
+
+  typedef cub::BlockReduce<std::size_t, block_size> BlockReduce;
+  __shared__ typename BlockReduce::TempStorage temp_storage;
+  std::size_t thread_num_items = 0;
+
+  while (first + key_idx < last) {
+    auto key          = *(first + key_idx);
+    auto current_slot = view.initial_slot(tile, key);
+
+    bool found_match = false;
+
+    while (true) {
+      pair<Key, Value> arr[2];
+      if constexpr (sizeof(Key) == 4) {
+        auto const tmp = *reinterpret_cast<uint4 const*>(current_slot);
+        memcpy(&arr[0], &tmp, 2 * sizeof(pair<Key, Value>));
+      } else {
+        auto const tmp = *reinterpret_cast<ulonglong4 const*>(current_slot);
+        memcpy(&arr[0], &tmp, 2 * sizeof(pair<Key, Value>));
+      }
+
+      auto const first_slot_is_empty  = (arr[0].first == view.get_empty_key_sentinel());
+      auto const second_slot_is_empty = (arr[1].first == view.get_empty_key_sentinel());
+      auto const first_equals         = (not first_slot_is_empty and key_equal(arr[0].first, key));
+      auto const second_equals        = (not second_slot_is_empty and key_equal(arr[1].first, key));
+      auto const first_exists         = tile.any(first_equals);
+      auto const second_exists        = tile.any(second_equals);
+
+      if (first_exists or second_exists) { found_match = true; }
+
+      thread_num_items += (first_equals + second_equals);
+
+      if (tile.any(first_slot_is_empty or second_slot_is_empty)) {
+        if ((not found_match) && (tile.thread_rank() == 0)) { thread_num_items++; }
+        break;
+      }
+
+      current_slot = view.next_slot(tile, current_slot);
+    }
+    key_idx += (gridDim.x * block_size) / tile_size;
+  }
+
+  // compute number of successfully inserted elements for each block
+  // and atomically add to the grand total
+  std::size_t block_num_items = BlockReduce(temp_storage).Sum(thread_num_items);
+  if (threadIdx.x == 0) { num_items->fetch_add(block_num_items, cuda::std::memory_order_relaxed); }
+}
+
 /**
  * @brief Finds all the values corresponding to all keys in the range `[first, last)`.
  *
  * If the key `k = *(first + i)` exists in the map, copies `k` and all associated values to
- * unspecified locations in `[output_begin, output_begin + *num_items - 1)`. Else, copies `k` and
- * the empty value sentinel.
+ * unspecified locations in `[output_begin, output_begin + *num_items - 1)`. Else, does nothing.
  *
  * Behavior is undefined if the total number of matching keys exceeds `std::distance(output_begin,
  * output_begin + *num_items - 1)`. Use `count()` to determine the number of matching keys.
  *
  * @tparam block_size The size of the thread block
- * @tparam tile_size The number of threads in the Cooperative Groups used to perform
- * inserts
+ * @tparam tile_size The number of threads in the Cooperative Groups
  * @tparam buffer_size Size of the output buffer
  * @tparam Key key type
  * @tparam Value The type of the mapped value for the map
@@ -223,12 +372,12 @@ template <uint32_t block_size,
           typename atomicT,
           typename viewT,
           typename KeyEqual>
-__global__ void find_all(InputIt first,
-                         InputIt last,
-                         OutputIt output_begin,
-                         atomicT* num_items,
-                         viewT view,
-                         KeyEqual key_equal)
+__global__ void retrieve_inner(InputIt first,
+                               InputIt last,
+                               OutputIt output_begin,
+                               atomicT* num_items,
+                               viewT view,
+                               KeyEqual key_equal)
 {
   auto tile    = cg::tiled_partition<tile_size>(cg::this_thread_block());
   auto tid     = block_size * blockIdx.x + threadIdx.x;
@@ -245,8 +394,7 @@ __global__ void find_all(InputIt first,
     auto key = *(first + key_idx);
     typename viewT::iterator current_slot;
 
-    bool running     = ((first + key_idx) < last);
-    bool found_match = false;
+    bool running = ((first + key_idx) < last);
 
     if (running) { current_slot = view.initial_slot(tile, key); }
 
@@ -269,8 +417,6 @@ __global__ void find_all(InputIt first,
         auto const second_exists = tile.ballot(second_equals);
 
         if (first_exists or second_exists) {
-          found_match = true;
-
           auto num_first_matches  = __popc(first_exists);
           auto num_second_matches = __popc(second_exists);
 
@@ -293,14 +439,7 @@ __global__ void find_all(InputIt first,
               cuco::make_pair<Key, Value>(std::move(k), std::move(arr[1].second));
           }
         }
-        if (tile.any(first_slot_is_empty or second_slot_is_empty)) {
-          running = false;
-          if ((not found_match) && (lane_id == 0)) {
-            auto output_idx = atomicAdd_block(&block_counter, 1);
-            output_buffer[output_idx] =
-              cuco::make_pair<Key, Value>(key, view.get_empty_key_sentinel());
-          }
-        }
+        if (tile.any(first_slot_is_empty or second_slot_is_empty)) { running = false; }
       }  // if running
 
       __syncthreads();
@@ -323,73 +462,142 @@ __global__ void find_all(InputIt first,
 }
 
 /**
- * @brief Counts the occurrences of keys in `[first, last)` contained in the multimap.
+ * @brief Finds all the values corresponding to all keys in the range `[first, last)`.
+ *
+ * If the key `k = *(first + i)` exists in the map, copies `k` and all associated values to
+ * unspecified locations in `[output_begin, output_begin + *num_items - 1)`. Else, copies `k` and
+ * the empty value sentinel.
+ *
+ * Behavior is undefined if the total number of matching keys exceeds `std::distance(output_begin,
+ * output_begin + *num_items - 1)`. Use `count()` to determine the number of matching keys.
  *
  * @tparam block_size The size of the thread block
- * @tparam tile_size The number of threads in the Cooperative Groups used to perform inserts
+ * @tparam tile_size The number of threads in the Cooperative Groups
+ * @tparam buffer_size Size of the output buffer
+ * @tparam Key key type
  * @tparam Value The type of the mapped value for the map
- * @tparam InputIt Device accessible input iterator whose `value_type` is convertible to the map's
- * `key_type`
+ * @tparam InputIt Device accessible input iterator whose `value_type` is
+ * convertible to the map's `key_type`
+ * @tparam OutputIt Device accessible output iterator whose `value_type` is
+ * convertible to the map's `mapped_type`
  * @tparam atomicT Type of atomic storage
  * @tparam viewT Type of device view allowing access of hash map storage
- * @tparam KeyEqual Binary callable
- * @param first Beginning of the sequence of keys to count
- * @param last End of the sequence of keys to count
- * @param num_items The number of all the matches for a sequence of keys
+ * @tparam KeyEqual Binary callable type
+ * @param first Beginning of the sequence of keys
+ * @param last End of the sequence of keys
+ * @param output_begin Beginning of the sequence of values retrieved for each key
+ * @param num_items Size of the output sequence
  * @param view Device view used to access the hash map's slot storage
- * @param key_equal Binary function to compare two keys for equality
+ * @param key_equal The binary function to compare two keys for equality
  */
+
 template <uint32_t block_size,
           uint32_t tile_size,
+          uint32_t buffer_size,
           typename Key,
           typename Value,
           typename InputIt,
+          typename OutputIt,
           typename atomicT,
           typename viewT,
           typename KeyEqual>
-__global__ void count(
-  InputIt first, InputIt last, atomicT* num_items, viewT view, KeyEqual key_equal)
+__global__ void retrieve_outer(InputIt first,
+                               InputIt last,
+                               OutputIt output_begin,
+                               atomicT* num_items,
+                               viewT view,
+                               KeyEqual key_equal)
 {
   auto tile    = cg::tiled_partition<tile_size>(cg::this_thread_block());
   auto tid     = block_size * blockIdx.x + threadIdx.x;
   auto key_idx = tid / tile_size;
 
-  typedef cub::BlockReduce<std::size_t, block_size> BlockReduce;
-  __shared__ typename BlockReduce::TempStorage temp_storage;
-  std::size_t thread_num_items = 0;
+  const uint32_t lane_id = tile.thread_rank();
 
-  while (first + key_idx < last) {
-    auto key          = *(first + key_idx);
-    auto current_slot = view.initial_slot(tile, key);
+  __shared__ uint32_t block_counter;
+  __shared__ cuco::pair_type<Key, Value> output_buffer[buffer_size];
 
-    while (true) {
-      pair<Key, Value> arr[2];
-      if constexpr (sizeof(Key) == 4) {
-        auto const tmp = *reinterpret_cast<uint4 const*>(current_slot);
-        memcpy(&arr[0], &tmp, 2 * sizeof(pair<Key, Value>));
-      } else {
-        auto const tmp = *reinterpret_cast<ulonglong4 const*>(current_slot);
-        memcpy(&arr[0], &tmp, 2 * sizeof(pair<Key, Value>));
-      }
+  if (0 == threadIdx.x) { block_counter = 0; }
 
-      auto const first_slot_is_empty  = (arr[0].first == view.get_empty_key_sentinel());
-      auto const second_slot_is_empty = (arr[1].first == view.get_empty_key_sentinel());
-      auto const first_equals         = (not first_slot_is_empty and key_equal(arr[0].first, key));
-      auto const second_equals        = (not second_slot_is_empty and key_equal(arr[1].first, key));
+  while (__syncthreads_or(first + key_idx < last)) {
+    auto key = *(first + key_idx);
+    typename viewT::iterator current_slot;
 
-      thread_num_items += (first_equals + second_equals);
+    bool running     = ((first + key_idx) < last);
+    bool found_match = false;
 
-      if (tile.any(first_slot_is_empty or second_slot_is_empty)) { break; }
+    if (running) { current_slot = view.initial_slot(tile, key); }
 
-      current_slot = view.next_slot(tile, current_slot);
-    }
+    while (__syncthreads_or(running)) {
+      if (running) {
+        pair<Key, Value> arr[2];
+        if constexpr (sizeof(Key) == 4) {
+          auto const tmp = *reinterpret_cast<uint4 const*>(current_slot);
+          memcpy(&arr[0], &tmp, 2 * sizeof(pair<Key, Value>));
+        } else {
+          auto const tmp = *reinterpret_cast<ulonglong4 const*>(current_slot);
+          memcpy(&arr[0], &tmp, 2 * sizeof(pair<Key, Value>));
+        }
+
+        auto const first_slot_is_empty  = (arr[0].first == view.get_empty_key_sentinel());
+        auto const second_slot_is_empty = (arr[1].first == view.get_empty_key_sentinel());
+        auto const first_equals  = (not first_slot_is_empty and key_equal(arr[0].first, key));
+        auto const second_equals = (not second_slot_is_empty and key_equal(arr[1].first, key));
+        auto const first_exists  = tile.ballot(first_equals);
+        auto const second_exists = tile.ballot(second_equals);
+
+        if (first_exists or second_exists) {
+          found_match = true;
+
+          auto num_first_matches  = __popc(first_exists);
+          auto num_second_matches = __popc(second_exists);
+
+          uint32_t output_idx;
+          if (0 == lane_id) {
+            output_idx = atomicAdd_block(&block_counter, (num_first_matches + num_second_matches));
+          }
+          output_idx = tile.shfl(output_idx, 0);
+
+          if (first_equals) {
+            auto lane_offset = __popc(first_exists & ((1 << lane_id) - 1));
+            Key k            = key;
+            output_buffer[output_idx + lane_offset] =
+              cuco::make_pair<Key, Value>(std::move(k), std::move(arr[0].second));
+          }
+          if (second_equals) {
+            auto lane_offset = __popc(second_exists & ((1 << lane_id) - 1));
+            Key k            = key;
+            output_buffer[output_idx + num_first_matches + lane_offset] =
+              cuco::make_pair<Key, Value>(std::move(k), std::move(arr[1].second));
+          }
+        }
+        if (tile.any(first_slot_is_empty or second_slot_is_empty)) {
+          running = false;
+          if ((not found_match) && (lane_id == 0)) {
+            auto output_idx = atomicAdd_block(&block_counter, 1);
+            output_buffer[output_idx] =
+              cuco::make_pair<Key, Value>(key, view.get_empty_key_sentinel());
+          }
+        }
+      }  // if running
+
+      __syncthreads();
+
+      if ((block_counter + 2 * block_size) > buffer_size) {
+        flush_output_buffer<block_size>(block_counter, output_buffer, num_items, output_begin);
+
+        if (0 == threadIdx.x) { block_counter = 0; }
+      }
+
+      if (running) { current_slot = view.next_slot(tile, current_slot); }
+    }  // while running
     key_idx += (gridDim.x * block_size) / tile_size;
   }
 
-  // compute number of successfully inserted elements for each block
-  // and atomically add to the grand total
-  std::size_t block_num_items = BlockReduce(temp_storage).Sum(thread_num_items);
-  if (threadIdx.x == 0) { num_items->fetch_add(block_num_items, cuda::std::memory_order_relaxed); }
+  // Final remainder flush
+  if (block_counter > 0) {
+    flush_output_buffer<block_size>(block_counter, output_buffer, num_items, output_begin);
+  }
 }
 
 }  // namespace detail
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index 3455bb248..943406e1f 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -217,12 +217,44 @@ class static_multimap {
                 cudaStream_t stream = 0,
                 KeyEqual key_equal  = KeyEqual{});
 
+  /**
+   * @brief Counts the occurrences of keys in `[first, last)` contained in the multimap.
+   *
+   * @tparam Input Device accesible input iterator whose `value_type` is convertible to `key_type`
+   * @tparam KeyEqual Binary callable
+   * @param first Beginning of the sequence of keys to count
+   * @param last End of the sequence of keys to count
+   * @param key_equal Binary function to compare two keys for equality
+   * @return The sum of total occurrences of all keys in `[first,last)`
+   */
+  template <typename InputIt, typename KeyEqual = thrust::equal_to<key_type>>
+  std::size_t count_inner(InputIt first,
+                          InputIt last,
+                          cudaStream_t stream = 0,
+                          KeyEqual key_equal  = KeyEqual{});
+
+  /**
+   * @brief Counts the occurrences of keys in `[first, last)` contained in the multimap. If no
+   * matches can be found for a given key, the corresponding occurrence is 1.
+   *
+   * @tparam Input Device accesible input iterator whose `value_type` is convertible to `key_type`
+   * @tparam KeyEqual Binary callable
+   * @param first Beginning of the sequence of keys to count
+   * @param last End of the sequence of keys to count
+   * @param key_equal Binary function to compare two keys for equality
+   * @return The sum of total occurrences of all keys in `[first,last)`
+   */
+  template <typename InputIt, typename KeyEqual = thrust::equal_to<key_type>>
+  std::size_t count_outer(InputIt first,
+                          InputIt last,
+                          cudaStream_t stream = 0,
+                          KeyEqual key_equal  = KeyEqual{});
+
   /**
    * @brief Finds all the values corresponding to all keys in the range `[first, last)`.
    *
    * If the key `k = *(first + i)` exists in the map, copies `k` and all associated values to
-   * unspecified locations in `[output_begin, output_end)`. Else, copies `k` and the empty value
-   * sentinel.
+   * unspecified locations in `[output_begin, output_end)`. Else, does nothing.
    *
    * Behavior is undefined if the total number of matching keys exceeds `std::distance(output_begin,
    * output_end)`. Use `count()` to determine the number of matching keys.
@@ -239,27 +271,39 @@ class static_multimap {
    * @return The iterator indicating the last valid key/value pairs in the output
    */
   template <typename InputIt, typename OutputIt, typename KeyEqual = thrust::equal_to<key_type>>
-  OutputIt find_all(InputIt first,
-                    InputIt last,
-                    OutputIt output_begin,
-                    cudaStream_t stream = 0,
-                    KeyEqual key_equal  = KeyEqual{});
+  OutputIt retrieve_inner(InputIt first,
+                          InputIt last,
+                          OutputIt output_begin,
+                          cudaStream_t stream = 0,
+                          KeyEqual key_equal  = KeyEqual{});
 
   /**
-   * @brief Counts the occurrences of keys in `[first, last)` contained in the multimap.
+   * @brief Finds all the values corresponding to all keys in the range `[first, last)`.
    *
-   * @tparam Input Device accesible input iterator whose `value_type` is convertible to `key_type`
-   * @tparam KeyEqual Binary callable
-   * @param first Beginning of the sequence of keys to count
-   * @param last End of the sequence of keys to count
-   * @param key_equal Binary function to compare two keys for equality
-   * @return The sum of total occurrences of all keys in `[first,last)`
+   * If the key `k = *(first + i)` exists in the map, copies `k` and all associated values to
+   * unspecified locations in `[output_begin, output_end)`. Else, copies `k` and the empty value
+   * sentinel.
+   *
+   * Behavior is undefined if the total number of matching keys exceeds `std::distance(output_begin,
+   * output_end)`. Use `count()` to determine the number of matching keys.
+   *
+   * @tparam InputIt Device accessible input iterator whose `value_type` is
+   * convertible to the map's `key_type`
+   * @tparam OutputIt Device accessible output iterator whose `value_type` is
+   * convertible to the map's `value_type`
+   * @tparam KeyEqual Binary callable type
+   * @param first Beginning of the sequence of keys
+   * @param last End of the sequence of keys
+   * @param output_begin Beginning of the sequence of key/value pairs retrieved for each key
+   * @param key_equal The binary function to compare two keys for equality
+   * @return The iterator indicating the last valid key/value pairs in the output
    */
-  template <typename InputIt, typename KeyEqual = thrust::equal_to<key_type>>
-  std::size_t count(InputIt first,
-                    InputIt last,
-                    cudaStream_t stream = 0,
-                    KeyEqual key_equal  = KeyEqual{});
+  template <typename InputIt, typename OutputIt, typename KeyEqual = thrust::equal_to<key_type>>
+  OutputIt retrieve_outer(InputIt first,
+                          InputIt last,
+                          OutputIt output_begin,
+                          cudaStream_t stream = 0,
+                          KeyEqual key_equal  = KeyEqual{});
 
  private:
   class device_view_base {

From 08a9d005dad2c5c0453b987f388a947cf1bf2e12 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Mon, 31 May 2021 12:06:31 -0400
Subject: [PATCH 113/267] Update benchmarks, examples and tests for inner/outer
 functions

---
 .../hash_table/static_multimap/find_all_bench.cu   |  2 +-
 .../static_multimap/static_multimap_bench.cu       | 14 ++++++--------
 .../static_multimap/static_multimap_example.cu     |  5 ++---
 tests/static_multimap/static_multimap_test.cu      |  6 +++---
 4 files changed, 12 insertions(+), 15 deletions(-)

diff --git a/benchmarks/hash_table/static_multimap/find_all_bench.cu b/benchmarks/hash_table/static_multimap/find_all_bench.cu
index 40582eb38..496a4adb2 100644
--- a/benchmarks/hash_table/static_multimap/find_all_bench.cu
+++ b/benchmarks/hash_table/static_multimap/find_all_bench.cu
@@ -102,7 +102,7 @@ std::enable_if_t<(sizeof(Key) == sizeof(Value)), void> nvbench_find_all(
       CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_items, sizeof(atomic_ctr_type), device_id));
 
       // Use timers to explicitly mark the target region
-      cuco::detail::find_all<block_size, CGSize, buffer_size, Key, Value>
+      cuco::detail::retrieve_outer<block_size, CGSize, buffer_size, Key, Value>
         <<<grid_size, block_size, 0, launch.get_stream()>>>(d_unique_keys.begin(),
                                                             d_unique_keys.end(),
                                                             d_results.data().get(),
diff --git a/benchmarks/hash_table/static_multimap/static_multimap_bench.cu b/benchmarks/hash_table/static_multimap/static_multimap_bench.cu
index a4a6a0a55..a22ce4fb7 100644
--- a/benchmarks/hash_table/static_multimap/static_multimap_bench.cu
+++ b/benchmarks/hash_table/static_multimap/static_multimap_bench.cu
@@ -134,7 +134,7 @@ std::enable_if_t<(sizeof(Key) == sizeof(Value)), void> nvbench_static_multimap_c
   state.exec(nvbench::exec_tag::sync | nvbench::exec_tag::timer,
              [&](nvbench::launch& launch, auto& timer) {
                timer.start();
-               auto count = map.count(d_keys.begin(), d_keys.end(), launch.get_stream());
+               auto count = map.count_inner(d_keys.begin(), d_keys.end(), launch.get_stream());
                timer.stop();
              });
 }
@@ -185,14 +185,13 @@ std::enable_if_t<(sizeof(Key) == sizeof(Value)), void> nvbench_static_multimap_f
   cuco::static_multimap<Key, Value> map{size, -1, -1};
   map.insert(d_pairs.begin(), d_pairs.end());
 
-  auto num_matches        = map.count(d_keys.begin(), d_keys.end());
-  std::size_t output_size = num_matches + num_keys;
+  auto const output_size = map.count_outer(d_keys.begin(), d_keys.end());
   thrust::device_vector<cuco::pair_type<Key, Value>> d_results(output_size);
 
   state.exec(
     nvbench::exec_tag::sync | nvbench::exec_tag::timer, [&](nvbench::launch& launch, auto& timer) {
       timer.start();
-      map.find_all(d_keys.begin(), d_keys.end(), d_results.data().get(), launch.get_stream());
+      map.retrieve_outer(d_keys.begin(), d_keys.end(), d_results.data().get(), launch.get_stream());
       timer.stop();
     });
 }
@@ -238,15 +237,14 @@ std::enable_if_t<(sizeof(Key) == sizeof(Value)), void> nvbench_static_multimap_r
   cuco::static_multimap<Key, Value> map{size, -1, -1};
   map.insert(d_pairs.begin(), d_pairs.end());
 
-  auto num_matches        = map.count(d_keys.begin(), d_keys.end());
-  std::size_t output_size = num_matches + num_keys;
+  auto const output_size = map.count_outer(d_keys.begin(), d_keys.end());
   thrust::device_vector<cuco::pair_type<Key, Value>> d_results(output_size);
 
   state.exec(
     nvbench::exec_tag::sync | nvbench::exec_tag::timer, [&](nvbench::launch& launch, auto& timer) {
       timer.start();
-      auto count = map.count(d_keys.begin(), d_keys.end(), launch.get_stream());
-      map.find_all(d_keys.begin(), d_keys.end(), d_results.data().get(), launch.get_stream());
+      auto count = map.count_outer(d_keys.begin(), d_keys.end(), launch.get_stream());
+      map.retrieve_outer(d_keys.begin(), d_keys.end(), d_results.data().get(), launch.get_stream());
       timer.stop();
     });
 }
diff --git a/examples/static_multimap/static_multimap_example.cu b/examples/static_multimap/static_multimap_example.cu
index 4b0292e06..5c4b5af05 100644
--- a/examples/static_multimap/static_multimap_example.cu
+++ b/examples/static_multimap/static_multimap_example.cu
@@ -49,14 +49,13 @@ int main(void)
   thrust::sequence(keys_to_find.begin(), keys_to_find.end(), 0);
 
   // Counts number of matches for all keys {0, 1, 2, ... 50'000}
-  auto num_matches = map.count(keys_to_find.begin(), keys_to_find.end());
+  auto const output_size = map.count_outer(keys_to_find.begin(), keys_to_find.end());
 
-  std::size_t output_size = num_matches + 25'000;
   thrust::device_vector<cuco::pair_type<int, int>> d_results(output_size);
 
   // Finds all keys {0, 1, 2, ...} and stores associated key/value pairs into `d_results`
   // If a key `keys_to_find[i]` doesn't exist, `d_results[i].second == empty_value_sentinel`
-  map.find_all(keys_to_find.begin(), keys_to_find.end(), d_results.data().get());
+  map.retrieve_outer(keys_to_find.begin(), keys_to_find.end(), d_results.data().get());
 
   return 0;
 }
diff --git a/tests/static_multimap/static_multimap_test.cu b/tests/static_multimap/static_multimap_test.cu
index bdf75b88d..aa6f9ed76 100644
--- a/tests/static_multimap/static_multimap_test.cu
+++ b/tests/static_multimap/static_multimap_test.cu
@@ -126,13 +126,13 @@ TEMPLATE_TEST_CASE_SIG("Each key appears twice",
   {
     map.insert(d_pairs.begin(), d_pairs.end());
     // Count matching keys
-    size_t num = map.count(d_unique_keys.begin(), d_unique_keys.end());
+    size_t num = map.count_inner(d_unique_keys.begin(), d_unique_keys.end());
 
     REQUIRE(num == num_items);
 
     auto output_begin = d_results.data().get();
-    auto output_end   = map.find_all(d_unique_keys.begin(), d_unique_keys.end(), output_begin);
-    auto size         = thrust::distance(output_begin, output_end);
+    auto output_end = map.retrieve_inner(d_unique_keys.begin(), d_unique_keys.end(), output_begin);
+    auto size       = thrust::distance(output_begin, output_end);
 
     REQUIRE(size == num_items);
   }

From be4bf73db16eb2252fe1cb500369b4c637499861 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Mon, 31 May 2021 13:46:24 -0400
Subject: [PATCH 114/267] Add pair_count_inner/outer functions

---
 include/cuco/detail/static_multimap.inl       |  64 ++++++
 .../cuco/detail/static_multimap_kernels.cuh   | 190 +++++++++++++++++-
 include/cuco/static_multimap.cuh              |  43 ++++
 3 files changed, 292 insertions(+), 5 deletions(-)

diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index 455f8f158..141461f00 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -165,6 +165,70 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::count_
   return result;
 }
 
+template <typename Key,
+          typename Value,
+          class ProbeSequence,
+          cuda::thread_scope Scope,
+          typename Allocator>
+template <typename InputIt, typename KeyEqual, typename ValEqual>
+std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::pair_count_inner(
+  InputIt first, InputIt last, cudaStream_t stream, KeyEqual key_equal, ValEqual val_equal)
+{
+  auto num_keys         = std::distance(first, last);
+  auto const block_size = 128;
+  auto const stride     = 1;
+  auto const grid_size  = (cg_size() * num_keys + stride * block_size - 1) / (stride * block_size);
+  auto view             = get_device_view();
+
+  atomic_ctr_type* num_items;
+  CUCO_CUDA_TRY(cudaMallocManaged(&num_items, sizeof(atomic_ctr_type)));
+  *num_items = 0;
+  int device_id;
+  CUCO_CUDA_TRY(cudaGetDevice(&device_id));
+  CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_items, sizeof(atomic_ctr_type), device_id));
+
+  detail::pair_count_inner<block_size, cg_size(), Key, Value>
+    <<<grid_size, block_size, 0, stream>>>(first, last, num_items, view, key_equal, val_equal);
+  CUCO_CUDA_TRY(cudaDeviceSynchronize());
+
+  size_t result = *num_items;
+  CUCO_CUDA_TRY(cudaFree(num_items));
+
+  return result;
+}
+
+template <typename Key,
+          typename Value,
+          class ProbeSequence,
+          cuda::thread_scope Scope,
+          typename Allocator>
+template <typename InputIt, typename KeyEqual, typename ValEqual>
+std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::pair_count_outer(
+  InputIt first, InputIt last, cudaStream_t stream, KeyEqual key_equal, ValEqual val_equal)
+{
+  auto num_keys         = std::distance(first, last);
+  auto const block_size = 128;
+  auto const stride     = 1;
+  auto const grid_size  = (cg_size() * num_keys + stride * block_size - 1) / (stride * block_size);
+  auto view             = get_device_view();
+
+  atomic_ctr_type* num_items;
+  CUCO_CUDA_TRY(cudaMallocManaged(&num_items, sizeof(atomic_ctr_type)));
+  *num_items = 0;
+  int device_id;
+  CUCO_CUDA_TRY(cudaGetDevice(&device_id));
+  CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_items, sizeof(atomic_ctr_type), device_id));
+
+  detail::pair_count_outer<block_size, cg_size(), Key, Value>
+    <<<grid_size, block_size, 0, stream>>>(first, last, num_items, view, key_equal, val_equal);
+  CUCO_CUDA_TRY(cudaDeviceSynchronize());
+
+  size_t result = *num_items;
+  CUCO_CUDA_TRY(cudaFree(num_items));
+
+  return result;
+}
+
 template <typename Key,
           typename Value,
           class ProbeSequence,
diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap_kernels.cuh
index 9723381f4..90f3e82e0 100644
--- a/include/cuco/detail/static_multimap_kernels.cuh
+++ b/include/cuco/detail/static_multimap_kernels.cuh
@@ -310,10 +310,9 @@ __global__ void count_outer(
       auto const second_slot_is_empty = (arr[1].first == view.get_empty_key_sentinel());
       auto const first_equals         = (not first_slot_is_empty and key_equal(arr[0].first, key));
       auto const second_equals        = (not second_slot_is_empty and key_equal(arr[1].first, key));
-      auto const first_exists         = tile.any(first_equals);
-      auto const second_exists        = tile.any(second_equals);
+      auto const exists               = tile.any(first_equals or second_equals);
 
-      if (first_exists or second_exists) { found_match = true; }
+      if (exists) { found_match = true; }
 
       thread_num_items += (first_equals + second_equals);
 
@@ -333,6 +332,189 @@ __global__ void count_outer(
   if (threadIdx.x == 0) { num_items->fetch_add(block_num_items, cuda::std::memory_order_relaxed); }
 }
 
+/**
+ * @brief Counts the occurrences of key/value pairs in `[first, last)` contained in the multimap.
+ *
+ * @tparam block_size The size of the thread block
+ * @tparam tile_size The number of threads in the Cooperative Groups used to perform counts
+ * @tparam Key key type
+ * @tparam Value The type of the mapped value for the map
+ * @tparam Input Device accesible input iterator of key/value pairs
+ * @tparam atomicT Type of atomic storage
+ * @tparam viewT Type of device view allowing access of hash map storage
+ * @tparam KeyEqual Binary callable
+ * @tparam ValEqual Binary callable
+ * @param first Beginning of the sequence of pairs to count
+ * @param last End of the sequence of pairs to count
+ * @param num_items The number of all the matches for a sequence of pairs
+ * @param view Device view used to access the hash map's slot storage
+ * @param key_equal Binary function to compare two keys for equality
+ * @param val_equal Binary function to compare two values for equality
+ */
+template <uint32_t block_size,
+          uint32_t tile_size,
+          typename Key,
+          typename Value,
+          typename InputIt,
+          typename atomicT,
+          typename viewT,
+          typename KeyEqual,
+          typename ValEqual>
+__global__ void pair_count_inner(InputIt first,
+                                 InputIt last,
+                                 atomicT* num_items,
+                                 viewT view,
+                                 KeyEqual key_equal,
+                                 ValEqual val_equal)
+{
+  auto tile     = cg::tiled_partition<tile_size>(cg::this_thread_block());
+  auto tid      = block_size * blockIdx.x + threadIdx.x;
+  auto pair_idx = tid / tile_size;
+
+  typedef cub::BlockReduce<std::size_t, block_size> BlockReduce;
+  __shared__ typename BlockReduce::TempStorage temp_storage;
+  std::size_t thread_num_items = 0;
+
+  while (first + pair_idx < last) {
+    auto pair         = *(first + pair_idx);
+    auto key          = pair.first;
+    auto value        = pair.second;
+    auto current_slot = view.initial_slot(tile, key);
+
+    while (true) {
+      cuco::pair_type<Key, Value> arr[2];
+      if constexpr (sizeof(Key) == 4) {
+        auto const tmp = *reinterpret_cast<uint4 const*>(current_slot);
+        memcpy(&arr[0], &tmp, 2 * sizeof(cuco::pair_type<Key, Value>));
+      } else {
+        auto const tmp = *reinterpret_cast<ulonglong4 const*>(current_slot);
+        memcpy(&arr[0], &tmp, 2 * sizeof(cuco::pair_type<Key, Value>));
+      }
+
+      auto const first_key_is_empty  = (arr[0].first == view.get_empty_key_sentinel());
+      auto const second_key_is_empty = (arr[1].first == view.get_empty_key_sentinel());
+      auto const first_val_is_empty  = (arr[0].second == view.get_empty_value_sentinel());
+      auto const second_val_is_empty = (arr[1].second == view.get_empty_value_sentinel());
+
+      auto const first_key_equals  = (not first_key_is_empty and key_equal(arr[0].first, key));
+      auto const second_key_equals = (not second_key_is_empty and key_equal(arr[1].first, key));
+      auto const first_val_equals  = (not first_val_is_empty and key_equal(arr[0].second, value));
+      auto const second_val_equals = (not second_val_is_empty and key_equal(arr[1].second, value));
+
+      auto const first_equals  = first_key_equals and first_val_equals;
+      auto const second_equals = second_key_equals and second_val_equals;
+
+      thread_num_items += (first_equals + second_equals);
+
+      if (tile.any(first_key_is_empty or second_key_is_empty)) { break; }
+
+      current_slot = view.next_slot(tile, current_slot);
+    }
+    pair_idx += (gridDim.x * block_size) / tile_size;
+  }
+
+  // compute number of successfully inserted elements for each block
+  // and atomically add to the grand total
+  std::size_t block_num_items = BlockReduce(temp_storage).Sum(thread_num_items);
+  if (threadIdx.x == 0) { num_items->fetch_add(block_num_items, cuda::std::memory_order_relaxed); }
+}
+
+/**
+ * @brief Counts the occurrences of key/value pairs in `[first, last)` contained in the multimap.
+ * If no matches can be found for a given key/value pair, the corresponding occurrence is 1.
+ *
+ * @tparam block_size The size of the thread block
+ * @tparam tile_size The number of threads in the Cooperative Groups used to perform counts
+ * @tparam Key key type
+ * @tparam Value The type of the mapped value for the map
+ * @tparam Input Device accesible input iterator of key/value pairs
+ * @tparam atomicT Type of atomic storage
+ * @tparam viewT Type of device view allowing access of hash map storage
+ * @tparam KeyEqual Binary callable
+ * @tparam ValEqual Binary callable
+ * @param first Beginning of the sequence of pairs to count
+ * @param last End of the sequence of pairs to count
+ * @param num_items The number of all the matches for a sequence of pairs
+ * @param view Device view used to access the hash map's slot storage
+ * @param key_equal Binary function to compare two keys for equality
+ * @param val_equal Binary function to compare two values for equality
+ */
+template <uint32_t block_size,
+          uint32_t tile_size,
+          typename Key,
+          typename Value,
+          typename InputIt,
+          typename atomicT,
+          typename viewT,
+          typename KeyEqual,
+          typename ValEqual>
+__global__ void pair_count_outer(InputIt first,
+                                 InputIt last,
+                                 atomicT* num_items,
+                                 viewT view,
+                                 KeyEqual key_equal,
+                                 ValEqual val_equal)
+{
+  auto tile     = cg::tiled_partition<tile_size>(cg::this_thread_block());
+  auto tid      = block_size * blockIdx.x + threadIdx.x;
+  auto pair_idx = tid / tile_size;
+
+  typedef cub::BlockReduce<std::size_t, block_size> BlockReduce;
+  __shared__ typename BlockReduce::TempStorage temp_storage;
+  std::size_t thread_num_items = 0;
+
+  while (first + pair_idx < last) {
+    auto pair         = *(first + pair_idx);
+    auto key          = pair.first;
+    auto value        = pair.second;
+    auto current_slot = view.initial_slot(tile, key);
+
+    bool found_match = false;
+
+    while (true) {
+      cuco::pair_type<Key, Value> arr[2];
+      if constexpr (sizeof(Key) == 4) {
+        auto const tmp = *reinterpret_cast<uint4 const*>(current_slot);
+        memcpy(&arr[0], &tmp, 2 * sizeof(cuco::pair_type<Key, Value>));
+      } else {
+        auto const tmp = *reinterpret_cast<ulonglong4 const*>(current_slot);
+        memcpy(&arr[0], &tmp, 2 * sizeof(cuco::pair_type<Key, Value>));
+      }
+
+      auto const first_key_is_empty  = (arr[0].first == view.get_empty_key_sentinel());
+      auto const second_key_is_empty = (arr[1].first == view.get_empty_key_sentinel());
+      auto const first_val_is_empty  = (arr[0].second == view.get_empty_value_sentinel());
+      auto const second_val_is_empty = (arr[1].second == view.get_empty_value_sentinel());
+
+      auto const first_key_equals  = (not first_key_is_empty and key_equal(arr[0].first, key));
+      auto const second_key_equals = (not second_key_is_empty and key_equal(arr[1].first, key));
+      auto const first_val_equals  = (not first_val_is_empty and key_equal(arr[0].second, value));
+      auto const second_val_equals = (not second_val_is_empty and key_equal(arr[1].second, value));
+
+      auto const first_equals  = first_key_equals and first_val_equals;
+      auto const second_equals = second_key_equals and second_val_equals;
+      auto const exists        = tile.any(first_equals or second_equals);
+
+      if (exists) { found_match = true; }
+
+      thread_num_items += (first_equals + second_equals);
+
+      if (tile.any(first_key_is_empty or second_key_is_empty)) {
+        if ((not found_match) && (tile.thread_rank() == 0)) { thread_num_items++; }
+        break;
+      }
+
+      current_slot = view.next_slot(tile, current_slot);
+    }
+    pair_idx += (gridDim.x * block_size) / tile_size;
+  }
+
+  // compute number of successfully inserted elements for each block
+  // and atomically add to the grand total
+  std::size_t block_num_items = BlockReduce(temp_storage).Sum(thread_num_items);
+  if (threadIdx.x == 0) { num_items->fetch_add(block_num_items, cuda::std::memory_order_relaxed); }
+}
+
 /**
  * @brief Finds all the values corresponding to all keys in the range `[first, last)`.
  *
@@ -361,7 +543,6 @@ __global__ void count_outer(
  * @param view Device view used to access the hash map's slot storage
  * @param key_equal The binary function to compare two keys for equality
  */
-
 template <uint32_t block_size,
           uint32_t tile_size,
           uint32_t buffer_size,
@@ -490,7 +671,6 @@ __global__ void retrieve_inner(InputIt first,
  * @param view Device view used to access the hash map's slot storage
  * @param key_equal The binary function to compare two keys for equality
  */
-
 template <uint32_t block_size,
           uint32_t tile_size,
           uint32_t buffer_size,
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index 943406e1f..ee1446022 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -250,6 +250,49 @@ class static_multimap {
                           cudaStream_t stream = 0,
                           KeyEqual key_equal  = KeyEqual{});
 
+  /**
+   * @brief Counts the occurrences of key/value pairs in `[first, last)` contained in the multimap.
+   *
+   * @tparam Input Device accesible input iterator of key/value pairs
+   * @tparam KeyEqual Binary callable
+   * @tparam ValEqual Binary callable
+   * @param first Beginning of the sequence of pairs to count
+   * @param last End of the sequence of pairs to count
+   * @param key_equal Binary function to compare two keys for equality
+   * @param val_equal Binary function to compare two values for equality
+   * @return The sum of total occurrences of all pairs in `[first,last)`
+   */
+  template <typename InputIt,
+            typename KeyEqual = thrust::equal_to<key_type>,
+            typename ValEqual = thrust::equal_to<mapped_type>>
+  std::size_t pair_count_inner(InputIt first,
+                               InputIt last,
+                               cudaStream_t stream = 0,
+                               KeyEqual key_equal  = KeyEqual{},
+                               ValEqual val_equal  = ValEqual{});
+
+  /**
+   * @brief Counts the occurrences of key/value pairs in `[first, last)` contained in the multimap.
+   * If no matches can be found for a given key/value pair, the corresponding occurrence is 1.
+   *
+   * @tparam Input Device accesible input iterator of key/value pairs
+   * @tparam KeyEqual Binary callable
+   * @tparam ValEqual Binary callable
+   * @param first Beginning of the sequence of pairs to count
+   * @param last End of the sequence of pairs to count
+   * @param key_equal Binary function to compare two keys for equality
+   * @param val_equal Binary function to compare two values for equality
+   * @return The sum of total occurrences of all pairs in `[first,last)`
+   */
+  template <typename InputIt,
+            typename KeyEqual = thrust::equal_to<key_type>,
+            typename ValEqual = thrust::equal_to<mapped_type>>
+  std::size_t pair_count_outer(InputIt first,
+                               InputIt last,
+                               cudaStream_t stream = 0,
+                               KeyEqual key_equal  = KeyEqual{},
+                               ValEqual val_equal  = ValEqual{});
+
   /**
    * @brief Finds all the values corresponding to all keys in the range `[first, last)`.
    *

From e6c98ebb9c60b498cb6eaa77c699aa8662a94ea7 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Mon, 31 May 2021 15:05:39 -0400
Subject: [PATCH 115/267] Add linear probing class + next_slot cleanups

---
 include/cuco/detail/probe_sequences.cuh       | 49 ++++++++++++++++++-
 include/cuco/detail/static_multimap.inl       | 46 +----------------
 .../cuco/detail/static_multimap_kernels.cuh   | 12 ++---
 include/cuco/static_multimap.cuh              | 42 ++--------------
 4 files changed, 58 insertions(+), 91 deletions(-)

diff --git a/include/cuco/detail/probe_sequences.cuh b/include/cuco/detail/probe_sequences.cuh
index 0eb35bb46..80c8f7b62 100644
--- a/include/cuco/detail/probe_sequences.cuh
+++ b/include/cuco/detail/probe_sequences.cuh
@@ -61,8 +61,7 @@ class DoubleHashing {
     return slots_ + index;
   }
 
-  template <typename CG>
-  __device__ iterator next_slot(CG const& g, iterator s) noexcept
+  __device__ iterator next_slot(iterator s) noexcept
   {
     std::size_t index = s - slots_;
     return &slots_[(index + step_size_) % capacity_];
@@ -76,4 +75,50 @@ class DoubleHashing {
   Hash2 hash2_{};
 };  // class DoubleHashing
 
+template <typename Key,
+          typename Value,
+          uint32_t CGSize          = 8,
+          typename Hash            = cuco::detail::MurmurHash3_32<Key>,
+          cuda::thread_scope Scope = cuda::thread_scope_device>
+class LinearProbing {
+ public:
+  using value_type         = cuco::pair_type<Key, Value>;
+  using key_type           = Key;
+  using mapped_type        = Value;
+  using atomic_key_type    = cuda::atomic<key_type, Scope>;
+  using atomic_mapped_type = cuda::atomic<mapped_type, Scope>;
+  using pair_atomic_type   = cuco::pair_type<atomic_key_type, atomic_mapped_type>;
+  using iterator           = pair_atomic_type*;
+  using const_iterator     = pair_atomic_type const*;
+
+  __host__ __device__ static constexpr uint32_t cg_size() noexcept { return CGSize; }
+
+  __host__ __device__ explicit LinearProbing(iterator slots, std::size_t capacity)
+    : slots_{slots}, capacity_{capacity}
+  {
+  }
+
+  __host__ __device__ std::size_t get_capacity() const noexcept { return capacity_; }
+
+  __device__ iterator get_slots() noexcept { return slots_; }
+  __device__ const_iterator get_slots() const noexcept { return slots_; }
+
+  template <typename CG>
+  __device__ iterator initial_slot(CG const& g, Key const k) noexcept
+  {
+    return &slots_[(hash_(k) + g.thread_rank() * 2) % capacity_];
+  }
+
+  __device__ iterator next_slot(iterator s) noexcept
+  {
+    std::size_t index = s - slots_;
+    return &slots_[(index + cg_size() * 2) % capacity_];
+  }
+
+ private:
+  iterator slots_;
+  const std::size_t capacity_;
+  Hash hash_{};
+};  // class LinearProbing
+
 }  // namespace cuco
diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index 141461f00..45a70bcd6 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -295,48 +295,6 @@ OutputIt static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::retrieve_
   return output_end;
 }
 
-template <typename Key,
-          typename Value,
-          class ProbeSequence,
-          cuda::thread_scope Scope,
-          typename Allocator>
-template <typename KeyEqual>
-__device__ void
-static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_mutable_view::insert(
-  value_type const& insert_pair, KeyEqual key_equal) noexcept
-{
-  auto current_slot{initial_slot(insert_pair.first)};
-
-  while (true) {
-    using cuda::std::memory_order_relaxed;
-    auto expected_key   = this->get_empty_key_sentinel();
-    auto expected_value = this->get_empty_value_sentinel();
-    auto& slot_key      = current_slot->first;
-    auto& slot_value    = current_slot->second;
-
-    bool key_success =
-      slot_key.compare_exchange_strong(expected_key, insert_pair.first, memory_order_relaxed);
-    bool value_success =
-      slot_value.compare_exchange_strong(expected_value, insert_pair.second, memory_order_relaxed);
-
-    if (key_success) {
-      while (not value_success) {
-        value_success =
-          slot_value.compare_exchange_strong(expected_value = this->get_empty_value_sentinel(),
-                                             insert_pair.second,
-                                             memory_order_relaxed);
-      }
-      return;
-    } else if (value_success) {
-      slot_value.store(this->get_empty_value_sentinel(), memory_order_relaxed);
-    }
-
-    // If we couldn't insert the key, then there must have been some other key there. So we keep
-    // looking for a slot
-    current_slot = next_slot(current_slot);
-  }
-}
-
 template <typename Key,
           typename Value,
           class ProbeSequence,
@@ -405,7 +363,7 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_mutable_vie
     // if there are no empty slots in the current window,
     // we move onto the next window
     else {
-      current_slot = next_slot(g, current_slot);
+      current_slot = next_slot(current_slot);
     }
   }
 }
@@ -444,7 +402,7 @@ __device__ bool static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::de
 
     // otherwise, all slots in the current window are full with other keys, so we move onto the next
     // window
-    current_slot = next_slot(g, current_slot);
+    current_slot = next_slot(current_slot);
   }
 }
 
diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap_kernels.cuh
index 90f3e82e0..a088a70a6 100644
--- a/include/cuco/detail/static_multimap_kernels.cuh
+++ b/include/cuco/detail/static_multimap_kernels.cuh
@@ -241,7 +241,7 @@ __global__ void count_inner(
 
       if (tile.any(first_slot_is_empty or second_slot_is_empty)) { break; }
 
-      current_slot = view.next_slot(tile, current_slot);
+      current_slot = view.next_slot(current_slot);
     }
     key_idx += (gridDim.x * block_size) / tile_size;
   }
@@ -321,7 +321,7 @@ __global__ void count_outer(
         break;
       }
 
-      current_slot = view.next_slot(tile, current_slot);
+      current_slot = view.next_slot(current_slot);
     }
     key_idx += (gridDim.x * block_size) / tile_size;
   }
@@ -408,7 +408,7 @@ __global__ void pair_count_inner(InputIt first,
 
       if (tile.any(first_key_is_empty or second_key_is_empty)) { break; }
 
-      current_slot = view.next_slot(tile, current_slot);
+      current_slot = view.next_slot(current_slot);
     }
     pair_idx += (gridDim.x * block_size) / tile_size;
   }
@@ -504,7 +504,7 @@ __global__ void pair_count_outer(InputIt first,
         break;
       }
 
-      current_slot = view.next_slot(tile, current_slot);
+      current_slot = view.next_slot(current_slot);
     }
     pair_idx += (gridDim.x * block_size) / tile_size;
   }
@@ -631,7 +631,7 @@ __global__ void retrieve_inner(InputIt first,
         if (0 == threadIdx.x) { block_counter = 0; }
       }
 
-      if (running) { current_slot = view.next_slot(tile, current_slot); }
+      if (running) { current_slot = view.next_slot(current_slot); }
     }  // while running
     key_idx += (gridDim.x * block_size) / tile_size;
   }
@@ -769,7 +769,7 @@ __global__ void retrieve_outer(InputIt first,
         if (0 == threadIdx.x) { block_counter = 0; }
       }
 
-      if (running) { current_slot = view.next_slot(tile, current_slot); }
+      if (running) { current_slot = view.next_slot(current_slot); }
     }  // while running
     key_idx += (gridDim.x * block_size) / tile_size;
   }
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index ee1446022..fc77ebdb5 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -429,16 +429,10 @@ class static_multimap {
      * If `s` is the last slot, wraps back around to the first slot. To
      * be used for Cooperative Group based probing.
      *
-     * @tparam CG The Cooperative Group type
-     * @param g The Cooperative Group for which the next slot is needed
      * @param s The slot to advance
      * @return The next slot after `s`
      */
-    template <typename CG>
-    __device__ iterator next_slot(CG const& g, iterator s) noexcept
-    {
-      return probe_sequence_.next_slot(g, s);
-    }
+    __device__ iterator next_slot(iterator s) noexcept { return probe_sequence_.next_slot(s); }
 
     /**
      * @brief Given a slot `s`, returns the next slot.
@@ -446,15 +440,13 @@ class static_multimap {
      * If `s` is the last slot, wraps back around to the first slot. To
      * be used for Cooperative Group based probing.
      *
-     * @tparam CG The Cooperative Group type
-     * @param g The Cooperative Group for which the next slot is needed
      * @param s The slot to advance
      * @return The next slot after `s`
      */
     template <typename CG>
-    __device__ const_iterator next_slot(CG const& g, const_iterator s) const noexcept
+    __device__ const_iterator next_slot(const_iterator s) const noexcept
     {
-      return probe_sequence_.next_slot(g, s);
+      return probe_sequence_.next_slot(s);
     }
 
    public:
@@ -534,17 +526,6 @@ class static_multimap {
     {
     }
 
-    /**
-     * @brief Inserts the specified key/value pair into the map.
-     *
-     * @tparam KeyEqual Binary callable type
-     * @param insert_pair The pair to insert
-     * @param key_equal The binary callable used to compare two keys for
-     * equality
-     * @return void.
-     */
-    template <typename KeyEqual = thrust::equal_to<key_type>>
-    __device__ void insert(value_type const& insert_pair, KeyEqual key_equal = KeyEqual{}) noexcept;
     /**
      * @brief Inserts the specified key/value pair into the map.
      *
@@ -672,23 +653,6 @@ class static_multimap {
                          source_device_view.get_empty_value_sentinel());
     }
 
-    /**
-     * @brief Indicates whether the key `k` was inserted into the map.
-     *
-     * If the key `k` was inserted into the map, find returns
-     * true. Otherwise, it returns false.
-     *
-     * @tparam KeyEqual Binary callable type
-     * @param k The key to search for
-     * @param key_equal The binary callable used to compare two keys
-     * for equality
-     * @return A boolean indicating whether the key/value pair
-     * containing `k` was inserted
-     */
-
-    template <typename KeyEqual = thrust::equal_to<key_type>>
-    __device__ bool contains(Key const& k, KeyEqual key_equal = KeyEqual{}) noexcept;
-
     /**
      * @brief Indicates whether the key `k` was inserted into the map.
      *

From 3868b10e566434d1c6f98c003099435c966fc458 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Mon, 31 May 2021 16:35:52 -0400
Subject: [PATCH 116/267] Create & use probe_sequence_base class

---
 .../static_multimap/find_all_bench.cu         |  2 +-
 include/cuco/detail/probe_sequences.cuh       | 65 ++++++++++---------
 include/cuco/static_multimap.cuh              |  2 +-
 3 files changed, 38 insertions(+), 31 deletions(-)

diff --git a/benchmarks/hash_table/static_multimap/find_all_bench.cu b/benchmarks/hash_table/static_multimap/find_all_bench.cu
index 496a4adb2..a7bb02cd8 100644
--- a/benchmarks/hash_table/static_multimap/find_all_bench.cu
+++ b/benchmarks/hash_table/static_multimap/find_all_bench.cu
@@ -78,7 +78,7 @@ std::enable_if_t<(sizeof(Key) == sizeof(Value)), void> nvbench_find_all(
 
   state.exec(
     nvbench::exec_tag::sync | nvbench::exec_tag::timer, [&](nvbench::launch& launch, auto& timer) {
-      cuco::static_multimap<Key, Value, cuco::DoubleHashing<Key, Value, CGSize>> map{size, -1, -1};
+      cuco::static_multimap<Key, Value, cuco::double_hashing<Key, Value, CGSize>> map{size, -1, -1};
       map.insert(d_pairs.begin(), d_pairs.end());
 
       timer.start();
diff --git a/include/cuco/detail/probe_sequences.cuh b/include/cuco/detail/probe_sequences.cuh
index 80c8f7b62..b0b1009a6 100644
--- a/include/cuco/detail/probe_sequences.cuh
+++ b/include/cuco/detail/probe_sequences.cuh
@@ -26,11 +26,9 @@ namespace cuco {
 template <typename Key,
           typename Value,
           uint32_t CGSize          = 8,
-          typename Hash1           = cuco::detail::MurmurHash3_32<Key>,
-          typename Hash2           = cuco::detail::MurmurHash3_32<Key>,
           cuda::thread_scope Scope = cuda::thread_scope_device>
-class DoubleHashing {
- public:
+class probe_sequence_base {
+ protected:
   using value_type         = cuco::pair_type<Key, Value>;
   using key_type           = Key;
   using mapped_type        = Value;
@@ -40,9 +38,10 @@ class DoubleHashing {
   using iterator           = pair_atomic_type*;
   using const_iterator     = pair_atomic_type const*;
 
+ public:
   __host__ __device__ static constexpr uint32_t cg_size() noexcept { return CGSize; }
 
-  __host__ __device__ explicit DoubleHashing(iterator slots, std::size_t capacity)
+  __host__ __device__ explicit probe_sequence_base(iterator slots, std::size_t capacity)
     : slots_{slots}, capacity_{capacity}
   {
   }
@@ -52,6 +51,29 @@ class DoubleHashing {
   __device__ iterator get_slots() noexcept { return slots_; }
   __device__ const_iterator get_slots() const noexcept { return slots_; }
 
+ protected:
+  iterator slots_;
+  const std::size_t capacity_;
+};  // class probe_sequence_base
+
+template <typename Key,
+          typename Value,
+          uint32_t CGSize          = 8,
+          typename Hash1           = cuco::detail::MurmurHash3_32<Key>,
+          typename Hash2           = cuco::detail::MurmurHash3_32<Key>,
+          cuda::thread_scope Scope = cuda::thread_scope_device>
+class double_hashing : public probe_sequence_base<Key, Value, CGSize, Scope> {
+ public:
+  using iterator = typename probe_sequence_base<Key, Value, CGSize, Scope>::iterator;
+  using probe_sequence_base<Key, Value, CGSize, Scope>::capacity_;
+  using probe_sequence_base<Key, Value, CGSize, Scope>::slots_;
+  using probe_sequence_base<Key, Value, CGSize, Scope>::cg_size;
+
+  __host__ __device__ explicit double_hashing(iterator slots, std::size_t capacity) noexcept
+    : probe_sequence_base<Key, Value, CGSize, Scope>{slots, capacity}
+  {
+  }
+
   template <typename CG>
   __device__ iterator initial_slot(CG const& g, Key const k) noexcept
   {
@@ -68,41 +90,28 @@ class DoubleHashing {
   }
 
  private:
-  iterator slots_;
-  const std::size_t capacity_;
   std::size_t step_size_;
   Hash1 hash1_{};
   Hash2 hash2_{};
-};  // class DoubleHashing
+};  // class double_hashing
 
 template <typename Key,
           typename Value,
           uint32_t CGSize          = 8,
           typename Hash            = cuco::detail::MurmurHash3_32<Key>,
           cuda::thread_scope Scope = cuda::thread_scope_device>
-class LinearProbing {
+class linear_probing : public probe_sequence_base<Key, Value, CGSize, Scope> {
  public:
-  using value_type         = cuco::pair_type<Key, Value>;
-  using key_type           = Key;
-  using mapped_type        = Value;
-  using atomic_key_type    = cuda::atomic<key_type, Scope>;
-  using atomic_mapped_type = cuda::atomic<mapped_type, Scope>;
-  using pair_atomic_type   = cuco::pair_type<atomic_key_type, atomic_mapped_type>;
-  using iterator           = pair_atomic_type*;
-  using const_iterator     = pair_atomic_type const*;
+  using iterator = typename probe_sequence_base<Key, Value, CGSize, Scope>::iterator;
+  using probe_sequence_base<Key, Value, CGSize, Scope>::capacity_;
+  using probe_sequence_base<Key, Value, CGSize, Scope>::slots_;
+  using probe_sequence_base<Key, Value, CGSize, Scope>::cg_size;
 
-  __host__ __device__ static constexpr uint32_t cg_size() noexcept { return CGSize; }
-
-  __host__ __device__ explicit LinearProbing(iterator slots, std::size_t capacity)
-    : slots_{slots}, capacity_{capacity}
+  __host__ __device__ explicit linear_probing(iterator slots, std::size_t capacity)
+    : probe_sequence_base<Key, Value, CGSize, Scope>{slots, capacity}
   {
   }
 
-  __host__ __device__ std::size_t get_capacity() const noexcept { return capacity_; }
-
-  __device__ iterator get_slots() noexcept { return slots_; }
-  __device__ const_iterator get_slots() const noexcept { return slots_; }
-
   template <typename CG>
   __device__ iterator initial_slot(CG const& g, Key const k) noexcept
   {
@@ -116,9 +125,7 @@ class LinearProbing {
   }
 
  private:
-  iterator slots_;
-  const std::size_t capacity_;
   Hash hash_{};
-};  // class LinearProbing
+};  // class linear_probing
 
 }  // namespace cuco
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index fc77ebdb5..693193103 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -110,7 +110,7 @@ namespace cuco {
  */
 template <typename Key,
           typename Value,
-          class ProbeSequence      = DoubleHashing<Key, Value>,
+          class ProbeSequence      = cuco::double_hashing<Key, Value>,
           cuda::thread_scope Scope = cuda::thread_scope_device,
           typename Allocator       = cuco::cuda_allocator<char>>
 class static_multimap {

From 414bd33f51ae50af5959db99952931cfdc9c13c8 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Tue, 1 Jun 2021 12:04:49 -0400
Subject: [PATCH 117/267] Renaming: remove _inner suffix

---
 .../static_multimap/static_multimap_bench.cu  |  2 +-
 .../static_multimap_example.cu                |  4 +--
 include/cuco/detail/static_multimap.inl       | 16 +++++-----
 .../cuco/detail/static_multimap_kernels.cuh   | 26 ++++++++---------
 include/cuco/static_multimap.cuh              | 29 +++++++++----------
 tests/static_multimap/static_multimap_test.cu |  6 ++--
 6 files changed, 42 insertions(+), 41 deletions(-)

diff --git a/benchmarks/hash_table/static_multimap/static_multimap_bench.cu b/benchmarks/hash_table/static_multimap/static_multimap_bench.cu
index a22ce4fb7..87ff31d7e 100644
--- a/benchmarks/hash_table/static_multimap/static_multimap_bench.cu
+++ b/benchmarks/hash_table/static_multimap/static_multimap_bench.cu
@@ -134,7 +134,7 @@ std::enable_if_t<(sizeof(Key) == sizeof(Value)), void> nvbench_static_multimap_c
   state.exec(nvbench::exec_tag::sync | nvbench::exec_tag::timer,
              [&](nvbench::launch& launch, auto& timer) {
                timer.start();
-               auto count = map.count_inner(d_keys.begin(), d_keys.end(), launch.get_stream());
+               auto count = map.count(d_keys.begin(), d_keys.end(), launch.get_stream());
                timer.stop();
              });
 }
diff --git a/examples/static_multimap/static_multimap_example.cu b/examples/static_multimap/static_multimap_example.cu
index 5c4b5af05..98d821462 100644
--- a/examples/static_multimap/static_multimap_example.cu
+++ b/examples/static_multimap/static_multimap_example.cu
@@ -49,13 +49,13 @@ int main(void)
   thrust::sequence(keys_to_find.begin(), keys_to_find.end(), 0);
 
   // Counts number of matches for all keys {0, 1, 2, ... 50'000}
-  auto const output_size = map.count_outer(keys_to_find.begin(), keys_to_find.end());
+  auto const output_size = map.count(keys_to_find.begin(), keys_to_find.end());
 
   thrust::device_vector<cuco::pair_type<int, int>> d_results(output_size);
 
   // Finds all keys {0, 1, 2, ...} and stores associated key/value pairs into `d_results`
   // If a key `keys_to_find[i]` doesn't exist, `d_results[i].second == empty_value_sentinel`
-  map.retrieve_outer(keys_to_find.begin(), keys_to_find.end(), d_results.data().get());
+  map.retrieve(keys_to_find.begin(), keys_to_find.end(), d_results.data().get());
 
   return 0;
 }
diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index 45a70bcd6..0f27f0cd2 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -107,8 +107,10 @@ template <typename Key,
           cuda::thread_scope Scope,
           typename Allocator>
 template <typename InputIt, typename KeyEqual>
-std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::count_inner(
-  InputIt first, InputIt last, cudaStream_t stream, KeyEqual key_equal)
+std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::count(InputIt first,
+                                                                                InputIt last,
+                                                                                cudaStream_t stream,
+                                                                                KeyEqual key_equal)
 {
   auto num_keys         = std::distance(first, last);
   auto const block_size = 128;
@@ -123,7 +125,7 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::count_
   CUCO_CUDA_TRY(cudaGetDevice(&device_id));
   CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_items, sizeof(atomic_ctr_type), device_id));
 
-  detail::count_inner<block_size, cg_size(), Key, Value>
+  detail::count<block_size, cg_size(), Key, Value>
     <<<grid_size, block_size, 0, stream>>>(first, last, num_items, view, key_equal);
   CUCO_CUDA_TRY(cudaDeviceSynchronize());
 
@@ -171,7 +173,7 @@ template <typename Key,
           cuda::thread_scope Scope,
           typename Allocator>
 template <typename InputIt, typename KeyEqual, typename ValEqual>
-std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::pair_count_inner(
+std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::pair_count(
   InputIt first, InputIt last, cudaStream_t stream, KeyEqual key_equal, ValEqual val_equal)
 {
   auto num_keys         = std::distance(first, last);
@@ -187,7 +189,7 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::pair_c
   CUCO_CUDA_TRY(cudaGetDevice(&device_id));
   CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_items, sizeof(atomic_ctr_type), device_id));
 
-  detail::pair_count_inner<block_size, cg_size(), Key, Value>
+  detail::pair_count<block_size, cg_size(), Key, Value>
     <<<grid_size, block_size, 0, stream>>>(first, last, num_items, view, key_equal, val_equal);
   CUCO_CUDA_TRY(cudaDeviceSynchronize());
 
@@ -235,7 +237,7 @@ template <typename Key,
           cuda::thread_scope Scope,
           typename Allocator>
 template <typename InputIt, typename OutputIt, typename KeyEqual>
-OutputIt static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::retrieve_inner(
+OutputIt static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::retrieve(
   InputIt first, InputIt last, OutputIt output_begin, cudaStream_t stream, KeyEqual key_equal)
 {
   auto num_keys          = std::distance(first, last);
@@ -252,7 +254,7 @@ OutputIt static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::retrieve_
   CUCO_CUDA_TRY(cudaGetDevice(&device_id));
   CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_items, sizeof(atomic_ctr_type), device_id));
 
-  detail::retrieve_inner<block_size, cg_size(), buffer_size, Key, Value>
+  detail::retrieve<block_size, cg_size(), buffer_size, Key, Value>
     <<<grid_size, block_size, 0, stream>>>(first, last, output_begin, num_items, view, key_equal);
   CUCO_CUDA_TRY(cudaDeviceSynchronize());
 
diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap_kernels.cuh
index a088a70a6..156358a8e 100644
--- a/include/cuco/detail/static_multimap_kernels.cuh
+++ b/include/cuco/detail/static_multimap_kernels.cuh
@@ -207,7 +207,7 @@ template <uint32_t block_size,
           typename atomicT,
           typename viewT,
           typename KeyEqual>
-__global__ void count_inner(
+__global__ void count(
   InputIt first, InputIt last, atomicT* num_items, viewT view, KeyEqual key_equal)
 {
   auto tile    = cg::tiled_partition<tile_size>(cg::this_thread_block());
@@ -360,12 +360,12 @@ template <uint32_t block_size,
           typename viewT,
           typename KeyEqual,
           typename ValEqual>
-__global__ void pair_count_inner(InputIt first,
-                                 InputIt last,
-                                 atomicT* num_items,
-                                 viewT view,
-                                 KeyEqual key_equal,
-                                 ValEqual val_equal)
+__global__ void pair_count(InputIt first,
+                           InputIt last,
+                           atomicT* num_items,
+                           viewT view,
+                           KeyEqual key_equal,
+                           ValEqual val_equal)
 {
   auto tile     = cg::tiled_partition<tile_size>(cg::this_thread_block());
   auto tid      = block_size * blockIdx.x + threadIdx.x;
@@ -553,12 +553,12 @@ template <uint32_t block_size,
           typename atomicT,
           typename viewT,
           typename KeyEqual>
-__global__ void retrieve_inner(InputIt first,
-                               InputIt last,
-                               OutputIt output_begin,
-                               atomicT* num_items,
-                               viewT view,
-                               KeyEqual key_equal)
+__global__ void retrieve(InputIt first,
+                         InputIt last,
+                         OutputIt output_begin,
+                         atomicT* num_items,
+                         viewT view,
+                         KeyEqual key_equal)
 {
   auto tile    = cg::tiled_partition<tile_size>(cg::this_thread_block());
   auto tid     = block_size * blockIdx.x + threadIdx.x;
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index 693193103..8d4d7b5bb 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -228,10 +228,10 @@ class static_multimap {
    * @return The sum of total occurrences of all keys in `[first,last)`
    */
   template <typename InputIt, typename KeyEqual = thrust::equal_to<key_type>>
-  std::size_t count_inner(InputIt first,
-                          InputIt last,
-                          cudaStream_t stream = 0,
-                          KeyEqual key_equal  = KeyEqual{});
+  std::size_t count(InputIt first,
+                    InputIt last,
+                    cudaStream_t stream = 0,
+                    KeyEqual key_equal  = KeyEqual{});
 
   /**
    * @brief Counts the occurrences of keys in `[first, last)` contained in the multimap. If no
@@ -265,11 +265,11 @@ class static_multimap {
   template <typename InputIt,
             typename KeyEqual = thrust::equal_to<key_type>,
             typename ValEqual = thrust::equal_to<mapped_type>>
-  std::size_t pair_count_inner(InputIt first,
-                               InputIt last,
-                               cudaStream_t stream = 0,
-                               KeyEqual key_equal  = KeyEqual{},
-                               ValEqual val_equal  = ValEqual{});
+  std::size_t pair_count(InputIt first,
+                         InputIt last,
+                         cudaStream_t stream = 0,
+                         KeyEqual key_equal  = KeyEqual{},
+                         ValEqual val_equal  = ValEqual{});
 
   /**
    * @brief Counts the occurrences of key/value pairs in `[first, last)` contained in the multimap.
@@ -314,11 +314,11 @@ class static_multimap {
    * @return The iterator indicating the last valid key/value pairs in the output
    */
   template <typename InputIt, typename OutputIt, typename KeyEqual = thrust::equal_to<key_type>>
-  OutputIt retrieve_inner(InputIt first,
-                          InputIt last,
-                          OutputIt output_begin,
-                          cudaStream_t stream = 0,
-                          KeyEqual key_equal  = KeyEqual{});
+  OutputIt retrieve(InputIt first,
+                    InputIt last,
+                    OutputIt output_begin,
+                    cudaStream_t stream = 0,
+                    KeyEqual key_equal  = KeyEqual{});
 
   /**
    * @brief Finds all the values corresponding to all keys in the range `[first, last)`.
@@ -443,7 +443,6 @@ class static_multimap {
      * @param s The slot to advance
      * @return The next slot after `s`
      */
-    template <typename CG>
     __device__ const_iterator next_slot(const_iterator s) const noexcept
     {
       return probe_sequence_.next_slot(s);
diff --git a/tests/static_multimap/static_multimap_test.cu b/tests/static_multimap/static_multimap_test.cu
index aa6f9ed76..5643084ab 100644
--- a/tests/static_multimap/static_multimap_test.cu
+++ b/tests/static_multimap/static_multimap_test.cu
@@ -126,13 +126,13 @@ TEMPLATE_TEST_CASE_SIG("Each key appears twice",
   {
     map.insert(d_pairs.begin(), d_pairs.end());
     // Count matching keys
-    size_t num = map.count_inner(d_unique_keys.begin(), d_unique_keys.end());
+    size_t num = map.count(d_unique_keys.begin(), d_unique_keys.end());
 
     REQUIRE(num == num_items);
 
     auto output_begin = d_results.data().get();
-    auto output_end = map.retrieve_inner(d_unique_keys.begin(), d_unique_keys.end(), output_begin);
-    auto size       = thrust::distance(output_begin, output_end);
+    auto output_end   = map.retrieve(d_unique_keys.begin(), d_unique_keys.end(), output_begin);
+    auto size         = thrust::distance(output_begin, output_end);
 
     REQUIRE(size == num_items);
   }

From 5344bdd33b1dcf710250c58ffa3f89d8fc1020ee Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Tue, 1 Jun 2021 13:28:19 -0400
Subject: [PATCH 118/267] Add static is_vector_load in probe sequence class

---
 include/cuco/detail/probe_sequences.cuh | 53 +++++++++++++++++--------
 1 file changed, 36 insertions(+), 17 deletions(-)

diff --git a/include/cuco/detail/probe_sequences.cuh b/include/cuco/detail/probe_sequences.cuh
index b0b1009a6..7782e2c8a 100644
--- a/include/cuco/detail/probe_sequences.cuh
+++ b/include/cuco/detail/probe_sequences.cuh
@@ -26,6 +26,7 @@ namespace cuco {
 template <typename Key,
           typename Value,
           uint32_t CGSize          = 8,
+          bool IsVectorLoad        = sizeof(Key) == 4,
           cuda::thread_scope Scope = cuda::thread_scope_device>
 class probe_sequence_base {
  protected:
@@ -40,6 +41,7 @@ class probe_sequence_base {
 
  public:
   __host__ __device__ static constexpr uint32_t cg_size() noexcept { return CGSize; }
+  __host__ __device__ static constexpr bool is_vector_load() noexcept { return IsVectorLoad; }
 
   __host__ __device__ explicit probe_sequence_base(iterator slots, std::size_t capacity)
     : slots_{slots}, capacity_{capacity}
@@ -59,27 +61,34 @@ class probe_sequence_base {
 template <typename Key,
           typename Value,
           uint32_t CGSize          = 8,
+          bool IsVectorLoad        = sizeof(Key) == 4,
           typename Hash1           = cuco::detail::MurmurHash3_32<Key>,
           typename Hash2           = cuco::detail::MurmurHash3_32<Key>,
           cuda::thread_scope Scope = cuda::thread_scope_device>
-class double_hashing : public probe_sequence_base<Key, Value, CGSize, Scope> {
+class double_hashing : public probe_sequence_base<Key, Value, CGSize, IsVectorLoad, Scope> {
  public:
-  using iterator = typename probe_sequence_base<Key, Value, CGSize, Scope>::iterator;
-  using probe_sequence_base<Key, Value, CGSize, Scope>::capacity_;
-  using probe_sequence_base<Key, Value, CGSize, Scope>::slots_;
-  using probe_sequence_base<Key, Value, CGSize, Scope>::cg_size;
+  using iterator = typename probe_sequence_base<Key, Value, CGSize, IsVectorLoad, Scope>::iterator;
+  using probe_sequence_base<Key, Value, CGSize, IsVectorLoad, Scope>::capacity_;
+  using probe_sequence_base<Key, Value, CGSize, IsVectorLoad, Scope>::slots_;
+  using probe_sequence_base<Key, Value, CGSize, IsVectorLoad, Scope>::cg_size;
+  using probe_sequence_base<Key, Value, CGSize, IsVectorLoad, Scope>::is_vector_load;
 
   __host__ __device__ explicit double_hashing(iterator slots, std::size_t capacity) noexcept
-    : probe_sequence_base<Key, Value, CGSize, Scope>{slots, capacity}
+    : probe_sequence_base<Key, Value, CGSize, IsVectorLoad, Scope>{slots, capacity}
   {
   }
 
   template <typename CG>
   __device__ iterator initial_slot(CG const& g, Key const k) noexcept
   {
-    step_size_ = (hash2_(k + 1) % (capacity_ / (cg_size() * 2) - 1) + 1) * cg_size() * 2;
-    std::size_t index =
-      hash1_(k) % (capacity_ / (cg_size() * 2)) * cg_size() * 2 + g.thread_rank() * 2;
+    std::size_t index;
+    if constexpr (IsVectorLoad) {
+      step_size_ = (hash2_(k + 1) % (capacity_ / (cg_size() * 2) - 1) + 1) * cg_size() * 2;
+      index      = hash1_(k) % (capacity_ / (cg_size() * 2)) * cg_size() * 2 + g.thread_rank() * 2;
+    } else {
+      step_size_ = (hash2_(k + 1) % (capacity_ / cg_size() - 1) + 1) * cg_size();
+      index      = (hash1_(k) + g.thread_rank()) % capacity_;
+    }
     return slots_ + index;
   }
 
@@ -98,30 +107,40 @@ class double_hashing : public probe_sequence_base<Key, Value, CGSize, Scope> {
 template <typename Key,
           typename Value,
           uint32_t CGSize          = 8,
+          bool IsVectorLoad        = sizeof(Key) == 4,
           typename Hash            = cuco::detail::MurmurHash3_32<Key>,
           cuda::thread_scope Scope = cuda::thread_scope_device>
-class linear_probing : public probe_sequence_base<Key, Value, CGSize, Scope> {
+class linear_probing : public probe_sequence_base<Key, Value, CGSize, IsVectorLoad, Scope> {
  public:
-  using iterator = typename probe_sequence_base<Key, Value, CGSize, Scope>::iterator;
-  using probe_sequence_base<Key, Value, CGSize, Scope>::capacity_;
-  using probe_sequence_base<Key, Value, CGSize, Scope>::slots_;
-  using probe_sequence_base<Key, Value, CGSize, Scope>::cg_size;
+  using iterator = typename probe_sequence_base<Key, Value, CGSize, IsVectorLoad, Scope>::iterator;
+  using probe_sequence_base<Key, Value, CGSize, IsVectorLoad, Scope>::capacity_;
+  using probe_sequence_base<Key, Value, CGSize, IsVectorLoad, Scope>::slots_;
+  using probe_sequence_base<Key, Value, CGSize, IsVectorLoad, Scope>::cg_size;
+  using probe_sequence_base<Key, Value, CGSize, IsVectorLoad, Scope>::is_vector_load;
 
   __host__ __device__ explicit linear_probing(iterator slots, std::size_t capacity)
-    : probe_sequence_base<Key, Value, CGSize, Scope>{slots, capacity}
+    : probe_sequence_base<Key, Value, CGSize, IsVectorLoad, Scope>{slots, capacity}
   {
   }
 
   template <typename CG>
   __device__ iterator initial_slot(CG const& g, Key const k) noexcept
   {
-    return &slots_[(hash_(k) + g.thread_rank() * 2) % capacity_];
+    if constexpr (IsVectorLoad) {
+      return &slots_[(hash_(k) + g.thread_rank() * 2) % capacity_];
+    } else {
+      return &slots_[(hash_(k) + g.thread_rank()) % capacity_];
+    }
   }
 
   __device__ iterator next_slot(iterator s) noexcept
   {
     std::size_t index = s - slots_;
-    return &slots_[(index + cg_size() * 2) % capacity_];
+    if constexpr (IsVectorLoad) {
+      return &slots_[(index + cg_size() * 2) % capacity_];
+    } else {
+      return &slots_[(index + cg_size()) % capacity_];
+    }
   }
 
  private:

From e568706781cb8510077389c92095495ee1266b2c Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Tue, 1 Jun 2021 17:38:29 -0400
Subject: [PATCH 119/267] Use is_vector_load & is_outer template booleans to
 simplify probing method switching

---
 .../static_multimap/find_all_bench.cu         |   5 +-
 include/cuco/detail/prime.hpp                 |   4 +-
 include/cuco/detail/static_multimap.inl       | 172 +++-
 .../cuco/detail/static_multimap_kernels.cuh   | 734 ++++++++++++------
 include/cuco/static_multimap.cuh              |  54 +-
 5 files changed, 700 insertions(+), 269 deletions(-)

diff --git a/benchmarks/hash_table/static_multimap/find_all_bench.cu b/benchmarks/hash_table/static_multimap/find_all_bench.cu
index a7bb02cd8..5a0ab0fa8 100644
--- a/benchmarks/hash_table/static_multimap/find_all_bench.cu
+++ b/benchmarks/hash_table/static_multimap/find_all_bench.cu
@@ -53,6 +53,9 @@ std::enable_if_t<(sizeof(Key) == sizeof(Value)), void> nvbench_find_all(
   std::size_t const size     = num_keys / occupancy;
   std::size_t const num_reps = state.get_int64("NumReps");
 
+  constexpr bool is_vector_load = true;
+  constexpr bool is_outer       = true;
+
   std::vector<Key> h_keys(num_keys);
   std::vector<cuco::pair_type<Key, Value>> h_pairs(num_keys);
 
@@ -102,7 +105,7 @@ std::enable_if_t<(sizeof(Key) == sizeof(Value)), void> nvbench_find_all(
       CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_items, sizeof(atomic_ctr_type), device_id));
 
       // Use timers to explicitly mark the target region
-      cuco::detail::retrieve_outer<block_size, CGSize, buffer_size, Key, Value>
+      cuco::detail::retrieve<block_size, CGSize, buffer_size, Key, Value, is_vector_load, is_outer>
         <<<grid_size, block_size, 0, launch.get_stream()>>>(d_unique_keys.begin(),
                                                             d_unique_keys.end(),
                                                             d_results.data().get(),
diff --git a/include/cuco/detail/prime.hpp b/include/cuco/detail/prime.hpp
index 0c8365edf..6dd96fa71 100644
--- a/include/cuco/detail/prime.hpp
+++ b/include/cuco/detail/prime.hpp
@@ -69,8 +69,8 @@ constexpr std::size_t compute_prime(std::size_t num) noexcept
 template <std::size_t CGSize>
 constexpr std::size_t get_valid_capacity(std::size_t capacity) noexcept
 {
-  auto const min_prime = compute_prime(SDIV(capacity, CGSize * 2));
-  return min_prime * CGSize * 2;
+  auto const min_prime = compute_prime(SDIV(capacity, CGSize));
+  return min_prime * CGSize;
 }
 
 }  // namespace detail
diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index 0f27f0cd2..effc706c5 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -35,11 +35,16 @@ template <typename Key,
           typename Allocator>
 static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::static_multimap(
   std::size_t capacity, Key empty_key_sentinel, Value empty_value_sentinel, Allocator const& alloc)
-  : capacity_{cuco::detail::get_valid_capacity<cg_size()>(capacity)},
-    empty_key_sentinel_{empty_key_sentinel},
+  : empty_key_sentinel_{empty_key_sentinel},
     empty_value_sentinel_{empty_value_sentinel},
     slot_allocator_{alloc}
 {
+  if constexpr (is_vector_load()) {
+    capacity_ = cuco::detail::get_valid_capacity<cg_size() * 2>(capacity);
+  } else {
+    capacity_ = cuco::detail::get_valid_capacity<cg_size()>(capacity);
+  }
+
   slots_ = std::allocator_traits<slot_allocator_type>::allocate(slot_allocator_, get_capacity());
 
   auto constexpr block_size = 256;
@@ -76,7 +81,7 @@ void static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::insert(InputI
   auto const grid_size  = (cg_size() * num_keys + stride * block_size - 1) / (stride * block_size);
   auto view             = get_device_mutable_view();
 
-  detail::insert<block_size, cg_size()>
+  detail::insert<block_size, cg_size(), is_vector_load()>
     <<<grid_size, block_size, 0, stream>>>(first, first + num_keys, view, key_equal);
   CUCO_CUDA_TRY(cudaDeviceSynchronize());
 }
@@ -96,7 +101,7 @@ void static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::contains(
   auto const grid_size  = (cg_size() * num_keys + stride * block_size - 1) / (stride * block_size);
   auto view             = get_device_view();
 
-  detail::contains<block_size, cg_size>
+  detail::contains<block_size, cg_size(), is_vector_load()>
     <<<grid_size, block_size, 0, stream>>>(first, last, output_begin, view, key_equal);
   CUCO_CUDA_TRY(cudaDeviceSynchronize());
 }
@@ -125,7 +130,7 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::count(
   CUCO_CUDA_TRY(cudaGetDevice(&device_id));
   CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_items, sizeof(atomic_ctr_type), device_id));
 
-  detail::count<block_size, cg_size(), Key, Value>
+  detail::count<block_size, cg_size(), Key, Value, is_vector_load()>
     <<<grid_size, block_size, 0, stream>>>(first, last, num_items, view, key_equal);
   CUCO_CUDA_TRY(cudaDeviceSynchronize());
 
@@ -150,6 +155,8 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::count_
   auto const grid_size  = (cg_size() * num_keys + stride * block_size - 1) / (stride * block_size);
   auto view             = get_device_view();
 
+  constexpr bool is_outer = true;
+
   atomic_ctr_type* num_items;
   CUCO_CUDA_TRY(cudaMallocManaged(&num_items, sizeof(atomic_ctr_type)));
   *num_items = 0;
@@ -157,7 +164,7 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::count_
   CUCO_CUDA_TRY(cudaGetDevice(&device_id));
   CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_items, sizeof(atomic_ctr_type), device_id));
 
-  detail::count_outer<block_size, cg_size(), Key, Value>
+  detail::count<block_size, cg_size(), Key, Value, is_vector_load(), is_outer>
     <<<grid_size, block_size, 0, stream>>>(first, last, num_items, view, key_equal);
   CUCO_CUDA_TRY(cudaDeviceSynchronize());
 
@@ -189,7 +196,7 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::pair_c
   CUCO_CUDA_TRY(cudaGetDevice(&device_id));
   CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_items, sizeof(atomic_ctr_type), device_id));
 
-  detail::pair_count<block_size, cg_size(), Key, Value>
+  detail::pair_count<block_size, cg_size(), Key, Value, is_vector_load()>
     <<<grid_size, block_size, 0, stream>>>(first, last, num_items, view, key_equal, val_equal);
   CUCO_CUDA_TRY(cudaDeviceSynchronize());
 
@@ -214,6 +221,8 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::pair_c
   auto const grid_size  = (cg_size() * num_keys + stride * block_size - 1) / (stride * block_size);
   auto view             = get_device_view();
 
+  constexpr bool is_outer = true;
+
   atomic_ctr_type* num_items;
   CUCO_CUDA_TRY(cudaMallocManaged(&num_items, sizeof(atomic_ctr_type)));
   *num_items = 0;
@@ -221,7 +230,7 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::pair_c
   CUCO_CUDA_TRY(cudaGetDevice(&device_id));
   CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_items, sizeof(atomic_ctr_type), device_id));
 
-  detail::pair_count_outer<block_size, cg_size(), Key, Value>
+  detail::pair_count<block_size, cg_size(), Key, Value, is_vector_load(), is_outer>
     <<<grid_size, block_size, 0, stream>>>(first, last, num_items, view, key_equal, val_equal);
   CUCO_CUDA_TRY(cudaDeviceSynchronize());
 
@@ -240,12 +249,16 @@ template <typename InputIt, typename OutputIt, typename KeyEqual>
 OutputIt static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::retrieve(
   InputIt first, InputIt last, OutputIt output_begin, cudaStream_t stream, KeyEqual key_equal)
 {
-  auto num_keys          = std::distance(first, last);
-  auto const block_size  = 128;
-  auto const buffer_size = block_size * 3;
-  auto const stride      = 1;
-  auto const grid_size   = (cg_size() * num_keys + stride * block_size - 1) / (stride * block_size);
-  auto view              = get_device_view();
+  auto num_keys         = std::distance(first, last);
+  auto const block_size = 128;
+  // Using per-block buffer for vector loads and per-CG buffer for scalar loads
+  auto const buffer_size = [&]() {
+    if constexpr (is_vector_load()) { return block_size * 3u; }
+    return cg_size() * 3u;
+  }();
+  auto const stride    = 1;
+  auto const grid_size = (cg_size() * num_keys + stride * block_size - 1) / (stride * block_size);
+  auto view            = get_device_view();
 
   atomic_ctr_type* num_items;
   CUCO_CUDA_TRY(cudaMallocManaged(&num_items, sizeof(atomic_ctr_type)));
@@ -254,7 +267,7 @@ OutputIt static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::retrieve(
   CUCO_CUDA_TRY(cudaGetDevice(&device_id));
   CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_items, sizeof(atomic_ctr_type), device_id));
 
-  detail::retrieve<block_size, cg_size(), buffer_size, Key, Value>
+  detail::retrieve<block_size, cg_size(), buffer_size, Key, Value, is_vector_load()>
     <<<grid_size, block_size, 0, stream>>>(first, last, output_begin, num_items, view, key_equal);
   CUCO_CUDA_TRY(cudaDeviceSynchronize());
 
@@ -273,12 +286,18 @@ template <typename InputIt, typename OutputIt, typename KeyEqual>
 OutputIt static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::retrieve_outer(
   InputIt first, InputIt last, OutputIt output_begin, cudaStream_t stream, KeyEqual key_equal)
 {
-  auto num_keys          = std::distance(first, last);
-  auto const block_size  = 128;
-  auto const buffer_size = block_size * 3;
-  auto const stride      = 1;
-  auto const grid_size   = (cg_size() * num_keys + stride * block_size - 1) / (stride * block_size);
-  auto view              = get_device_view();
+  auto num_keys         = std::distance(first, last);
+  auto const block_size = 128;
+  // Using per-block buffer for vector loads and per-CG buffer for scalar loads
+  auto const buffer_size = [&]() {
+    if constexpr (is_vector_load()) { return block_size * 3u; }
+    return cg_size() * 3u;
+  }();
+  auto const stride    = 1;
+  auto const grid_size = (cg_size() * num_keys + stride * block_size - 1) / (stride * block_size);
+  auto view            = get_device_view();
+
+  constexpr bool is_outer = true;
 
   atomic_ctr_type* num_items;
   CUCO_CUDA_TRY(cudaMallocManaged(&num_items, sizeof(atomic_ctr_type)));
@@ -287,7 +306,7 @@ OutputIt static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::retrieve_
   CUCO_CUDA_TRY(cudaGetDevice(&device_id));
   CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_items, sizeof(atomic_ctr_type), device_id));
 
-  detail::retrieve_outer<block_size, cg_size(), buffer_size, Key, Value>
+  detail::retrieve<block_size, cg_size(), buffer_size, Key, Value, is_vector_load(), is_outer>
     <<<grid_size, block_size, 0, stream>>>(first, last, output_begin, num_items, view, key_equal);
   CUCO_CUDA_TRY(cudaDeviceSynchronize());
 
@@ -302,8 +321,8 @@ template <typename Key,
           class ProbeSequence,
           cuda::thread_scope Scope,
           typename Allocator>
-template <typename CG, typename KeyEqual>
-__device__ void
+template <bool is_vector_load, typename CG, typename KeyEqual>
+__device__ std::enable_if_t<is_vector_load, void>
 static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_mutable_view::insert(
   CG g, value_type const& insert_pair, KeyEqual key_equal) noexcept
 {
@@ -375,8 +394,76 @@ template <typename Key,
           class ProbeSequence,
           cuda::thread_scope Scope,
           typename Allocator>
-template <typename CG, typename KeyEqual>
-__device__ bool static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::contains(
+template <bool is_vector_load, typename CG, typename KeyEqual>
+__device__ std::enable_if_t<not is_vector_load, void>
+static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_mutable_view::insert(
+  CG g, value_type const& insert_pair, KeyEqual key_equal) noexcept
+{
+  auto current_slot = initial_slot(g, insert_pair.first);
+
+  while (true) {
+    key_type const existing_key = current_slot->first.load(cuda::memory_order_relaxed);
+
+    // The user provide `key_equal` can never be used to compare against `empty_key_sentinel` as the
+    // sentinel is not a valid key value. Therefore, first check for the sentinel
+    auto const slot_is_empty         = (existing_key == this->get_empty_key_sentinel());
+    auto const window_contains_empty = g.ballot(slot_is_empty);
+
+    if (window_contains_empty) {
+      // the first lane in the group with an empty slot will attempt the insert
+      insert_result status{insert_result::CONTINUE};
+      uint32_t src_lane = __ffs(window_contains_empty) - 1;
+
+      if (g.thread_rank() == src_lane) {
+        using cuda::std::memory_order_relaxed;
+        auto expected_key   = this->get_empty_key_sentinel();
+        auto expected_value = this->get_empty_value_sentinel();
+        auto& slot_key      = current_slot->first;
+        auto& slot_value    = current_slot->second;
+
+        bool key_success =
+          slot_key.compare_exchange_strong(expected_key, insert_pair.first, memory_order_relaxed);
+        bool value_success = slot_value.compare_exchange_strong(
+          expected_value, insert_pair.second, memory_order_relaxed);
+
+        if (key_success) {
+          while (not value_success) {
+            value_success =
+              slot_value.compare_exchange_strong(expected_value = this->get_empty_value_sentinel(),
+                                                 insert_pair.second,
+                                                 memory_order_relaxed);
+          }
+          status = insert_result::SUCCESS;
+        } else if (value_success) {
+          slot_value.store(this->get_empty_value_sentinel(), memory_order_relaxed);
+        }
+
+        // another key was inserted in the slot we wanted to try
+        // so we need to try the next empty slot in the window
+      }
+
+      // successful insert
+      if (g.any(status == insert_result::SUCCESS)) { return; }
+      // if we've gotten this far, a different key took our spot
+      // before we could insert. We need to retry the insert on the
+      // same window
+    }
+    // if there are no empty slots in the current window,
+    // we move onto the next window
+    else {
+      current_slot = next_slot(current_slot);
+    }
+  }
+}
+
+template <typename Key,
+          typename Value,
+          class ProbeSequence,
+          cuda::thread_scope Scope,
+          typename Allocator>
+template <bool is_vector_load, typename CG, typename KeyEqual>
+__device__ std::enable_if_t<is_vector_load, bool>
+static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::contains(
   CG g, Key const& k, KeyEqual key_equal) noexcept
 {
   auto current_slot = initial_slot(g, k);
@@ -408,4 +495,37 @@ __device__ bool static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::de
   }
 }
 
+template <typename Key,
+          typename Value,
+          class ProbeSequence,
+          cuda::thread_scope Scope,
+          typename Allocator>
+template <bool is_vector_load, typename CG, typename KeyEqual>
+__device__ std::enable_if_t<not is_vector_load, bool>
+static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::contains(
+  CG g, Key const& k, KeyEqual key_equal) noexcept
+{
+  auto current_slot = initial_slot(g, k);
+
+  while (true) {
+    key_type const existing_key = current_slot->first.load(cuda::std::memory_order_relaxed);
+
+    // The user provide `key_equal` can never be used to compare against `empty_key_sentinel` as the
+    // sentinel is not a valid key value. Therefore, first check for the sentinel
+    auto const slot_is_empty = (existing_key == this->get_empty_key_sentinel());
+
+    auto const equals = (not slot_is_empty and key_equal(existing_key, k));
+
+    // the key we were searching for was found by one of the threads, so we return true
+    if (g.any(equals)) { return true; }
+
+    // we found an empty slot, meaning that the key we're searching for isn't present
+    if (g.any(slot_is_empty)) { return false; }
+
+    // otherwise, all slots in the current window are full with other keys, so we move onto the next
+    // window
+    current_slot = next_slot(current_slot);
+  }
+}
+
 }  // namespace cuco
diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap_kernels.cuh
index 156358a8e..bb09565f5 100644
--- a/include/cuco/detail/static_multimap_kernels.cuh
+++ b/include/cuco/detail/static_multimap_kernels.cuh
@@ -25,7 +25,81 @@ namespace detail {
 namespace cg = cooperative_groups;
 
 /**
- * @brief Flushes shared memory buffer into the output sequence.
+ * @brief Flushes per-CG shared memory buffer into the output sequence.
+ *
+ * @tparam Key key type
+ * @tparam Value value type
+ * @tparam atomicT Type of atomic storage
+ * @tparam OutputIt Device accessible output iterator whose `value_type` is
+ * convertible to the map's `mapped_type`
+ * @param output_size Number of valid output in the buffer
+ * @param output_buffer Shared memory buffer of the key/value pair sequence
+ * @param num_items Size of the output sequence
+ * @param output_begin Beginning of the output sequence of key/value pairs
+ */
+template <uint32_t cg_size,
+          typename CG,
+          typename Key,
+          typename Value,
+          typename atomicT,
+          typename OutputIt>
+__inline__ __device__ std::enable_if_t<thrust::is_contiguous_iterator<OutputIt>::value, void>
+flush_output_buffer(CG const& g,
+                    uint32_t const num_outputs,
+                    cuco::pair_type<Key, Value>* output_buffer,
+                    atomicT* num_items,
+                    OutputIt output_begin)
+{
+  std::size_t offset;
+  const auto lane_id = g.thread_rank();
+  if (0 == lane_id) { offset = num_items->fetch_add(num_outputs, cuda::std::memory_order_relaxed); }
+  offset = g.shfl(offset, 0);
+
+  cg::memcpy_async(g,
+                   output_begin + offset,
+                   output_buffer,
+                   cuda::aligned_size_t<alignof(cuco::pair_type<Key, Value>)>(
+                     sizeof(cuco::pair_type<Key, Value>) * num_outputs));
+}
+
+/**
+ * @brief Flushes per-CG shared memory buffer into the output sequence.
+ *
+ * @tparam Key key type
+ * @tparam Value value type
+ * @tparam atomicT Type of atomic storage
+ * @tparam OutputIt Device accessible output iterator whose `value_type` is
+ * convertible to the map's `mapped_type`
+ * @param output_size Number of valid output in the buffer
+ * @param output_buffer Shared memory buffer of the key/value pair sequence
+ * @param num_items Size of the output sequence
+ * @param output_begin Beginning of the output sequence of key/value pairs
+ */
+template <uint32_t cg_size,
+          typename CG,
+          typename Key,
+          typename Value,
+          typename atomicT,
+          typename OutputIt>
+__inline__ __device__ std::enable_if_t<not thrust::is_contiguous_iterator<OutputIt>::value, void>
+flush_output_buffer(CG const& g,
+                    uint32_t const num_outputs,
+                    cuco::pair_type<Key, Value>* output_buffer,
+                    atomicT* num_items,
+                    OutputIt output_begin)
+{
+  std::size_t offset;
+  const auto lane_id = g.thread_rank();
+  if (0 == lane_id) { offset = num_items->fetch_add(num_outputs, cuda::std::memory_order_relaxed); }
+  offset = g.shfl(offset, 0);
+
+  for (auto index = lane_id; index < num_outputs; index += cg_size) {
+    *(output_begin + offset + index) = output_buffer[index];
+  }
+}
+
+/**
+ * @brief Flushes per-block shared memory buffer into the output sequence.
  *
  * @tparam Key key type
  * @tparam Value value type
@@ -108,6 +182,7 @@ __global__ void initialize(pair_atomic_type* const slots, Key k, Value v, std::s
  */
 template <uint32_t block_size,
           uint32_t tile_size,
+          bool is_vector_load,
           typename InputIt,
           typename viewT,
           typename KeyEqual>
@@ -120,7 +195,7 @@ __global__ void insert(InputIt first, InputIt last, viewT view, KeyEqual key_equ
   while (it < last) {
     // force conversion to value_type
     typename viewT::value_type const insert_pair{*it};
-    view.insert(tile, insert_pair, key_equal);
+    view.insert<is_vector_load>(tile, insert_pair, key_equal);
     it += (gridDim.x * block_size) / tile_size;
   }
 }
@@ -149,6 +224,7 @@ __global__ void insert(InputIt first, InputIt last, viewT view, KeyEqual key_equ
  */
 template <uint32_t block_size,
           uint32_t tile_size,
+          bool is_vector_load,
           typename InputIt,
           typename OutputIt,
           typename viewT,
@@ -163,7 +239,7 @@ __global__ void contains(
 
   while (first + key_idx < last) {
     auto key   = *(first + key_idx);
-    auto found = view.contains(tile, key, key_equal);
+    auto found = view.contains<is_vector_load>(tile, key, key_equal);
 
     /*
      * The ld.relaxed.gpu instruction used in view.find causes L1 to
@@ -182,7 +258,8 @@ __global__ void contains(
 }
 
 /**
- * @brief Counts the occurrences of keys in `[first, last)` contained in the multimap.
+ * @brief Counts the occurrences of keys in `[first, last)` contained in the multimap. If is_outer
+ * is true, the corresponding occurrence for non-matches is 1. Otherwise, it's 0.
  *
  * @tparam block_size The size of the thread block
  * @tparam tile_size The number of threads in the Cooperative Groups used to perform counts
@@ -203,11 +280,13 @@ template <uint32_t block_size,
           uint32_t tile_size,
           typename Key,
           typename Value,
+          bool is_vector_load,
+          bool is_outer = false,
           typename InputIt,
           typename atomicT,
           typename viewT,
           typename KeyEqual>
-__global__ void count(
+__global__ std::enable_if_t<is_vector_load, void> count(
   InputIt first, InputIt last, atomicT* num_items, viewT view, KeyEqual key_equal)
 {
   auto tile    = cg::tiled_partition<tile_size>(cg::this_thread_block());
@@ -222,26 +301,57 @@ __global__ void count(
     auto key          = *(first + key_idx);
     auto current_slot = view.initial_slot(tile, key);
 
-    while (true) {
-      pair<Key, Value> arr[2];
-      if constexpr (sizeof(Key) == 4) {
-        auto const tmp = *reinterpret_cast<uint4 const*>(current_slot);
-        memcpy(&arr[0], &tmp, 2 * sizeof(pair<Key, Value>));
-      } else {
-        auto const tmp = *reinterpret_cast<ulonglong4 const*>(current_slot);
-        memcpy(&arr[0], &tmp, 2 * sizeof(pair<Key, Value>));
+    if constexpr (is_outer) {
+      bool found_match = false;
+
+      while (true) {
+        pair<Key, Value> arr[2];
+        if constexpr (sizeof(Key) == 4) {
+          auto const tmp = *reinterpret_cast<uint4 const*>(current_slot);
+          memcpy(&arr[0], &tmp, 2 * sizeof(pair<Key, Value>));
+        } else {
+          auto const tmp = *reinterpret_cast<ulonglong4 const*>(current_slot);
+          memcpy(&arr[0], &tmp, 2 * sizeof(pair<Key, Value>));
+        }
+
+        auto const first_slot_is_empty  = (arr[0].first == view.get_empty_key_sentinel());
+        auto const second_slot_is_empty = (arr[1].first == view.get_empty_key_sentinel());
+        auto const first_equals  = (not first_slot_is_empty and key_equal(arr[0].first, key));
+        auto const second_equals = (not second_slot_is_empty and key_equal(arr[1].first, key));
+
+        found_match = tile.any(first_equals or second_equals);
+
+        thread_num_items += (first_equals + second_equals);
+
+        if (tile.any(first_slot_is_empty or second_slot_is_empty)) {
+          if ((not found_match) && (tile.thread_rank() == 0)) { thread_num_items++; }
+          break;
+        }
+
+        current_slot = view.next_slot(current_slot);
       }
+    } else {
+      while (true) {
+        pair<Key, Value> arr[2];
+        if constexpr (sizeof(Key) == 4) {
+          auto const tmp = *reinterpret_cast<uint4 const*>(current_slot);
+          memcpy(&arr[0], &tmp, 2 * sizeof(pair<Key, Value>));
+        } else {
+          auto const tmp = *reinterpret_cast<ulonglong4 const*>(current_slot);
+          memcpy(&arr[0], &tmp, 2 * sizeof(pair<Key, Value>));
+        }
 
-      auto const first_slot_is_empty  = (arr[0].first == view.get_empty_key_sentinel());
-      auto const second_slot_is_empty = (arr[1].first == view.get_empty_key_sentinel());
-      auto const first_equals         = (not first_slot_is_empty and key_equal(arr[0].first, key));
-      auto const second_equals        = (not second_slot_is_empty and key_equal(arr[1].first, key));
+        auto const first_slot_is_empty  = (arr[0].first == view.get_empty_key_sentinel());
+        auto const second_slot_is_empty = (arr[1].first == view.get_empty_key_sentinel());
+        auto const first_equals  = (not first_slot_is_empty and key_equal(arr[0].first, key));
+        auto const second_equals = (not second_slot_is_empty and key_equal(arr[1].first, key));
 
-      thread_num_items += (first_equals + second_equals);
+        thread_num_items += (first_equals + second_equals);
 
-      if (tile.any(first_slot_is_empty or second_slot_is_empty)) { break; }
+        if (tile.any(first_slot_is_empty or second_slot_is_empty)) { break; }
 
-      current_slot = view.next_slot(current_slot);
+        current_slot = view.next_slot(current_slot);
+      }
     }
     key_idx += (gridDim.x * block_size) / tile_size;
   }
@@ -253,8 +363,8 @@ __global__ void count(
 }
 
 /**
- * @brief Counts the occurrences of keys in `[first, last)` contained in the multimap. If no matches
- * can be found for a given key, the corresponding occurrence is 1.
+ * @brief Counts the occurrences of keys in `[first, last)` contained in the multimap. If is_outer
+ * is true, the corresponding occurrence for non-matches is 1. Otherwise, it's 0.
  *
  * @tparam block_size The size of the thread block
  * @tparam tile_size The number of threads in the Cooperative Groups used to perform counts
@@ -275,11 +385,13 @@ template <uint32_t block_size,
           uint32_t tile_size,
           typename Key,
           typename Value,
+          bool is_vector_load,
+          bool is_outer = false,
           typename InputIt,
           typename atomicT,
           typename viewT,
           typename KeyEqual>
-__global__ void count_outer(
+__global__ std::enable_if_t<not is_vector_load, void> count(
   InputIt first, InputIt last, atomicT* num_items, viewT view, KeyEqual key_equal)
 {
   auto tile    = cg::tiled_partition<tile_size>(cg::this_thread_block());
@@ -294,46 +406,53 @@ __global__ void count_outer(
     auto key          = *(first + key_idx);
     auto current_slot = view.initial_slot(tile, key);
 
-    bool found_match = false;
+    if constexpr (is_outer) {
+      bool found_match = false;
 
-    while (true) {
-      pair<Key, Value> arr[2];
-      if constexpr (sizeof(Key) == 4) {
-        auto const tmp = *reinterpret_cast<uint4 const*>(current_slot);
-        memcpy(&arr[0], &tmp, 2 * sizeof(pair<Key, Value>));
-      } else {
-        auto const tmp = *reinterpret_cast<ulonglong4 const*>(current_slot);
-        memcpy(&arr[0], &tmp, 2 * sizeof(pair<Key, Value>));
-      }
+      while (true) {
+        pair<Key, Value> slot_contents =
+          *reinterpret_cast<cuco::pair_type<Key, Value> const*>(current_slot);
+        auto const& current_key = slot_contents.first;
+
+        auto const slot_is_empty = (current_key == view.get_empty_key_sentinel());
+        auto const equals        = not slot_is_empty and key_equal(current_key, key);
 
-      auto const first_slot_is_empty  = (arr[0].first == view.get_empty_key_sentinel());
-      auto const second_slot_is_empty = (arr[1].first == view.get_empty_key_sentinel());
-      auto const first_equals         = (not first_slot_is_empty and key_equal(arr[0].first, key));
-      auto const second_equals        = (not second_slot_is_empty and key_equal(arr[1].first, key));
-      auto const exists               = tile.any(first_equals or second_equals);
+        found_match = tile.any(equals);
 
-      if (exists) { found_match = true; }
+        thread_num_items += equals;
 
-      thread_num_items += (first_equals + second_equals);
+        if (tile.any(slot_is_empty)) {
+          if ((not found_match) && (tile.thread_rank() == 0)) { thread_num_items++; }
+          break;
+        }
 
-      if (tile.any(first_slot_is_empty or second_slot_is_empty)) {
-        if ((not found_match) && (tile.thread_rank() == 0)) { thread_num_items++; }
-        break;
+        current_slot = view.next_slot(current_slot);
       }
+    } else {
+      while (true) {
+        pair<Key, Value> slot_contents =
+          *reinterpret_cast<cuco::pair_type<Key, Value> const*>(current_slot);
+        auto const& current_key = slot_contents.first;
 
-      current_slot = view.next_slot(current_slot);
+        auto const slot_is_empty = (current_key == view.get_empty_key_sentinel());
+        auto const equals        = not slot_is_empty and key_equal(current_key, key);
+
+        thread_num_items += equals;
+
+        if (tile.any(slot_is_empty)) { break; }
+
+        current_slot = view.next_slot(current_slot);
+      }
     }
     key_idx += (gridDim.x * block_size) / tile_size;
   }
-
-  // compute number of successfully inserted elements for each block
-  // and atomically add to the grand total
-  std::size_t block_num_items = BlockReduce(temp_storage).Sum(thread_num_items);
+  auto const block_num_items = BlockReduce(temp_storage).Sum(thread_num_items);
   if (threadIdx.x == 0) { num_items->fetch_add(block_num_items, cuda::std::memory_order_relaxed); }
 }
 
 /**
  * @brief Counts the occurrences of key/value pairs in `[first, last)` contained in the multimap.
+ * If no matches can be found for a given key/value pair, the corresponding occurrence is 1.
  *
  * @tparam block_size The size of the thread block
  * @tparam tile_size The number of threads in the Cooperative Groups used to perform counts
@@ -355,17 +474,19 @@ template <uint32_t block_size,
           uint32_t tile_size,
           typename Key,
           typename Value,
+          bool is_vector_load,
+          bool is_outer = false,
           typename InputIt,
           typename atomicT,
           typename viewT,
           typename KeyEqual,
           typename ValEqual>
-__global__ void pair_count(InputIt first,
-                           InputIt last,
-                           atomicT* num_items,
-                           viewT view,
-                           KeyEqual key_equal,
-                           ValEqual val_equal)
+__global__ std::enable_if_t<is_vector_load, void> pair_count(InputIt first,
+                                                             InputIt last,
+                                                             atomicT* num_items,
+                                                             viewT view,
+                                                             KeyEqual key_equal,
+                                                             ValEqual val_equal)
 {
   auto tile     = cg::tiled_partition<tile_size>(cg::this_thread_block());
   auto tid      = block_size * blockIdx.x + threadIdx.x;
@@ -381,34 +502,75 @@ __global__ void pair_count(InputIt first,
     auto value        = pair.second;
     auto current_slot = view.initial_slot(tile, key);
 
-    while (true) {
-      cuco::pair_type<Key, Value> arr[2];
-      if constexpr (sizeof(Key) == 4) {
-        auto const tmp = *reinterpret_cast<uint4 const*>(current_slot);
-        memcpy(&arr[0], &tmp, 2 * sizeof(cuco::pair_type<Key, Value>));
-      } else {
-        auto const tmp = *reinterpret_cast<ulonglong4 const*>(current_slot);
-        memcpy(&arr[0], &tmp, 2 * sizeof(cuco::pair_type<Key, Value>));
+    if constexpr (is_outer) {
+      bool found_match = false;
+
+      while (true) {
+        cuco::pair_type<Key, Value> arr[2];
+        if constexpr (sizeof(Key) == 4) {
+          auto const tmp = *reinterpret_cast<uint4 const*>(current_slot);
+          memcpy(&arr[0], &tmp, 2 * sizeof(cuco::pair_type<Key, Value>));
+        } else {
+          auto const tmp = *reinterpret_cast<ulonglong4 const*>(current_slot);
+          memcpy(&arr[0], &tmp, 2 * sizeof(cuco::pair_type<Key, Value>));
+        }
+
+        auto const first_key_is_empty  = (arr[0].first == view.get_empty_key_sentinel());
+        auto const second_key_is_empty = (arr[1].first == view.get_empty_key_sentinel());
+        auto const first_val_is_empty  = (arr[0].second == view.get_empty_value_sentinel());
+        auto const second_val_is_empty = (arr[1].second == view.get_empty_value_sentinel());
+
+        auto const first_key_equals  = (not first_key_is_empty and key_equal(arr[0].first, key));
+        auto const second_key_equals = (not second_key_is_empty and key_equal(arr[1].first, key));
+        auto const first_val_equals  = (not first_val_is_empty and key_equal(arr[0].second, value));
+        auto const second_val_equals =
+          (not second_val_is_empty and key_equal(arr[1].second, value));
+
+        auto const first_equals  = first_key_equals and first_val_equals;
+        auto const second_equals = second_key_equals and second_val_equals;
+
+        found_match = tile.any(first_equals or second_equals);
+
+        thread_num_items += (first_equals + second_equals);
+
+        if (tile.any(first_key_is_empty or second_key_is_empty)) {
+          if ((not found_match) && (tile.thread_rank() == 0)) { thread_num_items++; }
+          break;
+        }
+
+        current_slot = view.next_slot(current_slot);
       }
+    } else {
+      while (true) {
+        cuco::pair_type<Key, Value> arr[2];
+        if constexpr (sizeof(Key) == 4) {
+          auto const tmp = *reinterpret_cast<uint4 const*>(current_slot);
+          memcpy(&arr[0], &tmp, 2 * sizeof(cuco::pair_type<Key, Value>));
+        } else {
+          auto const tmp = *reinterpret_cast<ulonglong4 const*>(current_slot);
+          memcpy(&arr[0], &tmp, 2 * sizeof(cuco::pair_type<Key, Value>));
+        }
 
-      auto const first_key_is_empty  = (arr[0].first == view.get_empty_key_sentinel());
-      auto const second_key_is_empty = (arr[1].first == view.get_empty_key_sentinel());
-      auto const first_val_is_empty  = (arr[0].second == view.get_empty_value_sentinel());
-      auto const second_val_is_empty = (arr[1].second == view.get_empty_value_sentinel());
+        auto const first_key_is_empty  = (arr[0].first == view.get_empty_key_sentinel());
+        auto const second_key_is_empty = (arr[1].first == view.get_empty_key_sentinel());
+        auto const first_val_is_empty  = (arr[0].second == view.get_empty_value_sentinel());
+        auto const second_val_is_empty = (arr[1].second == view.get_empty_value_sentinel());
 
-      auto const first_key_equals  = (not first_key_is_empty and key_equal(arr[0].first, key));
-      auto const second_key_equals = (not second_key_is_empty and key_equal(arr[1].first, key));
-      auto const first_val_equals  = (not first_val_is_empty and key_equal(arr[0].second, value));
-      auto const second_val_equals = (not second_val_is_empty and key_equal(arr[1].second, value));
+        auto const first_key_equals  = (not first_key_is_empty and key_equal(arr[0].first, key));
+        auto const second_key_equals = (not second_key_is_empty and key_equal(arr[1].first, key));
+        auto const first_val_equals  = (not first_val_is_empty and key_equal(arr[0].second, value));
+        auto const second_val_equals =
+          (not second_val_is_empty and key_equal(arr[1].second, value));
 
-      auto const first_equals  = first_key_equals and first_val_equals;
-      auto const second_equals = second_key_equals and second_val_equals;
+        auto const first_equals  = first_key_equals and first_val_equals;
+        auto const second_equals = second_key_equals and second_val_equals;
 
-      thread_num_items += (first_equals + second_equals);
+        thread_num_items += (first_equals + second_equals);
 
-      if (tile.any(first_key_is_empty or second_key_is_empty)) { break; }
+        if (tile.any(first_key_is_empty or second_key_is_empty)) { break; }
 
-      current_slot = view.next_slot(current_slot);
+        current_slot = view.next_slot(current_slot);
+      }
     }
     pair_idx += (gridDim.x * block_size) / tile_size;
   }
@@ -443,17 +605,19 @@ template <uint32_t block_size,
           uint32_t tile_size,
           typename Key,
           typename Value,
+          bool is_vector_load,
+          bool is_outer = false,
           typename InputIt,
           typename atomicT,
           typename viewT,
           typename KeyEqual,
           typename ValEqual>
-__global__ void pair_count_outer(InputIt first,
-                                 InputIt last,
-                                 atomicT* num_items,
-                                 viewT view,
-                                 KeyEqual key_equal,
-                                 ValEqual val_equal)
+__global__ std::enable_if_t<not is_vector_load, void> pair_count(InputIt first,
+                                                                 InputIt last,
+                                                                 atomicT* num_items,
+                                                                 viewT view,
+                                                                 KeyEqual key_equal,
+                                                                 ValEqual val_equal)
 {
   auto tile     = cg::tiled_partition<tile_size>(cg::this_thread_block());
   auto tid      = block_size * blockIdx.x + threadIdx.x;
@@ -469,42 +633,49 @@ __global__ void pair_count_outer(InputIt first,
     auto value        = pair.second;
     auto current_slot = view.initial_slot(tile, key);
 
-    bool found_match = false;
+    if constexpr (is_outer) {
+      bool found_match = false;
 
-    while (true) {
-      cuco::pair_type<Key, Value> arr[2];
-      if constexpr (sizeof(Key) == 4) {
-        auto const tmp = *reinterpret_cast<uint4 const*>(current_slot);
-        memcpy(&arr[0], &tmp, 2 * sizeof(cuco::pair_type<Key, Value>));
-      } else {
-        auto const tmp = *reinterpret_cast<ulonglong4 const*>(current_slot);
-        memcpy(&arr[0], &tmp, 2 * sizeof(cuco::pair_type<Key, Value>));
-      }
+      while (true) {
+        auto slot_contents = *reinterpret_cast<cuco::pair_type<Key, Value> const*>(current_slot);
+
+        auto const key_is_empty = (slot_contents.first == view.get_empty_key_sentinel());
+        auto const val_is_empty = (slot_contents.second == view.get_empty_value_sentinel());
 
-      auto const first_key_is_empty  = (arr[0].first == view.get_empty_key_sentinel());
-      auto const second_key_is_empty = (arr[1].first == view.get_empty_key_sentinel());
-      auto const first_val_is_empty  = (arr[0].second == view.get_empty_value_sentinel());
-      auto const second_val_is_empty = (arr[1].second == view.get_empty_value_sentinel());
+        auto const key_equals = (not key_is_empty and key_equal(slot_contents.first, key));
+        auto const val_equals = (not val_is_empty and key_equal(slot_contents.second, value));
 
-      auto const first_key_equals  = (not first_key_is_empty and key_equal(arr[0].first, key));
-      auto const second_key_equals = (not second_key_is_empty and key_equal(arr[1].first, key));
-      auto const first_val_equals  = (not first_val_is_empty and key_equal(arr[0].second, value));
-      auto const second_val_equals = (not second_val_is_empty and key_equal(arr[1].second, value));
+        auto const equals = key_equals and val_equals;
 
-      auto const first_equals  = first_key_equals and first_val_equals;
-      auto const second_equals = second_key_equals and second_val_equals;
-      auto const exists        = tile.any(first_equals or second_equals);
+        found_match = tile.any(equals);
 
-      if (exists) { found_match = true; }
+        thread_num_items += equals;
 
-      thread_num_items += (first_equals + second_equals);
+        if (tile.any(key_is_empty)) {
+          if ((not found_match) && (tile.thread_rank() == 0)) { thread_num_items++; }
+          break;
+        }
 
-      if (tile.any(first_key_is_empty or second_key_is_empty)) {
-        if ((not found_match) && (tile.thread_rank() == 0)) { thread_num_items++; }
-        break;
+        current_slot = view.next_slot(current_slot);
       }
+    } else {
+      while (true) {
+        auto slot_contents = *reinterpret_cast<cuco::pair_type<Key, Value> const*>(current_slot);
+
+        auto const key_is_empty = (slot_contents.first == view.get_empty_key_sentinel());
+        auto const val_is_empty = (slot_contents.second == view.get_empty_value_sentinel());
+
+        auto const key_equals = (not key_is_empty and key_equal(slot_contents.first, key));
+        auto const val_equals = (not val_is_empty and key_equal(slot_contents.second, value));
+
+        auto const equals = key_equals and val_equals;
 
-      current_slot = view.next_slot(current_slot);
+        thread_num_items += equals;
+
+        if (tile.any(key_is_empty)) { break; }
+
+        current_slot = view.next_slot(current_slot);
+      }
     }
     pair_idx += (gridDim.x * block_size) / tile_size;
   }
@@ -519,7 +690,8 @@ __global__ void pair_count_outer(InputIt first,
  * @brief Finds all the values corresponding to all keys in the range `[first, last)`.
  *
  * If the key `k = *(first + i)` exists in the map, copies `k` and all associated values to
- * unspecified locations in `[output_begin, output_begin + *num_items - 1)`. Else, does nothing.
+ * unspecified locations in `[output_begin, output_begin + *num_items - 1)`. Else, copies `k` and
+ * the empty value sentinel.
  *
  * Behavior is undefined if the total number of matching keys exceeds `std::distance(output_begin,
  * output_begin + *num_items - 1)`. Use `count()` to determine the number of matching keys.
@@ -548,17 +720,19 @@ template <uint32_t block_size,
           uint32_t buffer_size,
           typename Key,
           typename Value,
+          bool is_vector_load,
+          bool is_outer = false,
           typename InputIt,
           typename OutputIt,
           typename atomicT,
           typename viewT,
           typename KeyEqual>
-__global__ void retrieve(InputIt first,
-                         InputIt last,
-                         OutputIt output_begin,
-                         atomicT* num_items,
-                         viewT view,
-                         KeyEqual key_equal)
+__global__ std::enable_if_t<is_vector_load, void> retrieve(InputIt first,
+                                                           InputIt last,
+                                                           OutputIt output_begin,
+                                                           atomicT* num_items,
+                                                           viewT view,
+                                                           KeyEqual key_equal)
 {
   auto tile    = cg::tiled_partition<tile_size>(cg::this_thread_block());
   auto tid     = block_size * blockIdx.x + threadIdx.x;
@@ -577,62 +751,134 @@ __global__ void retrieve(InputIt first,
 
     bool running = ((first + key_idx) < last);
 
-    if (running) { current_slot = view.initial_slot(tile, key); }
+    if constexpr (is_outer) {
+      bool found_match = false;
 
-    while (__syncthreads_or(running)) {
-      if (running) {
-        pair<Key, Value> arr[2];
-        if constexpr (sizeof(Key) == 4) {
-          auto const tmp = *reinterpret_cast<uint4 const*>(current_slot);
-          memcpy(&arr[0], &tmp, 2 * sizeof(pair<Key, Value>));
-        } else {
-          auto const tmp = *reinterpret_cast<ulonglong4 const*>(current_slot);
-          memcpy(&arr[0], &tmp, 2 * sizeof(pair<Key, Value>));
-        }
+      if (running) { current_slot = view.initial_slot(tile, key); }
 
-        auto const first_slot_is_empty  = (arr[0].first == view.get_empty_key_sentinel());
-        auto const second_slot_is_empty = (arr[1].first == view.get_empty_key_sentinel());
-        auto const first_equals  = (not first_slot_is_empty and key_equal(arr[0].first, key));
-        auto const second_equals = (not second_slot_is_empty and key_equal(arr[1].first, key));
-        auto const first_exists  = tile.ballot(first_equals);
-        auto const second_exists = tile.ballot(second_equals);
-
-        if (first_exists or second_exists) {
-          auto num_first_matches  = __popc(first_exists);
-          auto num_second_matches = __popc(second_exists);
-
-          uint32_t output_idx;
-          if (0 == lane_id) {
-            output_idx = atomicAdd_block(&block_counter, (num_first_matches + num_second_matches));
+      while (__syncthreads_or(running)) {
+        if (running) {
+          pair<Key, Value> arr[2];
+          if constexpr (sizeof(Key) == 4) {
+            auto const tmp = *reinterpret_cast<uint4 const*>(current_slot);
+            memcpy(&arr[0], &tmp, 2 * sizeof(pair<Key, Value>));
+          } else {
+            auto const tmp = *reinterpret_cast<ulonglong4 const*>(current_slot);
+            memcpy(&arr[0], &tmp, 2 * sizeof(pair<Key, Value>));
           }
-          output_idx = tile.shfl(output_idx, 0);
 
-          if (first_equals) {
-            auto lane_offset = __popc(first_exists & ((1 << lane_id) - 1));
-            Key k            = key;
-            output_buffer[output_idx + lane_offset] =
-              cuco::make_pair<Key, Value>(std::move(k), std::move(arr[0].second));
+          auto const first_slot_is_empty  = (arr[0].first == view.get_empty_key_sentinel());
+          auto const second_slot_is_empty = (arr[1].first == view.get_empty_key_sentinel());
+          auto const first_equals  = (not first_slot_is_empty and key_equal(arr[0].first, key));
+          auto const second_equals = (not second_slot_is_empty and key_equal(arr[1].first, key));
+          auto const first_exists  = tile.ballot(first_equals);
+          auto const second_exists = tile.ballot(second_equals);
+
+          if (first_exists or second_exists) {
+            found_match = true;
+
+            auto num_first_matches  = __popc(first_exists);
+            auto num_second_matches = __popc(second_exists);
+
+            uint32_t output_idx;
+            if (0 == lane_id) {
+              output_idx =
+                atomicAdd_block(&block_counter, (num_first_matches + num_second_matches));
+            }
+            output_idx = tile.shfl(output_idx, 0);
+
+            if (first_equals) {
+              auto lane_offset = __popc(first_exists & ((1 << lane_id) - 1));
+              Key k            = key;
+              output_buffer[output_idx + lane_offset] =
+                cuco::make_pair<Key, Value>(std::move(k), std::move(arr[0].second));
+            }
+            if (second_equals) {
+              auto lane_offset = __popc(second_exists & ((1 << lane_id) - 1));
+              Key k            = key;
+              output_buffer[output_idx + num_first_matches + lane_offset] =
+                cuco::make_pair<Key, Value>(std::move(k), std::move(arr[1].second));
+            }
           }
-          if (second_equals) {
-            auto lane_offset = __popc(second_exists & ((1 << lane_id) - 1));
-            Key k            = key;
-            output_buffer[output_idx + num_first_matches + lane_offset] =
-              cuco::make_pair<Key, Value>(std::move(k), std::move(arr[1].second));
+          if (tile.any(first_slot_is_empty or second_slot_is_empty)) {
+            running = false;
+            if ((not found_match) && (lane_id == 0)) {
+              auto output_idx = atomicAdd_block(&block_counter, 1);
+              output_buffer[output_idx] =
+                cuco::make_pair<Key, Value>(key, view.get_empty_key_sentinel());
+            }
           }
+        }  // if running
+
+        __syncthreads();
+
+        if ((block_counter + 2 * block_size) > buffer_size) {
+          flush_output_buffer<block_size>(block_counter, output_buffer, num_items, output_begin);
+
+          if (0 == threadIdx.x) { block_counter = 0; }
         }
-        if (tile.any(first_slot_is_empty or second_slot_is_empty)) { running = false; }
-      }  // if running
 
-      __syncthreads();
+        if (running) { current_slot = view.next_slot(current_slot); }
+      }  // while running
+    } else {
+      if (running) { current_slot = view.initial_slot(tile, key); }
+
+      while (__syncthreads_or(running)) {
+        if (running) {
+          pair<Key, Value> arr[2];
+          if constexpr (sizeof(Key) == 4) {
+            auto const tmp = *reinterpret_cast<uint4 const*>(current_slot);
+            memcpy(&arr[0], &tmp, 2 * sizeof(pair<Key, Value>));
+          } else {
+            auto const tmp = *reinterpret_cast<ulonglong4 const*>(current_slot);
+            memcpy(&arr[0], &tmp, 2 * sizeof(pair<Key, Value>));
+          }
 
-      if ((block_counter + 2 * block_size) > buffer_size) {
-        flush_output_buffer<block_size>(block_counter, output_buffer, num_items, output_begin);
+          auto const first_slot_is_empty  = (arr[0].first == view.get_empty_key_sentinel());
+          auto const second_slot_is_empty = (arr[1].first == view.get_empty_key_sentinel());
+          auto const first_equals  = (not first_slot_is_empty and key_equal(arr[0].first, key));
+          auto const second_equals = (not second_slot_is_empty and key_equal(arr[1].first, key));
+          auto const first_exists  = tile.ballot(first_equals);
+          auto const second_exists = tile.ballot(second_equals);
+
+          if (first_exists or second_exists) {
+            auto num_first_matches  = __popc(first_exists);
+            auto num_second_matches = __popc(second_exists);
+
+            uint32_t output_idx;
+            if (0 == lane_id) {
+              output_idx =
+                atomicAdd_block(&block_counter, (num_first_matches + num_second_matches));
+            }
+            output_idx = tile.shfl(output_idx, 0);
+
+            if (first_equals) {
+              auto lane_offset = __popc(first_exists & ((1 << lane_id) - 1));
+              Key k            = key;
+              output_buffer[output_idx + lane_offset] =
+                cuco::make_pair<Key, Value>(std::move(k), std::move(arr[0].second));
+            }
+            if (second_equals) {
+              auto lane_offset = __popc(second_exists & ((1 << lane_id) - 1));
+              Key k            = key;
+              output_buffer[output_idx + num_first_matches + lane_offset] =
+                cuco::make_pair<Key, Value>(std::move(k), std::move(arr[1].second));
+            }
+          }
+          if (tile.any(first_slot_is_empty or second_slot_is_empty)) { running = false; }
+        }  // if running
 
-        if (0 == threadIdx.x) { block_counter = 0; }
-      }
+        __syncthreads();
+
+        if ((block_counter + 2 * block_size) > buffer_size) {
+          flush_output_buffer<block_size>(block_counter, output_buffer, num_items, output_begin);
 
-      if (running) { current_slot = view.next_slot(current_slot); }
-    }  // while running
+          if (0 == threadIdx.x) { block_counter = 0; }
+        }
+
+        if (running) { current_slot = view.next_slot(current_slot); }
+      }  // while running
+    }
     key_idx += (gridDim.x * block_size) / tile_size;
   }
 
@@ -676,107 +922,131 @@ template <uint32_t block_size,
           uint32_t buffer_size,
           typename Key,
           typename Value,
+          bool is_vector_load,
+          bool is_outer = false,
           typename InputIt,
           typename OutputIt,
           typename atomicT,
           typename viewT,
           typename KeyEqual>
-__global__ void retrieve_outer(InputIt first,
-                               InputIt last,
-                               OutputIt output_begin,
-                               atomicT* num_items,
-                               viewT view,
-                               KeyEqual key_equal)
+__global__ std::enable_if_t<not is_vector_load, void> retrieve(InputIt first,
+                                                               InputIt last,
+                                                               OutputIt output_begin,
+                                                               atomicT* num_items,
+                                                               viewT view,
+                                                               KeyEqual key_equal)
 {
   auto tile    = cg::tiled_partition<tile_size>(cg::this_thread_block());
   auto tid     = block_size * blockIdx.x + threadIdx.x;
   auto key_idx = tid / tile_size;
 
-  const uint32_t lane_id = tile.thread_rank();
-
-  __shared__ uint32_t block_counter;
-  __shared__ cuco::pair_type<Key, Value> output_buffer[buffer_size];
-
-  if (0 == threadIdx.x) { block_counter = 0; }
-
-  while (__syncthreads_or(first + key_idx < last)) {
-    auto key = *(first + key_idx);
-    typename viewT::iterator current_slot;
-
-    bool running     = ((first + key_idx) < last);
-    bool found_match = false;
-
-    if (running) { current_slot = view.initial_slot(tile, key); }
-
-    while (__syncthreads_or(running)) {
-      if (running) {
-        pair<Key, Value> arr[2];
-        if constexpr (sizeof(Key) == 4) {
-          auto const tmp = *reinterpret_cast<uint4 const*>(current_slot);
-          memcpy(&arr[0], &tmp, 2 * sizeof(pair<Key, Value>));
-        } else {
-          auto const tmp = *reinterpret_cast<ulonglong4 const*>(current_slot);
-          memcpy(&arr[0], &tmp, 2 * sizeof(pair<Key, Value>));
-        }
-
-        auto const first_slot_is_empty  = (arr[0].first == view.get_empty_key_sentinel());
-        auto const second_slot_is_empty = (arr[1].first == view.get_empty_key_sentinel());
-        auto const first_equals  = (not first_slot_is_empty and key_equal(arr[0].first, key));
-        auto const second_equals = (not second_slot_is_empty and key_equal(arr[1].first, key));
-        auto const first_exists  = tile.ballot(first_equals);
-        auto const second_exists = tile.ballot(second_equals);
+  constexpr uint32_t num_cgs = block_size / tile_size;
+  const uint32_t cg_id       = threadIdx.x / tile_size;
+  const uint32_t lane_id     = tile.thread_rank();
 
-        if (first_exists or second_exists) {
-          found_match = true;
+  __shared__ cuco::pair_type<Key, Value> output_buffer[num_cgs][buffer_size];
+  __shared__ uint32_t cg_counter[num_cgs];
 
-          auto num_first_matches  = __popc(first_exists);
-          auto num_second_matches = __popc(second_exists);
+  if (lane_id == 0) { cg_counter[cg_id] = 0; }
 
-          uint32_t output_idx;
-          if (0 == lane_id) {
-            output_idx = atomicAdd_block(&block_counter, (num_first_matches + num_second_matches));
-          }
-          output_idx = tile.shfl(output_idx, 0);
+  while (first + key_idx < last) {
+    auto key          = *(first + key_idx);
+    auto current_slot = view.initial_slot(tile, key);
 
-          if (first_equals) {
-            auto lane_offset = __popc(first_exists & ((1 << lane_id) - 1));
-            Key k            = key;
-            output_buffer[output_idx + lane_offset] =
-              cuco::make_pair<Key, Value>(std::move(k), std::move(arr[0].second));
-          }
-          if (second_equals) {
-            auto lane_offset = __popc(second_exists & ((1 << lane_id) - 1));
+    bool running = true;
+
+    if constexpr (is_outer) {
+      bool found_match = false;
+
+      while (running) {
+        // TODO: Replace reinterpret_cast with atomic ref when possible. The current implementation
+        // is unsafe!
+        static_assert(sizeof(Key) == sizeof(cuda::atomic<Key>));
+        static_assert(sizeof(Value) == sizeof(cuda::atomic<Value>));
+        pair<Key, Value> slot_contents = *reinterpret_cast<pair<Key, Value> const*>(current_slot);
+
+        auto const slot_is_empty = (slot_contents.first == view.get_empty_key_sentinel());
+        auto const equals        = (not slot_is_empty and key_equal(slot_contents.first, key));
+        auto const exists        = tile.ballot(equals);
+
+        if (exists) {
+          found_match         = true;
+          auto num_matches    = __popc(exists);
+          uint32_t output_idx = cg_counter[cg_id];
+          if (equals) {
+            // Each match computes its lane-level offset
+            auto lane_offset = __popc(exists & ((1 << lane_id) - 1));
             Key k            = key;
-            output_buffer[output_idx + num_first_matches + lane_offset] =
-              cuco::make_pair<Key, Value>(std::move(k), std::move(arr[1].second));
+            output_buffer[cg_id][output_idx + lane_offset] =
+              cuco::make_pair<Key, Value>(std::move(k), std::move(slot_contents.second));
           }
+          if (0 == lane_id) { cg_counter[cg_id] += num_matches; }
         }
-        if (tile.any(first_slot_is_empty or second_slot_is_empty)) {
+        if (tile.any(slot_is_empty)) {
           running = false;
           if ((not found_match) && (lane_id == 0)) {
-            auto output_idx = atomicAdd_block(&block_counter, 1);
-            output_buffer[output_idx] =
+            auto output_idx = cg_counter[cg_id]++;
+            output_buffer[cg_id][output_idx] =
               cuco::make_pair<Key, Value>(key, view.get_empty_key_sentinel());
           }
         }
-      }  // if running
 
-      __syncthreads();
+        tile.sync();
 
-      if ((block_counter + 2 * block_size) > buffer_size) {
-        flush_output_buffer<block_size>(block_counter, output_buffer, num_items, output_begin);
+        // Flush if the next iteration won't fit into buffer
+        if ((cg_counter[cg_id] + tile_size) > buffer_size) {
+          flush_output_buffer<tile_size>(
+            tile, cg_counter[cg_id], output_buffer[cg_id], num_items, output_begin);
+          // First lane reset CG-level counter
+          if (lane_id == 0) { cg_counter[cg_id] = 0; }
+        }
+        current_slot = view.next_slot(current_slot);
+      }  // while running
+    } else {
+      while (running) {
+        // TODO: Replace reinterpret_cast with atomic ref when possible. The current implementation
+        // is unsafe!
+        static_assert(sizeof(Key) == sizeof(cuda::atomic<Key>));
+        static_assert(sizeof(Value) == sizeof(cuda::atomic<Value>));
+        pair<Key, Value> slot_contents = *reinterpret_cast<pair<Key, Value> const*>(current_slot);
+
+        auto const slot_is_empty = (slot_contents.first == view.get_empty_key_sentinel());
+        auto const equals        = (not slot_is_empty and key_equal(slot_contents.first, key));
+        auto const exists        = tile.ballot(equals);
+
+        if (exists) {
+          auto num_matches    = __popc(exists);
+          uint32_t output_idx = cg_counter[cg_id];
+          if (equals) {
+            // Each match computes its lane-level offset
+            auto lane_offset = __popc(exists & ((1 << lane_id) - 1));
+            Key k            = key;
+            output_buffer[cg_id][output_idx + lane_offset] =
+              cuco::make_pair<Key, Value>(std::move(k), std::move(slot_contents.second));
+          }
+          if (0 == lane_id) { cg_counter[cg_id] += num_matches; }
+        }
+        if (tile.any(slot_is_empty)) { running = false; }
 
-        if (0 == threadIdx.x) { block_counter = 0; }
-      }
+        tile.sync();
 
-      if (running) { current_slot = view.next_slot(current_slot); }
-    }  // while running
+        // Flush if the next iteration won't fit into buffer
+        if ((cg_counter[cg_id] + tile_size) > buffer_size) {
+          flush_output_buffer<tile_size>(
+            tile, cg_counter[cg_id], output_buffer[cg_id], num_items, output_begin);
+          // First lane reset CG-level counter
+          if (lane_id == 0) { cg_counter[cg_id] = 0; }
+        }
+        current_slot = view.next_slot(current_slot);
+      }  // while running
+    }
     key_idx += (gridDim.x * block_size) / tile_size;
   }
 
-  // Final remainder flush
-  if (block_counter > 0) {
-    flush_output_buffer<block_size>(block_counter, output_buffer, num_items, output_begin);
+  // Final flush of output buffer
+  if (cg_counter[cg_id] > 0) {
+    flush_output_buffer<tile_size>(
+      tile, cg_counter[cg_id], output_buffer[cg_id], num_items, output_begin);
   }
 }
 
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index 8d4d7b5bb..b0d1b4ec3 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -141,6 +141,7 @@ class static_multimap {
   static_multimap& operator=(static_multimap&&) = delete;
 
   static constexpr uint32_t cg_size() noexcept { return ProbeSequence::cg_size(); }
+  static constexpr bool is_vector_load() noexcept { return ProbeSequence::is_vector_load(); }
 
   /**
    * @brief Construct a fixed-size map with the specified capacity and sentinel values.
@@ -526,7 +527,7 @@ class static_multimap {
     }
 
     /**
-     * @brief Inserts the specified key/value pair into the map.
+     * @brief Inserts the specified key/value pair into the map using vector loads.
      *
      * @tparam Cooperative Group type
      * @tparam KeyEqual Binary callable type
@@ -537,11 +538,25 @@ class static_multimap {
      * equality
      * @return void.
      */
-    template <typename CG, typename KeyEqual = thrust::equal_to<key_type>>
-    __device__ void insert(CG g,
-                           value_type const& insert_pair,
-                           KeyEqual key_equal = KeyEqual{}) noexcept;
+    template <bool is_vector_load, typename CG, typename KeyEqual = thrust::equal_to<key_type>>
+    __device__ std::enable_if_t<is_vector_load, void> insert(
+      CG g, value_type const& insert_pair, KeyEqual key_equal = KeyEqual{}) noexcept;
 
+    /**
+     * @brief Inserts the specified key/value pair into the map using scalar loads.
+     *
+     * @tparam Cooperative Group type
+     * @tparam KeyEqual Binary callable type
+     *
+     * @param g The Cooperative Group that performs the insert
+     * @param insert_pair The pair to insert
+     * @param key_equal The binary callable used to compare two keys for
+     * equality
+     * @return void.
+     */
+    template <bool is_vector_load, typename CG, typename KeyEqual = thrust::equal_to<key_type>>
+    __device__ std::enable_if_t<not is_vector_load, void> insert(
+      CG g, value_type const& insert_pair, KeyEqual key_equal = KeyEqual{}) noexcept;
   };  // class device mutable view
 
   /**
@@ -653,7 +668,29 @@ class static_multimap {
     }
 
     /**
-     * @brief Indicates whether the key `k` was inserted into the map.
+     * @brief Indicates whether the key `k` was inserted into the map using vector loads.
+     *
+     * If the key `k` was inserted into the map, find returns
+     * true. Otherwise, it returns false. Uses the CUDA Cooperative Groups API to
+     * to leverage multiple threads to perform a single contains operation. This provides a
+     * significant boost in throughput compared to the non Cooperative Group
+     * `contains` at moderate to high load factors.
+     *
+     * @tparam CG Cooperative Group type
+     * @tparam KeyEqual Binary callable type
+     * @param g The Cooperative Group used to perform the contains operation
+     * @param k The key to search for
+     * @param key_equal The binary callable used to compare two keys
+     * for equality
+     * @return A boolean indicating whether the key/value pair
+     * containing `k` was inserted
+     */
+    template <bool is_vector_load, typename CG, typename KeyEqual = thrust::equal_to<key_type>>
+    __device__ std::enable_if_t<is_vector_load, bool> contains(
+      CG g, Key const& k, KeyEqual key_equal = KeyEqual{}) noexcept;
+
+    /**
+     * @brief Indicates whether the key `k` was inserted into the map using scalar loads.
      *
      * If the key `k` was inserted into the map, find returns
      * true. Otherwise, it returns false. Uses the CUDA Cooperative Groups API to
@@ -670,8 +707,9 @@ class static_multimap {
      * @return A boolean indicating whether the key/value pair
      * containing `k` was inserted
      */
-    template <typename CG, typename KeyEqual = thrust::equal_to<key_type>>
-    __device__ bool contains(CG g, Key const& k, KeyEqual key_equal = KeyEqual{}) noexcept;
+    template <bool is_vector_load, typename CG, typename KeyEqual = thrust::equal_to<key_type>>
+    __device__ std::enable_if_t<not is_vector_load, bool> contains(
+      CG g, Key const& k, KeyEqual key_equal = KeyEqual{}) noexcept;
   };  // class device_view
 
   /**

From cd6566ddbb513e5799ea55d21dcece03d397d17c Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Wed, 2 Jun 2021 14:11:54 -0400
Subject: [PATCH 120/267] Fix bugs: wrong logic in count_outer + use this
 pointer

---
 include/cuco/detail/static_multimap.inl         | 4 ++--
 include/cuco/detail/static_multimap_kernels.cuh | 4 ++--
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index effc706c5..a07927597 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -478,8 +478,8 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::conta
       memcpy(&arr[0], &tmp, 2 * sizeof(pair<Key, Value>));
     }
 
-    auto const first_slot_is_empty  = (arr[0].first == get_empty_key_sentinel());
-    auto const second_slot_is_empty = (arr[1].first == get_empty_key_sentinel());
+    auto const first_slot_is_empty  = (arr[0].first == this->get_empty_key_sentinel());
+    auto const second_slot_is_empty = (arr[1].first == this->get_empty_key_sentinel());
     auto const first_equals         = (not first_slot_is_empty and key_equal(arr[0].first, k));
     auto const second_equals        = (not second_slot_is_empty and key_equal(arr[1].first, k));
 
diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap_kernels.cuh
index bb09565f5..ec73dcb7f 100644
--- a/include/cuco/detail/static_multimap_kernels.cuh
+++ b/include/cuco/detail/static_multimap_kernels.cuh
@@ -319,7 +319,7 @@ __global__ std::enable_if_t<is_vector_load, void> count(
         auto const first_equals  = (not first_slot_is_empty and key_equal(arr[0].first, key));
         auto const second_equals = (not second_slot_is_empty and key_equal(arr[1].first, key));
 
-        found_match = tile.any(first_equals or second_equals);
+        if (tile.any(first_equals or second_equals)) { found_match = true; }
 
         thread_num_items += (first_equals + second_equals);
 
@@ -417,7 +417,7 @@ __global__ std::enable_if_t<not is_vector_load, void> count(
         auto const slot_is_empty = (current_key == view.get_empty_key_sentinel());
         auto const equals        = not slot_is_empty and key_equal(current_key, key);
 
-        found_match = tile.any(equals);
+        if (tile.any(equals)) { found_match = true; }
 
         thread_num_items += equals;
 

From a5c83e3b87db66889cde0fccd69bbdbdca9266d2 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Wed, 2 Jun 2021 14:18:38 -0400
Subject: [PATCH 121/267] Update multimap unit tests

---
 tests/static_multimap/static_multimap_test.cu | 142 +++++++++++++++---
 1 file changed, 124 insertions(+), 18 deletions(-)

diff --git a/tests/static_multimap/static_multimap_test.cu b/tests/static_multimap/static_multimap_test.cu
index 5643084ab..dfafc3ef0 100644
--- a/tests/static_multimap/static_multimap_test.cu
+++ b/tests/static_multimap/static_multimap_test.cu
@@ -70,20 +70,6 @@ static void generate_keys(OutputIt output_begin, OutputIt output_end)
       }
       break;
     }
-    case dist_type::UNIFORM: {
-      std::uniform_int_distribution<Key> distribution{0, std::numeric_limits<Key>::max()};
-      for (auto i = 0; i < num_items; ++i) {
-        output_begin[i] = distribution(gen);
-      }
-      break;
-    }
-    case dist_type::GAUSSIAN: {
-      std::normal_distribution<> dg{1e9, 1e7};
-      for (auto i = 0; i < num_items; ++i) {
-        output_begin[i] = std::abs(static_cast<Key>(dg(gen)));
-      }
-      break;
-    }
   }
 }
 
@@ -116,17 +102,35 @@ TEMPLATE_TEST_CASE_SIG("Each key appears twice",
   thrust::device_vector<cuco::pair_type<Key, Value>> d_pairs(h_pairs);
   thrust::device_vector<cuco::pair_type<Key, Value>> d_results(num_items);
 
-  // Get array of unique keys
+  // Get unique keys
   std::set<Key> key_set(h_keys.begin(), h_keys.end());
   std::vector<Key> h_unique_keys(key_set.begin(), key_set.end());
   thrust::device_vector<Key> d_unique_keys(h_unique_keys);
 
-  // Bulk function test cases
+  thrust::device_vector<bool> d_contained(num_items / 2);
+
+  SECTION("Non-inserted key/value pairs should not be contained.")
+  {
+    map.contains(d_unique_keys.begin(), d_unique_keys.end(), d_contained.begin());
+
+    REQUIRE(
+      none_of(d_contained.begin(), d_contained.end(), [] __device__(bool const& b) { return b; }));
+  }
+
+  map.insert(d_pairs.begin(), d_pairs.end());
+
+  SECTION("All inserted key/value pairs should be contained.")
+  {
+    map.contains(d_unique_keys.begin(), d_unique_keys.end(), d_contained.begin());
+
+    REQUIRE(
+      all_of(d_contained.begin(), d_contained.end(), [] __device__(bool const& b) { return b; }));
+  }
+
   SECTION("Total count should be equal to the number of inserted pairs.")
   {
-    map.insert(d_pairs.begin(), d_pairs.end());
     // Count matching keys
-    size_t num = map.count(d_unique_keys.begin(), d_unique_keys.end());
+    auto num = map.count(d_unique_keys.begin(), d_unique_keys.end());
 
     REQUIRE(num == num_items);
 
@@ -136,4 +140,106 @@ TEMPLATE_TEST_CASE_SIG("Each key appears twice",
 
     REQUIRE(size == num_items);
   }
+
+  SECTION("count and count_outer should return the same value.")
+  {
+    auto num       = map.count(d_unique_keys.begin(), d_unique_keys.end());
+    auto num_outer = map.count_outer(d_unique_keys.begin(), d_unique_keys.end());
+
+    REQUIRE(num == num_outer);
+  }
+
+  SECTION("Output size of retrieve and retrieve_outer should be the same.")
+  {
+    auto output_begin = d_results.data().get();
+    auto output_end   = map.retrieve(d_unique_keys.begin(), d_unique_keys.end(), output_begin);
+    auto size         = thrust::distance(output_begin, output_end);
+
+    output_end      = map.retrieve_outer(d_unique_keys.begin(), d_unique_keys.end(), output_begin);
+    auto size_outer = thrust::distance(output_begin, output_end);
+
+    REQUIRE(size == size_outer);
+  }
+}
+
+TEMPLATE_TEST_CASE_SIG("Handling of non-matches",
+                       "",
+                       ((typename T, dist_type Dist), T, Dist),
+                       (int32_t, dist_type::UNIQUE),
+                       (int64_t, dist_type::UNIQUE))
+{
+  using Key   = T;
+  using Value = T;
+
+  constexpr std::size_t num_keys{1'000'000};
+  cuco::static_multimap<Key, Value> map{2'000'000, -1, -1};
+
+  auto m_view = map.get_device_mutable_view();
+  auto view   = map.get_device_view();
+
+  std::vector<Key> h_keys(num_keys);
+  std::vector<cuco::pair_type<Key, Value>> h_pairs(num_keys);
+
+  generate_keys<Dist, Key>(h_keys.begin(), h_keys.end());
+
+  for (auto i = 0; i < num_keys; ++i) {
+    h_pairs[i].first  = h_keys[i] / 2;
+    h_pairs[i].second = h_keys[i];
+  }
+
+  thrust::device_vector<Key> d_keys(h_keys);
+  thrust::device_vector<cuco::pair_type<Key, Value>> d_pairs(h_pairs);
+
+  map.insert(d_pairs.begin(), d_pairs.end());
+
+  SECTION("Output of count and retrieve should be coherent.")
+  {
+    auto num = map.count(d_keys.begin(), d_keys.end());
+    thrust::device_vector<cuco::pair_type<Key, Value>> d_results(num);
+
+    REQUIRE(num == num_keys);
+
+    auto output_begin = d_results.data().get();
+    auto output_end   = map.retrieve(d_keys.begin(), d_keys.end(), output_begin);
+    auto size         = thrust::distance(output_begin, output_end);
+
+    REQUIRE(num == size);
+  }
+
+  SECTION("Output of count_outer and retrieve_outer should be coherent.")
+  {
+    auto num = map.count_outer(d_keys.begin(), d_keys.end());
+    thrust::device_vector<cuco::pair_type<Key, Value>> d_results(num);
+
+    REQUIRE(num == (num_keys + num_keys / 2));
+
+    auto output_begin = d_results.data().get();
+    auto output_end   = map.retrieve_outer(d_keys.begin(), d_keys.end(), output_begin);
+    auto size         = thrust::distance(output_begin, output_end);
+
+    REQUIRE(num == size);
+  }
+
+  SECTION("count_outer handles non-matches wile count doesn't.")
+  {
+    auto num_outer = map.count_outer(d_keys.begin(), d_keys.end());
+    auto num       = map.count(d_keys.begin(), d_keys.end());
+
+    REQUIRE(num_outer == (num + num_keys / 2));
+  }
+
+  SECTION("retrieve_outer handles non-matches wile retrieve doesn't.")
+  {
+    auto num_outer = map.count_outer(d_keys.begin(), d_keys.end());
+    thrust::device_vector<cuco::pair_type<Key, Value>> d_results(num_outer);
+
+    auto output_begin = d_results.data().get();
+    auto output_end   = map.retrieve(d_keys.begin(), d_keys.end(), output_begin);
+    auto size         = thrust::distance(output_begin, output_end);
+
+    output_end      = map.retrieve_outer(d_keys.begin(), d_keys.end(), output_begin);
+    auto size_outer = thrust::distance(output_begin, output_end);
+
+    REQUIRE(size_outer == (size + num_keys / 2));
+  }
 }

From 451b0bb66e63b33983d51a31d408789e5262278e Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Wed, 2 Jun 2021 18:39:54 -0400
Subject: [PATCH 122/267] Pass pair_equal callables to pair_count + update
 comments

---
 include/cuco/detail/static_multimap.inl       |  12 +-
 .../cuco/detail/static_multimap_kernels.cuh   | 141 ++++++++----------
 include/cuco/static_multimap.cuh              |  49 +++---
 3 files changed, 98 insertions(+), 104 deletions(-)

diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index a07927597..37d7ff18a 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -179,9 +179,9 @@ template <typename Key,
           class ProbeSequence,
           cuda::thread_scope Scope,
           typename Allocator>
-template <typename InputIt, typename KeyEqual, typename ValEqual>
+template <typename InputIt, typename PairEqual>
 std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::pair_count(
-  InputIt first, InputIt last, cudaStream_t stream, KeyEqual key_equal, ValEqual val_equal)
+  InputIt first, InputIt last, PairEqual pair_equal, cudaStream_t stream)
 {
   auto num_keys         = std::distance(first, last);
   auto const block_size = 128;
@@ -197,7 +197,7 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::pair_c
   CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_items, sizeof(atomic_ctr_type), device_id));
 
   detail::pair_count<block_size, cg_size(), Key, Value, is_vector_load()>
-    <<<grid_size, block_size, 0, stream>>>(first, last, num_items, view, key_equal, val_equal);
+    <<<grid_size, block_size, 0, stream>>>(first, last, num_items, view, pair_equal);
   CUCO_CUDA_TRY(cudaDeviceSynchronize());
 
   size_t result = *num_items;
@@ -211,9 +211,9 @@ template <typename Key,
           class ProbeSequence,
           cuda::thread_scope Scope,
           typename Allocator>
-template <typename InputIt, typename KeyEqual, typename ValEqual>
+template <typename InputIt, typename PairEqual>
 std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::pair_count_outer(
-  InputIt first, InputIt last, cudaStream_t stream, KeyEqual key_equal, ValEqual val_equal)
+  InputIt first, InputIt last, PairEqual pair_equal, cudaStream_t stream)
 {
   auto num_keys         = std::distance(first, last);
   auto const block_size = 128;
@@ -231,7 +231,7 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::pair_c
   CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_items, sizeof(atomic_ctr_type), device_id));
 
   detail::pair_count<block_size, cg_size(), Key, Value, is_vector_load(), is_outer>
-    <<<grid_size, block_size, 0, stream>>>(first, last, num_items, view, key_equal, val_equal);
+    <<<grid_size, block_size, 0, stream>>>(first, last, num_items, view, pair_equal);
   CUCO_CUDA_TRY(cudaDeviceSynchronize());
 
   size_t result = *num_items;
diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap_kernels.cuh
index ec73dcb7f..956f3c15f 100644
--- a/include/cuco/detail/static_multimap_kernels.cuh
+++ b/include/cuco/detail/static_multimap_kernels.cuh
@@ -25,14 +25,17 @@ namespace detail {
 namespace cg = cooperative_groups;
 
 /**
- * @brief Flushes per-CG shared memory buffer into the output sequence.
+ * @brief Flushes per-CG shared memory buffer into the output sequence using CG memcpy_async.
  *
+ * @tparam cg_size The number of threads in the Cooperative Groups
+ * @tparam CG Cooperative Group type
  * @tparam Key key type
  * @tparam Value value type
  * @tparam atomicT Type of atomic storage
  * @tparam OutputIt Device accessible output iterator whose `value_type` is
  * convertible to the map's `mapped_type`
- * @param output_size Number of valid output in the buffer
+ * @param g The Cooperative Group used to flush output buffer
+ * @param num_outputs Number of valid output in the buffer
  * @param output_buffer Shared memory buffer of the key/value pair sequence
  * @param num_items Size of the output sequence
  * @param output_begin Beginning of the output sequence of key/value pairs
@@ -65,12 +68,15 @@ flush_output_buffer(CG const& g,
 /**
  * @brief Flushes per-CG shared memory buffer into the output sequence.
  *
+ * @tparam cg_size The number of threads in the Cooperative Groups
+ * @tparam CG Cooperative Group type
  * @tparam Key key type
  * @tparam Value value type
  * @tparam atomicT Type of atomic storage
  * @tparam OutputIt Device accessible output iterator whose `value_type` is
  * convertible to the map's `mapped_type`
- * @param output_size Number of valid output in the buffer
+ * @param g The Cooperative Group used to flush output buffer
+ * @param num_outputs Number of valid output in the buffer
  * @param output_buffer Shared memory buffer of the key/value pair sequence
  * @param num_items Size of the output sequence
  * @param output_begin Beginning of the output sequence of key/value pairs
@@ -101,12 +107,13 @@ flush_output_buffer(CG const& g,
 /**
  * @brief Flushes per-block shared memory buffer into the output sequence.
  *
+ * @tparam block_size The size of the thread block
  * @tparam Key key type
  * @tparam Value value type
  * @tparam atomicT Type of atomic storage
  * @tparam OutputIt Device accessible output iterator whose `value_type` is
  * convertible to the map's `mapped_type`
- * @param output_size Number of valid output in the buffer
+ * @param num_outputs Number of valid output in the buffer
  * @param output_buffer Shared memory buffer of the key/value pair sequence
  * @param num_items Size of the output sequence
  * @param output_begin Beginning of the output sequence of key/value pairs
@@ -168,9 +175,10 @@ __global__ void initialize(pair_atomic_type* const slots, Key k, Value v, std::s
  * significant boost in throughput compared to the non Cooperative Group
  * `insert` at moderate to high load factors.
  *
- * @tparam block_size
+ * @tparam block_size The size of the thread block
  * @tparam tile_size The number of threads in the Cooperative Groups used to perform
  * inserts
+ * @tparam is_vector_load Boolean flag indicating whether vector loads are used or not
  * @tparam InputIt Device accessible input iterator whose `value_type` is
  * convertible to the map's `value_type`
  * @tparam viewT Type of device view allowing access of hash map storage
@@ -210,6 +218,7 @@ __global__ void insert(InputIt first, InputIt last, viewT view, KeyEqual key_equ
  *
  * @tparam block_size The size of the thread block
  * @tparam tile_size The number of threads in the Cooperative Groups
+ * @tparam is_vector_load Boolean flag indicating whether vector loads are used or not
  * @tparam InputIt Device accessible input iterator whose `value_type` is
  * convertible to the map's `key_type`
  * @tparam OutputIt Device accessible output iterator whose `value_type` is
@@ -265,6 +274,9 @@ __global__ void contains(
  * @tparam tile_size The number of threads in the Cooperative Groups used to perform counts
  * @tparam Key key type
  * @tparam Value The type of the mapped value for the map
+ * @tparam is_vector_load Boolean flag indicating whether vector loads are used or not
+ * @tparam is_outer Boolean flag indicating whether the current functions is used for outer join
+ * operations or not
  * @tparam InputIt Device accessible input iterator whose `value_type` is convertible to the map's
  * `key_type`
  * @tparam atomicT Type of atomic storage
@@ -370,6 +382,9 @@ __global__ std::enable_if_t<is_vector_load, void> count(
  * @tparam tile_size The number of threads in the Cooperative Groups used to perform counts
  * @tparam Key key type
  * @tparam Value The type of the mapped value for the map
+ * @tparam is_vector_load Boolean flag indicating whether vector loads are used or not
+ * @tparam is_outer Boolean flag indicating whether the current functions is used for outer join
+ * operations or not
  * @tparam InputIt Device accessible input iterator whose `value_type` is convertible to the map's
  * `key_type`
  * @tparam atomicT Type of atomic storage
@@ -458,17 +473,18 @@ __global__ std::enable_if_t<not is_vector_load, void> count(
  * @tparam tile_size The number of threads in the Cooperative Groups used to perform counts
  * @tparam Key key type
  * @tparam Value The type of the mapped value for the map
+ * @tparam is_vector_load Boolean flag indicating whether vector loads are used or not
+ * @tparam is_outer Boolean flag indicating whether the current functions is used for outer join
+ * operations or not
  * @tparam Input Device accesible input iterator of key/value pairs
  * @tparam atomicT Type of atomic storage
  * @tparam viewT Type of device view allowing access of hash map storage
- * @tparam KeyEqual Binary callable
- * @tparam ValEqual Binary callable
+ * @tparam PairEqual Binary callable
  * @param first Beginning of the sequence of pairs to count
  * @param last End of the sequence of pairs to count
  * @param num_items The number of all the matches for a sequence of pairs
  * @param view Device view used to access the hash map's slot storage
- * @param key_equal Binary function to compare two keys for equality
- * @param val_equal Binary function to compare two values for equality
+ * @param pair_equal Binary function to compare two pairs for equality
  */
 template <uint32_t block_size,
           uint32_t tile_size,
@@ -479,14 +495,9 @@ template <uint32_t block_size,
           typename InputIt,
           typename atomicT,
           typename viewT,
-          typename KeyEqual,
-          typename ValEqual>
-__global__ std::enable_if_t<is_vector_load, void> pair_count(InputIt first,
-                                                             InputIt last,
-                                                             atomicT* num_items,
-                                                             viewT view,
-                                                             KeyEqual key_equal,
-                                                             ValEqual val_equal)
+          typename PairEqual>
+__global__ std::enable_if_t<is_vector_load, void> pair_count(
+  InputIt first, InputIt last, atomicT* num_items, viewT view, PairEqual pair_equal)
 {
   auto tile     = cg::tiled_partition<tile_size>(cg::this_thread_block());
   auto tid      = block_size * blockIdx.x + threadIdx.x;
@@ -515,25 +526,17 @@ __global__ std::enable_if_t<is_vector_load, void> pair_count(InputIt first,
           memcpy(&arr[0], &tmp, 2 * sizeof(cuco::pair_type<Key, Value>));
         }
 
-        auto const first_key_is_empty  = (arr[0].first == view.get_empty_key_sentinel());
-        auto const second_key_is_empty = (arr[1].first == view.get_empty_key_sentinel());
-        auto const first_val_is_empty  = (arr[0].second == view.get_empty_value_sentinel());
-        auto const second_val_is_empty = (arr[1].second == view.get_empty_value_sentinel());
-
-        auto const first_key_equals  = (not first_key_is_empty and key_equal(arr[0].first, key));
-        auto const second_key_equals = (not second_key_is_empty and key_equal(arr[1].first, key));
-        auto const first_val_equals  = (not first_val_is_empty and key_equal(arr[0].second, value));
-        auto const second_val_equals =
-          (not second_val_is_empty and key_equal(arr[1].second, value));
+        auto const first_slot_is_empty  = (arr[0].first == view.get_empty_key_sentinel());
+        auto const second_slot_is_empty = (arr[1].first == view.get_empty_key_sentinel());
 
-        auto const first_equals  = first_key_equals and first_val_equals;
-        auto const second_equals = second_key_equals and second_val_equals;
+        auto const first_slot_equals  = (not first_slot_is_empty and pair_equal(arr[0], pair));
+        auto const second_slot_equals = (not second_slot_is_empty and pair_equal(arr[1], pair));
 
-        found_match = tile.any(first_equals or second_equals);
+        if (tile.any(first_slot_equals or second_slot_equals)) { found_match = true; }
 
-        thread_num_items += (first_equals + second_equals);
+        thread_num_items += (first_slot_equals + second_slot_equals);
 
-        if (tile.any(first_key_is_empty or second_key_is_empty)) {
+        if (tile.any(first_slot_is_empty or second_slot_is_empty)) {
           if ((not found_match) && (tile.thread_rank() == 0)) { thread_num_items++; }
           break;
         }
@@ -551,23 +554,15 @@ __global__ std::enable_if_t<is_vector_load, void> pair_count(InputIt first,
           memcpy(&arr[0], &tmp, 2 * sizeof(cuco::pair_type<Key, Value>));
         }
 
-        auto const first_key_is_empty  = (arr[0].first == view.get_empty_key_sentinel());
-        auto const second_key_is_empty = (arr[1].first == view.get_empty_key_sentinel());
-        auto const first_val_is_empty  = (arr[0].second == view.get_empty_value_sentinel());
-        auto const second_val_is_empty = (arr[1].second == view.get_empty_value_sentinel());
-
-        auto const first_key_equals  = (not first_key_is_empty and key_equal(arr[0].first, key));
-        auto const second_key_equals = (not second_key_is_empty and key_equal(arr[1].first, key));
-        auto const first_val_equals  = (not first_val_is_empty and key_equal(arr[0].second, value));
-        auto const second_val_equals =
-          (not second_val_is_empty and key_equal(arr[1].second, value));
+        auto const first_slot_is_empty  = (arr[0].first == view.get_empty_key_sentinel());
+        auto const second_slot_is_empty = (arr[1].first == view.get_empty_key_sentinel());
 
-        auto const first_equals  = first_key_equals and first_val_equals;
-        auto const second_equals = second_key_equals and second_val_equals;
+        auto const first_slot_equals  = (not first_slot_is_empty and pair_equal(arr[0], pair));
+        auto const second_slot_equals = (not second_slot_is_empty and pair_equal(arr[1], pair));
 
-        thread_num_items += (first_equals + second_equals);
+        thread_num_items += (first_slot_equals + second_slot_equals);
 
-        if (tile.any(first_key_is_empty or second_key_is_empty)) { break; }
+        if (tile.any(first_slot_is_empty or second_slot_is_empty)) { break; }
 
         current_slot = view.next_slot(current_slot);
       }
@@ -589,17 +584,18 @@ __global__ std::enable_if_t<is_vector_load, void> pair_count(InputIt first,
  * @tparam tile_size The number of threads in the Cooperative Groups used to perform counts
  * @tparam Key key type
  * @tparam Value The type of the mapped value for the map
+ * @tparam is_vector_load Boolean flag indicating whether vector loads are used or not
+ * @tparam is_outer Boolean flag indicating whether the current functions is used for outer join
+ * operations or not
  * @tparam Input Device accesible input iterator of key/value pairs
  * @tparam atomicT Type of atomic storage
  * @tparam viewT Type of device view allowing access of hash map storage
- * @tparam KeyEqual Binary callable
- * @tparam ValEqual Binary callable
+ * @tparam PairEqual Binary callable
  * @param first Beginning of the sequence of pairs to count
  * @param last End of the sequence of pairs to count
  * @param num_items The number of all the matches for a sequence of pairs
  * @param view Device view used to access the hash map's slot storage
- * @param key_equal Binary function to compare two keys for equality
- * @param val_equal Binary function to compare two values for equality
+ * @param pair_equal Binary function to compare two pairs for equality
  */
 template <uint32_t block_size,
           uint32_t tile_size,
@@ -610,14 +606,9 @@ template <uint32_t block_size,
           typename InputIt,
           typename atomicT,
           typename viewT,
-          typename KeyEqual,
-          typename ValEqual>
-__global__ std::enable_if_t<not is_vector_load, void> pair_count(InputIt first,
-                                                                 InputIt last,
-                                                                 atomicT* num_items,
-                                                                 viewT view,
-                                                                 KeyEqual key_equal,
-                                                                 ValEqual val_equal)
+          typename PairEqual>
+__global__ std::enable_if_t<not is_vector_load, void> pair_count(
+  InputIt first, InputIt last, atomicT* num_items, viewT view, PairEqual pair_equal)
 {
   auto tile     = cg::tiled_partition<tile_size>(cg::this_thread_block());
   auto tid      = block_size * blockIdx.x + threadIdx.x;
@@ -639,19 +630,15 @@ __global__ std::enable_if_t<not is_vector_load, void> pair_count(InputIt first,
       while (true) {
         auto slot_contents = *reinterpret_cast<cuco::pair_type<Key, Value> const*>(current_slot);
 
-        auto const key_is_empty = (slot_contents.first == view.get_empty_key_sentinel());
-        auto const val_is_empty = (slot_contents.second == view.get_empty_value_sentinel());
-
-        auto const key_equals = (not key_is_empty and key_equal(slot_contents.first, key));
-        auto const val_equals = (not val_is_empty and key_equal(slot_contents.second, value));
+        auto const slot_is_empty = (slot_contents.first == view.get_empty_key_sentinel());
 
-        auto const equals = key_equals and val_equals;
+        auto const equals = not slot_is_empty and pair_equal(slot_contents, pair);
 
-        found_match = tile.any(equals);
+        if (tile.any(equals)) { found_match = true; }
 
         thread_num_items += equals;
 
-        if (tile.any(key_is_empty)) {
+        if (tile.any(slot_is_empty)) {
           if ((not found_match) && (tile.thread_rank() == 0)) { thread_num_items++; }
           break;
         }
@@ -662,17 +649,13 @@ __global__ std::enable_if_t<not is_vector_load, void> pair_count(InputIt first,
       while (true) {
         auto slot_contents = *reinterpret_cast<cuco::pair_type<Key, Value> const*>(current_slot);
 
-        auto const key_is_empty = (slot_contents.first == view.get_empty_key_sentinel());
-        auto const val_is_empty = (slot_contents.second == view.get_empty_value_sentinel());
-
-        auto const key_equals = (not key_is_empty and key_equal(slot_contents.first, key));
-        auto const val_equals = (not val_is_empty and key_equal(slot_contents.second, value));
+        auto const slot_is_empty = (slot_contents.first == view.get_empty_key_sentinel());
 
-        auto const equals = key_equals and val_equals;
+        auto const equals = not slot_is_empty and pair_equal(slot_contents, pair);
 
         thread_num_items += equals;
 
-        if (tile.any(key_is_empty)) { break; }
+        if (tile.any(slot_is_empty)) { break; }
 
         current_slot = view.next_slot(current_slot);
       }
@@ -687,7 +670,8 @@ __global__ std::enable_if_t<not is_vector_load, void> pair_count(InputIt first,
 }
 
 /**
- * @brief Finds all the values corresponding to all keys in the range `[first, last)`.
+ * @brief Finds all the values corresponding to all keys in the range `[first, last)` using vector
+ * oads combined with per-block shared memory buffer.
  *
  * If the key `k = *(first + i)` exists in the map, copies `k` and all associated values to
  * unspecified locations in `[output_begin, output_begin + *num_items - 1)`. Else, copies `k` and
@@ -701,6 +685,9 @@ __global__ std::enable_if_t<not is_vector_load, void> pair_count(InputIt first,
  * @tparam buffer_size Size of the output buffer
  * @tparam Key key type
  * @tparam Value The type of the mapped value for the map
+ * @tparam is_vector_load Boolean flag indicating whether vector loads are used or not
+ * @tparam is_outer Boolean flag indicating whether the current functions is used for outer join
+ * operations or not
  * @tparam InputIt Device accessible input iterator whose `value_type` is
  * convertible to the map's `key_type`
  * @tparam OutputIt Device accessible output iterator whose `value_type` is
@@ -889,7 +876,8 @@ __global__ std::enable_if_t<is_vector_load, void> retrieve(InputIt first,
 }
 
 /**
- * @brief Finds all the values corresponding to all keys in the range `[first, last)`.
+ * @brief Finds all the values corresponding to all keys in the range `[first, last)` using scalar
+ * loads combined with per-CG shared memory buffer.
  *
  * If the key `k = *(first + i)` exists in the map, copies `k` and all associated values to
  * unspecified locations in `[output_begin, output_begin + *num_items - 1)`. Else, copies `k` and
@@ -903,6 +891,9 @@ __global__ std::enable_if_t<is_vector_load, void> retrieve(InputIt first,
  * @tparam buffer_size Size of the output buffer
  * @tparam Key key type
  * @tparam Value The type of the mapped value for the map
+ * @tparam is_vector_load Boolean flag indicating whether vector loads are used or not
+ * @tparam is_outer Boolean flag indicating whether the current functions is used for outer join
+ * operations or not
  * @tparam InputIt Device accessible input iterator whose `value_type` is
  * convertible to the map's `key_type`
  * @tparam OutputIt Device accessible output iterator whose `value_type` is
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index b0d1b4ec3..1a355e7bb 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -104,6 +104,7 @@ namespace cuco {
  *
  * @tparam Key Arithmetic type used for key
  * @tparam Value Type of the mapped values
+ * @tparam ProbeSequence Probe sequence used in multimap
  * @tparam Scope The scope in which insert/find operations will be performed by
  * individual threads.
  * @tparam Allocator Type of allocator used for device storage
@@ -188,6 +189,7 @@ class static_multimap {
    * @tparam KeyEqual Binary callable type
    * @param first Beginning of the sequence of key/value pairs
    * @param last End of the sequence of key/value pairs
+   * @param stream CUDA stream used for insert
    * @param key_equal The binary function to compare two keys for equality
    */
   template <typename InputIt, typename KeyEqual = thrust::equal_to<key_type>>
@@ -209,6 +211,7 @@ class static_multimap {
    * @param first Beginning of the sequence of keys
    * @param last End of the sequence of keys
    * @param output_begin Beginning of the sequence of booleans for the presence of each key
+   * @param stream CUDA stream used for contains
    * @param key_equal The binary function to compare two keys for equality
    */
   template <typename InputIt, typename OutputIt, typename KeyEqual = thrust::equal_to<key_type>>
@@ -225,6 +228,7 @@ class static_multimap {
    * @tparam KeyEqual Binary callable
    * @param first Beginning of the sequence of keys to count
    * @param last End of the sequence of keys to count
+   * @param stream CUDA stream used for count
    * @param key_equal Binary function to compare two keys for equality
    * @return The sum of total occurrences of all keys in `[first,last)`
    */
@@ -242,6 +246,7 @@ class static_multimap {
    * @tparam KeyEqual Binary callable
    * @param first Beginning of the sequence of keys to count
    * @param last End of the sequence of keys to count
+   * @param stream CUDA stream used for count_outer
    * @param key_equal Binary function to compare two keys for equality
    * @return The sum of total occurrences of all keys in `[first,last)`
    */
@@ -255,44 +260,36 @@ class static_multimap {
    * @brief Counts the occurrences of key/value pairs in `[first, last)` contained in the multimap.
    *
    * @tparam Input Device accesible input iterator of key/value pairs
-   * @tparam KeyEqual Binary callable
-   * @tparam ValEqual Binary callable
+   * @tparam PairEqual Binary callable
    * @param first Beginning of the sequence of pairs to count
    * @param last End of the sequence of pairs to count
-   * @param key_equal Binary function to compare two keys for equality
-   * @param val_equal Binary function to compare two values for equality
+   * @param pair_equal Binary function to compare two pairs for equality
+   * @param stream CUDA stream used for pair_count
    * @return The sum of total occurrences of all pairs in `[first,last)`
    */
-  template <typename InputIt,
-            typename KeyEqual = thrust::equal_to<key_type>,
-            typename ValEqual = thrust::equal_to<mapped_type>>
+  template <typename InputIt, typename PairEqual>
   std::size_t pair_count(InputIt first,
                          InputIt last,
-                         cudaStream_t stream = 0,
-                         KeyEqual key_equal  = KeyEqual{},
-                         ValEqual val_equal  = ValEqual{});
+                         PairEqual pair_equal,
+                         cudaStream_t stream = 0);
 
   /**
    * @brief Counts the occurrences of key/value pairs in `[first, last)` contained in the multimap.
    * If no matches can be found for a given key/value pair, the corresponding occurrence is 1.
    *
    * @tparam Input Device accesible input iterator of key/value pairs
-   * @tparam KeyEqual Binary callable
-   * @tparam ValEqual Binary callable
+   * @tparam PairEqual Binary callable
    * @param first Beginning of the sequence of pairs to count
    * @param last End of the sequence of pairs to count
-   * @param key_equal Binary function to compare two keys for equality
-   * @param val_equal Binary function to compare two values for equality
+   * @param pair_equal Binary function to compare two pairs for equality
+   * @param stream CUDA stream used for pair_count_outer
    * @return The sum of total occurrences of all pairs in `[first,last)`
    */
-  template <typename InputIt,
-            typename KeyEqual = thrust::equal_to<key_type>,
-            typename ValEqual = thrust::equal_to<mapped_type>>
+  template <typename InputIt, typename PairEqual>
   std::size_t pair_count_outer(InputIt first,
                                InputIt last,
-                               cudaStream_t stream = 0,
-                               KeyEqual key_equal  = KeyEqual{},
-                               ValEqual val_equal  = ValEqual{});
+                               PairEqual pair_equal,
+                               cudaStream_t stream = 0);
 
   /**
    * @brief Finds all the values corresponding to all keys in the range `[first, last)`.
@@ -311,6 +308,7 @@ class static_multimap {
    * @param first Beginning of the sequence of keys
    * @param last End of the sequence of keys
    * @param output_begin Beginning of the sequence of key/value pairs retrieved for each key
+   * @param stream CUDA stream used for retrieve
    * @param key_equal The binary function to compare two keys for equality
    * @return The iterator indicating the last valid key/value pairs in the output
    */
@@ -339,6 +337,7 @@ class static_multimap {
    * @param first Beginning of the sequence of keys
    * @param last End of the sequence of keys
    * @param output_begin Beginning of the sequence of key/value pairs retrieved for each key
+   * @param stream CUDA stream used for retrieve_outer
    * @param key_equal The binary function to compare two keys for equality
    * @return The iterator indicating the last valid key/value pairs in the output
    */
@@ -529,7 +528,8 @@ class static_multimap {
     /**
      * @brief Inserts the specified key/value pair into the map using vector loads.
      *
-     * @tparam Cooperative Group type
+     * @tparam is_vector_load Boolean flag indicating whether vector loads are used or not
+     * @tparam CG Cooperative Group type
      * @tparam KeyEqual Binary callable type
      *
      * @param g The Cooperative Group that performs the insert
@@ -545,7 +545,8 @@ class static_multimap {
     /**
      * @brief Inserts the specified key/value pair into the map using scalar loads.
      *
-     * @tparam Cooperative Group type
+     * @tparam is_vector_load Boolean flag indicating whether vector loads are used or not
+     * @tparam CG Cooperative Group type
      * @tparam KeyEqual Binary callable type
      *
      * @param g The Cooperative Group that performs the insert
@@ -625,7 +626,7 @@ class static_multimap {
      * @endcode
      *
      * @tparam CG The type of the cooperative thread group
-     * @param g The ooperative thread group used to copy the slots
+     * @param g The cooperative thread group used to copy the slots
      * @param source_device_view `device_view` to copy from
      * @param memory_to_use Array large enough to support `capacity` elements. Object does not take
      * the ownership of the memory
@@ -676,6 +677,7 @@ class static_multimap {
      * significant boost in throughput compared to the non Cooperative Group
      * `contains` at moderate to high load factors.
      *
+     * @tparam is_vector_load Boolean flag indicating whether vector loads are used or not
      * @tparam CG Cooperative Group type
      * @tparam KeyEqual Binary callable type
      * @param g The Cooperative Group used to perform the contains operation
@@ -698,6 +700,7 @@ class static_multimap {
      * significant boost in throughput compared to the non Cooperative Group
      * `contains` at moderate to high load factors.
      *
+     * @tparam is_vector_load Boolean flag indicating whether vector loads are used or not
      * @tparam CG Cooperative Group type
      * @tparam KeyEqual Binary callable type
      * @param g The Cooperative Group used to perform the contains operation

From 8e3d2ad15bc441e303311d1ae703d57ca12cca2f Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Wed, 2 Jun 2021 19:39:14 -0400
Subject: [PATCH 123/267] Add pair_count unit tests

---
 .../cuco/detail/static_multimap_kernels.cuh   | 14 ++---
 tests/static_multimap/static_multimap_test.cu | 57 ++++++++++++++++++-
 2 files changed, 61 insertions(+), 10 deletions(-)

diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap_kernels.cuh
index 956f3c15f..bcc0a98a5 100644
--- a/include/cuco/detail/static_multimap_kernels.cuh
+++ b/include/cuco/detail/static_multimap_kernels.cuh
@@ -508,10 +508,9 @@ __global__ std::enable_if_t<is_vector_load, void> pair_count(
   std::size_t thread_num_items = 0;
 
   while (first + pair_idx < last) {
-    auto pair         = *(first + pair_idx);
-    auto key          = pair.first;
-    auto value        = pair.second;
-    auto current_slot = view.initial_slot(tile, key);
+    cuco::pair_type<Key, Value> pair = *(first + pair_idx);
+    auto key                         = pair.first;
+    auto current_slot                = view.initial_slot(tile, key);
 
     if constexpr (is_outer) {
       bool found_match = false;
@@ -619,10 +618,9 @@ __global__ std::enable_if_t<not is_vector_load, void> pair_count(
   std::size_t thread_num_items = 0;
 
   while (first + pair_idx < last) {
-    auto pair         = *(first + pair_idx);
-    auto key          = pair.first;
-    auto value        = pair.second;
-    auto current_slot = view.initial_slot(tile, key);
+    cuco::pair_type<Key, Value> pair = *(first + pair_idx);
+    auto key                         = pair.first;
+    auto current_slot                = view.initial_slot(tile, key);
 
     if constexpr (is_outer) {
       bool found_match = false;
diff --git a/tests/static_multimap/static_multimap_test.cu b/tests/static_multimap/static_multimap_test.cu
index dfafc3ef0..f0bc79398 100644
--- a/tests/static_multimap/static_multimap_test.cu
+++ b/tests/static_multimap/static_multimap_test.cu
@@ -45,6 +45,15 @@ bool none_of(Iterator begin, Iterator end, Predicate p)
 {
   return not all_of(begin, end, p);
 }
+
+template <typename Key, typename Value>
+struct pair_equal {
+  __host__ __device__ bool operator()(const cuco::pair_type<Key, Value>& lhs,
+                                      const cuco::pair_type<Key, Value>& rhs) const
+  {
+    return lhs.first == rhs.first;
+  }
+};
 }  // namespace
 
 enum class dist_type { UNIQUE, DUAL, UNIFORM, GAUSSIAN };
@@ -82,8 +91,8 @@ TEMPLATE_TEST_CASE_SIG("Each key appears twice",
   using Key   = T;
   using Value = T;
 
-  constexpr std::size_t num_items{50'000'000};
-  cuco::static_multimap<Key, Value> map{100'000'000, -1, -1};
+  constexpr std::size_t num_items{400};
+  cuco::static_multimap<Key, Value> map{500, -1, -1};
 
   auto m_view = map.get_device_mutable_view();
   auto view   = map.get_device_view();
@@ -243,3 +252,47 @@ TEMPLATE_TEST_CASE_SIG("Handling of non-matches",
     REQUIRE(size_outer == (size + num_keys / 2));
   }
 }
+
+TEMPLATE_TEST_CASE_SIG("Evaluation of pair functions",
+                       "",
+                       ((typename T, dist_type Dist), T, Dist),
+                       (int32_t, dist_type::UNIQUE),
+                       (int64_t, dist_type::UNIQUE))
+{
+  using Key   = T;
+  using Value = T;
+
+  constexpr std::size_t num_pairs{5'000'000};
+  cuco::static_multimap<Key, Value> map{10'000'000, -1, -1};
+
+  auto m_view = map.get_device_mutable_view();
+  auto view   = map.get_device_view();
+
+  std::vector<Key> h_keys(num_pairs);
+  std::vector<cuco::pair_type<Key, Value>> h_pairs(num_pairs);
+
+  generate_keys<Dist, Key>(h_keys.begin(), h_keys.end());
+
+  for (auto i = 0; i < num_pairs; ++i) {
+    h_pairs[i].first  = h_keys[i] / 2;
+    h_pairs[i].second = h_keys[i];
+  }
+
+  thrust::device_vector<Key> d_keys(h_keys);
+  thrust::device_vector<cuco::pair_type<Key, Value>> d_pairs(h_pairs);
+
+  map.insert(d_pairs.begin(), d_pairs.end());
+
+  for (auto i = 0; i < num_pairs; ++i) {
+    h_pairs[i].first = h_keys[i];
+  }
+  d_pairs = h_pairs;
+
+  SECTION("pair_count_outer handles non-matches wile pair_count doesn't.")
+  {
+    auto num_outer = map.pair_count_outer(d_pairs.begin(), d_pairs.end(), pair_equal<Key, Value>{});
+    auto num       = map.pair_count(d_pairs.begin(), d_pairs.end(), pair_equal<Key, Value>{});
+
+    REQUIRE(num_outer == (num + num_pairs / 2));
+  }
+}

From 87e0050044197c6aa70b40919ebbb7ae5bce9bf7 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Wed, 2 Jun 2021 20:22:18 -0400
Subject: [PATCH 124/267] Change the default nvbench repo in CMake

---
 benchmarks/CMakeLists.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/benchmarks/CMakeLists.txt b/benchmarks/CMakeLists.txt
index 604d10ec7..2066ef9ac 100644
--- a/benchmarks/CMakeLists.txt
+++ b/benchmarks/CMakeLists.txt
@@ -16,7 +16,7 @@ CPMAddPackage(
 
 CPMAddPackage(
   NAME nvbench
-  GITHUB_REPOSITORY allisonvacanti/nvbench
+  GITHUB_REPOSITORY NVIDIA/nvbench
   GIT_TAG main
   GIT_SHALLOW TRUE
 )

From 476f8fe3a88737093371b2b5c4b9b567c6fdea85 Mon Sep 17 00:00:00 2001
From: Jake Hemstad <jhemstad@nvidia.com>
Date: Wed, 2 Jun 2021 11:38:34 -0500
Subject: [PATCH 125/267] Add trait for is_bitwise_comparable.

---
 include/cuco/static_map.cuh | 52 +++++++++++++++++++++++++++++++------
 1 file changed, 44 insertions(+), 8 deletions(-)

diff --git a/include/cuco/static_map.cuh b/include/cuco/static_map.cuh
index cf7de0a7e..5abe40b19 100644
--- a/include/cuco/static_map.cuh
+++ b/include/cuco/static_map.cuh
@@ -44,6 +44,40 @@ namespace cuco {
 template <typename Key, typename Value, cuda::thread_scope Scope, typename Allocator>
 class dynamic_map;
 
+/**
+ * @brief Customization point that can be specialized to indicate that it is safe to perform bitwise
+ * equality comparisons on objects of type `T`.
+ *
+ * By default, only types where `std::has_unique_object_representations_v<T>` is true are safe for
+ * bitwise equality. However, this can be too restrictive for some types, e.g., floating point
+ * types.
+ *
+ * User-defined specializations of `is_bitwise_comparable` are allowed, but it is the users
+ * responsibility to ensure values do not occur that would lead to unexpected behavior. For example,
+ * if a `NaN` bit pattern were used as the empty sentinel value, it may not compare bitwise equal to
+ * other `NaN` bit patterns.
+ *
+ */
+template <typename T, typename = void>
+struct is_bitwise_comparable : std::false_type {
+};
+
+template <typename T>
+  struct is_bitwise_comparable < T,
+  std::enable_if_t<std::has_unique_object_representations_v<T>> : std::true_type {
+};
+
+/**
+ * @brief Declares that a type `Type` is bitwise comparable.
+ * 
+ */
+#define CUCO_DECLARE_BITWISE_COMPARABLE(Type)           \
+  namespace cuco {                                      \
+  template <>                                           \
+  struct is_bitwise_comparable<Type> : std::true_type { \
+  };                                                    \
+  }
+
 /**
  * @brief A GPU-accelerated, unordered, associative container of key-value
  * pairs with unique keys.
@@ -118,14 +152,16 @@ template <typename Key,
           cuda::thread_scope Scope = cuda::thread_scope_device,
           typename Allocator       = cuco::cuda_allocator<char>>
 class static_map {
-  template <typename T>
-  static constexpr bool is_CAS_safe =
-    std::is_trivially_copyable_v<T>and std::has_unique_object_representations_v<T>;
-
-  static_assert(is_CAS_safe<Key>,
-                "Key type must be trivially copyable and have unique object representation.");
-  static_assert(is_CAS_safe<Value>,
-                "Value type must be trivially copyable and have unique object representation.");
+
+  static_assert(
+    is_bitwise_comparable<Key>::value,
+    "Key type must have unique object representations or have been explicitly declared as safe for "
+    "bitwise comparison via specialization of cuco::is_bitwise_comparable<Key>.");
+
+  static_assert(
+    is_bitwise_comparable<Value>::value,
+    "Key type must have unique object representations or have been explicitly declared as safe for "
+    "bitwise comparison via specialization of cuco::is_bitwise_comparable<Value>.");
 
   friend class dynamic_map<Key, Value, Scope, Allocator>;
 

From 228c1538f20857d82ee25ad1c549577d6ef3f768 Mon Sep 17 00:00:00 2001
From: Jake Hemstad <jhemstad@nvidia.com>
Date: Wed, 2 Jun 2021 11:46:53 -0500
Subject: [PATCH 126/267] Update docs.

---
 include/cuco/static_map.cuh | 9 ++++-----
 1 file changed, 4 insertions(+), 5 deletions(-)

diff --git a/include/cuco/static_map.cuh b/include/cuco/static_map.cuh
index 5abe40b19..3ae42b5a7 100644
--- a/include/cuco/static_map.cuh
+++ b/include/cuco/static_map.cuh
@@ -62,6 +62,7 @@ template <typename T, typename = void>
 struct is_bitwise_comparable : std::false_type {
 };
 
+/// By default, only types with unique object representations are allowed
 template <typename T>
   struct is_bitwise_comparable < T,
   std::enable_if_t<std::has_unique_object_representations_v<T>> : std::true_type {
@@ -86,14 +87,12 @@ template <typename T>
  * concurrent insert and find) from threads in device code.
  *
  * Current limitations:
- * - Requires keys and values that are trivially copyable and have unique object representations
+ * - Requires keys and values that where `cuco::is_bitwise_comparable<T>::value` is true
  *    - Comparisons against the "sentinel" values will always be done with bitwise comparisons.
- *      Therefore, the objects must have unique, bitwise object representations (e.g., no padding
- *      bits).
  * - Does not support erasing keys
  * - Capacity is fixed and will not grow automatically
- * - Requires the user to specify sentinel values for both key and mapped value
- * to indicate empty slots
+ * - Requires the user to specify sentinel values for both key and mapped value to indicate empty
+ * slots
  * - Does not support concurrent insert and find operations
  *
  * The `static_map` supports two types of operations:

From cf966396bb9aea2a4a72da46b41bd28f8ea14af5 Mon Sep 17 00:00:00 2001
From: Jake Hemstad <jhemstad@nvidia.com>
Date: Wed, 2 Jun 2021 11:51:41 -0500
Subject: [PATCH 127/267] fix typo.

---
 include/cuco/static_map.cuh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/include/cuco/static_map.cuh b/include/cuco/static_map.cuh
index 3ae42b5a7..5258a1894 100644
--- a/include/cuco/static_map.cuh
+++ b/include/cuco/static_map.cuh
@@ -159,7 +159,7 @@ class static_map {
 
   static_assert(
     is_bitwise_comparable<Value>::value,
-    "Key type must have unique object representations or have been explicitly declared as safe for "
+    "Value type must have unique object representations or have been explicitly declared as safe for "
     "bitwise comparison via specialization of cuco::is_bitwise_comparable<Value>.");
 
   friend class dynamic_map<Key, Value, Scope, Allocator>;

From c619954990e13941ea04d5af1621727f5ad39bae Mon Sep 17 00:00:00 2001
From: Jake Hemstad <jhemstad@nvidia.com>
Date: Wed, 2 Jun 2021 11:59:41 -0500
Subject: [PATCH 128/267] Missing angle bracket.

---
 include/cuco/static_map.cuh | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/include/cuco/static_map.cuh b/include/cuco/static_map.cuh
index 5258a1894..8a1f7acff 100644
--- a/include/cuco/static_map.cuh
+++ b/include/cuco/static_map.cuh
@@ -64,8 +64,8 @@ struct is_bitwise_comparable : std::false_type {
 
 /// By default, only types with unique object representations are allowed
 template <typename T>
-  struct is_bitwise_comparable < T,
-  std::enable_if_t<std::has_unique_object_representations_v<T>> : std::true_type {
+struct is_bitwise_comparable<T, std::enable_if_t<std::has_unique_object_representations_v<T>>>
+  : std::true_type {
 };
 
 /**

From fb7660ff631b0afa2379c52bfc712295fc563240 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Mon, 7 Jun 2021 11:02:02 -0400
Subject: [PATCH 129/267] Revert dynamic map and static map implementations to
 their main dev version

---
 include/cuco/detail/dynamic_map.inl         |   4 +-
 include/cuco/detail/dynamic_map_kernels.cuh | 301 ++++++++---------
 include/cuco/detail/static_map.inl          |  16 +-
 include/cuco/detail/static_map_kernels.cuh  | 339 ++++++++++----------
 include/cuco/dynamic_map.cuh                |   4 +-
 include/cuco/static_map.cuh                 |  40 ++-
 6 files changed, 347 insertions(+), 357 deletions(-)

diff --git a/include/cuco/detail/dynamic_map.inl b/include/cuco/detail/dynamic_map.inl
index e511489a4..57950ea45 100644
--- a/include/cuco/detail/dynamic_map.inl
+++ b/include/cuco/detail/dynamic_map.inl
@@ -35,7 +35,7 @@ dynamic_map<Key, Value, Scope, Allocator>::dynamic_map(std::size_t initial_capac
   submap_mutable_views_.push_back(submaps_[0]->get_device_mutable_view());
 
   CUCO_CUDA_TRY(cudaMallocManaged(&num_successes_, sizeof(atomic_ctr_type)));
-}
+}  // namespace cuco
 
 template <typename Key, typename Value, cuda::thread_scope Scope, typename Allocator>
 dynamic_map<Key, Value, Scope, Allocator>::~dynamic_map()
@@ -153,4 +153,4 @@ void dynamic_map<Key, Value, Scope, Allocator>::contains(
   CUCO_CUDA_TRY(cudaDeviceSynchronize());
 }
 
-}  // namespace cuco
+}  // namespace cuco
\ No newline at end of file
diff --git a/include/cuco/detail/dynamic_map_kernels.cuh b/include/cuco/detail/dynamic_map_kernels.cuh
index c1e21e863..28690b0dc 100644
--- a/include/cuco/detail/dynamic_map_kernels.cuh
+++ b/include/cuco/detail/dynamic_map_kernels.cuh
@@ -19,17 +19,17 @@ namespace detail {
 namespace cg = cooperative_groups;
 
 /**
- * @brief Inserts all key/value pairs in the range `[first, last)`.
- *
+ * @brief Inserts all key/value pairs in the range `[first, last)`. 
+ *  
  * If multiple keys in `[first, last)` compare equal, it is unspecified which
  * element is inserted.
- *
- * @tparam block_size
+ * 
+ * @tparam block_size 
  * @tparam pair_type Type of the pairs contained in the map
  * @tparam InputIt Device accessible input iterator whose `value_type` is
  * convertible to the map's `value_type`
  * @tparam viewT Type of the `static_map` device views
- * @tparam mutableViewT Type of the `static_map` device mutable views
+ * @tparam mutableViewT Type of the `static_map` device mutable views 
  * @tparam atomicT Type of atomic storage
  * @tparam viewT Type of device view allowing access of hash map storage
  * @tparam Hash Unary callable type
@@ -38,7 +38,7 @@ namespace cg = cooperative_groups;
  * @param last End of the sequence of key/value pairs
  * @param submap_views Array of `static_map::device_view` objects used to
  * perform `contains` operations on each underlying `static_map`
- * @param submap_mutable_views Array of `static_map::device_mutable_view` objects
+ * @param submap_mutable_views Array of `static_map::device_mutable_view` objects 
  * used to perform an `insert` into the target `static_map` submap
  * @param num_successes The number of successfully inserted key/value pairs
  * @param insert_idx The index of the submap we are inserting into
@@ -46,14 +46,14 @@ namespace cg = cooperative_groups;
  * @param hash The unary function to apply to hash each key
  * @param key_equal The binary function used to compare two keys for equality
  */
-template <uint32_t block_size,
-          typename pair_type,
-          typename InputIt,
-          typename viewT,
-          typename mutableViewT,
-          typename atomicT,
-          typename Hash,
-          typename KeyEqual>
+template<uint32_t block_size,
+         typename pair_type,
+         typename InputIt,
+         typename viewT,
+         typename mutableViewT,
+         typename atomicT,
+         typename Hash, 
+         typename KeyEqual>
 __global__ void insert(InputIt first,
                        InputIt last,
                        viewT* submap_views,
@@ -62,55 +62,58 @@ __global__ void insert(InputIt first,
                        uint32_t insert_idx,
                        uint32_t num_submaps,
                        Hash hash,
-                       KeyEqual key_equal)
-{
+                       KeyEqual key_equal) {
   typedef cub::BlockReduce<std::size_t, block_size> BlockReduce;
   __shared__ typename BlockReduce::TempStorage temp_storage;
   std::size_t thread_num_successes = 0;
-
+  
   auto tid = blockDim.x * blockIdx.x + threadIdx.x;
 
-  while (first + tid < last) {
+  while(first + tid < last) {
     pair_type insert_pair = *(first + tid);
-    auto exists           = false;
-
+    auto exists = false;
+    
     // manually check for duplicates in those submaps we are not inserting into
-    for (auto i = 0; i < num_submaps; ++i) {
-      if (i != insert_idx) {
+    for(auto i = 0; i < num_submaps; ++i) {
+      if(i != insert_idx) {
         exists = submap_views[i].contains(insert_pair.first, hash, key_equal);
-        if (exists) { break; }
+        if(exists) {
+          break;
+        }
       }
     }
-    if (!exists) {
-      if (submap_mutable_views[insert_idx].insert(insert_pair, hash, key_equal)) {
+    if(!exists) {
+      if(submap_mutable_views[insert_idx].insert(insert_pair, hash, key_equal)) {
         thread_num_successes++;
       }
     }
 
     tid += gridDim.x * blockDim.x;
   }
-
+  
   std::size_t block_num_successes = BlockReduce(temp_storage).Sum(thread_num_successes);
-  if (threadIdx.x == 0) { *num_successes += block_num_successes; }
+  if(threadIdx.x == 0) {
+    *num_successes += block_num_successes;
+  }
 }
 
 /**
- * @brief Inserts all key/value pairs in the range `[first, last)`.
- *
+ * @brief Inserts all key/value pairs in the range `[first, last)`. 
+ *  
  * If multiple keys in `[first, last)` compare equal, it is unspecified which
  * element is inserted. Uses the CUDA Cooperative Groups API to leverage groups
- * of multiple threads to perform each key/value insertion. This provides a
+ * of multiple threads to perform each key/value insertion. This provides a 
  * significant boost in throughput compared to the non Cooperative Group
  * `insert` at moderate to high load factors.
- *
- * @tparam block_size
+ * 
+ * @tparam block_size 
  * @tparam tile_size The number of threads in the Cooperative Groups used to perform
  * inserts
  * @tparam pair_type Type of the pairs contained in the map
  * @tparam InputIt Device accessible input iterator whose `value_type` is
  * convertible to the map's `value_type`
  * @tparam viewT Type of the `static_map` device views
- * @tparam mutableViewT Type of the `static_map` device mutable views
+ * @tparam mutableViewT Type of the `static_map` device mutable views 
  * @tparam atomicT Type of atomic storage
  * @tparam viewT Type of device view allowing access of hash map storage
  * @tparam Hash Unary callable type
@@ -119,7 +122,7 @@ __global__ void insert(InputIt first,
  * @param last End of the sequence of key/value pairs
  * @param submap_views Array of `static_map::device_view` objects used to
  * perform `contains` operations on each underlying `static_map`
- * @param submap_mutable_views Array of `static_map::device_mutable_view` objects
+ * @param submap_mutable_views Array of `static_map::device_mutable_view` objects 
  * used to perform an `insert` into the target `static_map` submap
  * @param num_successes The number of successfully inserted key/value pairs
  * @param insert_idx The index of the submap we are inserting into
@@ -127,15 +130,14 @@ __global__ void insert(InputIt first,
  * @param hash The unary function to apply to hash each key
  * @param key_equal The binary function used to compare two keys for equality
  */
-template <uint32_t block_size,
-          uint32_t tile_size,
-          typename pair_type,
-          typename InputIt,
-          typename viewT,
-          typename mutableViewT,
-          typename atomicT,
-          typename Hash,
-          typename KeyEqual>
+template<uint32_t block_size, uint32_t tile_size,
+         typename pair_type,
+         typename InputIt,
+         typename viewT,
+         typename mutableViewT,
+         typename atomicT,
+         typename Hash, 
+         typename KeyEqual>
 __global__ void insert(InputIt first,
                        InputIt last,
                        viewT* submap_views,
@@ -144,51 +146,54 @@ __global__ void insert(InputIt first,
                        uint32_t insert_idx,
                        uint32_t num_submaps,
                        Hash hash,
-                       KeyEqual key_equal)
-{
+                       KeyEqual key_equal) {
   typedef cub::BlockReduce<std::size_t, block_size> BlockReduce;
   __shared__ typename BlockReduce::TempStorage temp_storage;
   std::size_t thread_num_successes = 0;
-
+  
   auto tile = cg::tiled_partition<tile_size>(cg::this_thread_block());
-  auto tid  = blockDim.x * blockIdx.x + threadIdx.x;
-  auto it   = first + tid / tile_size;
+  auto tid = blockDim.x * blockIdx.x + threadIdx.x;
+  auto it = first + tid / tile_size;
 
-  while (it < last) {
+  while(it < last) {
     pair_type insert_pair = *it;
-    auto exists           = false;
-
+    auto exists = false;
+    
     // manually check for duplicates in those submaps we are not inserting into
-    for (auto i = 0; i < num_submaps; ++i) {
-      if (i != insert_idx) {
+    for(auto i = 0; i < num_submaps; ++i) {
+      if(i != insert_idx) {
         exists = submap_views[i].contains(tile, insert_pair.first, hash, key_equal);
-        if (exists) { break; }
+        if(exists) {
+          break;
+        }
       }
     }
-    if (!exists) {
-      if (submap_mutable_views[insert_idx].insert(tile, insert_pair, hash, key_equal) &&
-          tile.thread_rank() == 0) {
+    if(!exists) {
+      if(submap_mutable_views[insert_idx].insert(tile, insert_pair, hash, key_equal) && 
+         tile.thread_rank() == 0) {
         thread_num_successes++;
       }
     }
 
     it += (gridDim.x * blockDim.x) / tile_size;
   }
-
+  
   std::size_t block_num_successes = BlockReduce(temp_storage).Sum(thread_num_successes);
-  if (threadIdx.x == 0) { *num_successes += block_num_successes; }
+  if(threadIdx.x == 0) {
+    *num_successes += block_num_successes;
+  }
 }
 
 /**
  * @brief Finds the values corresponding to all keys in the range `[first, last)`.
- *
- * If the key `*(first + i)` exists in the map, copies its associated value to `(output_begin + i)`.
- * Else, copies the empty value sentinel.
+ * 
+ * If the key `*(first + i)` exists in the map, copies its associated value to `(output_begin + i)`. 
+ * Else, copies the empty value sentinel. 
  * @tparam block_size The number of threads in the thread block
- * @tparam Value The mapped value type for the map
+ * @tparam Value The mapped value type for the map 
  * @tparam InputIt Device accessible input iterator whose `value_type` is
  * convertible to the map's `key_type`
- * @tparam OutputIt Device accessible output iterator whose `value_type` is
+ * @tparam OutputIt Device accessible output iterator whose `value_type` is 
  * convertible to the map's `mapped_type`
  * @tparam viewT Type of `static_map` device view
  * @tparam Hash Unary callable type
@@ -202,39 +207,37 @@ __global__ void insert(InputIt first,
  * @param hash The unary function to apply to hash each key
  * @param key_equal The binary function to compare two keys for equality
  */
-template <uint32_t block_size,
-          typename Value,
-          typename InputIt,
-          typename OutputIt,
-          typename viewT,
-          typename Hash,
-          typename KeyEqual>
+template<uint32_t block_size,
+         typename Value,
+         typename InputIt, typename OutputIt, 
+         typename viewT,
+         typename Hash, 
+         typename KeyEqual>
 __global__ void find(InputIt first,
                      InputIt last,
                      OutputIt output_begin,
                      viewT* submap_views,
                      uint32_t num_submaps,
                      Hash hash,
-                     KeyEqual key_equal)
-{
-  auto tid                  = blockDim.x * blockIdx.x + threadIdx.x;
+                     KeyEqual key_equal) {
+  auto tid = blockDim.x * blockIdx.x + threadIdx.x;
   auto empty_value_sentinel = submap_views[0].get_empty_value_sentinel();
   __shared__ Value writeBuffer[block_size];
 
-  while (first + tid < last) {
-    auto key         = *(first + tid);
+  while(first + tid < last) {
+    auto key = *(first + tid);
     auto found_value = empty_value_sentinel;
-    for (auto i = 0; i < num_submaps; ++i) {
+    for(auto i = 0; i < num_submaps; ++i) {
       auto submap_view = submap_views[i];
-      auto found       = submap_view.find(key, hash, key_equal);
-      if (found != submap_view.end()) {
+      auto found = submap_view.find(key, hash, key_equal);
+      if(found != submap_view.end()) {
         found_value = found->second;
         break;
       }
     }
 
-    /*
-     * The ld.relaxed.gpu instruction used in view.find causes L1 to
+    /* 
+     * The ld.relaxed.gpu instruction used in view.find causes L1 to 
      * flush more frequently, causing increased sector stores from L2 to global memory.
      * By writing results to shared memory and then synchronizing before writing back
      * to global, we no longer rely on L1, preventing the increase in sector stores from
@@ -249,10 +252,10 @@ __global__ void find(InputIt first,
 
 /**
  * @brief Finds the values corresponding to all keys in the range `[first, last)`.
- *
- * If the key `*(first + i)` exists in the map, copies its associated value to `(output_begin + i)`.
+ * 
+ * If the key `*(first + i)` exists in the map, copies its associated value to `(output_begin + i)`. 
  * Else, copies the empty value sentinel. Uses the CUDA Cooperative Groups API to leverage groups
- * of multiple threads to find each key. This provides a significant boost in throughput compared
+ * of multiple threads to find each key. This provides a significant boost in throughput compared 
  * to the non Cooperative Group `find` at moderate to high load factors.
  *
  * @tparam block_size The number of threads in the thread block
@@ -261,7 +264,7 @@ __global__ void find(InputIt first,
  * @tparam Value The mapped value type for the map
  * @tparam InputIt Device accessible input iterator whose `value_type` is
  * convertible to the map's `key_type`
- * @tparam OutputIt Device accessible output iterator whose `value_type` is
+ * @tparam OutputIt Device accessible output iterator whose `value_type` is 
  * convertible to the map's `mapped_type`
  * @tparam viewT Type of `static_map` device view
  * @tparam Hash Unary callable type
@@ -275,50 +278,49 @@ __global__ void find(InputIt first,
  * @param hash The unary function to apply to hash each key
  * @param key_equal The binary function to compare two keys for equality
  */
-template <uint32_t block_size,
-          uint32_t tile_size,
-          typename Value,
-          typename InputIt,
-          typename OutputIt,
-          typename viewT,
-          typename Hash,
-          typename KeyEqual>
+template<uint32_t block_size, uint32_t tile_size,
+         typename Value,
+         typename InputIt, typename OutputIt, 
+         typename viewT,
+         typename Hash, 
+         typename KeyEqual>
 __global__ void find(InputIt first,
                      InputIt last,
                      OutputIt output_begin,
                      viewT* submap_views,
                      uint32_t num_submaps,
                      Hash hash,
-                     KeyEqual key_equal)
-{
-  auto tile                 = cg::tiled_partition<tile_size>(cg::this_thread_block());
-  auto tid                  = blockDim.x * blockIdx.x + threadIdx.x;
-  auto key_idx              = tid / tile_size;
+                     KeyEqual key_equal) {
+  auto tile = cg::tiled_partition<tile_size>(cg::this_thread_block());
+  auto tid = blockDim.x * blockIdx.x + threadIdx.x;
+  auto key_idx = tid / tile_size;
   auto empty_value_sentinel = submap_views[0].get_empty_value_sentinel();
   __shared__ Value writeBuffer[block_size];
 
-  while (first + key_idx < last) {
-    auto key         = *(first + key_idx);
+  while(first + key_idx < last) {
+    auto key = *(first + key_idx);
     auto found_value = empty_value_sentinel;
-    for (auto i = 0; i < num_submaps; ++i) {
+    for(auto i = 0; i < num_submaps; ++i) {
       auto submap_view = submap_views[i];
-      auto found       = submap_view.find(tile, key, hash, key_equal);
-      if (found != submap_view.end()) {
+      auto found = submap_view.find(tile, key, hash, key_equal);
+      if(found != submap_view.end()) {
         found_value = found->second;
         break;
       }
     }
 
-    /*
-     * The ld.relaxed.gpu instruction used in view.find causes L1 to
+    /* 
+     * The ld.relaxed.gpu instruction used in view.find causes L1 to 
      * flush more frequently, causing increased sector stores from L2 to global memory.
      * By writing results to shared memory and then synchronizing before writing back
      * to global, we no longer rely on L1, preventing the increase in sector stores from
      * L2 to global and improving performance.
      */
-    if (tile.thread_rank() == 0) { writeBuffer[threadIdx.x / tile_size] = found_value; }
+    if(tile.thread_rank() == 0) {
+      writeBuffer[threadIdx.x / tile_size] = found_value;
+    }
     __syncthreads();
-    if (tile.thread_rank() == 0) {
+    if(tile.thread_rank() == 0) {
       *(output_begin + key_idx) = writeBuffer[threadIdx.x / tile_size];
     }
     key_idx += (gridDim.x * blockDim.x) / tile_size;
@@ -327,13 +329,13 @@ __global__ void find(InputIt first,
 
 /**
  * @brief Indicates whether the keys in the range `[first, last)` are contained in the map.
- *
+ * 
  * Writes a `bool` to `(output + i)` indicating if the key `*(first + i)` exists in the map.
  *
  * @tparam block_size The number of threads in the thread block
  * @tparam InputIt Device accessible input iterator whose `value_type` is
  * convertible to the map's `key_type`
- * @tparam OutputIt Device accessible output iterator whose `value_type` is
+ * @tparam OutputIt Device accessible output iterator whose `value_type` is 
  * convertible to the map's `mapped_type`
  * @tparam viewT Type of `static_map` device view
  * @tparam Hash Unary callable type
@@ -347,33 +349,33 @@ __global__ void find(InputIt first,
  * @param hash The unary function to apply to hash each key
  * @param key_equal The binary function to compare two keys for equality
  */
-template <uint32_t block_size,
-          typename InputIt,
-          typename OutputIt,
-          typename viewT,
-          typename Hash,
-          typename KeyEqual>
+template<uint32_t block_size,
+         typename InputIt, typename OutputIt, 
+         typename viewT,
+         typename Hash, 
+         typename KeyEqual>
 __global__ void contains(InputIt first,
                          InputIt last,
                          OutputIt output_begin,
                          viewT* submap_views,
                          uint32_t num_submaps,
                          Hash hash,
-                         KeyEqual key_equal)
-{
+                         KeyEqual key_equal) {
   auto tid = blockDim.x * blockIdx.x + threadIdx.x;
   __shared__ bool writeBuffer[block_size];
 
-  while (first + tid < last) {
-    auto key   = *(first + tid);
+  while(first + tid < last) {
+    auto key = *(first + tid);
     auto found = false;
-    for (auto i = 0; i < num_submaps; ++i) {
+    for(auto i = 0; i < num_submaps; ++i) {
       found = submap_views[i].contains(key, hash, key_equal);
-      if (found) { break; }
+      if(found) {
+        break;
+      }
     }
-
-    /*
-     * The ld.relaxed.gpu instruction used in view.find causes L1 to
+    
+    /* 
+     * The ld.relaxed.gpu instruction used in view.find causes L1 to 
      * flush more frequently, causing increased sector stores from L2 to global memory.
      * By writing results to shared memory and then synchronizing before writing back
      * to global, we no longer rely on L1, preventing the increase in sector stores from
@@ -388,10 +390,10 @@ __global__ void contains(InputIt first,
 
 /**
  * @brief Indicates whether the keys in the range `[first, last)` are contained in the map.
- *
+ * 
  * Writes a `bool` to `(output + i)` indicating if the key `*(first + i)` exists in the map.
- * Uses the CUDA Cooperative Groups API to leverage groups of multiple threads to perform the
- * contains operation for each key. This provides a significant boost in throughput compared
+ * Uses the CUDA Cooperative Groups API to leverage groups of multiple threads to perform the 
+ * contains operation for each key. This provides a significant boost in throughput compared 
  * to the non Cooperative Group `contains` at moderate to high load factors.
  *
  * @tparam block_size The number of threads in the thread block
@@ -399,7 +401,7 @@ __global__ void contains(InputIt first,
  * perform find operations
  * @tparam InputIt Device accessible input iterator whose `value_type` is
  * convertible to the map's `key_type`
- * @tparam OutputIt Device accessible output iterator whose `value_type` is
+ * @tparam OutputIt Device accessible output iterator whose `value_type` is 
  * convertible to the map's `mapped_type`
  * @tparam viewT Type of `static_map` device view
  * @tparam Hash Unary callable type
@@ -413,48 +415,49 @@ __global__ void contains(InputIt first,
  * @param hash The unary function to apply to hash each key
  * @param key_equal The binary function to compare two keys for equality
  */
-template <uint32_t block_size,
-          uint32_t tile_size,
-          typename InputIt,
-          typename OutputIt,
-          typename viewT,
-          typename Hash,
-          typename KeyEqual>
+template<uint32_t block_size, uint32_t tile_size,
+         typename InputIt, typename OutputIt, 
+         typename viewT,
+         typename Hash, 
+         typename KeyEqual>
 __global__ void contains(InputIt first,
                          InputIt last,
                          OutputIt output_begin,
                          viewT* submap_views,
                          uint32_t num_submaps,
                          Hash hash,
-                         KeyEqual key_equal)
-{
-  auto tile    = cg::tiled_partition<tile_size>(cg::this_thread_block());
-  auto tid     = blockDim.x * blockIdx.x + threadIdx.x;
+                         KeyEqual key_equal) {
+  auto tile = cg::tiled_partition<tile_size>(cg::this_thread_block());
+  auto tid = blockDim.x * blockIdx.x + threadIdx.x;
   auto key_idx = tid / tile_size;
   __shared__ bool writeBuffer[block_size];
 
-  while (first + key_idx < last) {
-    auto key   = *(first + key_idx);
+  while(first + key_idx < last) {
+    auto key = *(first + key_idx);
     auto found = false;
-    for (auto i = 0; i < num_submaps; ++i) {
+    for(auto i = 0; i < num_submaps; ++i) {
       found = submap_views[i].contains(tile, key, hash, key_equal);
-      if (found) { break; }
+      if(found) {
+        break;
+      }
     }
 
-    /*
-     * The ld.relaxed.gpu instruction used in view.find causes L1 to
+    /* 
+     * The ld.relaxed.gpu instruction used in view.find causes L1 to 
      * flush more frequently, causing increased sector stores from L2 to global memory.
      * By writing results to shared memory and then synchronizing before writing back
      * to global, we no longer rely on L1, preventing the increase in sector stores from
      * L2 to global and improving performance.
      */
-    if (tile.thread_rank() == 0) { writeBuffer[threadIdx.x / tile_size] = found; }
+    if(tile.thread_rank() == 0) {
+      writeBuffer[threadIdx.x / tile_size] = found;
+    }
     __syncthreads();
-    if (tile.thread_rank() == 0) {
+    if(tile.thread_rank() == 0) {
       *(output_begin + key_idx) = writeBuffer[threadIdx.x / tile_size];
     }
     key_idx += (gridDim.x * blockDim.x) / tile_size;
   }
 }
-}  // namespace detail
-}  // namespace cuco
\ No newline at end of file
+} // namespace detail
+} // namespace cuco
\ No newline at end of file
diff --git a/include/cuco/detail/static_map.inl b/include/cuco/detail/static_map.inl
index 2b72cfa32..b8f7ccdba 100644
--- a/include/cuco/detail/static_map.inl
+++ b/include/cuco/detail/static_map.inl
@@ -63,7 +63,7 @@ void static_map<Key, Value, Scope, Allocator>::insert(InputIt first,
                                                       Hash hash,
                                                       KeyEqual key_equal)
 {
-  auto num_keys = std::distance(first, last);
+  auto num_keys         = std::distance(first, last);
   if (num_keys == 0) { return; }
 
   auto const block_size = 128;
@@ -87,9 +87,9 @@ void static_map<Key, Value, Scope, Allocator>::insert(InputIt first,
 template <typename Key, typename Value, cuda::thread_scope Scope, typename Allocator>
 template <typename InputIt, typename OutputIt, typename Hash, typename KeyEqual>
 void static_map<Key, Value, Scope, Allocator>::find(
-  InputIt first, InputIt last, OutputIt output_begin, Hash hash, KeyEqual key_equal)
+  InputIt first, InputIt last, OutputIt output_begin, Hash hash, KeyEqual key_equal) 
 {
-  auto num_keys = std::distance(first, last);
+  auto num_keys         = std::distance(first, last);
   if (num_keys == 0) { return; }
 
   auto const block_size = 128;
@@ -108,7 +108,7 @@ template <typename InputIt, typename OutputIt, typename Hash, typename KeyEqual>
 void static_map<Key, Value, Scope, Allocator>::contains(
   InputIt first, InputIt last, OutputIt output_begin, Hash hash, KeyEqual key_equal)
 {
-  auto num_keys = std::distance(first, last);
+  auto num_keys         = std::distance(first, last);
   if (num_keys == 0) { return; }
 
   auto const block_size = 128;
@@ -175,8 +175,7 @@ __device__ bool static_map<Key, Value, Scope, Allocator>::device_mutable_view::i
 
     // The user provide `key_equal` can never be used to compare against `empty_key_sentinel` as the
     // sentinel is not a valid key value. Therefore, first check for the sentinel
-    auto const slot_is_empty =
-      detail::bitwise_compare(existing_key, this->get_empty_key_sentinel());
+    auto const slot_is_empty = detail::bitwise_compare(existing_key,this->get_empty_key_sentinel());
 
     // the key we are trying to insert is already in the map, so we return with failure to insert
     if (g.ballot(not slot_is_empty and key_equal(existing_key, insert_pair.first))) {
@@ -375,7 +374,7 @@ __device__ bool static_map<Key, Value, Scope, Allocator>::device_view::contains(
   while (true) {
     auto const existing_key = current_slot->first.load(cuda::std::memory_order_relaxed);
 
-    if (detail::bitwise_compare(existing_key, empty_key_sentinel_)) { return false; }
+    if (detail::bitwise_compare(existing_key,empty_key_sentinel_)) { return false; }
 
     if (key_equal(existing_key, k)) { return true; }
 
@@ -395,8 +394,7 @@ __device__ bool static_map<Key, Value, Scope, Allocator>::device_view::contains(
 
     // The user provide `key_equal` can never be used to compare against `empty_key_sentinel` as the
     // sentinel is not a valid key value. Therefore, first check for the sentinel
-    auto const slot_is_empty =
-      detail::bitwise_compare(existing_key, this->get_empty_key_sentinel());
+    auto const slot_is_empty = detail::bitwise_compare(existing_key,this->get_empty_key_sentinel());
 
     // the key we were searching for was found by one of the threads, so we return an iterator to
     // the entry
diff --git a/include/cuco/detail/static_map_kernels.cuh b/include/cuco/detail/static_map_kernels.cuh
index c243414e8..aa4606aa6 100644
--- a/include/cuco/detail/static_map_kernels.cuh
+++ b/include/cuco/detail/static_map_kernels.cuh
@@ -12,9 +12,9 @@
  * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  * See the License for the specific language governing permissions and
  * limitations under the License.
- */
+ */ 
 
-namespace cuco {
+namespace cuco{
 namespace detail {
 namespace cg = cooperative_groups;
 
@@ -23,7 +23,7 @@ namespace cg = cooperative_groups;
  *
  * Each space in `slots` that can hold a key value pair is initialized to a
  * `pair_atomic_type` containing the key `k` and the value `v`.
- *
+ * 
  * @tparam atomic_key_type Type of the `Key` atomic container
  * @tparam atomic_mapped_type Type of the `Value` atomic container
  * @tparam Key key type
@@ -34,14 +34,16 @@ namespace cg = cooperative_groups;
  * @param v Value to which all values in `slots` are initialized
  * @param size Size of the storage pointed to by `slots`
  */
-template <std::size_t block_size,
-          typename atomic_key_type,
-          typename atomic_mapped_type,
-          typename Key,
-          typename Value,
-          typename pair_atomic_type>
-__global__ void initialize(pair_atomic_type* const slots, Key k, Value v, std::size_t size)
-{
+template<std::size_t block_size,
+         typename atomic_key_type,
+         typename atomic_mapped_type,
+         typename Key,
+         typename Value,
+         typename pair_atomic_type>
+__global__ void initialize(
+  pair_atomic_type* const slots, Key k,
+  Value v, std::size_t size) {
+  
   auto tid = block_size * blockIdx.x + threadIdx.x;
   while (tid < size) {
     new (&slots[tid].first) atomic_key_type{k};
@@ -51,45 +53,14 @@ __global__ void initialize(pair_atomic_type* const slots, Key k, Value v, std::s
 }
 
 /**
- * @brief Initializes each slot in the flat `slots` storage to contain `k` and `v`.
- *
- * Each space in `slots` that can hold a key value pair is initialized to a
- * `pair_atomic_type` containing the key `k` and the value `v`.
- *
- * @tparam atomic_key_type Type of the `Key` atomic container
- * @tparam atomic_mapped_type Type of the `Value` atomic container
- * @tparam Key key type
- * @tparam Value value type
- * @tparam pair_atomic_type key/value pair type
- * @param slots Pointer to flat storage for the map's key/value pairs
- * @param k Key to which all keys in `slots` are initialized
- * @param v Value to which all values in `slots` are initialized
- * @param size Size of the storage pointed to by `slots`
- */
-template <typename atomic_key_type,
-          typename atomic_mapped_type,
-          typename Key,
-          typename Value,
-          typename pair_atomic_type>
-__global__ void initialize(pair_atomic_type* const slots, Key k, Value v, std::size_t size)
-{
-  auto tid = threadIdx.x + blockIdx.x * blockDim.x;
-  while (tid < size) {
-    new (&slots[tid].first) atomic_key_type{k};
-    new (&slots[tid].second) atomic_mapped_type{v};
-    tid += gridDim.x * blockDim.x;
-  }
-}
-
-/**
- * @brief Inserts all key/value pairs in the range `[first, last)`.
- *
+ * @brief Inserts all key/value pairs in the range `[first, last)`. 
+ *  
  * If multiple keys in `[first, last)` compare equal, it is unspecified which
  * element is inserted.
- *
- * @tparam block_size
+ * 
+ * @tparam block_size 
  * @tparam InputIt Device accessible input iterator whose `value_type` is
- * convertible to the map's `value_type`
+* convertible to the map's `value_type`
  * @tparam atomicT Type of atomic storage
  * @tparam viewT Type of device view allowing access of hash map storage
  * @tparam Hash Unary callable type
@@ -101,22 +72,25 @@ __global__ void initialize(pair_atomic_type* const slots, Key k, Value v, std::s
  * @param hash The unary function to apply to hash each key
  * @param key_equal The binary function used to compare two keys for equality
  */
-template <uint32_t block_size,
-          typename InputIt,
-          typename atomicT,
-          typename viewT,
-          typename Hash,
-          typename KeyEqual>
-__global__ void insert(
-  InputIt first, InputIt last, atomicT* num_successes, viewT view, Hash hash, KeyEqual key_equal)
-{
+template<std::size_t block_size,
+         typename InputIt,
+         typename atomicT,
+         typename viewT,
+         typename Hash, 
+         typename KeyEqual>
+__global__ void insert(InputIt first,
+                       InputIt last,
+                       atomicT* num_successes,
+                       viewT view,
+                       Hash hash,
+                       KeyEqual key_equal) {
   typedef cub::BlockReduce<std::size_t, block_size> BlockReduce;
   __shared__ typename BlockReduce::TempStorage temp_storage;
   std::size_t thread_num_successes = 0;
-
+  
   auto tid = block_size * blockIdx.x + threadIdx.x;
-  auto it  = first + tid;
-
+  auto it = first + tid;
+  
   while (it < last) {
     typename viewT::value_type const insert_pair{*it};
     if (view.insert(insert_pair, hash, key_equal)) { thread_num_successes++; }
@@ -126,23 +100,25 @@ __global__ void insert(
   // compute number of successfully inserted elements for each block
   // and atomically add to the grand total
   std::size_t block_num_successes = BlockReduce(temp_storage).Sum(thread_num_successes);
-  if (threadIdx.x == 0) { *num_successes += block_num_successes; }
+  if(threadIdx.x == 0) {
+    *num_successes += block_num_successes;
+  }
 }
 
 /**
- * @brief Inserts all key/value pairs in the range `[first, last)`.
- *
+ * @brief Inserts all key/value pairs in the range `[first, last)`. 
+ *  
  * If multiple keys in `[first, last)` compare equal, it is unspecified which
  * element is inserted. Uses the CUDA Cooperative Groups API to leverage groups
- * of multiple threads to perform each key/value insertion. This provides a
+ * of multiple threads to perform each key/value insertion. This provides a 
  * significant boost in throughput compared to the non Cooperative Group
  * `insert` at moderate to high load factors.
- *
- * @tparam block_size
+ * 
+ * @tparam block_size 
  * @tparam tile_size The number of threads in the Cooperative Groups used to perform
  * inserts
  * @tparam InputIt Device accessible input iterator whose `value_type` is
- * convertible to the map's `value_type`
+* convertible to the map's `value_type`
  * @tparam atomicT Type of atomic storage
  * @tparam viewT Type of device view allowing access of hash map storage
  * @tparam Hash Unary callable type
@@ -154,24 +130,27 @@ __global__ void insert(
  * @param hash The unary function to apply to hash each key
  * @param key_equal The binary function used to compare two keys for equality
  */
-template <uint32_t block_size,
-          uint32_t tile_size,
-          typename InputIt,
-          typename atomicT,
-          typename viewT,
-          typename Hash,
-          typename KeyEqual>
-__global__ void insert(
-  InputIt first, InputIt last, atomicT* num_successes, viewT view, Hash hash, KeyEqual key_equal)
-{
+template<std::size_t block_size,
+         uint32_t tile_size,
+         typename InputIt,
+         typename atomicT,
+         typename viewT,
+         typename Hash, 
+         typename KeyEqual>
+__global__ void insert(InputIt first,
+                       InputIt last,
+                       atomicT* num_successes,
+                       viewT view,
+                       Hash hash,
+                       KeyEqual key_equal) {
   typedef cub::BlockReduce<std::size_t, block_size> BlockReduce;
   __shared__ typename BlockReduce::TempStorage temp_storage;
   std::size_t thread_num_successes = 0;
 
   auto tile = cg::tiled_partition<tile_size>(cg::this_thread_block());
-  auto tid  = block_size * blockIdx.x + threadIdx.x;
-  auto it   = first + tid / tile_size;
-
+  auto tid = block_size * blockIdx.x + threadIdx.x;
+  auto it = first + tid / tile_size;
+  
   while (it < last) {
     // force conversion to value_type
     typename viewT::value_type const insert_pair{*it};
@@ -180,23 +159,25 @@ __global__ void insert(
     }
     it += (gridDim.x * block_size) / tile_size;
   }
-
+  
   // compute number of successfully inserted elements for each block
   // and atomically add to the grand total
   std::size_t block_num_successes = BlockReduce(temp_storage).Sum(thread_num_successes);
-  if (threadIdx.x == 0) { *num_successes += block_num_successes; }
+  if(threadIdx.x == 0) {
+    *num_successes += block_num_successes;
+  }
 }
 
 /**
  * @brief Finds the values corresponding to all keys in the range `[first, last)`.
- *
- * If the key `*(first + i)` exists in the map, copies its associated value to `(output_begin + i)`.
- * Else, copies the empty value sentinel.
+ * 
+ * If the key `*(first + i)` exists in the map, copies its associated value to `(output_begin + i)`. 
+ * Else, copies the empty value sentinel. 
  * @tparam block_size The size of the thread block
  * @tparam Value The type of the mapped value for the map
  * @tparam InputIt Device accessible input iterator whose `value_type` is
  * convertible to the map's `key_type`
- * @tparam OutputIt Device accessible output iterator whose `value_type` is
+ * @tparam OutputIt Device accessible output iterator whose `value_type` is 
  * convertible to the map's `mapped_type`
  * @tparam viewT Type of device view allowing access of hash map storage
  * @tparam Hash Unary callable type
@@ -208,34 +189,37 @@ __global__ void insert(
  * @param hash The unary function to apply to hash each key
  * @param key_equal The binary function to compare two keys for equality
  */
-template <uint32_t block_size,
-          typename Value,
-          typename InputIt,
-          typename OutputIt,
-          typename viewT,
-          typename Hash,
-          typename KeyEqual>
-__global__ void find(
-  InputIt first, InputIt last, OutputIt output_begin, viewT view, Hash hash, KeyEqual key_equal)
-{
-  auto tid     = block_size * blockIdx.x + threadIdx.x;
+template<std::size_t block_size,
+         typename Value,
+         typename InputIt, typename OutputIt, 
+         typename viewT,
+         typename Hash, 
+         typename KeyEqual>
+__global__ void find(InputIt first,
+                     InputIt last,
+                     OutputIt output_begin,
+                     viewT view,
+                     Hash hash,
+                     KeyEqual key_equal) {
+  auto tid = block_size * blockIdx.x + threadIdx.x;
   auto key_idx = tid;
   __shared__ Value writeBuffer[block_size];
-
-  while (first + key_idx < last) {
-    auto key   = *(first + key_idx);
+  
+  while(first + key_idx < last) {
+    auto key = *(first + key_idx);
     auto found = view.find(key, hash, key_equal);
 
-    /*
-     * The ld.relaxed.gpu instruction used in view.find causes L1 to
+    /* 
+     * The ld.relaxed.gpu instruction used in view.find causes L1 to 
      * flush more frequently, causing increased sector stores from L2 to global memory.
      * By writing results to shared memory and then synchronizing before writing back
      * to global, we no longer rely on L1, preventing the increase in sector stores from
      * L2 to global and improving performance.
      */
-    writeBuffer[threadIdx.x] = found == view.end()
-                                 ? view.get_empty_value_sentinel()
-                                 : found->second.load(cuda::std::memory_order_relaxed);
+    writeBuffer[threadIdx.x] =
+      found == view.end()
+        ? view.get_empty_value_sentinel()
+        : found->second.load(cuda::std::memory_order_relaxed);
     __syncthreads();
     *(output_begin + key_idx) = writeBuffer[threadIdx.x];
     key_idx += gridDim.x * block_size;
@@ -244,10 +228,10 @@ __global__ void find(
 
 /**
  * @brief Finds the values corresponding to all keys in the range `[first, last)`.
- *
- * If the key `*(first + i)` exists in the map, copies its associated value to `(output_begin + i)`.
+ * 
+ * If the key `*(first + i)` exists in the map, copies its associated value to `(output_begin + i)`. 
  * Else, copies the empty value sentinel. Uses the CUDA Cooperative Groups API to leverage groups
- * of multiple threads to find each key. This provides a significant boost in throughput compared
+ * of multiple threads to find each key. This provides a significant boost in throughput compared 
  * to the non Cooperative Group `find` at moderate to high load factors.
  *
  * @tparam block_size The size of the thread block
@@ -256,7 +240,7 @@ __global__ void find(
  * @tparam Value The type of the mapped value for the map
  * @tparam InputIt Device accessible input iterator whose `value_type` is
  * convertible to the map's `key_type`
- * @tparam OutputIt Device accessible output iterator whose `value_type` is
+ * @tparam OutputIt Device accessible output iterator whose `value_type` is 
  * convertible to the map's `mapped_type`
  * @tparam viewT Type of device view allowing access of hash map storage
  * @tparam Hash Unary callable type
@@ -268,40 +252,42 @@ __global__ void find(
  * @param hash The unary function to apply to hash each key
  * @param key_equal The binary function to compare two keys for equality
  */
-template <uint32_t block_size,
-          uint32_t tile_size,
-          typename Value,
-          typename InputIt,
-          typename OutputIt,
-          typename viewT,
-          typename Hash,
-          typename KeyEqual>
-__global__ void find(
-  InputIt first, InputIt last, OutputIt output_begin, viewT view, Hash hash, KeyEqual key_equal)
-{
-  auto tile    = cg::tiled_partition<tile_size>(cg::this_thread_block());
-  auto tid     = block_size * blockIdx.x + threadIdx.x;
+template<std::size_t block_size, uint32_t tile_size,
+         typename Value,
+         typename InputIt, typename OutputIt, 
+         typename viewT,
+         typename Hash, 
+         typename KeyEqual>
+__global__ void find(InputIt first,
+                     InputIt last,
+                     OutputIt output_begin,
+                     viewT view,
+                     Hash hash,
+                     KeyEqual key_equal) {
+  auto tile = cg::tiled_partition<tile_size>(cg::this_thread_block());
+  auto tid = block_size * blockIdx.x + threadIdx.x;
   auto key_idx = tid / tile_size;
   __shared__ Value writeBuffer[block_size];
-
-  while (first + key_idx < last) {
-    auto key   = *(first + key_idx);
+  
+  while(first + key_idx < last) {
+    auto key = *(first + key_idx);
     auto found = view.find(tile, key, hash, key_equal);
-
-    /*
-     * The ld.relaxed.gpu instruction used in view.find causes L1 to
+    
+    /* 
+     * The ld.relaxed.gpu instruction used in view.find causes L1 to 
      * flush more frequently, causing increased sector stores from L2 to global memory.
      * By writing results to shared memory and then synchronizing before writing back
      * to global, we no longer rely on L1, preventing the increase in sector stores from
      * L2 to global and improving performance.
      */
-    if (tile.thread_rank() == 0) {
+    if(tile.thread_rank() == 0) {
       writeBuffer[threadIdx.x / tile_size] =
-        found == view.end() ? view.get_empty_value_sentinel()
-                            : found->second.load(cuda::std::memory_order_relaxed);
+        found == view.end()
+          ? view.get_empty_value_sentinel()
+          : found->second.load(cuda::std::memory_order_relaxed);
     }
     __syncthreads();
-    if (tile.thread_rank() == 0) {
+    if(tile.thread_rank() == 0) {
       *(output_begin + key_idx) = writeBuffer[threadIdx.x / tile_size];
     }
     key_idx += (gridDim.x * block_size) / tile_size;
@@ -310,7 +296,7 @@ __global__ void find(
 
 /**
  * @brief Indicates whether the keys in the range `[first, last)` are contained in the map.
- *
+ * 
  * Writes a `bool` to `(output + i)` indicating if the key `*(first + i)` exists in the map.
  *
  * @tparam block_size The size of the thread block
@@ -320,7 +306,7 @@ __global__ void find(
  * convertible to the map's `mapped_type`
  * @tparam viewT Type of device view allowing access of hash map storage
  * @tparam Hash Unary callable type
- * @tparam KeyEqual Binary callable type
+ * @tparam KeyEqual Binary callable type 
  * @param first Beginning of the sequence of keys
  * @param last End of the sequence of keys
  * @param output_begin Beginning of the sequence of booleans for the presence of each key
@@ -328,24 +314,26 @@ __global__ void find(
  * @param hash The unary function to apply to hash each key
  * @param key_equal The binary function to compare two keys for equality
  */
-template <uint32_t block_size,
-          typename InputIt,
-          typename OutputIt,
-          typename viewT,
-          typename Hash,
-          typename KeyEqual>
-__global__ void contains(
-  InputIt first, InputIt last, OutputIt output_begin, viewT view, Hash hash, KeyEqual key_equal)
-{
-  auto tid     = block_size * blockIdx.x + threadIdx.x;
+template<std::size_t block_size,
+         typename InputIt, typename OutputIt, 
+         typename viewT,
+         typename Hash, 
+         typename KeyEqual>
+__global__ void contains(InputIt first,
+                         InputIt last,
+                         OutputIt output_begin,
+                         viewT view,
+                         Hash hash,
+                         KeyEqual key_equal) {
+  auto tid = block_size * blockIdx.x + threadIdx.x;
   auto key_idx = tid;
   __shared__ bool writeBuffer[block_size];
-
-  while (first + key_idx < last) {
+  
+  while(first + key_idx < last) {
     auto key = *(first + key_idx);
-
-    /*
-     * The ld.relaxed.gpu instruction used in view.find causes L1 to
+    
+    /* 
+     * The ld.relaxed.gpu instruction used in view.find causes L1 to 
      * flush more frequently, causing increased sector stores from L2 to global memory.
      * By writing results to shared memory and then synchronizing before writing back
      * to global, we no longer rely on L1, preventing the increase in sector stores from
@@ -360,10 +348,10 @@ __global__ void contains(
 
 /**
  * @brief Indicates whether the keys in the range `[first, last)` are contained in the map.
- *
+ * 
  * Writes a `bool` to `(output + i)` indicating if the key `*(first + i)` exists in the map.
- * Uses the CUDA Cooperative Groups API to leverage groups of multiple threads to perform the
- * contains operation for each key. This provides a significant boost in throughput compared
+ * Uses the CUDA Cooperative Groups API to leverage groups of multiple threads to perform the 
+ * contains operation for each key. This provides a significant boost in throughput compared 
  * to the non Cooperative Group `contains` at moderate to high load factors.
  *
  * @tparam block_size The size of the thread block
@@ -375,7 +363,7 @@ __global__ void contains(
  * convertible to the map's `mapped_type`
  * @tparam viewT Type of device view allowing access of hash map storage
  * @tparam Hash Unary callable type
- * @tparam KeyEqual Binary callable type
+ * @tparam KeyEqual Binary callable type 
  * @param first Beginning of the sequence of keys
  * @param last End of the sequence of keys
  * @param output_begin Beginning of the sequence of booleans for the presence of each key
@@ -383,40 +371,43 @@ __global__ void contains(
  * @param hash The unary function to apply to hash each key
  * @param key_equal The binary function to compare two keys for equality
  */
-template <uint32_t block_size,
-          uint32_t tile_size,
-          typename InputIt,
-          typename OutputIt,
-          typename viewT,
-          typename Hash,
-          typename KeyEqual>
-__global__ void contains(
-  InputIt first, InputIt last, OutputIt output_begin, viewT view, Hash hash, KeyEqual key_equal)
-{
-  auto tile    = cg::tiled_partition<tile_size>(cg::this_thread_block());
-  auto tid     = block_size * blockIdx.x + threadIdx.x;
+template<std::size_t block_size, uint32_t tile_size,
+         typename InputIt, typename OutputIt, 
+         typename viewT,
+         typename Hash, 
+         typename KeyEqual>
+__global__ void contains(InputIt first,
+                         InputIt last,
+                         OutputIt output_begin,
+                         viewT view,
+                         Hash hash,
+                         KeyEqual key_equal) {
+  auto tile = cg::tiled_partition<tile_size>(cg::this_thread_block());
+  auto tid = block_size * blockIdx.x + threadIdx.x;
   auto key_idx = tid / tile_size;
   __shared__ bool writeBuffer[block_size];
-
-  while (first + key_idx < last) {
-    auto key   = *(first + key_idx);
+  
+  while(first + key_idx < last) {
+    auto key = *(first + key_idx);
     auto found = view.contains(tile, key, hash, key_equal);
-
-    /*
-     * The ld.relaxed.gpu instruction used in view.find causes L1 to
+    
+    /* 
+     * The ld.relaxed.gpu instruction used in view.find causes L1 to 
      * flush more frequently, causing increased sector stores from L2 to global memory.
      * By writing results to shared memory and then synchronizing before writing back
      * to global, we no longer rely on L1, preventing the increase in sector stores from
      * L2 to global and improving performance.
      */
-    if (tile.thread_rank() == 0) { writeBuffer[threadIdx.x / tile_size] = found; }
+    if(tile.thread_rank() == 0) {
+      writeBuffer[threadIdx.x / tile_size] = found;
+    }
     __syncthreads();
-    if (tile.thread_rank() == 0) {
+    if(tile.thread_rank() == 0) {
       *(output_begin + key_idx) = writeBuffer[threadIdx.x / tile_size];
     }
     key_idx += (gridDim.x * block_size) / tile_size;
   }
 }
 
-}  // namespace detail
-}  // namespace cuco
+} // namespace detail
+} // namespace cuco
diff --git a/include/cuco/dynamic_map.cuh b/include/cuco/dynamic_map.cuh
index 957d8dc33..c234a3579 100644
--- a/include/cuco/dynamic_map.cuh
+++ b/include/cuco/dynamic_map.cuh
@@ -51,7 +51,7 @@ namespace cuco {
  * Example:
  * \code{.cpp}
  * int empty_key_sentinel = -1;
- * int empty_value_sentinel = -1;
+ * int empty_value_sentine = -1;
  *
  * // Constructs a map with 100,000 initial slots using -1 and -1 as the empty key/value
  * // sentinels. Performs one bulk insert of 50,000 keys and a second bulk insert of
@@ -257,4 +257,4 @@ class dynamic_map {
 };
 }  // namespace cuco
 
-#include <cuco/detail/dynamic_map.inl>
+#include <cuco/detail/dynamic_map.inl>
\ No newline at end of file
diff --git a/include/cuco/static_map.cuh b/include/cuco/static_map.cuh
index 8a1f7acff..0fb74bed8 100644
--- a/include/cuco/static_map.cuh
+++ b/include/cuco/static_map.cuh
@@ -25,8 +25,7 @@
 
 #include <cuco/allocator.hpp>
 
-#if defined(CUDART_VERSION) && (CUDART_VERSION >= 11000) && defined(__CUDA_ARCH__) && \
-  (__CUDA_ARCH__ >= 700)
+#if defined(CUDART_VERSION) && (CUDART_VERSION >= 11000) && defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 700)
 #define CUCO_HAS_CUDA_BARRIER
 #endif
 
@@ -116,7 +115,7 @@ struct is_bitwise_comparable<T, std::enable_if_t<std::has_unique_object_represen
  * Example:
  * \code{.cpp}
  * int empty_key_sentinel = -1;
- * int empty_value_sentinel = -1;
+ * int empty_value_sentine = -1;
  *
  * // Constructs a map with 100,000 slots using -1 and -1 as the empty key/value
  * // sentinels. Note the capacity is chosen knowing we will insert 50,000 keys,
@@ -584,10 +583,10 @@ class static_map {
      * @param empty_value_sentinel The reserved value for mapped values to
      * represent empty slots
      */
-    __host__ __device__ device_mutable_view(pair_atomic_type* slots,
-                                            std::size_t capacity,
-                                            Key empty_key_sentinel,
-                                            Value empty_value_sentinel) noexcept
+     __host__ __device__ device_mutable_view(pair_atomic_type* slots,
+                                             std::size_t capacity,
+                                             Key empty_key_sentinel,
+                                             Value empty_value_sentinel) noexcept
       : device_view_base{slots, capacity, empty_key_sentinel, empty_value_sentinel}
     {
     }
@@ -669,10 +668,10 @@ class static_map {
      * @param empty_value_sentinel The reserved value for mapped values to
      * represent empty slots
      */
-    __host__ __device__ device_view(pair_atomic_type* slots,
-                                    std::size_t capacity,
-                                    Key empty_key_sentinel,
-                                    Value empty_value_sentinel) noexcept
+     __host__ __device__ device_view(pair_atomic_type* slots,
+                                     std::size_t capacity,
+                                     Key empty_key_sentinel,
+                                     Value empty_value_sentinel) noexcept
       : device_view_base{slots, capacity, empty_key_sentinel, empty_value_sentinel}
     {
     }
@@ -680,8 +679,7 @@ class static_map {
     /**
      * @brief Makes a copy of given `device_view` using non-owned memory.
      *
-     * This function is intended to be used to create shared memory copies of small static maps,
-     * although global memory can be used as well.
+     * This function is intended to be used to create shared memory copies of small static maps, although global memory can be used as well.
      *
      * Example:
      * @code{.cpp}
@@ -710,8 +708,7 @@ class static_map {
      * @tparam CG The type of the cooperative thread group
      * @param g The ooperative thread group used to copy the slots
      * @param source_device_view `device_view` to copy from
-     * @param memory_to_use Array large enough to support `capacity` elements. Object does not take
-     * the ownership of the memory
+     * @param memory_to_use Array large enough to support `capacity` elements. Object does not take the ownership of the memory
      * @return Copy of passed `device_view`
      */
     template <typename CG>
@@ -721,7 +718,9 @@ class static_map {
     {
 #if defined(CUDA_HAS_CUDA_BARRIER)
       __shared__ cuda::barrier<cuda::thread_scope::thread_scope_block> barrier;
-      if (g.thread_rank() == 0) { init(&barrier, g.size()); }
+      if (g.thread_rank() == 0) {
+        init(&barrier, g.size());
+      }
       g.sync();
 
       cuda::memcpy_async(g,
@@ -733,11 +732,10 @@ class static_map {
       barrier.arrive_and_wait();
 #else
       pair_atomic_type const* const slots_ptr = source_device_view.get_slots();
-      for (std::size_t i = g.thread_rank(); i < source_device_view.get_capacity(); i += g.size()) {
-        new (&memory_to_use[i].first)
-          atomic_key_type{slots_ptr[i].first.load(cuda::memory_order_relaxed)};
-        new (&memory_to_use[i].second)
-          atomic_mapped_type{slots_ptr[i].second.load(cuda::memory_order_relaxed)};
+      for (std::size_t i = g.thread_rank(); i < source_device_view.get_capacity(); i += g.size())
+      {
+        new (&memory_to_use[i].first) atomic_key_type{slots_ptr[i].first.load(cuda::memory_order_relaxed)};
+        new (&memory_to_use[i].second) atomic_mapped_type{slots_ptr[i].second.load(cuda::memory_order_relaxed)};
       }
       g.sync();
 #endif

From 32cb584302075b6d8911088aa3bd1e613ece3d34 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Mon, 7 Jun 2021 11:04:44 -0400
Subject: [PATCH 130/267] Revert dynamic map and static map benchmarks to their
 main dev version

---
 benchmarks/hash_table/dynamic_map_bench.cu | 100 ++++++++++---------
 benchmarks/hash_table/static_map_bench.cu  | 110 +++++++++++----------
 2 files changed, 111 insertions(+), 99 deletions(-)

diff --git a/benchmarks/hash_table/dynamic_map_bench.cu b/benchmarks/hash_table/dynamic_map_bench.cu
index 995e53903..cd6eadb47 100644
--- a/benchmarks/hash_table/dynamic_map_bench.cu
+++ b/benchmarks/hash_table/dynamic_map_bench.cu
@@ -15,118 +15,120 @@
  */
 
 #include <benchmark/benchmark.h>
+#include <synchronization.hpp>
 #include <cuco/dynamic_map.cuh>
 #include <iostream>
 #include <random>
-#include <synchronization.hpp>
 
-enum class dist_type { UNIQUE, UNIFORM, GAUSSIAN };
+enum class dist_type {
+  UNIQUE,
+  UNIFORM,
+  GAUSSIAN
+};
 
-template <dist_type Dist, typename Key, typename OutputIt>
-static void generate_keys(OutputIt output_begin, OutputIt output_end)
-{
+template<dist_type Dist, typename Key, typename OutputIt>
+static void generate_keys(OutputIt output_begin, OutputIt output_end) {
   auto num_keys = std::distance(output_begin, output_end);
-
+  
   std::random_device rd;
   std::mt19937 gen{rd()};
 
-  switch (Dist) {
+  switch(Dist) {
     case dist_type::UNIQUE:
-      for (auto i = 0; i < num_keys; ++i) {
+      for(auto i = 0; i < num_keys; ++i) {
         output_begin[i] = i;
       }
       break;
     case dist_type::UNIFORM:
-      for (auto i = 0; i < num_keys; ++i) {
+      for(auto i = 0; i < num_keys; ++i) {
         output_begin[i] = std::abs(static_cast<Key>(gen()));
       }
       break;
     case dist_type::GAUSSIAN:
       std::normal_distribution<> dg{1e9, 1e7};
-      for (auto i = 0; i < num_keys; ++i) {
+      for(auto i = 0; i < num_keys; ++i) {
         output_begin[i] = std::abs(static_cast<Key>(dg(gen)));
       }
       break;
   }
 }
 
-static void gen_final_size(benchmark::internal::Benchmark* b)
-{
-  for (auto size = 10'000'000; size <= 150'000'000; size += 20'000'000) {
+static void gen_final_size(benchmark::internal::Benchmark* b) {
+  for(auto size = 10'000'000; size <= 150'000'000; size += 20'000'000) {
     b->Args({size});
   }
 }
 
 template <typename Key, typename Value, dist_type Dist>
-static void BM_dynamic_insert(::benchmark::State& state)
-{
+static void BM_dynamic_insert(::benchmark::State& state) {
   using map_type = cuco::dynamic_map<Key, Value>;
-
-  std::size_t num_keys     = state.range(0);
-  std::size_t initial_size = 1 << 27;
-
-  std::vector<Key> h_keys(num_keys);
-  std::vector<cuco::pair_type<Key, Value>> h_pairs(num_keys);
-
+  
+  std::size_t num_keys = state.range(0);
+  std::size_t initial_size = 1<<27;
+  
+  std::vector<Key> h_keys( num_keys );
+  std::vector<cuco::pair_type<Key, Value>> h_pairs ( num_keys );
+  
   generate_keys<Dist, Key>(h_keys.begin(), h_keys.end());
 
-  for (auto i = 0; i < num_keys; ++i) {
-    Key key           = h_keys[i];
-    Value val         = h_keys[i];
-    h_pairs[i].first  = key;
+  for(auto i = 0; i < num_keys; ++i) {
+    Key key = h_keys[i];
+    Value val = h_keys[i];
+    h_pairs[i].first = key;
     h_pairs[i].second = val;
   }
 
-  thrust::device_vector<cuco::pair_type<Key, Value>> d_pairs(h_pairs);
+  thrust::device_vector<cuco::pair_type<Key, Value>> d_pairs( h_pairs );
 
   std::size_t batch_size = 1E6;
-  for (auto _ : state) {
+  for(auto _ : state) {
     map_type map{initial_size, -1, -1};
     {
-      cuda_event_timer raii{state};
-      for (auto i = 0; i < num_keys; i += batch_size) {
+      cuda_event_timer raii{state}; 
+      for(auto i = 0; i < num_keys; i += batch_size) {
         map.insert(d_pairs.begin() + i, d_pairs.begin() + i + batch_size);
       }
     }
   }
 
-  state.SetBytesProcessed((sizeof(Key) + sizeof(Value)) * int64_t(state.iterations()) *
+  state.SetBytesProcessed((sizeof(Key) + sizeof(Value)) *
+                          int64_t(state.iterations()) *
                           int64_t(state.range(0)));
 }
 
 template <typename Key, typename Value, dist_type Dist>
-static void BM_dynamic_search_all(::benchmark::State& state)
-{
+static void BM_dynamic_search_all(::benchmark::State& state) {
   using map_type = cuco::dynamic_map<Key, Value>;
+  
+  std::size_t num_keys = state.range(0);
+  std::size_t initial_size = 1<<27;
 
-  std::size_t num_keys     = state.range(0);
-  std::size_t initial_size = 1 << 27;
-
-  std::vector<Key> h_keys(num_keys);
-  std::vector<cuco::pair_type<Key, Value>> h_pairs(num_keys);
+  std::vector<Key> h_keys( num_keys );
+  std::vector<cuco::pair_type<Key, Value>> h_pairs ( num_keys );
 
   generate_keys<Dist, Key>(h_keys.begin(), h_keys.end());
-
-  for (auto i = 0; i < num_keys; ++i) {
-    Key key           = h_keys[i];
-    Value val         = h_keys[i];
-    h_pairs[i].first  = key;
+  
+  for(auto i = 0; i < num_keys; ++i) {
+    Key key = h_keys[i];
+    Value val = h_keys[i];
+    h_pairs[i].first = key;
     h_pairs[i].second = val;
   }
 
-  thrust::device_vector<Key> d_keys(h_keys);
-  thrust::device_vector<cuco::pair_type<Key, Value>> d_pairs(h_pairs);
-  thrust::device_vector<Value> d_results(num_keys);
+  thrust::device_vector<Key> d_keys( h_keys );
+  thrust::device_vector<cuco::pair_type<Key, Value>> d_pairs( h_pairs );
+  thrust::device_vector<Value> d_results( num_keys );
 
   map_type map{initial_size, -1, -1};
   map.insert(d_pairs.begin(), d_pairs.end());
 
-  for (auto _ : state) {
+  for(auto _ : state) {
     cuda_event_timer raii{state};
     map.find(d_keys.begin(), d_keys.end(), d_results.begin());
   }
 
-  state.SetBytesProcessed((sizeof(Key) + sizeof(Value)) * int64_t(state.iterations()) *
+  state.SetBytesProcessed((sizeof(Key) + sizeof(Value)) *
+                          int64_t(state.iterations()) *
                           int64_t(state.range(0)));
 }
 
@@ -159,7 +161,7 @@ BENCHMARK_TEMPLATE(BM_dynamic_search_all, int32_t, int32_t, dist_type::GAUSSIAN)
   ->Unit(benchmark::kMillisecond)
   ->Apply(gen_final_size)
   ->UseManualTime();
-
+  
 BENCHMARK_TEMPLATE(BM_dynamic_insert, int64_t, int64_t, dist_type::UNIQUE)
   ->Unit(benchmark::kMillisecond)
   ->Apply(gen_final_size)
diff --git a/benchmarks/hash_table/static_map_bench.cu b/benchmarks/hash_table/static_map_bench.cu
index 52e8eb140..165465518 100644
--- a/benchmarks/hash_table/static_map_bench.cu
+++ b/benchmarks/hash_table/static_map_bench.cu
@@ -15,37 +15,40 @@
  */
 
 #include <benchmark/benchmark.h>
-#include <thrust/device_vector.h>
+#include "cuco/static_map.cuh"
 #include <thrust/for_each.h>
-#include <fstream>
+#include <thrust/device_vector.h>
 #include <iostream>
+#include <fstream>
 #include <random>
-#include "cuco/static_map.cuh"
 
-enum class dist_type { UNIQUE, UNIFORM, GAUSSIAN };
+enum class dist_type {
+  UNIQUE,
+  UNIFORM,
+  GAUSSIAN
+};
 
-template <dist_type Dist, typename Key, typename OutputIt>
-static void generate_keys(OutputIt output_begin, OutputIt output_end)
-{
+template<dist_type Dist, typename Key, typename OutputIt>
+static void generate_keys(OutputIt output_begin, OutputIt output_end) {
   auto num_keys = std::distance(output_begin, output_end);
-
+  
   std::random_device rd;
   std::mt19937 gen{rd()};
 
-  switch (Dist) {
+  switch(Dist) {
     case dist_type::UNIQUE:
-      for (auto i = 0; i < num_keys; ++i) {
+      for(auto i = 0; i < num_keys; ++i) {
         output_begin[i] = i;
       }
       break;
     case dist_type::UNIFORM:
-      for (auto i = 0; i < num_keys; ++i) {
+      for(auto i = 0; i < num_keys; ++i) {
         output_begin[i] = std::abs(static_cast<Key>(gen()));
       }
       break;
     case dist_type::GAUSSIAN:
       std::normal_distribution<> dg{1e9, 1e7};
-      for (auto i = 0; i < num_keys; ++i) {
+      for(auto i = 0; i < num_keys; ++i) {
         output_begin[i] = std::abs(static_cast<Key>(dg(gen)));
       }
       break;
@@ -56,8 +59,7 @@ static void generate_keys(OutputIt output_begin, OutputIt output_end)
  * @brief Generates input sizes and hash table occupancies
  *
  */
-static void generate_size_and_occupancy(benchmark::internal::Benchmark* b)
-{
+static void generate_size_and_occupancy(benchmark::internal::Benchmark* b) {
   for (auto size = 100'000'000; size <= 100'000'000; size *= 10) {
     for (auto occupancy = 10; occupancy <= 90; occupancy += 10) {
       b->Args({size, occupancy});
@@ -65,74 +67,80 @@ static void generate_size_and_occupancy(benchmark::internal::Benchmark* b)
   }
 }
 
+
+
 template <typename Key, typename Value, dist_type Dist>
-static void BM_static_map_insert(::benchmark::State& state)
-{
+static void BM_static_map_insert(::benchmark::State& state) {
   using map_type = cuco::static_map<Key, Value>;
-
+  
   std::size_t num_keys = state.range(0);
-  float occupancy      = state.range(1) / float{100};
-  std::size_t size     = num_keys / occupancy;
-
-  std::vector<Key> h_keys(num_keys);
-  std::vector<cuco::pair_type<Key, Value>> h_pairs(num_keys);
+  float occupancy = state.range(1) / float{100};
+  std::size_t size = num_keys / occupancy;
 
+  std::vector<Key> h_keys( num_keys );
+  std::vector<cuco::pair_type<Key, Value>> h_pairs( num_keys );
+  
   generate_keys<Dist, Key>(h_keys.begin(), h_keys.end());
-
-  for (auto i = 0; i < num_keys; ++i) {
-    Key key           = h_keys[i];
-    Value val         = h_keys[i];
-    h_pairs[i].first  = key;
+  
+  for(auto i = 0; i < num_keys; ++i) {
+    Key key = h_keys[i];
+    Value val = h_keys[i];
+    h_pairs[i].first = key;
     h_pairs[i].second = val;
   }
 
-  thrust::device_vector<cuco::pair_type<Key, Value>> d_pairs(h_pairs);
+  thrust::device_vector<cuco::pair_type<Key, Value>> d_pairs( h_pairs );
 
-  for (auto _ : state) {
+  for(auto _ : state) {
+    state.ResumeTiming();
     state.PauseTiming();
     map_type map{size, -1, -1};
     state.ResumeTiming();
 
     map.insert(d_pairs.begin(), d_pairs.end());
+
+    state.PauseTiming();
   }
 
-  state.SetBytesProcessed((sizeof(Key) + sizeof(Value)) * int64_t(state.iterations()) *
+  state.SetBytesProcessed((sizeof(Key) + sizeof(Value)) *
+                          int64_t(state.iterations()) *
                           int64_t(state.range(0)));
 }
 
+
+
 template <typename Key, typename Value, dist_type Dist>
-static void BM_static_map_search_all(::benchmark::State& state)
-{
+static void BM_static_map_search_all(::benchmark::State& state) {
   using map_type = cuco::static_map<Key, Value>;
-
+  
   std::size_t num_keys = state.range(0);
-  float occupancy      = state.range(1) / float{100};
-  std::size_t size     = num_keys / occupancy;
+  float occupancy = state.range(1) / float{100};
+  std::size_t size = num_keys / occupancy;
 
   map_type map{size, -1, -1};
   auto view = map.get_device_mutable_view();
 
-  std::vector<Key> h_keys(num_keys);
-  std::vector<Value> h_values(num_keys);
-  std::vector<cuco::pair_type<Key, Value>> h_pairs(num_keys);
-  std::vector<Value> h_results(num_keys);
+  std::vector<Key> h_keys( num_keys );
+  std::vector<Value> h_values( num_keys );
+  std::vector<cuco::pair_type<Key, Value>> h_pairs ( num_keys );
+  std::vector<Value> h_results (num_keys);
 
   generate_keys<Dist, Key>(h_keys.begin(), h_keys.end());
-
-  for (auto i = 0; i < num_keys; ++i) {
-    Key key           = h_keys[i];
-    Value val         = h_keys[i];
-    h_pairs[i].first  = key;
+  
+  for(auto i = 0; i < num_keys; ++i) {
+    Key key = h_keys[i];
+    Value val = h_keys[i];
+    h_pairs[i].first = key;
     h_pairs[i].second = val;
   }
 
-  thrust::device_vector<Key> d_keys(h_keys);
-  thrust::device_vector<Value> d_results(num_keys);
-  thrust::device_vector<cuco::pair_type<Key, Value>> d_pairs(h_pairs);
+  thrust::device_vector<Key> d_keys( h_keys ); 
+  thrust::device_vector<Value> d_results( num_keys);
+  thrust::device_vector<cuco::pair_type<Key, Value>> d_pairs( h_pairs );
 
   map.insert(d_pairs.begin(), d_pairs.end());
-
-  for (auto _ : state) {
+  
+  for(auto _ : state) {
     map.find(d_keys.begin(), d_keys.end(), d_results.begin());
   }
 
@@ -140,6 +148,8 @@ static void BM_static_map_search_all(::benchmark::State& state)
                           int64_t(state.range(0)));
 }
 
+
+
 BENCHMARK_TEMPLATE(BM_static_map_insert, int32_t, int32_t, dist_type::UNIQUE)
   ->Unit(benchmark::kMillisecond)
   ->Apply(generate_size_and_occupancy);
@@ -186,4 +196,4 @@ BENCHMARK_TEMPLATE(BM_static_map_insert, int64_t, int64_t, dist_type::GAUSSIAN)
 
 BENCHMARK_TEMPLATE(BM_static_map_search_all, int64_t, int64_t, dist_type::GAUSSIAN)
   ->Unit(benchmark::kMillisecond)
-  ->Apply(generate_size_and_occupancy);
+  ->Apply(generate_size_and_occupancy);
\ No newline at end of file

From f65b61f8d95233f346bc4d37792ce4e78124e232 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Mon, 7 Jun 2021 11:06:31 -0400
Subject: [PATCH 131/267] Revert reduce_by_key to its main dev version

---
 benchmarks/reduce_by_key/reduce_by_key.cu | 12 +++++++-----
 1 file changed, 7 insertions(+), 5 deletions(-)

diff --git a/benchmarks/reduce_by_key/reduce_by_key.cu b/benchmarks/reduce_by_key/reduce_by_key.cu
index 3e4776c6b..0ca08144f 100644
--- a/benchmarks/reduce_by_key/reduce_by_key.cu
+++ b/benchmarks/reduce_by_key/reduce_by_key.cu
@@ -16,13 +16,13 @@
 
 #include <benchmark/benchmark.h>
 
-#include <thrust/device_vector.h>
 #include <thrust/iterator/iterator_traits.h>
 #include <thrust/iterator/zip_iterator.h>
 #include <thrust/pair.h>
 #include <thrust/reduce.h>
 #include <thrust/sort.h>
 #include <thrust/transform.h>
+#include <thrust/device_vector.h>
 
 /**
  * @brief Generates input sizes and number of unique keys
@@ -76,11 +76,13 @@ static void BM_thrust(::benchmark::State& state)
   }
 }
 BENCHMARK_TEMPLATE(BM_thrust, int32_t, int32_t)
-  ->Unit(benchmark::kMillisecond)
-  ->Apply(generate_size_and_num_unique);
+    ->Unit(benchmark::kMillisecond)
+    ->Apply(generate_size_and_num_unique);
 
 BENCHMARK_TEMPLATE(BM_thrust, int64_t, int64_t)
-  ->Unit(benchmark::kMillisecond)
-  ->Apply(generate_size_and_num_unique);
+    ->Unit(benchmark::kMillisecond)
+    ->Apply(generate_size_and_num_unique);
 
 // TODO: Hash based reduce by key benchmark
+
+

From 47c262467cec61840d97bbc833b72d04a0985b81 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Mon, 7 Jun 2021 11:09:08 -0400
Subject: [PATCH 132/267] Revert static map and dynamic map tests to their main
 dev version

---
 tests/dynamic_map/dynamic_map_test.cu | 86 +++++++++++++--------------
 tests/static_map/static_map_test.cu   | 32 +++++-----
 2 files changed, 54 insertions(+), 64 deletions(-)

diff --git a/tests/dynamic_map/dynamic_map_test.cu b/tests/dynamic_map/dynamic_map_test.cu
index 0e52b4b64..3e4b94f02 100644
--- a/tests/dynamic_map/dynamic_map_test.cu
+++ b/tests/dynamic_map/dynamic_map_test.cu
@@ -22,37 +22,36 @@
 #include <cuco/dynamic_map.cuh>
 #include <random>
 
-enum class dist_type { UNIQUE, UNIFORM, GAUSSIAN };
-
-template <dist_type Dist, typename Key, typename OutputIt>
-static void generate_keys(OutputIt output_begin, OutputIt output_end)
-{
+enum class dist_type {
+  UNIQUE,
+  UNIFORM,
+  GAUSSIAN
+};
+
+template<dist_type Dist, typename Key, typename OutputIt>
+static void generate_keys(OutputIt output_begin, OutputIt output_end) {
   auto num_keys = std::distance(output_begin, output_end);
 
   std::random_device rd;
   std::mt19937 gen{rd()};
 
-  switch (Dist) {
-    case dist_type::UNIQUE: {
-      for (auto i = 0; i < num_keys; ++i) {
+  switch(Dist) {
+    case dist_type::UNIQUE:
+      for(auto i = 0; i < num_keys; ++i) {
         output_begin[i] = i;
       }
       break;
-    }
-    case dist_type::UNIFORM: {
-      std::uniform_int_distribution<Key> distribution{0, std::numeric_limits<Key>::max()};
-      for (auto i = 0; i < num_keys; ++i) {
-        output_begin[i] = distribution(gen);
+    case dist_type::UNIFORM:
+      for(auto i = 0; i < num_keys; ++i) {
+        output_begin[i] = std::abs(static_cast<Key>(gen()));
       }
       break;
-    }
-    case dist_type::GAUSSIAN: {
+    case dist_type::GAUSSIAN:
       std::normal_distribution<> dg{1e9, 1e7};
-      for (auto i = 0; i < num_keys; ++i) {
+      for(auto i = 0; i < num_keys; ++i) {
         output_begin[i] = std::abs(static_cast<Key>(dg(gen)));
       }
       break;
-    }
   }
 }
 
@@ -79,15 +78,12 @@ bool none_of(Iterator begin, Iterator end, Predicate p)
 }
 }  // namespace
 
-TEMPLATE_TEST_CASE_SIG("Unique sequence of keys",
-                       "",
+
+TEMPLATE_TEST_CASE_SIG("Unique sequence of keys", "", 
                        ((typename T, dist_type Dist), T, Dist),
-                       (int32_t, dist_type::UNIQUE),
-                       (int64_t, dist_type::UNIQUE),
-                       (int32_t, dist_type::UNIFORM),
-                       (int64_t, dist_type::UNIFORM),
-                       (int32_t, dist_type::GAUSSIAN),
-                       (int64_t, dist_type::GAUSSIAN))
+   (int32_t, dist_type::UNIQUE), (int64_t, dist_type::UNIQUE),
+   (int32_t, dist_type::UNIFORM), (int64_t, dist_type::UNIFORM),
+   (int32_t, dist_type::GAUSSIAN), (int64_t, dist_type::GAUSSIAN))
 {
   using Key   = T;
   using Value = T;
@@ -95,25 +91,25 @@ TEMPLATE_TEST_CASE_SIG("Unique sequence of keys",
   constexpr std::size_t num_keys{50'000'000};
   cuco::dynamic_map<Key, Value> map{30'000'000, -1, -1};
 
-  std::vector<Key> h_keys(num_keys);
-  std::vector<Value> h_values(num_keys);
-  std::vector<cuco::pair_type<Key, Value>> h_pairs(num_keys);
+  std::vector<Key> h_keys( num_keys );
+  std::vector<Value> h_values( num_keys );
+  std::vector<cuco::pair_type<Key, Value>> h_pairs ( num_keys );
 
   generate_keys<Dist, Key>(h_keys.begin(), h_keys.end());
 
-  for (auto i = 0; i < num_keys; ++i) {
-    Key key           = h_keys[i];
-    Value val         = h_keys[i];
-    h_values[i]       = val;
-    h_pairs[i].first  = key;
+  for(auto i = 0; i < num_keys; ++i) {
+    Key key = h_keys[i];
+    Value val = h_keys[i];
+    h_values[i] = val;
+    h_pairs[i].first = key;
     h_pairs[i].second = val;
   }
 
-  thrust::device_vector<Key> d_keys(h_keys);
-  thrust::device_vector<Value> d_values(h_values);
-  thrust::device_vector<cuco::pair_type<Key, Value>> d_pairs(h_pairs);
-  thrust::device_vector<Value> d_results(num_keys);
-  thrust::device_vector<bool> d_contained(num_keys);
+  thrust::device_vector<Key> d_keys( h_keys );
+  thrust::device_vector<Value> d_values( h_values );
+  thrust::device_vector<cuco::pair_type<Key, Value>> d_pairs( h_pairs );
+  thrust::device_vector<Value> d_results( num_keys );
+  thrust::device_vector<bool> d_contained( num_keys );
 
   // bulk function test cases
   SECTION("All inserted keys-value pairs should be correctly recovered during find")
@@ -122,7 +118,8 @@ TEMPLATE_TEST_CASE_SIG("Unique sequence of keys",
     map.find(d_keys.begin(), d_keys.end(), d_results.begin());
     auto zip = thrust::make_zip_iterator(thrust::make_tuple(d_results.begin(), d_values.begin()));
 
-    REQUIRE(all_of(zip, zip + num_keys, [] __device__(auto const& p) {
+    REQUIRE(all_of(zip, zip + num_keys, 
+      [] __device__(auto const& p) {
       return thrust::get<0>(p) == thrust::get<1>(p);
     }));
   }
@@ -131,8 +128,7 @@ TEMPLATE_TEST_CASE_SIG("Unique sequence of keys",
   {
     map.find(d_keys.begin(), d_keys.end(), d_results.begin());
 
-    REQUIRE(
-      all_of(d_results.begin(), d_results.end(), [] __device__(auto const& p) { return p == -1; }));
+    REQUIRE(all_of(d_results.begin(), d_results.end(), [] __device__(auto const& p) { return p == -1; }));
   }
 
   SECTION("All inserted keys-value pairs should be contained")
@@ -140,15 +136,13 @@ TEMPLATE_TEST_CASE_SIG("Unique sequence of keys",
     map.insert(d_pairs.begin(), d_pairs.end());
     map.contains(d_keys.begin(), d_keys.end(), d_contained.begin());
 
-    REQUIRE(
-      all_of(d_contained.begin(), d_contained.end(), [] __device__(bool const& b) { return b; }));
+    REQUIRE(all_of(d_contained.begin(), d_contained.end(), [] __device__(bool const& b) { return b; }));
   }
 
   SECTION("Non-inserted keys-value pairs should not be contained")
   {
     map.contains(d_keys.begin(), d_keys.end(), d_contained.begin());
 
-    REQUIRE(
-      none_of(d_contained.begin(), d_contained.end(), [] __device__(bool const& b) { return b; }));
+    REQUIRE(none_of(d_contained.begin(), d_contained.end(), [] __device__(bool const& b) { return b; }));
   }
-}
+}
\ No newline at end of file
diff --git a/tests/static_map/static_map_test.cu b/tests/static_map/static_map_test.cu
index a9d4a4520..f27ee77c9 100644
--- a/tests/static_map/static_map_test.cu
+++ b/tests/static_map/static_map_test.cu
@@ -59,26 +59,22 @@ static void generate_keys(OutputIt output_begin, OutputIt output_end)
   std::mt19937 gen{rd()};
 
   switch (Dist) {
-    case dist_type::UNIQUE: {
+    case dist_type::UNIQUE:
       for (auto i = 0; i < num_keys; ++i) {
         output_begin[i] = i;
       }
       break;
-    }
-    case dist_type::UNIFORM: {
-      std::uniform_int_distribution<Key> distribution{0, std::numeric_limits<Key>::max()};
+    case dist_type::UNIFORM:
       for (auto i = 0; i < num_keys; ++i) {
-        output_begin[i] = distribution(gen);
+        output_begin[i] = std::abs(static_cast<Key>(gen()));
       }
       break;
-    }
-    case dist_type::GAUSSIAN: {
+    case dist_type::GAUSSIAN:
       std::normal_distribution<> dg{1e9, 1e7};
       for (auto i = 0; i < num_keys; ++i) {
         output_begin[i] = std::abs(static_cast<Key>(dg(gen)));
       }
       break;
-    }
   }
 }
 
@@ -221,6 +217,7 @@ TEST_CASE("User defined key and value type", "")
   }
 }
 
+
 TEMPLATE_TEST_CASE_SIG("Unique sequence of keys",
                        "",
                        ((typename T, dist_type Dist), T, Dist),
@@ -286,10 +283,10 @@ TEMPLATE_TEST_CASE_SIG("Unique sequence of keys",
     map.contains(d_keys.begin(), d_keys.end(), d_contained.begin());
 
     REQUIRE(
-      none_of(d_contained.begin(), d_contained.end(), [] __device__(bool const& b) { return b; }));
+      none_of(d_contained.begin(), d_contained.end(), [] __device__(bool const& b) { return b;
+}));
   }
 
-  // device funtion test cases
   SECTION("Inserting unique keys should return insert success.")
   {
     if (Dist == dist_type::UNIQUE) {
@@ -314,8 +311,8 @@ TEMPLATE_TEST_CASE_SIG("Unique sequence of keys",
     SECTION("const view")
     {
       REQUIRE(all_of(
-        d_pairs.begin(), d_pairs.end(), [view] __device__(cuco::pair_type<Key, Value> const& pair) {
-          return view.find(pair.first) == view.end();
+        d_pairs.begin(), d_pairs.end(), [view] __device__(cuco::pair_type<Key, Value> const& pair)
+{ return view.find(pair.first) == view.end();
         }));
     }
   }
@@ -345,10 +342,9 @@ TEMPLATE_TEST_CASE_SIG("Unique sequence of keys",
     {
       // All keys should be found
       REQUIRE(all_of(
-        d_pairs.begin(), d_pairs.end(), [view] __device__(cuco::pair_type<Key, Value> const& pair) {
-          auto const found = view.find(pair.first);
-          return (found != view.end()) and
-                 (found->first.load() == pair.first and found->second.load() == pair.second);
+        d_pairs.begin(), d_pairs.end(), [view] __device__(cuco::pair_type<Key, Value> const& pair)
+{ auto const found = view.find(pair.first); return (found != view.end()) and (found->first.load()
+== pair.first and found->second.load() == pair.second);
         }));
     }
   }
@@ -493,8 +489,8 @@ TEMPLATE_TEST_CASE_SIG("Shared memory static map",
                                d_keys_exist.data().get(),
                                d_keys_and_values_correct.data().get());
 
-    REQUIRE(none_of(d_keys_exist.begin(), d_keys_exist.end(), [] __device__(const bool key_found) {
-      return key_found;
+    REQUIRE(none_of(d_keys_exist.begin(), d_keys_exist.end(), [] __device__(const bool key_found)
+{ return key_found;
     }));
   }
 }

From dce099744cc0475858e2379ea88981b1e8fa1f1a Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Mon, 7 Jun 2021 15:40:01 -0400
Subject: [PATCH 133/267] Move is_bitwise_comparable to traits.hpp and update
 map/multimap accordingly

---
 include/cuco/detail/traits.hpp   | 54 ++++++++++++++++++++++++++++++++
 include/cuco/static_map.cuh      | 36 +--------------------
 include/cuco/static_multimap.cuh | 20 ++++++------
 3 files changed, 66 insertions(+), 44 deletions(-)
 create mode 100644 include/cuco/detail/traits.hpp

diff --git a/include/cuco/detail/traits.hpp b/include/cuco/detail/traits.hpp
new file mode 100644
index 000000000..3adfc227c
--- /dev/null
+++ b/include/cuco/detail/traits.hpp
@@ -0,0 +1,54 @@
+/*
+ * Copyright (c) 2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ */
+
+#pragma once
+
+namespace cuco {
+/**
+ * @brief Customization point that can be specialized to indicate that it is safe to perform bitwise
+ * equality comparisons on objects of type `T`.
+ *
+ * By default, only types where `std::has_unique_object_representations_v<T>` is true are safe for
+ * bitwise equality. However, this can be too restrictive for some types, e.g., floating point
+ * types.
+ *
+ * User-defined specializations of `is_bitwise_comparable` are allowed, but it is the users
+ * responsibility to ensure values do not occur that would lead to unexpected behavior. For example,
+ * if a `NaN` bit pattern were used as the empty sentinel value, it may not compare bitwise equal to
+ * other `NaN` bit patterns.
+ *
+ */
+template <typename T, typename = void>
+struct is_bitwise_comparable : std::false_type {
+};
+
+/// By default, only types with unique object representations are allowed
+template <typename T>
+struct is_bitwise_comparable<T, std::enable_if_t<std::has_unique_object_representations_v<T>>>
+  : std::true_type {
+};
+
+/**
+ * @brief Declares that a type `Type` is bitwise comparable.
+ *
+ */
+#define CUCO_DECLARE_BITWISE_COMPARABLE(Type)           \
+  namespace cuco {                                      \
+  template <>                                           \
+  struct is_bitwise_comparable<Type> : std::true_type { \
+  };                                                    \
+  }
+
+}  // namespace cuco
diff --git a/include/cuco/static_map.cuh b/include/cuco/static_map.cuh
index 0fb74bed8..6952044c9 100644
--- a/include/cuco/static_map.cuh
+++ b/include/cuco/static_map.cuh
@@ -37,47 +37,13 @@
 #include <cuco/detail/hash_functions.cuh>
 #include <cuco/detail/pair.cuh>
 #include <cuco/detail/static_map_kernels.cuh>
+#include <cuco/detail/traits.hpp>
 
 namespace cuco {
 
 template <typename Key, typename Value, cuda::thread_scope Scope, typename Allocator>
 class dynamic_map;
 
-/**
- * @brief Customization point that can be specialized to indicate that it is safe to perform bitwise
- * equality comparisons on objects of type `T`.
- *
- * By default, only types where `std::has_unique_object_representations_v<T>` is true are safe for
- * bitwise equality. However, this can be too restrictive for some types, e.g., floating point
- * types.
- *
- * User-defined specializations of `is_bitwise_comparable` are allowed, but it is the users
- * responsibility to ensure values do not occur that would lead to unexpected behavior. For example,
- * if a `NaN` bit pattern were used as the empty sentinel value, it may not compare bitwise equal to
- * other `NaN` bit patterns.
- *
- */
-template <typename T, typename = void>
-struct is_bitwise_comparable : std::false_type {
-};
-
-/// By default, only types with unique object representations are allowed
-template <typename T>
-struct is_bitwise_comparable<T, std::enable_if_t<std::has_unique_object_representations_v<T>>>
-  : std::true_type {
-};
-
-/**
- * @brief Declares that a type `Type` is bitwise comparable.
- * 
- */
-#define CUCO_DECLARE_BITWISE_COMPARABLE(Type)           \
-  namespace cuco {                                      \
-  template <>                                           \
-  struct is_bitwise_comparable<Type> : std::true_type { \
-  };                                                    \
-  }
-
 /**
  * @brief A GPU-accelerated, unordered, associative container of key-value
  * pairs with unique keys.
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index 1a355e7bb..03a7f4765 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -37,6 +37,7 @@
 #include <cuco/detail/prime.hpp>
 #include <cuco/detail/probe_sequences.cuh>
 #include <cuco/detail/static_multimap_kernels.cuh>
+#include <cuco/detail/traits.hpp>
 
 namespace cuco {
 
@@ -48,7 +49,7 @@ namespace cuco {
  * concurrent insert and find) from threads in device code.
  *
  * Current limitations:
- * - Requires keys and values that are trivially copyable and have unique object representations
+ * - Requires keys and values that where `cuco::is_bitwise_comparable<T>::value` is true
  * - Comparisons against the "sentinel" values will always be done with bitwise comparisons
  * Therefore, the objects must have unique, bitwise object representations (e.g., no padding bits).
  * - Does not support erasing keys
@@ -115,14 +116,15 @@ template <typename Key,
           cuda::thread_scope Scope = cuda::thread_scope_device,
           typename Allocator       = cuco::cuda_allocator<char>>
 class static_multimap {
-  template <typename T>
-  static constexpr bool is_CAS_safe =
-    std::is_trivially_copyable_v<T>and std::has_unique_object_representations_v<T>;
-
-  static_assert(is_CAS_safe<Key>,
-                "Key type must be trivially copyable and have unique object representation.");
-  static_assert(is_CAS_safe<Value>,
-                "Value type must be trivially copyable and have unique object representation.");
+  static_assert(
+    is_bitwise_comparable<Key>::value,
+    "Key type must have unique object representations or have been explicitly declared as safe for "
+    "bitwise comparison via specialization of cuco::is_bitwise_comparable<Key>.");
+
+  static_assert(is_bitwise_comparable<Value>::value,
+                "Value type must have unique object representations or have been explicitly "
+                "declared as safe for "
+                "bitwise comparison via specialization of cuco::is_bitwise_comparable<Value>.");
 
  public:
   using value_type         = cuco::pair_type<Key, Value>;

From 6229de57fd38ffb8abb10284fdb1facc32ca95aa Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Tue, 8 Jun 2021 13:40:32 -0400
Subject: [PATCH 134/267] Per-warp buffer by default for vector loads

---
 include/cuco/detail/static_multimap.inl       |  26 ++--
 .../cuco/detail/static_multimap_kernels.cuh   | 115 +++++++++---------
 2 files changed, 69 insertions(+), 72 deletions(-)

diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index 37d7ff18a..985a9932d 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -251,14 +251,11 @@ OutputIt static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::retrieve(
 {
   auto num_keys         = std::distance(first, last);
   auto const block_size = 128;
-  // Using per-block buffer for vector loads and per-CG buffer for scalar loads
-  auto const buffer_size = [&]() {
-    if constexpr (is_vector_load()) { return block_size * 3u; }
-    return cg_size() * 3u;
-  }();
-  auto const stride    = 1;
-  auto const grid_size = (cg_size() * num_keys + stride * block_size - 1) / (stride * block_size);
-  auto view            = get_device_view();
+  // Using per-warp buffer for vector loads and per-CG buffer for scalar loads
+  auto const buffer_size = is_vector_load() ? (32u * 3u) : (cg_size() * 3u);
+  auto const stride      = 1;
+  auto const grid_size   = (cg_size() * num_keys + stride * block_size - 1) / (stride * block_size);
+  auto view              = get_device_view();
 
   atomic_ctr_type* num_items;
   CUCO_CUDA_TRY(cudaMallocManaged(&num_items, sizeof(atomic_ctr_type)));
@@ -288,14 +285,11 @@ OutputIt static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::retrieve_
 {
   auto num_keys         = std::distance(first, last);
   auto const block_size = 128;
-  // Using per-block buffer for vector loads and per-CG buffer for scalar loads
-  auto const buffer_size = [&]() {
-    if constexpr (is_vector_load()) { return block_size * 3u; }
-    return cg_size() * 3u;
-  }();
-  auto const stride    = 1;
-  auto const grid_size = (cg_size() * num_keys + stride * block_size - 1) / (stride * block_size);
-  auto view            = get_device_view();
+  // Using per-warp buffer for vector loads and per-CG buffer for scalar loads
+  auto const buffer_size = is_vector_load() ? (32u * 3u) : (cg_size() * 3u);
+  auto const stride      = 1;
+  auto const grid_size   = (cg_size() * num_keys + stride * block_size - 1) / (stride * block_size);
+  auto view              = get_device_view();
 
   constexpr bool is_outer = true;
 
diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap_kernels.cuh
index bcc0a98a5..60bd6ac13 100644
--- a/include/cuco/detail/static_multimap_kernels.cuh
+++ b/include/cuco/detail/static_multimap_kernels.cuh
@@ -105,32 +105,34 @@ flush_output_buffer(CG const& g,
 }
 
 /**
- * @brief Flushes per-block shared memory buffer into the output sequence.
+ * @brief Flushes per-warp shared memory buffer into the output sequence.
  *
- * @tparam block_size The size of the thread block
  * @tparam Key key type
  * @tparam Value value type
  * @tparam atomicT Type of atomic storage
  * @tparam OutputIt Device accessible output iterator whose `value_type` is
  * convertible to the map's `mapped_type`
+ * @param activemask Mask of active threads in the warp
  * @param num_outputs Number of valid output in the buffer
  * @param output_buffer Shared memory buffer of the key/value pair sequence
  * @param num_items Size of the output sequence
  * @param output_begin Beginning of the output sequence of key/value pairs
  */
-template <uint32_t block_size, typename Key, typename Value, typename atomicT, typename OutputIt>
-__inline__ __device__ void flush_output_buffer(uint32_t const num_outputs,
+template <typename Key, typename Value, typename atomicT, typename OutputIt>
+__inline__ __device__ void flush_output_buffer(const unsigned int activemask,
+                                               uint32_t const num_outputs,
                                                cuco::pair_type<Key, Value>* output_buffer,
                                                atomicT* num_items,
                                                OutputIt output_begin)
 {
-  __shared__ std::size_t offset;
-  if (0 == threadIdx.x) {
-    offset = num_items->fetch_add(num_outputs, cuda::std::memory_order_relaxed);
-  }
-  __syncthreads();
+  int num_threads = __popc(activemask);
+
+  std::size_t offset;
+  const auto lane_id = threadIdx.x % 32;
+  if (0 == lane_id) { offset = num_items->fetch_add(num_outputs, cuda::std::memory_order_relaxed); }
+  offset = __shfl_sync(activemask, offset, 0);
 
-  for (auto index = threadIdx.x; index < num_outputs; index += block_size) {
+  for (auto index = lane_id; index < num_outputs; index += num_threads) {
     *(output_begin + offset + index) = output_buffer[index];
   }
 }
@@ -723,25 +725,27 @@ __global__ std::enable_if_t<is_vector_load, void> retrieve(InputIt first,
   auto tid     = block_size * blockIdx.x + threadIdx.x;
   auto key_idx = tid / tile_size;
 
-  const uint32_t lane_id = tile.thread_rank();
+  constexpr uint32_t num_warps = block_size / 32;
+  const uint32_t warp_id       = threadIdx.x / 32;
+  const uint32_t warp_lane_id  = threadIdx.x % 32;
+  const uint32_t tile_lane_id  = tile.thread_rank();
 
-  __shared__ uint32_t block_counter;
-  __shared__ cuco::pair_type<Key, Value> output_buffer[buffer_size];
+  __shared__ cuco::pair_type<Key, Value> output_buffer[num_warps][buffer_size];
+  __shared__ uint32_t warp_counter[num_warps];
 
-  if (0 == threadIdx.x) { block_counter = 0; }
+  if (warp_lane_id == 0) { warp_counter[warp_id] = 0; }
 
-  while (__syncthreads_or(first + key_idx < last)) {
-    auto key = *(first + key_idx);
-    typename viewT::iterator current_slot;
+  const unsigned int activemask = __ballot_sync(0xffffffff, first + key_idx < last);
 
-    bool running = ((first + key_idx) < last);
+  while (first + key_idx < last) {
+    auto key          = *(first + key_idx);
+    auto current_slot = view.initial_slot(tile, key);
 
+    bool running = true;
     if constexpr (is_outer) {
       bool found_match = false;
 
-      if (running) { current_slot = view.initial_slot(tile, key); }
-
-      while (__syncthreads_or(running)) {
+      while (__any_sync(activemask, running)) {
         if (running) {
           pair<Key, Value> arr[2];
           if constexpr (sizeof(Key) == 4) {
@@ -766,49 +770,47 @@ __global__ std::enable_if_t<is_vector_load, void> retrieve(InputIt first,
             auto num_second_matches = __popc(second_exists);
 
             uint32_t output_idx;
-            if (0 == lane_id) {
+            if (0 == tile_lane_id) {
               output_idx =
-                atomicAdd_block(&block_counter, (num_first_matches + num_second_matches));
+                atomicAdd(&warp_counter[warp_id], (num_first_matches + num_second_matches));
             }
             output_idx = tile.shfl(output_idx, 0);
 
             if (first_equals) {
-              auto lane_offset = __popc(first_exists & ((1 << lane_id) - 1));
+              auto lane_offset = __popc(first_exists & ((1 << tile_lane_id) - 1));
               Key k            = key;
-              output_buffer[output_idx + lane_offset] =
+              output_buffer[warp_id][output_idx + lane_offset] =
                 cuco::make_pair<Key, Value>(std::move(k), std::move(arr[0].second));
             }
             if (second_equals) {
-              auto lane_offset = __popc(second_exists & ((1 << lane_id) - 1));
+              auto lane_offset = __popc(second_exists & ((1 << tile_lane_id) - 1));
               Key k            = key;
-              output_buffer[output_idx + num_first_matches + lane_offset] =
+              output_buffer[warp_id][output_idx + num_first_matches + lane_offset] =
                 cuco::make_pair<Key, Value>(std::move(k), std::move(arr[1].second));
             }
           }
           if (tile.any(first_slot_is_empty or second_slot_is_empty)) {
             running = false;
-            if ((not found_match) && (lane_id == 0)) {
-              auto output_idx = atomicAdd_block(&block_counter, 1);
-              output_buffer[output_idx] =
+            if ((not found_match) && (tile_lane_id == 0)) {
+              auto output_idx = atomicAdd(&warp_counter[warp_id], 1);
+              output_buffer[warp_id][output_idx] =
                 cuco::make_pair<Key, Value>(key, view.get_empty_key_sentinel());
             }
           }
         }  // if running
 
-        __syncthreads();
-
-        if ((block_counter + 2 * block_size) > buffer_size) {
-          flush_output_buffer<block_size>(block_counter, output_buffer, num_items, output_begin);
-
-          if (0 == threadIdx.x) { block_counter = 0; }
+        __syncwarp(activemask);
+        if (warp_counter[warp_id] + 32 * 2 > buffer_size) {
+          flush_output_buffer(
+            activemask, warp_counter[warp_id], output_buffer[warp_id], num_items, output_begin);
+          // First lane reset warp-level counter
+          if (warp_lane_id == 0) { warp_counter[warp_id] = 0; }
         }
 
-        if (running) { current_slot = view.next_slot(current_slot); }
+        current_slot = view.next_slot(current_slot);
       }  // while running
     } else {
-      if (running) { current_slot = view.initial_slot(tile, key); }
-
-      while (__syncthreads_or(running)) {
+      while (__any_sync(activemask, running)) {
         if (running) {
           pair<Key, Value> arr[2];
           if constexpr (sizeof(Key) == 4) {
@@ -831,45 +833,46 @@ __global__ std::enable_if_t<is_vector_load, void> retrieve(InputIt first,
             auto num_second_matches = __popc(second_exists);
 
             uint32_t output_idx;
-            if (0 == lane_id) {
+            if (0 == tile_lane_id) {
               output_idx =
-                atomicAdd_block(&block_counter, (num_first_matches + num_second_matches));
+                atomicAdd(&warp_counter[warp_id], (num_first_matches + num_second_matches));
             }
             output_idx = tile.shfl(output_idx, 0);
 
             if (first_equals) {
-              auto lane_offset = __popc(first_exists & ((1 << lane_id) - 1));
+              auto lane_offset = __popc(first_exists & ((1 << tile_lane_id) - 1));
               Key k            = key;
-              output_buffer[output_idx + lane_offset] =
+              output_buffer[warp_id][output_idx + lane_offset] =
                 cuco::make_pair<Key, Value>(std::move(k), std::move(arr[0].second));
             }
             if (second_equals) {
-              auto lane_offset = __popc(second_exists & ((1 << lane_id) - 1));
+              auto lane_offset = __popc(second_exists & ((1 << tile_lane_id) - 1));
               Key k            = key;
-              output_buffer[output_idx + num_first_matches + lane_offset] =
+              output_buffer[warp_id][output_idx + num_first_matches + lane_offset] =
                 cuco::make_pair<Key, Value>(std::move(k), std::move(arr[1].second));
             }
           }
           if (tile.any(first_slot_is_empty or second_slot_is_empty)) { running = false; }
         }  // if running
 
-        __syncthreads();
-
-        if ((block_counter + 2 * block_size) > buffer_size) {
-          flush_output_buffer<block_size>(block_counter, output_buffer, num_items, output_begin);
-
-          if (0 == threadIdx.x) { block_counter = 0; }
+        __syncwarp(activemask);
+        if (warp_counter[warp_id] + 32 * 2 > buffer_size) {
+          flush_output_buffer(
+            activemask, warp_counter[warp_id], output_buffer[warp_id], num_items, output_begin);
+          // First lane reset warp-level counter
+          if (warp_lane_id == 0) { warp_counter[warp_id] = 0; }
         }
 
-        if (running) { current_slot = view.next_slot(current_slot); }
+        current_slot = view.next_slot(current_slot);
       }  // while running
     }
     key_idx += (gridDim.x * block_size) / tile_size;
   }
 
-  // Final remainder flush
-  if (block_counter > 0) {
-    flush_output_buffer<block_size>(block_counter, output_buffer, num_items, output_begin);
+  // Final flush of output buffer
+  if (warp_counter[warp_id] > 0) {
+    flush_output_buffer(
+      activemask, warp_counter[warp_id], output_buffer[warp_id], num_items, output_begin);
   }
 }
 

From 8af4d1d5f9cebe394bcdf4a5d9b564c73901f3d4 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Tue, 8 Jun 2021 15:39:51 -0400
Subject: [PATCH 135/267] Use aggregate CAS for 32-bit integers during
 insertions

---
 include/cuco/detail/pair.cuh            |   7 ++
 include/cuco/detail/static_multimap.inl | 103 ++++++++++++++++--------
 2 files changed, 77 insertions(+), 33 deletions(-)

diff --git a/include/cuco/detail/pair.cuh b/include/cuco/detail/pair.cuh
index 1299ee16c..ac4d9f9c0 100644
--- a/include/cuco/detail/pair.cuh
+++ b/include/cuco/detail/pair.cuh
@@ -137,4 +137,11 @@ __host__ __device__ pair_type<F, S> make_pair(F&& f, S&& s) noexcept
 {
   return pair_type<F, S>{std::forward<F>(f), std::forward<S>(s)};
 }
+
+template <typename K, typename V>
+union pair2uint64 {
+  uint64_t uint64;
+  pair_type<K, V> pair;
+};
+
 }  // namespace cuco
diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index 985a9932d..3f7110c3d 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -348,22 +348,41 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_mutable_vie
         auto expected_key    = this->get_empty_key_sentinel();
         auto expected_value  = this->get_empty_value_sentinel();
         auto insert_location = first_slot_is_empty ? current_slot : current_slot + 1;
-        auto& slot_key       = insert_location->first;
-        auto& slot_value     = insert_location->second;
-        bool key_success =
-          slot_key.compare_exchange_strong(expected_key, insert_pair.first, memory_order_relaxed);
-        bool value_success = slot_value.compare_exchange_strong(
-          expected_value, insert_pair.second, memory_order_relaxed);
-        if (key_success) {
-          while (not value_success) {
-            value_success =
-              slot_value.compare_exchange_strong(expected_value = this->get_empty_value_sentinel(),
-                                                 insert_pair.second,
-                                                 memory_order_relaxed);
+
+        if constexpr (sizeof(Key) == 4 and sizeof(Value) == 4) {
+          static_assert(sizeof(cuda::atomic<uint64_t>) ==
+                        (sizeof(cuda::atomic<Key>) + sizeof(cuda::atomic<Value>)));
+
+          cuco::pair2uint64<Key, Value> converter;
+          auto slot = reinterpret_cast<cuda::atomic<uint64_t>*>(insert_location);
+
+          converter.pair =
+            cuco::make_pair<Key, Value>(std::move(expected_key), std::move(expected_value));
+          auto empty_sentinel = converter.uint64;
+          converter.pair      = insert_pair;
+          auto tmp_pair       = converter.uint64;
+
+          bool success =
+            slot->compare_exchange_strong(empty_sentinel, tmp_pair, memory_order_relaxed);
+          if (success) { status = insert_result::SUCCESS; }
+        } else {
+          auto& slot_key   = insert_location->first;
+          auto& slot_value = insert_location->second;
+          bool key_success =
+            slot_key.compare_exchange_strong(expected_key, insert_pair.first, memory_order_relaxed);
+          bool value_success = slot_value.compare_exchange_strong(
+            expected_value, insert_pair.second, memory_order_relaxed);
+          if (key_success) {
+            while (not value_success) {
+              value_success = slot_value.compare_exchange_strong(
+                expected_value = this->get_empty_value_sentinel(),
+                insert_pair.second,
+                memory_order_relaxed);
+            }
+            status = insert_result::SUCCESS;
+          } else if (value_success) {
+            slot_value.store(this->get_empty_value_sentinel(), memory_order_relaxed);
           }
-          status = insert_result::SUCCESS;
-        } else if (value_success) {
-          slot_value.store(this->get_empty_value_sentinel(), memory_order_relaxed);
         }
         // another key was inserted in both slots we wanted to try
         // so we need to try the next empty slots in the window
@@ -412,26 +431,44 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_mutable_vie
         using cuda::std::memory_order_relaxed;
         auto expected_key   = this->get_empty_key_sentinel();
         auto expected_value = this->get_empty_value_sentinel();
-        auto& slot_key      = current_slot->first;
-        auto& slot_value    = current_slot->second;
-
-        bool key_success =
-          slot_key.compare_exchange_strong(expected_key, insert_pair.first, memory_order_relaxed);
-        bool value_success = slot_value.compare_exchange_strong(
-          expected_value, insert_pair.second, memory_order_relaxed);
-
-        if (key_success) {
-          while (not value_success) {
-            value_success =
-              slot_value.compare_exchange_strong(expected_value = this->get_empty_value_sentinel(),
-                                                 insert_pair.second,
-                                                 memory_order_relaxed);
+
+        if constexpr (sizeof(Key) == 4 and sizeof(Value) == 4) {
+          static_assert(sizeof(cuda::atomic<uint64_t>) ==
+                        (sizeof(cuda::atomic<Key>) + sizeof(cuda::atomic<Value>)));
+
+          cuco::pair2uint64<Key, Value> converter;
+          auto slot = reinterpret_cast<cuda::atomic<uint64_t>*>(current_slot);
+
+          converter.pair =
+            cuco::make_pair<Key, Value>(std::move(expected_key), std::move(expected_value));
+          auto empty_sentinel = converter.uint64;
+          converter.pair      = insert_pair;
+          auto tmp_pair       = converter.uint64;
+
+          bool success =
+            slot->compare_exchange_strong(empty_sentinel, tmp_pair, memory_order_relaxed);
+          if (success) { status = insert_result::SUCCESS; }
+        } else {
+          auto& slot_key   = current_slot->first;
+          auto& slot_value = current_slot->second;
+
+          bool key_success =
+            slot_key.compare_exchange_strong(expected_key, insert_pair.first, memory_order_relaxed);
+          bool value_success = slot_value.compare_exchange_strong(
+            expected_value, insert_pair.second, memory_order_relaxed);
+
+          if (key_success) {
+            while (not value_success) {
+              value_success = slot_value.compare_exchange_strong(
+                expected_value = this->get_empty_value_sentinel(),
+                insert_pair.second,
+                memory_order_relaxed);
+            }
+            status = insert_result::SUCCESS;
+          } else if (value_success) {
+            slot_value.store(this->get_empty_value_sentinel(), memory_order_relaxed);
           }
-          status = insert_result::SUCCESS;
-        } else if (value_success) {
-          slot_value.store(this->get_empty_value_sentinel(), memory_order_relaxed);
         }
-
         // another key was inserted in the slot we wanted to try
         // so we need to try the next empty slot in the window
       }

From b223ec9bd8149b781dd04eda3faddda1fe1d119c Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Wed, 9 Jun 2021 10:49:00 -0400
Subject: [PATCH 136/267] Update performance analysis notebook

---
 .../analysis/notebooks/StaticMultimap.ipynb   | 22 +++++++++----------
 1 file changed, 11 insertions(+), 11 deletions(-)

diff --git a/benchmarks/analysis/notebooks/StaticMultimap.ipynb b/benchmarks/analysis/notebooks/StaticMultimap.ipynb
index 8e3b6c05c..9b6675a43 100644
--- a/benchmarks/analysis/notebooks/StaticMultimap.ipynb
+++ b/benchmarks/analysis/notebooks/StaticMultimap.ipynb
@@ -134,7 +134,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 360x360 with 1 Axes>"
       ]
@@ -174,7 +174,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1080x360 with 3 Axes>"
       ]
@@ -217,7 +217,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 360x360 with 1 Axes>"
       ]
@@ -258,7 +258,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1080x360 with 3 Axes>"
       ]
@@ -299,7 +299,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1080x360 with 3 Axes>"
       ]
@@ -342,7 +342,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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7jokTJ3LmmWduP0a9evWoUqUK7777LsOGDeOzzz6L+fzxKO09adXKSp/HH79j24cfWisxQJ065XtPGzZsyKGHHsoBBxwAwNlnn82UKVOoV68eubm5NG/eHIANGzbQvHlzcnNziz1OmN/Ltm3hT3+CYcPg009h+XIYMQK6do05xNDzEmiUQYMgUo21XfXqtj0Z8vLy2LhxIwCrV69m8uTJtGzZEoDHHnuMt99+m7Fjx27/sJekS5cuTJw4kd8iHe/Gjx/PIYccstOlGsA+++xDt27ddmpJLUvz5s059NBDt18KAtx555106NBhe1IoVL9+fQYPHszdd9+9y3Fuv/12/v3vf+8SU0UZMMCqXj78ELZutd+9etn2ZDj88MNZvXo1K1asAOCDDz6gdevWnH766fz8888sXLiQhQsXUr169RKTJ4T7vSwogFdegRNOsH6gb72VXckTsqwEWpYePez3gAGweLH1bxs0aMf28po3bx7XX389IoKqcsMNN9C2bVsArrzySpo0aULnzp0BOPfcc7n11luLPU67du249tprOfrooxER6tatW2Kr/fXXX8+DDz4YV5yPP/44ffr0oXnz5qgqnTt3LvEf9+yzz2bgwIFMmjRpp+1HHnlkXOdMtsKrzT59YN48K5EOGrRje3nl5OQwZMgQTjzxRFSVww47jCuuuCLu44T5vaxUCV5/3RrpspWoatAxJE3Hjh3VJ1R2ziWbiMxQ1Y5Ft/slvHMubosXQ/fuUErtRFbwBJrGRo8evX1kSeHPNddcE3RYLgGZ9l4++ii88AJUqRJ0JMHyS3jnXFy2brX2gY4drR9oNvBLeOdcUrzyCvz8M1x5ZdCRBM8TqHMuLo88Yn2js63LUnG8G5NzLmYFBdC5M5x3XvaMNiqNJ1DnXMwqVYKofvlZzy/hnXMx2bgRXnsN8vODjiQxqZiq0hOocy4m48bZtHWffhp0JPFL1VSV3o3JOReTzp1tns9586CESaTSVmkTBRVZSKBY3o3JOZewWbNsvtwrrwxf8gQbORXP9lh5AnXOlWnECJtpvmfPoCNJTOPG8W2PlSdQ51ypCgqs3rN7d9hnn6CjScxtt+1ack7GVJXejck5V6pKlewSPszrvletao1H++5ry7kka6pKT6DOuRKp2tj3qlWhyCT5oTJsGLRoAd98Y18IyeKX8M65Ek2ZAo0aweefBx1J4qZOtZ++fZObPCHFCVREuorItyKSKyI3FXN/DxH5KvLzmYgcEnXfQhGZLSKzRMT7JjkXgOHDrQN969ZBR5K4YcOgZk24+OLkHztll/AikgM8BJwM5AHTRGSiqn4dtdsPwLGqulpETgVGAkdE3X+8qq5MVYzOuZKtWgXPP29rSdWoEXQ0ifnxRxsA0LevLWudbKksgXYCclV1gapuAZ4DzoreQVU/U9XVkZtTgIYpjMc5F4cnn7Qlvf/yl6AjSdzDD1svgmuvTc3xU5lA9weWRN3Oi2wrSS/gzajbCrwjIjNEpHcK4nPOlaCgwC7fjzwSiiwVHxobN1r/1TPPhGbNUnOOVLbCFzdeodhxoyJyPJZAj47afJSqLhWRusC7IvKNqn5SzGN7A70BGpe3V6xzDrA+k489Fs5RR4XGjLFqiOuuS905UlkCzQMaRd1uCCwtupOItAMeA85S1VWF21V1aeT3cmACViWwC1UdqaodVbXjvvvum8TwncteItClCxxzTNCRJEbVGo8OOQSOPTZ150llAp0GtBCRZiJSFegOTIzeQUQaA+OBP6vqd1Hb9xCRPQv/Bk4B5qQwVudcxE8/WaPLkiVl75uuPvwQ5syx0mcqS9EpS6Cqug24FngbmAe8oKpzReRKESlcTeVWoDbwcJHuSvsBn4rIl8DnwOuq+laqYnXO7fD44/DAA7BpU9CRJG7oUBt1dOGFqT2PT2fnnNsuP98aXFq2hHffDTqaxMyfb6OObr4Zbr89Ocf06eycc2V64w27dL/qqqAjSdwDD0DlyhXzHDyBOue2Gz4c6te3mefD6NdfYdQo6NbNnkeq+WQizjnA+n42bGgt71WqBB1NYkaPtlmj+vWrmPN5AnXOATbRxogRQUeRuPx8u3w/8kjouEttZWr4Jbxzji1bYNo06z8ZVq+/bg1Iqew4X5QnUOccEyZAp04waVLQkSRu2DCbeu/ccyvunJ5AnXMMH27dl44+uux909Hs2fDBB3DNNdYCX1E8gTqX5ebNg48+slmXkj3hcEUZNgx23x2uuKJizxvSl8s5lywjRlir+6WXBh1JYlautIlDevas+EXvPIE6l8UKCmDiRDjvPKhbN+hoEjNypA077du34s/t3Zicy2KVKtmkG2vXBh1JYrZuhYcegpNPDmbZEU+gzmUxVVsfvXr1oCNJzIsvwtKl8OijwZzfL+Gdy1IzZkCrVrbme1gNGwYHHQRduwZzfk+gzmWpESNg8WJo2jToSBIzZYotV9ynT3C9BzyBOpeF1q6FZ5+1+TJr1Qo6msSkcrniWHkCdS4LPfMMrF8PV15Z9r7pKC/P6j8vvzw1yxXHyhOoc1lG1UYeHXYYHH540NEkJtXLFcfKW+GdyzKqcOutUKNG0JEkZuNG6/uZyuWKY+UJ1LksU6kS/PGPQUeRuMLliitqzs/S+CW8c1lk5Uq4805YsSLoSBITvVxxly5BR+MJ1LmsMno03HILLFsWdCSJ+eCDilmuOFaeQJ3LEgUF1vfz6KPh4IODjiYxw4ZVzHLFsfIE6lyWeP99m7E9rCtu5ubCa69Z16vddgs6GuMJ1Lks8cgjUKeOzbwURhW5XHGsPIE6lwUKCuz3FVdAtWrBxpKIX3+1+tsLLqiY5Ypj5d2YnMsClSrB+PHhXTSucLniilwwLhZeAnUuw23bBgsW2N/p0HIdr/x8uP/+il2uOFaeQJ3LcK+/DgceCJMnBx1JYl5/3b4A0qHjfFGeQJ3LcMOHw/77wxFHBB1JYgqXKz7nnKAj2ZUnUOcy2IIF8Pbb1nhUkcv9JstXXwWzXHGsPIE6l8FGjrQGpMsvDzqSxNx/fzDLFcfKE6hzGaqgwOb9PPNMu4QPmxUrLP4gliuOVRoWip1zyVCpEnzxhXX/CaORI2Hz5mCWK46VJ1DnMljduuFc733rVps0OajlimPll/DOZaC5c+GYY+x3GBUuV5yOXZeieQJ1LgONGAGffx7O0ifA0KHBLlccK0+gzmWY9evhqafg/PNt6rewmTLFkn+QyxXHKs3Dc87F67nnbNnidJq1KB7DhsFee8EllwQdSdk8gTqXYYYPhzZt4Kijgo4kfnl5MG4c9OoVjkXvvBXeuQxSUGAlt333DefEIQ8/bDNGBb1ccaw8gTqXQSpVsmGPYbRhg/X9POus4JcrjpVfwjuXIdautQS0bl3QkSSmcLnidJvzszSeQJ3LEE89BX/5C3z7bdCRxC/dliuOlV/CO5cBVK3x6PDD4bDDgo4mfh98YJ3+R48OV92tJ1DnMsCkSfD11zBqVNCRJKZwueLu3YOOJD5+Ce9cBhg+3PpOXnBB0JHELx2XK46VJ1DnQi4/H378ES6+GKpXDzqa+KXjcsWx8kt450IuJwc+/thmMAqbdF2uOFYpLYGKSFcR+VZEckXkpmLu7yEiX0V+PhORQ2J9rHPOOs7/8ov9XaVKsLEkYtSo9FyuOFYpS6AikgM8BJwKtAYuFJGiM/v9AByrqu2AO4CRcTzWuaz3zjvQoIFNwBE2+fl2+Z6OyxXHKpUl0E5ArqouUNUtwHPAWdE7qOpnqro6cnMK0DDWxzrnrPGoZk3o0CHoSOKXzssVxyqVCXR/YEnU7bzItpL0At6M97Ei0ltEpovI9BUrVpQjXOfCJS8PXn3VJt6oWjXoaOI3dGj6Llccq1Qm0OK6w2qxO4ocjyXQv8f7WFUdqaodVbXjvmGc/NC5BD36qHWg79076Eji99VX8OGH6btccaxSGXoe0CjqdkNgadGdRKQd8BhwqqquiuexzmWr/Hx4/HGbsT0sE29ES/flimOVygQ6DWghIs2AH4HuwEXRO4hIY2A88GdV/S6exzqXzXJy4P33YcuWoCOJX+FyxZdckr7LFccqpgQqIh2BY4AGwEZgDvCeqv5S0mNUdZuIXAu8DeQAo1R1rohcGbl/OHArUBt4WGwA7LbI5Xixj030STqXiVq2DDqCxIRhueJYiWqxVYt2p8glQF+su9EMYDmwG3AQcBSWSG9R1cUpjzQGHTt21OnTpwcdhnMplZsLN90EgwdD8+ZBRxOfLVusyuHgg+Htt4OOJnYiMkNVd+lsVVYJdA/gKFXdWMJB2wMtgLRIoM5lgxEj4OWXrQ9l2BQuV/zoo0FHkhyllkDDxkugLtNt2gQNG8Jxx1kyCpsjjoA1a2DevPRfcTNaSSXQmJ6CiDwpIrWibu8tIiGdOMu58HrxRZu1PYwTbxQuV9y3b7iSZ2lifRrtVHVN4Y3I6KFDUxKRc65Ew4dDixZw/PFBRxK/oUNtyr2LLw46kuSJtRtTJRHZu3DYpYjsE8djnXNJkJ8PJ54IjRuHrwSXl2el5+uuC8dyxbGKNQn+B/hMRF7ERgR1AwalLCrn3C5ycuC224KOIjFhW644VjF9j6nqU8B5wDJgBXCuqj6dysCcczusW2ct79u2BR1J/DZssJ4DYVquOFbxXAjsA6xX1QeAFZFRQs65CvDsszbpxrRpQUcSvzFjbM7SsM75WZqYujGJyL+AjkBLVT1IRBoA41T1qFQHGA/vxuQykaqttJmfD7NmhWvVSlVo29Yme/7ii3DFHi3RjvSFzsFa3b8AUNWlIrJnEuNzzpVg2jSYOdPqEcOWgN5/P5zLFccq1kv4LWpFVQUQkT1SF5JzLtojj1jL9Z/+FHQk8QvrcsWxirUE+oKIjABqicgVwGVAhgzGci79bNwIEybAkiW2YNxFF8GeIbvm+/57m3X+5pvDt1xxrGJKoKo6REROBn4FWgK3quq7KY3MuSz1zTdwyinQpg20bm2zzS9caDMYVasWdHSxC/NyxbGKdSjnHsAHqtofK3nuLiIhXAPQufR3+eU229Ibb8CgQTBnjjXCDBsWdGSxW7s23MsVxyrWOtBPgGoisj/wHnAp8ESqgnIuW/34o5VAe/eGd9+1iUO+/BJuvBFeeCHo6GI3erT1Xc3ErkvRYk2goqobgHOBB1T1HGy5YedcEqnaMM3Vq225i5o17TI+J8fWgA+DwuWKjzoqvMsVxyrWRiQRkc5AD2zxt3ge65yLUcOG0KQJ/P738NNPMHmy1Xvedx+cd17Q0cXmtddsueLBg4OOJPViLYFeB/wDmBBZluMA4MPUheVc9jrhBOv32bq1JaNOnWDlSvjrX4OOLDbDhoV/ueJYxdoK/wlWD1p4ewG21IdzLsl++QVOPdXGjv/4IwwcaLdzcoKOrGyFyxX/+9/hXq44VqU+RREZidV5zi7mvj2AC4DNqjomRfE5l3UefTR8XZYKDRtmyxVffnnQkVSMsr4jHgZuEZG22AJyK7BF5VoANYFRgCdP58pJFf7xD+jZ0y7dw5g8V6ywiUMyYbniWJWaQFV1FtBNRGpgk4nUx5Y1nqeq36Y+POeyw/Dhdtlbu7Yl0DDKpOWKYxVrHeg64KPUhuJcdpo1yxqITj0Vrr8+6GjiN2YM/POfsHixDdksbADLBllQzetc+vrtN+jWzUqeTz4ZvqU6xoyxTv8bNtjtTZvsNkCPHsHFVVFC9nY5l1mGDIH582HsWJu1KGwGDNiRPAtt2GDbs0FcJVAR2UNV16cqGOeyzT//aSN2unQJOpLELF4c3/ZME+tkIkeKyNfAvMjtQ0Tk4ZRG5lwGy8214ZrVqtnMS2FVr17x2xs3rtg4ghLrJfx9wO+BVQCq+iUQ0u9M54K1fj2ceSacdpp1XwqrtWst/qIzzVevbrNIZYOY60BVdUmRTflJjsW5rHDNNTbj0p13hneZC1W47DLr+zlggI3fF7HfI0dmRwMSxF4HukREjgRURKpiwzjnpS4s5zLTk0/az623woknBh1N4v77Xxg/3hrBrr8e7rgj6IiCEeuqnHWAYcBJgADvANep6qrUhhcfX5XTpbN582x6t8MPt8XWwjC2vTiTJsHxx9tY/RdfDG8pOh7lWpVTVVdiU9k55xJUq5bVew4bFt7k+fPPNst8s2YwalR2JM/SxJRARaQZ0AdoGv0YVT0zNWE5l1kKCmxpi3Hjgo4kcdu22eqaa9bAW2/BXnsFHVHwYq0DfRl4HHgVCMm82M6lh7Fj4fHHLXnuvXfQ0STu5ptthdAnn4R27YKOJj3EmkA3qer9KY3EuQz0/fc2tPGQQ8K3LHG0V16xyU5697YZo5yJNYEOE5F/YY1Hmws3quoXKYnKuQywaZONc69a1UqhYZ1geP58uPhi6NAhXCuDVoRY39K2wJ+BE9hxCa+R2865Ytxwg8209OqrtsRFGG3caGsxVapkLe677RZ0ROkl1gR6DnCAqm5JZTDOZYo1a2w9o+uvhz/8IehoEnfttbas8muvWcu721msCfRLoBawPHWhOJc5atWyeTH32CPoSBL3+OPWVWnAADj99KCjSU+xDuXcD/hGRN4WkYmFP6kMzLkw2rLFRuls2WIt7lWrBh1RYmbOtCGnJ54It90WdDTpK9YS6L9SGoVzGeLvf4ehQ+Hgg8M7y9KaNXD++VCnjjV+hbXTf0WIdSTSx6kOxLmwe+UVS559+oQ3eRYUWDelxYvhk0/COclzRSprWeNPVfVoEfkNa3XffhegqlozpdE5FxKLFtlqlB06wL33Bh1N4u65x3oNDB0KnTsHHU36K6sE2h9AVUPcBdi51OvVC/Lz4fnnw7kkMcCHH1qDUbdu2bWyZnmUlUAfAjpURCDOhdnQoXbZ27x50JEkZulSG+d+0EHw2GM+SUisykqg/jI6V4qlS6FBA2s0OvjgoKNJzNatVupctw4++CDcQ04rWlkJtFlp3ZV8NiaXzfLyoH17uO46uOWWoKNJ3E03weTJtkRxmzZBRxMuZSXQFcB/Ej24iHTFJmLOAR5T1cFF7v8dMBqrJhigqkOi7lsI/IYtHbKtuMlMnQvKtm1w0UU7xruH1UsvWb/Va66x5+PiU1YC/S3RLkwikoPVoZ4M5AHTRGSiqn4dtdsv2PIgZ5dwmOMjkzk7l1YGDrSZ2Z95Blq2DDqaxHz3HVx6KXTqBP9JuJiU3coaibSwHMfuBOSq6oLIGPrngLOid1DV5ao6DdhajvM4V6HefRfuussWVQvr4mnr19skIVWr2jylYe05ELRSE6iqnluOY+8PRK/kmRfZFisF3hGRGSLSuxxxOJdUmzbBkUfCAw8EHUliVOGqq2DuXHj22exZwz0VUjlDYXEt+PGsgn2Uqi4VkbrAuyLyjap+sstJLLn2BmjsnwRXAc44w2ZYCmtXn5Ej4emnrRoirCOm0kXM68InIA+IngWxIbA01ger6tLI7+XABKxKoLj9RqpqR1XtuK+PO3MpdPfd1t9TNbzJc/p06yT/+9+Hu+dAuig1gYpIhyI/h4pIrFPDTgNaiEizyFry3YGYZnASkT1EZM/Cv4FTgDkxnte5pPvoI1sTaMaMoCNJ3KpVNklIvXrW+FUplcWnLFHWJXxxbXP7RBLihao6q6QHquo2EbkWeBvrxjRKVeeKyJWR+4eLSD1gOlATKBCRfkBroA4wQexrvjLwrKq+Fdczcy5Jli+3Lj4tWsAjj4Sz9FlQAH/+s3X8//RTm2nJlV+pCVRVjy9uu4h0BO4HupTx+DeAN4psGx7198/YpX1RvwKHlHZs5ypCYeJZvdqW8q1RI+iIEnPXXfDmm/DQQ9ZtySVHQo1IqjpdREL6UXIudpMnW7el4cPDu5Tvu+/Crbdal6urrgo6msySUAIVkf2Ir0XduVA65hhbGK5t26AjScySJVb90Lo1jBgRzuqHdFbWfKAPsGui3Ac4ErguVUE5F7RVq2D2bDjuuPCWPLdssWGmmzbZkM0wr8+UrsoqgU4vcluBVcDfIt2LnMs4qjY58nvvwcKFsN9+QUeUmBtugClT4IUXwjvcNN2V1Yj0pIgcChwIzFXVeRUTlnPB+e9/bRnf++8Pb/J87jkbKdWvH/zxj0FHk7nK6gd6C/A8cB7wuohcUSFROReQqVNterdzz7U10cNo3jy4/HIbbnrPPUFHk9nKuoTvDrRX1Q0iUht4C3g09WE5V/F+/RUuuAAaNrQ10cPY4LJunU0SUr26XbpXqRJ0RJmtrAS6SVU3AKjqKhHxsQsuY+25p02OfOSRUKtW0NHETxWuuAK+/RbeeQf2j2fqHpeQshLogVEz0kuR2z4jvcsYGzfC7rvDX/8adCSJe+ghq/scNAhOPDHoaLJDWQn0rCK3hxS7l3MhNn26za700ktw1FFBR5OYKVPgb3+z53HTTUFHkz3KaoVPaDZ658Ji7Vqr96xaFX73u6CjScyKFdbSvv/+8NRTPklIRSqrI/1ZQENVfShyeypQOGfcjar6Yorjcy5lVKF3b1i0CD7+GGrXDjqi+OXn2xDNFSvgs89g772Djii7lHUJfyPWEl+oGnA4sAe2GJwnUBcaqjahxrPP2iidGjWspfruu8N76X777TbWfeRI6NAh6GiyT1kJtKqqRi/L8amqrgJWRebpdC40broJJk60zuW77w79+0PdujZiJ4zefNMS6MUXW79PV/HKSqA7XRCoanTXYp/+3YXG99/D6NHWxafwMrd7dzjsMBuy2bVrsPHFa9Ei+NOfbJz+ww+Hs89qJiirunlqcaOPROQvwOepCcm55PvgA2uh3rLF6j3nzrWGox49rM9kmGzebDPLb9tmPQeqVw86ouxVVgn0r8DLInIR8EVk22FYXejZKYzLuaSqVQu++spKbL/+anWebdrATz+Fb3b2fv2s69X48dC8edDRZLeyujEtB44UkROANpHNr6vqBymPzLkk2bTJWtlnzIAmTaw02qaN3R4zBqZNCzrC2D3zjE3u3L8/nHNO0NG4mCZUjiRMT5oulIYMsbWMune3GeZ79rRGpG+/tdbrZs2CjjA2c+ZY9UOXLrZEhwteKteFdy4wqrBsma1Aef31Nr79hBOs3vCzz6wu9KijLJGGwa+/2iQhNWvacM3K/p+bFvxtcBln2TK49FJreZ81y2ZiP+EEu69yZSvBhYkqXHYZzJ9v1Q/16wcdkSvkg75cRnnjDWso+vBDa2wJawv1mDHQtKkNy6xd21rb7747fMk/03kJ1GWEzZutYeWBB2wBuMKGojAaM8bqOjdssNurV0NOjpc805GXQF1GyMmBmTOt1Pn55+FNngADBuxInoXy8+Hmm4OJx5XMS6AutFRtqd5zz7Uhme+/b53jw27x4vi2u+B4CdSF0rJlcPrpcNVV8GhkkZlMSJ4A+5YwSLpx44qNw5XNS6AudN54w1rZf/0VHnwQrr466IiSQ9VWAl2xwsa2q+64r3p1m2nepRcvgbpQGTXKSp777WfDGa+5JjMm0ti61b4I+vWDs86yUnWTJvbcmjSxDv89egQdpSvKS6AuFFQtmZx5pjWy3Hwz7LZb0FElx5o1NqP8e+/B3/9uo4wqVYJevYKOzJXFS6AurRVe1p54oo0iqlMH7rwzc5Ln/PnQubON1R81CgYP9iU5wsTfKpe2ChuKrrvO6gDXrw86ouT65BM44ghYvtxmlb/00qAjcvHyBOrSUvSIogcfhFdfhb32Cjqq5HniCTjpJCtRT50Kxx4bdEQuEV4H6tLO1q22Pvt++4V7RFFxCgqsDnfwYKuWGDfOF4ILM0+gLm3MnWtTy1Wvbuv9NGiQOXWdYFUQPXvaRMi9e1vJukqVoKNy5eGX8C5whQ1Fhx0G//qXbTvggMxKnj/+aBOBTJgA991nkyJ78gw/L4G6QBVOPffmm7ZmUf/+QUeUfF98AWecYR3/J0605+kyg5dAXWA++WTnhqKJE21MeyaZMAGOOcbmIZ082ZNnpvEE6gLToAG0aJFZI4oKqcK//20TnbRtay3t7doFHZVLNk+grkLNnm2jbVRtRclJkzKrlR1suZBeveCmm+CCC6yEXa9e0FG5VPAE6ipEYUPR4YfDk09aowpkVqkTYOVKOPlkGD3aGsTGjg3Puksuft6I5FKuaEPRqFElT9kWZt98Y88vL89mlb/ooqAjcqnmCdSlVEGBjbjJzd0x9VymlTrBJgI5/3yoVs0u2Tt3DjoiVxE8gbpyU4Vp0+Cdd2xUzQUXQI0a1s8xJ8cu3evWzby6zkIjRlgjWKtWNuS0adOgI3IVxetAXbkUFMDll0P37tbPcepUaxxq1cpaoQGOPz4zk2d+vg05vfJKOOUU66bkyTO7eAJ15TJ+vC3mNmeOJcyOHW1BtMWLrftOpvrtN5v4eOhQ6NvX+rDWrBl0VK6i+SW8K5eXXrLL19xcK4198IE1pCxdCnvsEXR0qbFokY0s+vprePhhW5fJZScvgbqErV0LGzfa3/n51sfzoYesNFa5cmY2Fk2ZAp06WQn7zTc9eWY7T6AubnPmWOLYf39bjuLBB+Ggg6xv59VXw0cfWYI56qigI02u556D446zBrL//c/6e7rsltIEKiJdReRbEckVkZuKuf93IvI/EdksIjfE81hX8V57zRJI27Y2IXC3bjBkiNV7tmkD//iHLXz2xz/C009nzjLDqnDbbXDhhTYQYOpUayRzLmV1oCKSAzwEnAzkAdNEZKKqfh212y9AX+DsBB7rKsCyZdYFScTqNxctgnvugcsug9q1bZ+OHW08+zvvwIEHWrelwvvCbtMme65jx9pcniNHWl9P5yC1jUidgFxVXQAgIs8BZwHbk6CqLgeWi8jp8T7WpY6qXaI++CC8+KItr3HSSXD77XDvvda3s6iOHe0nkyxbBmefbfWed99tY/gzsV7XJS6VCXR/YEnU7TzgiAp4rEvQli3wzDOWOGfOtG45V19t/TrB6v6yxezZ1ptgxQr7EjnvvKAjcukolQm0uO9qTfZjRaQ30BugcePGMR7eRVu/3rocidh667Vr24zpPXpkV9Is9MYbNppqzz1tztJMK1m75EllI1Ie0CjqdkNgabIfq6ojVbWjqnbcNxNnqEiRggJ4+23rz9i6ta25XqUKfP45fPUV/OUv2Zc8C2eMOuMMm6f08889ebrSpTKBTgNaiEgzEakKdAcmVsBjXSnWrLHRM7/7HXTtamPYL74YNm+2+xs2zM56vq1brbriuuvgzDOt5NmwYdBRuXSXskt4Vd0mItcCbwM5wChVnSsiV0buHy4i9YDpQE2gQET6Aa1V9dfiHpuqWLNBfr41/kydaiOGjjzSuuacd17mdDdK1Jo11iXr3XfhxhutwaiS95B2MRDVWKsl01/Hjh11+vTpQYeRNrZutTV5HnoIjjjCuh8VFFgDySGHBB1depg/3xqLcnNtVqXLLgs6IpeORGSGqu5SoePfsxno55+ty1HTptYYsmSJ9c8EK1llc/IcM8Zel0qVbJmN9u1h+XIrfXrydPHyyUQyhOqOussbb7SRQF27Wsfvrl2L77uZbcaMgd69bbYosH6eIjBwoI2wci5efgkfchs2wLPPWt/Np56ylR9zcy2htmgRdHTppWlTG0lVVJMmsHBhRUfjwqSkS3gvgaaxxYstMc6da5N1XHPNjk7t8+fDI4/A449bI0jbtvYbduzjdli4sPjkCfY6O5cIrwNNU3Pm2MQVqjbjefXq1nL++ec2hVyHDjBsmM2E/skn8OWX0KVL0FGnnwUL4IorSi+N+/gLlygvgaapAQPsp29fWL0avv8eGjWC/v3h44/tsv3QQ6FBg6AjTU+5uXDXXVatUbmyfQm1aGEzRhXWgYJ9MQ0aFFycLtw8gaaJ/HxrLa9XD3bbzWY22rLFhlTm5lqXpKOOgkmTbN/Ti06/4gD49ltLiGPGWP/Wa6+1RrXCL5rate2LafFiK3kOGmRDVp1LhDciVaBt26wfZtWqtob4yJFWsszNtUvNLVvg008tUe67L9SqBQcfDC1b2lyUtWpZt5tffsnO0UKlmTcP7rzTJj2uVs1GFd1wg30hOVde3ohUwVatsn/mwgT5/ffwww/WveiCC2DlSuu43by5jUU/80y7xDzgAHt8375W2nzqKZvUYuNGKyn16uXJM9qcOZY4X3jBLsdvuAGuv97mMHUu1bIygf7vf/DoozZV2bHHWt/AeFdU3LoV3ntvR3LMzbWfq6+Gfv1g3Tq7fNxjD0uS7drBuedaaRKsQWjdupKT4U032WVm06bW8X32bGswuvPO8jzzzPHll3DHHbaoXY0a9nr97W9Qp07QkblsknWX8KNH25RtN9xg/f+ef95WV5w0yS6Ro3333c4J8vvv4ZhjrA5tyxbYfXe7JK9Rw0qPzZvDRRfZJLwFBdZRu1698pUY8/Lscr95c19zHOCLLyxxvvyyfeldd519Ye2zT9CRuUzml/DYZfCNN1orduvWNgNR69bWQtujhyWoevXgllts/+OOg59+sr9r1rQkWTnyilWtaiXZJk12LHkRrVIlqF+//DE3bOizAoHNGnXHHfDqq/ZFN3CgVXPsvXfQkblsllUJdNYsS3itW9vtY4+12YkK7bUXnHbajtujRu1InHXq7JokO3VKechZb8oUG9f/5puWLO+4A/r0sffKuaBlVQLdZx+baKNwarf+/a1UOneuXca//PLOSbJr18BCzXqTJ1vifOcd63p0991WvxxvXbVzqZRVI5FatrTL9LvusjrK886zOs0XXrBhkt66HbxPPrEF7I4+2tZluuceG4Z5002ePF36yaoECta16PXXbXq3Y4+1fpXXXGMt3C4YqvDhh1bnfOyx1jXpP/+xbl/9+2ff0iIuPLLqEh6sQeZ//7NuQStW2Jo3Xp8WDFV4/32bGf/TT63RbehQ61a2++5BR+dc2bIugYJdqrdrF3QU2UvVFrS7/Xb7MmvY0Gad6tXLhrE6FxZZdwnvgqNq1Sf/939w6qnw4482JV9urlWjePJ0YeMJ1KWcKrzyilWX/OEPtoRG4TwAV15pY9edCyNPoC5lCgpg/Hibdu/ss2HtWutb+913Nkdntq8G6sLPE6hLiujF2po0sVFC7dtbV7ENG+DJJ21I6qWXQpUqQUfrXHJkZSOSS66ii7UtXgwPPGBzcD7zDHTv7ovauczkCdSVS0GBTcwSPct7ocqVfbJil9n8Et7FbcsWG2J51VXWBennn4vfb8mSio3LuYrmJVAXk3Xr4K23YMIE64q0dq1NYHzqqTa71cqVuz7GF2tzmc4TqCvRypU2fdyECfDuu7Bpk03sce65cM45NmZ99913rQMFX6zNZQdPoG4nixfbrFQTJuxYwK5RI0uQ55xjk3xULvKpKazn9MXaXLbJuhnp3c5UbUG2CRPsZ8YM2966tSXMc86xNeh9piqXzXxGerddQYHN8F6YNL/7zrYfcQQMHmxJ86CDgo3RuTDwBJoltm61xp4JE+wSfelSuxQ/7jhbV+iss2D//YOO0rlw8QSawTZssFmPJkyA116D1aut0adrVytl/uEPvqaQc+XhCTTD/PKLJcsJEyx5btxoSfKMMyxpnnKKtZA758rPE2gGyMuz2Y4mTICPPrKW8/33h8sus6TZpYuPP3cuFXwkUpqLnqSjaVO7DTYxx91328qgjRrBtddaIu3f31YaXbzYJik+8URPns6lindjSmPFdVCvXNnWoV+61G537Liju1GrVsHE6Vym825MIZOfb6XJopN0bNsGq1bB/ffbHJuNGgUSnnMOT6BpYcsWW5f+iy92/Hz5ZfEzHBXu36dPxcbonNuVJ9AKtnEjfPXVzslyzhxLigB77mkTEV9xhc2luWrVrsfwSTqcSw+eQFPo119h1iyYOXNHspw3zy7PAfbZx4ZJ9utnvzt0sPXqK0Wa9g4/3CfpcC6deQJNklWrdk6UX3xhi6YVql/fEuQ559gaQR06WEmytDHmPkmHc+nNW+ET8NNPOyfKmTNh0aId9zdpsqNE2aGDJcz69VMelnMuRbwVPsqYMbGV6lQtMRYtWUbPwH7QQdC5s61r3qGD1V/Wrl1hT8U5F6CsS6BF+1YuWmS3CwpsNqLoRPnFFzZ+HGxRtNat4fe/33EJfsghULNmcM/FOResrEugAwbs2j1owwa4+GIrcYKtV962LZx//o7L8LZtbSIO55wrlHUJdPHi4rerwqhRlixbtbIk6pxzpcm6BNq48c4NPoWaNIFLL634eJxz4ZV1k4kMGrTrdG7et9I5l4iUJlAR6Soi34pIrojcVMz9IiL3R+7/SkQ6RN23UERmi8gsEUla36QePWDkSCtxitjvkSO9b6VzLn4pu4QXkRzgIeBkIA+YJiITVfXrqN1OBVpEfo4AHon8LnS8qhaz4nj59OjhCdM5V36pLIF2AnJVdYGqbgGeA84qss9ZwFNqpgC1RMS7nDvnQiGVCXR/YEnU7bzItlj3UeAdEZkhIr1TFqVzziUola3wxY3yLjputLR9jlLVpSJSF3hXRL5R1U92OYkl194AjX2aIudcBUplCTQPiJ7utyGwNNZ9VLXw93JgAlYlsAtVHamqHVW147777puk0J1zrmypTKDTgBYi0kxEqgLdgYlF9pkI9Iy0xv8fsFZVfxKRPURkTwAR2QM4BZiTwlidcy5uKbuEV9VtInIt8DaQA4xS1bkicmXk/uHAG8BpQC6wASjsyr4fMEFsrrfKwLOq+laqYnXOuUT4dHbOOVeGkqazy7qRSM45lywZVQIVkRVAMSPd2QtYW8z2ZsAPKQ0qeVIda0mvUbwSiTPec8eyf1n7lBZnsl6LZEj0fa+I51D0HBXx3ifyuFjiLOt4TVR111ZqVc34H2BkCdvXBx1bHM8hpbGW9BpVRJzxnjuW/cvap7Q4k/VaBPm+V8RzKHqOinjvE3lcLHEmGke2XMK/GnQAIRDkaxTvuWPZvzzPJxM+LxXxHJJxjkSPEc/jUvZ5yahL+HiJyHpV3SPoOGIRllg9zuQKS5wQnliTGWe2lEBLMj7oAOIQllg9zuQKS5wQnliTFmdWl0Cdc648sr0E6pxzCcvKBCoi34lIgYhsCjqW0ohIJxFZLSKbRWSTiLwUdEylEZEqIrJBRJYFHUtpROTlyOu5SUQWicheQccEJX8uRWSciGyJxDs1qPii4tlLRNaJyMZITB9Ftn8e+axuFJGlItIk4FARkSYikheJa7OIXBF136sioiJyUKLHz8oECtwP/CnoIGKwGbhGVathfdfOEJEzAo6pNC8Cy4MOojQichhwOlBfVXfD/gf+E2xU2+3yuRSRvwInALUj8abDyl2/Ageq6u7A3kBHEemF1S3WjGxfDDwfYIyF3gfei/wP7QO8A1Y4AToD+eU5eFYmUFV9EHuD05qqfqmqz0b+/glYBbQONqriiUhH4BgsCaQ7AfYWkWpAVWwuhsCV8Ln8GzBYVX+L7PP1Lg+sYGoKrzKqY3lEVXWwqm6ObP8Im9MiMCKyP9CEyJeOqq5X1cKBNuOBq8p7jqxMoGEkIkcD+wLPBB1LCV4G+gAFAcdRKlWdAbwCzAc2AhtUdXCwUZWqDnB65JJ5jYhcHHRAsL26ZiOwEvhKVUcV2eUS4M0KD2xnR2NXcd9Hqpa+EZF9ReROYIWqjivvCTyBhoCI7IfNavUfVf0x6HiKEpHbgDWqOiboWMoiIk2B44FWwB7AbiLycKBBlU6AWsCeQD/gMYlMUxYkVd0auVRvCrQUkbML7xORd7Av0muCiW67qth7fJeqVse+MF/HXsczk3ECT6BpTkR2B+Zi9Th/DzqeEpwM/E5EtmH1iXVFZEHAMZWkD1b6+EZVN2Jz0nYJOKbS/AY8EblsfiKyrWWA8ewkckk8E+gFICIjsbrFthp8H8lZQH5U6fgxoDlW7fBD5POaA3wtIm0TOYEn0DQWKWnMAZaoatEF+dKGqh6pqpVVtTJwPbBcVQ8IOq4SzAWaiEjtyOt7IjAv4JhK8w5wDoCInIKVSL8NMiAR+V1hC7uI7A0cBnwhIgOAi4HDVXVVkDECqOpsYL2IdI1s+iO20GWlqM9rPtA6sm/cUrkmUtoSkUXY8iGVIt9CT6tqOrRuFnUlcACwKVLfBHC3qt4eYEyhpqqjRKQntnSMRn5fFmxUprjPJbbe1+xI16YC4MY0KNm1AZ6OfAEJMFlV/yUiW7BC2cxILcMCVW0TYJxgr99LIlIJWIOVjpPGRyI551yC/BLeOecS5AnUOecS5AnUOecS5AnUOecS5AnUOecS5AnUpZXI7DhPR92uLCIrROS1GB67LvK7qYhcFLW9o4iUOkY/8pg5cez/hojUivxcXVZsLjN5AnXpZj1wcGQEFtgop3iHrzYFtidQVZ2uqn1jfXAs+6vqaaq6Bhtm6Qk0S3kCdenoTWzKOYALgbGFd4jIQBG5Ier2nMj49miDgWNEZJaI/FVEjisswUYe/7SIfCAi30fPDxl1zOj9a4jIaBGZLSJfich5ke0LRaRO5FwHRs51b+TYZ0Uda4yIJGXctUs/nkBdOnoO6C4iuwHtgHgnEb4JmKSq7VX1vmLub4cl6M7ArSLSoJRj3QKsVdW2qtoO+KCYc82PnKs/Nt76UrCJh4EjgTfijN+FhCdQl3ZU9SvsMvxCUpN8XlHVjaq6EvgQ6FTKvicBD0XFtrq0A6vqx0BzEamLxf+Sqm5LQswuDWXlWHgXChOBIcBxQO2o7dvY+Yt/twSOXXT8cmnjmaWM+4vzNNAD6E6ajLN3qeElUJeuRgG3FzNLzkKgA4CIdMCWOinqN2z+zJKcJSK7iUhtLEFPK2Xfd4BrC29EZh8q61xPYHNOoqpzSzm2CzlPoC4tqWqeqg4r5q6XgH1EZBa2JMN3xezzFbBNRL6MrClU1OfYxLpTgDtUdWkpodyJLf8xR0S+xCZjjo5zFTA5cv+9kW3LsCnyRpf6JF3o+WxMLquIyEBgnaoOSeE5qgOzgQ6qujZV53HB8xKoc0kkIicB3wAPePLMfF4Cdc65BHkJ1DnnEuQJ1DnnEuQJ1DnnEuQJ1DnnEuQJ1DnnEuQJ1DnnEvT/dTIkIWx7L/sAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 360x360 with 1 Axes>"
       ]
@@ -383,7 +383,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1080x360 with 3 Axes>"
       ]
@@ -424,7 +424,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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RRxg/fjwAy5cv54knniBjxoy0bNmSJUuWhCYhkVWFDNn2zDPPcOTIEbp168bBgwepXLky58+fp2DBghw6dIjRo0eTJk0aGjRowO+///7AdebNm0fbtm0BaNu27QNJSdGiRalRowbfJdFe7UOHIFu28NuyZQNHjhRvgYGBrF+/nvHjx7N161aOHDnC7NmzOXXqFIsWLaJv376xvmZy+PxX/LeC41eOU6hIIerWqstPP/xE5nSZKZ2rdKzfb1S0WqlSymmMsQnhlCkwfz7kzm3vtTw8YP9+Ow/l6lUYP94ON3J3tz2KcWjTlVIqdnbuhMmT7Ti369ehTBnbAIXInt1OloookiFgqdGnnz58v7e3HUoaUZEisHZtwsaSP39+ypUrx/r162nVqhUAI0eO5Pz580ydOvWh55YrV44dO3ZQqVIlcubMya5duxg/fjzXr18H7E36xo0b8fb2BmwCsWbNGp566ily5sxJQEAAuXLlAuDSpUuhvwPkyJGD9u3b0759e5599ln+/PNPWrZsSbp06WjcuDGNGzcmb968LF26lAYNGoSed/HiRf744w/27t2LiBAUFISIPDBMcujQobRq1SrcEMjE9LDPsUwZ21v4xBP3t61Zc///77lyxe/fQcGCBalcuTLFHKXNW7RowebNm8mXLx+HDx+mhOMLnps3b1KiRAkOHz4c6XWS0+d/78l7TFw1kQzBGbh8+zKfjvqUWs/U4tF6j8b9DxkJ7TlUSjnF6tVQtSrUrAkLF8LLL8PSpffvtRzD7cmaFd57D/75xy5r8cYbNnlUSqkEd+nS/VLIP/5oJ8C9+CJs3Aj79kG/fuGPjaxaqYuWsRCRRiJySEQOi8jgSPaXFpG/ROSOiLwZYZ+niHwvIgdF5ICIPObseEeNAscUsFAZM9rtCcHf359bt24BEBAQwMaNGylVqhQA06dPZ9WqVcybNy90bldU3nrrLUaNGsWBAwdCt910jIe9evUqGzZs4Pjx4/j5+eHn58ekSZNCe3Hq16/PN998A9h5bt9++y1POLKhP/74I/Q6165dw9fXl8KFC7Njx47QIYbBwcHs3r2bIkWKhIvp+++/p2PHjhw7dgw/Pz9OnDhB0aJF2bBhQ7jjSpcuTdmyZVm+fHns/4BONmyYHUK8Zo39T27NGvt82LCEuX716tUJCAjg/PnzgP17ly1blqZNm3LmzJnQzytjxoxRJoaQPD7/v/b9xeUMl5m4aCKdfTpTNHtRcmXMRenSpXmq5lNsXbs1If6k9xljUvyjatWqRinlfLt3G3PqlP19+XJjKlQw5ssvjblyJeFfC9hmkkD7Ep+Htk1KJYKgIGP++MOY9u2NSZfOmMWL7faAAOc0Tibh2yfADfAFigEewD9A2QjH5AGqA6OANyPsmwO86vjdA/CM7jUTon369ltjihQxRsT+/PbbeF/SZMqUyRhjzOrVq02FChVMxYoVTYUKFczUqVNDj3FzczPFihUzlSpVMpUqVTIjR4586DWXL19uqlWrZkqVKmVq1apl2rZtaw4dOmRmzZpl2rRpE+7Yixcvmly5cpnbt2+by5cvm3bt2oXGMHDgQBMUFGSMMWbs2LGmTJkypkKFCqZcuXJm/PjxxhhjVq5caapUqWLKlStnypUrZ7p06WJu3bpljDGmSJEi5vz586ZevXpm5cqV4V534sSJpmfPnubo0aOmXLlyodt37dplRMTMmjUrbn9QJ/ruO2PKlTMmTRr787vv4n/NkM/fmPv/BsqXL286depk7ty589Djo5KUP//g4GBT86uaxuNZD/NU66fi/fnHpG0Se1zKVq1aNbNt2zZXh6FUsmWMLR7zxRdw8iQ8+iiMGGHnBd6+Dd9/b4eObtwIQ4bAhx/acwAiGZKfIERkuzGmmnOunji0bVIqAUS1WL2nJ7z1FsyYAb6+9vlLL0GfPuDoYXKWhG6fHD19I4wxzzieDwEwxoyO5NgRwHVjzHjH86zYZLKYicVNn7ZPSrlOsAkmKDgIdzd3/jnzD+nSpkuQeYUxaZt0zqFSKlqjR8OiRTY5LF3ajsZq0gSaN4clS+woq5Il4eOPoVMne46zkkKllAonZLF6gMBA+PdfKFvWNkIzZ9piMiNGQMuWkCGDS0ONhwLAiTDP/YGaMTy3GHAemCUilYDtQH9jzI2EDVEplRAu3rzIy0tepqhnUSY1nUSlfJUS9fU1OVRKPdTNmzbp277d3mP9+Sd062YLyUyaBE8+CT172knn0UztUEop5zh+3A5vmDnTDmc4edJu37HD1s9P/iL7ui2mvYBpgSpAX2PMFhGZCAwGhj/wIiLdge4AhQsXjmOoSdOsWbOYOHFiuG21a9dm0qRJLopIJabk8vn/ffJvXlz0Imeun+HTZz51SQyaHCqlHur4cVucb8kSW53u+HFbt+HJJ2HOHNujqJRSLvHPP/Zn0aK297BRI/vtVVrH7U3KSAzB9hQWCvO8IBDTRQH8AX9jzBbH8++xyeEDjDHTgGlgh5XGLdSkqUuXLnTp0sXVYSgXSeqfvzGGydsmM+CXAeTPkp+Nr2ykWn7XzJzR7/mVUlG6dMlWevf1hddft6XJf/zRTtfZutUOJVVKqUR1+TKcOWN/v+EYGfnWW3adnBUr4Pnn7yeHKcdWoKSIFBURD6AtsCwmJxpjzgAnRCRkomUDYL9zwlRKxcXJayd569e3eLr40+zoscNliSFoz6FSKhI3bkCmTHbKzpw58MgjdvHaWbNsgrh6Nbz7ri1Eo5RSieKff+xY9rlz7do4U6bYhevBToxOwYwxgSLSB1iFrVw60xizT0R6OvZPEZF8wDYgKxAsIgOwFU2vAn2BuY7E8giQdLtQlEpFTl49Sf4s+SmYtSBbXt1CmdxlSCOu7bvT5FApBdgRWatW2fmFFy/aOYbZs9thpBkz2sVsa9Swa0U/8ghMnw61a7s6aqVUird4MXzyiS2HnCEDtG8PPXrYfSK2oYqsAlYKW6zeGLMCWBFh25Qwv5/BDjeN7NxdQLKu7qxUSjNvzzy6/dSNzxp/xiuVX6FcnnKuDgnQ5FCpVO/OHZg3zyaFe/dC/vx2HejAQHB3t4vUA7z/vi34d+eOvT/TaqRKKac5dQq8vGxD89tvcPasTRA7d34w6XPRovRKKRUXdwLv8Pqq1/ly25fUKVyHZ4o/4+qQwtE5h0qlcgsWQJcu94eQHj0KgwbZxDAiNzfbi6iJoVIqwQUHw6+/2jmDhQrBpk12+9ixcOgQvPZaiusNTNHy5bP/s4j4yJcvXpfNnDkzAMeOHaNq1ar4+PhQrlw5pkwJ7USlQ4cOlCpVivLly/PKK69w7969h17zl19+oUaNGpQuXRofHx/atGnD8ePHAejcuTNFixbFx8cHHx8fatWqFXre0qVLqVixIqVLl6ZChQosXbo0dF/nzp3JmDEj165dC93Wv39/RIQLFy4A4ObmFnpdHx8fxowZw/PPP4+Pjw8lSpQgW7Zsofs2bdpE/fr1KVWqFJUqVaJ69ers2rUr9Nre3t6h1z1z5gxt27alePHilC1bliZNmvDvv//G7Q8eV97ekX/+3t7xumzI5w9w/Phxnn76acqUKUPZsmXx8/MLd2zfvn3DHR8VZ3z+zds0x83DjSqfV6H6V9Wp/lV18jbIi4hQYkwJvtz2JfKecHXiVZrWbZq0Pn9jTIp/VK1a1SilLF9fY/r2NWbKFPv89m1jVq82JjjYtXHFFrDNJIH2JT4PbZuUMsbcvGnMhAnGPPKIMWBM7tzGDBlijL+/qyOLM22fjP0so3rEQ6ZMmYwxxty5c8fcvn3bGGPMtWvXTJEiRczJkyeNMcb8/PPPJjg42AQHB5u2bduaL7/8Msrr7dmzx5QoUcLs378/dNuPP/5o1q1bZ4wxplOnTmbRokUPnLdr1y5TvHhxc+TIEWOMMUeOHDHFixc3//zzT+h5FSpUMN98840xxpigoCBToUIFU6BAAXP+/Plw7yUya9asMU2bNg23rV69embr1q3GGGNmzpxpnnrqqdB9RYoUMefPnzfBwcHm0UcfNZMnTw7dt3PnTvPnn39G+VpOEdXnnECfvzH277F69WpjjP03cOPGjdB9W7duNS+99NJD/8bGOO/zb/tSW5MmXxrT+8Pe5m//v83m45tNidIlTJqsaUyGoRnMD/t/cMnnH5O2SYeVKpWCBATY6qLr10OePHZaTsiXXJs326GjixfbHsA337Tb06WDhg1dF7NSKhXIkcM2UBFlz26HkI4aBSVKwDffwIsv2oZJJW0DBkCYnotYqV8/8u0+PnbNpBjw8PAI/f3OnTsEBweHPm/SpEno7zVq1MDf3z/K63z00UcMHTqUMmXKhG5r1qxZtK8/fvx4hg4dStGiRQEoWrQoQ4YMYdy4cXzzzTcAtGvXjgULFvDSSy+xdu1aateuzcqVK2P0/qLz2GOPMW7cuAe2r1mzBnd3d3r27Bm6zcfHJ0Fe8wGRfY6tW0OvXlHvD3HhArRqFX7b2rUxfun9+/cTGBhIQ8cNTNgewqCgIAYOHMh3333HkiVLHnodZ33+6dzSUa9pPZYtWcbEQRP5cuGXlPApwbFzx9jZYyelCpeiIx1j/H4jcubnr8NKlUohLl60hfsOHrTtcpUqto2ePRv697f7fvvNVnz384MPP3R1xEqpVCMgwPYZ3b5tq43WqgXFitnt6dPbCc9//QUvvaSJoYqxEydOULFiRQoVKsSgQYPInz9/uP337t3jm2++oVGjRlFeY9++fVSpUuWhrzNw4MDQ4X0dOnQIPa9q1arhjqtWrRr79u0LfV6yZEnOnz9PQEAA8+bNo23btuGOv3XrVrhhpQsWLIjR+wY7FLJFixYPbN+7d+8DcaVE//77L56enrzwwgtUrlyZgQMHEhQUBMAXX3xBs2bN8PLyivY6zvz8Oz7ZkVNnTlFrUi36j+vP/rz78UzvSc6MOYGk+/lrz6FSKcTnn9v7rZkz4eZN8Pe3X8K3aQNffw3Fi8Mrr0AMht8rpVTCGzvW9gqdPm17Cfv0sQuoAuTN69LQVBxE18P3sMnpseghephChQqxe/duTp06RYsWLWjVqhV5w/xb6tWrF3Xr1uXxxx+P0fUuXrxIgwYNuHnzJt27d+dNxxCbcePG0SpCL5cxBonwHiPb9sILLzB//ny2bNnC1KlTw+3LkCFDuHljMdGhQwdu3LhBUFAQO3bsiNW5CS66zzGy/SF/n1y54vXvIDAwkPXr17Nz504KFy5MmzZtmD17No0bN2bRokWsjcO1E/rzPxpwFLeybmxdtZUcl3Lg+Ygn4n5/f1L9/J3acygijUTkkIgcFpHBkewXEfnMsX+3iFQJs+81EdknIntFZJ6IpHdsHyEiJ0Vkl+PRJOJ1lUqN1qyBZ5+1S04UKWJ7D/futQX/cuWyFUg1MbSc1DblEJFfReQ/x0+tnKFUWIMGQbly8Msv9wvMKJUA8ufPT7ly5Vi/fn3otpEjR3L+/Hk++eSTh55brly50JvsnDlzsmvXLrp3787169ejPW/btm3htu3YsYOyZcuG29a2bVuGDx9Ow4YNSZMm/rfdc+fO5ejRo7Rv357evXtHGtf27dvj/TpJXcGCBalcuTLFihUjbdq0tGjRgh07drBz504OHz5MiRIl8Pb25ubNm5QoUSLK6zjr8999djfvrXuP/I/lhzUQXDSYEU+MiN+bJnE+f6clhyLiBkwCGgNlgXYiUjbCYY2Bko5Hd2Cy49wCQD+gmjGmPHbB17B98ROMMT6OxwqUSuWMsT2F7drZ5SYeewzWrbNzDs+etdN9lOXEtmkw8LsxpiTwu+O5UqnT/v12qMKoUfe37dxpq5E+8wwkwE2ySuKi6g1OoF5if39/bt26BUBAQAAbN26kVKlSAEyfPp1Vq1Yxb968aBOyt956i1GjRnHgwIHQbTdv3oz29d98801Gjx4dWiHTz8+PDz/8kDfeeCPccYULF2bUqFH0CpmHlwDc3d354IMP2Lx5c7i4AZ588knu3LnDV199Fbpt69atrFu3LsFeP0aKFIm8WmmRIgly+erVqxMQEMD58+cB+OOPPyhbtixNmzblzJkz+Pn54efnR8aMGTl8+HCU13Hm51+3SF32DNlDo+6NyPtEXpqXbh7Hdxuesz9/Z7bONYDDxpgjxpi7wHwg4l+lOfC1o5jOZsBTREIGCKcFMohIWiAjcMqJsSqVLIVUbRaBwoXtVJ3Vq2HZMrtA/ciRUL68ndqjQjmrbWoOzHH8Pgdo4cT3oFTSYwxs2ADNmtkewvnz7cKoIZxVFEMlTWfORF6r9MyZBLn8gQMHqFmzJpUqVaJevXq8+eabVKhQAYCePXty9uxZHnvsMXx8fHjvvfeivE6FChWYOHEiHTt2pHTp0tSuXZsDBw7Qvn370GPCzjnz8fHh7t27+Pj48NFHH/Hcc89RunRpnnvuOcaOHRtp8Y8ePXpQvHjxB7ZHnHM2eHDMv1PMkCEDb7zxBuPHjw+3XURYsmQJv/76K8WLF6dcuXKMGDHigfmYTufnF/nnH2G5ibhyc3Nj/PjxNGjQgAoVKmCMoVu3brG+TkJ+/i+98RIXstrlJCrmrUifGn3I7JGZnz7+ib8G/kUaCZ92JdnPP7pypnF9AK2A6WGevwx8EeGY5UCdMM9/x34jD9AfuA6cB+aGOWYE4AfsBmYC2aOLRcvFq5QkONiY33835plnjBExJqT6cnCwMR9/bIynpzG1axtTsKAxdesac/q0a+N1FuJYKt6JbdPlCNcIiC4WbZtUijJokL39y5nTmBEjjHGU6zfGGJM9e+RLGmTP7rp4nSiu7VNSemj7pFTMBAYFmnf+eMfICDF1Z9U1wUl4bbCYtE3O7DmMbCayickxjrk6zYGiQH4gk4i85Ng/GSgO+ACngY8jfXGR7iKyTUS2hXQ5K5WcBQXBokVQowY0aGBHaH3wgZ1TCLb38PXX4dgxu331aju0NJ7rDadEzmqbYvbi2japlOL2bZg+HXx97fOWLeGLL+D4cXj3XTvZOcSlS5H3Ily65JrYlVIqAZy+dpqG3zTkvT/fo2Oljqxov+KBQjXJjTOTQ3+gUJjnBXlwaGhUxzwFHDXGnDfG3AMWA7UAjDFnjTFBxphg4CvsELEHGGOmGWOqGWOq5c6dO0HekFKuFBAAnTrB5cswZYpNAocOBU/P8MdlzWqXFgqzZI8KzyltE3A2ZOip4+e5yF5c2yaV7AUEwOjR4O0N3brBvHl2e/Xq0Ls3ZMzo0vCUisysWbPCDeHz8fGJtKCHSpmc8fmfuHICn6k+bPbfzKzms5jdYjaZPDIlUMSu48ylLLYCJUWkKHASW7ShfYRjlgF9RGQ+UBO4Yow5LSLHgUdFJCNwC2gAbAN702WMOe04/3lgrxPfg1IuE7Kg/ZYtsHSp/RJ+0yaoUMEuYq/izCltk+OcTsAYx88fnf5OlEpsw4bBZ5/B9eu2sMzAgfDkk66OSqlodenShS5durg6DOUizvj8C2YtSOdKnelYqSPl8pRL0Gu7ktN6Do0xgUAfYBVwAFhojNknIj1FpKfjsBXAEeAwthewl+PcLcD3wA5gjyPOaY5zxorIHhHZDTwBaC1slewYA9u3w/r1dmRWWCdOwBtv2AIzw4bB3bv2PgxsPQdNDOPHiW3TGKChiPwHNHQ8Vyp5yZEj8gqDISWPL1+G5s1h1y67JEWDBg9fz04ppVKQ09dO03x+cw5fOoyI8FHDj1JUYgggdm5iylatWjUTcS0SpVxl715o3drOIcyWzQ4P/fJLO13njz/sl/HGQNu29kv5SpVcHXHSJCLbjTHVXB1HfGjbpJIckfvzAf/80y5cv8KxYlTIdk0Go6Xtk1Ipz6++v/LSkpe4fvc681vO57lSz7k6pFiLSdukCw0plYju3bML1Q8aBAcP2iGjo0bZ5cAOHbLrE77+Ohw+DN9+q4mhUsoFFi2CRx+1k5e3boX337+/TxNDpVQqExgcyNt/vM0z3z5D7oy52dpta7JMDGNKk0OlEtGvv0KBAtCxIyxfDrVq2XoOmTLBnDmQIQN89JGt86CUUokm7CiiHj3g4kU76fnYMXj7bdfFpZRSLvbp5k8ZtX4UXXy68He3vymbu6yrQ3IqTQ6VSkSXLoGHB1SrZteJPnPGVn5/8017L6aUUonqzBmb/NWuDcHBdtvGjXYoQ8+e9hsrpeKg8tTKyEh54FF5auV4XTdz5swAHDt2jKpVq+Lj40O5cuWYMmVK6DEdOnSgVKlSlC9fnldeeYV79+5Feb0RI0Y8sJC4t7c3Fy7YxcxFhDfeeCN03/jx4xkxYsQD53bu3JmiRYuGVsL87LPPALhy5QodO3akePHiFC9enI4dO3LlyhUA/Pz8yJAhAz4+PpQtW5aOHTuGxrp27VpEhBkzZoS+9s6dOxGRB+JNiurMrBPp519nZp14XTfk8wc4fvw4Tz/9NGXKlKFs2bL4+fmFO7Zv377hjo/Mwz7/W/duISKcWHSCH1r/wIzmM/hy4pcp/vPX5FCpRGCMLSzz+ON2lFZAAMyaBf/+C//7HyxeDE884eoolVKpxoED8OqrUKQIfPihXRDVccNCmTJa+UrF22MFH8PDzSPcNg83D2oVrBXFGbHj5eXFpk2b2LVrF1u2bGHMmDGcOmVXJerQoQMHDx5kz5493Lp1i+nTp8f5ddKlS8fixYtDk8WHGTduHLt27WLXrl3069cPgK5du1KsWDF8fX3x9fWlaNGivPrqq6HnFC9enF27drFnzx78/f1ZuHBh6L4KFSqwYMGC0Ofz58+nUjKZb1I9f3UG1ByAedeEPgbUHECNApGuQBcnHTt2ZODAgRw4cIC///6bPHnyhO7btm0bly9fjvYa3+7+ltHrR1NsYrHQh/9Vf8pOKkuVaVVIly4dy5Yuo27uutFeK6V8/s5cykKpVM8YWLYMRo6E556zP/v1s8lgUJDdN22a7U1s2dLV0SqlUoV16+x8wvTpoWtXeO01KFnS7suePfJ5hdmzJ2qIKukb8MsAdp3ZFeX+O4F3CAwODLctMDiQnWd2Un92/UjP8cnnw6eNPo3R63t43E8879y5Q3BIzzfQpEmT0N9r1KiBv79/jK4ZmbRp09K9e3cmTJjAqFGjYnXu4cOH2b59e7gb/HfeeYcSJUrg6+uLW5gvYdzc3KhRowYnT54M3Va4cGGuXr3K2bNnyZMnD7/88ku49+ZqkX2Orcu1plf1XvSp2YfSX5Rms/9m0qVNx53AO2w7vY3xDW2v14WbF2i1sFW4c9d2Xhvj196/fz+BgYE0bNgQCN+jGBQUxMCBA/nuu+9YsmTJQ6+TI0MOsqXNxqKOiwA4c/0MdUfW5fyN8zSr3IwTaU+kus9few6VcgJj7NqEVatCixZw9SqUdQxRHzUKxo2D1att7+Hzz9tigO7uroxYKZViBQbCggXw9df2ee3athE6ftyWSg5JDMGOfQ+pShr2cemSa2JXyVa6tOnImykvgv2yQRDyZcr3QG9ifJw4cYKKFStSqFAhBg0aRP78+cPtv3fvHt988w2NGjWK1+v07t2buXPnhg4HjMrAgQNDhxXu2bOH/fv34+Pj80AS4OPjw759+8Kde/v2bbZs2fJArK1atWLRokVs2rSJKlVsT1Zy4JXZi7yZ8nLi6gkATlw9Qd5MecmWPluCXP/ff//F09OTF154gcqVKzNw4ECCgoIA+OKLL2jWrBleXl7RXqdO4TocuHCAzB6Z+ffivzSb14yg4CCGPj6U6c1sj3Nq+/y151ApJxgwwK4TXaIEzJ4NHTpAWsd/bSK2F/G5lFvoSimVFFy/DjNmwIQJtrDM44/balhp09qJzkrFQ0x6+E5fO02xz4pxO/A26dOmZ3uP7eTLnC/BYihUqBC7d+/m1KlTtGjRglatWpE3b97Q/b169aJu3bo8/vjjUV5DoqjAG3Z71qxZ6dixI5999hkZHjIPd9y4cbRqdb837MiRI5Fe3xgTut3X1xcfHx/+++8/WrVqRcWKFcMd27p1a9q0acPBgwdp164dmzZtivL1E9vDevoyumdka7etlPuyHBOemcBT3zzFvl77Qj//XBlzxaqnMKLAwEDWr1/Pzp07KVy4MG3atGH27Nk0btyYRYsWsXZtzK6dLX02KuerzLiN4/jL/y/c3dxJ756e12u9HnpMavv8tedQqQQQHAxLlsDRo/Z55862+uiBA9Cp0/3EUCmlEsW330KhQvabqkKF7FCGGN4sKZVQvLJ40cWnC2kkDV18uiRoYhhW/vz5KVeuHOvXrw/dNnLkSM6fP88nn3zy0HNz5sxJQEBAuG3Xrl3D09Mz3LYBAwYwY8YMbty4EeO4ypUrx86dO8MNeQ0ODuaff/6hTJkywP05Z4cPH2bz5s0sW7Ys3DXy5cuHu7s7v/76Kw0aNIjxaycFXlm86FSpEw2+bkCnSp0S9PMvWLAglStXplixYqRNm5YWLVqwY8cOdu7cyeHDhylRogTe3t7cvHmTEiVKRH2hDFA2S1lm7JzBhGcmEBwcTLqgdKn689fkUKl4CA628wcrV4YXXoCQYmmVK9//gl4ppRJUjhx2CELER7ZsEDJfpWBBaNAA/voL1q+H5s0hjf4vXyW+4XWHU6dwHYbXG56g1/X39+fWrVsABAQEsHHjRkqVKgXA9OnTWbVqFfPmzSNNNP/u69aty7Jly7h27RoAixcvplKlSuGGAgLkyJGD1q1bh6seGZ0SJUpQuXJlPvjgg9BtH3zwAVWqVHkgYfHy8mLMmDGMHj36geu89957fPTRRw/ElBy8VfstahSowVu130rQ61avXp2AgADOnz8PwB9//EHZsmVp2rQpZ86cwc/PDz8/PzJmzMjhw4cjvcaifYsYf3I8i5YsolXxVrT+vjXFzhSjik+VVP356/8plIqjH3+0SWDLlnD7NnzzjZ1PqJRSThUQcH8uYHAw/P47NG5sJzeHlDivXx++/94uZq+UC3ll8WJd53UJ3mt44MABatasSaVKlahXrx5vvvkmFSpUAKBnz56cPXuWxx57DB8fH957770or1OxYkX69OlDnTp18PHxYcqUKVFWN33jjTdiVLU0rBkzZvDvv/9SokQJihcvzr///htlgtGiRQtu3rwZrgcUoFatWrRo0SJWr5tUeGXx4peXfknwz9/NzY3x48fToEEDKlSogDGGbt26xejcm/du0uOnHrT+vjXlKpRj4ICBbBi5geMfHYftpPrPX0zYhW9TqGrVqplt27a5OgyVzCxcaJO9/fuhVCkYOhTatbtfyK9nT1izBt55B9q21crviU1Ethtjqrk6jvjQtknFiYhNDL//HkaPhh07IE8eOHcOLlyAnDldHWGqp+2TUknTfxf/4/kFz7Pv/D4G1x7Me0+8h7ubrQgYMjc2JYtJ26Q9h0pFYuFCeOst+OQTW9Phs89stfciRSBkLvC4cTZx7NBBE0OlVCJxVOMDYPlyuHEDvvrKFpwBTQyVUuohsqXPhrubO6teWsXop0aHJoZAik8MY0pnRCkViQ8/tPdbTzxh5xS+9579Ut7Dww4hBciSxbUxKqVSkVu3YOZM+61UiM8+g8yZdS6hUjEwa9YsJk6cGG5b7dq1mTRpkosiUonlyu0rdHq3E0dXHQ1dWuWtyW/xY+0f9fOPhCaHSkXiwAG7FNiTT9r1okuXtsX/XnrJJoxKKZUorl+3la4+/hjOnIFate73EmbN6trYlEpGunTpQpcuXVwdhkpkW/y30PaHtpzIdII/fvyDukXqujqkJE+/blQqjOBg+OknO8dw0yZ4+WWYOxf27oXChe32KJZEUkqphBUUBBUrwsCBUL68neS8YQNkzx55tdLs2V0dsVJKJQnBJpixG8dSZ1YdjDGs77JeE8MY0uRQKWxSuHChvQ9r1gyeeQa6dYNixaB1a9i4Ebp0sUVplFLKac6ds5OdjbGTmd9/HzZvhl9/tRVIReDSpfvVSsM+Ll1ydfRKKZUk9Pq5F4N+G0SL0i3Y1XMXjxV6zNUhJRs6rFSlasHBsGiRnVO4fz+UKQPz5sGLL0LVqtCvn91eujS8+67tSVRKqQTn72+XoZg2zU5srlsXqlWzFa+UUkrFiDEGEeHVKq9SOV9lulftjuiQr1jR5FClasHB8Pbb4O4O8+dDq1b3K4+2bWsfxuhQUqWUk1y+DIMGwaxZtrF56SUYPNiOYVdKKRUj94Lu8c6ad7h+9zqfN/mcavmrUS1/sl5NxmV0WKlKVYKCbM9g/fq2AnzatPDbb7BnD7RpE/mSFJoYKqUS3PXr9mfGjLB2Lbz6Kvz3n00SNTFUSqkY87vsR93ZdRmzcQx3gu4QbIJdHVKypj2HKlUICrJzCt97Dw4ehLJl4cQJO1y0SBFXR6eUSnFy5ICAgAe3Z8kCTz8NW7bA4cOQLp2teOXu/uCxSimlOHfjHDtO74hyX7+V/TAYFrRaQOtyrRM5upRHk0OV4l24AI8/bpPCcuVsktiypS4NppRyooAAO0w0xMaNMGoUrFxpi8v07Qv37tnkUBNDpZSK0oK9Cxjy+xDqFK4Tus1g+M33NzzSelAxb0Xmt5xP0exFXRhlyqHJoUrW7t6Fb76BZcsgfXpbu+G55+xcwl27bFGZnDmhTh1b9O+FFzQpVEolss2bbSOUK5d9fvw4ZMvm2piUUiqZ6OTTiff/fJ9PG31K6VylOX7lOP9e+Be/y37MaTGHql5VcXfTL9kSiiaHKtkKCoIWLWxhvx497BzCwYNtsb/Dh+060UePQr588NVXro5WKZVqBDvmu3z5JfTqBTVrwuzZtuJV5syaGCqlVCxkTZeVAY8O4P1171Pfuz79f+lPwawFeafeOzxa8FFXh5fiaB+KSrZ++gnOn4fVq+0wUQ8PO0rr559tEZm5cyFPHldHqZRKNe7etUlguXL2+Vdf2URRBDp1gkyZXBqeUkolVx0qduD7A9/TfXl3yuQqQ5AJom35tq4OK0XS5FAlW3/8YZeaSJsWjhyBjh1t4b969eD113UIqVIqEa1aBSVKQJcudh4hwLZt2ggppVQ8bTy+kcdnPk5gcCA++XzI4J6BkfVHkjaNDoB0Bv2/lkqWgoLg1Cm7LAXAI4/YaT07d9p7sZCpPUop5TQBAbYhAjt+3dsbVqywDVH27PabK5Hwj+zZXRqyUkolNwG3A0iXNh2/dfyNE1dOcP7mee01dCJNuVWyEhwMP/wAI0bA/v12XcL162010urVYcECOHQImjRxdaRKqRTr9GmYMAGmTLEVsObOhUqV4M8/7x9z6ZLr4lNKqWTu2OVjbDyxkfYV2vPsI8/ydPGn8XDz4LPGn5E7Y27tNXQip/5lRaQRMBFwA6YbY8ZE2C+O/U2Am0BnY8wOx77XgFcBA+wBuhhjbotIDmAB4A34Aa2NMZEsJqVSmt274aWX7IL1ZcrYRDBtWlvjwdvbFqS5exeWL78/qksppRKMry+MG2fnFd67B61bw1tvuToqpZRKURbtW0S3n7rhlsaNpiWbki19NjzcPABoX6G9i6NL+ZyWHIqIGzAJaAj4A1tFZJkxZn+YwxoDJR2PmsBkoKaIFAD6AWWNMbdEZCHQFpgNDAZ+N8aMEZHBjueDnPU+lGsZY9cpzJ0bvLxs0ZnvvrP3ZG5u9pimTe2Q0vTpbe+hTvFRSjnFpEkwaxZ07gwDB9o5hkoppRLEjbs36P9Lf2bsnEGNAjWY13Ie2dJrdefE5szb6BrAYWPMEWPMXWA+0DzCMc2Br421GfAUES/HvrRABhFJC2QEToU5Z47j9zlACye+B+UixthqpNWqwbPP2ue5c9v6Du3a3U8MwfYS1qtnq8VrYqiUSjCbNtlho7/9Zp8PGWLXx5k6VRNDpZRKQHeD7vLojEeZuXMmQ+oMYUOXDRTLXszVYaVKzhxWWgA4Eea5P7Z3MLpjChhjtonIeOA4cAtYbYxZ7TgmrzHmNIAx5rSI6GIFKYgxsHIlvPuuTQSLFYPhw+12EVdHp5RKcXLksIVlIkqbFgIDIWdO+40U2G+olFJKJRhjDCKCh5sHPar2oGzusjxZ9ElXh5WqObOfJbJbeROTY0QkO7aHsCiQH8gkIi/F6sVFuovINhHZdv78+dicqlxo3jw7TPTCBZgxAw4etCO4tEdQKeUUAQH226eQR9OmdntgIHz6KRw7Bu11jotSSiW0czfO8ey8Z/nl8C8A9KnRRxPDJMCZt9z+QKEwzwtyf2hodMc8BRw1xpw3xtwDFgO1HMecDRl66vh5LrIXN8ZMM8ZUM8ZUy63f9rqcMfDPP3YpsAsXwm//9VdbRAbs2oQzZ9qKo6+8Au7urolXKZWKLF5sk0GAF1+030wB9O+vC9crpZQTrPZdTcXJFfn9yO+cvX7W1eGoMJyZHG4FSopIURHxwBaUWRbhmGVAR7EeBa44howeBx4VkYyOiqYNgANhzunk+L0T8KMT34NKAGfO2KUmWrSwhf5KloSRI+0i9nXrwtNPw0cf2WPTp7drSHt4uDRkpVRKZwwsXWp/b9nSJogAnTrZb6aUSmJEpJGIHBKRw46CfBH3lxaRv0Tkjoi8Gcl+NxHZKSLLEydipR50N+guA1cP5JlvnyFXxlxs7baVTj6doj9RJRqnJYfGmECgD7AKm9gtNMbsE5GeItLTcdgK4AhwGPgK6OU4dwvwPbADu4xFGmCa45wxQEMR+Q9bCTXc8hgq6enY0RaM8fW1dR2++w7GjoUGDeDIEVsAMKTeg1KJIQY3WSIinzn27xaRKo7tpURkV5jHVREZ4Ng3QkROhtmnq20mRcbA6tW2gtXzz9tt8+bZNXGUSqLCVIBvDJQF2olI2QiHXcJWeh8fxWX6c/+LdqVcYsmBJYz/azz/q/Y/tnbbSoW8FVwdkorAqescGmNWYBPAsNumhPndAL2jOPdd4N1Itl/E9iSqZODECdi58/6wUYA7d2zPYOHCdl/69K6LT6U+8VlmxxhzCPAJc52TwJIw500wxkR1Y6aSAmPg9dfh2jU7fLRrV2jb1tVRKRWd0ArwACISUgE+tN0yxpwDzolI04gni0hBoCkwCng9USJWysEYg99lP4pmL0rrcq0plK0QtQrViv5E5RJOTQ6VunzZTtlp1Qp8fOC996B5c1i40FYh1cRQuUC0N1mEWWYH2CwiniLiFVIp2aEB4GuMOZZYgas42r7dDleYNg2yZYMff4SCBe06OG++GXkp5OzZEz9OpaIWkwrwD/Mp8BaQJQFjUipUwK0Aykwqw52gO+G2G2PImi4rAbcD2Pu/vRTxLKKJYRKnNSCV0xw5AmPG2GJ/a9bcv9cSgQULoGFD18anUq1Il9CJwzFtgXkRtvVxDEOd6ai6rFxp3z47n7BaNTt2fe9eu714cZsYAly6FL5aacjj0iXXxa3Ug2JSAT7yE0WeBc4ZY7bH4Fit9K7ixDO9J6VyleL9J97nSL8jHOl3hPkt53Mn6A4nr51kSJ0hFMxa0NVhqhjQ5FA5xdSpULq0rfHQvDlkyAC3b8P339uq8Bs2wIABro5SpVJxXmYndKctstUMWBRm/2SgOHbY6Wng40hfXG++nO/uXXj5ZahQwZZDfvdd+21V7dqujkypuIpJBfio1AaaiYgfMB94UkS+jexArfSu4kpEGFFvBJ///TmZPTIzaeskmn7XlHtB9/ij4x8MfXwobmncXB2migFNDlWCuXwZzjqqEVevbgv+HT5sCwKuWgXHj8O330KVKrB5s11bWikXiM8yOyEaAzuMMaH1t40xZ40xQcaYYGyBrRqRvbjefDnRtWv2p4cH3Lhhh4wePQojRtjhpEolXzGpAB8pY8wQY0xBY4y347w/jDGxWjtaqZio712ffJnzsWDfAk5cOUG+zPkY9eQo6nnXc3VoKhY0OVTxdvOmnc5TrBi89ZbdVqUKTJkCBRwD8SpXhsmTbaL45pvg6emqaJWK1zI7IdoRYUhpyPqrDs8DexM+dBWps2ftUIQCBcDPz2774QfbMOm3UCoFiEkFeBHJJyL+2IIzb4uIv4hkdV3UKrX589ifvFzhZd7/8306+3Qm2ATTr2Y/V4elYkkL0qg4u3fPLlg/ciScPg2NG+tQUZX0GWMCRSTkJssNmBlyk+XYPwVbZbkJdpmdm0CXkPNFJCO20mmPCJceKyI+2OGnfpHsVwktIMAunjpxoi2D3KnT/UVSIysyo1QyFoMK8Gewoxwedo21wFonhKdSsWATzJgNYxi+ZjiNSzQmX+Z8vLDwBYbUGUIG9wyuDk/FkiaHKs7efRdGj4ZatWD+fLugvVLJQTyX2bkJPNAdZYx5OYHDVCFy5LCJYEQitnhM27b2W6pHHkn82JRSKhW7ePMiLy95mZWHV9K2fFumPTuNHad30HVZV7pV6ebq8FQcaHKoYswYWLkS8uWzw0Z797aJYdOm+iW9UsqJAgJsA3TrFqxYYSuQgm14/vkHKlZ0bXxKKZUK+V7y5Yk5T3D2xlkmN51Mj6o9EBHqedfj377/kkZ09lpypJ+aCufkSViyBP76y96Lhdi4EerVs4ngJ5/YbQUKwLPPamKolEoE06ZByZJ20dR9++5v18RQKaVconC2wtQtUpe/uv5Fz2o9kTA3hJoYJl/6ySnAJoKDBtnK77Nm2UqjVavaKvDPPQd16sC//8KkSXaeoVJKOV1wMMxz1P3p0QOKFLGLppYr59q4lFIqlbp8+zI9furB+RvncXdz59sXvqWKVxVXh6USkA4rVYC9/1q1yi49kSOHTRY/+AB69rRrQX/4IfTrB5kyuTpSpVSqcfUq9Oplf1++HJo00aEKSinlIttPbefFRS9y4uoJGpdsTIvSLVwdknIC7TlUAMyeDcOH27Wje/e2i9UPHmzvzf78E4YM0cRQKZUI1q6Frl1tr6GnJ2zaZLfr5GallHIJYwyT/p5ErZm1CAwOZH2X9ZoYpmDac6gAW+9h6VLo2NEmiPnzg7s7ZM+u92NKqUSwbRsMGwarV99fr7BYMShTJuqGKHv2RA9TKaVSm3GbxjHot0E0KdmEr1t8Tc6Mun5sSqbJoeLrr219h23boE0bO5y0RAnYsMEuHVamjKsjVEqlWBcv2vmEP/xgF6z/+GP43/8gQ5i1sS5dcl18SimVShljEBG6+HQhnVs6+tbsq4VmUgH9hFOpoCDbQwiQNi08+qhNCG/ftqO4hg+HF16AL74ANzfXxqqUSoHu3LE/s2aFI0fswqlHjsDrr4dPDJVSSiUqYwwzd86k0dxGBAYHkjtTbvo/2l8Tw1RCP+VUxhj46Sfw8YEJE+y2du3gjz9g505o2BB++QVu3oR162ylUqWUSjBnz9rqVqVK2YbG3d0OWxgxwiaKSimlXObG3Rt0+bELXZd1JSg4iOt3r7s6JJXIdFhpKrJxo12uYuNG20tYurTdHjKVJ3NmW4ymd2/XxaiUSqEuX4Zx4+DTT22v4Suv2KEKGTNCGv2eUimlXO3A+QO8uOhF9p/fz7v13mV43eG4pdHhY6mNJoepxPDhdi5hvnwwebItBuju7uqolFIpSo4ctrpVRFmz2gTw8mVo2xbee88uaK+UUipRDft9GD//9/MD29OmScutwFucv3GeVS+tomHxhi6ITiUFmhymYMePQ/r0kCePXR4sfXoYMECXpFBKOUlAgB27DnZS87ZtUKuWHZ4weLCteOXj49IQlVIqNfPK4oW7mztfPfcVAHcC7/DXib/47O/PWNxmMbkz5qZA1gIujlK5ko7lSYEuXoQ33oBHHoGRI+22xx6zVeI1MVRKOVVQkC2BXKoUNGgA587Z7aNHa2KolFIu9mqVVzl97TRBwUFk9shMz597Mv6v8bxd92188vloYqi05zA5MgYOHLDTdipWvF9N9MYNO51n7Fi4ft2uWThokEtDVUqlNtWr2+pWVarAlCmQO7erI1JKKeWQPm16BtcZzP9+/h+HLh7CGENG94y8XPFlV4emkghNDpOZAwegfXu77FfGjLbY31dfwdNPw8CBdj5hs2bw4YdQrpyro1VKpRp+fvbnhQswd66dW6iFZpRSKkkJCg7i+JXjbD21lbK5yuKZ3pOuVbri7qaFKJSl/+dORu7ehcaNbTXRo0dh/37o1Alat7bzCwcNsgvX//ijJoZKqURw/Tp8/7393dvb/jx40H6DpYmhUkolOceuHGPytsnUKlSLQBPI6euntddQhROjnkMRqQY8DuQHbgF7gd+MMZecGJuK4JdfoHBhePVVWLvW9hRu2wbly9spPm+/DUWKuDpKpRKPtk0uEhwM33wDQ4bAmTPw339QvDhkzx75xObs2RM/RqWSKG23lCscCThCUc+iFMtejD3/20O+zPko8VkJ3nviPe01VOE89KtdEeksIjuAIUAG4BBwDqgD/Coic0SksPPDVGDrOuTJA61awRNP2OezZ0PnznZdaaVSi9mzZwOUQdumxLdpEzz6qG14ChWyz4sXt/suXbKToiM+Luk9r1J6T6VcZfqO6ZSZVIbZu2YD4O3pTfq06dnXax9dfLq4NjiV5ETXc5gJqG2MuRXZThHxAUoCxxM4LhWJxx+3S1EYA++/byuSpksHderAa6+5OjqlEs+NGzcADhpjWka2X9smJ7lyBZ55BrJkgTlz4KWXdPioUjGn91QqUd0OvE2fFX2YsXMGDYs15LlSz4Xbny19NhdFppKyh/5f3RgzKapGzLF/lzHm94QPS4UwBubPhz/+sJXh27WzX9I/8gisWWOLz3h4QIsWro5UqcTTu3dvABPVfm2bElBI1StjIFs2WL4c/v3XlkPWxFCpGNN7KpWYjl0+Rp2ZdZixcwbDHh/Gyg4ryZUxl6vDUslAjP7P7hjq4BnmeXYRmem0qBRgq8HXq2cTwqlT7bapU+Gtt+xw0vHj4amnYOVKcNfh4ioV0rbJiYyBefPst1Ldu9vho2AbpcyZXRubUsmYtlsqMew7vw/fAF9+bPsjHzz5AW5p3FwdkkomYvq1b0VjzOWQJ8aYAKBydCeJSCMROSQih0VkcCT7RUQ+c+zfLSJVHNtLiciuMI+rIjLAsW+EiJwMs69JDN9DsnH+PPToAVWr2qUrpk2D776z+9KksSO5VqywvYkDBkCGDC4NVylXilPbpKKxdasdr96+vZ3o/OefULu2q6NSKqXQdks5RbAJZov/FgCalGzC0f5HaVaqmYujUslNTJPDNCISWm5ORHIQzXxFEXEDJgGNgbJAOxEpG+Gwxtjx9SWB7sBkAGPMIWOMjzHGB6gK3ASWhDlvQsh+Y8yKGL6HZGPZMpg5E/r3t0UAu3W7v9C9UiqcWLdNKhqBgfDii+DrCzNmwN9/2wnPSqmEou2WSnCXb1/m+QXPU3tmbfae2wuAZ3pP1walkqWYNkYfA5tE5HvsPJ/WwKhozqkBHDbGHAEQkflAc2B/mGOaA18bYwywWUQ8RcTLGHM6zDENAF9jzLEYxpos/forBATYNQs7d7b3Yo884uqolEry4tI2pW45ctjGJqIMGWxV0fTpYckSO7k5a9bEj0+plE/bLZWg9pzdwwsLX8Dvsh+fPPMJ5XLrYtcq7mLUc2iM+RpoCZwFzgMvGGO+iea0AsCJMM/9Hdtie0xbYF6EbX0cw1Bnhv32LTny9bXFZJ5+Gj7+2E7zcXPTxFCpmIhj25S6BQTcX2IiOBgWLrQLpN66BT//bI+pXFkTQ6WcRNstlZDm7ZnHozMe5frd66zptIZ+NfshIq4OSyVjsSk1lwO4YYz5HDgvIkWjOT6yf5kRqws+9BgR8QCaAYvC7J8MFAd8gNPYb+AefHGR7iKyTUS2nT9/PppQE9/16zB0KJQtC7/9BqNH22k9+t+zUrEW27ZJwf2KV61b2yqkAC0jXRlEKZXwtN1SCcLvsh9VvKqwo/sO6hSu4+pwVAoQ02ql7wKDsAu3ArgD30Zzmj9QKMzzgsCpWB7TGNhhjAld4t0Yc9YYE2SMCQa+wg5ffYAxZpoxppoxplru3LmjCdU5goPh6lX7M6K//7YJYZs2tir84MF2zUKlVMzFsW1SxkCvXrbi1dSpsGOHqyNSKtXQdkvF1+lrp9l0wlaQHlxnMH90/AOvLF4ujkqlFDGdc/g8tpLWDgBjzCkRyRLNOVuBko5vw05ih4e2j3DMMuwQ0flATeBKhPmG7YgwpDTCnMTngb0xfA+Jxhj44gv46CO4fNlO8Rk6FKpVg+3bbSXSJ5+092WlS7s6WqWStbi0TanTvXswZYr9XQS++QZy5QJPT5eGpVQqpO2WeihjDCeunsCW5Ajvv0v/8fKSl3FP487hfofxcPPA3U3XM1MJJ6bJ4V1jjBERAyAimaI7wRgTKCJ9gFWAGzDTGLNPRHo69k8BVgBNgMPYiqRdQs4XkYxAQ6BHhEuPFREf7PBTv0j2u9zUqXb5iRUroGJFW2zmxRdtL2LBgnbt6AwZNDFUKgHEum1KlX791a57sz9MPbASJVwWjlKpnLZb6qF+P/o7Db9pSOFshUO3GWM4de0UBkPx7MVZ3GYxHm4eLoxSpVQxTQ4XishUwFNEugGvYId0PpRjmYkVEbZNCfO7AXpHce5NIGck21+OYcwuM26cXTu6TBn45BMYORJu3LA9iHv36rqESiWgOLVNqcaRI/DGG7B0KRQrZn927hz55Obsybq2l1LJibZb6qHqe9enePbizGg2g3re9bgbdJcOizvw/f7vearoU3zf+nuypc/m6jBVChWj5NAYM15EGgJXgVLAO8aYX50aWTIVHGzvx6pVg0OH7FzCp56y8wtr1tQCgEolJG2bHKJansLDA9KmhQ8/hNdes8tURHacUirRaLulopM2TVrervs2I9eN5A/vP3BP486hC4eokq8Kq15eRRqJTT1JpWInpgVpMgF/GGMGYr/dyiAiOsA5EkFBtir877/bnsM9e+zw0uPHoVIlV0enVMqibZNDyPIUwcEwd64dPmoM3L1rK14NGWITQ6WUy2m7pWLipYovcfDCQebunsuVO1c4efUkC15coImhcrqY/gv7E0gnIgWA37BzA2c7K6jkaudOqF4djh2Dl16yI7iyZ4fvv7dFaIYPd3WESqU42jaFOHoU6taFDh1g8uT72wtEXDpWKeVi2m6phzLG8OnmTzlz/Qyvr3qdiZsn8lyp5yiRQ+eKK+eL6ZxDMcbcFJGuwOfGmLEistOZgSUnd+/CBx/YoaO5ctmk0M3NVivt1s2uZThrFjzzjKsjVSrF0bYpRJUqtrfwq6+gS5foj1dKuYq2WypKt+7dovvy7ny7+1talmnJjtM7GLdpHLt67nJ1aCqViHFyKCKPAR2ArrE8N8X47z/49lu4cgWefhoaNbJ1HZ56Ctavt1VIJ0yw038Ann3WtfEqlQrEqW0SkUbARGwl5enGmDERL+rY3wRbSbmzMWaHiJQCFoQ5tBh2vtCnIpLDsc8bW0m5tTHG+RP8fvjBEUkxWLTI/lRKJWV6T6Uidfb6WZ6d9yzbTm3j/SfeZ9jjw1hycAl/n/xbew1VoonpsNL+2MValziWoygGrHFeWEnP/PlQq5atOpo/PwwaBK1a2Sk+ffvC8uUwZ879xFAplShi3TaJiBswCWgMlAXaiUjZCIc1Bko6Ht2ByQDGmEPGGB9jjA9QFZs4LnGcMxj43RhTEvjd8dx5Qta/atzY/ty4URNDpZKHVH9PpSKXNV1WMntkZmmbpbxd921EhBfKvMCYp8ZEf7JSCSSm1Ur/xI6RD3l+BOjnrKCSmuvXoXdvWLPGrlu4ZYsdJrpzp/2ivm1bV0eoVOoUx7apBnDYcSwiMh9oDoRZBJDmwNeO5XY2i4iniHgZY06HOaYB4GuMORbmnPqO3+cAa4FBcXlf0Vqzxq6R89NPkCWLndwc2Ro5ujyFUklOar+nUg9asHcBjUs2Jmu6rPzR8Q8ksuWGlEokD+05FJFpIlIhin2ZROQVEengnNCSjvXrbVL4yCO2x7BWLZswPv88LFvm6uiUSn26d+8OEOmKoTFomwoAJ8I893dsi+0xbYF5YZ7nDUkeHT/zRBFfdxHZJiLbzp8/H0WIYeTIYcevh308+aRtmELOv3TJ9iRGfFy6FP31lVKJQu+pVET3gu7Rb2U/2v7Qlgl/TQDQxFC5XHTDSr8EhovIARFZJCJfishMEVkPbAKyAN87PUoXS5cOzp6FypVh7Fh45RW7mH3RonafUipx9erVC8Arjm1TZP/nNbE5RkQ8gGbAotjGboyZZoypZoypljt37uhPCFmm4sIFaNLEbmvb1o5p12GkSiUnek+lQl28eZFGcxvx+d+f89qjrzGs7jBXh6QUEM2wUmPMLqC1iGQGqgFewC3ggDHmkPPDSxoef9x+QS8Cq1bZYjQXLsBnn4WvGK+UShw+Pj4AR7DDOGPbNvkDhcI8LwiciuUxjYEdxpizYbadDRl6KiJewLkYvZmY+t//4LffYNIk+/v8+Ql6eaWUc+k9lQpx4PwBmn7XlJPXTjK7+Ww6+XRydUhKhYrpnMPr2PkzKdZ//8F339mCM40awRNPwObNsG2bLTjz00/QvDl8/LGtWLpiBfTqZSuVKqVcI45t01agpIgUBU5ih4e2j3DMMqCPYz5iTeBKhPmG7Qg/pDTknE7AGMfPH2MZ18ONHw8DB9rFVJVSyVZquKdSD5clXRY803syr+U8ahas6epwlApHSycD33wDr78OL79sp/f07Anu7nDggB06+uqr8Oij4OcHP/8Mly/De++Bt7eLA1dKxZoxJlBE+gCrsEtZzHRUDOzp2D8FWIFdxuIwtiJp6MKBIpIRaAj0iHDpMcBCx9plx4EXEzTwwoXtQymlVLITbIKZt2cebcu3pWDWgmzvvl3nF6okKdUnh5cvQ79+sGkTlCljf379Nezfb3sQFyy4XwQwQwa7fIVSKnkzxqzAJoBht00J87sBekdx7k0gZyTbL2IrmCqllFKhrt+9TqelnVh8YDHp0qajVdlWmhiqJCtWyaGIZDLG3HBWMK7w+++2+miZMnDuHDRoAHnz2iGjd+9C1qyujlApFZ2U2DaFyp7dTniObLtSKtlK0e2WCnUk4AjN5zdn//n9THhmAi3LtHR1SEo9VHTVSgEQkVoish844HheSUS+dGpkicTDA27ftr/nyQOLF8OePXb0loeHa2NTSj1cSm6bQukyFUqlKKmi3VIArPNbR/WvqnPy6kl+6fALAx4doD2GKsmLUXIITACeAS4CGGP+Aeo6K6jE9NRTNhlct84+b9wYbt2yVUjbtXNtbEqpaKXYtkkplWJpu5VKeLh5UCRbEf7u9jcNizd0dThKxUiMh5UaY05E+LYjKOHDSXwZMsC8edCyJdSubUdqLV8O/ftDnTqujk4pFZ2U2jYppVIubbdShm7LuuF/zT/ctmATzJ3AO6ztvJbHCj3Gtu7bSCMx7YtRyvVimhyeEJFagHEs/twPx3CIlKBBAzhyBJYts0tZvPuurVKqlEryUnTbpJRKkeLUbolII2AitsrydGPMmAj7SwOzgCrAMGPMeMf2QsDXQD4gGJhmjJmYgO8n1bp46yLZ02fn5YovA3Dp1iVeW/Ua52+eZ++5vZTPU14TQ5XsxPRfbE9s5b4C2MWhfYiikl9ylTUrvPQS9OihiaFSyUiKb5uUUilOrNstEXEDJgGNgbJAOxEpG+GwS9hEc3yE7YHAG8aYMsCjQO9IzlVx8E69d1jrt5b63vXJnSk3g34bxIWbF3j/ifcpn6e8q8NTKk5i1HNojLkAdHByLEopFSvaNimlkps4tls1gMPGmCMAIjIfaA7sD3Pdc8A5EWka4fVOA6cdv18TkQPYxHQ/Kl588vnwaMFH+d/P/2PBvgVkcs9EzYI1ebvu264OTak4i1FyKCJFgb6Ad9hzjDHNnBOWUkpFT9smpVRyE8d2qwBwIsxzf6BmHF7bG6gMbIntuSpy79R7h/qz61M5X2WOXznOx09/7OqQlIqXmM45XArMAH7CjldXSqmkYCnaNimlkpelxL7dimz9AxObFxWRzMAPwABjzNUojukOdAcoXLhwbC6f6twNusuuM7uoUaAGTxZ9klPXTlEuTzlqFarl6tCUipeYJoe3jTGfOTUSpZSKPW2blFLJTVzaLX+gUJjnBYFTMT1ZRNyxieFcY8ziqI4zxkwDpgFUq1YtVslnanL59mVaLmzJphObONz3MO/Ue4fKUyuz8ZWNrg5NqXiLaXI4UUTeBVYDd0I2GmN2OCUqpZSKGW2blFLJTVzara1ASceQ1JNAW6B9TF5M7JoZM4ADxphP4hy1AuBowFGafteUw5cOM73ZdApkLUCBrAXw7edLsezFXB2eUvEW0+SwAvAy8CT3h0AYx3OllHIVbZuUUslNrNstY0ygiPQBVmGXsphpjNknIj0d+6eISD5gG5AVCBaRAdjKphUdr7dHRHY5LjnUGLMiod9YSvfXib9oPr85gcGB/Pryr9Tzrhe6TxNDlVLENDl8HihmjLnrzGCUUiqWtG1SSiU3cWq3HMncigjbpoT5/Qx2uGlEG4h8zqKKpcUHFpM1XVZ+bv8zpXKVcnU4SjlFTNc5/AfwdGIcSikVF9o2KaWSG223khFjDGeunwFgzFNj+Lvb35oYqhQtpslhXuCgiKwSkWUhj+hOEpFGInJIRA6LyOBI9ouIfObYv1tEqji2lxKRXWEeVx3DIxCRHCLyq4j85/iZPRbvVymVssSpbVJKKRfSdiuZuBd0j24/daPatGpcuHkBtzRu5MiQw9VhKeVUMR1W+m5sLywibsAkoCG2ytZWEVlmjAm76GpjoKTjUROYDNQ0xhwCfMJc5ySwxHHOYOB3Y8wYR8I5GBgU2/iUUilCrNsmpZRyMW23koHLty/TamErfj/6O28//rYmhSrViFFyaIxZF4dr1wAOG2OOAIjIfKA5EDY5bA58bYwxwGYR8RQRL2PM6TDHNAB8jTHHwpxT3/H7HGAtmhwqlSrFsW1SSimX0XYr6QtbkXR289l08unk6pCUSjQPTQ5FZIMxpo6IXCP8YqsCGGNM1oecXgA4Eea5P7Z3MLpjCgBhk8O2wLwwz/OGJI/GmNMikudh70EplfLUqVMHgDi2TUoplejieU+lEtHba97m9PXTrHppFU8UfcLV4SiVqKLrORwIYIzJEodrR1YZK+KCqg89RkQ8gGbAkFi/uEh3oDtA4cKFY3u6UioJGzduHLVq1Ypr26SUUq4Qn3sqlQjuBd3D3c2dL5t8yZnrZ7TwjEqVoitIMyke1/YHCoV5XhA4FctjGgM7jDFnw2w7KyJeAI6f5yJ7cWPMNGNMNWNMtdy5c8fxLSilkqLevXu7OgSllIqt+NxTKScyxvDRho+oP6c+t+7dIlv6bJoYqlQrup7D+KyLsxUoKSJFsQVl2gLtIxyzDOjjmI9YE7gSYb5hO8IPKQ05pxMwxvHzx3jEqJRKhuw0ZaWUSlZ0rcEk6F7QPXr93IvpO6fTplwbRPRjUklfwU8KcvLayQe2F8hSAP/X/eN17eiSw6IPK69sjGn2kH2BItIHWAW4ATONMftEpKdj/xTsYq5NgMPATaBLyPkikhFb6bRHhEuPARaKSFfgOPBiNO9BKZXCHD16FKBEVO3Tw9ompZRykTjfUynnuHz7Mi8uepHfjvzGsMeH8d4T75FGYrrKm1Ku06xUM2bsnMHdoLuh2zzcPGheqnm8rx1dcnge+DiuFzfGrMAmgGG3TQnzuwEiHR9mjLkJ5Ixk+0VsBVOlVCqVO3durly5coZ4tE9KKZXI4nVPpeLm+JXjbDu1LdJ9M3bOYK3fWmY1n0Vnn86JG5hS8TC87nBm7pwZbpubuDG83vB4Xzu65PCallxWSiU1WbJkAbiu7ZNSKhnReyoX+GH/D4xcN5Iniz4Zus1g+PHgj8xoNoM3H3tTK5KqZOXmvZss2LeAtGnScifoDmB7Dbv4dCFf5nzxvn50fed+8X4FpZRKYN7e3q4OQSmlYsvP1QGkRq9UfgV3N3fGPDWGxW0W06FCB27fu03FvBXp7NNZE0OVbFy5fYUP139IkU+L8Nqq16iYtyLuadyBhOs1hGh6Do0xLyTIqyilVAJavHixFg1QSiUrek/lGtnSZ6N/zf68v+59KuatyFu/vUVG94xMf266/n9EJQsXbl7g082f8sXfX3DlzhUal2jMsMeHUbtwbXr93Iup26cmWK8hRD+sVCmllFJKqWSrd/Xe5P8kP9/u+ZZahWpx/e512pZv6+qwlHqoU9dOMX7TeKZun8qte7d4ocwLDH18KFW8qoQeM7zucFb5rkqwXkPQ5FAppZRSSqVgb/36FrcDb1M6V2lu3L3ByPojtddQJVlHA47y0caPmLVrFkHBQbSv0J4hdYZQJneZB471yuKFbz/fBH39hyaHIlIlwiYDXDDGnEjQKJRSKhZ27NgBkDFMG6Vtk1IqSdN7KtdpXa41RbIV4eO/PsY7u3eClPtXKqEdvHCQ0RtGM3f3XNzSuNHFpwtv1X6LYtmLJWoc0fUcRlZyOYeIeADtjDG7Ej4kpZR6uDfeeAOgIOHbKG2blFJJmd5TJaIz18+w5uga2lVoR8PiDWlYvCElc5akYNaC2muokpSdp3fy4YYP+WH/D2Rwz0C/mv1447E3KJC1gEviia4gTaQlnESkGvAZUNcZQSml1MOsWbMGEfk3YhulbZNSKqnSe6rE89/F/3jm22e4cPMCTxV7ityZcgPQpnwbF0em1H0bj29k1PpRrDy8kqzpsjL08aH0r9k/9N+rq8RpzqExZpuIZE7oYJRSKj60bVJKJTfabiWsrSe30uS7JgD81vE3l99oq9Sr4CcFOXnt5APbc2bISYW8FVjrt5ZcGXMx6slR9K7em2zps7kgygdFt85hpEQkL3asvFJKJRnaNimlkhtttxLOL4d/of6c+mTxyMKmVzZRo0ANV4ekUrFmpZrh4eYRbpsgXLx1kX8v/suEZybg19+PoY8PTTKJIURfkOZzHmywcgC1gP7OCkoppR6mb9++AIVE5LMwm2PcNolII2Ai4AZMN8aMibBfHPubADeBzsaYHY59nsB0oDy2fXzFGPOXiIwAugHnHZcZaoxZEce3qJRKYfSeyvkOnD/AIzkfYWWHlQm25ptScTW87nBm7pz5wPaxDcfSr0Y/0qVN54KoohfdsNJtEZ4b4CLwujHmnHNCUkqph6tWrRrYpG27Y1OM2yYRcQMmAQ0Bf2CriCwzxuwPc1hjoKTjUROY7PgJNmn8xRjTylFIImOY8yYYY8bH570ppVIsvadyAmMMJ66eoHC2wrz22Gv8r/r/SJ82vavDUqncncA7/PTvT6RLm447QXcAcBM3ulXpxsBaA10c3cM9dFipMWYOsBu4AfxtjPnaGPOzNmJKKVfq1KkT2OQwLm1TDeCwMeaIMeYuMB+IWNe8OfC1sTYDniLiJSJZsUUjZgAYY+4aYy4nzLtSSqVkek+V8IJNMK+vep3yX5bH95Jd600TQ+VK1+5cY/ym8RSdWJQey3tQPHtx3NO4A+Dh5sG79d91cYTRe2hyKCLDgQVAS+BnEemWKFEppdRDvP/++wDFiVvbVAAIu66Yv2NbTI4phh02OktEdorIdBHJFOa4PiKyW0Rmikj2WMSklErh9J4qYd0JvEP7H9rz6ZZP6Vq5K0WzF3V1SCoVu3jzIu+ueZcinxZh4K8DKZu7LL+9/Bvbu2/n1SqvkkbS0MWnS7IY7hxdQZq2gI8xph1QHeju/JCUUurh5s+fD7A/jm1TZAtcRZwHFNUxaYEqwGRjTGVsD8Bgx/7J2ITVBzhN5GuaISLdRWSbiGw7f/58ZIcopVImvadKIFfvXKXJd01YsG8BHz31EZ888wlpJE41FpWKl5NXT/L6qtcp8mkR3vvzPep512PLq1v4reNvNCjWABFheN3heHt6M7zecFeHGyPRzTm8bYy5CWCMuSii/+UppVwvffr0AMEQp7bJHygU5nlB4FQMjzGAvzFmi2P79ziSQ2PM2ZCDReQrYHlkL26MmQZMA6hWrZpWKFQq9dB7qgTy8aaP+fPYn8xpMYeOlTq6OhyVCv138T/GbhzLnH/mEGyC6VCxA4NqD6Js7rIPHOuVxQvffr4uiDJuoksOi4vIMsfvEuE5xphmTotMKaWi4OvrC1DC0R7Ftm3aCpQUkaLASey3+e0jHLMMO0R0PrYQzRVjzGkAETkhIqWMMYeABsB+x3avkGOA54G98XybSqmURe+p4skYg4gwrO4wGpVoxGOFHnN1SCqV2XVmF6M3jOb7/d/j4eZB96rdebPWm3h7ers6tAQTXXIYsUiDVuFTSrncjz/+SP369c9wf+hmjNsmY0ygiPQBVmGXsphpjNknIj0d+6cAK7DLWBzGFr7pEuYSfYG5jkqlR8LsGysiPtjeRT+gR9zenVIqhdJ7qnjYenIrr616jSVtlpA7U25NDFWiWn9sPaM3jGbl4ZVkTZeVt2q9xYBHB5A3c15Xh5bgHpocGmPWJVYgSikVU/Xq1QO4Htc2yrH+4IoI26aE+d0AvaM4dxdQLZLtL8clFqVU6qD3VHG36vAqWi5sSe5Mubly5wq5M+V2dUgqFTDGsPLwSkZvGM2G4xvInTE3o54cRa/qvfBM7+nq8JzmocmhiDQHChpjJjmebwFC/ot8yxjzvZPjU0qpB/z4449wvy3StkkpleTpPVXcfPPPN7yy7BXK5S7Hyg4r8cri5eqQVApT8JOCnLx28oHt7mncuRd8j0JZC/FZo8/oWqUrGd0zRnKFlCW6YaVvYefjhEiHrbCVCZiFLcaglFKJauzYsQCXw2zStkkpldTpPVUUzl4/S8nPS3I36G647UHBQQSaQJ4s+iSLWy8mW/psLopQpWTNSjVjxs4ZD/z7y+SRiQnPTKB9hfZ4uHm4KLrEF11y6GGMCbvW1wZjzEXgYoS1vZRSKtHcvXsX4F6YTdo2KaWSOr2nikKeTHmokLcCXSt3pX2F+/XBnpjzBFk9srKs3TLSpU3nwghVStaneh++2vFVuG0eaTzY12sf+bPkd1FUrhNdGeVwizgbY/qEeaoDvpVSLhEQEBDuubZNSqlkQO+poiAijKg3grEbx2KM4fMtn7PpxCbO3TjH8vbLNTFUTnHowiF6/dyL6tOrExgciDiWOHZP486rVV5NlYkhRN9zuEVEuhljwqXTItID+Nt5YSmlVNRq1qyJr69vrojbtW1SSiVhek/1EE8VewrP9J48Ov1Rdp/bTcW8FXn78bdxd3N3dWgqBTHG8PvR35mweQIr/ltBOrd0vFTxJdqXb0/TeU25HXibtGnSJpsF650huuTwNWCpiLQHdji2VcWOk2/hxLiUUipKEyZM4LvvvsslImvQtkkplTzoPdVDXL1zlRv3brD33F7erPUmiw8s5qWKL7k6LJVC3A68zdzdc/l0y6fsPbeXvJnyMrL+SHpW60meTHkA6OLThanbp9LFpwv5MudzccSuE91SFueAWiLyJFDOsflnY8wfTo9MKaWikCdPHoCDwPto26SUSgb0nipqF25eoNG3jTh44SCP5HyE2btmM/apsdprqOLtzPUzfLn1S6Zsm8L5m+eplLcSs5vPpm35tg8MVx5edzirfFel6l5DiL7nEABHw5XqGy+lVNKibZNSKrnRdutB/lf98b/qz9I2S8ngnoHXV72uvYYqXnad2cWnmz9l3t553Au6x3OlnuO1R1+jXpF6iEik53hl8cK3n28iR5r0xCg5jCsRaQRMBNyA6caYMRH2i2N/E+Am0NkYs8OxzxOYDpQHDPCKMeYvERkBdAPOOy4z1LGgtVJKKaVUwsiaFa5de3B7lixw9Wrix5MCXb59Gc/0nvjk8+FI/yOha8jt7LEzyht4paISbIJZ/u9yJmyewFq/tWRyz0T3Kt3pV7MfJXOWdHV4yYbTkkMRcQMmAQ0Bf2CriCwzxuwPc1hjoKTjUROY7PgJNmn8xRjTSkQ8gLCrTk4wxox3VuxKKaWUSuUiSwwftl3FysELB3nq66cYWGsg/R/tH25xcU0MVWxcv3udWTtn8dnfn3H40mEKZyvMuIbjeLXKq3im93R1eMmOM3sOawCHjTFHAERkPtAcCJscNge+NsYYYLOIeIqIF3ADqAt0BjDG3AXCr0yplFJKKaWSnV1ndvH0N0+TRtLwRNEnXB2OSgYKflKQk9dOPrA9s3tm3NK4ceXOFR4r+BgfPvkhz5d5nrRpnDo4MkVz5l+uABB2sVd/7vcKPuyYAkAgdtjoLBGpBGwH+htjbjiO6yMiHYFtwBvGmPCLnimllFJKxVRgIBw+DHv23H+oeLl06xJ/n3xwhY4D5w8wYt0IsqXLxm8df+ORnI+4IDqV3DQr1YwZO2dwNyh8X9GNezdoU74NA2oOoGbBiGmGigtnJoeRjQkwMTwmLVAF6GuM2SIiE4HBwHDs0NP3Hce9D3wMvPLAi4t0B7oDFC5cOI5vQSmllFJJWmzmBhoDJ0/a5G/v3vuJ4IEDcOeOPSZNGihRwvlxp3A///sz3X7qRn3v+qHb7gbdZY3fGvJmysuGVzZQOJven6mYGVhrINN3TA+3La2kZfOrm6mav6qLokqZnJkc+gOFwjwvCJyK4TEG8DfGbHFs/x6bHGKMORtysIh8BSyP7MWNMdOAaQDVqlWLmJQqpZRSKrE4s7jLw+YGbthwPwEMSQYvX75/TP78UKECPPUUlC9vfy9TBjJkAJ33Fi+ty7Vm6B9Def+J96leoDoA209tp+E3Ddn86mZNDFWM+F32Y8q2KczYOYN7wfdCt7uncadblW6aGDqBM5PDrUBJESkKnATaAu0jHLMMO0R0PnbI6RVjzGkAETkhIqWMMYeABjjmKoqIV8gxwPPAXie+B6WUUkrFV1yLu9y7B7dvw61bUf98mMcftz+zZrXJX5s2NgGsUME+z5Ej6nOzZIk6oVXRSpc2HUPqDGHkupG8VPElcmbIyed/f857T7yHt6e3q8NTSViwCeZX31+ZtHUSy/9dThpJQ/PSzWlbri0vL3mZO0F3SJsmbapfj9BZnJYcGmMCRaQPsAq7lMVMY8w+Eenp2D8FWIFdxuIwdimLLmEu0ReY66hUeiTMvrEi4oPtXfQDejjrPSillFLKyWrWjDr5CwqK37WXL7eJYKFCse8J1OUq4q1r5a4MXzOclYdX8ljBxzgacJSFLy50dVgqiQq4FcDsXbOZvG0y/136jzyZ8jDs8WH0qNaDglkLArDGbw1Tt0+li08X8mXO5+KIUyanlvJxrD+4IsK2KWF+N0DvKM7dBVSLZPvLCRulUkoppZzm+vWH78+eHby87FDO9Olj/7NWraiv3bRpwr4XFStLDy4l4FYA2TNkJ2u6rAx5fAjp06Z3dVgqifnnzD9M2jqJb3d/y63AW9QqVIsR9UfQskxL0qVNF+7Y4XWHs8p3lfYaOpHWeVVKKaVUwrtyBb74AiZMePhxv/ySOPGoRLVw30I6LO5A7cK1ORJwhN1nd7O4zWJXh6WSiLtBd/lh/w9M2jqJjSc2kiFtBjpU6EDvGr3xyecT5XleWbzw7eebeIGmQpocKqVcbu5cGDYMjh+HwoVh1Cjo0MHVUSml4uTCBZg4ET7/3CaITZrAihXRnxdXOjcwSfrtyG88VugxVnZYyR9H/yDYBGuvocL/qj9Tt03lqx1fcfbGWYpnL84nT39CZ5/OZM+Q3dXhKTQ5VErFkLMSuLlzoXt3uHnTPj92zD4HTRCVSlbOnIGPP4bJk+HGDWjZEoYOhSpVHl6tNL5SydxAEWkETMTWcZhujBkTYX9pYBZ2KbBhxpjxMT03Id0NuouHmwdTnp3C7cDbZHTPSLNSzZz1cioJiWqh+vyZ8/PNC98waeskfjz4I8EmmKaPNKV39d48Xfxp0kgaF0SroqLJoVIqWnFN4Iyx94hXr9rHtWv3fw95Pnz4/euGuHnTJqKaHCqVDBw/DuPGwVdf2eqi7drBkCFQrtz9Y1JJAucsIuIGTAIaYpcB2yoiy4wx+8McdgnoB7SIw7mxcuveLXwDHhza99uR3/hy65f83vF3CmUrREb3jHF9CZUMRbZQvZu4cSvwFg2+bkDODDl547E36FmtJ0WzF3VhpOphNDlUSkVr2LDIE7j//Q9+//3BpC8k8bt2DYKD4/aax4/HP26llBP5+sKYMTBnjv0mqFMnGDxYF5B3jhrAYWPMEQDHEmDNcSzzBWCMOQecE5GIVXiiPTe2pu+YzsBfB1Iix/3P+uqdq5y4eoKSOUqSLX22uF5aJWPD6w5n1q5Z4bYFmSAKZyvMhGcm0LpcazK4Z3BRdCqmNDlUSkUpOBg2b7Y9hZG5dg1+/dWOGAt5FCgQ/nnWrHbkWFTPq1aNPBEsrOsjK5U07d8PH34I8+aBu7sdRvDWW/ofrXMVAE6Eee6PXR86Qc8Vke5Ad4DCD/k8O1TswIh1I1jWbhnFshfjx4M/0mpRK9zTuLO+y3qypssaw9BUSnEn8A5r/daSPX12Tl+3y5GnkTQ8X+p5vm/zvYujU7GhyaFSKpw7d+CPP2DJEli2DM6ejfrYIkXAzy9+r/fhh+GHrAJkzGjnNCqlkpCdO+1/mIsX22UkXnsN3njDLkOhnC2yRRpNQp9rjJkGTAOoVq1alNfPkSEHvar14sP1H9KufDteXPQiWTyy8Fatt8ibOW8Mw1IpwdGAo0zdPpWZO2dy/uZ5vLN5kzZNWgKDA0nnlo4vmn7h6hBVLOkMUKUUV67YToA2bSBXLltccP58qF/fbp82zSZsYSVUAtehg71+kSJ2jeoiRexznW+olAtkzWr/Q4z4SJvWFpb59VdbZObYMRg/XhPDxOMPFArzvCBwKhHOjdJrj73GkoNLyJ4hO88+8iwebh70f7R/fC+rkoGg4CB+OvQTTeY2ofhnxRm3aRy1CtXilw6/4Nvfl25VupFG0uhC9cmU9hwqlUqdPGl7BpcuhTVrbB2JvHmhfXt4/nl44glIF2bt2YwZnbfcRIcOmgwqlSREVlEUICgIPvgAevcGT89EDUkBsBUoKSJFgZNAW6B9IpwbpZDewy+3fgnAkDpDdD5ZCnfm+hlm7JjBtB3TOH7lOF6ZvRhedzivVnmVQtnuf/+gC9Unb5ocKpXCPGzJiQMHbDK4dCn8/bfdVrKkHR3WogXUrAlpohhPoAmcUqncsGGujiDVMsYEikgfYBV2OYqZxph9ItLTsX+KiOQDtgFZgWARGQCUNcZcjezchIjrtcdeo8RnJUifNj1zX5ibEJdUSYwxhnXH1jF522QWH1hMYHAgDYo24JOnP6FZqWa4u7k/cI4uVJ+8aXKoVAoS2ZITXbvaIaL//QeHDtntNWrYuX4tWkDp0nbUmFIqFbp9G9ats4vUO3OhehVvxpgVwIoI26aE+f0MdshojM5NCDky5OD9J97HM72n9hqmMJdvX2bOrjlM2T6FgxcOkj19dvrW6EvPaj15JOcjrg5POZEmh0qlIJEtOXHnDixfDg0bQr9+0KwZFIz09kEplSr4+dlEcOVKuxbNrVu2wMwTT8Dhw66OTiUzvWv0dnUIKgFtO7WNyVsnM2/vPG4F3qJmgZrMbj5bl6FIRTQ5VCqZu3wZ1q+3X/5HteQEwOrViRaSUiopuXsXNmy43zt44IDdXqwYvPqqrUBVr55NEHUYgVIpXsFPCnLy2skHtruncede8D0yumfkpYov8b9q/6OyV2UXRKhcSZNDpZKZixfvJ4Pr1sGuXXb9aQ8PW0Dmzp0HzylSJNHDVEq5kr+/7RlcsQJ++w2uX7eNRL16dux5kyZ2wnHEZDBLlsiL0mTJkjhxK6WcrlmpZszYOYO7QXfDbc/skZn3nniPlyu+TLb02VwUnXI1TQ6VSuLOnYM//7yfDO7ZY7enTw+PPQbvvmvv92rWtMuP6ZqBSqVwWbNGnsBlyAD9+9uEcPduu61QIXjpJZsMPvEEZM788GtfvZrw8SqlkgxjDE8Xf5qp26eG2+7h5sG+XvvwyqLL06R2mhwq5QIPqyh6+vT9RHDduvsjwDJmhNq17VqE9epB9erhl5qA+9dw1pITSqkkIKrlJm7dsmsP1qkDY8fahLBsWR0qqpQi2ASz7NAyxm4cy1/+f5E+bXruBd0jyATh4ebBq5Vf1cRQAZocKpXoIqso+sorMH26XXvwv//s9ixZ7D1ep042GaxaFdwfrBj9AF1yQqkkIKrevSxZHt47d+OG/Ybo1Kn7P8P+fvr0w1/3wgXIpsPBlFLW7cDbfLv7W8ZvGs+hi4co6lmULxp/QaMSjSg/uTxBgUG4iZuuSahCaXKoVCIbPPjBiqJ379qho02bQo8eNhn08YG0+l+oU4hII2Aids2v6caYMRH2i2N/E+Am0NkYs8OxzxOYDpQHDPCKMeYvEckBLAC8AT+gtTEmIDHej0qCourdu3bNri0TMeEL+RlZ4pguHeTPD15eUL48HDwY9etqYqiUwi5FMWXbFCZumciZ62eo4lWF+S3n07JsS9KmsTcXXXy6MHX7VLr4dCFf5nwujlglFXrrqZSTXblih4f+/rt9+PtHflxwMCxblrixpUYi4gZMAhoC/sBWEVlmjNkf5rDGQEnHoyYw2fETbNL4izGmlYh4ABkd2wcDvxtjxojIYMfzQU5/Qyr5adfO/kyf3iZ8+fNDxYrwzDP3k8CwPz09ww8N1WGiSqkonLhygk83f8q0HdO4fvc6Txd/mm+f/5Yniz6JRGg7htcdzirfVdprqMLR5FCpBHb7NmzadD8Z3LYNgoJsrYg6dezQ0cuXHzxPK4ommhrAYWPMEQARmQ80B8Imh82Br40xBtgsIp4i4gXcAOoCnQGMMXeBu2HOqe/4fQ6wFk0OUydjHr5/3z6b+EVM+pRSKo72ntvLuE3j+G7PdxhjaFu+LW/WehOffD5RnuOVxQvffr6JF6RKFjQ5VCqegoJg+/b7yeDGjTZBdHODGjVgyBBo0MBWFk2X7sE5h6AVRRNZAeBEmOf+3O8VfNgxBYBA4DwwS0QqAduB/saYG0BeY8xpAGPMaRHJ46T4VVK2dy/0jmZR8LJl4/cautyEUgpbefTPY38ydtNYVvy3gozuGeldvTevPfoaRTz1G2cVN5ocKvUQkVUVbd/eVhANSQbXrrVDRwEqVICePW0yWLeurUkRkVYUdbnIumoidvVEdUxaoArQ1xizRUQmYoePxnhMjoh0B7oDFC5cOKanqaTu6lUYORImTnT+vD9dbkKpVC0oOIilB5cydtNY/j75N7kz5ub9J97nf9X+R86MOV0dnkrmNDlUKgqRVRXt1An69Lk/LNTbG1q1ssngk09C3rwxu7ZWFHUpf6BQmOcFgVMxPMYA/saYLY7t32OTQ4CzIuLl6DX0As5F9uLGmGnANIBq1apFM/5QJXnGwLx58OabcOYMdOsGH34IRYtq755SKs4KflKQk9dOPrDdM50nuTLl4vClwxTPXpzJTSfTqVInMrhncEGUKiXS5FCpSNy8Ca+//mBV0aAgW1l02jSbEBYr5pr4VLxsBUqKSFHgJNAWaB/hmGVAH8d8xJrAlZAhoyJyQkRKGWMOAQ24P1dxGdAJGOP4+aPT34lyrX377BDSdeugWjVYutSOJQft3VNKxUuzUs2YsXMGd4Puhtt++c5lSuQswaIXF/F86edxS+PmoghVSqXJoVLYL/8PHIBffrGPP/+EO3ciP/bWLds5oJInY0ygiPQBVmGXsphpjNknIj0d+6cAK7DLWBzGLmXRJcwl+gJzHZVKj4TZNwZYKCJdgePAi4nxfpQLRBxCOnUqdO1qJxorpVQCGF53ODN3zgy3LY2kCU0KI1YeVSqhaHKoUq2rV+2cwZCE8Phxu71MGejVyw4rPRfJwECdJpb8GWNWYBPAsNumhPndAJFWFTHG7AKqRbL9IrYnUaVUxtg1Ct94ww4hffVVO4Q0Vy5XR6aUSkF8L/kyduNY7gXfC92WNk1aulfpzgtlXnBhZCo10ORQpRrGwO7dsHKlTQY3boTAQMicGZ56yhaIeeaZ+0tKVK2qVUWVUg779tkJx2vXPjiEVCmlEsDec3sZvWE08/fOxz2NOy9XfJl5e+dxN+gu7mncdT1ClSg0OVTJXmQVRUOKvVy6BL/+er938MwZu71SJVs/olEju8SEh8eD19Wqokoprl27P4Q0SxaYMsX2GOoQUqVUAtniv4XRG0bz46EfyeSeidcffZ3XH3sdryxeZHTPyNTtU+ni04V8mfO5OlSVCjg1ORSRRsBE7Lye6caYMRH2i2N/E+y8ns7GmB2OfZ7AdKA8tkLgK8aYv0QkB7AA8Ab8gNbGmABnvg+VdEVWUbRrV1iyBE6dgi1bIDgYsmeHp5+2yeDTT0P+/DG7vlYVVSqVMgYWLLBDSE+dsgnh6NE6hFQplSCMMazxW8OH6z/k96O/kz19dkbUG0Hfmn3JkSFH6HHD6w5nle8q7TVUicZpyaGIuAGTgIbYsvBbRWSZMWZ/mMMaAyUdj5rAZO4vRj0R+MUY08pR+CGjY/tg4HdjzBgRGex4PshZ70MlbcOGPVhR9M4d+OEHO+Lr7bdtQli9OqTVfnKlVERZs0a+5ISbmy1PXLUqLF4MNWs+eIxSSsVSsAlm+b/L+XD9h2w5uYV8mfMxruE4elTtQZZ0Dy5145XFC99+vi6IVKVWzrxdrgEcNsYcAXCUhG/O/bLvOJ5/7Sj+sFlEPB3rg90A6gKdAYwxd4G7Yc6p7/h9DrAWTQ5TndOn4ccfbU9hVLZsiXqfUkoBkSeGYBPDyZNtaWIdQqqUiqfA4EAW7VvE6A2j2XNuD96e3kxuOpnOPp1Jnza9q8NTKpQzk8MCwIkwz/253yv4sGMKAIHAeWCWiFQCtgP9jTE3gLwh6405FpvO46T4VRJz+LAdLrpkCWzebEd9pU1ri8pEFFJURiml4qxnT1dHoJRK5u4E3uGb3d8wZsMYfAN8KZOrDF+3+Jq25dvi7ubu6vCUeoAzk8PIFmAxMTwmLVAF6GuM2SIiE7HDR2M84FpEugPdAQrr2gPJkjHwzz92RNeSJbB3r91eubKtD/H887BrF/TooRVFlVKxdOECLF/u6iiUUinUjbs3+GrHV4zfNJ6T105S1asqi1svpnnp5qSRNK4OT6koOTM59AcKhXleEDgVw2MM4G+MCRkY+D02OQQ4KyJejl5DLyCSlejAGDMNmAZQrVq1iEmpSqKCgmDTpvs9hH5+kCYN1KkDEyZAixbg7X3/+PLlQUQriiqlYsDPz45HX7oU/vzTVqtSSql4KPhJQU5eO/nA9jSShmATTL0i9ZjZfCYNizXUhetVsuDMry62AiVFpKijoExbYFmEY5YBHcV6FLhijDltjDkDnBCRUo7jGnB/ruIyoJPj907Aj058DyqBzJ1rk7o0aezPuXPv77tzB1assFN78ueHunVh0iQoVw6mT7fLT6xbBwMGhE8MQ3ToYO/5goPtT00MlVLA/cVNR460Qw6KFrUNyYULMHQobN/u6giVUslcs1LN8HB7cD2sglkLsqHLBtZ2XsvTxZ/WxFAlG07rOTTGBIpIH2AVdimLmcaYfSLS07F/CrACu4zFYexSFl3CXKIvMNeRWB4Js28MsFBEugLHgRed9R5UwohsuYlu3ey8wfPnbWJ47ZpdQqxpUztctHFj+1wppWIlKAg2brS9g0uXwtGjdnhB7dowfjw0bw4lStw/PkuWyIvSaAOklIqBPtX7MG37tHDb0rmlY8urW3RdQpUsObW4vzFmBTYBDLttSpjfDdA7inN3AdUi2X4R25OokonIlpu4dQu++ALy5IG2bW1C+OSTkC6da2JUSiVhUS03kSULXL1qG5TffrNj0X/6yfYMenhAw4a2h/C55yBv3sivffWqc2NXSqVI1+9eZ+LmiYzbNI4gExQ6jNTDzYOulbtqYqiSLV35TTnNjRt2Ws/Dlps4dUqrxCulohHVchPXrkHLlvDLL/YbqGzZ7PCDFi3sAqfa+6eUSmB3g+4ybfs03v/zfc7dOEezUs3oX6M/Tec15XbgbdzETResV8maJocqwQQFwc6dsHo1/PqrLSxz927UxxcpoomhUiqeNm+GTp3s8IN69WyPoVJKJbCg4CC+2/Md76x9B7/LftQrUo+lbZbyWKHHAOji04Wp26fSxaeL9hqqZE2TQxUvx47ZRHD1avj9d7h0yW6vVAn697ejuvz9oU8fXW5CKeUEJ07YSldKKeUExhiWHVrGsD+Gse/8Pqp4VWFK0ykPFJkZXnc4q3xXaa+hSvY0OVSxcvUqrFlzv3fwv//s9vz57bSep5+GBg0enN7j4aHLTSilYunQIRgz5uHHaGKolHKStX5rGfL7EDb7b+aRnI+wsNVCWpZtGek6hV5ZvPDt5+uCKJVKWJocqnDmzg2fxL33ni3sF5IMbtlih49mzAj160OvXjYhLFPGFgSMSocOmgwqpWJo1y748EP4/nutUqWUSnTbT21n6B9DWe27mgJZCvDVc1/R2aczadPobbNK+fRfuQoV2ZITnRwrSopAtWowaJBNBh97TKf2KKUS2KZNdljBihW2QungwXZdwhIldLkJpZTTHbpwiOFrhrNo/yJyZsjJ+Ibj6VW9FxncM7g6NKUSjSaHCrC9ga+99uCSEwC5csHBg5AzZ+LHpZRK4Yyxy1CMGgXr1tmG5oMPoHdv8PS0x+hyE0opJzpx5QQj141k9q7ZpE+bnnfqvsMbtd4ga7qsrg5NqUSnyWEqd/EizJgBX35pF6SP6hhNDJVSCSo4GJYts8NHt261E5cnTIBu3SBTJldHp5RKBS7cvMDo9aOZtHUSBkOfGn0Y+vhQ8mTK4+rQlHIZTQ5TqR077CL08+bB7dt2/uCNG3bt6IgKF0708JRSKVVgIMyfD6NHw/79UKwYTJsGHTvq/EKllFMU/KQgJ6+dfGC7IIgInSp14t1671LEs4gLolMqadEyb6nI3bvw3XdQuzZUrQoLFkDnzrBnj61A+umnttBMWLrkhFIqQdy5A1OnwiOPwMsv24nMc+faiqTdumliqJRymmalmuHh9mChhKKeRdnzvz3MbD5TE0OlHLTnMBU4edLek02bBmfPQsmSNhHs1On+lB64X01Ul5xQSsVJ1qyRF45Jl86OTT91CqpXt8NHn3tOl6FQSiWKtx9/m+k7pofbls4tHRu7btQF65WKQJPDFMoY2LABPv8cFi+203uaNrWL0TdsGPU9mS45oZSKs8gSQ7C9hqVKwZw5diHUh617o5RSCejA+QP0+6Uf94LvIQgGg4ebB10rd9XEUKlI6Ne2KcyNG/DVV+DjA3Xr2iKAr70Ghw/DTz/BM8/ol/VKKRf44w946ilNDJVSieLK7Su8vup1Kk6pyLZT2/jgiQ9Ch5a6iRvD6w13cYRKJU2aJiRDc+eCt7dN8ry97XNfX3jjDShY0K5VKALTp4O/P4wbZ2s+KKWU05w+7eoIlFKKYBPMzJ0zeeSLR/h086d0rdyV//r+x7C6w3il8iukkTR08emivYZKRUGHlSYzkS1U37GjHUbq5gatWtmho7Vq6Rf0SqlE4O8PY8faSc1KKeVCW/y30HdlX7ae2kqtQrVY2WElVbyqhO4fXnc4q3xXaa+hUg+hyWEyM2zYgwvVBwdDtmxw4AB4ebkmLqVUKnPsGIwZAzNn2kaoUye7aKpSSiWyM9fPMOT3IczeNRuvzF58+/y3tK/QHonwLblXFi98+/m6KEqlkgdNDpOREyfs/Vhkrl7VxFAplQh8fe0ahXPm2OEJXbvCoEF2jPvChZEXpcmSJdHDVEqlfHeD7vL5ls8ZuW4ktwNvM7j2YIY+PpQs6bTNUSquNDlMBrZvh08+sfddUdGF6pVSTnXoEHz4oR3bnjYt/O9/8NZbdqJziKtXXRefUipVWe27mv6/9OfghYM0LdmUCc9MoGTOkq4OS6lkT5PDJCo4GJYvt0nhunX2i/d+/WwSOHRo+KGlulC9Uspp9u2zDcyCBXa9wv794c03daiCUsoljgQc4fVVr/PjoR8pkaMEy9stp+kjTV0dllIphiaHSczNm3a01oQJ8N9/Nhn8+GM7citbNntMrly6UL1Sysn++Qc++AB++MF+A/Xmm7Ykcp48ro5MKZUK3bh7g9EbRjN+03jSpknLmAZjGPDoANKlTefq0JRKUTQ5TCJOn4ZJk2DyZLh0CapXh/nzoWVLO4IrLF2oXinlNNu3w/vvw48/QtasdqjCgAH2WymllEpkxhgW7lvIm/9v786DpKzvPI6/PzPDbERR2EUUJYi3Ag4ISPAIkfVYwUTLxIpElJV1Aa9k11SqsNYdtdRk1XU3ajyQeEQtDKbWozTxwCQazRouBQE1sfBAUcsrRl0jcn33j+cZaIZupoHup5+e+byqpqb7eZ7u/kxDf6t/z/M7Hv8BKz5ZwWktp3Hl0VeyW4/dah3NrFNy47DGFi9OrhLefTesXg0nnpicnD/8cC9FYWZVsOOOxSeN6d4djjwSHn4YevaESy5J+rL36pVxQDMrRtJxwLVAI3BLRFzRbr/S/eOAvwJnRMRz6b7zgX8GAlgCTIqIlRnG71C//+7HW5++tcn25sZmVq1dxcG7Hsysb83i8P6H1yCdWdfRUOsAXVEEPPooHHssDBmSTDQzeTK8/DLcfz8ccYQbhmZWJcUahpD0aZ87N+mnvnw5XHyxG4ZmOSGpEbgBGAsMBL4jaWC7w8YC+6Y/U4Cb0sfuDnwPGBERg0kal+Mzil62E/Y/gebG5k22N6iBm79+M/Mnz3fD0CwDbhxWycyZyczuDQ3J75kzYeXKZBmwwYNh7FhYujSZ/O/NN+H662GffWqd2qxrkHScpD9JWibpgiL7Jem6dP9iScMK9r0uaYmkRZIWFGy/RNJb6fZFksZl9fdUzOuvJ91Id9yx1knMbGMjgWUR8WpErAJmASe2O+ZE4M5IzAF6SmqbOaoJ2E5SE9AdeDur4OVqHd2K2PjMeKMaWTh1IVOGT6GxobFGycy6FncrrYKZM2HKlA0zii5fDpMmwTnnJDO9t7Qkk86MHw/Nm54kM7MqKjgDfwywApgv6cGIeLHgsMIz8F8hOQP/lYL9YyLigyJP/+OIuLo6yTOwww61TmBmxe0OvFlwfwUb16RSx+weEQskXQ28AXwOzI6I2dUMuzVWr1tNn+378OYnyZ/Q1NDElGFTOKD3ATVOZta1+MphFVx44cZLTUAynnD1avj1r2HRIpg40Q1DsxrZ1jPw9enDD5NlKMysHhUbbBLlHCOpF0lN2xPYDdhe0mlFX0SaImmBpAXvv//+NgUu17pYx/QF0xl04yA+/PxDmhqS6xbdGrrR+rXWTDKY2QZuHG6BYl1F23z6aTKOcNq05EphMStXwlFHeTyhWY0VPbu+BccEMFvSs5KmtHvceWk31NvSL2SbyPzL16pVyaxX++yT9F83s3q0Avhywf1+bNo1tNQxRwOvRcT7EbEauA84rNiLRMSMiBgRESN23nnnioUv5ZU/v8LRdx7N2b86m1H9RvHCOS8wedhkGtTApKGT2HWHXauewcw2VtXGYWca19PWVXT58mRCmeXLk7UHTzgBDj00mbdh7NjkO9jflFhyp3//LJKaWQe2+gx8+vvwiBhG0vX0XEmj0+03AXsDQ4F3gP8q9uKZffmKSGa4GjQIvv99GDkyWbuwR4/ix5fabmZ5MB/YV9KekppJJpR5sN0xDwIT0+9Wo4CPI+Idku6koyR1T2c0PQp4Kcvw7a1dt5Zr51xLy/QWnn3nWW75xi3MPm02A3oOoHV0a/LbVw3NaqJqYw4727ieYl1Fv/gCHnooaRxOmwZjxiS3H3hg4zGHkMwS/8MfZpnYzErYljPwRETb7/ck3U/STfWpiHi37WBJPwV+WfnoJZRangJg4EB45BE47rjk/iefZBbLzCojItZIOg94jGS20dsi4gVJZ6X7pwMPkyxjsYxkKYtJ6b65kv4HeA5YAywEZmSRu+TyFA3NrFq3iuP3PZ7pX59Ovx37rd/Xt0dfXvneK1nEM7MiqjkhzfpxPQCS2sb1FDYO14/rAeZI6impb3qmK1feeKP0vmee2fh+2wL1F16YPK5//6Rh6IXrzXJh/Rl44C2SM/CntjvmQZIuorNITlh9HBHvSNoeaIiIT9PbxwKXArSrXScBSzP4WxKlGoaQXC1s8txjZvUuIh4maQAWbptecDuAc0s89mLg4qoGLOKE/U/g1oW3smrtqo22S+Kuk+5iwkETkMfamOVKNbuV1nRcT6XttFPx7XvsUXz7hAnJrPDr1iW/3TA0y4eIWAO0nYF/CfhF2xn4trPwJF/AXiU5A/9T4Jx0+y7A7yU9D8wDfhURj6b7rkq7wi8GxgDnZ/MXdcANQzOrkdbRrTRo46+aDWpg3uR5nNZymhuGZjlUzW8NlRjX87akPsDjkv4YEU+RdD29LD3uMpJxPf+0yYsnDcopAP23YbBfBFxwAfzlL9DYCGvXbtjnrqJm9Wlrz8CnPSGGlHjO0yscszybu2poZlZDvbv3pqVPC/PengdsWJ6iZZeWGiczs1KqeeWwYuN6gLZxPUTEuxGxNiLWkZzRH1nsxSsx6cOaNTB5Mlx1FZx9Ntx+e3KlUEp+z5jhK4JmVkMLF8KwYR0fZ2aWsSXvLmHUraOY9/a89VcPvTyFWf5Vs3G41TNrSdpeUg+AgnE9S9P7hWuNVW1cz8qVcMopcOut0NoKN9wAp5/urqJmlgMRSVEaNQo+/7zWaczM1lu9djWXP3U5w2cMZ8UnK7j32/cydfhUL09hVieq1q10W2bWIhnXc3/aF70JuLvduJ6hJN1KXwemViLvzJkbJpDp1y+Z1f3FF+Gaa7xutJnV0OZmIh03Du64A/baq/gxXp7CzKqk1Eyk3Rq6sXrdasYPHs9Pxv6E3t17c2i/Q3nslcd81dCsDlR1poJ6GdfTtoZh29ITb6ZT5Jx1lhuGZlZjmxtT+NBD0NDg5SnMLHOlZiJtamhi1smz+OaB31y/zctTmNWPanYrrRvF1jCEZGkwM7PcanAJN7PaKDYTaaMamT95/kYNQzOrL/5mQek1DDe3tqGZmZlZV9W3R1/OGHLG+vtNDU1MHT6VQX0G1S6UmW0zNw5JFqnfku1mZmZmXd1FX7uI5sZmwDORmnUWbhySrFXYvfvG27yGoZmZmVlpfXv05cyDz/RMpGadiBuHJEtSzJjhNQzNLIdKzTjqmUjNLAdaR7cyoOcAXzU06ySqOltpPZkwwY1BM8shz0RqZjnmmUjNOhdfOTQzMzMzMzM3Ds3MzMzMzMyNQzMzMzMzM8ONQzMzMzMzM8ONQzMzMzMzM8ONQzMzMzMzM8ONQzMzMzMzM8ONQzMzMzMzMwMUEbXOUHWS3geWd3BYb+CDDOJUkjNnpx5zd/bMe0TEztUMU22duDZBfeZ25mx0hcyuT/nlzNmox8xQn7kr+t2pSzQOyyFpQUSMqHWOLeHM2anH3M7cOdTre1KPuZ05G87cedTj++LM2ajHzFCfuSud2d1KzczMzMzMzI1DMzMzMzMzc+Ow0IxaB9gKzpydesztzJ1Dvb4n9ZjbmbPhzJ1HPb4vzpyNeswM9Zm7opk95tDMzMzMzMx85dDMzMzMzMy6YONQ0nGS/iRpmaQLiuyXpOvS/YslDatFznaZOso8Ic26WNIzkobUIme7TJvNXHDcIZLWSjo5y3wlsnSYWdKRkhZJekHS77LOWCRPR/83dpL0kKTn08yTapGzXabbJL0naWmJ/bn7DGbBtSkb9VibwPUpC65NxdVjbQLXp6y4NmUj0/oUEV3mB2gEXgH2ApqB54GB7Y4ZBzwCCBgFzK2DzIcBvdLbY+shc8FxvwUeBk7Oe2agJ/Ai0D+936cOMv8bcGV6e2fgz0BzjXOPBoYBS0vsz9VnMEf/lrl6X1yb8pXb9akimV2btu7fMXfvi+tTfjK7NlUsd2b1qatdORwJLIuIVyNiFTALOLHdMScCd0ZiDtBTUt+sgxboMHNEPBMRH6V35wD9Ms7YXjnvM8B3gXuB97IMV0I5mU8F7ouINwAiota5y8kcQA9JAnYgKXBrso3ZLlDEU2mOUvL2GcyCa1M26rE2getTJlybiqrH2gSuT1lxbcpIlvWpqzUOdwfeLLi/It22pcdkaUvznEly5qCWOswsaXfgJGB6hrk2p5z3eT+gl6QnJT0raWJm6YorJ/P1wIHA28AS4F8iYl028bZa3j6DWXBtykY91iZwfcqLvH0Gs1CPtQlcn7Li2pQfFfscNlUkTv1QkW3tp2st55gslZ1H0hiSAndEVRN1rJzM1wDTImJtcmKm5srJ3AQMB44CtgP+IGlORLxc7XAllJP5H4BFwN8DewOPS3o6Ij6pcrZtkbfPYBZcm7JRj7UJXJ/yIm+fwSzUY20C16esuDblR8U+h12tcbgC+HLB/X4kZwW29JgslZVHUgtwCzA2Ij7MKFsp5WQeAcxKi1tvYJykNRHxQCYJN1Xu/40PIuIz4DNJTwFDgFoVuHIyTwKuiKRD+jJJrwEHAPOyibhV8vYZzIJrUzbqsTaB61Ne5O0zmIV6rE3g+pQV16b8qNzncGsHK9bjD0lj+FVgTzYMQh3U7pjj2XhA57w6yNwfWAYcVuv3uNzM7Y7/GbUfVF3O+3wg8Jv02O7AUmBwzjPfBFyS3t4FeAvonYP/IwMoPag6V5/BHP1b5up9cW3KV27Xp4rldm3a8n/H3L0vrk/5yezaVNHsmdSnLnXlMCLWSDoPeIxktqLbIuIFSWel+6eTzP40jqRg/JXk7EHNlJn5IuDvgBvTs0lrImJEzjPnSjmZI+IlSY8Ci4F1wC0RUXRK4bxkBi4DfiZpCUnBmBYRH9QqM4CknwNHAr0lrQAuBrpBPj+DWXBtylXm3HF9yoZr06bqsTaB61NWXJuyk2V9UtraNDMzMzMzsy6sq81WamZmZmZmZkW4cWhmZmZmZmZuHJqZmZmZmZkbh2ZmZmZmZoYbh2ZmZmZmZoYbh1ZBkkLSXQX3myS9L+mXHTxuqKRxZTz//5XYfpakiVueeP3jX5e0RNJiSb+TtEcHxw+QdOrWvp6ZZcu1yczyyLXJ8siNQ6ukz4DBkrZL7x9DsnBoR4aSrM2yVdJ1dO7c2senxkREC/Ak8O8dHDsAcJEzqx+uTWaWR65NljtuHFqlPQIcn97+DvDzth2SRkp6RtLC9Pf+kpqBS4FTJC2SdIqkHSTdXnBW6lsFz/FDSc9LmiNpl3TbJZJ+kN5+UtKVkuZJelnSV9Pt3SX9In2+eyTNlVRssds/ALunjxkg6WlJz6U/h6XHXAF8Nc17vqRGSf8paX76/FMr+o6aWSW4Nrk2meWRa5NrU664cWiVNgsYL+lLQAswt2DfH4HREXEwcBHwo4hYld6+JyKGRsQ9QCvwcUQclJ6V+m36+O2BORExBHgKmFwiQ1NEjAT+Fbg43XYO8FH6fJcBw0s89jjggfT2e8AxETEMOAW4Lt1+AfB0mvfHwJlp3kOAQ4DJkvbc7LtkZllzbXJtMssj1ybXplxpqnUA61wiYrGkASRnvx5ut3sn4A5J+wIBdCvxNEcD4wue86P05iqgrR/+syTdL4q5r+CYAentI4Br0+dbKmlxu8c8kZ5Re48N3SO6AddLGgqsBfYr8XrHAi2STk7v7wTsC7xW4ngzy5hrE+DaZJY7rk2Aa1Ou+MqhVcODwNUUdI1IXQY8ERGDgW8AXyrxeJEUwfZWR0Tb9rWUPrnxRZFj1EHmMcAewAsk3TUAzgfeBYYAI4DmzeT9bnpGbGhE7BkRszt4PTPLnmuTa5NZHrk2uTblhhuHVg23AZdGxJJ223diw0DrMwq2fwr0KLg/Gziv7Y6kXhXI9Hvg2+nzDQQOan9ARHxO0qVioqS/TfO+ExHrgNOBxhJ5HwPOltQtff79JG1fgcxmVlmuTa5NZnnk2uTalBtuHFrFRcSKiLi2yK6rgP+Q9L9sKBgATwAD2wZWA5cDvSQtlfQ8ydmpbXUjsHPaLWIasBj4uEj2d0jO3J2bPuYfJc0h6RrxWXrYYmBNOsD7fOAW4EXgOUlLgZtxl22z3HFtcm0yyyPXJtemPNGGq81mnZekRqBbRKyUtDfwG2C/dGC3mVlNuDaZWR65NnVdbqVbV9GdZPB0N5K+7me7wJlZDrg2mVkeuTZ1Ub5yaGZmZmZmZh5zaGZmZmZmZm4cmpmZmZmZGW4cmpmZmZmZGW4cmpmZmZmZGW4cmpmZmZmZGW4cmpmZmZmZGfD/HsQhIa5H4YgAAAAASUVORK5CYII=\n",
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\n",
       "text/plain": [
        "<Figure size 1080x360 with 3 Axes>"
       ]
@@ -467,7 +467,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 360x360 with 1 Axes>"
       ]
@@ -508,7 +508,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1080x360 with 3 Axes>"
       ]
@@ -549,7 +549,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1080x360 with 3 Axes>"
       ]

From cdcbbd4cd2ae559cc9e127612c6e1fb9d508c524 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Wed, 9 Jun 2021 15:02:32 -0400
Subject: [PATCH 137/267] Rename num_items to num_matches

---
 include/cuco/detail/static_multimap.inl       |  84 ++++++------
 .../cuco/detail/static_multimap_kernels.cuh   | 124 ++++++++++--------
 2 files changed, 111 insertions(+), 97 deletions(-)

diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index 3f7110c3d..42ed2bfc7 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -123,19 +123,19 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::count(
   auto const grid_size  = (cg_size() * num_keys + stride * block_size - 1) / (stride * block_size);
   auto view             = get_device_view();
 
-  atomic_ctr_type* num_items;
-  CUCO_CUDA_TRY(cudaMallocManaged(&num_items, sizeof(atomic_ctr_type)));
-  *num_items = 0;
+  atomic_ctr_type* num_matches;
+  CUCO_CUDA_TRY(cudaMallocManaged(&num_matches, sizeof(atomic_ctr_type)));
+  *num_matches = 0;
   int device_id;
   CUCO_CUDA_TRY(cudaGetDevice(&device_id));
-  CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_items, sizeof(atomic_ctr_type), device_id));
+  CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_matches, sizeof(atomic_ctr_type), device_id));
 
   detail::count<block_size, cg_size(), Key, Value, is_vector_load()>
-    <<<grid_size, block_size, 0, stream>>>(first, last, num_items, view, key_equal);
+    <<<grid_size, block_size, 0, stream>>>(first, last, num_matches, view, key_equal);
   CUCO_CUDA_TRY(cudaDeviceSynchronize());
 
-  size_t result = *num_items;
-  CUCO_CUDA_TRY(cudaFree(num_items));
+  size_t result = *num_matches;
+  CUCO_CUDA_TRY(cudaFree(num_matches));
 
   return result;
 }
@@ -157,19 +157,19 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::count_
 
   constexpr bool is_outer = true;
 
-  atomic_ctr_type* num_items;
-  CUCO_CUDA_TRY(cudaMallocManaged(&num_items, sizeof(atomic_ctr_type)));
-  *num_items = 0;
+  atomic_ctr_type* num_matches;
+  CUCO_CUDA_TRY(cudaMallocManaged(&num_matches, sizeof(atomic_ctr_type)));
+  *num_matches = 0;
   int device_id;
   CUCO_CUDA_TRY(cudaGetDevice(&device_id));
-  CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_items, sizeof(atomic_ctr_type), device_id));
+  CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_matches, sizeof(atomic_ctr_type), device_id));
 
   detail::count<block_size, cg_size(), Key, Value, is_vector_load(), is_outer>
-    <<<grid_size, block_size, 0, stream>>>(first, last, num_items, view, key_equal);
+    <<<grid_size, block_size, 0, stream>>>(first, last, num_matches, view, key_equal);
   CUCO_CUDA_TRY(cudaDeviceSynchronize());
 
-  size_t result = *num_items;
-  CUCO_CUDA_TRY(cudaFree(num_items));
+  size_t result = *num_matches;
+  CUCO_CUDA_TRY(cudaFree(num_matches));
 
   return result;
 }
@@ -189,19 +189,19 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::pair_c
   auto const grid_size  = (cg_size() * num_keys + stride * block_size - 1) / (stride * block_size);
   auto view             = get_device_view();
 
-  atomic_ctr_type* num_items;
-  CUCO_CUDA_TRY(cudaMallocManaged(&num_items, sizeof(atomic_ctr_type)));
-  *num_items = 0;
+  atomic_ctr_type* num_matches;
+  CUCO_CUDA_TRY(cudaMallocManaged(&num_matches, sizeof(atomic_ctr_type)));
+  *num_matches = 0;
   int device_id;
   CUCO_CUDA_TRY(cudaGetDevice(&device_id));
-  CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_items, sizeof(atomic_ctr_type), device_id));
+  CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_matches, sizeof(atomic_ctr_type), device_id));
 
   detail::pair_count<block_size, cg_size(), Key, Value, is_vector_load()>
-    <<<grid_size, block_size, 0, stream>>>(first, last, num_items, view, pair_equal);
+    <<<grid_size, block_size, 0, stream>>>(first, last, num_matches, view, pair_equal);
   CUCO_CUDA_TRY(cudaDeviceSynchronize());
 
-  size_t result = *num_items;
-  CUCO_CUDA_TRY(cudaFree(num_items));
+  size_t result = *num_matches;
+  CUCO_CUDA_TRY(cudaFree(num_matches));
 
   return result;
 }
@@ -223,19 +223,19 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::pair_c
 
   constexpr bool is_outer = true;
 
-  atomic_ctr_type* num_items;
-  CUCO_CUDA_TRY(cudaMallocManaged(&num_items, sizeof(atomic_ctr_type)));
-  *num_items = 0;
+  atomic_ctr_type* num_matches;
+  CUCO_CUDA_TRY(cudaMallocManaged(&num_matches, sizeof(atomic_ctr_type)));
+  *num_matches = 0;
   int device_id;
   CUCO_CUDA_TRY(cudaGetDevice(&device_id));
-  CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_items, sizeof(atomic_ctr_type), device_id));
+  CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_matches, sizeof(atomic_ctr_type), device_id));
 
   detail::pair_count<block_size, cg_size(), Key, Value, is_vector_load(), is_outer>
-    <<<grid_size, block_size, 0, stream>>>(first, last, num_items, view, pair_equal);
+    <<<grid_size, block_size, 0, stream>>>(first, last, num_matches, view, pair_equal);
   CUCO_CUDA_TRY(cudaDeviceSynchronize());
 
-  size_t result = *num_items;
-  CUCO_CUDA_TRY(cudaFree(num_items));
+  size_t result = *num_matches;
+  CUCO_CUDA_TRY(cudaFree(num_matches));
 
   return result;
 }
@@ -257,19 +257,19 @@ OutputIt static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::retrieve(
   auto const grid_size   = (cg_size() * num_keys + stride * block_size - 1) / (stride * block_size);
   auto view              = get_device_view();
 
-  atomic_ctr_type* num_items;
-  CUCO_CUDA_TRY(cudaMallocManaged(&num_items, sizeof(atomic_ctr_type)));
-  *num_items = 0;
+  atomic_ctr_type* num_matches;
+  CUCO_CUDA_TRY(cudaMallocManaged(&num_matches, sizeof(atomic_ctr_type)));
+  *num_matches = 0;
   int device_id;
   CUCO_CUDA_TRY(cudaGetDevice(&device_id));
-  CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_items, sizeof(atomic_ctr_type), device_id));
+  CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_matches, sizeof(atomic_ctr_type), device_id));
 
   detail::retrieve<block_size, cg_size(), buffer_size, Key, Value, is_vector_load()>
-    <<<grid_size, block_size, 0, stream>>>(first, last, output_begin, num_items, view, key_equal);
+    <<<grid_size, block_size, 0, stream>>>(first, last, output_begin, num_matches, view, key_equal);
   CUCO_CUDA_TRY(cudaDeviceSynchronize());
 
-  auto output_end = output_begin + *num_items;
-  CUCO_CUDA_TRY(cudaFree(num_items));
+  auto output_end = output_begin + *num_matches;
+  CUCO_CUDA_TRY(cudaFree(num_matches));
 
   return output_end;
 }
@@ -293,19 +293,19 @@ OutputIt static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::retrieve_
 
   constexpr bool is_outer = true;
 
-  atomic_ctr_type* num_items;
-  CUCO_CUDA_TRY(cudaMallocManaged(&num_items, sizeof(atomic_ctr_type)));
-  *num_items = 0;
+  atomic_ctr_type* num_matches;
+  CUCO_CUDA_TRY(cudaMallocManaged(&num_matches, sizeof(atomic_ctr_type)));
+  *num_matches = 0;
   int device_id;
   CUCO_CUDA_TRY(cudaGetDevice(&device_id));
-  CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_items, sizeof(atomic_ctr_type), device_id));
+  CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_matches, sizeof(atomic_ctr_type), device_id));
 
   detail::retrieve<block_size, cg_size(), buffer_size, Key, Value, is_vector_load(), is_outer>
-    <<<grid_size, block_size, 0, stream>>>(first, last, output_begin, num_items, view, key_equal);
+    <<<grid_size, block_size, 0, stream>>>(first, last, output_begin, num_matches, view, key_equal);
   CUCO_CUDA_TRY(cudaDeviceSynchronize());
 
-  auto output_end = output_begin + *num_items;
-  CUCO_CUDA_TRY(cudaFree(num_items));
+  auto output_end = output_begin + *num_matches;
+  CUCO_CUDA_TRY(cudaFree(num_matches));
 
   return output_end;
 }
diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap_kernels.cuh
index 60bd6ac13..79afeadfb 100644
--- a/include/cuco/detail/static_multimap_kernels.cuh
+++ b/include/cuco/detail/static_multimap_kernels.cuh
@@ -37,7 +37,7 @@ namespace cg = cooperative_groups;
  * @param g The Cooperative Group used to flush output buffer
  * @param num_outputs Number of valid output in the buffer
  * @param output_buffer Shared memory buffer of the key/value pair sequence
- * @param num_items Size of the output sequence
+ * @param num_matches Size of the output sequence
  * @param output_begin Beginning of the output sequence of key/value pairs
  */
 template <uint32_t cg_size,
@@ -50,12 +50,14 @@ __inline__ __device__ std::enable_if_t<thrust::is_contiguous_iterator<OutputIt>:
 flush_output_buffer(CG const& g,
                     uint32_t const num_outputs,
                     cuco::pair_type<Key, Value>* output_buffer,
-                    atomicT* num_items,
+                    atomicT* num_matches,
                     OutputIt output_begin)
 {
   std::size_t offset;
   const auto lane_id = g.thread_rank();
-  if (0 == lane_id) { offset = num_items->fetch_add(num_outputs, cuda::std::memory_order_relaxed); }
+  if (0 == lane_id) {
+    offset = num_matches->fetch_add(num_outputs, cuda::std::memory_order_relaxed);
+  }
   offset = g.shfl(offset, 0);
 
   cg::memcpy_async(g,
@@ -78,7 +80,7 @@ flush_output_buffer(CG const& g,
  * @param g The Cooperative Group used to flush output buffer
  * @param num_outputs Number of valid output in the buffer
  * @param output_buffer Shared memory buffer of the key/value pair sequence
- * @param num_items Size of the output sequence
+ * @param num_matches Size of the output sequence
  * @param output_begin Beginning of the output sequence of key/value pairs
  */
 template <uint32_t cg_size,
@@ -91,12 +93,14 @@ __inline__ __device__ std::enable_if_t<not thrust::is_contiguous_iterator<Output
 flush_output_buffer(CG const& g,
                     uint32_t const num_outputs,
                     cuco::pair_type<Key, Value>* output_buffer,
-                    atomicT* num_items,
+                    atomicT* num_matches,
                     OutputIt output_begin)
 {
   std::size_t offset;
   const auto lane_id = g.thread_rank();
-  if (0 == lane_id) { offset = num_items->fetch_add(num_outputs, cuda::std::memory_order_relaxed); }
+  if (0 == lane_id) {
+    offset = num_matches->fetch_add(num_outputs, cuda::std::memory_order_relaxed);
+  }
   offset = g.shfl(offset, 0);
 
   for (auto index = lane_id; index < num_outputs; index += cg_size) {
@@ -115,21 +119,23 @@ flush_output_buffer(CG const& g,
  * @param activemask Mask of active threads in the warp
  * @param num_outputs Number of valid output in the buffer
  * @param output_buffer Shared memory buffer of the key/value pair sequence
- * @param num_items Size of the output sequence
+ * @param num_matches Size of the output sequence
  * @param output_begin Beginning of the output sequence of key/value pairs
  */
 template <typename Key, typename Value, typename atomicT, typename OutputIt>
 __inline__ __device__ void flush_output_buffer(const unsigned int activemask,
                                                uint32_t const num_outputs,
                                                cuco::pair_type<Key, Value>* output_buffer,
-                                               atomicT* num_items,
+                                               atomicT* num_matches,
                                                OutputIt output_begin)
 {
   int num_threads = __popc(activemask);
 
   std::size_t offset;
   const auto lane_id = threadIdx.x % 32;
-  if (0 == lane_id) { offset = num_items->fetch_add(num_outputs, cuda::std::memory_order_relaxed); }
+  if (0 == lane_id) {
+    offset = num_matches->fetch_add(num_outputs, cuda::std::memory_order_relaxed);
+  }
   offset = __shfl_sync(activemask, offset, 0);
 
   for (auto index = lane_id; index < num_outputs; index += num_threads) {
@@ -286,7 +292,7 @@ __global__ void contains(
  * @tparam KeyEqual Binary callable
  * @param first Beginning of the sequence of keys to count
  * @param last End of the sequence of keys to count
- * @param num_items The number of all the matches for a sequence of keys
+ * @param num_matches The number of all the matches for a sequence of keys
  * @param view Device view used to access the hash map's slot storage
  * @param key_equal Binary function to compare two keys for equality
  */
@@ -301,7 +307,7 @@ template <uint32_t block_size,
           typename viewT,
           typename KeyEqual>
 __global__ std::enable_if_t<is_vector_load, void> count(
-  InputIt first, InputIt last, atomicT* num_items, viewT view, KeyEqual key_equal)
+  InputIt first, InputIt last, atomicT* num_matches, viewT view, KeyEqual key_equal)
 {
   auto tile    = cg::tiled_partition<tile_size>(cg::this_thread_block());
   auto tid     = block_size * blockIdx.x + threadIdx.x;
@@ -309,7 +315,7 @@ __global__ std::enable_if_t<is_vector_load, void> count(
 
   typedef cub::BlockReduce<std::size_t, block_size> BlockReduce;
   __shared__ typename BlockReduce::TempStorage temp_storage;
-  std::size_t thread_num_items = 0;
+  std::size_t thread_num_matches = 0;
 
   while (first + key_idx < last) {
     auto key          = *(first + key_idx);
@@ -335,10 +341,10 @@ __global__ std::enable_if_t<is_vector_load, void> count(
 
         if (tile.any(first_equals or second_equals)) { found_match = true; }
 
-        thread_num_items += (first_equals + second_equals);
+        thread_num_matches += (first_equals + second_equals);
 
         if (tile.any(first_slot_is_empty or second_slot_is_empty)) {
-          if ((not found_match) && (tile.thread_rank() == 0)) { thread_num_items++; }
+          if ((not found_match) && (tile.thread_rank() == 0)) { thread_num_matches++; }
           break;
         }
 
@@ -360,7 +366,7 @@ __global__ std::enable_if_t<is_vector_load, void> count(
         auto const first_equals  = (not first_slot_is_empty and key_equal(arr[0].first, key));
         auto const second_equals = (not second_slot_is_empty and key_equal(arr[1].first, key));
 
-        thread_num_items += (first_equals + second_equals);
+        thread_num_matches += (first_equals + second_equals);
 
         if (tile.any(first_slot_is_empty or second_slot_is_empty)) { break; }
 
@@ -372,8 +378,10 @@ __global__ std::enable_if_t<is_vector_load, void> count(
 
   // compute number of successfully inserted elements for each block
   // and atomically add to the grand total
-  std::size_t block_num_items = BlockReduce(temp_storage).Sum(thread_num_items);
-  if (threadIdx.x == 0) { num_items->fetch_add(block_num_items, cuda::std::memory_order_relaxed); }
+  std::size_t block_num_matches = BlockReduce(temp_storage).Sum(thread_num_matches);
+  if (threadIdx.x == 0) {
+    num_matches->fetch_add(block_num_matches, cuda::std::memory_order_relaxed);
+  }
 }
 
 /**
@@ -394,7 +402,7 @@ __global__ std::enable_if_t<is_vector_load, void> count(
  * @tparam KeyEqual Binary callable
  * @param first Beginning of the sequence of keys to count
  * @param last End of the sequence of keys to count
- * @param num_items The number of all the matches for a sequence of keys
+ * @param num_matches The number of all the matches for a sequence of keys
  * @param view Device view used to access the hash map's slot storage
  * @param key_equal Binary function to compare two keys for equality
  */
@@ -409,7 +417,7 @@ template <uint32_t block_size,
           typename viewT,
           typename KeyEqual>
 __global__ std::enable_if_t<not is_vector_load, void> count(
-  InputIt first, InputIt last, atomicT* num_items, viewT view, KeyEqual key_equal)
+  InputIt first, InputIt last, atomicT* num_matches, viewT view, KeyEqual key_equal)
 {
   auto tile    = cg::tiled_partition<tile_size>(cg::this_thread_block());
   auto tid     = block_size * blockIdx.x + threadIdx.x;
@@ -417,7 +425,7 @@ __global__ std::enable_if_t<not is_vector_load, void> count(
 
   typedef cub::BlockReduce<std::size_t, block_size> BlockReduce;
   __shared__ typename BlockReduce::TempStorage temp_storage;
-  std::size_t thread_num_items = 0;
+  std::size_t thread_num_matches = 0;
 
   while (first + key_idx < last) {
     auto key          = *(first + key_idx);
@@ -436,10 +444,10 @@ __global__ std::enable_if_t<not is_vector_load, void> count(
 
         if (tile.any(equals)) { found_match = true; }
 
-        thread_num_items += equals;
+        thread_num_matches += equals;
 
         if (tile.any(slot_is_empty)) {
-          if ((not found_match) && (tile.thread_rank() == 0)) { thread_num_items++; }
+          if ((not found_match) && (tile.thread_rank() == 0)) { thread_num_matches++; }
           break;
         }
 
@@ -454,7 +462,7 @@ __global__ std::enable_if_t<not is_vector_load, void> count(
         auto const slot_is_empty = (current_key == view.get_empty_key_sentinel());
         auto const equals        = not slot_is_empty and key_equal(current_key, key);
 
-        thread_num_items += equals;
+        thread_num_matches += equals;
 
         if (tile.any(slot_is_empty)) { break; }
 
@@ -463,8 +471,10 @@ __global__ std::enable_if_t<not is_vector_load, void> count(
     }
     key_idx += (gridDim.x * block_size) / tile_size;
   }
-  auto const block_num_items = BlockReduce(temp_storage).Sum(thread_num_items);
-  if (threadIdx.x == 0) { num_items->fetch_add(block_num_items, cuda::std::memory_order_relaxed); }
+  auto const block_num_matches = BlockReduce(temp_storage).Sum(thread_num_matches);
+  if (threadIdx.x == 0) {
+    num_matches->fetch_add(block_num_matches, cuda::std::memory_order_relaxed);
+  }
 }
 
 /**
@@ -484,7 +494,7 @@ __global__ std::enable_if_t<not is_vector_load, void> count(
  * @tparam PairEqual Binary callable
  * @param first Beginning of the sequence of pairs to count
  * @param last End of the sequence of pairs to count
- * @param num_items The number of all the matches for a sequence of pairs
+ * @param num_matches The number of all the matches for a sequence of pairs
  * @param view Device view used to access the hash map's slot storage
  * @param pair_equal Binary function to compare two pairs for equality
  */
@@ -499,7 +509,7 @@ template <uint32_t block_size,
           typename viewT,
           typename PairEqual>
 __global__ std::enable_if_t<is_vector_load, void> pair_count(
-  InputIt first, InputIt last, atomicT* num_items, viewT view, PairEqual pair_equal)
+  InputIt first, InputIt last, atomicT* num_matches, viewT view, PairEqual pair_equal)
 {
   auto tile     = cg::tiled_partition<tile_size>(cg::this_thread_block());
   auto tid      = block_size * blockIdx.x + threadIdx.x;
@@ -507,7 +517,7 @@ __global__ std::enable_if_t<is_vector_load, void> pair_count(
 
   typedef cub::BlockReduce<std::size_t, block_size> BlockReduce;
   __shared__ typename BlockReduce::TempStorage temp_storage;
-  std::size_t thread_num_items = 0;
+  std::size_t thread_num_matches = 0;
 
   while (first + pair_idx < last) {
     cuco::pair_type<Key, Value> pair = *(first + pair_idx);
@@ -535,10 +545,10 @@ __global__ std::enable_if_t<is_vector_load, void> pair_count(
 
         if (tile.any(first_slot_equals or second_slot_equals)) { found_match = true; }
 
-        thread_num_items += (first_slot_equals + second_slot_equals);
+        thread_num_matches += (first_slot_equals + second_slot_equals);
 
         if (tile.any(first_slot_is_empty or second_slot_is_empty)) {
-          if ((not found_match) && (tile.thread_rank() == 0)) { thread_num_items++; }
+          if ((not found_match) && (tile.thread_rank() == 0)) { thread_num_matches++; }
           break;
         }
 
@@ -561,7 +571,7 @@ __global__ std::enable_if_t<is_vector_load, void> pair_count(
         auto const first_slot_equals  = (not first_slot_is_empty and pair_equal(arr[0], pair));
         auto const second_slot_equals = (not second_slot_is_empty and pair_equal(arr[1], pair));
 
-        thread_num_items += (first_slot_equals + second_slot_equals);
+        thread_num_matches += (first_slot_equals + second_slot_equals);
 
         if (tile.any(first_slot_is_empty or second_slot_is_empty)) { break; }
 
@@ -573,8 +583,10 @@ __global__ std::enable_if_t<is_vector_load, void> pair_count(
 
   // compute number of successfully inserted elements for each block
   // and atomically add to the grand total
-  std::size_t block_num_items = BlockReduce(temp_storage).Sum(thread_num_items);
-  if (threadIdx.x == 0) { num_items->fetch_add(block_num_items, cuda::std::memory_order_relaxed); }
+  std::size_t block_num_matches = BlockReduce(temp_storage).Sum(thread_num_matches);
+  if (threadIdx.x == 0) {
+    num_matches->fetch_add(block_num_matches, cuda::std::memory_order_relaxed);
+  }
 }
 
 /**
@@ -594,7 +606,7 @@ __global__ std::enable_if_t<is_vector_load, void> pair_count(
  * @tparam PairEqual Binary callable
  * @param first Beginning of the sequence of pairs to count
  * @param last End of the sequence of pairs to count
- * @param num_items The number of all the matches for a sequence of pairs
+ * @param num_matches The number of all the matches for a sequence of pairs
  * @param view Device view used to access the hash map's slot storage
  * @param pair_equal Binary function to compare two pairs for equality
  */
@@ -609,7 +621,7 @@ template <uint32_t block_size,
           typename viewT,
           typename PairEqual>
 __global__ std::enable_if_t<not is_vector_load, void> pair_count(
-  InputIt first, InputIt last, atomicT* num_items, viewT view, PairEqual pair_equal)
+  InputIt first, InputIt last, atomicT* num_matches, viewT view, PairEqual pair_equal)
 {
   auto tile     = cg::tiled_partition<tile_size>(cg::this_thread_block());
   auto tid      = block_size * blockIdx.x + threadIdx.x;
@@ -617,7 +629,7 @@ __global__ std::enable_if_t<not is_vector_load, void> pair_count(
 
   typedef cub::BlockReduce<std::size_t, block_size> BlockReduce;
   __shared__ typename BlockReduce::TempStorage temp_storage;
-  std::size_t thread_num_items = 0;
+  std::size_t thread_num_matches = 0;
 
   while (first + pair_idx < last) {
     cuco::pair_type<Key, Value> pair = *(first + pair_idx);
@@ -636,10 +648,10 @@ __global__ std::enable_if_t<not is_vector_load, void> pair_count(
 
         if (tile.any(equals)) { found_match = true; }
 
-        thread_num_items += equals;
+        thread_num_matches += equals;
 
         if (tile.any(slot_is_empty)) {
-          if ((not found_match) && (tile.thread_rank() == 0)) { thread_num_items++; }
+          if ((not found_match) && (tile.thread_rank() == 0)) { thread_num_matches++; }
           break;
         }
 
@@ -653,7 +665,7 @@ __global__ std::enable_if_t<not is_vector_load, void> pair_count(
 
         auto const equals = not slot_is_empty and pair_equal(slot_contents, pair);
 
-        thread_num_items += equals;
+        thread_num_matches += equals;
 
         if (tile.any(slot_is_empty)) { break; }
 
@@ -665,8 +677,10 @@ __global__ std::enable_if_t<not is_vector_load, void> pair_count(
 
   // compute number of successfully inserted elements for each block
   // and atomically add to the grand total
-  std::size_t block_num_items = BlockReduce(temp_storage).Sum(thread_num_items);
-  if (threadIdx.x == 0) { num_items->fetch_add(block_num_items, cuda::std::memory_order_relaxed); }
+  std::size_t block_num_matches = BlockReduce(temp_storage).Sum(thread_num_matches);
+  if (threadIdx.x == 0) {
+    num_matches->fetch_add(block_num_matches, cuda::std::memory_order_relaxed);
+  }
 }
 
 /**
@@ -674,11 +688,11 @@ __global__ std::enable_if_t<not is_vector_load, void> pair_count(
  * oads combined with per-block shared memory buffer.
  *
  * If the key `k = *(first + i)` exists in the map, copies `k` and all associated values to
- * unspecified locations in `[output_begin, output_begin + *num_items - 1)`. Else, copies `k` and
+ * unspecified locations in `[output_begin, output_begin + *num_matches - 1)`. Else, copies `k` and
  * the empty value sentinel.
  *
  * Behavior is undefined if the total number of matching keys exceeds `std::distance(output_begin,
- * output_begin + *num_items - 1)`. Use `count()` to determine the number of matching keys.
+ * output_begin + *num_matches - 1)`. Use `count()` to determine the number of matching keys.
  *
  * @tparam block_size The size of the thread block
  * @tparam tile_size The number of threads in the Cooperative Groups
@@ -698,7 +712,7 @@ __global__ std::enable_if_t<not is_vector_load, void> pair_count(
  * @param first Beginning of the sequence of keys
  * @param last End of the sequence of keys
  * @param output_begin Beginning of the sequence of values retrieved for each key
- * @param num_items Size of the output sequence
+ * @param num_matches Size of the output sequence
  * @param view Device view used to access the hash map's slot storage
  * @param key_equal The binary function to compare two keys for equality
  */
@@ -717,7 +731,7 @@ template <uint32_t block_size,
 __global__ std::enable_if_t<is_vector_load, void> retrieve(InputIt first,
                                                            InputIt last,
                                                            OutputIt output_begin,
-                                                           atomicT* num_items,
+                                                           atomicT* num_matches,
                                                            viewT view,
                                                            KeyEqual key_equal)
 {
@@ -802,7 +816,7 @@ __global__ std::enable_if_t<is_vector_load, void> retrieve(InputIt first,
         __syncwarp(activemask);
         if (warp_counter[warp_id] + 32 * 2 > buffer_size) {
           flush_output_buffer(
-            activemask, warp_counter[warp_id], output_buffer[warp_id], num_items, output_begin);
+            activemask, warp_counter[warp_id], output_buffer[warp_id], num_matches, output_begin);
           // First lane reset warp-level counter
           if (warp_lane_id == 0) { warp_counter[warp_id] = 0; }
         }
@@ -858,7 +872,7 @@ __global__ std::enable_if_t<is_vector_load, void> retrieve(InputIt first,
         __syncwarp(activemask);
         if (warp_counter[warp_id] + 32 * 2 > buffer_size) {
           flush_output_buffer(
-            activemask, warp_counter[warp_id], output_buffer[warp_id], num_items, output_begin);
+            activemask, warp_counter[warp_id], output_buffer[warp_id], num_matches, output_begin);
           // First lane reset warp-level counter
           if (warp_lane_id == 0) { warp_counter[warp_id] = 0; }
         }
@@ -872,7 +886,7 @@ __global__ std::enable_if_t<is_vector_load, void> retrieve(InputIt first,
   // Final flush of output buffer
   if (warp_counter[warp_id] > 0) {
     flush_output_buffer(
-      activemask, warp_counter[warp_id], output_buffer[warp_id], num_items, output_begin);
+      activemask, warp_counter[warp_id], output_buffer[warp_id], num_matches, output_begin);
   }
 }
 
@@ -881,11 +895,11 @@ __global__ std::enable_if_t<is_vector_load, void> retrieve(InputIt first,
  * loads combined with per-CG shared memory buffer.
  *
  * If the key `k = *(first + i)` exists in the map, copies `k` and all associated values to
- * unspecified locations in `[output_begin, output_begin + *num_items - 1)`. Else, copies `k` and
+ * unspecified locations in `[output_begin, output_begin + *num_matches - 1)`. Else, copies `k` and
  * the empty value sentinel.
  *
  * Behavior is undefined if the total number of matching keys exceeds `std::distance(output_begin,
- * output_begin + *num_items - 1)`. Use `count()` to determine the number of matching keys.
+ * output_begin + *num_matches - 1)`. Use `count()` to determine the number of matching keys.
  *
  * @tparam block_size The size of the thread block
  * @tparam tile_size The number of threads in the Cooperative Groups
@@ -905,7 +919,7 @@ __global__ std::enable_if_t<is_vector_load, void> retrieve(InputIt first,
  * @param first Beginning of the sequence of keys
  * @param last End of the sequence of keys
  * @param output_begin Beginning of the sequence of values retrieved for each key
- * @param num_items Size of the output sequence
+ * @param num_matches Size of the output sequence
  * @param view Device view used to access the hash map's slot storage
  * @param key_equal The binary function to compare two keys for equality
  */
@@ -924,7 +938,7 @@ template <uint32_t block_size,
 __global__ std::enable_if_t<not is_vector_load, void> retrieve(InputIt first,
                                                                InputIt last,
                                                                OutputIt output_begin,
-                                                               atomicT* num_items,
+                                                               atomicT* num_matches,
                                                                viewT view,
                                                                KeyEqual key_equal)
 {
@@ -988,7 +1002,7 @@ __global__ std::enable_if_t<not is_vector_load, void> retrieve(InputIt first,
         // Flush if the next iteration won't fit into buffer
         if ((cg_counter[cg_id] + tile_size) > buffer_size) {
           flush_output_buffer<tile_size>(
-            tile, cg_counter[cg_id], output_buffer[cg_id], num_items, output_begin);
+            tile, cg_counter[cg_id], output_buffer[cg_id], num_matches, output_begin);
           // First lane reset CG-level counter
           if (lane_id == 0) { cg_counter[cg_id] = 0; }
         }
@@ -1025,7 +1039,7 @@ __global__ std::enable_if_t<not is_vector_load, void> retrieve(InputIt first,
         // Flush if the next iteration won't fit into buffer
         if ((cg_counter[cg_id] + tile_size) > buffer_size) {
           flush_output_buffer<tile_size>(
-            tile, cg_counter[cg_id], output_buffer[cg_id], num_items, output_begin);
+            tile, cg_counter[cg_id], output_buffer[cg_id], num_matches, output_begin);
           // First lane reset CG-level counter
           if (lane_id == 0) { cg_counter[cg_id] = 0; }
         }
@@ -1038,7 +1052,7 @@ __global__ std::enable_if_t<not is_vector_load, void> retrieve(InputIt first,
   // Final flush of output buffer
   if (cg_counter[cg_id] > 0) {
     flush_output_buffer<tile_size>(
-      tile, cg_counter[cg_id], output_buffer[cg_id], num_items, output_begin);
+      tile, cg_counter[cg_id], output_buffer[cg_id], num_matches, output_begin);
   }
 }
 

From e4ece4f848b823f7685c3db1ec635f51e0150c33 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Wed, 9 Jun 2021 15:44:00 -0400
Subject: [PATCH 138/267] Create and use view::count functions

---
 include/cuco/detail/static_multimap.inl       | 117 ++++++++++++++++++
 .../cuco/detail/static_multimap_kernels.cuh   | 100 +--------------
 include/cuco/static_multimap.cuh              |  46 +++++++
 3 files changed, 167 insertions(+), 96 deletions(-)

diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index 42ed2bfc7..c8ba3f10b 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -559,4 +559,121 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::conta
   }
 }
 
+template <typename Key,
+          typename Value,
+          class ProbeSequence,
+          cuda::thread_scope Scope,
+          typename Allocator>
+template <bool is_vector_load, bool is_outer, typename CG, typename KeyEqual>
+__device__ std::enable_if_t<is_vector_load, void>
+static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::count(
+  CG const& g, std::size_t& thread_num_matches, Key const& k, KeyEqual key_equal) noexcept
+{
+  auto current_slot = initial_slot(g, k);
+
+  if constexpr (is_outer) {
+    bool found_match = false;
+
+    while (true) {
+      pair<Key, Value> arr[2];
+      if constexpr (sizeof(Key) == 4) {
+        auto const tmp = *reinterpret_cast<uint4 const*>(current_slot);
+        memcpy(&arr[0], &tmp, 2 * sizeof(pair<Key, Value>));
+      } else {
+        auto const tmp = *reinterpret_cast<ulonglong4 const*>(current_slot);
+        memcpy(&arr[0], &tmp, 2 * sizeof(pair<Key, Value>));
+      }
+
+      auto const first_slot_is_empty  = (arr[0].first == this->get_empty_key_sentinel());
+      auto const second_slot_is_empty = (arr[1].first == this->get_empty_key_sentinel());
+      auto const first_equals         = (not first_slot_is_empty and key_equal(arr[0].first, k));
+      auto const second_equals        = (not second_slot_is_empty and key_equal(arr[1].first, k));
+
+      if (g.any(first_equals or second_equals)) { found_match = true; }
+
+      thread_num_matches += (first_equals + second_equals);
+
+      if (g.any(first_slot_is_empty or second_slot_is_empty)) {
+        if ((not found_match) && (g.thread_rank() == 0)) { thread_num_matches++; }
+        return;
+      }
+
+      current_slot = next_slot(current_slot);
+    }
+  } else {
+    while (true) {
+      pair<Key, Value> arr[2];
+      if constexpr (sizeof(Key) == 4) {
+        auto const tmp = *reinterpret_cast<uint4 const*>(current_slot);
+        memcpy(&arr[0], &tmp, 2 * sizeof(pair<Key, Value>));
+      } else {
+        auto const tmp = *reinterpret_cast<ulonglong4 const*>(current_slot);
+        memcpy(&arr[0], &tmp, 2 * sizeof(pair<Key, Value>));
+      }
+
+      auto const first_slot_is_empty  = (arr[0].first == this->get_empty_key_sentinel());
+      auto const second_slot_is_empty = (arr[1].first == this->get_empty_key_sentinel());
+      auto const first_equals         = (not first_slot_is_empty and key_equal(arr[0].first, k));
+      auto const second_equals        = (not second_slot_is_empty and key_equal(arr[1].first, k));
+
+      thread_num_matches += (first_equals + second_equals);
+
+      if (g.any(first_slot_is_empty or second_slot_is_empty)) { return; }
+
+      current_slot = next_slot(current_slot);
+    }
+  }
+}
+
+template <typename Key,
+          typename Value,
+          class ProbeSequence,
+          cuda::thread_scope Scope,
+          typename Allocator>
+template <bool is_vector_load, bool is_outer, typename CG, typename KeyEqual>
+__device__ std::enable_if_t<not is_vector_load, void>
+static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::count(
+  CG const& g, std::size_t& thread_num_matches, Key const& k, KeyEqual key_equal) noexcept
+{
+  auto current_slot = initial_slot(g, k);
+
+  if constexpr (is_outer) {
+    bool found_match = false;
+
+    while (true) {
+      pair<Key, Value> slot_contents =
+        *reinterpret_cast<cuco::pair_type<Key, Value> const*>(current_slot);
+      auto const& current_key = slot_contents.first;
+
+      auto const slot_is_empty = (current_key == this->get_empty_key_sentinel());
+      auto const equals        = not slot_is_empty and key_equal(current_key, k);
+
+      if (g.any(equals)) { found_match = true; }
+
+      thread_num_matches += equals;
+
+      if (g.any(slot_is_empty)) {
+        if ((not found_match) && (g.thread_rank() == 0)) { thread_num_matches++; }
+        break;
+      }
+
+      current_slot = next_slot(current_slot);
+    }
+  } else {
+    while (true) {
+      pair<Key, Value> slot_contents =
+        *reinterpret_cast<cuco::pair_type<Key, Value> const*>(current_slot);
+      auto const& current_key = slot_contents.first;
+
+      auto const slot_is_empty = (current_key == this->get_empty_key_sentinel());
+      auto const equals        = not slot_is_empty and key_equal(current_key, k);
+
+      thread_num_matches += equals;
+
+      if (g.any(slot_is_empty)) { break; }
+
+      current_slot = next_slot(current_slot);
+    }
+  }
+}
 }  // namespace cuco
diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap_kernels.cuh
index 79afeadfb..ccaced8b8 100644
--- a/include/cuco/detail/static_multimap_kernels.cuh
+++ b/include/cuco/detail/static_multimap_kernels.cuh
@@ -318,61 +318,8 @@ __global__ std::enable_if_t<is_vector_load, void> count(
   std::size_t thread_num_matches = 0;
 
   while (first + key_idx < last) {
-    auto key          = *(first + key_idx);
-    auto current_slot = view.initial_slot(tile, key);
-
-    if constexpr (is_outer) {
-      bool found_match = false;
-
-      while (true) {
-        pair<Key, Value> arr[2];
-        if constexpr (sizeof(Key) == 4) {
-          auto const tmp = *reinterpret_cast<uint4 const*>(current_slot);
-          memcpy(&arr[0], &tmp, 2 * sizeof(pair<Key, Value>));
-        } else {
-          auto const tmp = *reinterpret_cast<ulonglong4 const*>(current_slot);
-          memcpy(&arr[0], &tmp, 2 * sizeof(pair<Key, Value>));
-        }
-
-        auto const first_slot_is_empty  = (arr[0].first == view.get_empty_key_sentinel());
-        auto const second_slot_is_empty = (arr[1].first == view.get_empty_key_sentinel());
-        auto const first_equals  = (not first_slot_is_empty and key_equal(arr[0].first, key));
-        auto const second_equals = (not second_slot_is_empty and key_equal(arr[1].first, key));
-
-        if (tile.any(first_equals or second_equals)) { found_match = true; }
-
-        thread_num_matches += (first_equals + second_equals);
-
-        if (tile.any(first_slot_is_empty or second_slot_is_empty)) {
-          if ((not found_match) && (tile.thread_rank() == 0)) { thread_num_matches++; }
-          break;
-        }
-
-        current_slot = view.next_slot(current_slot);
-      }
-    } else {
-      while (true) {
-        pair<Key, Value> arr[2];
-        if constexpr (sizeof(Key) == 4) {
-          auto const tmp = *reinterpret_cast<uint4 const*>(current_slot);
-          memcpy(&arr[0], &tmp, 2 * sizeof(pair<Key, Value>));
-        } else {
-          auto const tmp = *reinterpret_cast<ulonglong4 const*>(current_slot);
-          memcpy(&arr[0], &tmp, 2 * sizeof(pair<Key, Value>));
-        }
-
-        auto const first_slot_is_empty  = (arr[0].first == view.get_empty_key_sentinel());
-        auto const second_slot_is_empty = (arr[1].first == view.get_empty_key_sentinel());
-        auto const first_equals  = (not first_slot_is_empty and key_equal(arr[0].first, key));
-        auto const second_equals = (not second_slot_is_empty and key_equal(arr[1].first, key));
-
-        thread_num_matches += (first_equals + second_equals);
-
-        if (tile.any(first_slot_is_empty or second_slot_is_empty)) { break; }
-
-        current_slot = view.next_slot(current_slot);
-      }
-    }
+    auto key = *(first + key_idx);
+    view.count<is_vector_load, is_outer>(tile, thread_num_matches, key, key_equal);
     key_idx += (gridDim.x * block_size) / tile_size;
   }
 
@@ -428,47 +375,8 @@ __global__ std::enable_if_t<not is_vector_load, void> count(
   std::size_t thread_num_matches = 0;
 
   while (first + key_idx < last) {
-    auto key          = *(first + key_idx);
-    auto current_slot = view.initial_slot(tile, key);
-
-    if constexpr (is_outer) {
-      bool found_match = false;
-
-      while (true) {
-        pair<Key, Value> slot_contents =
-          *reinterpret_cast<cuco::pair_type<Key, Value> const*>(current_slot);
-        auto const& current_key = slot_contents.first;
-
-        auto const slot_is_empty = (current_key == view.get_empty_key_sentinel());
-        auto const equals        = not slot_is_empty and key_equal(current_key, key);
-
-        if (tile.any(equals)) { found_match = true; }
-
-        thread_num_matches += equals;
-
-        if (tile.any(slot_is_empty)) {
-          if ((not found_match) && (tile.thread_rank() == 0)) { thread_num_matches++; }
-          break;
-        }
-
-        current_slot = view.next_slot(current_slot);
-      }
-    } else {
-      while (true) {
-        pair<Key, Value> slot_contents =
-          *reinterpret_cast<cuco::pair_type<Key, Value> const*>(current_slot);
-        auto const& current_key = slot_contents.first;
-
-        auto const slot_is_empty = (current_key == view.get_empty_key_sentinel());
-        auto const equals        = not slot_is_empty and key_equal(current_key, key);
-
-        thread_num_matches += equals;
-
-        if (tile.any(slot_is_empty)) { break; }
-
-        current_slot = view.next_slot(current_slot);
-      }
-    }
+    auto key = *(first + key_idx);
+    view.count<is_vector_load, is_outer>(tile, thread_num_matches, key, key_equal);
     key_idx += (gridDim.x * block_size) / tile_size;
   }
   auto const block_num_matches = BlockReduce(temp_storage).Sum(thread_num_matches);
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index 03a7f4765..25f3612cd 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -715,6 +715,52 @@ class static_multimap {
     template <bool is_vector_load, typename CG, typename KeyEqual = thrust::equal_to<key_type>>
     __device__ std::enable_if_t<not is_vector_load, bool> contains(
       CG g, Key const& k, KeyEqual key_equal = KeyEqual{}) noexcept;
+
+    /**
+     * @brief Counts the occurrence of a given key contained in multimap using vector loads.
+     *
+     * @tparam is_vector_load Boolean flag indicating whether vector loads are used or not
+     * @tparam is_outer Boolean flag indicating whether outer join is peformed or not
+     * @tparam CG Cooperative Group type
+     * @tparam KeyEqual Binary callable type
+     * @param g The Cooperative Group used to perform the contains operation
+     * @param thread_num_matches Number of matches found by the current thread
+     * @param k The key to search for
+     * @param key_equal The binary callable used to compare two keys
+     * for equality
+     */
+    template <bool is_vector_load,
+              bool is_outer,
+              typename CG,
+              typename KeyEqual = thrust::equal_to<key_type>>
+    __device__ std::enable_if_t<is_vector_load, void> count(
+      CG const& g,
+      std::size_t& thread_num_matches,
+      Key const& k,
+      KeyEqual key_equal = KeyEqual{}) noexcept;
+
+    /**
+     * @brief Counts the occurrence of a given key contained in multimap using scalar loads.
+     *
+     * @tparam is_vector_load Boolean flag indicating whether vector loads are used or not
+     * @tparam is_outer Boolean flag indicating whether outer join is peformed or not
+     * @tparam CG Cooperative Group type
+     * @tparam KeyEqual Binary callable type
+     * @param g The Cooperative Group used to perform the contains operation
+     * @param thread_num_matches Number of matches found by the current thread
+     * @param k The key to search for
+     * @param key_equal The binary callable used to compare two keys
+     * for equality
+     */
+    template <bool is_vector_load,
+              bool is_outer,
+              typename CG,
+              typename KeyEqual = thrust::equal_to<key_type>>
+    __device__ std::enable_if_t<not is_vector_load, void> count(
+      CG const& g,
+      std::size_t& thread_num_matches,
+      Key const& k,
+      KeyEqual key_equal = KeyEqual{}) noexcept;
   };  // class device_view
 
   /**

From 4dea589bdd84a16d4291d93b444311e04e359ff9 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Wed, 9 Jun 2021 16:33:19 -0400
Subject: [PATCH 139/267] Refactor global pair_count to use view::pair_count
 functions

---
 include/cuco/detail/static_multimap.inl       | 135 +++++++++++++++++-
 .../cuco/detail/static_multimap_kernels.cuh   |  64 +--------
 include/cuco/static_multimap.cuh              |  53 ++++++-
 3 files changed, 182 insertions(+), 70 deletions(-)

diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index c8ba3f10b..0a20fde62 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -567,7 +567,7 @@ template <typename Key,
 template <bool is_vector_load, bool is_outer, typename CG, typename KeyEqual>
 __device__ std::enable_if_t<is_vector_load, void>
 static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::count(
-  CG const& g, std::size_t& thread_num_matches, Key const& k, KeyEqual key_equal) noexcept
+  CG const& g, Key const& k, std::size_t& thread_num_matches, KeyEqual key_equal) noexcept
 {
   auto current_slot = initial_slot(g, k);
 
@@ -595,7 +595,7 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::count
 
       if (g.any(first_slot_is_empty or second_slot_is_empty)) {
         if ((not found_match) && (g.thread_rank() == 0)) { thread_num_matches++; }
-        return;
+        break;
       }
 
       current_slot = next_slot(current_slot);
@@ -618,7 +618,7 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::count
 
       thread_num_matches += (first_equals + second_equals);
 
-      if (g.any(first_slot_is_empty or second_slot_is_empty)) { return; }
+      if (g.any(first_slot_is_empty or second_slot_is_empty)) { break; }
 
       current_slot = next_slot(current_slot);
     }
@@ -633,7 +633,7 @@ template <typename Key,
 template <bool is_vector_load, bool is_outer, typename CG, typename KeyEqual>
 __device__ std::enable_if_t<not is_vector_load, void>
 static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::count(
-  CG const& g, std::size_t& thread_num_matches, Key const& k, KeyEqual key_equal) noexcept
+  CG const& g, Key const& k, std::size_t& thread_num_matches, KeyEqual key_equal) noexcept
 {
   auto current_slot = initial_slot(g, k);
 
@@ -676,4 +676,131 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::count
     }
   }
 }
+
+template <typename Key,
+          typename Value,
+          class ProbeSequence,
+          cuda::thread_scope Scope,
+          typename Allocator>
+template <bool is_vector_load, bool is_outer, typename CG, typename PairEqual>
+__device__ std::enable_if_t<is_vector_load, void>
+static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_count(
+  CG const& g,
+  value_type const& pair,
+  std::size_t& thread_num_matches,
+  PairEqual pair_equal) noexcept
+{
+  auto key          = pair.first;
+  auto current_slot = initial_slot(g, key);
+
+  if constexpr (is_outer) {
+    bool found_match = false;
+
+    while (true) {
+      cuco::pair_type<Key, Value> arr[2];
+      if constexpr (sizeof(Key) == 4) {
+        auto const tmp = *reinterpret_cast<uint4 const*>(current_slot);
+        memcpy(&arr[0], &tmp, 2 * sizeof(cuco::pair_type<Key, Value>));
+      } else {
+        auto const tmp = *reinterpret_cast<ulonglong4 const*>(current_slot);
+        memcpy(&arr[0], &tmp, 2 * sizeof(cuco::pair_type<Key, Value>));
+      }
+
+      auto const first_slot_is_empty  = (arr[0].first == this->get_empty_key_sentinel());
+      auto const second_slot_is_empty = (arr[1].first == this->get_empty_key_sentinel());
+
+      auto const first_slot_equals  = (not first_slot_is_empty and pair_equal(arr[0], pair));
+      auto const second_slot_equals = (not second_slot_is_empty and pair_equal(arr[1], pair));
+
+      if (g.any(first_slot_equals or second_slot_equals)) { found_match = true; }
+
+      thread_num_matches += (first_slot_equals + second_slot_equals);
+
+      if (g.any(first_slot_is_empty or second_slot_is_empty)) {
+        if ((not found_match) && (g.thread_rank() == 0)) { thread_num_matches++; }
+        break;
+      }
+
+      current_slot = next_slot(current_slot);
+    }
+  } else {
+    while (true) {
+      cuco::pair_type<Key, Value> arr[2];
+      if constexpr (sizeof(Key) == 4) {
+        auto const tmp = *reinterpret_cast<uint4 const*>(current_slot);
+        memcpy(&arr[0], &tmp, 2 * sizeof(cuco::pair_type<Key, Value>));
+      } else {
+        auto const tmp = *reinterpret_cast<ulonglong4 const*>(current_slot);
+        memcpy(&arr[0], &tmp, 2 * sizeof(cuco::pair_type<Key, Value>));
+      }
+
+      auto const first_slot_is_empty  = (arr[0].first == this->get_empty_key_sentinel());
+      auto const second_slot_is_empty = (arr[1].first == this->get_empty_key_sentinel());
+
+      auto const first_slot_equals  = (not first_slot_is_empty and pair_equal(arr[0], pair));
+      auto const second_slot_equals = (not second_slot_is_empty and pair_equal(arr[1], pair));
+
+      thread_num_matches += (first_slot_equals + second_slot_equals);
+
+      if (g.any(first_slot_is_empty or second_slot_is_empty)) { break; }
+
+      current_slot = next_slot(current_slot);
+    }
+  }
+}
+
+template <typename Key,
+          typename Value,
+          class ProbeSequence,
+          cuda::thread_scope Scope,
+          typename Allocator>
+template <bool is_vector_load, bool is_outer, typename CG, typename PairEqual>
+__device__ std::enable_if_t<not is_vector_load, void>
+static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_count(
+  CG const& g,
+  value_type const& pair,
+  std::size_t& thread_num_matches,
+  PairEqual pair_equal) noexcept
+{
+  auto key          = pair.first;
+  auto current_slot = initial_slot(g, key);
+
+  if constexpr (is_outer) {
+    bool found_match = false;
+
+    while (true) {
+      auto slot_contents = *reinterpret_cast<cuco::pair_type<Key, Value> const*>(current_slot);
+
+      auto const slot_is_empty = (slot_contents.first == this->get_empty_key_sentinel());
+
+      auto const equals = not slot_is_empty and pair_equal(slot_contents, pair);
+
+      if (g.any(equals)) { found_match = true; }
+
+      thread_num_matches += equals;
+
+      if (g.any(slot_is_empty)) {
+        if ((not found_match) && (g.thread_rank() == 0)) { thread_num_matches++; }
+        break;
+      }
+
+      current_slot = next_slot(current_slot);
+    }
+  } else {
+    while (true) {
+      auto slot_contents = *reinterpret_cast<cuco::pair_type<Key, Value> const*>(current_slot);
+
+      auto const slot_is_empty = (slot_contents.first == this->get_empty_key_sentinel());
+
+      auto const equals = not slot_is_empty and pair_equal(slot_contents, pair);
+
+      thread_num_matches += equals;
+
+      if (g.any(slot_is_empty)) { break; }
+
+      current_slot = next_slot(current_slot);
+    }
+  }
+}
+
 }  // namespace cuco
diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap_kernels.cuh
index ccaced8b8..f6bbac3ee 100644
--- a/include/cuco/detail/static_multimap_kernels.cuh
+++ b/include/cuco/detail/static_multimap_kernels.cuh
@@ -319,7 +319,7 @@ __global__ std::enable_if_t<is_vector_load, void> count(
 
   while (first + key_idx < last) {
     auto key = *(first + key_idx);
-    view.count<is_vector_load, is_outer>(tile, thread_num_matches, key, key_equal);
+    view.count<is_vector_load, is_outer>(tile, key, thread_num_matches, key_equal);
     key_idx += (gridDim.x * block_size) / tile_size;
   }
 
@@ -376,7 +376,7 @@ __global__ std::enable_if_t<not is_vector_load, void> count(
 
   while (first + key_idx < last) {
     auto key = *(first + key_idx);
-    view.count<is_vector_load, is_outer>(tile, thread_num_matches, key, key_equal);
+    view.count<is_vector_load, is_outer>(tile, key, thread_num_matches, key_equal);
     key_idx += (gridDim.x * block_size) / tile_size;
   }
   auto const block_num_matches = BlockReduce(temp_storage).Sum(thread_num_matches);
@@ -428,64 +428,8 @@ __global__ std::enable_if_t<is_vector_load, void> pair_count(
   std::size_t thread_num_matches = 0;
 
   while (first + pair_idx < last) {
-    cuco::pair_type<Key, Value> pair = *(first + pair_idx);
-    auto key                         = pair.first;
-    auto current_slot                = view.initial_slot(tile, key);
-
-    if constexpr (is_outer) {
-      bool found_match = false;
-
-      while (true) {
-        cuco::pair_type<Key, Value> arr[2];
-        if constexpr (sizeof(Key) == 4) {
-          auto const tmp = *reinterpret_cast<uint4 const*>(current_slot);
-          memcpy(&arr[0], &tmp, 2 * sizeof(cuco::pair_type<Key, Value>));
-        } else {
-          auto const tmp = *reinterpret_cast<ulonglong4 const*>(current_slot);
-          memcpy(&arr[0], &tmp, 2 * sizeof(cuco::pair_type<Key, Value>));
-        }
-
-        auto const first_slot_is_empty  = (arr[0].first == view.get_empty_key_sentinel());
-        auto const second_slot_is_empty = (arr[1].first == view.get_empty_key_sentinel());
-
-        auto const first_slot_equals  = (not first_slot_is_empty and pair_equal(arr[0], pair));
-        auto const second_slot_equals = (not second_slot_is_empty and pair_equal(arr[1], pair));
-
-        if (tile.any(first_slot_equals or second_slot_equals)) { found_match = true; }
-
-        thread_num_matches += (first_slot_equals + second_slot_equals);
-
-        if (tile.any(first_slot_is_empty or second_slot_is_empty)) {
-          if ((not found_match) && (tile.thread_rank() == 0)) { thread_num_matches++; }
-          break;
-        }
-
-        current_slot = view.next_slot(current_slot);
-      }
-    } else {
-      while (true) {
-        cuco::pair_type<Key, Value> arr[2];
-        if constexpr (sizeof(Key) == 4) {
-          auto const tmp = *reinterpret_cast<uint4 const*>(current_slot);
-          memcpy(&arr[0], &tmp, 2 * sizeof(cuco::pair_type<Key, Value>));
-        } else {
-          auto const tmp = *reinterpret_cast<ulonglong4 const*>(current_slot);
-          memcpy(&arr[0], &tmp, 2 * sizeof(cuco::pair_type<Key, Value>));
-        }
-
-        auto const first_slot_is_empty  = (arr[0].first == view.get_empty_key_sentinel());
-        auto const second_slot_is_empty = (arr[1].first == view.get_empty_key_sentinel());
-
-        auto const first_slot_equals  = (not first_slot_is_empty and pair_equal(arr[0], pair));
-        auto const second_slot_equals = (not second_slot_is_empty and pair_equal(arr[1], pair));
-
-        thread_num_matches += (first_slot_equals + second_slot_equals);
-
-        if (tile.any(first_slot_is_empty or second_slot_is_empty)) { break; }
-
-        current_slot = view.next_slot(current_slot);
-      }
-    }
+    typename viewT::value_type const pair = *(first + pair_idx);
+    view.pair_count<is_vector_load, is_outer>(tile, pair, thread_num_matches, pair_equal);
     pair_idx += (gridDim.x * block_size) / tile_size;
   }
 
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index 25f3612cd..6ed96c820 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -723,9 +723,9 @@ class static_multimap {
      * @tparam is_outer Boolean flag indicating whether outer join is peformed or not
      * @tparam CG Cooperative Group type
      * @tparam KeyEqual Binary callable type
-     * @param g The Cooperative Group used to perform the contains operation
-     * @param thread_num_matches Number of matches found by the current thread
+     * @param g The Cooperative Group used to perform the count operation
      * @param k The key to search for
+     * @param thread_num_matches Number of matches found by the current thread
      * @param key_equal The binary callable used to compare two keys
      * for equality
      */
@@ -735,8 +735,8 @@ class static_multimap {
               typename KeyEqual = thrust::equal_to<key_type>>
     __device__ std::enable_if_t<is_vector_load, void> count(
       CG const& g,
-      std::size_t& thread_num_matches,
       Key const& k,
+      std::size_t& thread_num_matches,
       KeyEqual key_equal = KeyEqual{}) noexcept;
 
     /**
@@ -746,9 +746,9 @@ class static_multimap {
      * @tparam is_outer Boolean flag indicating whether outer join is peformed or not
      * @tparam CG Cooperative Group type
      * @tparam KeyEqual Binary callable type
-     * @param g The Cooperative Group used to perform the contains operation
-     * @param thread_num_matches Number of matches found by the current thread
+     * @param g The Cooperative Group used to perform the count operation
      * @param k The key to search for
+     * @param thread_num_matches Number of matches found by the current thread
      * @param key_equal The binary callable used to compare two keys
      * for equality
      */
@@ -758,9 +758,50 @@ class static_multimap {
               typename KeyEqual = thrust::equal_to<key_type>>
     __device__ std::enable_if_t<not is_vector_load, void> count(
       CG const& g,
-      std::size_t& thread_num_matches,
       Key const& k,
+      std::size_t& thread_num_matches,
       KeyEqual key_equal = KeyEqual{}) noexcept;
+
+    /**
+     * @brief Counts the occurrence of a given key/value pair contained in multimap using vector
+     * loads.
+     *
+     * @tparam is_vector_load Boolean flag indicating whether vector loads are used or not
+     * @tparam is_outer Boolean flag indicating whether outer join is peformed or not
+     * @tparam CG Cooperative Group type
+     * @tparam PairEqual Binary callable type
+     * @param g The Cooperative Group used to perform the pair_count operation
+     * @param pair The pair to search for
+     * @param thread_num_matches Number of matches found by the current thread
+     * @param pair_equal The binary callable used to compare two pairs
+     * for equality
+     */
+    template <bool is_vector_load, bool is_outer, typename CG, typename PairEqual>
+    __device__ std::enable_if_t<is_vector_load, void> pair_count(CG const& g,
+                                                                 value_type const& pair,
+                                                                 std::size_t& thread_num_matches,
+                                                                 PairEqual pair_equal) noexcept;
+
+    /**
+     * @brief Counts the occurrence of a given key/value pair contained in multimap using scalar
+     * loads.
+     *
+     * @tparam is_vector_load Boolean flag indicating whether vector loads are used or not
+     * @tparam is_outer Boolean flag indicating whether outer join is peformed or not
+     * @tparam CG Cooperative Group type
+     * @tparam PairEqual Binary callable type
+     * @param g The Cooperative Group used to perform the pair_count operation
+     * @param pair The pair to search for
+     * @param thread_num_matches Number of matches found by the current thread
+     * @param pair_equal The binary callable used to compare two pairs
+     * for equality
+     */
+    template <bool is_vector_load, bool is_outer, typename CG, typename PairEqual>
+    __device__ std::enable_if_t<not is_vector_load, void> pair_count(
+      CG const& g,
+      value_type const& pair,
+      std::size_t& thread_num_matches,
+      PairEqual pair_equal) noexcept;
   };  // class device_view
 
   /**

From 95c2ac9437d2cba4258efb34e9d4e4038f5e0e02 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Wed, 9 Jun 2021 16:45:07 -0400
Subject: [PATCH 140/267] Cleanups: remove redundant template specializations

---
 .../cuco/detail/static_multimap_kernels.cuh   | 157 +-----------------
 1 file changed, 5 insertions(+), 152 deletions(-)

diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap_kernels.cuh
index f6bbac3ee..54457dfdb 100644
--- a/include/cuco/detail/static_multimap_kernels.cuh
+++ b/include/cuco/detail/static_multimap_kernels.cuh
@@ -275,7 +275,7 @@ __global__ void contains(
 }
 
 /**
- * @brief Counts the occurrences of keys in `[first, last)` contained in the multimap. If is_outer
+ * @brief Counts the occurrences of keys in `[first, last)` contained in the multimap. If `is_outer`
  * is true, the corresponding occurrence for non-matches is 1. Otherwise, it's 0.
  *
  * @tparam block_size The size of the thread block
@@ -306,7 +306,7 @@ template <uint32_t block_size,
           typename atomicT,
           typename viewT,
           typename KeyEqual>
-__global__ std::enable_if_t<is_vector_load, void> count(
+__global__ void count(
   InputIt first, InputIt last, atomicT* num_matches, viewT view, KeyEqual key_equal)
 {
   auto tile    = cg::tiled_partition<tile_size>(cg::this_thread_block());
@@ -331,63 +331,10 @@ __global__ std::enable_if_t<is_vector_load, void> count(
   }
 }
 
-/**
- * @brief Counts the occurrences of keys in `[first, last)` contained in the multimap. If is_outer
- * is true, the corresponding occurrence for non-matches is 1. Otherwise, it's 0.
- *
- * @tparam block_size The size of the thread block
- * @tparam tile_size The number of threads in the Cooperative Groups used to perform counts
- * @tparam Key key type
- * @tparam Value The type of the mapped value for the map
- * @tparam is_vector_load Boolean flag indicating whether vector loads are used or not
- * @tparam is_outer Boolean flag indicating whether the current functions is used for outer join
- * operations or not
- * @tparam InputIt Device accessible input iterator whose `value_type` is convertible to the map's
- * `key_type`
- * @tparam atomicT Type of atomic storage
- * @tparam viewT Type of device view allowing access of hash map storage
- * @tparam KeyEqual Binary callable
- * @param first Beginning of the sequence of keys to count
- * @param last End of the sequence of keys to count
- * @param num_matches The number of all the matches for a sequence of keys
- * @param view Device view used to access the hash map's slot storage
- * @param key_equal Binary function to compare two keys for equality
- */
-template <uint32_t block_size,
-          uint32_t tile_size,
-          typename Key,
-          typename Value,
-          bool is_vector_load,
-          bool is_outer = false,
-          typename InputIt,
-          typename atomicT,
-          typename viewT,
-          typename KeyEqual>
-__global__ std::enable_if_t<not is_vector_load, void> count(
-  InputIt first, InputIt last, atomicT* num_matches, viewT view, KeyEqual key_equal)
-{
-  auto tile    = cg::tiled_partition<tile_size>(cg::this_thread_block());
-  auto tid     = block_size * blockIdx.x + threadIdx.x;
-  auto key_idx = tid / tile_size;
-
-  typedef cub::BlockReduce<std::size_t, block_size> BlockReduce;
-  __shared__ typename BlockReduce::TempStorage temp_storage;
-  std::size_t thread_num_matches = 0;
-
-  while (first + key_idx < last) {
-    auto key = *(first + key_idx);
-    view.count<is_vector_load, is_outer>(tile, key, thread_num_matches, key_equal);
-    key_idx += (gridDim.x * block_size) / tile_size;
-  }
-  auto const block_num_matches = BlockReduce(temp_storage).Sum(thread_num_matches);
-  if (threadIdx.x == 0) {
-    num_matches->fetch_add(block_num_matches, cuda::std::memory_order_relaxed);
-  }
-}
-
 /**
  * @brief Counts the occurrences of key/value pairs in `[first, last)` contained in the multimap.
- * If no matches can be found for a given key/value pair, the corresponding occurrence is 1.
+ * If no matches can be found for a given key/value pair and `is_outer` is true, the corresponding
+ * occurrence is 1.
  *
  * @tparam block_size The size of the thread block
  * @tparam tile_size The number of threads in the Cooperative Groups used to perform counts
@@ -416,7 +363,7 @@ template <uint32_t block_size,
           typename atomicT,
           typename viewT,
           typename PairEqual>
-__global__ std::enable_if_t<is_vector_load, void> pair_count(
+__global__ void pair_count(
   InputIt first, InputIt last, atomicT* num_matches, viewT view, PairEqual pair_equal)
 {
   auto tile     = cg::tiled_partition<tile_size>(cg::this_thread_block());
@@ -441,100 +388,6 @@ __global__ std::enable_if_t<is_vector_load, void> pair_count(
   }
 }
 
-/**
- * @brief Counts the occurrences of key/value pairs in `[first, last)` contained in the multimap.
- * If no matches can be found for a given key/value pair, the corresponding occurrence is 1.
- *
- * @tparam block_size The size of the thread block
- * @tparam tile_size The number of threads in the Cooperative Groups used to perform counts
- * @tparam Key key type
- * @tparam Value The type of the mapped value for the map
- * @tparam is_vector_load Boolean flag indicating whether vector loads are used or not
- * @tparam is_outer Boolean flag indicating whether the current functions is used for outer join
- * operations or not
- * @tparam Input Device accesible input iterator of key/value pairs
- * @tparam atomicT Type of atomic storage
- * @tparam viewT Type of device view allowing access of hash map storage
- * @tparam PairEqual Binary callable
- * @param first Beginning of the sequence of pairs to count
- * @param last End of the sequence of pairs to count
- * @param num_matches The number of all the matches for a sequence of pairs
- * @param view Device view used to access the hash map's slot storage
- * @param pair_equal Binary function to compare two pairs for equality
- */
-template <uint32_t block_size,
-          uint32_t tile_size,
-          typename Key,
-          typename Value,
-          bool is_vector_load,
-          bool is_outer = false,
-          typename InputIt,
-          typename atomicT,
-          typename viewT,
-          typename PairEqual>
-__global__ std::enable_if_t<not is_vector_load, void> pair_count(
-  InputIt first, InputIt last, atomicT* num_matches, viewT view, PairEqual pair_equal)
-{
-  auto tile     = cg::tiled_partition<tile_size>(cg::this_thread_block());
-  auto tid      = block_size * blockIdx.x + threadIdx.x;
-  auto pair_idx = tid / tile_size;
-
-  typedef cub::BlockReduce<std::size_t, block_size> BlockReduce;
-  __shared__ typename BlockReduce::TempStorage temp_storage;
-  std::size_t thread_num_matches = 0;
-
-  while (first + pair_idx < last) {
-    cuco::pair_type<Key, Value> pair = *(first + pair_idx);
-    auto key                         = pair.first;
-    auto current_slot                = view.initial_slot(tile, key);
-
-    if constexpr (is_outer) {
-      bool found_match = false;
-
-      while (true) {
-        auto slot_contents = *reinterpret_cast<cuco::pair_type<Key, Value> const*>(current_slot);
-
-        auto const slot_is_empty = (slot_contents.first == view.get_empty_key_sentinel());
-
-        auto const equals = not slot_is_empty and pair_equal(slot_contents, pair);
-
-        if (tile.any(equals)) { found_match = true; }
-
-        thread_num_matches += equals;
-
-        if (tile.any(slot_is_empty)) {
-          if ((not found_match) && (tile.thread_rank() == 0)) { thread_num_matches++; }
-          break;
-        }
-
-        current_slot = view.next_slot(current_slot);
-      }
-    } else {
-      while (true) {
-        auto slot_contents = *reinterpret_cast<cuco::pair_type<Key, Value> const*>(current_slot);
-
-        auto const slot_is_empty = (slot_contents.first == view.get_empty_key_sentinel());
-
-        auto const equals = not slot_is_empty and pair_equal(slot_contents, pair);
-
-        thread_num_matches += equals;
-
-        if (tile.any(slot_is_empty)) { break; }
-
-        current_slot = view.next_slot(current_slot);
-      }
-    }
-    pair_idx += (gridDim.x * block_size) / tile_size;
-  }
-
-  // compute number of successfully inserted elements for each block
-  // and atomically add to the grand total
-  std::size_t block_num_matches = BlockReduce(temp_storage).Sum(thread_num_matches);
-  if (threadIdx.x == 0) {
-    num_matches->fetch_add(block_num_matches, cuda::std::memory_order_relaxed);
-  }
-}
-
 /**
  * @brief Finds all the values corresponding to all keys in the range `[first, last)` using vector
  * oads combined with per-block shared memory buffer.

From e21e0371b7c7944eef2ab2a5780eeb9e714c0317 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Wed, 9 Jun 2021 19:43:27 -0400
Subject: [PATCH 141/267] Refactor retrieve kernel to use view-based retrieve
 functions

---
 include/cuco/detail/static_multimap.inl       | 347 +++++++++++++++++
 .../cuco/detail/static_multimap_kernels.cuh   | 354 +-----------------
 include/cuco/static_multimap.cuh              | 152 +++++++-
 3 files changed, 512 insertions(+), 341 deletions(-)

diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index 0a20fde62..8e412689f 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -487,6 +487,86 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_mutable_vie
   }
 }
 
+template <typename Key,
+          typename Value,
+          class ProbeSequence,
+          cuda::thread_scope Scope,
+          typename Allocator>
+template <uint32_t cg_size, typename CG, typename atomicT, typename OutputIt>
+__inline__ __device__ std::enable_if_t<thrust::is_contiguous_iterator<OutputIt>::value, void>
+static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::flush_output_buffer(
+  CG const& g,
+  uint32_t const num_outputs,
+  value_type* output_buffer,
+  atomicT* num_matches,
+  OutputIt output_begin) noexcept
+{
+  std::size_t offset;
+  const auto lane_id = g.thread_rank();
+  if (0 == lane_id) {
+    offset = num_matches->fetch_add(num_outputs, cuda::std::memory_order_relaxed);
+  }
+  offset = g.shfl(offset, 0);
+
+  cooperative_groups::memcpy_async(g,
+                                   output_begin + offset,
+                                   output_buffer,
+                                   cuda::aligned_size_t<alignof(cuco::pair_type<Key, Value>)>(
+                                     sizeof(cuco::pair_type<Key, Value>) * num_outputs));
+}
+
+template <typename Key,
+          typename Value,
+          class ProbeSequence,
+          cuda::thread_scope Scope,
+          typename Allocator>
+template <uint32_t cg_size, typename CG, typename atomicT, typename OutputIt>
+__inline__ __device__ std::enable_if_t<not thrust::is_contiguous_iterator<OutputIt>::value, void>
+static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::flush_output_buffer(
+  CG const& g,
+  uint32_t const num_outputs,
+  value_type* output_buffer,
+  atomicT* num_items,
+  OutputIt output_begin) noexcept
+{
+  std::size_t offset;
+  const auto lane_id = g.thread_rank();
+  if (0 == lane_id) { offset = num_items->fetch_add(num_outputs, cuda::std::memory_order_relaxed); }
+  offset = g.shfl(offset, 0);
+
+  for (auto index = lane_id; index < num_outputs; index += cg_size) {
+    *(output_begin + offset + index) = output_buffer[index];
+  }
+}
+
+template <typename Key,
+          typename Value,
+          class ProbeSequence,
+          cuda::thread_scope Scope,
+          typename Allocator>
+template <typename atomicT, typename OutputIt>
+__inline__ __device__ void
+static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::flush_output_buffer(
+  const unsigned int activemask,
+  uint32_t const num_outputs,
+  value_type* output_buffer,
+  atomicT* num_matches,
+  OutputIt output_begin) noexcept
+{
+  int num_threads = __popc(activemask);
+
+  std::size_t offset;
+  const auto lane_id = threadIdx.x % 32;
+  if (0 == lane_id) {
+    offset = num_matches->fetch_add(num_outputs, cuda::std::memory_order_relaxed);
+  }
+  offset = __shfl_sync(activemask, offset, 0);
+
+  for (auto index = lane_id; index < num_outputs; index += num_threads) {
+    *(output_begin + offset + index) = output_buffer[index];
+  }
+}
+
 template <typename Key,
           typename Value,
           class ProbeSequence,
@@ -803,4 +883,271 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_
   }
 }
 
+template <typename Key,
+          typename Value,
+          class ProbeSequence,
+          cuda::thread_scope Scope,
+          typename Allocator>
+template <uint32_t buffer_size,
+          bool is_vector_load,
+          bool is_outer,
+          typename CG,
+          typename atomicT,
+          typename OutputIt,
+          typename KeyEqual>
+__device__ std::enable_if_t<is_vector_load, void>
+static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::retrieve(
+  const unsigned int activemask,
+  CG const& g,
+  Key const& k,
+  uint32_t* warp_counter,
+  value_type* output_buffer,
+  atomicT* num_matches,
+  OutputIt output_begin,
+  KeyEqual key_equal) noexcept
+{
+  const uint32_t warp_lane_id = threadIdx.x % 32;
+  const uint32_t cg_lane_id   = g.thread_rank();
+
+  auto current_slot = initial_slot(g, k);
+
+  bool running = true;
+  if constexpr (is_outer) {
+    bool found_match = false;
+
+    while (__any_sync(activemask, running)) {
+      if (running) {
+        pair<Key, Value> arr[2];
+        if constexpr (sizeof(Key) == 4) {
+          auto const tmp = *reinterpret_cast<uint4 const*>(current_slot);
+          memcpy(&arr[0], &tmp, 2 * sizeof(pair<Key, Value>));
+        } else {
+          auto const tmp = *reinterpret_cast<ulonglong4 const*>(current_slot);
+          memcpy(&arr[0], &tmp, 2 * sizeof(pair<Key, Value>));
+        }
+
+        auto const first_slot_is_empty  = (arr[0].first == this->get_empty_key_sentinel());
+        auto const second_slot_is_empty = (arr[1].first == this->get_empty_key_sentinel());
+        auto const first_equals         = (not first_slot_is_empty and key_equal(arr[0].first, k));
+        auto const second_equals        = (not second_slot_is_empty and key_equal(arr[1].first, k));
+        auto const first_exists         = g.ballot(first_equals);
+        auto const second_exists        = g.ballot(second_equals);
+
+        if (first_exists or second_exists) {
+          found_match = true;
+
+          auto num_first_matches  = __popc(first_exists);
+          auto num_second_matches = __popc(second_exists);
+
+          uint32_t output_idx;
+          if (0 == cg_lane_id) {
+            output_idx = atomicAdd(warp_counter, (num_first_matches + num_second_matches));
+          }
+          output_idx = g.shfl(output_idx, 0);
+
+          if (first_equals) {
+            auto lane_offset = __popc(first_exists & ((1 << cg_lane_id) - 1));
+            Key key          = k;
+            output_buffer[output_idx + lane_offset] =
+              cuco::make_pair<Key, Value>(std::move(key), std::move(arr[0].second));
+          }
+          if (second_equals) {
+            auto lane_offset = __popc(second_exists & ((1 << cg_lane_id) - 1));
+            Key key          = k;
+            output_buffer[output_idx + num_first_matches + lane_offset] =
+              cuco::make_pair<Key, Value>(std::move(key), std::move(arr[1].second));
+          }
+        }
+        if (g.any(first_slot_is_empty or second_slot_is_empty)) {
+          running = false;
+          if ((not found_match) && (cg_lane_id == 0)) {
+            auto output_idx           = atomicAdd(warp_counter, 1);
+            Key key                   = k;
+            output_buffer[output_idx] = cuco::make_pair<Key, Value>(
+              std::move(key), std::move(this->get_empty_key_sentinel()));
+          }
+        }
+      }  // if running
+
+      __syncwarp(activemask);
+      if (*warp_counter + 32 * 2 > buffer_size) {
+        flush_output_buffer(activemask, *warp_counter, output_buffer, num_matches, output_begin);
+        // First lane reset warp-level counter
+        if (warp_lane_id == 0) { *warp_counter = 0; }
+      }
+
+      current_slot = next_slot(current_slot);
+    }  // while running
+  } else {
+    while (__any_sync(activemask, running)) {
+      if (running) {
+        pair<Key, Value> arr[2];
+        if constexpr (sizeof(Key) == 4) {
+          auto const tmp = *reinterpret_cast<uint4 const*>(current_slot);
+          memcpy(&arr[0], &tmp, 2 * sizeof(pair<Key, Value>));
+        } else {
+          auto const tmp = *reinterpret_cast<ulonglong4 const*>(current_slot);
+          memcpy(&arr[0], &tmp, 2 * sizeof(pair<Key, Value>));
+        }
+
+        auto const first_slot_is_empty  = (arr[0].first == this->get_empty_key_sentinel());
+        auto const second_slot_is_empty = (arr[1].first == this->get_empty_key_sentinel());
+        auto const first_equals         = (not first_slot_is_empty and key_equal(arr[0].first, k));
+        auto const second_equals        = (not second_slot_is_empty and key_equal(arr[1].first, k));
+        auto const first_exists         = g.ballot(first_equals);
+        auto const second_exists        = g.ballot(second_equals);
+
+        if (first_exists or second_exists) {
+          auto num_first_matches  = __popc(first_exists);
+          auto num_second_matches = __popc(second_exists);
+
+          uint32_t output_idx;
+          if (0 == cg_lane_id) {
+            output_idx = atomicAdd(warp_counter, (num_first_matches + num_second_matches));
+          }
+          output_idx = g.shfl(output_idx, 0);
+
+          if (first_equals) {
+            auto lane_offset = __popc(first_exists & ((1 << cg_lane_id) - 1));
+            Key key          = k;
+            output_buffer[output_idx + lane_offset] =
+              cuco::make_pair<Key, Value>(std::move(key), std::move(arr[0].second));
+          }
+          if (second_equals) {
+            auto lane_offset = __popc(second_exists & ((1 << cg_lane_id) - 1));
+            Key key          = k;
+            output_buffer[output_idx + num_first_matches + lane_offset] =
+              cuco::make_pair<Key, Value>(std::move(key), std::move(arr[1].second));
+          }
+        }
+        if (g.any(first_slot_is_empty or second_slot_is_empty)) { running = false; }
+      }  // if running
+
+      __syncwarp(activemask);
+      if (*warp_counter + 32 * 2 > buffer_size) {
+        flush_output_buffer(activemask, *warp_counter, output_buffer, num_matches, output_begin);
+        // First lane reset warp-level counter
+        if (warp_lane_id == 0) { *warp_counter = 0; }
+      }
+
+      current_slot = next_slot(current_slot);
+    }  // while running
+  }
+}
+
+template <typename Key,
+          typename Value,
+          class ProbeSequence,
+          cuda::thread_scope Scope,
+          typename Allocator>
+template <uint32_t cg_size,
+          uint32_t buffer_size,
+          bool is_vector_load,
+          bool is_outer,
+          typename CG,
+          typename atomicT,
+          typename OutputIt,
+          typename KeyEqual>
+__device__ std::enable_if_t<not is_vector_load, void>
+static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::retrieve(
+  CG const& g,
+  Key const& k,
+  uint32_t* cg_counter,
+  value_type* output_buffer,
+  atomicT* num_matches,
+  OutputIt output_begin,
+  KeyEqual key_equal) noexcept
+{
+  const uint32_t lane_id = g.thread_rank();
+
+  auto current_slot = initial_slot(g, k);
+
+  bool running = true;
+
+  if constexpr (is_outer) {
+    bool found_match = false;
+
+    while (running) {
+      // TODO: Replace reinterpret_cast with atomic ref when possible. The current implementation
+      // is unsafe!
+      static_assert(sizeof(Key) == sizeof(cuda::atomic<Key>));
+      static_assert(sizeof(Value) == sizeof(cuda::atomic<Value>));
+      pair<Key, Value> slot_contents = *reinterpret_cast<pair<Key, Value> const*>(current_slot);
+
+      auto const slot_is_empty = (slot_contents.first == this->get_empty_key_sentinel());
+      auto const equals        = (not slot_is_empty and key_equal(slot_contents.first, k));
+      auto const exists        = g.ballot(equals);
+
+      if (exists) {
+        found_match         = true;
+        auto num_matches    = __popc(exists);
+        uint32_t output_idx = *cg_counter;
+        if (equals) {
+          // Each match computes its lane-level offset
+          auto lane_offset = __popc(exists & ((1 << lane_id) - 1));
+          Key key          = k;
+          output_buffer[output_idx + lane_offset] =
+            cuco::make_pair<Key, Value>(std::move(key), std::move(slot_contents.second));
+        }
+        if (0 == lane_id) { (*cg_counter) += num_matches; }
+      }
+      if (g.any(slot_is_empty)) {
+        running = false;
+        if ((not found_match) && (lane_id == 0)) {
+          auto output_idx = (*cg_counter)++;
+          Key key         = k;
+          output_buffer[output_idx] =
+            cuco::make_pair<Key, Value>(std::move(key), std::move(this->get_empty_key_sentinel()));
+        }
+      }
+
+      g.sync();
+
+      // Flush if the next iteration won't fit into buffer
+      if ((*cg_counter + cg_size) > buffer_size) {
+        flush_output_buffer<cg_size>(g, *cg_counter, output_buffer, num_matches, output_begin);
+        // First lane reset CG-level counter
+        if (lane_id == 0) { *cg_counter = 0; }
+      }
+      current_slot = next_slot(current_slot);
+    }  // while running
+  } else {
+    while (running) {
+      // TODO: Replace reinterpret_cast with atomic ref when possible. The current implementation
+      // is unsafe!
+      static_assert(sizeof(Key) == sizeof(cuda::atomic<Key>));
+      static_assert(sizeof(Value) == sizeof(cuda::atomic<Value>));
+      pair<Key, Value> slot_contents = *reinterpret_cast<pair<Key, Value> const*>(current_slot);
+
+      auto const slot_is_empty = (slot_contents.first == this->get_empty_key_sentinel());
+      auto const equals        = (not slot_is_empty and key_equal(slot_contents.first, k));
+      auto const exists        = g.ballot(equals);
+
+      if (exists) {
+        auto num_matches    = __popc(exists);
+        uint32_t output_idx = *cg_counter;
+        if (equals) {
+          // Each match computes its lane-level offset
+          auto lane_offset = __popc(exists & ((1 << lane_id) - 1));
+          Key key          = k;
+          output_buffer[output_idx + lane_offset] =
+            cuco::make_pair<Key, Value>(std::move(key), std::move(slot_contents.second));
+        }
+        if (0 == lane_id) { (*cg_counter) += num_matches; }
+      }
+      if (g.any(slot_is_empty)) { running = false; }
+
+      g.sync();
+
+      // Flush if the next iteration won't fit into buffer
+      if ((*cg_counter + cg_size) > buffer_size) {
+        flush_output_buffer<cg_size>(g, *cg_counter, output_buffer, num_matches, output_begin);
+        // First lane reset CG-level counter
+        if (lane_id == 0) { *cg_counter = 0; }
+      }
+      current_slot = next_slot(current_slot);
+    }  // while running
+  }
+}
+
 }  // namespace cuco
diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap_kernels.cuh
index 54457dfdb..baef8fe4c 100644
--- a/include/cuco/detail/static_multimap_kernels.cuh
+++ b/include/cuco/detail/static_multimap_kernels.cuh
@@ -24,125 +24,6 @@ namespace cuco {
 namespace detail {
 namespace cg = cooperative_groups;
 
-/**
- * @brief Flushes per-CG shared memory buffer into the output sequence using CG memcpy_async.
- *
- * @tparam cg_size The number of threads in the Cooperative Groups
- * @tparam CG Cooperative Group type
- * @tparam Key key type
- * @tparam Value value type
- * @tparam atomicT Type of atomic storage
- * @tparam OutputIt Device accessible output iterator whose `value_type` is
- * convertible to the map's `mapped_type`
- * @param g The Cooperative Group used to flush output buffer
- * @param num_outputs Number of valid output in the buffer
- * @param output_buffer Shared memory buffer of the key/value pair sequence
- * @param num_matches Size of the output sequence
- * @param output_begin Beginning of the output sequence of key/value pairs
- */
-template <uint32_t cg_size,
-          typename CG,
-          typename Key,
-          typename Value,
-          typename atomicT,
-          typename OutputIt>
-__inline__ __device__ std::enable_if_t<thrust::is_contiguous_iterator<OutputIt>::value, void>
-flush_output_buffer(CG const& g,
-                    uint32_t const num_outputs,
-                    cuco::pair_type<Key, Value>* output_buffer,
-                    atomicT* num_matches,
-                    OutputIt output_begin)
-{
-  std::size_t offset;
-  const auto lane_id = g.thread_rank();
-  if (0 == lane_id) {
-    offset = num_matches->fetch_add(num_outputs, cuda::std::memory_order_relaxed);
-  }
-  offset = g.shfl(offset, 0);
-
-  cg::memcpy_async(g,
-                   output_begin + offset,
-                   output_buffer,
-                   cuda::aligned_size_t<alignof(cuco::pair_type<Key, Value>)>(
-                     sizeof(cuco::pair_type<Key, Value>) * num_outputs));
-}
-
-/**
- * @brief Flushes per-CG shared memory buffer into the output sequence.
- *
- * @tparam cg_size The number of threads in the Cooperative Groups
- * @tparam CG Cooperative Group type
- * @tparam Key key type
- * @tparam Value value type
- * @tparam atomicT Type of atomic storage
- * @tparam OutputIt Device accessible output iterator whose `value_type` is
- * convertible to the map's `mapped_type`
- * @param g The Cooperative Group used to flush output buffer
- * @param num_outputs Number of valid output in the buffer
- * @param output_buffer Shared memory buffer of the key/value pair sequence
- * @param num_matches Size of the output sequence
- * @param output_begin Beginning of the output sequence of key/value pairs
- */
-template <uint32_t cg_size,
-          typename CG,
-          typename Key,
-          typename Value,
-          typename atomicT,
-          typename OutputIt>
-__inline__ __device__ std::enable_if_t<not thrust::is_contiguous_iterator<OutputIt>::value, void>
-flush_output_buffer(CG const& g,
-                    uint32_t const num_outputs,
-                    cuco::pair_type<Key, Value>* output_buffer,
-                    atomicT* num_matches,
-                    OutputIt output_begin)
-{
-  std::size_t offset;
-  const auto lane_id = g.thread_rank();
-  if (0 == lane_id) {
-    offset = num_matches->fetch_add(num_outputs, cuda::std::memory_order_relaxed);
-  }
-  offset = g.shfl(offset, 0);
-
-  for (auto index = lane_id; index < num_outputs; index += cg_size) {
-    *(output_begin + offset + index) = output_buffer[index];
-  }
-}
-
-/**
- * @brief Flushes per-warp shared memory buffer into the output sequence.
- *
- * @tparam Key key type
- * @tparam Value value type
- * @tparam atomicT Type of atomic storage
- * @tparam OutputIt Device accessible output iterator whose `value_type` is
- * convertible to the map's `mapped_type`
- * @param activemask Mask of active threads in the warp
- * @param num_outputs Number of valid output in the buffer
- * @param output_buffer Shared memory buffer of the key/value pair sequence
- * @param num_matches Size of the output sequence
- * @param output_begin Beginning of the output sequence of key/value pairs
- */
-template <typename Key, typename Value, typename atomicT, typename OutputIt>
-__inline__ __device__ void flush_output_buffer(const unsigned int activemask,
-                                               uint32_t const num_outputs,
-                                               cuco::pair_type<Key, Value>* output_buffer,
-                                               atomicT* num_matches,
-                                               OutputIt output_begin)
-{
-  int num_threads = __popc(activemask);
-
-  std::size_t offset;
-  const auto lane_id = threadIdx.x % 32;
-  if (0 == lane_id) {
-    offset = num_matches->fetch_add(num_outputs, cuda::std::memory_order_relaxed);
-  }
-  offset = __shfl_sync(activemask, offset, 0);
-
-  for (auto index = lane_id; index < num_outputs; index += num_threads) {
-    *(output_begin + offset + index) = output_buffer[index];
-  }
-}
-
 /**
  * @brief Initializes each slot in the flat `slots` storage to contain `k` and `v`.
  *
@@ -447,7 +328,6 @@ __global__ std::enable_if_t<is_vector_load, void> retrieve(InputIt first,
   constexpr uint32_t num_warps = block_size / 32;
   const uint32_t warp_id       = threadIdx.x / 32;
   const uint32_t warp_lane_id  = threadIdx.x % 32;
-  const uint32_t tile_lane_id  = tile.thread_rank();
 
   __shared__ cuco::pair_type<Key, Value> output_buffer[num_warps][buffer_size];
   __shared__ uint32_t warp_counter[num_warps];
@@ -457,140 +337,21 @@ __global__ std::enable_if_t<is_vector_load, void> retrieve(InputIt first,
   const unsigned int activemask = __ballot_sync(0xffffffff, first + key_idx < last);
 
   while (first + key_idx < last) {
-    auto key          = *(first + key_idx);
-    auto current_slot = view.initial_slot(tile, key);
-
-    bool running = true;
-    if constexpr (is_outer) {
-      bool found_match = false;
-
-      while (__any_sync(activemask, running)) {
-        if (running) {
-          pair<Key, Value> arr[2];
-          if constexpr (sizeof(Key) == 4) {
-            auto const tmp = *reinterpret_cast<uint4 const*>(current_slot);
-            memcpy(&arr[0], &tmp, 2 * sizeof(pair<Key, Value>));
-          } else {
-            auto const tmp = *reinterpret_cast<ulonglong4 const*>(current_slot);
-            memcpy(&arr[0], &tmp, 2 * sizeof(pair<Key, Value>));
-          }
-
-          auto const first_slot_is_empty  = (arr[0].first == view.get_empty_key_sentinel());
-          auto const second_slot_is_empty = (arr[1].first == view.get_empty_key_sentinel());
-          auto const first_equals  = (not first_slot_is_empty and key_equal(arr[0].first, key));
-          auto const second_equals = (not second_slot_is_empty and key_equal(arr[1].first, key));
-          auto const first_exists  = tile.ballot(first_equals);
-          auto const second_exists = tile.ballot(second_equals);
-
-          if (first_exists or second_exists) {
-            found_match = true;
-
-            auto num_first_matches  = __popc(first_exists);
-            auto num_second_matches = __popc(second_exists);
-
-            uint32_t output_idx;
-            if (0 == tile_lane_id) {
-              output_idx =
-                atomicAdd(&warp_counter[warp_id], (num_first_matches + num_second_matches));
-            }
-            output_idx = tile.shfl(output_idx, 0);
-
-            if (first_equals) {
-              auto lane_offset = __popc(first_exists & ((1 << tile_lane_id) - 1));
-              Key k            = key;
-              output_buffer[warp_id][output_idx + lane_offset] =
-                cuco::make_pair<Key, Value>(std::move(k), std::move(arr[0].second));
-            }
-            if (second_equals) {
-              auto lane_offset = __popc(second_exists & ((1 << tile_lane_id) - 1));
-              Key k            = key;
-              output_buffer[warp_id][output_idx + num_first_matches + lane_offset] =
-                cuco::make_pair<Key, Value>(std::move(k), std::move(arr[1].second));
-            }
-          }
-          if (tile.any(first_slot_is_empty or second_slot_is_empty)) {
-            running = false;
-            if ((not found_match) && (tile_lane_id == 0)) {
-              auto output_idx = atomicAdd(&warp_counter[warp_id], 1);
-              output_buffer[warp_id][output_idx] =
-                cuco::make_pair<Key, Value>(key, view.get_empty_key_sentinel());
-            }
-          }
-        }  // if running
-
-        __syncwarp(activemask);
-        if (warp_counter[warp_id] + 32 * 2 > buffer_size) {
-          flush_output_buffer(
-            activemask, warp_counter[warp_id], output_buffer[warp_id], num_matches, output_begin);
-          // First lane reset warp-level counter
-          if (warp_lane_id == 0) { warp_counter[warp_id] = 0; }
-        }
-
-        current_slot = view.next_slot(current_slot);
-      }  // while running
-    } else {
-      while (__any_sync(activemask, running)) {
-        if (running) {
-          pair<Key, Value> arr[2];
-          if constexpr (sizeof(Key) == 4) {
-            auto const tmp = *reinterpret_cast<uint4 const*>(current_slot);
-            memcpy(&arr[0], &tmp, 2 * sizeof(pair<Key, Value>));
-          } else {
-            auto const tmp = *reinterpret_cast<ulonglong4 const*>(current_slot);
-            memcpy(&arr[0], &tmp, 2 * sizeof(pair<Key, Value>));
-          }
-
-          auto const first_slot_is_empty  = (arr[0].first == view.get_empty_key_sentinel());
-          auto const second_slot_is_empty = (arr[1].first == view.get_empty_key_sentinel());
-          auto const first_equals  = (not first_slot_is_empty and key_equal(arr[0].first, key));
-          auto const second_equals = (not second_slot_is_empty and key_equal(arr[1].first, key));
-          auto const first_exists  = tile.ballot(first_equals);
-          auto const second_exists = tile.ballot(second_equals);
-
-          if (first_exists or second_exists) {
-            auto num_first_matches  = __popc(first_exists);
-            auto num_second_matches = __popc(second_exists);
-
-            uint32_t output_idx;
-            if (0 == tile_lane_id) {
-              output_idx =
-                atomicAdd(&warp_counter[warp_id], (num_first_matches + num_second_matches));
-            }
-            output_idx = tile.shfl(output_idx, 0);
-
-            if (first_equals) {
-              auto lane_offset = __popc(first_exists & ((1 << tile_lane_id) - 1));
-              Key k            = key;
-              output_buffer[warp_id][output_idx + lane_offset] =
-                cuco::make_pair<Key, Value>(std::move(k), std::move(arr[0].second));
-            }
-            if (second_equals) {
-              auto lane_offset = __popc(second_exists & ((1 << tile_lane_id) - 1));
-              Key k            = key;
-              output_buffer[warp_id][output_idx + num_first_matches + lane_offset] =
-                cuco::make_pair<Key, Value>(std::move(k), std::move(arr[1].second));
-            }
-          }
-          if (tile.any(first_slot_is_empty or second_slot_is_empty)) { running = false; }
-        }  // if running
-
-        __syncwarp(activemask);
-        if (warp_counter[warp_id] + 32 * 2 > buffer_size) {
-          flush_output_buffer(
-            activemask, warp_counter[warp_id], output_buffer[warp_id], num_matches, output_begin);
-          // First lane reset warp-level counter
-          if (warp_lane_id == 0) { warp_counter[warp_id] = 0; }
-        }
-
-        current_slot = view.next_slot(current_slot);
-      }  // while running
-    }
+    auto key = *(first + key_idx);
+    view.retrieve<buffer_size, is_vector_load, is_outer>(activemask,
+                                                         tile,
+                                                         key,
+                                                         &warp_counter[warp_id],
+                                                         output_buffer[warp_id],
+                                                         num_matches,
+                                                         output_begin,
+                                                         key_equal);
     key_idx += (gridDim.x * block_size) / tile_size;
   }
 
   // Final flush of output buffer
   if (warp_counter[warp_id] > 0) {
-    flush_output_buffer(
+    view.flush_output_buffer(
       activemask, warp_counter[warp_id], output_buffer[warp_id], num_matches, output_begin);
   }
 }
@@ -661,102 +422,15 @@ __global__ std::enable_if_t<not is_vector_load, void> retrieve(InputIt first,
   if (lane_id == 0) { cg_counter[cg_id] = 0; }
 
   while (first + key_idx < last) {
-    auto key          = *(first + key_idx);
-    auto current_slot = view.initial_slot(tile, key);
-
-    bool running = true;
-
-    if constexpr (is_outer) {
-      bool found_match = false;
-
-      while (running) {
-        // TODO: Replace reinterpret_cast with atomic ref when possible. The current implementation
-        // is unsafe!
-        static_assert(sizeof(Key) == sizeof(cuda::atomic<Key>));
-        static_assert(sizeof(Value) == sizeof(cuda::atomic<Value>));
-        pair<Key, Value> slot_contents = *reinterpret_cast<pair<Key, Value> const*>(current_slot);
-
-        auto const slot_is_empty = (slot_contents.first == view.get_empty_key_sentinel());
-        auto const equals        = (not slot_is_empty and key_equal(slot_contents.first, key));
-        auto const exists        = tile.ballot(equals);
-
-        if (exists) {
-          found_match         = true;
-          auto num_matches    = __popc(exists);
-          uint32_t output_idx = cg_counter[cg_id];
-          if (equals) {
-            // Each match computes its lane-level offset
-            auto lane_offset = __popc(exists & ((1 << lane_id) - 1));
-            Key k            = key;
-            output_buffer[cg_id][output_idx + lane_offset] =
-              cuco::make_pair<Key, Value>(std::move(k), std::move(slot_contents.second));
-          }
-          if (0 == lane_id) { cg_counter[cg_id] += num_matches; }
-        }
-        if (tile.any(slot_is_empty)) {
-          running = false;
-          if ((not found_match) && (lane_id == 0)) {
-            auto output_idx = cg_counter[cg_id]++;
-            output_buffer[cg_id][output_idx] =
-              cuco::make_pair<Key, Value>(key, view.get_empty_key_sentinel());
-          }
-        }
-
-        tile.sync();
-
-        // Flush if the next iteration won't fit into buffer
-        if ((cg_counter[cg_id] + tile_size) > buffer_size) {
-          flush_output_buffer<tile_size>(
-            tile, cg_counter[cg_id], output_buffer[cg_id], num_matches, output_begin);
-          // First lane reset CG-level counter
-          if (lane_id == 0) { cg_counter[cg_id] = 0; }
-        }
-        current_slot = view.next_slot(current_slot);
-      }  // while running
-    } else {
-      while (running) {
-        // TODO: Replace reinterpret_cast with atomic ref when possible. The current implementation
-        // is unsafe!
-        static_assert(sizeof(Key) == sizeof(cuda::atomic<Key>));
-        static_assert(sizeof(Value) == sizeof(cuda::atomic<Value>));
-        pair<Key, Value> slot_contents = *reinterpret_cast<pair<Key, Value> const*>(current_slot);
-
-        auto const slot_is_empty = (slot_contents.first == view.get_empty_key_sentinel());
-        auto const equals        = (not slot_is_empty and key_equal(slot_contents.first, key));
-        auto const exists        = tile.ballot(equals);
-
-        if (exists) {
-          auto num_matches    = __popc(exists);
-          uint32_t output_idx = cg_counter[cg_id];
-          if (equals) {
-            // Each match computes its lane-level offset
-            auto lane_offset = __popc(exists & ((1 << lane_id) - 1));
-            Key k            = key;
-            output_buffer[cg_id][output_idx + lane_offset] =
-              cuco::make_pair<Key, Value>(std::move(k), std::move(slot_contents.second));
-          }
-          if (0 == lane_id) { cg_counter[cg_id] += num_matches; }
-        }
-        if (tile.any(slot_is_empty)) { running = false; }
-
-        tile.sync();
-
-        // Flush if the next iteration won't fit into buffer
-        if ((cg_counter[cg_id] + tile_size) > buffer_size) {
-          flush_output_buffer<tile_size>(
-            tile, cg_counter[cg_id], output_buffer[cg_id], num_matches, output_begin);
-          // First lane reset CG-level counter
-          if (lane_id == 0) { cg_counter[cg_id] = 0; }
-        }
-        current_slot = view.next_slot(current_slot);
-      }  // while running
-    }
+    auto key = *(first + key_idx);
+    view.retrieve<tile_size, buffer_size, is_vector_load, is_outer>(
+      tile, key, &cg_counter[cg_id], output_buffer[cg_id], num_matches, output_begin, key_equal);
     key_idx += (gridDim.x * block_size) / tile_size;
   }
 
   // Final flush of output buffer
   if (cg_counter[cg_id] > 0) {
-    flush_output_buffer<tile_size>(
+    view.flush_output_buffer<tile_size>(
       tile, cg_counter[cg_id], output_buffer[cg_id], num_matches, output_begin);
   }
 }
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index 6ed96c820..9dd097886 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -322,7 +322,7 @@ class static_multimap {
                     KeyEqual key_equal  = KeyEqual{});
 
   /**
-   * @brief Finds all the values corresponding to all keys in the range `[first, last)`.
+   * @brief Finds all the matches corresponding to all keys in the range `[first, last)`.
    *
    * If the key `k = *(first + i)` exists in the map, copies `k` and all associated values to
    * unspecified locations in `[output_begin, output_end)`. Else, copies `k` and the empty value
@@ -670,6 +670,70 @@ class static_multimap {
                          source_device_view.get_empty_value_sentinel());
     }
 
+    /**
+     * @brief Flushes per-CG shared memory buffer into the output sequence using CG memcpy_async.
+     *
+     * @tparam cg_size The number of threads in the Cooperative Groups
+     * @tparam CG Cooperative Group type
+     * @tparam atomicT Type of atomic storage
+     * @tparam OutputIt Device accessible output iterator whose `value_type` is
+     * convertible to the map's `mapped_type`
+     * @param g The Cooperative Group used to flush output buffer
+     * @param num_outputs Number of valid output in the buffer
+     * @param output_buffer Shared memory buffer of the key/value pair sequence
+     * @param num_matches Size of the output sequence
+     * @param output_begin Beginning of the output sequence of key/value pairs
+     */
+    template <uint32_t cg_size, typename CG, typename atomicT, typename OutputIt>
+    __inline__ __device__ std::enable_if_t<thrust::is_contiguous_iterator<OutputIt>::value, void>
+    flush_output_buffer(CG const& g,
+                        uint32_t const num_outputs,
+                        value_type* output_buffer,
+                        atomicT* num_matches,
+                        OutputIt output_begin) noexcept;
+
+    /**
+     * @brief Flushes per-CG shared memory buffer into the output sequence using CG memcpy_async.
+     *
+     * @tparam cg_size The number of threads in the Cooperative Groups
+     * @tparam CG Cooperative Group type
+     * @tparam atomicT Type of atomic storage
+     * @tparam OutputIt Device accessible output iterator whose `value_type` is
+     * convertible to the map's `mapped_type`
+     * @param g The Cooperative Group used to flush output buffer
+     * @param num_outputs Number of valid output in the buffer
+     * @param output_buffer Shared memory buffer of the key/value pair sequence
+     * @param num_matches Size of the output sequence
+     * @param output_begin Beginning of the output sequence of key/value pairs
+     */
+    template <uint32_t cg_size, typename CG, typename atomicT, typename OutputIt>
+    __inline__ __device__
+      std::enable_if_t<not thrust::is_contiguous_iterator<OutputIt>::value, void>
+      flush_output_buffer(CG const& g,
+                          uint32_t const num_outputs,
+                          value_type* output_buffer,
+                          atomicT* num_matches,
+                          OutputIt output_begin) noexcept;
+
+    /**
+     * @brief Flushes per-warp shared memory buffer into the output sequence.
+     *
+     * @tparam atomicT Type of atomic storage
+     * @tparam OutputIt Device accessible output iterator whose `value_type` is
+     * convertible to the map's `mapped_type`
+     * @param activemask Mask of active threads in the warp
+     * @param num_outputs Number of valid output in the buffer
+     * @param output_buffer Shared memory buffer of the key/value pair sequence
+     * @param num_matches Size of the output sequence
+     * @param output_begin Beginning of the output sequence of key/value pairs
+     */
+    template <typename atomicT, typename OutputIt>
+    __inline__ __device__ void flush_output_buffer(const unsigned int activemask,
+                                                   uint32_t const num_outputs,
+                                                   value_type* output_buffer,
+                                                   atomicT* num_matches,
+                                                   OutputIt output_begin) noexcept;
+
     /**
      * @brief Indicates whether the key `k` was inserted into the map using vector loads.
      *
@@ -802,6 +866,92 @@ class static_multimap {
       value_type const& pair,
       std::size_t& thread_num_matches,
       PairEqual pair_equal) noexcept;
+
+    /**
+     * @brief Find all the matches of a given key contained in multimap using vector
+     * loads with per-warp shared memory buffer.
+     *
+     * For keys `k = *(first + i)` existing in the map, copies `k` and all associated values to
+     * unspecified locations in `[output_begin, output_end)`. In case of non-matches, copies `k` and
+     * the empty value sentinel into the output only if `is_outer` is true.
+     *
+     * @tparam buffer_size Size of the output buffer
+     * @tparam is_vector_load Boolean flag indicating whether vector loads are used or not
+     * @tparam is_outer Boolean flag indicating whether outer join is peformed or not
+     * @tparam CG Cooperative Group type
+     * @tparam atomicT Type of atomic storage
+     * @tparam OutputIt Device accessible output iterator whose `value_type` is
+     * convertible to the map's `mapped_type`
+     * @tparam KeyEqual Binary callable type
+     * @param activemask Mask of active threads in the warp
+     * @param g The Cooperative Group used to retrieve
+     * @param k The key to search for
+     * @param warp_counter Pointer to the warp counter
+     * @param output_buffer Shared memory buffer of the key/value pair sequence
+     * @param num_matches Size of the output sequence
+     * @param output_begin Beginning of the output sequence of key/value pairs
+     * @param key_equal The binary callable used to compare two keys
+     * for equality
+     */
+    template <uint32_t buffer_size,
+              bool is_vector_load,
+              bool is_outer,
+              typename CG,
+              typename atomicT,
+              typename OutputIt,
+              typename KeyEqual = thrust::equal_to<key_type>>
+    __device__ std::enable_if_t<is_vector_load, void> retrieve(
+      const unsigned int activemask,
+      CG const& g,
+      Key const& k,
+      uint32_t* warp_counter,
+      value_type* output_buffer,
+      atomicT* num_matches,
+      OutputIt output_begin,
+      KeyEqual key_equal = KeyEqual{}) noexcept;
+
+    /**
+     * @brief Find all the matches of a given key contained in multimap using scalar
+     * loads with per-cg shared memory buffer.
+     *
+     * For keys `k = *(first + i)` existing in the map, copies `k` and all associated values to
+     * unspecified locations in `[output_begin, output_end)`. In case of non-matches, copies `k` and
+     * the empty value sentinel into the output only if `is_outer` is true.
+     *
+     * @tparam cg_size The number of threads in CUDA Cooperative Groups
+     * @tparam buffer_size Size of the output buffer
+     * @tparam is_vector_load Boolean flag indicating whether vector loads are used or not
+     * @tparam is_outer Boolean flag indicating whether outer join is peformed or not
+     * @tparam CG Cooperative Group type
+     * @tparam atomicT Type of atomic storage
+     * @tparam OutputIt Device accessible output iterator whose `value_type` is
+     * convertible to the map's `mapped_type`
+     * @tparam KeyEqual Binary callable type
+     * @param g The Cooperative Group used to retrieve
+     * @param k The key to search for
+     * @param cg_counter Pointer to the CG counter
+     * @param output_buffer Shared memory buffer of the key/value pair sequence
+     * @param num_matches Size of the output sequence
+     * @param output_begin Beginning of the output sequence of key/value pairs
+     * @param key_equal The binary callable used to compare two keys
+     * for equality
+     */
+    template <uint32_t cg_size,
+              uint32_t buffer_size,
+              bool is_vector_load,
+              bool is_outer,
+              typename CG,
+              typename atomicT,
+              typename OutputIt,
+              typename KeyEqual = thrust::equal_to<key_type>>
+    __device__ std::enable_if_t<not is_vector_load, void> retrieve(
+      CG const& g,
+      Key const& k,
+      uint32_t* cg_counter,
+      value_type* output_buffer,
+      atomicT* num_matches,
+      OutputIt output_begin,
+      KeyEqual key_equal = KeyEqual{}) noexcept;
   };  // class device_view
 
   /**

From cddc18564f1a7e33a4ae68ecaf6139f6462367b4 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Wed, 16 Jun 2021 17:30:00 -0400
Subject: [PATCH 142/267] Correction: update copyright to 2021

---
 include/cuco/detail/probe_sequences.cuh         | 2 +-
 include/cuco/detail/static_multimap_kernels.cuh | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/include/cuco/detail/probe_sequences.cuh b/include/cuco/detail/probe_sequences.cuh
index 7782e2c8a..fdfa2ff30 100644
--- a/include/cuco/detail/probe_sequences.cuh
+++ b/include/cuco/detail/probe_sequences.cuh
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2020, NVIDIA CORPORATION.
+ * Copyright (c) 2021, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap_kernels.cuh
index baef8fe4c..354aa4918 100644
--- a/include/cuco/detail/static_multimap_kernels.cuh
+++ b/include/cuco/detail/static_multimap_kernels.cuh
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2020, NVIDIA CORPORATION.
+ * Copyright (c) 2021, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.

From d61cbad0925034d341ca58b08803f77e3f8ef85a Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Thu, 17 Jun 2021 15:36:39 -0400
Subject: [PATCH 143/267] Add TODO comments: use cuda::atomic when it's ready

---
 include/cuco/detail/static_multimap_kernels.cuh | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap_kernels.cuh
index 354aa4918..a58b13255 100644
--- a/include/cuco/detail/static_multimap_kernels.cuh
+++ b/include/cuco/detail/static_multimap_kernels.cuh
@@ -330,6 +330,8 @@ __global__ std::enable_if_t<is_vector_load, void> retrieve(InputIt first,
   const uint32_t warp_lane_id  = threadIdx.x % 32;
 
   __shared__ cuco::pair_type<Key, Value> output_buffer[num_warps][buffer_size];
+  // TODO: replace this with shared memory cuda::atomic variables once the dynamiic initialization
+  // warning issue is solved __shared__ atomicT toto_countter[num_warps];
   __shared__ uint32_t warp_counter[num_warps];
 
   if (warp_lane_id == 0) { warp_counter[warp_id] = 0; }

From 80bd3256e937aaf95c537ed0f09c49223ab2b57b Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Mon, 21 Jun 2021 10:53:01 -0400
Subject: [PATCH 144/267] Update docs

---
 .../static_multimap/static_multimap_example.cu   |  2 +-
 include/cuco/static_multimap.cuh                 | 16 ++++++++--------
 2 files changed, 9 insertions(+), 9 deletions(-)

diff --git a/examples/static_multimap/static_multimap_example.cu b/examples/static_multimap/static_multimap_example.cu
index 98d821462..430dba292 100644
--- a/examples/static_multimap/static_multimap_example.cu
+++ b/examples/static_multimap/static_multimap_example.cu
@@ -27,7 +27,7 @@ int main(void)
   int empty_key_sentinel   = -1;
   int empty_value_sentinel = -1;
 
-  // Constructs a map with 100,000 slots using -1 and -1 as the empty key/value
+  // Constructs a multimap with 100,000 slots using -1 and -1 as the empty key/value
   // sentinels. Note the capacity is chosen knowing we will insert 50,000 keys,
   // for an load factor of 50%.
   cuco::static_multimap<int, int> map{100'000, empty_key_sentinel, empty_value_sentinel};
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index 9dd097886..5aebd9d81 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -43,7 +43,7 @@ namespace cuco {
 
 /**
  * @brief A GPU-accelerated, unordered, associative container of key-value
- * pairs with unique keys.
+ * pairs that supports equivalent keys.
  *
  * Allows constant time concurrent inserts or concurrent find operations (not
  * concurrent insert and find) from threads in device code.
@@ -62,16 +62,16 @@ namespace cuco {
  * - Host-side "bulk" operations
  * - Device-side "singular" operations
  *
- * The host-side bulk operations include `insert`, `find`, and `contains`. These
- * APIs should be used when there are a large number of keys to insert or lookup
- * in the map. For example, given a range of keys specified by device-accessible
- * iterators, the bulk `insert` function will insert all keys into the map.
+ * The host-side bulk operations include `insert`, `contains`, `count`, `retrieve` and their
+ * variants. These APIs should be used when there are a large number of keys to insert or lookup in
+ * the map. For example, given a range of keys specified by device-accessible iterators, the bulk
+ * `insert` function will insert all keys into the map.
  *
  * The singular device-side operations allow individual threads to perform
  * independent insert or find/contains operations from device code. These
  * operations are accessed through non-owning, trivially copyable "view" types:
  * `device_view` and `mutable_device_view`. The `device_view` class is an
- * immutable view that allows only non-modifying operations such as `find` or
+ * immutable view that allows only non-modifying operations such as `count` or
  * `contains`. The `mutable_device_view` class only allows `insert` operations.
  * The two types are separate to prevent erroneous concurrent insert/find
  * operations.
@@ -81,7 +81,7 @@ namespace cuco {
  * int empty_key_sentinel = -1;
  * int empty_value_sentinel = -1;
  *
- * // Constructs a map with 100,000 slots using -1 and -1 as the empty key/value
+ * // Constructs a multimap with 100,000 slots using -1 and -1 as the empty key/value
  * // sentinels. Note the capacity is chosen knowing we will insert 50,000 keys,
  * // for an load factor of 50%.
  * static_multimap<int, int> m{100'000, empty_key_sentinel, empty_value_sentinel};
@@ -98,7 +98,7 @@ namespace cuco {
  * m.insert(pairs.begin(), pairs.end());
  *
  * // Get a `device_view` and passes it to a kernel where threads may perform
- * // `find/contains` lookups
+ * // `contains/count/retrieve` lookups
  * kernel<<<...>>>(m.get_device_view());
  * \endcode
  *

From 68bf47d416e1cd0a19b2770442dfd99016d8987a Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Mon, 21 Jun 2021 11:03:24 -0400
Subject: [PATCH 145/267] Use lightweight stream sync instead of device sync

---
 include/cuco/detail/static_multimap.inl | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index 8e412689f..7dcfaa541 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -83,7 +83,7 @@ void static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::insert(InputI
 
   detail::insert<block_size, cg_size(), is_vector_load()>
     <<<grid_size, block_size, 0, stream>>>(first, first + num_keys, view, key_equal);
-  CUCO_CUDA_TRY(cudaDeviceSynchronize());
+  CUCO_CUDA_TRY(cudaStreamSynchronize(stream));
 }
 
 template <typename Key,
@@ -103,7 +103,7 @@ void static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::contains(
 
   detail::contains<block_size, cg_size(), is_vector_load()>
     <<<grid_size, block_size, 0, stream>>>(first, last, output_begin, view, key_equal);
-  CUCO_CUDA_TRY(cudaDeviceSynchronize());
+  CUCO_CUDA_TRY(cudaStreamSynchronize(stream));
 }
 
 template <typename Key,
@@ -132,7 +132,7 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::count(
 
   detail::count<block_size, cg_size(), Key, Value, is_vector_load()>
     <<<grid_size, block_size, 0, stream>>>(first, last, num_matches, view, key_equal);
-  CUCO_CUDA_TRY(cudaDeviceSynchronize());
+  CUCO_CUDA_TRY(cudaStreamSynchronize(stream));
 
   size_t result = *num_matches;
   CUCO_CUDA_TRY(cudaFree(num_matches));
@@ -166,7 +166,7 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::count_
 
   detail::count<block_size, cg_size(), Key, Value, is_vector_load(), is_outer>
     <<<grid_size, block_size, 0, stream>>>(first, last, num_matches, view, key_equal);
-  CUCO_CUDA_TRY(cudaDeviceSynchronize());
+  CUCO_CUDA_TRY(cudaStreamSynchronize(stream));
 
   size_t result = *num_matches;
   CUCO_CUDA_TRY(cudaFree(num_matches));
@@ -198,7 +198,7 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::pair_c
 
   detail::pair_count<block_size, cg_size(), Key, Value, is_vector_load()>
     <<<grid_size, block_size, 0, stream>>>(first, last, num_matches, view, pair_equal);
-  CUCO_CUDA_TRY(cudaDeviceSynchronize());
+  CUCO_CUDA_TRY(cudaStreamSynchronize(stream));
 
   size_t result = *num_matches;
   CUCO_CUDA_TRY(cudaFree(num_matches));
@@ -232,7 +232,7 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::pair_c
 
   detail::pair_count<block_size, cg_size(), Key, Value, is_vector_load(), is_outer>
     <<<grid_size, block_size, 0, stream>>>(first, last, num_matches, view, pair_equal);
-  CUCO_CUDA_TRY(cudaDeviceSynchronize());
+  CUCO_CUDA_TRY(cudaStreamSynchronize(stream));
 
   size_t result = *num_matches;
   CUCO_CUDA_TRY(cudaFree(num_matches));
@@ -266,7 +266,7 @@ OutputIt static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::retrieve(
 
   detail::retrieve<block_size, cg_size(), buffer_size, Key, Value, is_vector_load()>
     <<<grid_size, block_size, 0, stream>>>(first, last, output_begin, num_matches, view, key_equal);
-  CUCO_CUDA_TRY(cudaDeviceSynchronize());
+  CUCO_CUDA_TRY(cudaStreamSynchronize(stream));
 
   auto output_end = output_begin + *num_matches;
   CUCO_CUDA_TRY(cudaFree(num_matches));
@@ -302,7 +302,7 @@ OutputIt static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::retrieve_
 
   detail::retrieve<block_size, cg_size(), buffer_size, Key, Value, is_vector_load(), is_outer>
     <<<grid_size, block_size, 0, stream>>>(first, last, output_begin, num_matches, view, key_equal);
-  CUCO_CUDA_TRY(cudaDeviceSynchronize());
+  CUCO_CUDA_TRY(cudaStreamSynchronize(stream));
 
   auto output_end = output_begin + *num_matches;
   CUCO_CUDA_TRY(cudaFree(num_matches));

From b817c3428e5dc9bc215acb45e55f911b9ef9deeb Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Mon, 21 Jun 2021 11:59:30 -0400
Subject: [PATCH 146/267] Fix a cuda barrier bug: use CUCO_HAS_CUDA_BARRIER
 logic

---
 include/cuco/detail/static_multimap.inl         | 5 +++++
 include/cuco/detail/static_multimap_kernels.cuh | 1 -
 include/cuco/static_multimap.cuh                | 4 +---
 3 files changed, 6 insertions(+), 4 deletions(-)

diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index 7dcfaa541..9496fbcb1 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -508,11 +508,16 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::flush
   }
   offset = g.shfl(offset, 0);
 
+#if defined(CUCO_HAS_CUDA_BARRIER)
   cooperative_groups::memcpy_async(g,
                                    output_begin + offset,
                                    output_buffer,
                                    cuda::aligned_size_t<alignof(cuco::pair_type<Key, Value>)>(
                                      sizeof(cuco::pair_type<Key, Value>) * num_outputs));
+#else
+  cooperative_groups::memcpy_async(
+    g, output_begin + offset, output_buffer, sizeof(cuco::pair_type<Key, Value>) * num_outputs);
+#endif
 }
 
 template <typename Key,
diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap_kernels.cuh
index a58b13255..b48b0e350 100644
--- a/include/cuco/detail/static_multimap_kernels.cuh
+++ b/include/cuco/detail/static_multimap_kernels.cuh
@@ -16,7 +16,6 @@
 
 #include <cooperative_groups/memcpy_async.h>
 #include <thrust/type_traits/is_contiguous_iterator.h>
-#include <cuda/barrier>
 
 #include <cuco/detail/pair.cuh>
 
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index 5aebd9d81..d816246a5 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -639,9 +639,7 @@ class static_multimap {
                                             pair_atomic_type* const memory_to_use,
                                             device_view source_device_view) noexcept
     {
-#ifndef CUDART_VERSION
-#error CUDART_VERSION Undefined!
-#elif (CUDART_VERSION >= 11000)
+#if defined(CUCO_HAS_CUDA_BARRIER)
       __shared__ cuda::barrier<cuda::thread_scope::thread_scope_block> barrier;
       if (g.thread_rank() == 0) { init(&barrier, g.size()); }
       g.sync();

From a75b4ff4bc8a3ff6d6918091e7cdcc65ba6bc8d3 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Mon, 21 Jun 2021 12:05:33 -0400
Subject: [PATCH 147/267] Remove different signedness warnings

---
 include/cuco/detail/prime.hpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/include/cuco/detail/prime.hpp b/include/cuco/detail/prime.hpp
index 6dd96fa71..5042fabbd 100644
--- a/include/cuco/detail/prime.hpp
+++ b/include/cuco/detail/prime.hpp
@@ -32,10 +32,10 @@ constexpr bool is_prime(std::size_t num) noexcept
 {
   bool flag = true;
   // 0 and 1 are not prime numbers
-  if (num == 0 || num == 1) {
+  if (num == 0lu || num == 1lu) {
     flag = false;
   } else {
-    for (auto i = 2; i <= num / 2; ++i) {
+    for (auto i = 2lu; i <= num / 2lu; ++i) {
       if (num % i == 0) {
         flag = false;
         break;

From df8f1ba1758d5c701bd0ae37690527317b8e31ba Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Tue, 6 Jul 2021 16:03:00 -0400
Subject: [PATCH 148/267] Remove redundant codes

---
 include/cuco/detail/static_multimap.inl | 10 ----------
 1 file changed, 10 deletions(-)

diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index 6fc47f7fe..1473d552f 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -18,16 +18,6 @@
 
 namespace cuco {
 
-/**---------------------------------------------------------------------------*
- * @brief Enumeration of the possible results of attempting to insert into
- *a hash bucket
- *---------------------------------------------------------------------------**/
-enum class insert_result {
-  CONTINUE,  ///< Insert did not succeed, continue trying to insert
-  SUCCESS,   ///< New pair inserted successfully
-  DUPLICATE  ///< Insert did not succeed, key is already present
-};
-
 template <typename Key,
           typename Value,
           class ProbeSequence,

From ed4ee25e243a43feff116541cad13157d9d41e77 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Wed, 7 Jul 2021 13:04:52 -0400
Subject: [PATCH 149/267] Use uses_vector_load instead of is_vector_load

---
 include/cuco/detail/probe_sequences.cuh       |  6 +-
 include/cuco/detail/static_multimap.inl       | 62 +++++++--------
 .../cuco/detail/static_multimap_kernels.cuh   | 74 ++++++++---------
 include/cuco/static_multimap.cuh              | 79 +++++++++++--------
 4 files changed, 116 insertions(+), 105 deletions(-)

diff --git a/include/cuco/detail/probe_sequences.cuh b/include/cuco/detail/probe_sequences.cuh
index fdfa2ff30..73aebc4af 100644
--- a/include/cuco/detail/probe_sequences.cuh
+++ b/include/cuco/detail/probe_sequences.cuh
@@ -41,7 +41,7 @@ class probe_sequence_base {
 
  public:
   __host__ __device__ static constexpr uint32_t cg_size() noexcept { return CGSize; }
-  __host__ __device__ static constexpr bool is_vector_load() noexcept { return IsVectorLoad; }
+  __host__ __device__ static constexpr bool uses_vector_load() noexcept { return IsVectorLoad; }
 
   __host__ __device__ explicit probe_sequence_base(iterator slots, std::size_t capacity)
     : slots_{slots}, capacity_{capacity}
@@ -71,7 +71,7 @@ class double_hashing : public probe_sequence_base<Key, Value, CGSize, IsVectorLo
   using probe_sequence_base<Key, Value, CGSize, IsVectorLoad, Scope>::capacity_;
   using probe_sequence_base<Key, Value, CGSize, IsVectorLoad, Scope>::slots_;
   using probe_sequence_base<Key, Value, CGSize, IsVectorLoad, Scope>::cg_size;
-  using probe_sequence_base<Key, Value, CGSize, IsVectorLoad, Scope>::is_vector_load;
+  using probe_sequence_base<Key, Value, CGSize, IsVectorLoad, Scope>::uses_vector_load;
 
   __host__ __device__ explicit double_hashing(iterator slots, std::size_t capacity) noexcept
     : probe_sequence_base<Key, Value, CGSize, IsVectorLoad, Scope>{slots, capacity}
@@ -116,7 +116,7 @@ class linear_probing : public probe_sequence_base<Key, Value, CGSize, IsVectorLo
   using probe_sequence_base<Key, Value, CGSize, IsVectorLoad, Scope>::capacity_;
   using probe_sequence_base<Key, Value, CGSize, IsVectorLoad, Scope>::slots_;
   using probe_sequence_base<Key, Value, CGSize, IsVectorLoad, Scope>::cg_size;
-  using probe_sequence_base<Key, Value, CGSize, IsVectorLoad, Scope>::is_vector_load;
+  using probe_sequence_base<Key, Value, CGSize, IsVectorLoad, Scope>::uses_vector_load;
 
   __host__ __device__ explicit linear_probing(iterator slots, std::size_t capacity)
     : probe_sequence_base<Key, Value, CGSize, IsVectorLoad, Scope>{slots, capacity}
diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index 1473d552f..d589d6e87 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -29,7 +29,7 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::static_multimap(
     empty_value_sentinel_{empty_value_sentinel},
     slot_allocator_{alloc}
 {
-  if constexpr (is_vector_load()) {
+  if constexpr (uses_vector_load()) {
     capacity_ = cuco::detail::get_valid_capacity<cg_size() * 2>(capacity);
   } else {
     capacity_ = cuco::detail::get_valid_capacity<cg_size()>(capacity);
@@ -71,7 +71,7 @@ void static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::insert(InputI
   auto const grid_size  = (cg_size() * num_keys + stride * block_size - 1) / (stride * block_size);
   auto view             = get_device_mutable_view();
 
-  detail::insert<block_size, cg_size(), is_vector_load()>
+  detail::insert<block_size, cg_size(), uses_vector_load()>
     <<<grid_size, block_size, 0, stream>>>(first, first + num_keys, view, key_equal);
   CUCO_CUDA_TRY(cudaStreamSynchronize(stream));
 }
@@ -91,7 +91,7 @@ void static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::contains(
   auto const grid_size  = (cg_size() * num_keys + stride * block_size - 1) / (stride * block_size);
   auto view             = get_device_view();
 
-  detail::contains<block_size, cg_size(), is_vector_load()>
+  detail::contains<block_size, cg_size(), uses_vector_load()>
     <<<grid_size, block_size, 0, stream>>>(first, last, output_begin, view, key_equal);
   CUCO_CUDA_TRY(cudaStreamSynchronize(stream));
 }
@@ -120,7 +120,7 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::count(
   CUCO_CUDA_TRY(cudaGetDevice(&device_id));
   CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_matches, sizeof(atomic_ctr_type), device_id));
 
-  detail::count<block_size, cg_size(), Key, Value, is_vector_load()>
+  detail::count<block_size, cg_size(), Key, Value, uses_vector_load()>
     <<<grid_size, block_size, 0, stream>>>(first, last, num_matches, view, key_equal);
   CUCO_CUDA_TRY(cudaStreamSynchronize(stream));
 
@@ -154,7 +154,7 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::count_
   CUCO_CUDA_TRY(cudaGetDevice(&device_id));
   CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_matches, sizeof(atomic_ctr_type), device_id));
 
-  detail::count<block_size, cg_size(), Key, Value, is_vector_load(), is_outer>
+  detail::count<block_size, cg_size(), Key, Value, uses_vector_load(), is_outer>
     <<<grid_size, block_size, 0, stream>>>(first, last, num_matches, view, key_equal);
   CUCO_CUDA_TRY(cudaStreamSynchronize(stream));
 
@@ -186,7 +186,7 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::pair_c
   CUCO_CUDA_TRY(cudaGetDevice(&device_id));
   CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_matches, sizeof(atomic_ctr_type), device_id));
 
-  detail::pair_count<block_size, cg_size(), Key, Value, is_vector_load()>
+  detail::pair_count<block_size, cg_size(), Key, Value, uses_vector_load()>
     <<<grid_size, block_size, 0, stream>>>(first, last, num_matches, view, pair_equal);
   CUCO_CUDA_TRY(cudaStreamSynchronize(stream));
 
@@ -220,7 +220,7 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::pair_c
   CUCO_CUDA_TRY(cudaGetDevice(&device_id));
   CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_matches, sizeof(atomic_ctr_type), device_id));
 
-  detail::pair_count<block_size, cg_size(), Key, Value, is_vector_load(), is_outer>
+  detail::pair_count<block_size, cg_size(), Key, Value, uses_vector_load(), is_outer>
     <<<grid_size, block_size, 0, stream>>>(first, last, num_matches, view, pair_equal);
   CUCO_CUDA_TRY(cudaStreamSynchronize(stream));
 
@@ -242,7 +242,7 @@ OutputIt static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::retrieve(
   auto num_keys         = std::distance(first, last);
   auto const block_size = 128;
   // Using per-warp buffer for vector loads and per-CG buffer for scalar loads
-  auto const buffer_size = is_vector_load() ? (32u * 3u) : (cg_size() * 3u);
+  auto const buffer_size = uses_vector_load() ? (32u * 3u) : (cg_size() * 3u);
   auto const stride      = 1;
   auto const grid_size   = (cg_size() * num_keys + stride * block_size - 1) / (stride * block_size);
   auto view              = get_device_view();
@@ -254,7 +254,7 @@ OutputIt static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::retrieve(
   CUCO_CUDA_TRY(cudaGetDevice(&device_id));
   CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_matches, sizeof(atomic_ctr_type), device_id));
 
-  detail::retrieve<block_size, cg_size(), buffer_size, Key, Value, is_vector_load()>
+  detail::retrieve<block_size, cg_size(), buffer_size, Key, Value, uses_vector_load()>
     <<<grid_size, block_size, 0, stream>>>(first, last, output_begin, num_matches, view, key_equal);
   CUCO_CUDA_TRY(cudaStreamSynchronize(stream));
 
@@ -276,7 +276,7 @@ OutputIt static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::retrieve_
   auto num_keys         = std::distance(first, last);
   auto const block_size = 128;
   // Using per-warp buffer for vector loads and per-CG buffer for scalar loads
-  auto const buffer_size = is_vector_load() ? (32u * 3u) : (cg_size() * 3u);
+  auto const buffer_size = uses_vector_load() ? (32u * 3u) : (cg_size() * 3u);
   auto const stride      = 1;
   auto const grid_size   = (cg_size() * num_keys + stride * block_size - 1) / (stride * block_size);
   auto view              = get_device_view();
@@ -290,7 +290,7 @@ OutputIt static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::retrieve_
   CUCO_CUDA_TRY(cudaGetDevice(&device_id));
   CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_matches, sizeof(atomic_ctr_type), device_id));
 
-  detail::retrieve<block_size, cg_size(), buffer_size, Key, Value, is_vector_load(), is_outer>
+  detail::retrieve<block_size, cg_size(), buffer_size, Key, Value, uses_vector_load(), is_outer>
     <<<grid_size, block_size, 0, stream>>>(first, last, output_begin, num_matches, view, key_equal);
   CUCO_CUDA_TRY(cudaStreamSynchronize(stream));
 
@@ -406,8 +406,8 @@ template <typename Key,
           class ProbeSequence,
           cuda::thread_scope Scope,
           typename Allocator>
-template <bool is_vector_load, typename CG, typename KeyEqual>
-__device__ std::enable_if_t<is_vector_load, void>
+template <bool uses_vector_load, typename CG, typename KeyEqual>
+__device__ std::enable_if_t<uses_vector_load, void>
 static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_mutable_view::insert(
   CG g, value_type const& insert_pair, KeyEqual key_equal) noexcept
 {
@@ -469,8 +469,8 @@ template <typename Key,
           class ProbeSequence,
           cuda::thread_scope Scope,
           typename Allocator>
-template <bool is_vector_load, typename CG, typename KeyEqual>
-__device__ std::enable_if_t<not is_vector_load, void>
+template <bool uses_vector_load, typename CG, typename KeyEqual>
+__device__ std::enable_if_t<not uses_vector_load, void>
 static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_mutable_view::insert(
   CG g, value_type const& insert_pair, KeyEqual key_equal) noexcept
 {
@@ -608,8 +608,8 @@ template <typename Key,
           class ProbeSequence,
           cuda::thread_scope Scope,
           typename Allocator>
-template <bool is_vector_load, typename CG, typename KeyEqual>
-__device__ std::enable_if_t<is_vector_load, bool>
+template <bool uses_vector_load, typename CG, typename KeyEqual>
+__device__ std::enable_if_t<uses_vector_load, bool>
 static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::contains(
   CG g, Key const& k, KeyEqual key_equal) noexcept
 {
@@ -647,8 +647,8 @@ template <typename Key,
           class ProbeSequence,
           cuda::thread_scope Scope,
           typename Allocator>
-template <bool is_vector_load, typename CG, typename KeyEqual>
-__device__ std::enable_if_t<not is_vector_load, bool>
+template <bool uses_vector_load, typename CG, typename KeyEqual>
+__device__ std::enable_if_t<not uses_vector_load, bool>
 static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::contains(
   CG g, Key const& k, KeyEqual key_equal) noexcept
 {
@@ -680,8 +680,8 @@ template <typename Key,
           class ProbeSequence,
           cuda::thread_scope Scope,
           typename Allocator>
-template <bool is_vector_load, bool is_outer, typename CG, typename KeyEqual>
-__device__ std::enable_if_t<is_vector_load, void>
+template <bool uses_vector_load, bool is_outer, typename CG, typename KeyEqual>
+__device__ std::enable_if_t<uses_vector_load, void>
 static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::count(
   CG const& g, Key const& k, std::size_t& thread_num_matches, KeyEqual key_equal) noexcept
 {
@@ -746,8 +746,8 @@ template <typename Key,
           class ProbeSequence,
           cuda::thread_scope Scope,
           typename Allocator>
-template <bool is_vector_load, bool is_outer, typename CG, typename KeyEqual>
-__device__ std::enable_if_t<not is_vector_load, void>
+template <bool uses_vector_load, bool is_outer, typename CG, typename KeyEqual>
+__device__ std::enable_if_t<not uses_vector_load, void>
 static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::count(
   CG const& g, Key const& k, std::size_t& thread_num_matches, KeyEqual key_equal) noexcept
 {
@@ -798,8 +798,8 @@ template <typename Key,
           class ProbeSequence,
           cuda::thread_scope Scope,
           typename Allocator>
-template <bool is_vector_load, bool is_outer, typename CG, typename PairEqual>
-__device__ std::enable_if_t<is_vector_load, void>
+template <bool uses_vector_load, bool is_outer, typename CG, typename PairEqual>
+__device__ std::enable_if_t<uses_vector_load, void>
 static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_count(
   CG const& g,
   value_type const& pair,
@@ -870,8 +870,8 @@ template <typename Key,
           class ProbeSequence,
           cuda::thread_scope Scope,
           typename Allocator>
-template <bool is_vector_load, bool is_outer, typename CG, typename PairEqual>
-__device__ std::enable_if_t<not is_vector_load, void>
+template <bool uses_vector_load, bool is_outer, typename CG, typename PairEqual>
+__device__ std::enable_if_t<not uses_vector_load, void>
 static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_count(
   CG const& g,
   value_type const& pair,
@@ -925,13 +925,13 @@ template <typename Key,
           cuda::thread_scope Scope,
           typename Allocator>
 template <uint32_t buffer_size,
-          bool is_vector_load,
+          bool uses_vector_load,
           bool is_outer,
           typename CG,
           typename atomicT,
           typename OutputIt,
           typename KeyEqual>
-__device__ std::enable_if_t<is_vector_load, void>
+__device__ std::enable_if_t<uses_vector_load, void>
 static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::retrieve(
   const unsigned int activemask,
   CG const& g,
@@ -1078,13 +1078,13 @@ template <typename Key,
           typename Allocator>
 template <uint32_t cg_size,
           uint32_t buffer_size,
-          bool is_vector_load,
+          bool uses_vector_load,
           bool is_outer,
           typename CG,
           typename atomicT,
           typename OutputIt,
           typename KeyEqual>
-__device__ std::enable_if_t<not is_vector_load, void>
+__device__ std::enable_if_t<not uses_vector_load, void>
 static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::retrieve(
   CG const& g,
   Key const& k,
diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap_kernels.cuh
index b48b0e350..2ae6d791b 100644
--- a/include/cuco/detail/static_multimap_kernels.cuh
+++ b/include/cuco/detail/static_multimap_kernels.cuh
@@ -66,7 +66,7 @@ __global__ void initialize(pair_atomic_type* const slots, Key k, Value v, std::s
  * @tparam block_size The size of the thread block
  * @tparam tile_size The number of threads in the Cooperative Groups used to perform
  * inserts
- * @tparam is_vector_load Boolean flag indicating whether vector loads are used or not
+ * @tparam uses_vector_load Boolean flag indicating whether vector loads are used or not
  * @tparam InputIt Device accessible input iterator whose `value_type` is
  * convertible to the map's `value_type`
  * @tparam viewT Type of device view allowing access of hash map storage
@@ -78,7 +78,7 @@ __global__ void initialize(pair_atomic_type* const slots, Key k, Value v, std::s
  */
 template <uint32_t block_size,
           uint32_t tile_size,
-          bool is_vector_load,
+          bool uses_vector_load,
           typename InputIt,
           typename viewT,
           typename KeyEqual>
@@ -91,7 +91,7 @@ __global__ void insert(InputIt first, InputIt last, viewT view, KeyEqual key_equ
   while (it < last) {
     // force conversion to value_type
     typename viewT::value_type const insert_pair{*it};
-    view.insert<is_vector_load>(tile, insert_pair, key_equal);
+    view.insert<uses_vector_load>(tile, insert_pair, key_equal);
     it += (gridDim.x * block_size) / tile_size;
   }
 }
@@ -106,7 +106,7 @@ __global__ void insert(InputIt first, InputIt last, viewT view, KeyEqual key_equ
  *
  * @tparam block_size The size of the thread block
  * @tparam tile_size The number of threads in the Cooperative Groups
- * @tparam is_vector_load Boolean flag indicating whether vector loads are used or not
+ * @tparam uses_vector_load Boolean flag indicating whether vector loads are used or not
  * @tparam InputIt Device accessible input iterator whose `value_type` is
  * convertible to the map's `key_type`
  * @tparam OutputIt Device accessible output iterator whose `value_type` is
@@ -121,7 +121,7 @@ __global__ void insert(InputIt first, InputIt last, viewT view, KeyEqual key_equ
  */
 template <uint32_t block_size,
           uint32_t tile_size,
-          bool is_vector_load,
+          bool uses_vector_load,
           typename InputIt,
           typename OutputIt,
           typename viewT,
@@ -136,7 +136,7 @@ __global__ void contains(
 
   while (first + key_idx < last) {
     auto key   = *(first + key_idx);
-    auto found = view.contains<is_vector_load>(tile, key, key_equal);
+    auto found = view.contains<uses_vector_load>(tile, key, key_equal);
 
     /*
      * The ld.relaxed.gpu instruction used in view.find causes L1 to
@@ -162,7 +162,7 @@ __global__ void contains(
  * @tparam tile_size The number of threads in the Cooperative Groups used to perform counts
  * @tparam Key key type
  * @tparam Value The type of the mapped value for the map
- * @tparam is_vector_load Boolean flag indicating whether vector loads are used or not
+ * @tparam uses_vector_load Boolean flag indicating whether vector loads are used or not
  * @tparam is_outer Boolean flag indicating whether the current functions is used for outer join
  * operations or not
  * @tparam InputIt Device accessible input iterator whose `value_type` is convertible to the map's
@@ -180,7 +180,7 @@ template <uint32_t block_size,
           uint32_t tile_size,
           typename Key,
           typename Value,
-          bool is_vector_load,
+          bool uses_vector_load,
           bool is_outer = false,
           typename InputIt,
           typename atomicT,
@@ -199,7 +199,7 @@ __global__ void count(
 
   while (first + key_idx < last) {
     auto key = *(first + key_idx);
-    view.count<is_vector_load, is_outer>(tile, key, thread_num_matches, key_equal);
+    view.count<uses_vector_load, is_outer>(tile, key, thread_num_matches, key_equal);
     key_idx += (gridDim.x * block_size) / tile_size;
   }
 
@@ -220,7 +220,7 @@ __global__ void count(
  * @tparam tile_size The number of threads in the Cooperative Groups used to perform counts
  * @tparam Key key type
  * @tparam Value The type of the mapped value for the map
- * @tparam is_vector_load Boolean flag indicating whether vector loads are used or not
+ * @tparam uses_vector_load Boolean flag indicating whether vector loads are used or not
  * @tparam is_outer Boolean flag indicating whether the current functions is used for outer join
  * operations or not
  * @tparam Input Device accesible input iterator of key/value pairs
@@ -237,7 +237,7 @@ template <uint32_t block_size,
           uint32_t tile_size,
           typename Key,
           typename Value,
-          bool is_vector_load,
+          bool uses_vector_load,
           bool is_outer = false,
           typename InputIt,
           typename atomicT,
@@ -256,7 +256,7 @@ __global__ void pair_count(
 
   while (first + pair_idx < last) {
     typename viewT::value_type const pair = *(first + pair_idx);
-    view.pair_count<is_vector_load, is_outer>(tile, pair, thread_num_matches, pair_equal);
+    view.pair_count<uses_vector_load, is_outer>(tile, pair, thread_num_matches, pair_equal);
     pair_idx += (gridDim.x * block_size) / tile_size;
   }
 
@@ -284,7 +284,7 @@ __global__ void pair_count(
  * @tparam buffer_size Size of the output buffer
  * @tparam Key key type
  * @tparam Value The type of the mapped value for the map
- * @tparam is_vector_load Boolean flag indicating whether vector loads are used or not
+ * @tparam uses_vector_load Boolean flag indicating whether vector loads are used or not
  * @tparam is_outer Boolean flag indicating whether the current functions is used for outer join
  * operations or not
  * @tparam InputIt Device accessible input iterator whose `value_type` is
@@ -306,19 +306,19 @@ template <uint32_t block_size,
           uint32_t buffer_size,
           typename Key,
           typename Value,
-          bool is_vector_load,
+          bool uses_vector_load,
           bool is_outer = false,
           typename InputIt,
           typename OutputIt,
           typename atomicT,
           typename viewT,
           typename KeyEqual>
-__global__ std::enable_if_t<is_vector_load, void> retrieve(InputIt first,
-                                                           InputIt last,
-                                                           OutputIt output_begin,
-                                                           atomicT* num_matches,
-                                                           viewT view,
-                                                           KeyEqual key_equal)
+__global__ std::enable_if_t<uses_vector_load, void> retrieve(InputIt first,
+                                                             InputIt last,
+                                                             OutputIt output_begin,
+                                                             atomicT* num_matches,
+                                                             viewT view,
+                                                             KeyEqual key_equal)
 {
   auto tile    = cg::tiled_partition<tile_size>(cg::this_thread_block());
   auto tid     = block_size * blockIdx.x + threadIdx.x;
@@ -339,14 +339,14 @@ __global__ std::enable_if_t<is_vector_load, void> retrieve(InputIt first,
 
   while (first + key_idx < last) {
     auto key = *(first + key_idx);
-    view.retrieve<buffer_size, is_vector_load, is_outer>(activemask,
-                                                         tile,
-                                                         key,
-                                                         &warp_counter[warp_id],
-                                                         output_buffer[warp_id],
-                                                         num_matches,
-                                                         output_begin,
-                                                         key_equal);
+    view.retrieve<buffer_size, uses_vector_load, is_outer>(activemask,
+                                                           tile,
+                                                           key,
+                                                           &warp_counter[warp_id],
+                                                           output_buffer[warp_id],
+                                                           num_matches,
+                                                           output_begin,
+                                                           key_equal);
     key_idx += (gridDim.x * block_size) / tile_size;
   }
 
@@ -373,7 +373,7 @@ __global__ std::enable_if_t<is_vector_load, void> retrieve(InputIt first,
  * @tparam buffer_size Size of the output buffer
  * @tparam Key key type
  * @tparam Value The type of the mapped value for the map
- * @tparam is_vector_load Boolean flag indicating whether vector loads are used or not
+ * @tparam uses_vector_load Boolean flag indicating whether vector loads are used or not
  * @tparam is_outer Boolean flag indicating whether the current functions is used for outer join
  * operations or not
  * @tparam InputIt Device accessible input iterator whose `value_type` is
@@ -395,19 +395,19 @@ template <uint32_t block_size,
           uint32_t buffer_size,
           typename Key,
           typename Value,
-          bool is_vector_load,
+          bool uses_vector_load,
           bool is_outer = false,
           typename InputIt,
           typename OutputIt,
           typename atomicT,
           typename viewT,
           typename KeyEqual>
-__global__ std::enable_if_t<not is_vector_load, void> retrieve(InputIt first,
-                                                               InputIt last,
-                                                               OutputIt output_begin,
-                                                               atomicT* num_matches,
-                                                               viewT view,
-                                                               KeyEqual key_equal)
+__global__ std::enable_if_t<not uses_vector_load, void> retrieve(InputIt first,
+                                                                 InputIt last,
+                                                                 OutputIt output_begin,
+                                                                 atomicT* num_matches,
+                                                                 viewT view,
+                                                                 KeyEqual key_equal)
 {
   auto tile    = cg::tiled_partition<tile_size>(cg::this_thread_block());
   auto tid     = block_size * blockIdx.x + threadIdx.x;
@@ -424,7 +424,7 @@ __global__ std::enable_if_t<not is_vector_load, void> retrieve(InputIt first,
 
   while (first + key_idx < last) {
     auto key = *(first + key_idx);
-    view.retrieve<tile_size, buffer_size, is_vector_load, is_outer>(
+    view.retrieve<tile_size, buffer_size, uses_vector_load, is_outer>(
       tile, key, &cg_counter[cg_id], output_buffer[cg_id], num_matches, output_begin, key_equal);
     key_idx += (gridDim.x * block_size) / tile_size;
   }
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index e28d92b23..051dcf381 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -143,8 +143,19 @@ class static_multimap {
   static_multimap& operator=(static_multimap const&) = delete;
   static_multimap& operator=(static_multimap&&) = delete;
 
+  /**
+   * @brief The size of the CUDA cooperative thread group.
+   *
+   * @return Boolean indicating if concurrent insert/find is supported.
+   */
   static constexpr uint32_t cg_size() noexcept { return ProbeSequence::cg_size(); }
-  static constexpr bool is_vector_load() noexcept { return ProbeSequence::is_vector_load(); }
+
+  /**
+   * @brief Indicate if vector load is used.
+   *
+   * @return Boolean indicating if vector load is used.
+   */
+  static constexpr bool uses_vector_load() noexcept { return ProbeSequence::uses_vector_load(); }
 
   /**
    * @brief Construct a fixed-size map with the specified capacity and sentinel values.
@@ -587,7 +598,7 @@ class static_multimap {
     /**
      * @brief Inserts the specified key/value pair into the map using vector loads.
      *
-     * @tparam is_vector_load Boolean flag indicating whether vector loads are used or not
+     * @tparam uses_vector_load Boolean flag indicating whether vector loads are used or not
      * @tparam CG Cooperative Group type
      * @tparam KeyEqual Binary callable type
      *
@@ -597,14 +608,14 @@ class static_multimap {
      * equality
      * @return void.
      */
-    template <bool is_vector_load, typename CG, typename KeyEqual = thrust::equal_to<key_type>>
-    __device__ std::enable_if_t<is_vector_load, void> insert(
+    template <bool uses_vector_load, typename CG, typename KeyEqual = thrust::equal_to<key_type>>
+    __device__ std::enable_if_t<uses_vector_load, void> insert(
       CG g, value_type const& insert_pair, KeyEqual key_equal = KeyEqual{}) noexcept;
 
     /**
      * @brief Inserts the specified key/value pair into the map using scalar loads.
      *
-     * @tparam is_vector_load Boolean flag indicating whether vector loads are used or not
+     * @tparam uses_vector_load Boolean flag indicating whether vector loads are used or not
      * @tparam CG Cooperative Group type
      * @tparam KeyEqual Binary callable type
      *
@@ -614,8 +625,8 @@ class static_multimap {
      * equality
      * @return void.
      */
-    template <bool is_vector_load, typename CG, typename KeyEqual = thrust::equal_to<key_type>>
-    __device__ std::enable_if_t<not is_vector_load, void> insert(
+    template <bool uses_vector_load, typename CG, typename KeyEqual = thrust::equal_to<key_type>>
+    __device__ std::enable_if_t<not uses_vector_load, void> insert(
       CG g, value_type const& insert_pair, KeyEqual key_equal = KeyEqual{}) noexcept;
   };  // class device mutable view
 
@@ -798,7 +809,7 @@ class static_multimap {
      * significant boost in throughput compared to the non Cooperative Group
      * `contains` at moderate to high load factors.
      *
-     * @tparam is_vector_load Boolean flag indicating whether vector loads are used or not
+     * @tparam uses_vector_load Boolean flag indicating whether vector loads are used or not
      * @tparam CG Cooperative Group type
      * @tparam KeyEqual Binary callable type
      * @param g The Cooperative Group used to perform the contains operation
@@ -808,8 +819,8 @@ class static_multimap {
      * @return A boolean indicating whether the key/value pair
      * containing `k` was inserted
      */
-    template <bool is_vector_load, typename CG, typename KeyEqual = thrust::equal_to<key_type>>
-    __device__ std::enable_if_t<is_vector_load, bool> contains(
+    template <bool uses_vector_load, typename CG, typename KeyEqual = thrust::equal_to<key_type>>
+    __device__ std::enable_if_t<uses_vector_load, bool> contains(
       CG g, Key const& k, KeyEqual key_equal = KeyEqual{}) noexcept;
 
     /**
@@ -821,7 +832,7 @@ class static_multimap {
      * significant boost in throughput compared to the non Cooperative Group
      * `contains` at moderate to high load factors.
      *
-     * @tparam is_vector_load Boolean flag indicating whether vector loads are used or not
+     * @tparam uses_vector_load Boolean flag indicating whether vector loads are used or not
      * @tparam CG Cooperative Group type
      * @tparam KeyEqual Binary callable type
      * @param g The Cooperative Group used to perform the contains operation
@@ -831,14 +842,14 @@ class static_multimap {
      * @return A boolean indicating whether the key/value pair
      * containing `k` was inserted
      */
-    template <bool is_vector_load, typename CG, typename KeyEqual = thrust::equal_to<key_type>>
-    __device__ std::enable_if_t<not is_vector_load, bool> contains(
+    template <bool uses_vector_load, typename CG, typename KeyEqual = thrust::equal_to<key_type>>
+    __device__ std::enable_if_t<not uses_vector_load, bool> contains(
       CG g, Key const& k, KeyEqual key_equal = KeyEqual{}) noexcept;
 
     /**
      * @brief Counts the occurrence of a given key contained in multimap using vector loads.
      *
-     * @tparam is_vector_load Boolean flag indicating whether vector loads are used or not
+     * @tparam uses_vector_load Boolean flag indicating whether vector loads are used or not
      * @tparam is_outer Boolean flag indicating whether outer join is peformed or not
      * @tparam CG Cooperative Group type
      * @tparam KeyEqual Binary callable type
@@ -848,11 +859,11 @@ class static_multimap {
      * @param key_equal The binary callable used to compare two keys
      * for equality
      */
-    template <bool is_vector_load,
+    template <bool uses_vector_load,
               bool is_outer,
               typename CG,
               typename KeyEqual = thrust::equal_to<key_type>>
-    __device__ std::enable_if_t<is_vector_load, void> count(
+    __device__ std::enable_if_t<uses_vector_load, void> count(
       CG const& g,
       Key const& k,
       std::size_t& thread_num_matches,
@@ -861,7 +872,7 @@ class static_multimap {
     /**
      * @brief Counts the occurrence of a given key contained in multimap using scalar loads.
      *
-     * @tparam is_vector_load Boolean flag indicating whether vector loads are used or not
+     * @tparam uses_vector_load Boolean flag indicating whether vector loads are used or not
      * @tparam is_outer Boolean flag indicating whether outer join is peformed or not
      * @tparam CG Cooperative Group type
      * @tparam KeyEqual Binary callable type
@@ -871,11 +882,11 @@ class static_multimap {
      * @param key_equal The binary callable used to compare two keys
      * for equality
      */
-    template <bool is_vector_load,
+    template <bool uses_vector_load,
               bool is_outer,
               typename CG,
               typename KeyEqual = thrust::equal_to<key_type>>
-    __device__ std::enable_if_t<not is_vector_load, void> count(
+    __device__ std::enable_if_t<not uses_vector_load, void> count(
       CG const& g,
       Key const& k,
       std::size_t& thread_num_matches,
@@ -885,7 +896,7 @@ class static_multimap {
      * @brief Counts the occurrence of a given key/value pair contained in multimap using vector
      * loads.
      *
-     * @tparam is_vector_load Boolean flag indicating whether vector loads are used or not
+     * @tparam uses_vector_load Boolean flag indicating whether vector loads are used or not
      * @tparam is_outer Boolean flag indicating whether outer join is peformed or not
      * @tparam CG Cooperative Group type
      * @tparam PairEqual Binary callable type
@@ -895,17 +906,17 @@ class static_multimap {
      * @param pair_equal The binary callable used to compare two pairs
      * for equality
      */
-    template <bool is_vector_load, bool is_outer, typename CG, typename PairEqual>
-    __device__ std::enable_if_t<is_vector_load, void> pair_count(CG const& g,
-                                                                 value_type const& pair,
-                                                                 std::size_t& thread_num_matches,
-                                                                 PairEqual pair_equal) noexcept;
+    template <bool uses_vector_load, bool is_outer, typename CG, typename PairEqual>
+    __device__ std::enable_if_t<uses_vector_load, void> pair_count(CG const& g,
+                                                                   value_type const& pair,
+                                                                   std::size_t& thread_num_matches,
+                                                                   PairEqual pair_equal) noexcept;
 
     /**
      * @brief Counts the occurrence of a given key/value pair contained in multimap using scalar
      * loads.
      *
-     * @tparam is_vector_load Boolean flag indicating whether vector loads are used or not
+     * @tparam uses_vector_load Boolean flag indicating whether vector loads are used or not
      * @tparam is_outer Boolean flag indicating whether outer join is peformed or not
      * @tparam CG Cooperative Group type
      * @tparam PairEqual Binary callable type
@@ -915,8 +926,8 @@ class static_multimap {
      * @param pair_equal The binary callable used to compare two pairs
      * for equality
      */
-    template <bool is_vector_load, bool is_outer, typename CG, typename PairEqual>
-    __device__ std::enable_if_t<not is_vector_load, void> pair_count(
+    template <bool uses_vector_load, bool is_outer, typename CG, typename PairEqual>
+    __device__ std::enable_if_t<not uses_vector_load, void> pair_count(
       CG const& g,
       value_type const& pair,
       std::size_t& thread_num_matches,
@@ -931,7 +942,7 @@ class static_multimap {
      * the empty value sentinel into the output only if `is_outer` is true.
      *
      * @tparam buffer_size Size of the output buffer
-     * @tparam is_vector_load Boolean flag indicating whether vector loads are used or not
+     * @tparam uses_vector_load Boolean flag indicating whether vector loads are used or not
      * @tparam is_outer Boolean flag indicating whether outer join is peformed or not
      * @tparam CG Cooperative Group type
      * @tparam atomicT Type of atomic storage
@@ -949,13 +960,13 @@ class static_multimap {
      * for equality
      */
     template <uint32_t buffer_size,
-              bool is_vector_load,
+              bool uses_vector_load,
               bool is_outer,
               typename CG,
               typename atomicT,
               typename OutputIt,
               typename KeyEqual = thrust::equal_to<key_type>>
-    __device__ std::enable_if_t<is_vector_load, void> retrieve(
+    __device__ std::enable_if_t<uses_vector_load, void> retrieve(
       const unsigned int activemask,
       CG const& g,
       Key const& k,
@@ -975,7 +986,7 @@ class static_multimap {
      *
      * @tparam cg_size The number of threads in CUDA Cooperative Groups
      * @tparam buffer_size Size of the output buffer
-     * @tparam is_vector_load Boolean flag indicating whether vector loads are used or not
+     * @tparam uses_vector_load Boolean flag indicating whether vector loads are used or not
      * @tparam is_outer Boolean flag indicating whether outer join is peformed or not
      * @tparam CG Cooperative Group type
      * @tparam atomicT Type of atomic storage
@@ -993,13 +1004,13 @@ class static_multimap {
      */
     template <uint32_t cg_size,
               uint32_t buffer_size,
-              bool is_vector_load,
+              bool uses_vector_load,
               bool is_outer,
               typename CG,
               typename atomicT,
               typename OutputIt,
               typename KeyEqual = thrust::equal_to<key_type>>
-    __device__ std::enable_if_t<not is_vector_load, void> retrieve(
+    __device__ std::enable_if_t<not uses_vector_load, void> retrieve(
       CG const& g,
       Key const& k,
       uint32_t* cg_counter,

From e978f155760024347d2874bdf7fc3ef0c811c188 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Wed, 7 Jul 2021 13:46:31 -0400
Subject: [PATCH 150/267] Update copyrights

---
 include/cuco/detail/bitwise_compare.cuh | 2 +-
 include/cuco/detail/pair.cuh            | 2 +-
 include/cuco/detail/static_map.inl      | 2 +-
 include/cuco/static_map.cuh             | 2 +-
 4 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/include/cuco/detail/bitwise_compare.cuh b/include/cuco/detail/bitwise_compare.cuh
index 267f5b6d3..959e296bf 100644
--- a/include/cuco/detail/bitwise_compare.cuh
+++ b/include/cuco/detail/bitwise_compare.cuh
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2020, NVIDIA CORPORATION.
+ * Copyright (c) 2020-2021, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/include/cuco/detail/pair.cuh b/include/cuco/detail/pair.cuh
index 01c33b600..0d8a85e3e 100644
--- a/include/cuco/detail/pair.cuh
+++ b/include/cuco/detail/pair.cuh
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2020, NVIDIA CORPORATION.
+ * Copyright (c) 2020-2021, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/include/cuco/detail/static_map.inl b/include/cuco/detail/static_map.inl
index c44df3b63..4f0fffc51 100644
--- a/include/cuco/detail/static_map.inl
+++ b/include/cuco/detail/static_map.inl
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2020, NVIDIA CORPORATION.
+ * Copyright (c) 2020-2021, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/include/cuco/static_map.cuh b/include/cuco/static_map.cuh
index 86fa825df..42b7021c0 100644
--- a/include/cuco/static_map.cuh
+++ b/include/cuco/static_map.cuh
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2020, NVIDIA CORPORATION.
+ * Copyright (c) 2020-2021, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.

From cec11458ccae5b4766c7cb7d7ec4229588abb5ee Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Wed, 7 Jul 2021 14:47:11 -0400
Subject: [PATCH 151/267] Use is_packable logic to determine uses_vector_load

---
 include/cuco/detail/probe_sequences.cuh | 42 ++++++++++++-------------
 1 file changed, 21 insertions(+), 21 deletions(-)

diff --git a/include/cuco/detail/probe_sequences.cuh b/include/cuco/detail/probe_sequences.cuh
index 73aebc4af..ec5d03e86 100644
--- a/include/cuco/detail/probe_sequences.cuh
+++ b/include/cuco/detail/probe_sequences.cuh
@@ -26,7 +26,6 @@ namespace cuco {
 template <typename Key,
           typename Value,
           uint32_t CGSize          = 8,
-          bool IsVectorLoad        = sizeof(Key) == 4,
           cuda::thread_scope Scope = cuda::thread_scope_device>
 class probe_sequence_base {
  protected:
@@ -41,7 +40,10 @@ class probe_sequence_base {
 
  public:
   __host__ __device__ static constexpr uint32_t cg_size() noexcept { return CGSize; }
-  __host__ __device__ static constexpr bool uses_vector_load() noexcept { return IsVectorLoad; }
+  __host__ __device__ static constexpr bool uses_vector_load() noexcept
+  {
+    return cuco::detail::is_packable<value_type>();
+  }
 
   __host__ __device__ explicit probe_sequence_base(iterator slots, std::size_t capacity)
     : slots_{slots}, capacity_{capacity}
@@ -61,20 +63,19 @@ class probe_sequence_base {
 template <typename Key,
           typename Value,
           uint32_t CGSize          = 8,
-          bool IsVectorLoad        = sizeof(Key) == 4,
           typename Hash1           = cuco::detail::MurmurHash3_32<Key>,
           typename Hash2           = cuco::detail::MurmurHash3_32<Key>,
           cuda::thread_scope Scope = cuda::thread_scope_device>
-class double_hashing : public probe_sequence_base<Key, Value, CGSize, IsVectorLoad, Scope> {
+class double_hashing : public probe_sequence_base<Key, Value, CGSize, Scope> {
  public:
-  using iterator = typename probe_sequence_base<Key, Value, CGSize, IsVectorLoad, Scope>::iterator;
-  using probe_sequence_base<Key, Value, CGSize, IsVectorLoad, Scope>::capacity_;
-  using probe_sequence_base<Key, Value, CGSize, IsVectorLoad, Scope>::slots_;
-  using probe_sequence_base<Key, Value, CGSize, IsVectorLoad, Scope>::cg_size;
-  using probe_sequence_base<Key, Value, CGSize, IsVectorLoad, Scope>::uses_vector_load;
+  using iterator = typename probe_sequence_base<Key, Value, CGSize, Scope>::iterator;
+  using probe_sequence_base<Key, Value, CGSize, Scope>::capacity_;
+  using probe_sequence_base<Key, Value, CGSize, Scope>::slots_;
+  using probe_sequence_base<Key, Value, CGSize, Scope>::cg_size;
+  using probe_sequence_base<Key, Value, CGSize, Scope>::uses_vector_load;
 
   __host__ __device__ explicit double_hashing(iterator slots, std::size_t capacity) noexcept
-    : probe_sequence_base<Key, Value, CGSize, IsVectorLoad, Scope>{slots, capacity}
+    : probe_sequence_base<Key, Value, CGSize, Scope>{slots, capacity}
   {
   }
 
@@ -82,7 +83,7 @@ class double_hashing : public probe_sequence_base<Key, Value, CGSize, IsVectorLo
   __device__ iterator initial_slot(CG const& g, Key const k) noexcept
   {
     std::size_t index;
-    if constexpr (IsVectorLoad) {
+    if constexpr (uses_vector_load()) {
       step_size_ = (hash2_(k + 1) % (capacity_ / (cg_size() * 2) - 1) + 1) * cg_size() * 2;
       index      = hash1_(k) % (capacity_ / (cg_size() * 2)) * cg_size() * 2 + g.thread_rank() * 2;
     } else {
@@ -107,26 +108,25 @@ class double_hashing : public probe_sequence_base<Key, Value, CGSize, IsVectorLo
 template <typename Key,
           typename Value,
           uint32_t CGSize          = 8,
-          bool IsVectorLoad        = sizeof(Key) == 4,
           typename Hash            = cuco::detail::MurmurHash3_32<Key>,
           cuda::thread_scope Scope = cuda::thread_scope_device>
-class linear_probing : public probe_sequence_base<Key, Value, CGSize, IsVectorLoad, Scope> {
+class linear_probing : public probe_sequence_base<Key, Value, CGSize, Scope> {
  public:
-  using iterator = typename probe_sequence_base<Key, Value, CGSize, IsVectorLoad, Scope>::iterator;
-  using probe_sequence_base<Key, Value, CGSize, IsVectorLoad, Scope>::capacity_;
-  using probe_sequence_base<Key, Value, CGSize, IsVectorLoad, Scope>::slots_;
-  using probe_sequence_base<Key, Value, CGSize, IsVectorLoad, Scope>::cg_size;
-  using probe_sequence_base<Key, Value, CGSize, IsVectorLoad, Scope>::uses_vector_load;
+  using iterator = typename probe_sequence_base<Key, Value, CGSize, Scope>::iterator;
+  using probe_sequence_base<Key, Value, CGSize, Scope>::capacity_;
+  using probe_sequence_base<Key, Value, CGSize, Scope>::slots_;
+  using probe_sequence_base<Key, Value, CGSize, Scope>::cg_size;
+  using probe_sequence_base<Key, Value, CGSize, Scope>::uses_vector_load;
 
   __host__ __device__ explicit linear_probing(iterator slots, std::size_t capacity)
-    : probe_sequence_base<Key, Value, CGSize, IsVectorLoad, Scope>{slots, capacity}
+    : probe_sequence_base<Key, Value, CGSize, Scope>{slots, capacity}
   {
   }
 
   template <typename CG>
   __device__ iterator initial_slot(CG const& g, Key const k) noexcept
   {
-    if constexpr (IsVectorLoad) {
+    if constexpr (uses_vector_load()) {
       return &slots_[(hash_(k) + g.thread_rank() * 2) % capacity_];
     } else {
       return &slots_[(hash_(k) + g.thread_rank()) % capacity_];
@@ -136,7 +136,7 @@ class linear_probing : public probe_sequence_base<Key, Value, CGSize, IsVectorLo
   __device__ iterator next_slot(iterator s) noexcept
   {
     std::size_t index = s - slots_;
-    if constexpr (IsVectorLoad) {
+    if constexpr (uses_vector_load()) {
       return &slots_[(index + cg_size() * 2) % capacity_];
     } else {
       return &slots_[(index + cg_size()) % capacity_];

From 662a6438c6c455e87cb7293b121565b502449068 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Thu, 8 Jul 2021 18:39:45 -0400
Subject: [PATCH 152/267] Get rid of enable_if in public APIs

---
 include/cuco/detail/static_multimap.inl       | 384 +++++++++++----
 .../cuco/detail/static_multimap_kernels.cuh   |  67 ++-
 include/cuco/static_multimap.cuh              | 461 ++++++++++++++----
 3 files changed, 692 insertions(+), 220 deletions(-)

diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index d589d6e87..7e903ad3f 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -71,7 +71,7 @@ void static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::insert(InputI
   auto const grid_size  = (cg_size() * num_keys + stride * block_size - 1) / (stride * block_size);
   auto view             = get_device_mutable_view();
 
-  detail::insert<block_size, cg_size(), uses_vector_load()>
+  detail::insert<block_size, cg_size()>
     <<<grid_size, block_size, 0, stream>>>(first, first + num_keys, view, key_equal);
   CUCO_CUDA_TRY(cudaStreamSynchronize(stream));
 }
@@ -91,7 +91,7 @@ void static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::contains(
   auto const grid_size  = (cg_size() * num_keys + stride * block_size - 1) / (stride * block_size);
   auto view             = get_device_view();
 
-  detail::contains<block_size, cg_size(), uses_vector_load()>
+  detail::contains<block_size, cg_size()>
     <<<grid_size, block_size, 0, stream>>>(first, last, output_begin, view, key_equal);
   CUCO_CUDA_TRY(cudaStreamSynchronize(stream));
 }
@@ -113,6 +113,8 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::count(
   auto const grid_size  = (cg_size() * num_keys + stride * block_size - 1) / (stride * block_size);
   auto view             = get_device_view();
 
+  constexpr bool is_outer = false;
+
   atomic_ctr_type* num_matches;
   CUCO_CUDA_TRY(cudaMallocManaged(&num_matches, sizeof(atomic_ctr_type)));
   *num_matches = 0;
@@ -120,7 +122,7 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::count(
   CUCO_CUDA_TRY(cudaGetDevice(&device_id));
   CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_matches, sizeof(atomic_ctr_type), device_id));
 
-  detail::count<block_size, cg_size(), Key, Value, uses_vector_load()>
+  detail::count<block_size, cg_size(), Key, Value, is_outer>
     <<<grid_size, block_size, 0, stream>>>(first, last, num_matches, view, key_equal);
   CUCO_CUDA_TRY(cudaStreamSynchronize(stream));
 
@@ -154,7 +156,7 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::count_
   CUCO_CUDA_TRY(cudaGetDevice(&device_id));
   CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_matches, sizeof(atomic_ctr_type), device_id));
 
-  detail::count<block_size, cg_size(), Key, Value, uses_vector_load(), is_outer>
+  detail::count<block_size, cg_size(), Key, Value, is_outer>
     <<<grid_size, block_size, 0, stream>>>(first, last, num_matches, view, key_equal);
   CUCO_CUDA_TRY(cudaStreamSynchronize(stream));
 
@@ -179,6 +181,8 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::pair_c
   auto const grid_size  = (cg_size() * num_keys + stride * block_size - 1) / (stride * block_size);
   auto view             = get_device_view();
 
+  constexpr bool is_outer = false;
+
   atomic_ctr_type* num_matches;
   CUCO_CUDA_TRY(cudaMallocManaged(&num_matches, sizeof(atomic_ctr_type)));
   *num_matches = 0;
@@ -186,7 +190,7 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::pair_c
   CUCO_CUDA_TRY(cudaGetDevice(&device_id));
   CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_matches, sizeof(atomic_ctr_type), device_id));
 
-  detail::pair_count<block_size, cg_size(), Key, Value, uses_vector_load()>
+  detail::pair_count<block_size, cg_size(), Key, Value, is_outer>
     <<<grid_size, block_size, 0, stream>>>(first, last, num_matches, view, pair_equal);
   CUCO_CUDA_TRY(cudaStreamSynchronize(stream));
 
@@ -220,7 +224,7 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::pair_c
   CUCO_CUDA_TRY(cudaGetDevice(&device_id));
   CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_matches, sizeof(atomic_ctr_type), device_id));
 
-  detail::pair_count<block_size, cg_size(), Key, Value, uses_vector_load(), is_outer>
+  detail::pair_count<block_size, cg_size(), Key, Value, is_outer>
     <<<grid_size, block_size, 0, stream>>>(first, last, num_matches, view, pair_equal);
   CUCO_CUDA_TRY(cudaStreamSynchronize(stream));
 
@@ -247,6 +251,8 @@ OutputIt static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::retrieve(
   auto const grid_size   = (cg_size() * num_keys + stride * block_size - 1) / (stride * block_size);
   auto view              = get_device_view();
 
+  constexpr bool is_outer = false;
+
   atomic_ctr_type* num_matches;
   CUCO_CUDA_TRY(cudaMallocManaged(&num_matches, sizeof(atomic_ctr_type)));
   *num_matches = 0;
@@ -254,7 +260,7 @@ OutputIt static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::retrieve(
   CUCO_CUDA_TRY(cudaGetDevice(&device_id));
   CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_matches, sizeof(atomic_ctr_type), device_id));
 
-  detail::retrieve<block_size, cg_size(), buffer_size, Key, Value, uses_vector_load()>
+  detail::retrieve<block_size, cg_size(), buffer_size, Key, Value, uses_vector_load(), is_outer>
     <<<grid_size, block_size, 0, stream>>>(first, last, output_begin, num_matches, view, key_equal);
   CUCO_CUDA_TRY(cudaStreamSynchronize(stream));
 
@@ -408,7 +414,7 @@ template <typename Key,
           typename Allocator>
 template <bool uses_vector_load, typename CG, typename KeyEqual>
 __device__ std::enable_if_t<uses_vector_load, void>
-static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_mutable_view::insert(
+static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_mutable_view::insert_impl(
   CG g, value_type const& insert_pair, KeyEqual key_equal) noexcept
 {
   auto current_slot = initial_slot(g, insert_pair.first);
@@ -471,7 +477,7 @@ template <typename Key,
           typename Allocator>
 template <bool uses_vector_load, typename CG, typename KeyEqual>
 __device__ std::enable_if_t<not uses_vector_load, void>
-static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_mutable_view::insert(
+static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_mutable_view::insert_impl(
   CG g, value_type const& insert_pair, KeyEqual key_equal) noexcept
 {
   auto current_slot = initial_slot(g, insert_pair.first);
@@ -523,84 +529,12 @@ template <typename Key,
           class ProbeSequence,
           cuda::thread_scope Scope,
           typename Allocator>
-template <uint32_t cg_size, typename CG, typename atomicT, typename OutputIt>
-__inline__ __device__ std::enable_if_t<thrust::is_contiguous_iterator<OutputIt>::value, void>
-static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::flush_output_buffer(
-  CG const& g,
-  uint32_t const num_outputs,
-  value_type* output_buffer,
-  atomicT* num_matches,
-  OutputIt output_begin) noexcept
-{
-  std::size_t offset;
-  const auto lane_id = g.thread_rank();
-  if (0 == lane_id) {
-    offset = num_matches->fetch_add(num_outputs, cuda::std::memory_order_relaxed);
-  }
-  offset = g.shfl(offset, 0);
-
-#if defined(CUCO_HAS_CUDA_BARRIER)
-  cooperative_groups::memcpy_async(g,
-                                   output_begin + offset,
-                                   output_buffer,
-                                   cuda::aligned_size_t<alignof(cuco::pair_type<Key, Value>)>(
-                                     sizeof(cuco::pair_type<Key, Value>) * num_outputs));
-#else
-  cooperative_groups::memcpy_async(
-    g, output_begin + offset, output_buffer, sizeof(cuco::pair_type<Key, Value>) * num_outputs);
-#endif
-}
-
-template <typename Key,
-          typename Value,
-          class ProbeSequence,
-          cuda::thread_scope Scope,
-          typename Allocator>
-template <uint32_t cg_size, typename CG, typename atomicT, typename OutputIt>
-__inline__ __device__ std::enable_if_t<not thrust::is_contiguous_iterator<OutputIt>::value, void>
-static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::flush_output_buffer(
-  CG const& g,
-  uint32_t const num_outputs,
-  value_type* output_buffer,
-  atomicT* num_items,
-  OutputIt output_begin) noexcept
+template <typename CG, typename KeyEqual>
+__device__ void
+static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_mutable_view::insert(
+  CG g, value_type const& insert_pair, KeyEqual key_equal) noexcept
 {
-  std::size_t offset;
-  const auto lane_id = g.thread_rank();
-  if (0 == lane_id) { offset = num_items->fetch_add(num_outputs, cuda::std::memory_order_relaxed); }
-  offset = g.shfl(offset, 0);
-
-  for (auto index = lane_id; index < num_outputs; index += cg_size) {
-    *(output_begin + offset + index) = output_buffer[index];
-  }
-}
-
-template <typename Key,
-          typename Value,
-          class ProbeSequence,
-          cuda::thread_scope Scope,
-          typename Allocator>
-template <typename atomicT, typename OutputIt>
-__inline__ __device__ void
-static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::flush_output_buffer(
-  const unsigned int activemask,
-  uint32_t const num_outputs,
-  value_type* output_buffer,
-  atomicT* num_matches,
-  OutputIt output_begin) noexcept
-{
-  int num_threads = __popc(activemask);
-
-  std::size_t offset;
-  const auto lane_id = threadIdx.x % 32;
-  if (0 == lane_id) {
-    offset = num_matches->fetch_add(num_outputs, cuda::std::memory_order_relaxed);
-  }
-  offset = __shfl_sync(activemask, offset, 0);
-
-  for (auto index = lane_id; index < num_outputs; index += num_threads) {
-    *(output_begin + offset + index) = output_buffer[index];
-  }
+  insert_impl<uses_vector_load()>(g, insert_pair, key_equal);
 }
 
 template <typename Key,
@@ -610,7 +544,7 @@ template <typename Key,
           typename Allocator>
 template <bool uses_vector_load, typename CG, typename KeyEqual>
 __device__ std::enable_if_t<uses_vector_load, bool>
-static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::contains(
+static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::contains_impl(
   CG g, Key const& k, KeyEqual key_equal) noexcept
 {
   auto current_slot = initial_slot(g, k);
@@ -649,7 +583,7 @@ template <typename Key,
           typename Allocator>
 template <bool uses_vector_load, typename CG, typename KeyEqual>
 __device__ std::enable_if_t<not uses_vector_load, bool>
-static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::contains(
+static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::contains_impl(
   CG g, Key const& k, KeyEqual key_equal) noexcept
 {
   auto current_slot = initial_slot(g, k);
@@ -682,7 +616,7 @@ template <typename Key,
           typename Allocator>
 template <bool uses_vector_load, bool is_outer, typename CG, typename KeyEqual>
 __device__ std::enable_if_t<uses_vector_load, void>
-static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::count(
+static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::count_impl(
   CG const& g, Key const& k, std::size_t& thread_num_matches, KeyEqual key_equal) noexcept
 {
   auto current_slot = initial_slot(g, k);
@@ -748,7 +682,7 @@ template <typename Key,
           typename Allocator>
 template <bool uses_vector_load, bool is_outer, typename CG, typename KeyEqual>
 __device__ std::enable_if_t<not uses_vector_load, void>
-static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::count(
+static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::count_impl(
   CG const& g, Key const& k, std::size_t& thread_num_matches, KeyEqual key_equal) noexcept
 {
   auto current_slot = initial_slot(g, k);
@@ -800,7 +734,7 @@ template <typename Key,
           typename Allocator>
 template <bool uses_vector_load, bool is_outer, typename CG, typename PairEqual>
 __device__ std::enable_if_t<uses_vector_load, void>
-static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_count(
+static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_count_impl(
   CG const& g,
   value_type const& pair,
   std::size_t& thread_num_matches,
@@ -872,7 +806,7 @@ template <typename Key,
           typename Allocator>
 template <bool uses_vector_load, bool is_outer, typename CG, typename PairEqual>
 __device__ std::enable_if_t<not uses_vector_load, void>
-static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_count(
+static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_count_impl(
   CG const& g,
   value_type const& pair,
   std::size_t& thread_num_matches,
@@ -925,14 +859,13 @@ template <typename Key,
           cuda::thread_scope Scope,
           typename Allocator>
 template <uint32_t buffer_size,
-          bool uses_vector_load,
           bool is_outer,
           typename CG,
           typename atomicT,
           typename OutputIt,
           typename KeyEqual>
-__device__ std::enable_if_t<uses_vector_load, void>
-static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::retrieve(
+__device__ void
+static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::warp_retrieve_impl(
   const unsigned int activemask,
   CG const& g,
   Key const& k,
@@ -1078,14 +1011,13 @@ template <typename Key,
           typename Allocator>
 template <uint32_t cg_size,
           uint32_t buffer_size,
-          bool uses_vector_load,
           bool is_outer,
           typename CG,
           typename atomicT,
           typename OutputIt,
           typename KeyEqual>
-__device__ std::enable_if_t<not uses_vector_load, void>
-static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::retrieve(
+__device__ void
+static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::cg_retrieve_impl(
   CG const& g,
   Key const& k,
   uint32_t* cg_counter,
@@ -1186,4 +1118,260 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::retri
   }
 }
 
+// public APIs
+
+template <typename Key,
+          typename Value,
+          class ProbeSequence,
+          cuda::thread_scope Scope,
+          typename Allocator>
+template <uint32_t cg_size, typename CG, typename atomicT, typename OutputIt>
+__inline__ __device__ std::enable_if_t<thrust::is_contiguous_iterator<OutputIt>::value, void>
+static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::flush_output_buffer(
+  CG const& g,
+  uint32_t const num_outputs,
+  value_type* output_buffer,
+  atomicT* num_matches,
+  OutputIt output_begin) noexcept
+{
+  std::size_t offset;
+  const auto lane_id = g.thread_rank();
+  if (0 == lane_id) {
+    offset = num_matches->fetch_add(num_outputs, cuda::std::memory_order_relaxed);
+  }
+  offset = g.shfl(offset, 0);
+
+#if defined(CUCO_HAS_CUDA_BARRIER)
+  cooperative_groups::memcpy_async(g,
+                                   output_begin + offset,
+                                   output_buffer,
+                                   cuda::aligned_size_t<alignof(cuco::pair_type<Key, Value>)>(
+                                     sizeof(cuco::pair_type<Key, Value>) * num_outputs));
+#else
+  cooperative_groups::memcpy_async(
+    g, output_begin + offset, output_buffer, sizeof(cuco::pair_type<Key, Value>) * num_outputs);
+#endif
+}
+
+template <typename Key,
+          typename Value,
+          class ProbeSequence,
+          cuda::thread_scope Scope,
+          typename Allocator>
+template <uint32_t cg_size, typename CG, typename atomicT, typename OutputIt>
+__inline__ __device__ std::enable_if_t<not thrust::is_contiguous_iterator<OutputIt>::value, void>
+static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::flush_output_buffer(
+  CG const& g,
+  uint32_t const num_outputs,
+  value_type* output_buffer,
+  atomicT* num_items,
+  OutputIt output_begin) noexcept
+{
+  std::size_t offset;
+  const auto lane_id = g.thread_rank();
+  if (0 == lane_id) { offset = num_items->fetch_add(num_outputs, cuda::std::memory_order_relaxed); }
+  offset = g.shfl(offset, 0);
+
+  for (auto index = lane_id; index < num_outputs; index += cg_size) {
+    *(output_begin + offset + index) = output_buffer[index];
+  }
+}
+
+template <typename Key,
+          typename Value,
+          class ProbeSequence,
+          cuda::thread_scope Scope,
+          typename Allocator>
+template <typename atomicT, typename OutputIt>
+__inline__ __device__ void
+static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::flush_output_buffer(
+  const unsigned int activemask,
+  uint32_t const num_outputs,
+  value_type* output_buffer,
+  atomicT* num_matches,
+  OutputIt output_begin) noexcept
+{
+  int num_threads = __popc(activemask);
+
+  std::size_t offset;
+  const auto lane_id = threadIdx.x % 32;
+  if (0 == lane_id) {
+    offset = num_matches->fetch_add(num_outputs, cuda::std::memory_order_relaxed);
+  }
+  offset = __shfl_sync(activemask, offset, 0);
+
+  for (auto index = lane_id; index < num_outputs; index += num_threads) {
+    *(output_begin + offset + index) = output_buffer[index];
+  }
+}
+
+template <typename Key,
+          typename Value,
+          class ProbeSequence,
+          cuda::thread_scope Scope,
+          typename Allocator>
+template <typename CG, typename KeyEqual>
+__device__ bool static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::contains(
+  CG g, Key const& k, KeyEqual key_equal) noexcept
+{
+  return contains_impl<uses_vector_load()>(g, k, key_equal);
+}
+
+template <typename Key,
+          typename Value,
+          class ProbeSequence,
+          cuda::thread_scope Scope,
+          typename Allocator>
+template <typename CG, typename KeyEqual>
+__device__ void static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::count(
+  CG const& g, Key const& k, std::size_t& thread_num_matches, KeyEqual key_equal) noexcept
+{
+  constexpr bool is_outer = false;
+  count_impl<uses_vector_load(), is_outer>(g, k, thread_num_matches, key_equal);
+}
+
+template <typename Key,
+          typename Value,
+          class ProbeSequence,
+          cuda::thread_scope Scope,
+          typename Allocator>
+template <typename CG, typename KeyEqual>
+__device__ void
+static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::count_outer(
+  CG const& g, Key const& k, std::size_t& thread_num_matches, KeyEqual key_equal) noexcept
+{
+  constexpr bool is_outer = true;
+  count_impl<uses_vector_load(), is_outer>(g, k, thread_num_matches, key_equal);
+}
+
+template <typename Key,
+          typename Value,
+          class ProbeSequence,
+          cuda::thread_scope Scope,
+          typename Allocator>
+template <typename CG, typename PairEqual>
+__device__ void
+static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_count(
+  CG const& g,
+  value_type const& pair,
+  std::size_t& thread_num_matches,
+  PairEqual pair_equal) noexcept
+{
+  constexpr bool is_outer = false;
+  pair_count_impl<uses_vector_load(), is_outer>(g, pair, thread_num_matches, pair_equal);
+}
+
+template <typename Key,
+          typename Value,
+          class ProbeSequence,
+          cuda::thread_scope Scope,
+          typename Allocator>
+template <typename CG, typename PairEqual>
+__device__ void
+static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_count_outer(
+  CG const& g,
+  value_type const& pair,
+  std::size_t& thread_num_matches,
+  PairEqual pair_equal) noexcept
+{
+  constexpr bool is_outer = true;
+  pair_count_impl<uses_vector_load(), is_outer>(g, pair, thread_num_matches, pair_equal);
+}
+
+template <typename Key,
+          typename Value,
+          class ProbeSequence,
+          cuda::thread_scope Scope,
+          typename Allocator>
+template <uint32_t buffer_size, typename CG, typename atomicT, typename OutputIt, typename KeyEqual>
+__device__ void
+static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::warp_retrieve(
+  const unsigned int activemask,
+  CG const& g,
+  Key const& k,
+  uint32_t* warp_counter,
+  value_type* output_buffer,
+  atomicT* num_matches,
+  OutputIt output_begin,
+  KeyEqual key_equal) noexcept
+{
+  constexpr bool is_outer = false;
+  warp_retrieve_impl<buffer_size, is_outer>(
+    activemask, g, k, warp_counter, output_buffer, num_matches, output_begin);
+}
+
+template <typename Key,
+          typename Value,
+          class ProbeSequence,
+          cuda::thread_scope Scope,
+          typename Allocator>
+template <uint32_t buffer_size, typename CG, typename atomicT, typename OutputIt, typename KeyEqual>
+__device__ void
+static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::warp_retrieve_outer(
+  const unsigned int activemask,
+  CG const& g,
+  Key const& k,
+  uint32_t* warp_counter,
+  value_type* output_buffer,
+  atomicT* num_matches,
+  OutputIt output_begin,
+  KeyEqual key_equal) noexcept
+{
+  constexpr bool is_outer = true;
+  warp_retrieve_impl<buffer_size, is_outer>(
+    activemask, g, k, warp_counter, output_buffer, num_matches, output_begin);
+}
+
+template <typename Key,
+          typename Value,
+          class ProbeSequence,
+          cuda::thread_scope Scope,
+          typename Allocator>
+template <uint32_t cg_size,
+          uint32_t buffer_size,
+          typename CG,
+          typename atomicT,
+          typename OutputIt,
+          typename KeyEqual>
+__device__ void
+static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::cg_retrieve(
+  CG const& g,
+  Key const& k,
+  uint32_t* cg_counter,
+  value_type* output_buffer,
+  atomicT* num_matches,
+  OutputIt output_begin,
+  KeyEqual key_equal) noexcept
+{
+  constexpr bool is_outer = false;
+  cg_retrieve_impl<cg_size, buffer_size, is_outer>(
+    g, k, cg_counter, output_buffer, num_matches, output_begin);
+}
+
+template <typename Key,
+          typename Value,
+          class ProbeSequence,
+          cuda::thread_scope Scope,
+          typename Allocator>
+template <uint32_t cg_size,
+          uint32_t buffer_size,
+          typename CG,
+          typename atomicT,
+          typename OutputIt,
+          typename KeyEqual>
+__device__ void
+static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::cg_retrieve_outer(
+  CG const& g,
+  Key const& k,
+  uint32_t* cg_counter,
+  value_type* output_buffer,
+  atomicT* num_matches,
+  OutputIt output_begin,
+  KeyEqual key_equal) noexcept
+{
+  constexpr bool is_outer = true;
+  cg_retrieve_impl<cg_size, buffer_size, is_outer>(
+    g, k, cg_counter, output_buffer, num_matches, output_begin);
+}
+
 }  // namespace cuco
diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap_kernels.cuh
index 2ae6d791b..4d51fb512 100644
--- a/include/cuco/detail/static_multimap_kernels.cuh
+++ b/include/cuco/detail/static_multimap_kernels.cuh
@@ -66,7 +66,6 @@ __global__ void initialize(pair_atomic_type* const slots, Key k, Value v, std::s
  * @tparam block_size The size of the thread block
  * @tparam tile_size The number of threads in the Cooperative Groups used to perform
  * inserts
- * @tparam uses_vector_load Boolean flag indicating whether vector loads are used or not
  * @tparam InputIt Device accessible input iterator whose `value_type` is
  * convertible to the map's `value_type`
  * @tparam viewT Type of device view allowing access of hash map storage
@@ -78,7 +77,6 @@ __global__ void initialize(pair_atomic_type* const slots, Key k, Value v, std::s
  */
 template <uint32_t block_size,
           uint32_t tile_size,
-          bool uses_vector_load,
           typename InputIt,
           typename viewT,
           typename KeyEqual>
@@ -91,7 +89,7 @@ __global__ void insert(InputIt first, InputIt last, viewT view, KeyEqual key_equ
   while (it < last) {
     // force conversion to value_type
     typename viewT::value_type const insert_pair{*it};
-    view.insert<uses_vector_load>(tile, insert_pair, key_equal);
+    view.insert(tile, insert_pair, key_equal);
     it += (gridDim.x * block_size) / tile_size;
   }
 }
@@ -106,7 +104,6 @@ __global__ void insert(InputIt first, InputIt last, viewT view, KeyEqual key_equ
  *
  * @tparam block_size The size of the thread block
  * @tparam tile_size The number of threads in the Cooperative Groups
- * @tparam uses_vector_load Boolean flag indicating whether vector loads are used or not
  * @tparam InputIt Device accessible input iterator whose `value_type` is
  * convertible to the map's `key_type`
  * @tparam OutputIt Device accessible output iterator whose `value_type` is
@@ -121,7 +118,6 @@ __global__ void insert(InputIt first, InputIt last, viewT view, KeyEqual key_equ
  */
 template <uint32_t block_size,
           uint32_t tile_size,
-          bool uses_vector_load,
           typename InputIt,
           typename OutputIt,
           typename viewT,
@@ -136,7 +132,7 @@ __global__ void contains(
 
   while (first + key_idx < last) {
     auto key   = *(first + key_idx);
-    auto found = view.contains<uses_vector_load>(tile, key, key_equal);
+    auto found = view.contains(tile, key, key_equal);
 
     /*
      * The ld.relaxed.gpu instruction used in view.find causes L1 to
@@ -180,8 +176,7 @@ template <uint32_t block_size,
           uint32_t tile_size,
           typename Key,
           typename Value,
-          bool uses_vector_load,
-          bool is_outer = false,
+          bool is_outer,
           typename InputIt,
           typename atomicT,
           typename viewT,
@@ -199,7 +194,11 @@ __global__ void count(
 
   while (first + key_idx < last) {
     auto key = *(first + key_idx);
-    view.count<uses_vector_load, is_outer>(tile, key, thread_num_matches, key_equal);
+    if constexpr (is_outer) {
+      view.count_outer(tile, key, thread_num_matches, key_equal);
+    } else {
+      view.count(tile, key, thread_num_matches, key_equal);
+    }
     key_idx += (gridDim.x * block_size) / tile_size;
   }
 
@@ -220,7 +219,6 @@ __global__ void count(
  * @tparam tile_size The number of threads in the Cooperative Groups used to perform counts
  * @tparam Key key type
  * @tparam Value The type of the mapped value for the map
- * @tparam uses_vector_load Boolean flag indicating whether vector loads are used or not
  * @tparam is_outer Boolean flag indicating whether the current functions is used for outer join
  * operations or not
  * @tparam Input Device accesible input iterator of key/value pairs
@@ -237,8 +235,7 @@ template <uint32_t block_size,
           uint32_t tile_size,
           typename Key,
           typename Value,
-          bool uses_vector_load,
-          bool is_outer = false,
+          bool is_outer,
           typename InputIt,
           typename atomicT,
           typename viewT,
@@ -256,7 +253,11 @@ __global__ void pair_count(
 
   while (first + pair_idx < last) {
     typename viewT::value_type const pair = *(first + pair_idx);
-    view.pair_count<uses_vector_load, is_outer>(tile, pair, thread_num_matches, pair_equal);
+    if constexpr (is_outer) {
+      view.pair_count_outer(tile, pair, thread_num_matches, pair_equal);
+    } else {
+      view.pair_count(tile, pair, thread_num_matches, pair_equal);
+    }
     pair_idx += (gridDim.x * block_size) / tile_size;
   }
 
@@ -307,7 +308,7 @@ template <uint32_t block_size,
           typename Key,
           typename Value,
           bool uses_vector_load,
-          bool is_outer = false,
+          bool is_outer,
           typename InputIt,
           typename OutputIt,
           typename atomicT,
@@ -339,14 +340,25 @@ __global__ std::enable_if_t<uses_vector_load, void> retrieve(InputIt first,
 
   while (first + key_idx < last) {
     auto key = *(first + key_idx);
-    view.retrieve<buffer_size, uses_vector_load, is_outer>(activemask,
-                                                           tile,
-                                                           key,
-                                                           &warp_counter[warp_id],
-                                                           output_buffer[warp_id],
-                                                           num_matches,
-                                                           output_begin,
-                                                           key_equal);
+    if constexpr (is_outer) {
+      view.warp_retrieve_outer<buffer_size>(activemask,
+                                            tile,
+                                            key,
+                                            &warp_counter[warp_id],
+                                            output_buffer[warp_id],
+                                            num_matches,
+                                            output_begin,
+                                            key_equal);
+    } else {
+      view.warp_retrieve<buffer_size>(activemask,
+                                      tile,
+                                      key,
+                                      &warp_counter[warp_id],
+                                      output_buffer[warp_id],
+                                      num_matches,
+                                      output_begin,
+                                      key_equal);
+    }
     key_idx += (gridDim.x * block_size) / tile_size;
   }
 
@@ -396,7 +408,7 @@ template <uint32_t block_size,
           typename Key,
           typename Value,
           bool uses_vector_load,
-          bool is_outer = false,
+          bool is_outer,
           typename InputIt,
           typename OutputIt,
           typename atomicT,
@@ -424,8 +436,13 @@ __global__ std::enable_if_t<not uses_vector_load, void> retrieve(InputIt first,
 
   while (first + key_idx < last) {
     auto key = *(first + key_idx);
-    view.retrieve<tile_size, buffer_size, uses_vector_load, is_outer>(
-      tile, key, &cg_counter[cg_id], output_buffer[cg_id], num_matches, output_begin, key_equal);
+    if constexpr (is_outer) {
+      view.cg_retrieve_outer<tile_size, buffer_size>(
+        tile, key, &cg_counter[cg_id], output_buffer[cg_id], num_matches, output_begin, key_equal);
+    } else {
+      view.cg_retrieve<tile_size, buffer_size>(
+        tile, key, &cg_counter[cg_id], output_buffer[cg_id], num_matches, output_begin, key_equal);
+    }
     key_idx += (gridDim.x * block_size) / tile_size;
   }
 
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index 051dcf381..1e03a0738 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -371,6 +371,13 @@ class static_multimap {
     using iterator       = pair_atomic_type*;
     using const_iterator = pair_atomic_type const*;
 
+    /**
+     * @brief Indicate if vector load is used.
+     *
+     * @return Boolean indicating if vector load is used.
+     */
+    static constexpr bool uses_vector_load() noexcept { return ProbeSequence::uses_vector_load(); }
+
    private:
     ProbeSequence probe_sequence_;
     Key empty_key_sentinel_{};      ///< Key value that represents an empty slot
@@ -519,6 +526,8 @@ class static_multimap {
     using iterator       = typename device_view_base::iterator;
     using const_iterator = typename device_view_base::const_iterator;
 
+    using device_view_base::uses_vector_load;
+
     /**
      * @brief Construct a mutable view of the first `capacity` slots of the
      * slots array pointed to by `slots`.
@@ -594,7 +603,6 @@ class static_multimap {
                                                  value_type const& insert_pair,
                                                  KeyEqual key_equal) noexcept;
 
-   public:
     /**
      * @brief Inserts the specified key/value pair into the map using vector loads.
      *
@@ -609,7 +617,7 @@ class static_multimap {
      * @return void.
      */
     template <bool uses_vector_load, typename CG, typename KeyEqual = thrust::equal_to<key_type>>
-    __device__ std::enable_if_t<uses_vector_load, void> insert(
+    __device__ std::enable_if_t<uses_vector_load, void> insert_impl(
       CG g, value_type const& insert_pair, KeyEqual key_equal = KeyEqual{}) noexcept;
 
     /**
@@ -626,8 +634,26 @@ class static_multimap {
      * @return void.
      */
     template <bool uses_vector_load, typename CG, typename KeyEqual = thrust::equal_to<key_type>>
-    __device__ std::enable_if_t<not uses_vector_load, void> insert(
+    __device__ std::enable_if_t<not uses_vector_load, void> insert_impl(
       CG g, value_type const& insert_pair, KeyEqual key_equal = KeyEqual{}) noexcept;
+
+   public:
+    /**
+     * @brief Inserts the specified key/value pair into the map.
+     *
+     * @tparam CG Cooperative Group type
+     * @tparam KeyEqual Binary callable type
+     *
+     * @param g The Cooperative Group that performs the insert
+     * @param insert_pair The pair to insert
+     * @param key_equal The binary callable used to compare two keys for
+     * equality
+     * @return void.
+     */
+    template <typename CG, typename KeyEqual = thrust::equal_to<key_type>>
+    __device__ void insert(CG g,
+                           value_type const& insert_pair,
+                           KeyEqual key_equal = KeyEqual{}) noexcept;
   };  // class device mutable view
 
   /**
@@ -646,6 +672,8 @@ class static_multimap {
     using iterator       = typename device_view_base::iterator;
     using const_iterator = typename device_view_base::const_iterator;
 
+    using device_view_base::uses_vector_load;
+
     /**
      * @brief Construct a view of the first `capacity` slots of the
      * slots array pointed to by `slots`.
@@ -736,70 +764,7 @@ class static_multimap {
                          source_device_view.get_empty_value_sentinel());
     }
 
-    /**
-     * @brief Flushes per-CG shared memory buffer into the output sequence using CG memcpy_async.
-     *
-     * @tparam cg_size The number of threads in the Cooperative Groups
-     * @tparam CG Cooperative Group type
-     * @tparam atomicT Type of atomic storage
-     * @tparam OutputIt Device accessible output iterator whose `value_type` is
-     * convertible to the map's `mapped_type`
-     * @param g The Cooperative Group used to flush output buffer
-     * @param num_outputs Number of valid output in the buffer
-     * @param output_buffer Shared memory buffer of the key/value pair sequence
-     * @param num_matches Size of the output sequence
-     * @param output_begin Beginning of the output sequence of key/value pairs
-     */
-    template <uint32_t cg_size, typename CG, typename atomicT, typename OutputIt>
-    __inline__ __device__ std::enable_if_t<thrust::is_contiguous_iterator<OutputIt>::value, void>
-    flush_output_buffer(CG const& g,
-                        uint32_t const num_outputs,
-                        value_type* output_buffer,
-                        atomicT* num_matches,
-                        OutputIt output_begin) noexcept;
-
-    /**
-     * @brief Flushes per-CG shared memory buffer into the output sequence using CG memcpy_async.
-     *
-     * @tparam cg_size The number of threads in the Cooperative Groups
-     * @tparam CG Cooperative Group type
-     * @tparam atomicT Type of atomic storage
-     * @tparam OutputIt Device accessible output iterator whose `value_type` is
-     * convertible to the map's `mapped_type`
-     * @param g The Cooperative Group used to flush output buffer
-     * @param num_outputs Number of valid output in the buffer
-     * @param output_buffer Shared memory buffer of the key/value pair sequence
-     * @param num_matches Size of the output sequence
-     * @param output_begin Beginning of the output sequence of key/value pairs
-     */
-    template <uint32_t cg_size, typename CG, typename atomicT, typename OutputIt>
-    __inline__ __device__
-      std::enable_if_t<not thrust::is_contiguous_iterator<OutputIt>::value, void>
-      flush_output_buffer(CG const& g,
-                          uint32_t const num_outputs,
-                          value_type* output_buffer,
-                          atomicT* num_matches,
-                          OutputIt output_begin) noexcept;
-
-    /**
-     * @brief Flushes per-warp shared memory buffer into the output sequence.
-     *
-     * @tparam atomicT Type of atomic storage
-     * @tparam OutputIt Device accessible output iterator whose `value_type` is
-     * convertible to the map's `mapped_type`
-     * @param activemask Mask of active threads in the warp
-     * @param num_outputs Number of valid output in the buffer
-     * @param output_buffer Shared memory buffer of the key/value pair sequence
-     * @param num_matches Size of the output sequence
-     * @param output_begin Beginning of the output sequence of key/value pairs
-     */
-    template <typename atomicT, typename OutputIt>
-    __inline__ __device__ void flush_output_buffer(const unsigned int activemask,
-                                                   uint32_t const num_outputs,
-                                                   value_type* output_buffer,
-                                                   atomicT* num_matches,
-                                                   OutputIt output_begin) noexcept;
-
+   private:
     /**
      * @brief Indicates whether the key `k` was inserted into the map using vector loads.
      *
@@ -820,7 +785,7 @@ class static_multimap {
      * containing `k` was inserted
      */
     template <bool uses_vector_load, typename CG, typename KeyEqual = thrust::equal_to<key_type>>
-    __device__ std::enable_if_t<uses_vector_load, bool> contains(
+    __device__ std::enable_if_t<uses_vector_load, bool> contains_impl(
       CG g, Key const& k, KeyEqual key_equal = KeyEqual{}) noexcept;
 
     /**
@@ -843,7 +808,7 @@ class static_multimap {
      * containing `k` was inserted
      */
     template <bool uses_vector_load, typename CG, typename KeyEqual = thrust::equal_to<key_type>>
-    __device__ std::enable_if_t<not uses_vector_load, bool> contains(
+    __device__ std::enable_if_t<not uses_vector_load, bool> contains_impl(
       CG g, Key const& k, KeyEqual key_equal = KeyEqual{}) noexcept;
 
     /**
@@ -863,7 +828,7 @@ class static_multimap {
               bool is_outer,
               typename CG,
               typename KeyEqual = thrust::equal_to<key_type>>
-    __device__ std::enable_if_t<uses_vector_load, void> count(
+    __device__ std::enable_if_t<uses_vector_load, void> count_impl(
       CG const& g,
       Key const& k,
       std::size_t& thread_num_matches,
@@ -886,7 +851,7 @@ class static_multimap {
               bool is_outer,
               typename CG,
               typename KeyEqual = thrust::equal_to<key_type>>
-    __device__ std::enable_if_t<not uses_vector_load, void> count(
+    __device__ std::enable_if_t<not uses_vector_load, void> count_impl(
       CG const& g,
       Key const& k,
       std::size_t& thread_num_matches,
@@ -907,10 +872,11 @@ class static_multimap {
      * for equality
      */
     template <bool uses_vector_load, bool is_outer, typename CG, typename PairEqual>
-    __device__ std::enable_if_t<uses_vector_load, void> pair_count(CG const& g,
-                                                                   value_type const& pair,
-                                                                   std::size_t& thread_num_matches,
-                                                                   PairEqual pair_equal) noexcept;
+    __device__ std::enable_if_t<uses_vector_load, void> pair_count_impl(
+      CG const& g,
+      value_type const& pair,
+      std::size_t& thread_num_matches,
+      PairEqual pair_equal) noexcept;
 
     /**
      * @brief Counts the occurrence of a given key/value pair contained in multimap using scalar
@@ -927,7 +893,7 @@ class static_multimap {
      * for equality
      */
     template <bool uses_vector_load, bool is_outer, typename CG, typename PairEqual>
-    __device__ std::enable_if_t<not uses_vector_load, void> pair_count(
+    __device__ std::enable_if_t<not uses_vector_load, void> pair_count_impl(
       CG const& g,
       value_type const& pair,
       std::size_t& thread_num_matches,
@@ -942,7 +908,6 @@ class static_multimap {
      * the empty value sentinel into the output only if `is_outer` is true.
      *
      * @tparam buffer_size Size of the output buffer
-     * @tparam uses_vector_load Boolean flag indicating whether vector loads are used or not
      * @tparam is_outer Boolean flag indicating whether outer join is peformed or not
      * @tparam CG Cooperative Group type
      * @tparam atomicT Type of atomic storage
@@ -960,21 +925,19 @@ class static_multimap {
      * for equality
      */
     template <uint32_t buffer_size,
-              bool uses_vector_load,
               bool is_outer,
               typename CG,
               typename atomicT,
               typename OutputIt,
               typename KeyEqual = thrust::equal_to<key_type>>
-    __device__ std::enable_if_t<uses_vector_load, void> retrieve(
-      const unsigned int activemask,
-      CG const& g,
-      Key const& k,
-      uint32_t* warp_counter,
-      value_type* output_buffer,
-      atomicT* num_matches,
-      OutputIt output_begin,
-      KeyEqual key_equal = KeyEqual{}) noexcept;
+    __device__ void warp_retrieve_impl(const unsigned int activemask,
+                                       CG const& g,
+                                       Key const& k,
+                                       uint32_t* warp_counter,
+                                       value_type* output_buffer,
+                                       atomicT* num_matches,
+                                       OutputIt output_begin,
+                                       KeyEqual key_equal = KeyEqual{}) noexcept;
 
     /**
      * @brief Find all the matches of a given key contained in multimap using scalar
@@ -986,7 +949,6 @@ class static_multimap {
      *
      * @tparam cg_size The number of threads in CUDA Cooperative Groups
      * @tparam buffer_size Size of the output buffer
-     * @tparam uses_vector_load Boolean flag indicating whether vector loads are used or not
      * @tparam is_outer Boolean flag indicating whether outer join is peformed or not
      * @tparam CG Cooperative Group type
      * @tparam atomicT Type of atomic storage
@@ -1004,20 +966,325 @@ class static_multimap {
      */
     template <uint32_t cg_size,
               uint32_t buffer_size,
-              bool uses_vector_load,
               bool is_outer,
               typename CG,
               typename atomicT,
               typename OutputIt,
               typename KeyEqual = thrust::equal_to<key_type>>
-    __device__ std::enable_if_t<not uses_vector_load, void> retrieve(
-      CG const& g,
-      Key const& k,
-      uint32_t* cg_counter,
-      value_type* output_buffer,
-      atomicT* num_matches,
-      OutputIt output_begin,
-      KeyEqual key_equal = KeyEqual{}) noexcept;
+    __device__ void cg_retrieve_impl(CG const& g,
+                                     Key const& k,
+                                     uint32_t* cg_counter,
+                                     value_type* output_buffer,
+                                     atomicT* num_matches,
+                                     OutputIt output_begin,
+                                     KeyEqual key_equal = KeyEqual{}) noexcept;
+
+   public:
+    /**
+     * @brief Flushes per-CG shared memory buffer into the output sequence using CG memcpy_async.
+     *
+     * @tparam cg_size The number of threads in the Cooperative Groups
+     * @tparam CG Cooperative Group type
+     * @tparam atomicT Type of atomic storage
+     * @tparam OutputIt Device accessible output iterator whose `value_type` is
+     * convertible to the map's `mapped_type`
+     * @param g The Cooperative Group used to flush output buffer
+     * @param num_outputs Number of valid output in the buffer
+     * @param output_buffer Shared memory buffer of the key/value pair sequence
+     * @param num_matches Size of the output sequence
+     * @param output_begin Beginning of the output sequence of key/value pairs
+     */
+    template <uint32_t cg_size, typename CG, typename atomicT, typename OutputIt>
+    __inline__ __device__ std::enable_if_t<thrust::is_contiguous_iterator<OutputIt>::value, void>
+    flush_output_buffer(CG const& g,
+                        uint32_t const num_outputs,
+                        value_type* output_buffer,
+                        atomicT* num_matches,
+                        OutputIt output_begin) noexcept;
+
+    /**
+     * @brief Flushes per-CG shared memory buffer into the output sequence using CG memcpy_async.
+     *
+     * @tparam cg_size The number of threads in the Cooperative Groups
+     * @tparam CG Cooperative Group type
+     * @tparam atomicT Type of atomic storage
+     * @tparam OutputIt Device accessible output iterator whose `value_type` is
+     * convertible to the map's `mapped_type`
+     * @param g The Cooperative Group used to flush output buffer
+     * @param num_outputs Number of valid output in the buffer
+     * @param output_buffer Shared memory buffer of the key/value pair sequence
+     * @param num_matches Size of the output sequence
+     * @param output_begin Beginning of the output sequence of key/value pairs
+     */
+    template <uint32_t cg_size, typename CG, typename atomicT, typename OutputIt>
+    __inline__ __device__
+      std::enable_if_t<not thrust::is_contiguous_iterator<OutputIt>::value, void>
+      flush_output_buffer(CG const& g,
+                          uint32_t const num_outputs,
+                          value_type* output_buffer,
+                          atomicT* num_matches,
+                          OutputIt output_begin) noexcept;
+
+    /**
+     * @brief Flushes per-warp shared memory buffer into the output sequence.
+     *
+     * @tparam atomicT Type of atomic storage
+     * @tparam OutputIt Device accessible output iterator whose `value_type` is
+     * convertible to the map's `mapped_type`
+     * @param activemask Mask of active threads in the warp
+     * @param num_outputs Number of valid output in the buffer
+     * @param output_buffer Shared memory buffer of the key/value pair sequence
+     * @param num_matches Size of the output sequence
+     * @param output_begin Beginning of the output sequence of key/value pairs
+     */
+    template <typename atomicT, typename OutputIt>
+    __inline__ __device__ void flush_output_buffer(const unsigned int activemask,
+                                                   uint32_t const num_outputs,
+                                                   value_type* output_buffer,
+                                                   atomicT* num_matches,
+                                                   OutputIt output_begin) noexcept;
+
+    /**
+     * @brief Indicates whether the key `k` was inserted into the map.
+     *
+     * If the key `k` was inserted into the map, find returns
+     * true. Otherwise, it returns false. Uses the CUDA Cooperative Groups API to
+     * to leverage multiple threads to perform a single contains operation. This provides a
+     * significant boost in throughput compared to the non Cooperative Group
+     * `contains` at moderate to high load factors.
+     *
+     * @tparam CG Cooperative Group type
+     * @tparam KeyEqual Binary callable type
+     * @param g The Cooperative Group used to perform the contains operation
+     * @param k The key to search for
+     * @param key_equal The binary callable used to compare two keys
+     * for equality
+     * @return A boolean indicating whether the key/value pair
+     * containing `k` was inserted
+     */
+    template <typename CG, typename KeyEqual = thrust::equal_to<key_type>>
+    __device__ bool contains(CG g, Key const& k, KeyEqual key_equal = KeyEqual{}) noexcept;
+
+    /**
+     * @brief Counts the occurrence of a given key contained in multimap.
+     *
+     * @tparam CG Cooperative Group type
+     * @tparam KeyEqual Binary callable type
+     * @param g The Cooperative Group used to perform the count operation
+     * @param k The key to search for
+     * @param thread_num_matches Number of matches found by the current thread
+     * @param key_equal The binary callable used to compare two keys
+     * for equality
+     */
+    template <typename CG, typename KeyEqual = thrust::equal_to<key_type>>
+    __device__ void count(CG const& g,
+                          Key const& k,
+                          std::size_t& thread_num_matches,
+                          KeyEqual key_equal = KeyEqual{}) noexcept;
+
+    /**
+     * @brief Counts the occurrence of a given key contained in multimap. If no
+     * matches can be found for a given key, the corresponding occurrence is 1.
+     *
+     * @tparam CG Cooperative Group type
+     * @tparam KeyEqual Binary callable type
+     * @param g The Cooperative Group used to perform the count operation
+     * @param k The key to search for
+     * @param thread_num_matches Number of matches found by the current thread
+     * @param key_equal The binary callable used to compare two keys
+     * for equality
+     */
+    template <typename CG, typename KeyEqual = thrust::equal_to<key_type>>
+    __device__ void count_outer(CG const& g,
+                                Key const& k,
+                                std::size_t& thread_num_matches,
+                                KeyEqual key_equal = KeyEqual{}) noexcept;
+
+    /**
+     * @brief Counts the occurrence of a given key/value pair contained in multimap.
+     *
+     * @tparam CG Cooperative Group type
+     * @tparam PairEqual Binary callable type
+     * @param g The Cooperative Group used to perform the pair_count operation
+     * @param pair The pair to search for
+     * @param thread_num_matches Number of matches found by the current thread
+     * @param pair_equal The binary callable used to compare two pairs
+     * for equality
+     */
+    template <typename CG, typename PairEqual>
+    __device__ void pair_count(CG const& g,
+                               value_type const& pair,
+                               std::size_t& thread_num_matches,
+                               PairEqual pair_equal) noexcept;
+
+    /**
+     * @brief Counts the occurrence of a given key/value pair contained in multimap.
+     * If no matches can be found for a given key, the corresponding occurrence is 1.
+     *
+     * @tparam CG Cooperative Group type
+     * @tparam PairEqual Binary callable type
+     * @param g The Cooperative Group used to perform the pair_count operation
+     * @param pair The pair to search for
+     * @param thread_num_matches Number of matches found by the current thread
+     * @param pair_equal The binary callable used to compare two pairs
+     * for equality
+     */
+    template <typename CG, typename PairEqual>
+    __device__ void pair_count_outer(CG const& g,
+                                     value_type const& pair,
+                                     std::size_t& thread_num_matches,
+                                     PairEqual pair_equal) noexcept;
+
+    /**
+     * @brief Find all the matches of a given key contained in multimap using vector
+     * loads with per-warp shared memory buffer.
+     *
+     * For keys `k = *(first + i)` existing in the map, copies `k` and all associated values to
+     * unspecified locations in `[output_begin, output_end)`. In case of non-matches, copies `k` and
+     * the empty value sentinel into the output only if `is_outer` is true.
+     *
+     * @tparam buffer_size Size of the output buffer
+     * @tparam CG Cooperative Group type
+     * @tparam atomicT Type of atomic storage
+     * @tparam OutputIt Device accessible output iterator whose `value_type` is
+     * convertible to the map's `mapped_type`
+     * @tparam KeyEqual Binary callable type
+     * @param activemask Mask of active threads in the warp
+     * @param g The Cooperative Group used to retrieve
+     * @param k The key to search for
+     * @param warp_counter Pointer to the warp counter
+     * @param output_buffer Shared memory buffer of the key/value pair sequence
+     * @param num_matches Size of the output sequence
+     * @param output_begin Beginning of the output sequence of key/value pairs
+     * @param key_equal The binary callable used to compare two keys
+     * for equality
+     */
+    template <uint32_t buffer_size,
+              typename CG,
+              typename atomicT,
+              typename OutputIt,
+              typename KeyEqual = thrust::equal_to<key_type>>
+    __device__ void warp_retrieve(const unsigned int activemask,
+                                  CG const& g,
+                                  Key const& k,
+                                  uint32_t* warp_counter,
+                                  value_type* output_buffer,
+                                  atomicT* num_matches,
+                                  OutputIt output_begin,
+                                  KeyEqual key_equal = KeyEqual{}) noexcept;
+
+    /**
+     * @brief Find all the matches of a given key contained in multimap using vector
+     * loads with per-warp shared memory buffer.
+     *
+     * For keys `k = *(first + i)` existing in the map, copies `k` and all associated values to
+     * unspecified locations in `[output_begin, output_end)`. In case of non-matches, copies `k` and
+     * the empty value sentinel into the output only if `is_outer` is true.
+     *
+     * @tparam buffer_size Size of the output buffer
+     * @tparam CG Cooperative Group type
+     * @tparam atomicT Type of atomic storage
+     * @tparam OutputIt Device accessible output iterator whose `value_type` is
+     * convertible to the map's `mapped_type`
+     * @tparam KeyEqual Binary callable type
+     * @param activemask Mask of active threads in the warp
+     * @param g The Cooperative Group used to retrieve
+     * @param k The key to search for
+     * @param warp_counter Pointer to the warp counter
+     * @param output_buffer Shared memory buffer of the key/value pair sequence
+     * @param num_matches Size of the output sequence
+     * @param output_begin Beginning of the output sequence of key/value pairs
+     * @param key_equal The binary callable used to compare two keys
+     * for equality
+     */
+    template <uint32_t buffer_size,
+              typename CG,
+              typename atomicT,
+              typename OutputIt,
+              typename KeyEqual = thrust::equal_to<key_type>>
+    __device__ void warp_retrieve_outer(const unsigned int activemask,
+                                        CG const& g,
+                                        Key const& k,
+                                        uint32_t* warp_counter,
+                                        value_type* output_buffer,
+                                        atomicT* num_matches,
+                                        OutputIt output_begin,
+                                        KeyEqual key_equal = KeyEqual{}) noexcept;
+
+    /**
+     * @brief Find all the matches of a given key contained in multimap using scalar
+     * loads with per-cg shared memory buffer.
+     *
+     * For keys `k = *(first + i)` existing in the map, copies `k` and all associated values to
+     * unspecified locations in `[output_begin, output_end)`.
+     *
+     * @tparam cg_size The number of threads in CUDA Cooperative Groups
+     * @tparam buffer_size Size of the output buffer
+     * @tparam CG Cooperative Group type
+     * @tparam atomicT Type of atomic storage
+     * @tparam OutputIt Device accessible output iterator whose `value_type` is
+     * convertible to the map's `mapped_type`
+     * @tparam KeyEqual Binary callable type
+     * @param g The Cooperative Group used to retrieve
+     * @param k The key to search for
+     * @param cg_counter Pointer to the CG counter
+     * @param output_buffer Shared memory buffer of the key/value pair sequence
+     * @param num_matches Size of the output sequence
+     * @param output_begin Beginning of the output sequence of key/value pairs
+     * @param key_equal The binary callable used to compare two keys
+     * for equality
+     */
+    template <uint32_t cg_size,
+              uint32_t buffer_size,
+              typename CG,
+              typename atomicT,
+              typename OutputIt,
+              typename KeyEqual = thrust::equal_to<key_type>>
+    __device__ void cg_retrieve(CG const& g,
+                                Key const& k,
+                                uint32_t* cg_counter,
+                                value_type* output_buffer,
+                                atomicT* num_matches,
+                                OutputIt output_begin,
+                                KeyEqual key_equal = KeyEqual{}) noexcept;
+
+    /**
+     * @brief Find all the matches of a given key contained in multimap using scalar
+     * loads with per-cg shared memory buffer.
+     *
+     * For keys `k = *(first + i)` existing in the map, copies `k` and all associated values to
+     * unspecified locations in `[output_begin, output_end)`. In case of non-matches, copies `k` and
+     * the empty value sentinel into the output.
+     *
+     * @tparam cg_size The number of threads in CUDA Cooperative Groups
+     * @tparam buffer_size Size of the output buffer
+     * @tparam CG Cooperative Group type
+     * @tparam atomicT Type of atomic storage
+     * @tparam OutputIt Device accessible output iterator whose `value_type` is
+     * convertible to the map's `mapped_type`
+     * @tparam KeyEqual Binary callable type
+     * @param g The Cooperative Group used to retrieve
+     * @param k The key to search for
+     * @param cg_counter Pointer to the CG counter
+     * @param output_buffer Shared memory buffer of the key/value pair sequence
+     * @param num_matches Size of the output sequence
+     * @param output_begin Beginning of the output sequence of key/value pairs
+     * @param key_equal The binary callable used to compare two keys
+     * for equality
+     */
+    template <uint32_t cg_size,
+              uint32_t buffer_size,
+              typename CG,
+              typename atomicT,
+              typename OutputIt,
+              typename KeyEqual = thrust::equal_to<key_type>>
+    __device__ void cg_retrieve_outer(CG const& g,
+                                      Key const& k,
+                                      uint32_t* cg_counter,
+                                      value_type* output_buffer,
+                                      atomicT* num_matches,
+                                      OutputIt output_begin,
+                                      KeyEqual key_equal = KeyEqual{}) noexcept;
   };  // class device_view
 
   /**

From b8b523c9e0258f16dae0b691db17d50a96ead1d1 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Fri, 9 Jul 2021 13:39:19 -0400
Subject: [PATCH 153/267] Fix a key comparison bug

---
 include/cuco/detail/static_multimap.inl | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index 7e903ad3f..ef2e5b901 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -419,7 +419,6 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_mutable_vie
 {
   auto current_slot = initial_slot(g, insert_pair.first);
   while (true) {
-    // key_type const existing_key = current_slot->first.load(cuda::memory_order_relaxed);
     pair<Key, Value> arr[2];
     if constexpr (sizeof(Key) == 4) {
       auto const tmp = *reinterpret_cast<uint4 const*>(current_slot);
@@ -487,7 +486,8 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_mutable_vie
 
     // The user provide `key_equal` can never be used to compare against `empty_key_sentinel` as the
     // sentinel is not a valid key value. Therefore, first check for the sentinel
-    auto const slot_is_empty         = (existing_key == this->get_empty_key_sentinel());
+    auto const slot_is_empty =
+      detail::bitwise_compare(existing_key, this->get_empty_key_sentinel());
     auto const window_contains_empty = g.ballot(slot_is_empty);
 
     if (window_contains_empty) {

From b4b6c934b03cf4b99df4350a81554830af5d2aa3 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Fri, 9 Jul 2021 14:25:46 -0400
Subject: [PATCH 154/267] Simplify multimap insert_impl

---
 include/cuco/detail/static_multimap.inl | 42 ++++++++-----------------
 1 file changed, 13 insertions(+), 29 deletions(-)

diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index ef2e5b901..12f98823b 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -419,13 +419,13 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_mutable_vie
 {
   auto current_slot = initial_slot(g, insert_pair.first);
   while (true) {
-    pair<Key, Value> arr[2];
-    if constexpr (sizeof(Key) == 4) {
-      auto const tmp = *reinterpret_cast<uint4 const*>(current_slot);
-      memcpy(&arr[0], &tmp, 2 * sizeof(pair<Key, Value>));
+    value_type arr[2];
+    if constexpr (sizeof(cuco::detail::packed_t<value_type>) == 4) {
+      auto const tmp = *reinterpret_cast<ushort4 const*>(current_slot);
+      memcpy(&arr[0], &tmp, 2 * sizeof(value_type));
     } else {
-      auto const tmp = *reinterpret_cast<ulonglong4 const*>(current_slot);
-      memcpy(&arr[0], &tmp, 2 * sizeof(pair<Key, Value>));
+      auto const tmp = *reinterpret_cast<uint4 const*>(current_slot);
+      memcpy(&arr[0], &tmp, 2 * sizeof(value_type));
     }
 
     // The user provide `key_equal` can never be used to compare against `empty_key_sentinel` as
@@ -435,24 +435,15 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_mutable_vie
     auto const second_slot_is_empty =
       (detail::bitwise_compare(arr[1].first, this->get_empty_value_sentinel()));
     auto const window_contains_empty = g.ballot(first_slot_is_empty or second_slot_is_empty);
+
     if (window_contains_empty) {
       // the first lane in the group with an empty slot will attempt the insert
       insert_result status{insert_result::CONTINUE};
       uint32_t src_lane = __ffs(window_contains_empty) - 1;
       if (g.thread_rank() == src_lane) {
         auto insert_location = first_slot_is_empty ? current_slot : current_slot + 1;
-        // One single CAS operation if `value_type` is packable
-        if constexpr (cuco::detail::is_packable<value_type>()) {
-          status = packed_cas(insert_location, insert_pair, key_equal);
-        }
-        // Otherwise, two back-to-back CAS operations
-        else {
-#if __CUDA_ARCH__ < 700
-          status = cas_dependent_write(insert_location, insert_pair, key_equal);
-#else
-          status = back_to_back_cas(insert_location, insert_pair, key_equal);
-#endif
-        }
+        // One single CAS operation since vector loads are dedicated to packable pairs
+        status = packed_cas(insert_location, insert_pair, key_equal);
       }
 
       // successful insert
@@ -466,7 +457,7 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_mutable_vie
     else {
       current_slot = next_slot(current_slot);
     }
-  }
+  }  // while true
 }
 
 template <typename Key,
@@ -496,18 +487,11 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_mutable_vie
       uint32_t src_lane = __ffs(window_contains_empty) - 1;
 
       if (g.thread_rank() == src_lane) {
-        // One single CAS operation if `value_type` is packable
-        if constexpr (cuco::detail::is_packable<value_type>()) {
-          status = packed_cas(current_slot, insert_pair, key_equal);
-        }
-        // Otherwise, two back-to-back CAS operations
-        else {
 #if __CUDA_ARCH__ < 700
-          status = cas_dependent_write(current_slot, insert_pair, key_equal);
+        status = cas_dependent_write(current_slot, insert_pair, key_equal);
 #else
-          status = back_to_back_cas(current_slot, insert_pair, key_equal);
+        status = back_to_back_cas(current_slot, insert_pair, key_equal);
 #endif
-        }
       }
 
       // successful insert
@@ -521,7 +505,7 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_mutable_vie
     else {
       current_slot = next_slot(current_slot);
     }
-  }
+  }  // while true
 }
 
 template <typename Key,

From ff49d421493ac53976f56718779a6c2a2d1135d5 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Fri, 9 Jul 2021 15:16:20 -0400
Subject: [PATCH 155/267] Use bitwise compare instead of naive =

---
 include/cuco/detail/static_multimap.inl | 106 ++++++++++++++----------
 include/cuco/static_multimap.cuh        |   4 -
 2 files changed, 63 insertions(+), 47 deletions(-)

diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index 12f98823b..0490fc9c3 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -534,19 +534,21 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::conta
   auto current_slot = initial_slot(g, k);
 
   while (true) {
-    pair<Key, Value> arr[2];
+    value_type arr[2];
     if constexpr (sizeof(Key) == 4) {
       auto const tmp = *reinterpret_cast<uint4 const*>(current_slot);
-      memcpy(&arr[0], &tmp, 2 * sizeof(pair<Key, Value>));
+      memcpy(&arr[0], &tmp, 2 * sizeof(value_type));
     } else {
       auto const tmp = *reinterpret_cast<ulonglong4 const*>(current_slot);
-      memcpy(&arr[0], &tmp, 2 * sizeof(pair<Key, Value>));
+      memcpy(&arr[0], &tmp, 2 * sizeof(value_type));
     }
 
-    auto const first_slot_is_empty  = (arr[0].first == this->get_empty_key_sentinel());
-    auto const second_slot_is_empty = (arr[1].first == this->get_empty_key_sentinel());
-    auto const first_equals         = (not first_slot_is_empty and key_equal(arr[0].first, k));
-    auto const second_equals        = (not second_slot_is_empty and key_equal(arr[1].first, k));
+    auto const first_slot_is_empty =
+      detail::bitwise_compare(arr[0].first, this->get_empty_key_sentinel());
+    auto const second_slot_is_empty =
+      detail::bitwise_compare(arr[1].first, this->get_empty_key_sentinel());
+    auto const first_equals  = (not first_slot_is_empty and key_equal(arr[0].first, k));
+    auto const second_equals = (not second_slot_is_empty and key_equal(arr[1].first, k));
 
     // the key we were searching for was found by one of the threads, so we return true
     if (g.any(first_equals or second_equals)) { return true; }
@@ -577,7 +579,8 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::conta
 
     // The user provide `key_equal` can never be used to compare against `empty_key_sentinel` as the
     // sentinel is not a valid key value. Therefore, first check for the sentinel
-    auto const slot_is_empty = (existing_key == this->get_empty_key_sentinel());
+    auto const slot_is_empty =
+      detail::bitwise_compare(existing_key, this->get_empty_key_sentinel());
 
     auto const equals = (not slot_is_empty and key_equal(existing_key, k));
 
@@ -618,10 +621,12 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::count
         memcpy(&arr[0], &tmp, 2 * sizeof(pair<Key, Value>));
       }
 
-      auto const first_slot_is_empty  = (arr[0].first == this->get_empty_key_sentinel());
-      auto const second_slot_is_empty = (arr[1].first == this->get_empty_key_sentinel());
-      auto const first_equals         = (not first_slot_is_empty and key_equal(arr[0].first, k));
-      auto const second_equals        = (not second_slot_is_empty and key_equal(arr[1].first, k));
+      auto const first_slot_is_empty =
+        detail::bitwise_compare(arr[0].first, this->get_empty_key_sentinel());
+      auto const second_slot_is_empty =
+        detail::bitwise_compare(arr[1].first, this->get_empty_key_sentinel());
+      auto const first_equals  = (not first_slot_is_empty and key_equal(arr[0].first, k));
+      auto const second_equals = (not second_slot_is_empty and key_equal(arr[1].first, k));
 
       if (g.any(first_equals or second_equals)) { found_match = true; }
 
@@ -645,10 +650,12 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::count
         memcpy(&arr[0], &tmp, 2 * sizeof(pair<Key, Value>));
       }
 
-      auto const first_slot_is_empty  = (arr[0].first == this->get_empty_key_sentinel());
-      auto const second_slot_is_empty = (arr[1].first == this->get_empty_key_sentinel());
-      auto const first_equals         = (not first_slot_is_empty and key_equal(arr[0].first, k));
-      auto const second_equals        = (not second_slot_is_empty and key_equal(arr[1].first, k));
+      auto const first_slot_is_empty =
+        detail::bitwise_compare(arr[0].first, this->get_empty_key_sentinel());
+      auto const second_slot_is_empty =
+        detail::bitwise_compare(arr[1].first, this->get_empty_key_sentinel());
+      auto const first_equals  = (not first_slot_is_empty and key_equal(arr[0].first, k));
+      auto const second_equals = (not second_slot_is_empty and key_equal(arr[1].first, k));
 
       thread_num_matches += (first_equals + second_equals);
 
@@ -679,8 +686,9 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::count
         *reinterpret_cast<cuco::pair_type<Key, Value> const*>(current_slot);
       auto const& current_key = slot_contents.first;
 
-      auto const slot_is_empty = (current_key == this->get_empty_key_sentinel());
-      auto const equals        = not slot_is_empty and key_equal(current_key, k);
+      auto const slot_is_empty =
+        detail::bitwise_compare(current_key, this->get_empty_key_sentinel());
+      auto const equals = not slot_is_empty and key_equal(current_key, k);
 
       if (g.any(equals)) { found_match = true; }
 
@@ -699,8 +707,9 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::count
         *reinterpret_cast<cuco::pair_type<Key, Value> const*>(current_slot);
       auto const& current_key = slot_contents.first;
 
-      auto const slot_is_empty = (current_key == this->get_empty_key_sentinel());
-      auto const equals        = not slot_is_empty and key_equal(current_key, k);
+      auto const slot_is_empty =
+        detail::bitwise_compare(current_key, this->get_empty_key_sentinel());
+      auto const equals = not slot_is_empty and key_equal(current_key, k);
 
       thread_num_matches += equals;
 
@@ -740,8 +749,10 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_
         memcpy(&arr[0], &tmp, 2 * sizeof(cuco::pair_type<Key, Value>));
       }
 
-      auto const first_slot_is_empty  = (arr[0].first == this->get_empty_key_sentinel());
-      auto const second_slot_is_empty = (arr[1].first == this->get_empty_key_sentinel());
+      auto const first_slot_is_empty =
+        detail::bitwise_compare(arr[0].first, this->get_empty_key_sentinel());
+      auto const second_slot_is_empty =
+        detail::bitwise_compare(arr[1].first, this->get_empty_key_sentinel());
 
       auto const first_slot_equals  = (not first_slot_is_empty and pair_equal(arr[0], pair));
       auto const second_slot_equals = (not second_slot_is_empty and pair_equal(arr[1], pair));
@@ -768,8 +779,10 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_
         memcpy(&arr[0], &tmp, 2 * sizeof(cuco::pair_type<Key, Value>));
       }
 
-      auto const first_slot_is_empty  = (arr[0].first == this->get_empty_key_sentinel());
-      auto const second_slot_is_empty = (arr[1].first == this->get_empty_key_sentinel());
+      auto const first_slot_is_empty =
+        detail::bitwise_compare(arr[0].first, this->get_empty_key_sentinel());
+      auto const second_slot_is_empty =
+        detail::bitwise_compare(arr[1].first, this->get_empty_key_sentinel());
 
       auto const first_slot_equals  = (not first_slot_is_empty and pair_equal(arr[0], pair));
       auto const second_slot_equals = (not second_slot_is_empty and pair_equal(arr[1], pair));
@@ -824,7 +837,8 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_
     while (true) {
       auto slot_contents = *reinterpret_cast<cuco::pair_type<Key, Value> const*>(current_slot);
 
-      auto const slot_is_empty = (slot_contents.first == this->get_empty_key_sentinel());
+      auto const slot_is_empty =
+        detail::bitwise_compare(slot_contents.first, this->get_empty_key_sentinel());
 
       auto const equals = not slot_is_empty and pair_equal(slot_contents, pair);
 
@@ -879,12 +893,14 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::warp_
           memcpy(&arr[0], &tmp, 2 * sizeof(pair<Key, Value>));
         }
 
-        auto const first_slot_is_empty  = (arr[0].first == this->get_empty_key_sentinel());
-        auto const second_slot_is_empty = (arr[1].first == this->get_empty_key_sentinel());
-        auto const first_equals         = (not first_slot_is_empty and key_equal(arr[0].first, k));
-        auto const second_equals        = (not second_slot_is_empty and key_equal(arr[1].first, k));
-        auto const first_exists         = g.ballot(first_equals);
-        auto const second_exists        = g.ballot(second_equals);
+        auto const first_slot_is_empty =
+          detail::bitwise_compare(arr[0].first, this->get_empty_key_sentinel());
+        auto const second_slot_is_empty =
+          detail::bitwise_compare(arr[1].first, this->get_empty_key_sentinel());
+        auto const first_equals  = (not first_slot_is_empty and key_equal(arr[0].first, k));
+        auto const second_equals = (not second_slot_is_empty and key_equal(arr[1].first, k));
+        auto const first_exists  = g.ballot(first_equals);
+        auto const second_exists = g.ballot(second_equals);
 
         if (first_exists or second_exists) {
           found_match = true;
@@ -943,12 +959,14 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::warp_
           memcpy(&arr[0], &tmp, 2 * sizeof(pair<Key, Value>));
         }
 
-        auto const first_slot_is_empty  = (arr[0].first == this->get_empty_key_sentinel());
-        auto const second_slot_is_empty = (arr[1].first == this->get_empty_key_sentinel());
-        auto const first_equals         = (not first_slot_is_empty and key_equal(arr[0].first, k));
-        auto const second_equals        = (not second_slot_is_empty and key_equal(arr[1].first, k));
-        auto const first_exists         = g.ballot(first_equals);
-        auto const second_exists        = g.ballot(second_equals);
+        auto const first_slot_is_empty =
+          detail::bitwise_compare(arr[0].first, this->get_empty_key_sentinel());
+        auto const second_slot_is_empty =
+          detail::bitwise_compare(arr[1].first, this->get_empty_key_sentinel());
+        auto const first_equals  = (not first_slot_is_empty and key_equal(arr[0].first, k));
+        auto const second_equals = (not second_slot_is_empty and key_equal(arr[1].first, k));
+        auto const first_exists  = g.ballot(first_equals);
+        auto const second_exists = g.ballot(second_equals);
 
         if (first_exists or second_exists) {
           auto num_first_matches  = __popc(first_exists);
@@ -1026,9 +1044,10 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::cg_re
       static_assert(sizeof(Value) == sizeof(cuda::atomic<Value>));
       pair<Key, Value> slot_contents = *reinterpret_cast<pair<Key, Value> const*>(current_slot);
 
-      auto const slot_is_empty = (slot_contents.first == this->get_empty_key_sentinel());
-      auto const equals        = (not slot_is_empty and key_equal(slot_contents.first, k));
-      auto const exists        = g.ballot(equals);
+      auto const slot_is_empty =
+        detail::bitwise_compare(slot_contents.first, this->get_empty_key_sentinel());
+      auto const equals = (not slot_is_empty and key_equal(slot_contents.first, k));
+      auto const exists = g.ballot(equals);
 
       if (exists) {
         found_match         = true;
@@ -1071,9 +1090,10 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::cg_re
       static_assert(sizeof(Value) == sizeof(cuda::atomic<Value>));
       pair<Key, Value> slot_contents = *reinterpret_cast<pair<Key, Value> const*>(current_slot);
 
-      auto const slot_is_empty = (slot_contents.first == this->get_empty_key_sentinel());
-      auto const equals        = (not slot_is_empty and key_equal(slot_contents.first, k));
-      auto const exists        = g.ballot(equals);
+      auto const slot_is_empty =
+        detail::bitwise_compare(slot_contents.first, this->get_empty_key_sentinel());
+      auto const equals = (not slot_is_empty and key_equal(slot_contents.first, k));
+      auto const exists = g.ballot(equals);
 
       if (exists) {
         auto num_matches    = __popc(exists);
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index 1e03a0738..d01d9823b 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -526,8 +526,6 @@ class static_multimap {
     using iterator       = typename device_view_base::iterator;
     using const_iterator = typename device_view_base::const_iterator;
 
-    using device_view_base::uses_vector_load;
-
     /**
      * @brief Construct a mutable view of the first `capacity` slots of the
      * slots array pointed to by `slots`.
@@ -672,8 +670,6 @@ class static_multimap {
     using iterator       = typename device_view_base::iterator;
     using const_iterator = typename device_view_base::const_iterator;
 
-    using device_view_base::uses_vector_load;
-
     /**
      * @brief Construct a view of the first `capacity` slots of the
      * slots array pointed to by `slots`.

From f125ef398a90791ac8783df4a477eb183b6c4d62 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Fri, 9 Jul 2021 17:24:45 -0400
Subject: [PATCH 156/267] Cleanups: use load_pair_array function

---
 include/cuco/detail/static_multimap.inl | 94 +++++++++----------------
 include/cuco/static_multimap.cuh        |  8 +++
 2 files changed, 40 insertions(+), 62 deletions(-)

diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index 0490fc9c3..9fa32f754 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -306,6 +306,24 @@ OutputIt static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::retrieve_
   return output_end;
 }
 
+template <typename Key,
+          typename Value,
+          class ProbeSequence,
+          cuda::thread_scope Scope,
+          typename Allocator>
+__device__ void
+static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view_base::load_pair_array(
+  value_type* arr, const_iterator current_slot) noexcept
+{
+  if constexpr (sizeof(value_type) == 4) {
+    auto const tmp = *reinterpret_cast<ushort4 const*>(current_slot);
+    memcpy(&arr[0], &tmp, 2 * sizeof(value_type));
+  } else {
+    auto const tmp = *reinterpret_cast<uint4 const*>(current_slot);
+    memcpy(&arr[0], &tmp, 2 * sizeof(value_type));
+  }
+}
+
 template <typename Key,
           typename Value,
           class ProbeSequence,
@@ -420,13 +438,7 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_mutable_vie
   auto current_slot = initial_slot(g, insert_pair.first);
   while (true) {
     value_type arr[2];
-    if constexpr (sizeof(cuco::detail::packed_t<value_type>) == 4) {
-      auto const tmp = *reinterpret_cast<ushort4 const*>(current_slot);
-      memcpy(&arr[0], &tmp, 2 * sizeof(value_type));
-    } else {
-      auto const tmp = *reinterpret_cast<uint4 const*>(current_slot);
-      memcpy(&arr[0], &tmp, 2 * sizeof(value_type));
-    }
+    load_pair_array(&arr[0], current_slot);
 
     // The user provide `key_equal` can never be used to compare against `empty_key_sentinel` as
     // the sentinel is not a valid key value. Therefore, first check for the sentinel
@@ -535,13 +547,7 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::conta
 
   while (true) {
     value_type arr[2];
-    if constexpr (sizeof(Key) == 4) {
-      auto const tmp = *reinterpret_cast<uint4 const*>(current_slot);
-      memcpy(&arr[0], &tmp, 2 * sizeof(value_type));
-    } else {
-      auto const tmp = *reinterpret_cast<ulonglong4 const*>(current_slot);
-      memcpy(&arr[0], &tmp, 2 * sizeof(value_type));
-    }
+    load_pair_array(&arr[0], current_slot);
 
     auto const first_slot_is_empty =
       detail::bitwise_compare(arr[0].first, this->get_empty_key_sentinel());
@@ -612,14 +618,8 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::count
     bool found_match = false;
 
     while (true) {
-      pair<Key, Value> arr[2];
-      if constexpr (sizeof(Key) == 4) {
-        auto const tmp = *reinterpret_cast<uint4 const*>(current_slot);
-        memcpy(&arr[0], &tmp, 2 * sizeof(pair<Key, Value>));
-      } else {
-        auto const tmp = *reinterpret_cast<ulonglong4 const*>(current_slot);
-        memcpy(&arr[0], &tmp, 2 * sizeof(pair<Key, Value>));
-      }
+      value_type arr[2];
+      load_pair_array(&arr[0], current_slot);
 
       auto const first_slot_is_empty =
         detail::bitwise_compare(arr[0].first, this->get_empty_key_sentinel());
@@ -641,14 +641,8 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::count
     }
   } else {
     while (true) {
-      pair<Key, Value> arr[2];
-      if constexpr (sizeof(Key) == 4) {
-        auto const tmp = *reinterpret_cast<uint4 const*>(current_slot);
-        memcpy(&arr[0], &tmp, 2 * sizeof(pair<Key, Value>));
-      } else {
-        auto const tmp = *reinterpret_cast<ulonglong4 const*>(current_slot);
-        memcpy(&arr[0], &tmp, 2 * sizeof(pair<Key, Value>));
-      }
+      value_type arr[2];
+      load_pair_array(&arr[0], current_slot);
 
       auto const first_slot_is_empty =
         detail::bitwise_compare(arr[0].first, this->get_empty_key_sentinel());
@@ -740,14 +734,8 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_
     bool found_match = false;
 
     while (true) {
-      cuco::pair_type<Key, Value> arr[2];
-      if constexpr (sizeof(Key) == 4) {
-        auto const tmp = *reinterpret_cast<uint4 const*>(current_slot);
-        memcpy(&arr[0], &tmp, 2 * sizeof(cuco::pair_type<Key, Value>));
-      } else {
-        auto const tmp = *reinterpret_cast<ulonglong4 const*>(current_slot);
-        memcpy(&arr[0], &tmp, 2 * sizeof(cuco::pair_type<Key, Value>));
-      }
+      value_type arr[2];
+      load_pair_array(&arr[0], current_slot);
 
       auto const first_slot_is_empty =
         detail::bitwise_compare(arr[0].first, this->get_empty_key_sentinel());
@@ -770,14 +758,8 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_
     }
   } else {
     while (true) {
-      cuco::pair_type<Key, Value> arr[2];
-      if constexpr (sizeof(Key) == 4) {
-        auto const tmp = *reinterpret_cast<uint4 const*>(current_slot);
-        memcpy(&arr[0], &tmp, 2 * sizeof(cuco::pair_type<Key, Value>));
-      } else {
-        auto const tmp = *reinterpret_cast<ulonglong4 const*>(current_slot);
-        memcpy(&arr[0], &tmp, 2 * sizeof(cuco::pair_type<Key, Value>));
-      }
+      value_type arr[2];
+      load_pair_array(&arr[0], current_slot);
 
       auto const first_slot_is_empty =
         detail::bitwise_compare(arr[0].first, this->get_empty_key_sentinel());
@@ -884,14 +866,8 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::warp_
 
     while (__any_sync(activemask, running)) {
       if (running) {
-        pair<Key, Value> arr[2];
-        if constexpr (sizeof(Key) == 4) {
-          auto const tmp = *reinterpret_cast<uint4 const*>(current_slot);
-          memcpy(&arr[0], &tmp, 2 * sizeof(pair<Key, Value>));
-        } else {
-          auto const tmp = *reinterpret_cast<ulonglong4 const*>(current_slot);
-          memcpy(&arr[0], &tmp, 2 * sizeof(pair<Key, Value>));
-        }
+        value_type arr[2];
+        load_pair_array(&arr[0], current_slot);
 
         auto const first_slot_is_empty =
           detail::bitwise_compare(arr[0].first, this->get_empty_key_sentinel());
@@ -950,14 +926,8 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::warp_
   } else {
     while (__any_sync(activemask, running)) {
       if (running) {
-        pair<Key, Value> arr[2];
-        if constexpr (sizeof(Key) == 4) {
-          auto const tmp = *reinterpret_cast<uint4 const*>(current_slot);
-          memcpy(&arr[0], &tmp, 2 * sizeof(pair<Key, Value>));
-        } else {
-          auto const tmp = *reinterpret_cast<ulonglong4 const*>(current_slot);
-          memcpy(&arr[0], &tmp, 2 * sizeof(pair<Key, Value>));
-        }
+        value_type arr[2];
+        load_pair_array(&arr[0], current_slot);
 
         auto const first_slot_is_empty =
           detail::bitwise_compare(arr[0].first, this->get_empty_key_sentinel());
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index d01d9823b..1ef7994bd 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -378,6 +378,14 @@ class static_multimap {
      */
     static constexpr bool uses_vector_load() noexcept { return ProbeSequence::uses_vector_load(); }
 
+    /**
+     * @brief Load two key/value pairs from the given slot to the target pair array.
+     *
+     * @param arr The pair array to be loaded
+     * @param current_slot The given slot to load from
+     */
+    __device__ void load_pair_array(value_type* arr, const_iterator current_slot) noexcept;
+
    private:
     ProbeSequence probe_sequence_;
     Key empty_key_sentinel_{};      ///< Key value that represents an empty slot

From 6acf1483804d02a4235e00c12202d5d1ed47a542 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Mon, 12 Jul 2021 08:50:34 -0400
Subject: [PATCH 157/267] Rename flush buffer functions

---
 include/cuco/detail/static_multimap.inl       | 14 ++++-----
 .../cuco/detail/static_multimap_kernels.cuh   |  4 +--
 include/cuco/static_multimap.cuh              | 30 +++++++++----------
 3 files changed, 24 insertions(+), 24 deletions(-)

diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index 9fa32f754..025a24c1c 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -916,7 +916,7 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::warp_
 
       __syncwarp(activemask);
       if (*warp_counter + 32 * 2 > buffer_size) {
-        flush_output_buffer(activemask, *warp_counter, output_buffer, num_matches, output_begin);
+        flush_warp_buffer(activemask, *warp_counter, output_buffer, num_matches, output_begin);
         // First lane reset warp-level counter
         if (warp_lane_id == 0) { *warp_counter = 0; }
       }
@@ -966,7 +966,7 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::warp_
 
       __syncwarp(activemask);
       if (*warp_counter + 32 * 2 > buffer_size) {
-        flush_output_buffer(activemask, *warp_counter, output_buffer, num_matches, output_begin);
+        flush_warp_buffer(activemask, *warp_counter, output_buffer, num_matches, output_begin);
         // First lane reset warp-level counter
         if (warp_lane_id == 0) { *warp_counter = 0; }
       }
@@ -1046,7 +1046,7 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::cg_re
 
       // Flush if the next iteration won't fit into buffer
       if ((*cg_counter + cg_size) > buffer_size) {
-        flush_output_buffer<cg_size>(g, *cg_counter, output_buffer, num_matches, output_begin);
+        flush_cg_buffer<cg_size>(g, *cg_counter, output_buffer, num_matches, output_begin);
         // First lane reset CG-level counter
         if (lane_id == 0) { *cg_counter = 0; }
       }
@@ -1083,7 +1083,7 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::cg_re
 
       // Flush if the next iteration won't fit into buffer
       if ((*cg_counter + cg_size) > buffer_size) {
-        flush_output_buffer<cg_size>(g, *cg_counter, output_buffer, num_matches, output_begin);
+        flush_cg_buffer<cg_size>(g, *cg_counter, output_buffer, num_matches, output_begin);
         // First lane reset CG-level counter
         if (lane_id == 0) { *cg_counter = 0; }
       }
@@ -1101,7 +1101,7 @@ template <typename Key,
           typename Allocator>
 template <uint32_t cg_size, typename CG, typename atomicT, typename OutputIt>
 __inline__ __device__ std::enable_if_t<thrust::is_contiguous_iterator<OutputIt>::value, void>
-static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::flush_output_buffer(
+static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::flush_cg_buffer(
   CG const& g,
   uint32_t const num_outputs,
   value_type* output_buffer,
@@ -1134,7 +1134,7 @@ template <typename Key,
           typename Allocator>
 template <uint32_t cg_size, typename CG, typename atomicT, typename OutputIt>
 __inline__ __device__ std::enable_if_t<not thrust::is_contiguous_iterator<OutputIt>::value, void>
-static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::flush_output_buffer(
+static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::flush_cg_buffer(
   CG const& g,
   uint32_t const num_outputs,
   value_type* output_buffer,
@@ -1158,7 +1158,7 @@ template <typename Key,
           typename Allocator>
 template <typename atomicT, typename OutputIt>
 __inline__ __device__ void
-static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::flush_output_buffer(
+static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::flush_warp_buffer(
   const unsigned int activemask,
   uint32_t const num_outputs,
   value_type* output_buffer,
diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap_kernels.cuh
index 4d51fb512..7cb85e862 100644
--- a/include/cuco/detail/static_multimap_kernels.cuh
+++ b/include/cuco/detail/static_multimap_kernels.cuh
@@ -364,7 +364,7 @@ __global__ std::enable_if_t<uses_vector_load, void> retrieve(InputIt first,
 
   // Final flush of output buffer
   if (warp_counter[warp_id] > 0) {
-    view.flush_output_buffer(
+    view.flush_warp_buffer(
       activemask, warp_counter[warp_id], output_buffer[warp_id], num_matches, output_begin);
   }
 }
@@ -448,7 +448,7 @@ __global__ std::enable_if_t<not uses_vector_load, void> retrieve(InputIt first,
 
   // Final flush of output buffer
   if (cg_counter[cg_id] > 0) {
-    view.flush_output_buffer<tile_size>(
+    view.flush_cg_buffer<tile_size>(
       tile, cg_counter[cg_id], output_buffer[cg_id], num_matches, output_begin);
   }
 }
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index 1ef7994bd..2ebd51f48 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -1000,11 +1000,11 @@ class static_multimap {
      */
     template <uint32_t cg_size, typename CG, typename atomicT, typename OutputIt>
     __inline__ __device__ std::enable_if_t<thrust::is_contiguous_iterator<OutputIt>::value, void>
-    flush_output_buffer(CG const& g,
-                        uint32_t const num_outputs,
-                        value_type* output_buffer,
-                        atomicT* num_matches,
-                        OutputIt output_begin) noexcept;
+    flush_cg_buffer(CG const& g,
+                    uint32_t const num_outputs,
+                    value_type* output_buffer,
+                    atomicT* num_matches,
+                    OutputIt output_begin) noexcept;
 
     /**
      * @brief Flushes per-CG shared memory buffer into the output sequence using CG memcpy_async.
@@ -1023,11 +1023,11 @@ class static_multimap {
     template <uint32_t cg_size, typename CG, typename atomicT, typename OutputIt>
     __inline__ __device__
       std::enable_if_t<not thrust::is_contiguous_iterator<OutputIt>::value, void>
-      flush_output_buffer(CG const& g,
-                          uint32_t const num_outputs,
-                          value_type* output_buffer,
-                          atomicT* num_matches,
-                          OutputIt output_begin) noexcept;
+      flush_cg_buffer(CG const& g,
+                      uint32_t const num_outputs,
+                      value_type* output_buffer,
+                      atomicT* num_matches,
+                      OutputIt output_begin) noexcept;
 
     /**
      * @brief Flushes per-warp shared memory buffer into the output sequence.
@@ -1042,11 +1042,11 @@ class static_multimap {
      * @param output_begin Beginning of the output sequence of key/value pairs
      */
     template <typename atomicT, typename OutputIt>
-    __inline__ __device__ void flush_output_buffer(const unsigned int activemask,
-                                                   uint32_t const num_outputs,
-                                                   value_type* output_buffer,
-                                                   atomicT* num_matches,
-                                                   OutputIt output_begin) noexcept;
+    __inline__ __device__ void flush_warp_buffer(const unsigned int activemask,
+                                                 uint32_t const num_outputs,
+                                                 value_type* output_buffer,
+                                                 atomicT* num_matches,
+                                                 OutputIt output_begin) noexcept;
 
     /**
      * @brief Indicates whether the key `k` was inserted into the map.

From 52f3a3a3883a1b17b6603149bd101953704ffdc3 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Tue, 13 Jul 2021 11:53:25 -0400
Subject: [PATCH 158/267] Cleanups: remove duplcates by using maybe_unused

---
 include/cuco/detail/static_multimap.inl | 453 ++++++++----------------
 1 file changed, 152 insertions(+), 301 deletions(-)

diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index 025a24c1c..cf93f7701 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -614,49 +614,33 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::count
 {
   auto current_slot = initial_slot(g, k);
 
-  if constexpr (is_outer) {
-    bool found_match = false;
+  [[maybe_unused]] bool found_match = false;
 
-    while (true) {
-      value_type arr[2];
-      load_pair_array(&arr[0], current_slot);
+  while (true) {
+    value_type arr[2];
+    load_pair_array(&arr[0], current_slot);
 
-      auto const first_slot_is_empty =
-        detail::bitwise_compare(arr[0].first, this->get_empty_key_sentinel());
-      auto const second_slot_is_empty =
-        detail::bitwise_compare(arr[1].first, this->get_empty_key_sentinel());
-      auto const first_equals  = (not first_slot_is_empty and key_equal(arr[0].first, k));
-      auto const second_equals = (not second_slot_is_empty and key_equal(arr[1].first, k));
+    auto const first_slot_is_empty =
+      detail::bitwise_compare(arr[0].first, this->get_empty_key_sentinel());
+    auto const second_slot_is_empty =
+      detail::bitwise_compare(arr[1].first, this->get_empty_key_sentinel());
+    auto const first_equals  = (not first_slot_is_empty and key_equal(arr[0].first, k));
+    auto const second_equals = (not second_slot_is_empty and key_equal(arr[1].first, k));
 
+    if constexpr (is_outer) {
       if (g.any(first_equals or second_equals)) { found_match = true; }
+    }
 
-      thread_num_matches += (first_equals + second_equals);
+    thread_num_matches += (first_equals + second_equals);
 
-      if (g.any(first_slot_is_empty or second_slot_is_empty)) {
+    if (g.any(first_slot_is_empty or second_slot_is_empty)) {
+      if constexpr (is_outer) {
         if ((not found_match) && (g.thread_rank() == 0)) { thread_num_matches++; }
-        break;
       }
-
-      current_slot = next_slot(current_slot);
+      break;
     }
-  } else {
-    while (true) {
-      value_type arr[2];
-      load_pair_array(&arr[0], current_slot);
 
-      auto const first_slot_is_empty =
-        detail::bitwise_compare(arr[0].first, this->get_empty_key_sentinel());
-      auto const second_slot_is_empty =
-        detail::bitwise_compare(arr[1].first, this->get_empty_key_sentinel());
-      auto const first_equals  = (not first_slot_is_empty and key_equal(arr[0].first, k));
-      auto const second_equals = (not second_slot_is_empty and key_equal(arr[1].first, k));
-
-      thread_num_matches += (first_equals + second_equals);
-
-      if (g.any(first_slot_is_empty or second_slot_is_empty)) { break; }
-
-      current_slot = next_slot(current_slot);
-    }
+    current_slot = next_slot(current_slot);
   }
 }
 
@@ -672,45 +656,30 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::count
 {
   auto current_slot = initial_slot(g, k);
 
-  if constexpr (is_outer) {
-    bool found_match = false;
+  [[maybe_unused]] bool found_match = false;
 
-    while (true) {
-      pair<Key, Value> slot_contents =
-        *reinterpret_cast<cuco::pair_type<Key, Value> const*>(current_slot);
-      auto const& current_key = slot_contents.first;
+  while (true) {
+    pair<Key, Value> slot_contents =
+      *reinterpret_cast<cuco::pair_type<Key, Value> const*>(current_slot);
+    auto const& current_key = slot_contents.first;
 
-      auto const slot_is_empty =
-        detail::bitwise_compare(current_key, this->get_empty_key_sentinel());
-      auto const equals = not slot_is_empty and key_equal(current_key, k);
+    auto const slot_is_empty = detail::bitwise_compare(current_key, this->get_empty_key_sentinel());
+    auto const equals        = not slot_is_empty and key_equal(current_key, k);
 
+    if constexpr (is_outer) {
       if (g.any(equals)) { found_match = true; }
+    }
 
-      thread_num_matches += equals;
+    thread_num_matches += equals;
 
-      if (g.any(slot_is_empty)) {
+    if (g.any(slot_is_empty)) {
+      if constexpr (is_outer) {
         if ((not found_match) && (g.thread_rank() == 0)) { thread_num_matches++; }
-        break;
       }
-
-      current_slot = next_slot(current_slot);
+      break;
     }
-  } else {
-    while (true) {
-      pair<Key, Value> slot_contents =
-        *reinterpret_cast<cuco::pair_type<Key, Value> const*>(current_slot);
-      auto const& current_key = slot_contents.first;
-
-      auto const slot_is_empty =
-        detail::bitwise_compare(current_key, this->get_empty_key_sentinel());
-      auto const equals = not slot_is_empty and key_equal(current_key, k);
-
-      thread_num_matches += equals;
 
-      if (g.any(slot_is_empty)) { break; }
-
-      current_slot = next_slot(current_slot);
-    }
+    current_slot = next_slot(current_slot);
   }
 }
 
@@ -730,51 +699,34 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_
   auto key          = pair.first;
   auto current_slot = initial_slot(g, key);
 
-  if constexpr (is_outer) {
-    bool found_match = false;
+  [[maybe_unused]] bool found_match = false;
 
-    while (true) {
-      value_type arr[2];
-      load_pair_array(&arr[0], current_slot);
+  while (true) {
+    value_type arr[2];
+    load_pair_array(&arr[0], current_slot);
 
-      auto const first_slot_is_empty =
-        detail::bitwise_compare(arr[0].first, this->get_empty_key_sentinel());
-      auto const second_slot_is_empty =
-        detail::bitwise_compare(arr[1].first, this->get_empty_key_sentinel());
+    auto const first_slot_is_empty =
+      detail::bitwise_compare(arr[0].first, this->get_empty_key_sentinel());
+    auto const second_slot_is_empty =
+      detail::bitwise_compare(arr[1].first, this->get_empty_key_sentinel());
 
-      auto const first_slot_equals  = (not first_slot_is_empty and pair_equal(arr[0], pair));
-      auto const second_slot_equals = (not second_slot_is_empty and pair_equal(arr[1], pair));
+    auto const first_slot_equals  = (not first_slot_is_empty and pair_equal(arr[0], pair));
+    auto const second_slot_equals = (not second_slot_is_empty and pair_equal(arr[1], pair));
 
+    if constexpr (is_outer) {
       if (g.any(first_slot_equals or second_slot_equals)) { found_match = true; }
+    }
 
-      thread_num_matches += (first_slot_equals + second_slot_equals);
+    thread_num_matches += (first_slot_equals + second_slot_equals);
 
-      if (g.any(first_slot_is_empty or second_slot_is_empty)) {
+    if (g.any(first_slot_is_empty or second_slot_is_empty)) {
+      if constexpr (is_outer) {
         if ((not found_match) && (g.thread_rank() == 0)) { thread_num_matches++; }
-        break;
       }
-
-      current_slot = next_slot(current_slot);
+      break;
     }
-  } else {
-    while (true) {
-      value_type arr[2];
-      load_pair_array(&arr[0], current_slot);
-
-      auto const first_slot_is_empty =
-        detail::bitwise_compare(arr[0].first, this->get_empty_key_sentinel());
-      auto const second_slot_is_empty =
-        detail::bitwise_compare(arr[1].first, this->get_empty_key_sentinel());
-
-      auto const first_slot_equals  = (not first_slot_is_empty and pair_equal(arr[0], pair));
-      auto const second_slot_equals = (not second_slot_is_empty and pair_equal(arr[1], pair));
-
-      thread_num_matches += (first_slot_equals + second_slot_equals);
-
-      if (g.any(first_slot_is_empty or second_slot_is_empty)) { break; }
 
-      current_slot = next_slot(current_slot);
-    }
+    current_slot = next_slot(current_slot);
   }
 }
 
@@ -794,42 +746,29 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_
   auto key          = pair.first;
   auto current_slot = initial_slot(g, key);
 
-  if constexpr (is_outer) {
-    bool found_match = false;
+  [[maybe_unused]] bool found_match = false;
 
-    while (true) {
-      auto slot_contents = *reinterpret_cast<cuco::pair_type<Key, Value> const*>(current_slot);
+  while (true) {
+    auto slot_contents = *reinterpret_cast<cuco::pair_type<Key, Value> const*>(current_slot);
 
-      auto const slot_is_empty = (slot_contents.first == this->get_empty_key_sentinel());
+    auto const slot_is_empty = (slot_contents.first == this->get_empty_key_sentinel());
 
-      auto const equals = not slot_is_empty and pair_equal(slot_contents, pair);
+    auto const equals = not slot_is_empty and pair_equal(slot_contents, pair);
 
+    if constexpr (is_outer) {
       if (g.any(equals)) { found_match = true; }
+    }
 
-      thread_num_matches += equals;
+    thread_num_matches += equals;
 
-      if (g.any(slot_is_empty)) {
+    if (g.any(slot_is_empty)) {
+      if constexpr (is_outer) {
         if ((not found_match) && (g.thread_rank() == 0)) { thread_num_matches++; }
-        break;
       }
-
-      current_slot = next_slot(current_slot);
+      break;
     }
-  } else {
-    while (true) {
-      auto slot_contents = *reinterpret_cast<cuco::pair_type<Key, Value> const*>(current_slot);
-
-      auto const slot_is_empty =
-        detail::bitwise_compare(slot_contents.first, this->get_empty_key_sentinel());
-
-      auto const equals = not slot_is_empty and pair_equal(slot_contents, pair);
 
-      thread_num_matches += equals;
-
-      if (g.any(slot_is_empty)) { break; }
-
-      current_slot = next_slot(current_slot);
-    }
+    current_slot = next_slot(current_slot);
   }
 }
 
@@ -860,51 +799,51 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::warp_
 
   auto current_slot = initial_slot(g, k);
 
-  bool running = true;
-  if constexpr (is_outer) {
-    bool found_match = false;
-
-    while (__any_sync(activemask, running)) {
-      if (running) {
-        value_type arr[2];
-        load_pair_array(&arr[0], current_slot);
-
-        auto const first_slot_is_empty =
-          detail::bitwise_compare(arr[0].first, this->get_empty_key_sentinel());
-        auto const second_slot_is_empty =
-          detail::bitwise_compare(arr[1].first, this->get_empty_key_sentinel());
-        auto const first_equals  = (not first_slot_is_empty and key_equal(arr[0].first, k));
-        auto const second_equals = (not second_slot_is_empty and key_equal(arr[1].first, k));
-        auto const first_exists  = g.ballot(first_equals);
-        auto const second_exists = g.ballot(second_equals);
-
-        if (first_exists or second_exists) {
-          found_match = true;
-
-          auto num_first_matches  = __popc(first_exists);
-          auto num_second_matches = __popc(second_exists);
-
-          uint32_t output_idx;
-          if (0 == cg_lane_id) {
-            output_idx = atomicAdd(warp_counter, (num_first_matches + num_second_matches));
-          }
-          output_idx = g.shfl(output_idx, 0);
+  bool running                      = true;
+  [[maybe_unused]] bool found_match = false;
 
-          if (first_equals) {
-            auto lane_offset = __popc(first_exists & ((1 << cg_lane_id) - 1));
-            Key key          = k;
-            output_buffer[output_idx + lane_offset] =
-              cuco::make_pair<Key, Value>(std::move(key), std::move(arr[0].second));
-          }
-          if (second_equals) {
-            auto lane_offset = __popc(second_exists & ((1 << cg_lane_id) - 1));
-            Key key          = k;
-            output_buffer[output_idx + num_first_matches + lane_offset] =
-              cuco::make_pair<Key, Value>(std::move(key), std::move(arr[1].second));
-          }
+  while (__any_sync(activemask, running)) {
+    if (running) {
+      value_type arr[2];
+      load_pair_array(&arr[0], current_slot);
+
+      auto const first_slot_is_empty =
+        detail::bitwise_compare(arr[0].first, this->get_empty_key_sentinel());
+      auto const second_slot_is_empty =
+        detail::bitwise_compare(arr[1].first, this->get_empty_key_sentinel());
+      auto const first_equals  = (not first_slot_is_empty and key_equal(arr[0].first, k));
+      auto const second_equals = (not second_slot_is_empty and key_equal(arr[1].first, k));
+      auto const first_exists  = g.ballot(first_equals);
+      auto const second_exists = g.ballot(second_equals);
+
+      if (first_exists or second_exists) {
+        if constexpr (is_outer) { found_match = true; }
+
+        auto num_first_matches  = __popc(first_exists);
+        auto num_second_matches = __popc(second_exists);
+
+        uint32_t output_idx;
+        if (0 == cg_lane_id) {
+          output_idx = atomicAdd(warp_counter, (num_first_matches + num_second_matches));
+        }
+        output_idx = g.shfl(output_idx, 0);
+
+        if (first_equals) {
+          auto lane_offset = __popc(first_exists & ((1 << cg_lane_id) - 1));
+          Key key          = k;
+          output_buffer[output_idx + lane_offset] =
+            cuco::make_pair<Key, Value>(std::move(key), std::move(arr[0].second));
         }
-        if (g.any(first_slot_is_empty or second_slot_is_empty)) {
-          running = false;
+        if (second_equals) {
+          auto lane_offset = __popc(second_exists & ((1 << cg_lane_id) - 1));
+          Key key          = k;
+          output_buffer[output_idx + num_first_matches + lane_offset] =
+            cuco::make_pair<Key, Value>(std::move(key), std::move(arr[1].second));
+        }
+      }
+      if (g.any(first_slot_is_empty or second_slot_is_empty)) {
+        running = false;
+        if constexpr (is_outer) {
           if ((not found_match) && (cg_lane_id == 0)) {
             auto output_idx           = atomicAdd(warp_counter, 1);
             Key key                   = k;
@@ -912,68 +851,18 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::warp_
               std::move(key), std::move(this->get_empty_key_sentinel()));
           }
         }
-      }  // if running
-
-      __syncwarp(activemask);
-      if (*warp_counter + 32 * 2 > buffer_size) {
-        flush_warp_buffer(activemask, *warp_counter, output_buffer, num_matches, output_begin);
-        // First lane reset warp-level counter
-        if (warp_lane_id == 0) { *warp_counter = 0; }
       }
+    }  // if running
 
-      current_slot = next_slot(current_slot);
-    }  // while running
-  } else {
-    while (__any_sync(activemask, running)) {
-      if (running) {
-        value_type arr[2];
-        load_pair_array(&arr[0], current_slot);
-
-        auto const first_slot_is_empty =
-          detail::bitwise_compare(arr[0].first, this->get_empty_key_sentinel());
-        auto const second_slot_is_empty =
-          detail::bitwise_compare(arr[1].first, this->get_empty_key_sentinel());
-        auto const first_equals  = (not first_slot_is_empty and key_equal(arr[0].first, k));
-        auto const second_equals = (not second_slot_is_empty and key_equal(arr[1].first, k));
-        auto const first_exists  = g.ballot(first_equals);
-        auto const second_exists = g.ballot(second_equals);
-
-        if (first_exists or second_exists) {
-          auto num_first_matches  = __popc(first_exists);
-          auto num_second_matches = __popc(second_exists);
-
-          uint32_t output_idx;
-          if (0 == cg_lane_id) {
-            output_idx = atomicAdd(warp_counter, (num_first_matches + num_second_matches));
-          }
-          output_idx = g.shfl(output_idx, 0);
-
-          if (first_equals) {
-            auto lane_offset = __popc(first_exists & ((1 << cg_lane_id) - 1));
-            Key key          = k;
-            output_buffer[output_idx + lane_offset] =
-              cuco::make_pair<Key, Value>(std::move(key), std::move(arr[0].second));
-          }
-          if (second_equals) {
-            auto lane_offset = __popc(second_exists & ((1 << cg_lane_id) - 1));
-            Key key          = k;
-            output_buffer[output_idx + num_first_matches + lane_offset] =
-              cuco::make_pair<Key, Value>(std::move(key), std::move(arr[1].second));
-          }
-        }
-        if (g.any(first_slot_is_empty or second_slot_is_empty)) { running = false; }
-      }  // if running
-
-      __syncwarp(activemask);
-      if (*warp_counter + 32 * 2 > buffer_size) {
-        flush_warp_buffer(activemask, *warp_counter, output_buffer, num_matches, output_begin);
-        // First lane reset warp-level counter
-        if (warp_lane_id == 0) { *warp_counter = 0; }
-      }
+    __syncwarp(activemask);
+    if (*warp_counter + 32 * 2 > buffer_size) {
+      flush_warp_buffer(activemask, *warp_counter, output_buffer, num_matches, output_begin);
+      // First lane reset warp-level counter
+      if (warp_lane_id == 0) { *warp_counter = 0; }
+    }
 
-      current_slot = next_slot(current_slot);
-    }  // while running
-  }
+    current_slot = next_slot(current_slot);
+  }  // while running
 }
 
 template <typename Key,
@@ -1002,38 +891,37 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::cg_re
 
   auto current_slot = initial_slot(g, k);
 
-  bool running = true;
-
-  if constexpr (is_outer) {
-    bool found_match = false;
-
-    while (running) {
-      // TODO: Replace reinterpret_cast with atomic ref when possible. The current implementation
-      // is unsafe!
-      static_assert(sizeof(Key) == sizeof(cuda::atomic<Key>));
-      static_assert(sizeof(Value) == sizeof(cuda::atomic<Value>));
-      pair<Key, Value> slot_contents = *reinterpret_cast<pair<Key, Value> const*>(current_slot);
-
-      auto const slot_is_empty =
-        detail::bitwise_compare(slot_contents.first, this->get_empty_key_sentinel());
-      auto const equals = (not slot_is_empty and key_equal(slot_contents.first, k));
-      auto const exists = g.ballot(equals);
-
-      if (exists) {
-        found_match         = true;
-        auto num_matches    = __popc(exists);
-        uint32_t output_idx = *cg_counter;
-        if (equals) {
-          // Each match computes its lane-level offset
-          auto lane_offset = __popc(exists & ((1 << lane_id) - 1));
-          Key key          = k;
-          output_buffer[output_idx + lane_offset] =
-            cuco::make_pair<Key, Value>(std::move(key), std::move(slot_contents.second));
-        }
-        if (0 == lane_id) { (*cg_counter) += num_matches; }
+  bool running                      = true;
+  [[maybe_unused]] bool found_match = false;
+
+  while (running) {
+    // TODO: Replace reinterpret_cast with atomic ref when possible. The current implementation
+    // is unsafe!
+    static_assert(sizeof(Key) == sizeof(cuda::atomic<Key>));
+    static_assert(sizeof(Value) == sizeof(cuda::atomic<Value>));
+    pair<Key, Value> slot_contents = *reinterpret_cast<pair<Key, Value> const*>(current_slot);
+
+    auto const slot_is_empty =
+      detail::bitwise_compare(slot_contents.first, this->get_empty_key_sentinel());
+    auto const equals = (not slot_is_empty and key_equal(slot_contents.first, k));
+    auto const exists = g.ballot(equals);
+
+    if (exists) {
+      if constexpr (is_outer) { found_match = true; }
+      auto num_matches    = __popc(exists);
+      uint32_t output_idx = *cg_counter;
+      if (equals) {
+        // Each match computes its lane-level offset
+        auto lane_offset = __popc(exists & ((1 << lane_id) - 1));
+        Key key          = k;
+        output_buffer[output_idx + lane_offset] =
+          cuco::make_pair<Key, Value>(std::move(key), std::move(slot_contents.second));
       }
-      if (g.any(slot_is_empty)) {
-        running = false;
+      if (0 == lane_id) { (*cg_counter) += num_matches; }
+    }
+    if (g.any(slot_is_empty)) {
+      running = false;
+      if constexpr (is_outer) {
         if ((not found_match) && (lane_id == 0)) {
           auto output_idx = (*cg_counter)++;
           Key key         = k;
@@ -1041,55 +929,18 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::cg_re
             cuco::make_pair<Key, Value>(std::move(key), std::move(this->get_empty_key_sentinel()));
         }
       }
+    }
 
-      g.sync();
-
-      // Flush if the next iteration won't fit into buffer
-      if ((*cg_counter + cg_size) > buffer_size) {
-        flush_cg_buffer<cg_size>(g, *cg_counter, output_buffer, num_matches, output_begin);
-        // First lane reset CG-level counter
-        if (lane_id == 0) { *cg_counter = 0; }
-      }
-      current_slot = next_slot(current_slot);
-    }  // while running
-  } else {
-    while (running) {
-      // TODO: Replace reinterpret_cast with atomic ref when possible. The current implementation
-      // is unsafe!
-      static_assert(sizeof(Key) == sizeof(cuda::atomic<Key>));
-      static_assert(sizeof(Value) == sizeof(cuda::atomic<Value>));
-      pair<Key, Value> slot_contents = *reinterpret_cast<pair<Key, Value> const*>(current_slot);
-
-      auto const slot_is_empty =
-        detail::bitwise_compare(slot_contents.first, this->get_empty_key_sentinel());
-      auto const equals = (not slot_is_empty and key_equal(slot_contents.first, k));
-      auto const exists = g.ballot(equals);
-
-      if (exists) {
-        auto num_matches    = __popc(exists);
-        uint32_t output_idx = *cg_counter;
-        if (equals) {
-          // Each match computes its lane-level offset
-          auto lane_offset = __popc(exists & ((1 << lane_id) - 1));
-          Key key          = k;
-          output_buffer[output_idx + lane_offset] =
-            cuco::make_pair<Key, Value>(std::move(key), std::move(slot_contents.second));
-        }
-        if (0 == lane_id) { (*cg_counter) += num_matches; }
-      }
-      if (g.any(slot_is_empty)) { running = false; }
-
-      g.sync();
+    g.sync();
 
-      // Flush if the next iteration won't fit into buffer
-      if ((*cg_counter + cg_size) > buffer_size) {
-        flush_cg_buffer<cg_size>(g, *cg_counter, output_buffer, num_matches, output_begin);
-        // First lane reset CG-level counter
-        if (lane_id == 0) { *cg_counter = 0; }
-      }
-      current_slot = next_slot(current_slot);
-    }  // while running
-  }
+    // Flush if the next iteration won't fit into buffer
+    if ((*cg_counter + cg_size) > buffer_size) {
+      flush_cg_buffer<cg_size>(g, *cg_counter, output_buffer, num_matches, output_begin);
+      // First lane reset CG-level counter
+      if (lane_id == 0) { *cg_counter = 0; }
+    }
+    current_slot = next_slot(current_slot);
+  }  // while running
 }
 
 // public APIs

From 9163acd11ddf3fca6e85dbd76b928399a24fad64 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Thu, 15 Jul 2021 10:50:04 -0400
Subject: [PATCH 159/267] Replace warp logic with cooperative group

---
 .../static_multimap/find_all_bench.cu         |   4 +-
 include/cuco/detail/static_multimap.inl       |  96 +++++++++------
 .../cuco/detail/static_multimap_kernels.cuh   |  56 ++++-----
 include/cuco/static_multimap.cuh              | 113 ++++++++++--------
 4 files changed, 153 insertions(+), 116 deletions(-)

diff --git a/benchmarks/hash_table/static_multimap/find_all_bench.cu b/benchmarks/hash_table/static_multimap/find_all_bench.cu
index 5a0ab0fa8..b8b46a321 100644
--- a/benchmarks/hash_table/static_multimap/find_all_bench.cu
+++ b/benchmarks/hash_table/static_multimap/find_all_bench.cu
@@ -88,6 +88,7 @@ std::enable_if_t<(sizeof(Key) == sizeof(Value)), void> nvbench_find_all(
       auto view = map.get_device_view();
 
       auto const block_size  = 128;
+      auto const warp_size   = 32;
       auto const buffer_size = CGSize * BufferSize;
       auto const stride      = 1;
       auto const grid_size = (CGSize * num_keys + stride * block_size - 1) / (stride * block_size);
@@ -105,7 +106,8 @@ std::enable_if_t<(sizeof(Key) == sizeof(Value)), void> nvbench_find_all(
       CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_items, sizeof(atomic_ctr_type), device_id));
 
       // Use timers to explicitly mark the target region
-      cuco::detail::retrieve<block_size, CGSize, buffer_size, Key, Value, is_vector_load, is_outer>
+      cuco::detail::
+        retrieve<block_size, warp_size, CGSize, buffer_size, Key, Value, is_vector_load, is_outer>
         <<<grid_size, block_size, 0, launch.get_stream()>>>(d_unique_keys.begin(),
                                                             d_unique_keys.end(),
                                                             d_results.data().get(),
diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index cf93f7701..c914f7149 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -246,7 +246,7 @@ OutputIt static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::retrieve(
   auto num_keys         = std::distance(first, last);
   auto const block_size = 128;
   // Using per-warp buffer for vector loads and per-CG buffer for scalar loads
-  auto const buffer_size = uses_vector_load() ? (32u * 3u) : (cg_size() * 3u);
+  auto const buffer_size = uses_vector_load() ? (warp_size() * 3u) : (cg_size() * 3u);
   auto const stride      = 1;
   auto const grid_size   = (cg_size() * num_keys + stride * block_size - 1) / (stride * block_size);
   auto view              = get_device_view();
@@ -260,7 +260,14 @@ OutputIt static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::retrieve(
   CUCO_CUDA_TRY(cudaGetDevice(&device_id));
   CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_matches, sizeof(atomic_ctr_type), device_id));
 
-  detail::retrieve<block_size, cg_size(), buffer_size, Key, Value, uses_vector_load(), is_outer>
+  detail::retrieve<block_size,
+                   warp_size(),
+                   cg_size(),
+                   buffer_size,
+                   Key,
+                   Value,
+                   uses_vector_load(),
+                   is_outer>
     <<<grid_size, block_size, 0, stream>>>(first, last, output_begin, num_matches, view, key_equal);
   CUCO_CUDA_TRY(cudaStreamSynchronize(stream));
 
@@ -282,7 +289,7 @@ OutputIt static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::retrieve_
   auto num_keys         = std::distance(first, last);
   auto const block_size = 128;
   // Using per-warp buffer for vector loads and per-CG buffer for scalar loads
-  auto const buffer_size = uses_vector_load() ? (32u * 3u) : (cg_size() * 3u);
+  auto const buffer_size = uses_vector_load() ? (warp_size() * 3u) : (cg_size() * 3u);
   auto const stride      = 1;
   auto const grid_size   = (cg_size() * num_keys + stride * block_size - 1) / (stride * block_size);
   auto view              = get_device_view();
@@ -296,7 +303,14 @@ OutputIt static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::retrieve_
   CUCO_CUDA_TRY(cudaGetDevice(&device_id));
   CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_matches, sizeof(atomic_ctr_type), device_id));
 
-  detail::retrieve<block_size, cg_size(), buffer_size, Key, Value, uses_vector_load(), is_outer>
+  detail::retrieve<block_size,
+                   warp_size(),
+                   cg_size(),
+                   buffer_size,
+                   Key,
+                   Value,
+                   uses_vector_load(),
+                   is_outer>
     <<<grid_size, block_size, 0, stream>>>(first, last, output_begin, num_matches, view, key_equal);
   CUCO_CUDA_TRY(cudaStreamSynchronize(stream));
 
@@ -779,13 +793,14 @@ template <typename Key,
           typename Allocator>
 template <uint32_t buffer_size,
           bool is_outer,
+          typename warpT,
           typename CG,
           typename atomicT,
           typename OutputIt,
           typename KeyEqual>
 __device__ void
-static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::warp_retrieve_impl(
-  const unsigned int activemask,
+static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::retrieve_impl(
+  warpT const& warp,
   CG const& g,
   Key const& k,
   uint32_t* warp_counter,
@@ -794,15 +809,14 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::warp_
   OutputIt output_begin,
   KeyEqual key_equal) noexcept
 {
-  const uint32_t warp_lane_id = threadIdx.x % 32;
-  const uint32_t cg_lane_id   = g.thread_rank();
+  const uint32_t cg_lane_id = g.thread_rank();
 
   auto current_slot = initial_slot(g, k);
 
   bool running                      = true;
   [[maybe_unused]] bool found_match = false;
 
-  while (__any_sync(activemask, running)) {
+  while (warp.any(running)) {
     if (running) {
       value_type arr[2];
       load_pair_array(&arr[0], current_slot);
@@ -854,11 +868,11 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::warp_
       }
     }  // if running
 
-    __syncwarp(activemask);
-    if (*warp_counter + 32 * 2 > buffer_size) {
-      flush_warp_buffer(activemask, *warp_counter, output_buffer, num_matches, output_begin);
+    warp.sync();
+    if (*warp_counter + warp.size() * 2u > buffer_size) {
+      flush_warp_buffer(warp, *warp_counter, output_buffer, num_matches, output_begin);
       // First lane reset warp-level counter
-      if (warp_lane_id == 0) { *warp_counter = 0; }
+      if (warp.thread_rank() == 0) { *warp_counter = 0; }
     }
 
     current_slot = next_slot(current_slot);
@@ -878,7 +892,7 @@ template <uint32_t cg_size,
           typename OutputIt,
           typename KeyEqual>
 __device__ void
-static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::cg_retrieve_impl(
+static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::retrieve_impl(
   CG const& g,
   Key const& k,
   uint32_t* cg_counter,
@@ -997,7 +1011,7 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::flush
   if (0 == lane_id) { offset = num_items->fetch_add(num_outputs, cuda::std::memory_order_relaxed); }
   offset = g.shfl(offset, 0);
 
-  for (auto index = lane_id; index < num_outputs; index += cg_size) {
+  for (auto index = lane_id; index < num_outputs; index += g.size()) {
     *(output_begin + offset + index) = output_buffer[index];
   }
 }
@@ -1007,25 +1021,23 @@ template <typename Key,
           class ProbeSequence,
           cuda::thread_scope Scope,
           typename Allocator>
-template <typename atomicT, typename OutputIt>
+template <typename CG, typename atomicT, typename OutputIt>
 __inline__ __device__ void
 static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::flush_warp_buffer(
-  const unsigned int activemask,
+  CG const& g,
   uint32_t const num_outputs,
   value_type* output_buffer,
   atomicT* num_matches,
   OutputIt output_begin) noexcept
 {
-  int num_threads = __popc(activemask);
-
   std::size_t offset;
-  const auto lane_id = threadIdx.x % 32;
+  const auto lane_id = g.thread_rank();
   if (0 == lane_id) {
     offset = num_matches->fetch_add(num_outputs, cuda::std::memory_order_relaxed);
   }
-  offset = __shfl_sync(activemask, offset, 0);
+  offset = g.shfl(offset, 0);
 
-  for (auto index = lane_id; index < num_outputs; index += num_threads) {
+  for (auto index = lane_id; index < num_outputs; index += g.size()) {
     *(output_begin + offset + index) = output_buffer[index];
   }
 }
@@ -1108,10 +1120,14 @@ template <typename Key,
           class ProbeSequence,
           cuda::thread_scope Scope,
           typename Allocator>
-template <uint32_t buffer_size, typename CG, typename atomicT, typename OutputIt, typename KeyEqual>
-__device__ void
-static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::warp_retrieve(
-  const unsigned int activemask,
+template <uint32_t buffer_size,
+          typename warpT,
+          typename CG,
+          typename atomicT,
+          typename OutputIt,
+          typename KeyEqual>
+__device__ void static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::retrieve(
+  warpT const& warp,
   CG const& g,
   Key const& k,
   uint32_t* warp_counter,
@@ -1121,8 +1137,8 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::warp_
   KeyEqual key_equal) noexcept
 {
   constexpr bool is_outer = false;
-  warp_retrieve_impl<buffer_size, is_outer>(
-    activemask, g, k, warp_counter, output_buffer, num_matches, output_begin);
+  retrieve_impl<buffer_size, is_outer>(
+    warp, g, k, warp_counter, output_buffer, num_matches, output_begin);
 }
 
 template <typename Key,
@@ -1130,10 +1146,15 @@ template <typename Key,
           class ProbeSequence,
           cuda::thread_scope Scope,
           typename Allocator>
-template <uint32_t buffer_size, typename CG, typename atomicT, typename OutputIt, typename KeyEqual>
+template <uint32_t buffer_size,
+          typename warpT,
+          typename CG,
+          typename atomicT,
+          typename OutputIt,
+          typename KeyEqual>
 __device__ void
-static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::warp_retrieve_outer(
-  const unsigned int activemask,
+static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::retrieve_outer(
+  warpT const& warp,
   CG const& g,
   Key const& k,
   uint32_t* warp_counter,
@@ -1143,8 +1164,8 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::warp_
   KeyEqual key_equal) noexcept
 {
   constexpr bool is_outer = true;
-  warp_retrieve_impl<buffer_size, is_outer>(
-    activemask, g, k, warp_counter, output_buffer, num_matches, output_begin);
+  retrieve_impl<buffer_size, is_outer>(
+    warp, g, k, warp_counter, output_buffer, num_matches, output_begin);
 }
 
 template <typename Key,
@@ -1158,8 +1179,7 @@ template <uint32_t cg_size,
           typename atomicT,
           typename OutputIt,
           typename KeyEqual>
-__device__ void
-static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::cg_retrieve(
+__device__ void static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::retrieve(
   CG const& g,
   Key const& k,
   uint32_t* cg_counter,
@@ -1169,7 +1189,7 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::cg_re
   KeyEqual key_equal) noexcept
 {
   constexpr bool is_outer = false;
-  cg_retrieve_impl<cg_size, buffer_size, is_outer>(
+  retrieve_impl<cg_size, buffer_size, is_outer>(
     g, k, cg_counter, output_buffer, num_matches, output_begin);
 }
 
@@ -1185,7 +1205,7 @@ template <uint32_t cg_size,
           typename OutputIt,
           typename KeyEqual>
 __device__ void
-static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::cg_retrieve_outer(
+static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::retrieve_outer(
   CG const& g,
   Key const& k,
   uint32_t* cg_counter,
@@ -1195,7 +1215,7 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::cg_re
   KeyEqual key_equal) noexcept
 {
   constexpr bool is_outer = true;
-  cg_retrieve_impl<cg_size, buffer_size, is_outer>(
+  retrieve_impl<cg_size, buffer_size, is_outer>(
     g, k, cg_counter, output_buffer, num_matches, output_begin);
 }
 
diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap_kernels.cuh
index 7cb85e862..fd8bc9a52 100644
--- a/include/cuco/detail/static_multimap_kernels.cuh
+++ b/include/cuco/detail/static_multimap_kernels.cuh
@@ -281,6 +281,7 @@ __global__ void pair_count(
  * output_begin + *num_matches - 1)`. Use `count()` to determine the number of matching keys.
  *
  * @tparam block_size The size of the thread block
+ * @tparam warp_size The size of the warp
  * @tparam tile_size The number of threads in the Cooperative Groups
  * @tparam buffer_size Size of the output buffer
  * @tparam Key key type
@@ -303,6 +304,7 @@ __global__ void pair_count(
  * @param key_equal The binary function to compare two keys for equality
  */
 template <uint32_t block_size,
+          uint32_t warp_size,
           uint32_t tile_size,
           uint32_t buffer_size,
           typename Key,
@@ -321,43 +323,41 @@ __global__ std::enable_if_t<uses_vector_load, void> retrieve(InputIt first,
                                                              viewT view,
                                                              KeyEqual key_equal)
 {
+  constexpr uint32_t num_warps = block_size / warp_size;
+  const uint32_t warp_id       = threadIdx.x / warp_size;
+
+  auto warp    = cg::tiled_partition<warp_size>(cg::this_thread_block());
   auto tile    = cg::tiled_partition<tile_size>(cg::this_thread_block());
   auto tid     = block_size * blockIdx.x + threadIdx.x;
   auto key_idx = tid / tile_size;
 
-  constexpr uint32_t num_warps = block_size / 32;
-  const uint32_t warp_id       = threadIdx.x / 32;
-  const uint32_t warp_lane_id  = threadIdx.x % 32;
-
   __shared__ cuco::pair_type<Key, Value> output_buffer[num_warps][buffer_size];
   // TODO: replace this with shared memory cuda::atomic variables once the dynamiic initialization
   // warning issue is solved __shared__ atomicT toto_countter[num_warps];
   __shared__ uint32_t warp_counter[num_warps];
 
-  if (warp_lane_id == 0) { warp_counter[warp_id] = 0; }
+  if (warp.thread_rank() == 0) { warp_counter[warp_id] = 0; }
 
-  const unsigned int activemask = __ballot_sync(0xffffffff, first + key_idx < last);
-
-  while (first + key_idx < last) {
+  while (warp.any(first + key_idx < last)) {
     auto key = *(first + key_idx);
     if constexpr (is_outer) {
-      view.warp_retrieve_outer<buffer_size>(activemask,
-                                            tile,
-                                            key,
-                                            &warp_counter[warp_id],
-                                            output_buffer[warp_id],
-                                            num_matches,
-                                            output_begin,
-                                            key_equal);
+      view.retrieve_outer<buffer_size>(warp,
+                                       tile,
+                                       key,
+                                       &warp_counter[warp_id],
+                                       output_buffer[warp_id],
+                                       num_matches,
+                                       output_begin,
+                                       key_equal);
     } else {
-      view.warp_retrieve<buffer_size>(activemask,
-                                      tile,
-                                      key,
-                                      &warp_counter[warp_id],
-                                      output_buffer[warp_id],
-                                      num_matches,
-                                      output_begin,
-                                      key_equal);
+      view.retrieve<buffer_size>(warp,
+                                 tile,
+                                 key,
+                                 &warp_counter[warp_id],
+                                 output_buffer[warp_id],
+                                 num_matches,
+                                 output_begin,
+                                 key_equal);
     }
     key_idx += (gridDim.x * block_size) / tile_size;
   }
@@ -365,7 +365,7 @@ __global__ std::enable_if_t<uses_vector_load, void> retrieve(InputIt first,
   // Final flush of output buffer
   if (warp_counter[warp_id] > 0) {
     view.flush_warp_buffer(
-      activemask, warp_counter[warp_id], output_buffer[warp_id], num_matches, output_begin);
+      warp, warp_counter[warp_id], output_buffer[warp_id], num_matches, output_begin);
   }
 }
 
@@ -381,6 +381,7 @@ __global__ std::enable_if_t<uses_vector_load, void> retrieve(InputIt first,
  * output_begin + *num_matches - 1)`. Use `count()` to determine the number of matching keys.
  *
  * @tparam block_size The size of the thread block
+ * @tparam warp_size The size of the warp
  * @tparam tile_size The number of threads in the Cooperative Groups
  * @tparam buffer_size Size of the output buffer
  * @tparam Key key type
@@ -403,6 +404,7 @@ __global__ std::enable_if_t<uses_vector_load, void> retrieve(InputIt first,
  * @param key_equal The binary function to compare two keys for equality
  */
 template <uint32_t block_size,
+          uint32_t warp_size,
           uint32_t tile_size,
           uint32_t buffer_size,
           typename Key,
@@ -437,10 +439,10 @@ __global__ std::enable_if_t<not uses_vector_load, void> retrieve(InputIt first,
   while (first + key_idx < last) {
     auto key = *(first + key_idx);
     if constexpr (is_outer) {
-      view.cg_retrieve_outer<tile_size, buffer_size>(
+      view.retrieve_outer<tile_size, buffer_size>(
         tile, key, &cg_counter[cg_id], output_buffer[cg_id], num_matches, output_begin, key_equal);
     } else {
-      view.cg_retrieve<tile_size, buffer_size>(
+      view.retrieve<tile_size, buffer_size>(
         tile, key, &cg_counter[cg_id], output_buffer[cg_id], num_matches, output_begin, key_equal);
     }
     key_idx += (gridDim.x * block_size) / tile_size;
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index 2ebd51f48..5eb18dd6b 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -143,6 +143,13 @@ class static_multimap {
   static_multimap& operator=(static_multimap const&) = delete;
   static_multimap& operator=(static_multimap&&) = delete;
 
+  /**
+   * @brief Indicate the warp size.
+   *
+   * @return The warp size.
+   */
+  static constexpr uint32_t warp_size() noexcept { return 32u; }
+
   /**
    * @brief The size of the CUDA cooperative thread group.
    *
@@ -913,12 +920,13 @@ class static_multimap {
      *
      * @tparam buffer_size Size of the output buffer
      * @tparam is_outer Boolean flag indicating whether outer join is peformed or not
+     * @tparam warpT Warp (Cooperative Group) type
      * @tparam CG Cooperative Group type
      * @tparam atomicT Type of atomic storage
      * @tparam OutputIt Device accessible output iterator whose `value_type` is
      * convertible to the map's `mapped_type`
      * @tparam KeyEqual Binary callable type
-     * @param activemask Mask of active threads in the warp
+     * @param warp The Cooperative Group (or warp) used to flush output buffer
      * @param g The Cooperative Group used to retrieve
      * @param k The key to search for
      * @param warp_counter Pointer to the warp counter
@@ -930,18 +938,19 @@ class static_multimap {
      */
     template <uint32_t buffer_size,
               bool is_outer,
+              typename warpT,
               typename CG,
               typename atomicT,
               typename OutputIt,
               typename KeyEqual = thrust::equal_to<key_type>>
-    __device__ void warp_retrieve_impl(const unsigned int activemask,
-                                       CG const& g,
-                                       Key const& k,
-                                       uint32_t* warp_counter,
-                                       value_type* output_buffer,
-                                       atomicT* num_matches,
-                                       OutputIt output_begin,
-                                       KeyEqual key_equal = KeyEqual{}) noexcept;
+    __device__ void retrieve_impl(warpT const& warp,
+                                  CG const& g,
+                                  Key const& k,
+                                  uint32_t* warp_counter,
+                                  value_type* output_buffer,
+                                  atomicT* num_matches,
+                                  OutputIt output_begin,
+                                  KeyEqual key_equal = KeyEqual{}) noexcept;
 
     /**
      * @brief Find all the matches of a given key contained in multimap using scalar
@@ -975,13 +984,13 @@ class static_multimap {
               typename atomicT,
               typename OutputIt,
               typename KeyEqual = thrust::equal_to<key_type>>
-    __device__ void cg_retrieve_impl(CG const& g,
-                                     Key const& k,
-                                     uint32_t* cg_counter,
-                                     value_type* output_buffer,
-                                     atomicT* num_matches,
-                                     OutputIt output_begin,
-                                     KeyEqual key_equal = KeyEqual{}) noexcept;
+    __device__ void retrieve_impl(CG const& g,
+                                  Key const& k,
+                                  uint32_t* cg_counter,
+                                  value_type* output_buffer,
+                                  atomicT* num_matches,
+                                  OutputIt output_begin,
+                                  KeyEqual key_equal = KeyEqual{}) noexcept;
 
    public:
     /**
@@ -1041,8 +1050,8 @@ class static_multimap {
      * @param num_matches Size of the output sequence
      * @param output_begin Beginning of the output sequence of key/value pairs
      */
-    template <typename atomicT, typename OutputIt>
-    __inline__ __device__ void flush_warp_buffer(const unsigned int activemask,
+    template <typename CG, typename atomicT, typename OutputIt>
+    __inline__ __device__ void flush_warp_buffer(CG const& g,
                                                  uint32_t const num_outputs,
                                                  value_type* output_buffer,
                                                  atomicT* num_matches,
@@ -1148,12 +1157,13 @@ class static_multimap {
      * the empty value sentinel into the output only if `is_outer` is true.
      *
      * @tparam buffer_size Size of the output buffer
+     * @tparam warpT Warp (Cooperative Group) type
      * @tparam CG Cooperative Group type
      * @tparam atomicT Type of atomic storage
      * @tparam OutputIt Device accessible output iterator whose `value_type` is
      * convertible to the map's `mapped_type`
      * @tparam KeyEqual Binary callable type
-     * @param activemask Mask of active threads in the warp
+     * @param warp The Cooperative Group (or warp) used to flush output buffer
      * @param g The Cooperative Group used to retrieve
      * @param k The key to search for
      * @param warp_counter Pointer to the warp counter
@@ -1164,18 +1174,19 @@ class static_multimap {
      * for equality
      */
     template <uint32_t buffer_size,
+              typename warpT,
               typename CG,
               typename atomicT,
               typename OutputIt,
               typename KeyEqual = thrust::equal_to<key_type>>
-    __device__ void warp_retrieve(const unsigned int activemask,
-                                  CG const& g,
-                                  Key const& k,
-                                  uint32_t* warp_counter,
-                                  value_type* output_buffer,
-                                  atomicT* num_matches,
-                                  OutputIt output_begin,
-                                  KeyEqual key_equal = KeyEqual{}) noexcept;
+    __device__ void retrieve(warpT const& warp,
+                             CG const& g,
+                             Key const& k,
+                             uint32_t* warp_counter,
+                             value_type* output_buffer,
+                             atomicT* num_matches,
+                             OutputIt output_begin,
+                             KeyEqual key_equal = KeyEqual{}) noexcept;
 
     /**
      * @brief Find all the matches of a given key contained in multimap using vector
@@ -1186,12 +1197,13 @@ class static_multimap {
      * the empty value sentinel into the output only if `is_outer` is true.
      *
      * @tparam buffer_size Size of the output buffer
+     * @tparam warpT Warp (Cooperative Group) type
      * @tparam CG Cooperative Group type
      * @tparam atomicT Type of atomic storage
      * @tparam OutputIt Device accessible output iterator whose `value_type` is
      * convertible to the map's `mapped_type`
      * @tparam KeyEqual Binary callable type
-     * @param activemask Mask of active threads in the warp
+     * @param warp The Cooperative Group (or warp) used to flush output buffer
      * @param g The Cooperative Group used to retrieve
      * @param k The key to search for
      * @param warp_counter Pointer to the warp counter
@@ -1202,18 +1214,19 @@ class static_multimap {
      * for equality
      */
     template <uint32_t buffer_size,
+              typename warpT,
               typename CG,
               typename atomicT,
               typename OutputIt,
               typename KeyEqual = thrust::equal_to<key_type>>
-    __device__ void warp_retrieve_outer(const unsigned int activemask,
-                                        CG const& g,
-                                        Key const& k,
-                                        uint32_t* warp_counter,
-                                        value_type* output_buffer,
-                                        atomicT* num_matches,
-                                        OutputIt output_begin,
-                                        KeyEqual key_equal = KeyEqual{}) noexcept;
+    __device__ void retrieve_outer(warpT const& warp,
+                                   CG const& g,
+                                   Key const& k,
+                                   uint32_t* warp_counter,
+                                   value_type* output_buffer,
+                                   atomicT* num_matches,
+                                   OutputIt output_begin,
+                                   KeyEqual key_equal = KeyEqual{}) noexcept;
 
     /**
      * @brief Find all the matches of a given key contained in multimap using scalar
@@ -1244,13 +1257,13 @@ class static_multimap {
               typename atomicT,
               typename OutputIt,
               typename KeyEqual = thrust::equal_to<key_type>>
-    __device__ void cg_retrieve(CG const& g,
-                                Key const& k,
-                                uint32_t* cg_counter,
-                                value_type* output_buffer,
-                                atomicT* num_matches,
-                                OutputIt output_begin,
-                                KeyEqual key_equal = KeyEqual{}) noexcept;
+    __device__ void retrieve(CG const& g,
+                             Key const& k,
+                             uint32_t* cg_counter,
+                             value_type* output_buffer,
+                             atomicT* num_matches,
+                             OutputIt output_begin,
+                             KeyEqual key_equal = KeyEqual{}) noexcept;
 
     /**
      * @brief Find all the matches of a given key contained in multimap using scalar
@@ -1282,13 +1295,13 @@ class static_multimap {
               typename atomicT,
               typename OutputIt,
               typename KeyEqual = thrust::equal_to<key_type>>
-    __device__ void cg_retrieve_outer(CG const& g,
-                                      Key const& k,
-                                      uint32_t* cg_counter,
-                                      value_type* output_buffer,
-                                      atomicT* num_matches,
-                                      OutputIt output_begin,
-                                      KeyEqual key_equal = KeyEqual{}) noexcept;
+    __device__ void retrieve_outer(CG const& g,
+                                   Key const& k,
+                                   uint32_t* cg_counter,
+                                   value_type* output_buffer,
+                                   atomicT* num_matches,
+                                   OutputIt output_begin,
+                                   KeyEqual key_equal = KeyEqual{}) noexcept;
   };  // class device_view
 
   /**

From f2262c4da6fc504e555f736ce46a7f3a0ee38b2e Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Thu, 15 Jul 2021 11:06:08 -0400
Subject: [PATCH 160/267] Refactor flush_output_buffer function

---
 include/cuco/detail/static_multimap.inl       | 36 +++----------
 .../cuco/detail/static_multimap_kernels.cuh   |  4 +-
 include/cuco/static_multimap.cuh              | 53 ++++++-------------
 3 files changed, 24 insertions(+), 69 deletions(-)

diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index c914f7149..ad525fc11 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -870,7 +870,7 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::retri
 
     warp.sync();
     if (*warp_counter + warp.size() * 2u > buffer_size) {
-      flush_warp_buffer(warp, *warp_counter, output_buffer, num_matches, output_begin);
+      flush_output_buffer(warp, *warp_counter, output_buffer, num_matches, output_begin);
       // First lane reset warp-level counter
       if (warp.thread_rank() == 0) { *warp_counter = 0; }
     }
@@ -949,7 +949,7 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::retri
 
     // Flush if the next iteration won't fit into buffer
     if ((*cg_counter + cg_size) > buffer_size) {
-      flush_cg_buffer<cg_size>(g, *cg_counter, output_buffer, num_matches, output_begin);
+      flush_output_buffer(g, *cg_counter, output_buffer, num_matches, output_begin);
       // First lane reset CG-level counter
       if (lane_id == 0) { *cg_counter = 0; }
     }
@@ -964,9 +964,9 @@ template <typename Key,
           class ProbeSequence,
           cuda::thread_scope Scope,
           typename Allocator>
-template <uint32_t cg_size, typename CG, typename atomicT, typename OutputIt>
+template <typename CG, typename atomicT, typename OutputIt>
 __inline__ __device__ std::enable_if_t<thrust::is_contiguous_iterator<OutputIt>::value, void>
-static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::flush_cg_buffer(
+static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::flush_output_buffer(
   CG const& g,
   uint32_t const num_outputs,
   value_type* output_buffer,
@@ -992,38 +992,14 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::flush
 #endif
 }
 
-template <typename Key,
-          typename Value,
-          class ProbeSequence,
-          cuda::thread_scope Scope,
-          typename Allocator>
-template <uint32_t cg_size, typename CG, typename atomicT, typename OutputIt>
-__inline__ __device__ std::enable_if_t<not thrust::is_contiguous_iterator<OutputIt>::value, void>
-static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::flush_cg_buffer(
-  CG const& g,
-  uint32_t const num_outputs,
-  value_type* output_buffer,
-  atomicT* num_items,
-  OutputIt output_begin) noexcept
-{
-  std::size_t offset;
-  const auto lane_id = g.thread_rank();
-  if (0 == lane_id) { offset = num_items->fetch_add(num_outputs, cuda::std::memory_order_relaxed); }
-  offset = g.shfl(offset, 0);
-
-  for (auto index = lane_id; index < num_outputs; index += g.size()) {
-    *(output_begin + offset + index) = output_buffer[index];
-  }
-}
-
 template <typename Key,
           typename Value,
           class ProbeSequence,
           cuda::thread_scope Scope,
           typename Allocator>
 template <typename CG, typename atomicT, typename OutputIt>
-__inline__ __device__ void
-static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::flush_warp_buffer(
+__inline__ __device__ std::enable_if_t<not thrust::is_contiguous_iterator<OutputIt>::value, void>
+static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::flush_output_buffer(
   CG const& g,
   uint32_t const num_outputs,
   value_type* output_buffer,
diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap_kernels.cuh
index fd8bc9a52..98268a0bd 100644
--- a/include/cuco/detail/static_multimap_kernels.cuh
+++ b/include/cuco/detail/static_multimap_kernels.cuh
@@ -364,7 +364,7 @@ __global__ std::enable_if_t<uses_vector_load, void> retrieve(InputIt first,
 
   // Final flush of output buffer
   if (warp_counter[warp_id] > 0) {
-    view.flush_warp_buffer(
+    view.flush_output_buffer(
       warp, warp_counter[warp_id], output_buffer[warp_id], num_matches, output_begin);
   }
 }
@@ -450,7 +450,7 @@ __global__ std::enable_if_t<not uses_vector_load, void> retrieve(InputIt first,
 
   // Final flush of output buffer
   if (cg_counter[cg_id] > 0) {
-    view.flush_cg_buffer<tile_size>(
+    view.flush_output_buffer(
       tile, cg_counter[cg_id], output_buffer[cg_id], num_matches, output_begin);
   }
 }
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index 5eb18dd6b..d2af719b8 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -994,68 +994,47 @@ class static_multimap {
 
    public:
     /**
-     * @brief Flushes per-CG shared memory buffer into the output sequence using CG memcpy_async.
+     * @brief Flushes per-CG buffer into the output sequence using CG memcpy_async.
      *
-     * @tparam cg_size The number of threads in the Cooperative Groups
      * @tparam CG Cooperative Group type
      * @tparam atomicT Type of atomic storage
      * @tparam OutputIt Device accessible output iterator whose `value_type` is
      * convertible to the map's `mapped_type`
      * @param g The Cooperative Group used to flush output buffer
      * @param num_outputs Number of valid output in the buffer
-     * @param output_buffer Shared memory buffer of the key/value pair sequence
+     * @param output_buffer Buffer of the key/value pair sequence
      * @param num_matches Size of the output sequence
      * @param output_begin Beginning of the output sequence of key/value pairs
      */
-    template <uint32_t cg_size, typename CG, typename atomicT, typename OutputIt>
+    template <typename CG, typename atomicT, typename OutputIt>
     __inline__ __device__ std::enable_if_t<thrust::is_contiguous_iterator<OutputIt>::value, void>
-    flush_cg_buffer(CG const& g,
-                    uint32_t const num_outputs,
-                    value_type* output_buffer,
-                    atomicT* num_matches,
-                    OutputIt output_begin) noexcept;
+    flush_output_buffer(CG const& g,
+                        uint32_t const num_outputs,
+                        value_type* output_buffer,
+                        atomicT* num_matches,
+                        OutputIt output_begin) noexcept;
 
     /**
-     * @brief Flushes per-CG shared memory buffer into the output sequence using CG memcpy_async.
+     * @brief Flushes per-CG buffer into the output sequence.
      *
-     * @tparam cg_size The number of threads in the Cooperative Groups
      * @tparam CG Cooperative Group type
      * @tparam atomicT Type of atomic storage
      * @tparam OutputIt Device accessible output iterator whose `value_type` is
      * convertible to the map's `mapped_type`
      * @param g The Cooperative Group used to flush output buffer
      * @param num_outputs Number of valid output in the buffer
-     * @param output_buffer Shared memory buffer of the key/value pair sequence
+     * @param output_buffer Buffer of the key/value pair sequence
      * @param num_matches Size of the output sequence
      * @param output_begin Beginning of the output sequence of key/value pairs
      */
-    template <uint32_t cg_size, typename CG, typename atomicT, typename OutputIt>
+    template <typename CG, typename atomicT, typename OutputIt>
     __inline__ __device__
       std::enable_if_t<not thrust::is_contiguous_iterator<OutputIt>::value, void>
-      flush_cg_buffer(CG const& g,
-                      uint32_t const num_outputs,
-                      value_type* output_buffer,
-                      atomicT* num_matches,
-                      OutputIt output_begin) noexcept;
-
-    /**
-     * @brief Flushes per-warp shared memory buffer into the output sequence.
-     *
-     * @tparam atomicT Type of atomic storage
-     * @tparam OutputIt Device accessible output iterator whose `value_type` is
-     * convertible to the map's `mapped_type`
-     * @param activemask Mask of active threads in the warp
-     * @param num_outputs Number of valid output in the buffer
-     * @param output_buffer Shared memory buffer of the key/value pair sequence
-     * @param num_matches Size of the output sequence
-     * @param output_begin Beginning of the output sequence of key/value pairs
-     */
-    template <typename CG, typename atomicT, typename OutputIt>
-    __inline__ __device__ void flush_warp_buffer(CG const& g,
-                                                 uint32_t const num_outputs,
-                                                 value_type* output_buffer,
-                                                 atomicT* num_matches,
-                                                 OutputIt output_begin) noexcept;
+      flush_output_buffer(CG const& g,
+                          uint32_t const num_outputs,
+                          value_type* output_buffer,
+                          atomicT* num_matches,
+                          OutputIt output_begin) noexcept;
 
     /**
      * @brief Indicates whether the key `k` was inserted into the map.

From ee649ebfab60f2ecf61f61a04961c118011c5ff4 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Thu, 15 Jul 2021 11:28:30 -0700
Subject: [PATCH 161/267] Fix the build error related to CUDA 11.0

---
 include/cuco/detail/static_multimap.inl | 20 ++++++++------
 include/cuco/static_multimap.cuh        | 36 ++++++++++++++++---------
 2 files changed, 35 insertions(+), 21 deletions(-)

diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index ad525fc11..98111905b 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -965,13 +965,15 @@ template <typename Key,
           cuda::thread_scope Scope,
           typename Allocator>
 template <typename CG, typename atomicT, typename OutputIt>
-__inline__ __device__ std::enable_if_t<thrust::is_contiguous_iterator<OutputIt>::value, void>
-static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::flush_output_buffer(
-  CG const& g,
-  uint32_t const num_outputs,
-  value_type* output_buffer,
-  atomicT* num_matches,
-  OutputIt output_begin) noexcept
+__inline__ __device__
+  std::enable_if_t<thrust::is_contiguous_iterator<OutputIt>::value and SUPPORTS_CG_MEMCPY_ASYNC,
+                   void>
+  static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::flush_output_buffer(
+    CG const& g,
+    uint32_t const num_outputs,
+    value_type* output_buffer,
+    atomicT* num_matches,
+    OutputIt output_begin) noexcept
 {
   std::size_t offset;
   const auto lane_id = g.thread_rank();
@@ -998,7 +1000,9 @@ template <typename Key,
           cuda::thread_scope Scope,
           typename Allocator>
 template <typename CG, typename atomicT, typename OutputIt>
-__inline__ __device__ std::enable_if_t<not thrust::is_contiguous_iterator<OutputIt>::value, void>
+__inline__ __device__ std::enable_if_t<not(thrust::is_contiguous_iterator<OutputIt>::value and
+                                           SUPPORTS_CG_MEMCPY_ASYNC),
+                                       void>
 static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::flush_output_buffer(
   CG const& g,
   uint32_t const num_outputs,
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index d2af719b8..cc7caf77c 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -29,6 +29,13 @@
 #define CUCO_HAS_CUDA_BARRIER
 #endif
 
+// cg::memcpy_aysnc is supported for CUDA 11.1 and up
+#if defined(CUDART_VERSION) && (CUDART_VERSION >= 11100)
+#define SUPPORTS_CG_MEMCPY_ASYNC 1
+#else
+#define SUPPORTS_CG_MEMCPY_ASYNC 0
+#endif
+
 #if defined(CUCO_HAS_CUDA_BARRIER)
 #include <cuda/barrier>
 #endif
@@ -1007,12 +1014,14 @@ class static_multimap {
      * @param output_begin Beginning of the output sequence of key/value pairs
      */
     template <typename CG, typename atomicT, typename OutputIt>
-    __inline__ __device__ std::enable_if_t<thrust::is_contiguous_iterator<OutputIt>::value, void>
-    flush_output_buffer(CG const& g,
-                        uint32_t const num_outputs,
-                        value_type* output_buffer,
-                        atomicT* num_matches,
-                        OutputIt output_begin) noexcept;
+    __inline__ __device__
+      std::enable_if_t<thrust::is_contiguous_iterator<OutputIt>::value and SUPPORTS_CG_MEMCPY_ASYNC,
+                       void>
+      flush_output_buffer(CG const& g,
+                          uint32_t const num_outputs,
+                          value_type* output_buffer,
+                          atomicT* num_matches,
+                          OutputIt output_begin) noexcept;
 
     /**
      * @brief Flushes per-CG buffer into the output sequence.
@@ -1028,13 +1037,14 @@ class static_multimap {
      * @param output_begin Beginning of the output sequence of key/value pairs
      */
     template <typename CG, typename atomicT, typename OutputIt>
-    __inline__ __device__
-      std::enable_if_t<not thrust::is_contiguous_iterator<OutputIt>::value, void>
-      flush_output_buffer(CG const& g,
-                          uint32_t const num_outputs,
-                          value_type* output_buffer,
-                          atomicT* num_matches,
-                          OutputIt output_begin) noexcept;
+    __inline__ __device__ std::enable_if_t<not(thrust::is_contiguous_iterator<OutputIt>::value and
+                                               SUPPORTS_CG_MEMCPY_ASYNC),
+                                           void>
+    flush_output_buffer(CG const& g,
+                        uint32_t const num_outputs,
+                        value_type* output_buffer,
+                        atomicT* num_matches,
+                        OutputIt output_begin) noexcept;
 
     /**
      * @brief Indicates whether the key `k` was inserted into the map.

From f23c9e1473f202f4c19d5413f723549b99a816f7 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Mon, 19 Jul 2021 13:07:37 -0400
Subject: [PATCH 162/267] Update docs + move uses_vector_load to private

---
 include/cuco/static_multimap.cuh | 21 +++++++++++++--------
 1 file changed, 13 insertions(+), 8 deletions(-)

diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index cc7caf77c..c2cc8f3ed 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -75,7 +75,7 @@ namespace cuco {
  * `insert` function will insert all keys into the map.
  *
  * The singular device-side operations allow individual threads to perform
- * independent insert or find/contains operations from device code. These
+ * independent operations (e.g. `insert`, etc.) from device code. These
  * operations are accessed through non-owning, trivially copyable "view" types:
  * `device_view` and `mutable_device_view`. The `device_view` class is an
  * immutable view that allows only non-modifying operations such as `count` or
@@ -83,6 +83,11 @@ namespace cuco {
  * The two types are separate to prevent erroneous concurrent insert/find
  * operations.
  *
+ * Loading two consecutive slots instead of one for small pairs can improve cache utilization since
+ * 16B memory loads are natively supported at the SASS/hardware level. This `vector load` method is
+ * implicitly applied to all involved operations (e.g. `insert`, `count`, and `retrieve`, etc.) if
+ * the pair is packable (see `multimap::uses_vector_load` logic).
+ *
  * Example:
  * \code{.cpp}
  * int empty_key_sentinel = -1;
@@ -164,13 +169,6 @@ class static_multimap {
    */
   static constexpr uint32_t cg_size() noexcept { return ProbeSequence::cg_size(); }
 
-  /**
-   * @brief Indicate if vector load is used.
-   *
-   * @return Boolean indicating if vector load is used.
-   */
-  static constexpr bool uses_vector_load() noexcept { return ProbeSequence::uses_vector_load(); }
-
   /**
    * @brief Construct a fixed-size map with the specified capacity and sentinel values.
    * @brief Construct a statically sized map with the specified number of slots
@@ -376,6 +374,13 @@ class static_multimap {
                           KeyEqual key_equal  = KeyEqual{});
 
  private:
+  /**
+   * @brief Indicate if vector load is used.
+   *
+   * @return Boolean indicating if vector load is used.
+   */
+  static constexpr bool uses_vector_load() noexcept { return ProbeSequence::uses_vector_load(); }
+
   class device_view_base {
    protected:
     // Import member type definitions from `static_multimap`

From 14be940cd014cc04fd49c8808bf998ee0bec2d3c Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Mon, 19 Jul 2021 14:12:14 -0400
Subject: [PATCH 163/267] Add tests for 4B/8B pairs

---
 tests/static_multimap/static_multimap_test.cu | 30 ++++++++-----------
 1 file changed, 12 insertions(+), 18 deletions(-)

diff --git a/tests/static_multimap/static_multimap_test.cu b/tests/static_multimap/static_multimap_test.cu
index f0bc79398..32c891f1d 100644
--- a/tests/static_multimap/static_multimap_test.cu
+++ b/tests/static_multimap/static_multimap_test.cu
@@ -84,13 +84,11 @@ static void generate_keys(OutputIt output_begin, OutputIt output_end)
 
 TEMPLATE_TEST_CASE_SIG("Each key appears twice",
                        "",
-                       ((typename T, dist_type Dist), T, Dist),
-                       (int32_t, dist_type::DUAL),
-                       (int64_t, dist_type::DUAL))
+                       ((typename Key, typename Value, dist_type Dist), Key, Value, Dist),
+                       (int32_t, int32_t, dist_type::DUAL),
+                       (int32_t, int64_t, dist_type::DUAL),
+                       (int64_t, int64_t, dist_type::DUAL))
 {
-  using Key   = T;
-  using Value = T;
-
   constexpr std::size_t num_items{400};
   cuco::static_multimap<Key, Value> map{500, -1, -1};
 
@@ -173,13 +171,11 @@ TEMPLATE_TEST_CASE_SIG("Each key appears twice",
 
 TEMPLATE_TEST_CASE_SIG("Handling of non-matches",
                        "",
-                       ((typename T, dist_type Dist), T, Dist),
-                       (int32_t, dist_type::UNIQUE),
-                       (int64_t, dist_type::UNIQUE))
+                       ((typename Key, typename Value, dist_type Dist), Key, Value, Dist),
+                       (int32_t, int32_t, dist_type::UNIQUE),
+                       (int32_t, int64_t, dist_type::UNIQUE),
+                       (int64_t, int64_t, dist_type::UNIQUE))
 {
-  using Key   = T;
-  using Value = T;
-
   constexpr std::size_t num_keys{1'000'000};
   cuco::static_multimap<Key, Value> map{2'000'000, -1, -1};
 
@@ -255,13 +251,11 @@ TEMPLATE_TEST_CASE_SIG("Handling of non-matches",
 
 TEMPLATE_TEST_CASE_SIG("Evaluation of pair functions",
                        "",
-                       ((typename T, dist_type Dist), T, Dist),
-                       (int32_t, dist_type::UNIQUE),
-                       (int64_t, dist_type::UNIQUE))
+                       ((typename Key, typename Value, dist_type Dist), Key, Value, Dist),
+                       (int32_t, int32_t, dist_type::UNIQUE),
+                       (int32_t, int64_t, dist_type::UNIQUE),
+                       (int64_t, int64_t, dist_type::UNIQUE))
 {
-  using Key   = T;
-  using Value = T;
-
   constexpr std::size_t num_pairs{5'000'000};
   cuco::static_multimap<Key, Value> map{10'000'000, -1, -1};
 

From ace127303805f05c3bf2316e9be43b4d64e8b05f Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Mon, 19 Jul 2021 14:45:08 -0400
Subject: [PATCH 164/267] Use vector_width logic

---
 include/cuco/detail/probe_sequences.cuh | 15 +++++++++++----
 include/cuco/detail/static_multimap.inl |  4 ++--
 include/cuco/static_multimap.cuh        | 14 ++++++++++++++
 3 files changed, 27 insertions(+), 6 deletions(-)

diff --git a/include/cuco/detail/probe_sequences.cuh b/include/cuco/detail/probe_sequences.cuh
index ec5d03e86..bc71bfceb 100644
--- a/include/cuco/detail/probe_sequences.cuh
+++ b/include/cuco/detail/probe_sequences.cuh
@@ -40,6 +40,9 @@ class probe_sequence_base {
 
  public:
   __host__ __device__ static constexpr uint32_t cg_size() noexcept { return CGSize; }
+
+  __host__ __device__ static constexpr uint32_t vector_width() noexcept { return 2u; }
+
   __host__ __device__ static constexpr bool uses_vector_load() noexcept
   {
     return cuco::detail::is_packable<value_type>();
@@ -72,6 +75,7 @@ class double_hashing : public probe_sequence_base<Key, Value, CGSize, Scope> {
   using probe_sequence_base<Key, Value, CGSize, Scope>::capacity_;
   using probe_sequence_base<Key, Value, CGSize, Scope>::slots_;
   using probe_sequence_base<Key, Value, CGSize, Scope>::cg_size;
+  using probe_sequence_base<Key, Value, CGSize, Scope>::vector_width;
   using probe_sequence_base<Key, Value, CGSize, Scope>::uses_vector_load;
 
   __host__ __device__ explicit double_hashing(iterator slots, std::size_t capacity) noexcept
@@ -84,8 +88,10 @@ class double_hashing : public probe_sequence_base<Key, Value, CGSize, Scope> {
   {
     std::size_t index;
     if constexpr (uses_vector_load()) {
-      step_size_ = (hash2_(k + 1) % (capacity_ / (cg_size() * 2) - 1) + 1) * cg_size() * 2;
-      index      = hash1_(k) % (capacity_ / (cg_size() * 2)) * cg_size() * 2 + g.thread_rank() * 2;
+      step_size_ = (hash2_(k + 1) % (capacity_ / (cg_size() * vector_width()) - 1) + 1) *
+                   cg_size() * vector_width();
+      index = hash1_(k) % (capacity_ / (cg_size() * vector_width())) * cg_size() * vector_width() +
+              g.thread_rank() * vector_width();
     } else {
       step_size_ = (hash2_(k + 1) % (capacity_ / cg_size() - 1) + 1) * cg_size();
       index      = (hash1_(k) + g.thread_rank()) % capacity_;
@@ -116,6 +122,7 @@ class linear_probing : public probe_sequence_base<Key, Value, CGSize, Scope> {
   using probe_sequence_base<Key, Value, CGSize, Scope>::capacity_;
   using probe_sequence_base<Key, Value, CGSize, Scope>::slots_;
   using probe_sequence_base<Key, Value, CGSize, Scope>::cg_size;
+  using probe_sequence_base<Key, Value, CGSize, Scope>::vector_width;
   using probe_sequence_base<Key, Value, CGSize, Scope>::uses_vector_load;
 
   __host__ __device__ explicit linear_probing(iterator slots, std::size_t capacity)
@@ -127,7 +134,7 @@ class linear_probing : public probe_sequence_base<Key, Value, CGSize, Scope> {
   __device__ iterator initial_slot(CG const& g, Key const k) noexcept
   {
     if constexpr (uses_vector_load()) {
-      return &slots_[(hash_(k) + g.thread_rank() * 2) % capacity_];
+      return &slots_[(hash_(k) + g.thread_rank() * vector_width()) % capacity_];
     } else {
       return &slots_[(hash_(k) + g.thread_rank()) % capacity_];
     }
@@ -137,7 +144,7 @@ class linear_probing : public probe_sequence_base<Key, Value, CGSize, Scope> {
   {
     std::size_t index = s - slots_;
     if constexpr (uses_vector_load()) {
-      return &slots_[(index + cg_size() * 2) % capacity_];
+      return &slots_[(index + cg_size() * vector_width()) % capacity_];
     } else {
       return &slots_[(index + cg_size()) % capacity_];
     }
diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index 98111905b..cce22d103 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -30,7 +30,7 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::static_multimap(
     slot_allocator_{alloc}
 {
   if constexpr (uses_vector_load()) {
-    capacity_ = cuco::detail::get_valid_capacity<cg_size() * 2>(capacity);
+    capacity_ = cuco::detail::get_valid_capacity<cg_size() * vector_width()>(capacity);
   } else {
     capacity_ = cuco::detail::get_valid_capacity<cg_size()>(capacity);
   }
@@ -869,7 +869,7 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::retri
     }  // if running
 
     warp.sync();
-    if (*warp_counter + warp.size() * 2u > buffer_size) {
+    if (*warp_counter + warp.size() * vector_width() > buffer_size) {
       flush_output_buffer(warp, *warp_counter, output_buffer, num_matches, output_begin);
       // First lane reset warp-level counter
       if (warp.thread_rank() == 0) { *warp_counter = 0; }
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index c2cc8f3ed..a1541691b 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -381,6 +381,13 @@ class static_multimap {
    */
   static constexpr bool uses_vector_load() noexcept { return ProbeSequence::uses_vector_load(); }
 
+  /**
+   * @brief The number of elements for each vector load
+   *
+   * @return The number of elements for each vector load.
+   */
+  static constexpr uint32_t vector_width() noexcept { return ProbeSequence::vector_width(); }
+
   class device_view_base {
    protected:
     // Import member type definitions from `static_multimap`
@@ -397,6 +404,13 @@ class static_multimap {
      */
     static constexpr bool uses_vector_load() noexcept { return ProbeSequence::uses_vector_load(); }
 
+    /**
+     * @brief The number of elements for each vector load
+     *
+     * @return The number of elements for each vector load.
+     */
+    static constexpr uint32_t vector_width() noexcept { return ProbeSequence::vector_width(); }
+
     /**
      * @brief Load two key/value pairs from the given slot to the target pair array.
      *

From 843d83f2c8a89003d3aed532a6ce390dbd4a8937 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Tue, 20 Jul 2021 09:40:13 -0400
Subject: [PATCH 165/267] Fix empty key/value sentinel bugs

---
 include/cuco/detail/static_multimap.inl | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index cce22d103..ac1a06950 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -457,9 +457,9 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_mutable_vie
     // The user provide `key_equal` can never be used to compare against `empty_key_sentinel` as
     // the sentinel is not a valid key value. Therefore, first check for the sentinel
     auto const first_slot_is_empty =
-      (detail::bitwise_compare(arr[0].first, this->get_empty_value_sentinel()));
+      (detail::bitwise_compare(arr[0].first, this->get_empty_key_sentinel()));
     auto const second_slot_is_empty =
-      (detail::bitwise_compare(arr[1].first, this->get_empty_value_sentinel()));
+      (detail::bitwise_compare(arr[1].first, this->get_empty_key_sentinel()));
     auto const window_contains_empty = g.ballot(first_slot_is_empty or second_slot_is_empty);
 
     if (window_contains_empty) {
@@ -862,7 +862,7 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::retri
             auto output_idx           = atomicAdd(warp_counter, 1);
             Key key                   = k;
             output_buffer[output_idx] = cuco::make_pair<Key, Value>(
-              std::move(key), std::move(this->get_empty_key_sentinel()));
+              std::move(key), std::move(this->get_empty_value_sentinel()));
           }
         }
       }
@@ -937,10 +937,10 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::retri
       running = false;
       if constexpr (is_outer) {
         if ((not found_match) && (lane_id == 0)) {
-          auto output_idx = (*cg_counter)++;
-          Key key         = k;
-          output_buffer[output_idx] =
-            cuco::make_pair<Key, Value>(std::move(key), std::move(this->get_empty_key_sentinel()));
+          auto output_idx           = (*cg_counter)++;
+          Key key                   = k;
+          output_buffer[output_idx] = cuco::make_pair<Key, Value>(
+            std::move(key), std::move(this->get_empty_value_sentinel()));
         }
       }
     }

From af10fcaf5808bfb2416a0b3126befd542a1b02c0 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Mon, 26 Jul 2021 09:37:05 -0400
Subject: [PATCH 166/267] Cleanup: move self-defined enum types to the key
 generator header file

---
 .../static_multimap/static_multimap_bench.cu  | 32 +++----------------
 benchmarks/key_generator.hpp                  | 21 ++++++++++++
 2 files changed, 26 insertions(+), 27 deletions(-)

diff --git a/benchmarks/hash_table/static_multimap/static_multimap_bench.cu b/benchmarks/hash_table/static_multimap/static_multimap_bench.cu
index 87ff31d7e..47fd4c81b 100644
--- a/benchmarks/hash_table/static_multimap/static_multimap_bench.cu
+++ b/benchmarks/hash_table/static_multimap/static_multimap_bench.cu
@@ -22,27 +22,6 @@
 #include <cuco/static_multimap.cuh>
 #include <key_generator.hpp>
 
-NVBENCH_DECLARE_ENUM_TYPE_STRINGS(
-  // Enum type:
-  dist_type,
-  // Callable to generate input strings:
-  // Short identifier used for tables, command-line args, etc.
-  // Used when context is available to figure out the enum type.
-  [](dist_type d) {
-    switch (d) {
-      case dist_type::GAUSSIAN: return "GAUSSIAN";
-      case dist_type::GEOMETRIC: return "GEOMETRIC";
-      case dist_type::UNIFORM: return "UNIFORM";
-      default: return "ERROR";
-    }
-  },
-  // Callable to generate descriptions:
-  // If non-empty, these are used in `--list` to describe values.
-  // Used when context may not be available to figure out the type from the
-  // input string.
-  // Just use `[](auto) { return std::string{}; }` if you don't want these.
-  [](auto) { return std::string{}; })
-
 /**
  * @brief A benchmark evaluating multi-value `insert` performance:
  * - Total number of insertions: 100'000'000
@@ -204,6 +183,11 @@ std::enable_if_t<(sizeof(Key) != sizeof(Value)), void> nvbench_static_multimap_f
   state.skip("Key should be the same type as Value.");
 }
 
+/**
+ * @brief A benchmark evaluating multi-value retrieve (`count` + `find_all`) performance:
+ * - Total number of insertions: 100'000'000
+ * - CG size: 8
+ */
 template <typename Key, typename Value, dist_type Dist, nvbench::int32_t Multiplicity>
 std::enable_if_t<(sizeof(Key) == sizeof(Value)), void> nvbench_static_multimap_retrieve(
   nvbench::state& state,
@@ -257,12 +241,6 @@ std::enable_if_t<(sizeof(Key) != sizeof(Value)), void> nvbench_static_multimap_r
   state.skip("Key should be the same type as Value.");
 }
 
-/**
- * @brief A benchmark evaluating multi-value retrieve (`count` + `find_all`) performance:
- * - Total number of insertions: 100'000'000
- * - CG size: 8
- */
-
 using key_type   = nvbench::type_list<nvbench::int32_t, nvbench::int64_t>;
 using value_type = nvbench::type_list<nvbench::int32_t, nvbench::int64_t>;
 using d_type =
diff --git a/benchmarks/key_generator.hpp b/benchmarks/key_generator.hpp
index 29b7a5d5d..9f0d8d8a6 100644
--- a/benchmarks/key_generator.hpp
+++ b/benchmarks/key_generator.hpp
@@ -21,6 +21,27 @@
 
 enum class dist_type { GAUSSIAN, GEOMETRIC, UNIFORM };
 
+NVBENCH_DECLARE_ENUM_TYPE_STRINGS(
+  // Enum type:
+  dist_type,
+  // Callable to generate input strings:
+  // Short identifier used for tables, command-line args, etc.
+  // Used when context is available to figure out the enum type.
+  [](dist_type d) {
+    switch (d) {
+      case dist_type::GAUSSIAN: return "GAUSSIAN";
+      case dist_type::GEOMETRIC: return "GEOMETRIC";
+      case dist_type::UNIFORM: return "UNIFORM";
+      default: return "ERROR";
+    }
+  },
+  // Callable to generate descriptions:
+  // If non-empty, these are used in `--list` to describe values.
+  // Used when context may not be available to figure out the type from the
+  // input string.
+  // Just use `[](auto) { return std::string{}; }` if you don't want these.
+  [](auto) { return std::string{}; })
+
 template <dist_type Dist, std::size_t Multiplicity, typename Key, typename OutputIt>
 static void generate_keys(OutputIt output_begin, OutputIt output_end)
 {

From c65a03438e71b0d47a3af40985a4d3fb0aded910 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Mon, 26 Jul 2021 10:07:18 -0400
Subject: [PATCH 167/267] Use distinct names instead of SFINAE

---
 .../static_multimap/find_all_bench.cu         |  5 +--
 include/cuco/detail/static_multimap.inl       | 38 ++++++++++---------
 .../cuco/detail/static_multimap_kernels.cuh   | 28 ++++++--------
 3 files changed, 34 insertions(+), 37 deletions(-)

diff --git a/benchmarks/hash_table/static_multimap/find_all_bench.cu b/benchmarks/hash_table/static_multimap/find_all_bench.cu
index b8b46a321..e030f4cc6 100644
--- a/benchmarks/hash_table/static_multimap/find_all_bench.cu
+++ b/benchmarks/hash_table/static_multimap/find_all_bench.cu
@@ -53,8 +53,7 @@ std::enable_if_t<(sizeof(Key) == sizeof(Value)), void> nvbench_find_all(
   std::size_t const size     = num_keys / occupancy;
   std::size_t const num_reps = state.get_int64("NumReps");
 
-  constexpr bool is_vector_load = true;
-  constexpr bool is_outer       = true;
+  constexpr bool is_outer = true;
 
   std::vector<Key> h_keys(num_keys);
   std::vector<cuco::pair_type<Key, Value>> h_pairs(num_keys);
@@ -107,7 +106,7 @@ std::enable_if_t<(sizeof(Key) == sizeof(Value)), void> nvbench_find_all(
 
       // Use timers to explicitly mark the target region
       cuco::detail::
-        retrieve<block_size, warp_size, CGSize, buffer_size, Key, Value, is_vector_load, is_outer>
+        vectorized_retrieve<block_size, warp_size, CGSize, buffer_size, Key, Value, is_outer>
         <<<grid_size, block_size, 0, launch.get_stream()>>>(d_unique_keys.begin(),
                                                             d_unique_keys.end(),
                                                             d_results.data().get(),
diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index ac1a06950..6776159c5 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -260,15 +260,16 @@ OutputIt static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::retrieve(
   CUCO_CUDA_TRY(cudaGetDevice(&device_id));
   CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_matches, sizeof(atomic_ctr_type), device_id));
 
-  detail::retrieve<block_size,
-                   warp_size(),
-                   cg_size(),
-                   buffer_size,
-                   Key,
-                   Value,
-                   uses_vector_load(),
-                   is_outer>
-    <<<grid_size, block_size, 0, stream>>>(first, last, output_begin, num_matches, view, key_equal);
+  if constexpr (uses_vector_load()) {
+    detail::
+      vectorized_retrieve<block_size, warp_size(), cg_size(), buffer_size, Key, Value, is_outer>
+      <<<grid_size, block_size, 0, stream>>>(
+        first, last, output_begin, num_matches, view, key_equal);
+  } else {
+    detail::retrieve<block_size, warp_size(), cg_size(), buffer_size, Key, Value, is_outer>
+      <<<grid_size, block_size, 0, stream>>>(
+        first, last, output_begin, num_matches, view, key_equal);
+  }
   CUCO_CUDA_TRY(cudaStreamSynchronize(stream));
 
   auto output_end = output_begin + *num_matches;
@@ -303,15 +304,16 @@ OutputIt static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::retrieve_
   CUCO_CUDA_TRY(cudaGetDevice(&device_id));
   CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_matches, sizeof(atomic_ctr_type), device_id));
 
-  detail::retrieve<block_size,
-                   warp_size(),
-                   cg_size(),
-                   buffer_size,
-                   Key,
-                   Value,
-                   uses_vector_load(),
-                   is_outer>
-    <<<grid_size, block_size, 0, stream>>>(first, last, output_begin, num_matches, view, key_equal);
+  if constexpr (uses_vector_load()) {
+    detail::
+      vectorized_retrieve<block_size, warp_size(), cg_size(), buffer_size, Key, Value, is_outer>
+      <<<grid_size, block_size, 0, stream>>>(
+        first, last, output_begin, num_matches, view, key_equal);
+  } else {
+    detail::retrieve<block_size, warp_size(), cg_size(), buffer_size, Key, Value, is_outer>
+      <<<grid_size, block_size, 0, stream>>>(
+        first, last, output_begin, num_matches, view, key_equal);
+  }
   CUCO_CUDA_TRY(cudaStreamSynchronize(stream));
 
   auto output_end = output_begin + *num_matches;
diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap_kernels.cuh
index 98268a0bd..c30a72e16 100644
--- a/include/cuco/detail/static_multimap_kernels.cuh
+++ b/include/cuco/detail/static_multimap_kernels.cuh
@@ -286,7 +286,6 @@ __global__ void pair_count(
  * @tparam buffer_size Size of the output buffer
  * @tparam Key key type
  * @tparam Value The type of the mapped value for the map
- * @tparam uses_vector_load Boolean flag indicating whether vector loads are used or not
  * @tparam is_outer Boolean flag indicating whether the current functions is used for outer join
  * operations or not
  * @tparam InputIt Device accessible input iterator whose `value_type` is
@@ -309,19 +308,18 @@ template <uint32_t block_size,
           uint32_t buffer_size,
           typename Key,
           typename Value,
-          bool uses_vector_load,
           bool is_outer,
           typename InputIt,
           typename OutputIt,
           typename atomicT,
           typename viewT,
           typename KeyEqual>
-__global__ std::enable_if_t<uses_vector_load, void> retrieve(InputIt first,
-                                                             InputIt last,
-                                                             OutputIt output_begin,
-                                                             atomicT* num_matches,
-                                                             viewT view,
-                                                             KeyEqual key_equal)
+__global__ void vectorized_retrieve(InputIt first,
+                                    InputIt last,
+                                    OutputIt output_begin,
+                                    atomicT* num_matches,
+                                    viewT view,
+                                    KeyEqual key_equal)
 {
   constexpr uint32_t num_warps = block_size / warp_size;
   const uint32_t warp_id       = threadIdx.x / warp_size;
@@ -386,7 +384,6 @@ __global__ std::enable_if_t<uses_vector_load, void> retrieve(InputIt first,
  * @tparam buffer_size Size of the output buffer
  * @tparam Key key type
  * @tparam Value The type of the mapped value for the map
- * @tparam uses_vector_load Boolean flag indicating whether vector loads are used or not
  * @tparam is_outer Boolean flag indicating whether the current functions is used for outer join
  * operations or not
  * @tparam InputIt Device accessible input iterator whose `value_type` is
@@ -409,19 +406,18 @@ template <uint32_t block_size,
           uint32_t buffer_size,
           typename Key,
           typename Value,
-          bool uses_vector_load,
           bool is_outer,
           typename InputIt,
           typename OutputIt,
           typename atomicT,
           typename viewT,
           typename KeyEqual>
-__global__ std::enable_if_t<not uses_vector_load, void> retrieve(InputIt first,
-                                                                 InputIt last,
-                                                                 OutputIt output_begin,
-                                                                 atomicT* num_matches,
-                                                                 viewT view,
-                                                                 KeyEqual key_equal)
+__global__ void retrieve(InputIt first,
+                         InputIt last,
+                         OutputIt output_begin,
+                         atomicT* num_matches,
+                         viewT view,
+                         KeyEqual key_equal)
 {
   auto tile    = cg::tiled_partition<tile_size>(cg::this_thread_block());
   auto tid     = block_size * blockIdx.x + threadIdx.x;

From 7a66714bf9bcb41dc73f2f2996075d59bf526e23 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Mon, 26 Jul 2021 10:32:32 -0400
Subject: [PATCH 168/267] Move is_bitwise_comparable logic to bitwise_compare
 header file

---
 include/cuco/detail/bitwise_compare.cuh | 35 ++++++++++++++++
 include/cuco/detail/static_map.inl      |  2 -
 include/cuco/detail/static_multimap.inl |  2 -
 include/cuco/detail/traits.hpp          | 54 -------------------------
 include/cuco/static_map.cuh             | 23 +++--------
 include/cuco/static_multimap.cuh        | 16 ++++----
 6 files changed, 49 insertions(+), 83 deletions(-)
 delete mode 100644 include/cuco/detail/traits.hpp

diff --git a/include/cuco/detail/bitwise_compare.cuh b/include/cuco/detail/bitwise_compare.cuh
index 959e296bf..160e36921 100644
--- a/include/cuco/detail/bitwise_compare.cuh
+++ b/include/cuco/detail/bitwise_compare.cuh
@@ -13,6 +13,7 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
+
 #pragma once
 
 #include <cstdint>
@@ -74,5 +75,39 @@ __host__ __device__ constexpr bool bitwise_compare(T const& lhs, T const& rhs)
                                                           reinterpret_cast<char const*>(&rhs));
 }
 
+/**
+ * @brief Customization point that can be specialized to indicate that it is safe to perform bitwise
+ * equality comparisons on objects of type `T`.
+ *
+ * By default, only types where `std::has_unique_object_representations_v<T>` is true are safe for
+ * bitwise equality. However, this can be too restrictive for some types, e.g., floating point
+ * types.
+ *
+ * User-defined specializations of `is_bitwise_comparable` are allowed, but it is the users
+ * responsibility to ensure values do not occur that would lead to unexpected behavior. For example,
+ * if a `NaN` bit pattern were used as the empty sentinel value, it may not compare bitwise equal to
+ * other `NaN` bit patterns.
+ *
+ */
+template <typename T, typename = void>
+struct is_bitwise_comparable : std::false_type {
+};
+
+/// By default, only types with unique object representations are allowed
+template <typename T>
+struct is_bitwise_comparable<T, std::enable_if_t<std::has_unique_object_representations_v<T>>>
+  : std::true_type {
+};
+
+/**
+ * @brief Declares that a type `Type` is bitwise comparable.
+ *
+ */
+#define CUCO_DECLARE_BITWISE_COMPARABLE(Type)           \
+  namespace cuco {                                      \
+  template <>                                           \
+  struct is_bitwise_comparable<Type> : std::true_type { \
+  };                                                    \
+  }
 }  // namespace detail
 }  // namespace cuco
diff --git a/include/cuco/detail/static_map.inl b/include/cuco/detail/static_map.inl
index 4f0fffc51..96081fff5 100644
--- a/include/cuco/detail/static_map.inl
+++ b/include/cuco/detail/static_map.inl
@@ -14,8 +14,6 @@
  * limitations under the License.
  */
 
-#include <cuco/detail/bitwise_compare.cuh>
-
 namespace cuco {
 
 template <typename Key, typename Value, cuda::thread_scope Scope, typename Allocator>
diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index 6776159c5..913025aa6 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -14,8 +14,6 @@
  * limitations under the License.
  */
 
-#include <cuco/detail/bitwise_compare.cuh>
-
 namespace cuco {
 
 template <typename Key,
diff --git a/include/cuco/detail/traits.hpp b/include/cuco/detail/traits.hpp
deleted file mode 100644
index 3adfc227c..000000000
--- a/include/cuco/detail/traits.hpp
+++ /dev/null
@@ -1,54 +0,0 @@
-/*
- * Copyright (c) 2021, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- */
-
-#pragma once
-
-namespace cuco {
-/**
- * @brief Customization point that can be specialized to indicate that it is safe to perform bitwise
- * equality comparisons on objects of type `T`.
- *
- * By default, only types where `std::has_unique_object_representations_v<T>` is true are safe for
- * bitwise equality. However, this can be too restrictive for some types, e.g., floating point
- * types.
- *
- * User-defined specializations of `is_bitwise_comparable` are allowed, but it is the users
- * responsibility to ensure values do not occur that would lead to unexpected behavior. For example,
- * if a `NaN` bit pattern were used as the empty sentinel value, it may not compare bitwise equal to
- * other `NaN` bit patterns.
- *
- */
-template <typename T, typename = void>
-struct is_bitwise_comparable : std::false_type {
-};
-
-/// By default, only types with unique object representations are allowed
-template <typename T>
-struct is_bitwise_comparable<T, std::enable_if_t<std::has_unique_object_representations_v<T>>>
-  : std::true_type {
-};
-
-/**
- * @brief Declares that a type `Type` is bitwise comparable.
- *
- */
-#define CUCO_DECLARE_BITWISE_COMPARABLE(Type)           \
-  namespace cuco {                                      \
-  template <>                                           \
-  struct is_bitwise_comparable<Type> : std::true_type { \
-  };                                                    \
-  }
-
-}  // namespace cuco
diff --git a/include/cuco/static_map.cuh b/include/cuco/static_map.cuh
index 42b7021c0..885356872 100644
--- a/include/cuco/static_map.cuh
+++ b/include/cuco/static_map.cuh
@@ -34,28 +34,17 @@
 #include <cuda/barrier>
 #endif
 
+#include <cuco/detail/bitwise_compare.cuh>
 #include <cuco/detail/error.hpp>
 #include <cuco/detail/hash_functions.cuh>
 #include <cuco/detail/pair.cuh>
 #include <cuco/detail/static_map_kernels.cuh>
-#include <cuco/detail/traits.hpp>
 
 namespace cuco {
 
 template <typename Key, typename Value, cuda::thread_scope Scope, typename Allocator>
 class dynamic_map;
 
-/**
- * @brief Declares that a type `Type` is bitwise comparable.
- *
- */
-#define CUCO_DECLARE_BITWISE_COMPARABLE(Type)           \
-  namespace cuco {                                      \
-  template <>                                           \
-  struct is_bitwise_comparable<Type> : std::true_type { \
-  };                                                    \
-  }
-
 /**
  * @brief A GPU-accelerated, unordered, associative container of key-value
  * pairs with unique keys.
@@ -65,7 +54,7 @@ class dynamic_map;
  * `cuco::detail::is_packable` constexpr).
  *
  * Current limitations:
- * - Requires keys and values that where `cuco::is_bitwise_comparable<T>::value` is true
+ * - Requires keys and values that where `cuco::detail::is_bitwise_comparable<T>::value` is true
  *    - Comparisons against the "sentinel" values will always be done with bitwise comparisons.
  * - Does not support erasing keys
  * - Capacity is fixed and will not grow automatically
@@ -130,14 +119,14 @@ template <typename Key,
           typename Allocator       = cuco::cuda_allocator<char>>
 class static_map {
   static_assert(
-    is_bitwise_comparable<Key>::value,
+    detail::is_bitwise_comparable<Key>::value,
     "Key type must have unique object representations or have been explicitly declared as safe for "
-    "bitwise comparison via specialization of cuco::is_bitwise_comparable<Key>.");
+    "bitwise comparison via specialization of cuco::detail::is_bitwise_comparable<Key>.");
 
-  static_assert(is_bitwise_comparable<Value>::value,
+  static_assert(detail::is_bitwise_comparable<Value>::value,
                 "Value type must have unique object representations or have been explicitly "
                 "declared as safe for bitwise comparison via specialization of "
-                "cuco::is_bitwise_comparable<Value>.");
+                "cuco::detail::is_bitwise_comparable<Value>.");
 
   friend class dynamic_map<Key, Value, Scope, Allocator>;
 
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index a1541691b..097b85277 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -40,11 +40,11 @@
 #include <cuda/barrier>
 #endif
 
+#include <cuco/detail/bitwise_compare.cuh>
 #include <cuco/detail/error.hpp>
 #include <cuco/detail/prime.hpp>
 #include <cuco/detail/probe_sequences.cuh>
 #include <cuco/detail/static_multimap_kernels.cuh>
-#include <cuco/detail/traits.hpp>
 
 namespace cuco {
 
@@ -56,7 +56,7 @@ namespace cuco {
  * concurrent insert and find) from threads in device code.
  *
  * Current limitations:
- * - Requires keys and values that where `cuco::is_bitwise_comparable<T>::value` is true
+ * - Requires keys and values that where `cuco::detail::is_bitwise_comparable<T>::value` is true
  * - Comparisons against the "sentinel" values will always be done with bitwise comparisons
  * Therefore, the objects must have unique, bitwise object representations (e.g., no padding bits).
  * - Does not support erasing keys
@@ -129,14 +129,14 @@ template <typename Key,
           typename Allocator       = cuco::cuda_allocator<char>>
 class static_multimap {
   static_assert(
-    is_bitwise_comparable<Key>::value,
+    detail::is_bitwise_comparable<Key>::value,
     "Key type must have unique object representations or have been explicitly declared as safe for "
-    "bitwise comparison via specialization of cuco::is_bitwise_comparable<Key>.");
+    "bitwise comparison via specialization of cuco::detail::is_bitwise_comparable<Key>.");
 
-  static_assert(is_bitwise_comparable<Value>::value,
-                "Value type must have unique object representations or have been explicitly "
-                "declared as safe for "
-                "bitwise comparison via specialization of cuco::is_bitwise_comparable<Value>.");
+  static_assert(
+    detail::is_bitwise_comparable<Value>::value,
+    "Value type must have unique object representations or have been explicitly declared as safe "
+    "for bitwise comparison via specialization of cuco::detail::is_bitwise_comparable<Value>.");
 
  public:
   using value_type         = cuco::pair_type<Key, Value>;

From f95cc83a8cf72622b2c6117961836a6f9ebc80b2 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Mon, 26 Jul 2021 10:48:51 -0400
Subject: [PATCH 169/267] Prefetch the dedicated stream

---
 include/cuco/detail/static_multimap.inl | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index 913025aa6..64236f894 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -118,7 +118,7 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::count(
   *num_matches = 0;
   int device_id;
   CUCO_CUDA_TRY(cudaGetDevice(&device_id));
-  CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_matches, sizeof(atomic_ctr_type), device_id));
+  CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_matches, sizeof(atomic_ctr_type), device_id, stream));
 
   detail::count<block_size, cg_size(), Key, Value, is_outer>
     <<<grid_size, block_size, 0, stream>>>(first, last, num_matches, view, key_equal);
@@ -152,7 +152,7 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::count_
   *num_matches = 0;
   int device_id;
   CUCO_CUDA_TRY(cudaGetDevice(&device_id));
-  CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_matches, sizeof(atomic_ctr_type), device_id));
+  CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_matches, sizeof(atomic_ctr_type), device_id, stream));
 
   detail::count<block_size, cg_size(), Key, Value, is_outer>
     <<<grid_size, block_size, 0, stream>>>(first, last, num_matches, view, key_equal);
@@ -186,7 +186,7 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::pair_c
   *num_matches = 0;
   int device_id;
   CUCO_CUDA_TRY(cudaGetDevice(&device_id));
-  CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_matches, sizeof(atomic_ctr_type), device_id));
+  CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_matches, sizeof(atomic_ctr_type), device_id, stream));
 
   detail::pair_count<block_size, cg_size(), Key, Value, is_outer>
     <<<grid_size, block_size, 0, stream>>>(first, last, num_matches, view, pair_equal);
@@ -220,7 +220,7 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::pair_c
   *num_matches = 0;
   int device_id;
   CUCO_CUDA_TRY(cudaGetDevice(&device_id));
-  CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_matches, sizeof(atomic_ctr_type), device_id));
+  CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_matches, sizeof(atomic_ctr_type), device_id, stream));
 
   detail::pair_count<block_size, cg_size(), Key, Value, is_outer>
     <<<grid_size, block_size, 0, stream>>>(first, last, num_matches, view, pair_equal);
@@ -256,7 +256,7 @@ OutputIt static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::retrieve(
   *num_matches = 0;
   int device_id;
   CUCO_CUDA_TRY(cudaGetDevice(&device_id));
-  CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_matches, sizeof(atomic_ctr_type), device_id));
+  CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_matches, sizeof(atomic_ctr_type), device_id, stream));
 
   if constexpr (uses_vector_load()) {
     detail::
@@ -300,7 +300,7 @@ OutputIt static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::retrieve_
   *num_matches = 0;
   int device_id;
   CUCO_CUDA_TRY(cudaGetDevice(&device_id));
-  CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_matches, sizeof(atomic_ctr_type), device_id));
+  CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_matches, sizeof(atomic_ctr_type), device_id, stream));
 
   if constexpr (uses_vector_load()) {
     detail::

From f0943bc70744995748aa5ec82264814cfbaf5683 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Mon, 26 Jul 2021 14:58:51 -0400
Subject: [PATCH 170/267] Use counter allocator instead of unified memory

---
 include/cuco/detail/static_multimap.inl | 145 ++++++++++++++----------
 include/cuco/static_multimap.cuh        |  17 +--
 2 files changed, 98 insertions(+), 64 deletions(-)

diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index 64236f894..82dbaa675 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -25,7 +25,8 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::static_multimap(
   std::size_t capacity, Key empty_key_sentinel, Value empty_value_sentinel, Allocator const& alloc)
   : empty_key_sentinel_{empty_key_sentinel},
     empty_value_sentinel_{empty_value_sentinel},
-    slot_allocator_{alloc}
+    slot_allocator_{alloc},
+    counter_allocator_{alloc}
 {
   if constexpr (uses_vector_load()) {
     capacity_ = cuco::detail::get_valid_capacity<cg_size() * vector_width()>(capacity);
@@ -113,19 +114,24 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::count(
 
   constexpr bool is_outer = false;
 
-  atomic_ctr_type* num_matches;
-  CUCO_CUDA_TRY(cudaMallocManaged(&num_matches, sizeof(atomic_ctr_type)));
-  *num_matches = 0;
-  int device_id;
-  CUCO_CUDA_TRY(cudaGetDevice(&device_id));
-  CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_matches, sizeof(atomic_ctr_type), device_id, stream));
+  atomic_ctr_type *h_num_matches, *d_num_matches;
+
+  CUCO_CUDA_TRY(cudaMallocHost((void**)&h_num_matches, sizeof(atomic_ctr_type)));
+  d_num_matches = std::allocator_traits<counter_allocator_type>::allocate(counter_allocator_, 1);
+
+  h_num_matches->store(0ull, cuda::std::memory_order_relaxed);
+  CUCO_CUDA_TRY(cudaMemcpyAsync(
+    d_num_matches, h_num_matches, sizeof(atomic_ctr_type), cudaMemcpyHostToDevice, stream));
 
   detail::count<block_size, cg_size(), Key, Value, is_outer>
-    <<<grid_size, block_size, 0, stream>>>(first, last, num_matches, view, key_equal);
+    <<<grid_size, block_size, 0, stream>>>(first, last, d_num_matches, view, key_equal);
+  CUCO_CUDA_TRY(cudaMemcpyAsync(
+    h_num_matches, d_num_matches, sizeof(atomic_ctr_type), cudaMemcpyDeviceToHost, stream));
   CUCO_CUDA_TRY(cudaStreamSynchronize(stream));
 
-  size_t result = *num_matches;
-  CUCO_CUDA_TRY(cudaFree(num_matches));
+  auto result = h_num_matches->load(cuda::std::memory_order_relaxed);
+  CUCO_CUDA_TRY(cudaFreeHost(h_num_matches));
+  std::allocator_traits<counter_allocator_type>::deallocate(counter_allocator_, d_num_matches, 1);
 
   return result;
 }
@@ -147,19 +153,24 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::count_
 
   constexpr bool is_outer = true;
 
-  atomic_ctr_type* num_matches;
-  CUCO_CUDA_TRY(cudaMallocManaged(&num_matches, sizeof(atomic_ctr_type)));
-  *num_matches = 0;
-  int device_id;
-  CUCO_CUDA_TRY(cudaGetDevice(&device_id));
-  CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_matches, sizeof(atomic_ctr_type), device_id, stream));
+  atomic_ctr_type *h_num_matches, *d_num_matches;
+
+  CUCO_CUDA_TRY(cudaMallocHost((void**)&h_num_matches, sizeof(atomic_ctr_type)));
+  d_num_matches = std::allocator_traits<counter_allocator_type>::allocate(counter_allocator_, 1);
+
+  h_num_matches->store(0ull, cuda::std::memory_order_relaxed);
+  CUCO_CUDA_TRY(cudaMemcpyAsync(
+    d_num_matches, h_num_matches, sizeof(atomic_ctr_type), cudaMemcpyHostToDevice, stream));
 
   detail::count<block_size, cg_size(), Key, Value, is_outer>
-    <<<grid_size, block_size, 0, stream>>>(first, last, num_matches, view, key_equal);
+    <<<grid_size, block_size, 0, stream>>>(first, last, d_num_matches, view, key_equal);
+  CUCO_CUDA_TRY(cudaMemcpyAsync(
+    h_num_matches, d_num_matches, sizeof(atomic_ctr_type), cudaMemcpyDeviceToHost, stream));
   CUCO_CUDA_TRY(cudaStreamSynchronize(stream));
 
-  size_t result = *num_matches;
-  CUCO_CUDA_TRY(cudaFree(num_matches));
+  auto result = h_num_matches->load(cuda::std::memory_order_relaxed);
+  CUCO_CUDA_TRY(cudaFreeHost(h_num_matches));
+  std::allocator_traits<counter_allocator_type>::deallocate(counter_allocator_, d_num_matches, 1);
 
   return result;
 }
@@ -181,19 +192,24 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::pair_c
 
   constexpr bool is_outer = false;
 
-  atomic_ctr_type* num_matches;
-  CUCO_CUDA_TRY(cudaMallocManaged(&num_matches, sizeof(atomic_ctr_type)));
-  *num_matches = 0;
-  int device_id;
-  CUCO_CUDA_TRY(cudaGetDevice(&device_id));
-  CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_matches, sizeof(atomic_ctr_type), device_id, stream));
+  atomic_ctr_type *h_num_matches, *d_num_matches;
+
+  CUCO_CUDA_TRY(cudaMallocHost((void**)&h_num_matches, sizeof(atomic_ctr_type)));
+  d_num_matches = std::allocator_traits<counter_allocator_type>::allocate(counter_allocator_, 1);
+
+  h_num_matches->store(0ull, cuda::std::memory_order_relaxed);
+  CUCO_CUDA_TRY(cudaMemcpyAsync(
+    d_num_matches, h_num_matches, sizeof(atomic_ctr_type), cudaMemcpyHostToDevice, stream));
 
   detail::pair_count<block_size, cg_size(), Key, Value, is_outer>
-    <<<grid_size, block_size, 0, stream>>>(first, last, num_matches, view, pair_equal);
+    <<<grid_size, block_size, 0, stream>>>(first, last, d_num_matches, view, pair_equal);
+  CUCO_CUDA_TRY(cudaMemcpyAsync(
+    h_num_matches, d_num_matches, sizeof(atomic_ctr_type), cudaMemcpyDeviceToHost, stream));
   CUCO_CUDA_TRY(cudaStreamSynchronize(stream));
 
-  size_t result = *num_matches;
-  CUCO_CUDA_TRY(cudaFree(num_matches));
+  auto result = h_num_matches->load(cuda::std::memory_order_relaxed);
+  CUCO_CUDA_TRY(cudaFreeHost(h_num_matches));
+  std::allocator_traits<counter_allocator_type>::deallocate(counter_allocator_, d_num_matches, 1);
 
   return result;
 }
@@ -215,19 +231,24 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::pair_c
 
   constexpr bool is_outer = true;
 
-  atomic_ctr_type* num_matches;
-  CUCO_CUDA_TRY(cudaMallocManaged(&num_matches, sizeof(atomic_ctr_type)));
-  *num_matches = 0;
-  int device_id;
-  CUCO_CUDA_TRY(cudaGetDevice(&device_id));
-  CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_matches, sizeof(atomic_ctr_type), device_id, stream));
+  atomic_ctr_type *h_num_matches, *d_num_matches;
+
+  CUCO_CUDA_TRY(cudaMallocHost((void**)&h_num_matches, sizeof(atomic_ctr_type)));
+  d_num_matches = std::allocator_traits<counter_allocator_type>::allocate(counter_allocator_, 1);
+
+  h_num_matches->store(0ull, cuda::std::memory_order_relaxed);
+  CUCO_CUDA_TRY(cudaMemcpyAsync(
+    d_num_matches, h_num_matches, sizeof(atomic_ctr_type), cudaMemcpyHostToDevice, stream));
 
   detail::pair_count<block_size, cg_size(), Key, Value, is_outer>
-    <<<grid_size, block_size, 0, stream>>>(first, last, num_matches, view, pair_equal);
+    <<<grid_size, block_size, 0, stream>>>(first, last, d_num_matches, view, pair_equal);
+  CUCO_CUDA_TRY(cudaMemcpyAsync(
+    h_num_matches, d_num_matches, sizeof(atomic_ctr_type), cudaMemcpyDeviceToHost, stream));
   CUCO_CUDA_TRY(cudaStreamSynchronize(stream));
 
-  size_t result = *num_matches;
-  CUCO_CUDA_TRY(cudaFree(num_matches));
+  auto result = h_num_matches->load(cuda::std::memory_order_relaxed);
+  CUCO_CUDA_TRY(cudaFreeHost(h_num_matches));
+  std::allocator_traits<counter_allocator_type>::deallocate(counter_allocator_, d_num_matches, 1);
 
   return result;
 }
@@ -251,27 +272,32 @@ OutputIt static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::retrieve(
 
   constexpr bool is_outer = false;
 
-  atomic_ctr_type* num_matches;
-  CUCO_CUDA_TRY(cudaMallocManaged(&num_matches, sizeof(atomic_ctr_type)));
-  *num_matches = 0;
-  int device_id;
-  CUCO_CUDA_TRY(cudaGetDevice(&device_id));
-  CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_matches, sizeof(atomic_ctr_type), device_id, stream));
+  atomic_ctr_type *h_num_matches, *d_num_matches;
+
+  CUCO_CUDA_TRY(cudaMallocHost((void**)&h_num_matches, sizeof(atomic_ctr_type)));
+  d_num_matches = std::allocator_traits<counter_allocator_type>::allocate(counter_allocator_, 1);
+
+  h_num_matches->store(0ull, cuda::std::memory_order_relaxed);
+  CUCO_CUDA_TRY(cudaMemcpyAsync(
+    d_num_matches, h_num_matches, sizeof(atomic_ctr_type), cudaMemcpyHostToDevice, stream));
 
   if constexpr (uses_vector_load()) {
     detail::
       vectorized_retrieve<block_size, warp_size(), cg_size(), buffer_size, Key, Value, is_outer>
       <<<grid_size, block_size, 0, stream>>>(
-        first, last, output_begin, num_matches, view, key_equal);
+        first, last, output_begin, d_num_matches, view, key_equal);
   } else {
     detail::retrieve<block_size, warp_size(), cg_size(), buffer_size, Key, Value, is_outer>
       <<<grid_size, block_size, 0, stream>>>(
-        first, last, output_begin, num_matches, view, key_equal);
+        first, last, output_begin, d_num_matches, view, key_equal);
   }
+  CUCO_CUDA_TRY(cudaMemcpyAsync(
+    h_num_matches, d_num_matches, sizeof(atomic_ctr_type), cudaMemcpyDeviceToHost, stream));
   CUCO_CUDA_TRY(cudaStreamSynchronize(stream));
 
-  auto output_end = output_begin + *num_matches;
-  CUCO_CUDA_TRY(cudaFree(num_matches));
+  auto output_end = output_begin + h_num_matches->load(cuda::std::memory_order_relaxed);
+  CUCO_CUDA_TRY(cudaFreeHost(h_num_matches));
+  std::allocator_traits<counter_allocator_type>::deallocate(counter_allocator_, d_num_matches, 1);
 
   return output_end;
 }
@@ -295,27 +321,32 @@ OutputIt static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::retrieve_
 
   constexpr bool is_outer = true;
 
-  atomic_ctr_type* num_matches;
-  CUCO_CUDA_TRY(cudaMallocManaged(&num_matches, sizeof(atomic_ctr_type)));
-  *num_matches = 0;
-  int device_id;
-  CUCO_CUDA_TRY(cudaGetDevice(&device_id));
-  CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_matches, sizeof(atomic_ctr_type), device_id, stream));
+  atomic_ctr_type *h_num_matches, *d_num_matches;
+
+  CUCO_CUDA_TRY(cudaMallocHost((void**)&h_num_matches, sizeof(atomic_ctr_type)));
+  d_num_matches = std::allocator_traits<counter_allocator_type>::allocate(counter_allocator_, 1);
+
+  h_num_matches->store(0ull, cuda::std::memory_order_relaxed);
+  CUCO_CUDA_TRY(cudaMemcpyAsync(
+    d_num_matches, h_num_matches, sizeof(atomic_ctr_type), cudaMemcpyHostToDevice, stream));
 
   if constexpr (uses_vector_load()) {
     detail::
       vectorized_retrieve<block_size, warp_size(), cg_size(), buffer_size, Key, Value, is_outer>
       <<<grid_size, block_size, 0, stream>>>(
-        first, last, output_begin, num_matches, view, key_equal);
+        first, last, output_begin, d_num_matches, view, key_equal);
   } else {
     detail::retrieve<block_size, warp_size(), cg_size(), buffer_size, Key, Value, is_outer>
       <<<grid_size, block_size, 0, stream>>>(
-        first, last, output_begin, num_matches, view, key_equal);
+        first, last, output_begin, d_num_matches, view, key_equal);
   }
+  CUCO_CUDA_TRY(cudaMemcpyAsync(
+    h_num_matches, d_num_matches, sizeof(atomic_ctr_type), cudaMemcpyDeviceToHost, stream));
   CUCO_CUDA_TRY(cudaStreamSynchronize(stream));
 
-  auto output_end = output_begin + *num_matches;
-  CUCO_CUDA_TRY(cudaFree(num_matches));
+  auto output_end = output_begin + h_num_matches->load(cuda::std::memory_order_relaxed);
+  CUCO_CUDA_TRY(cudaFreeHost(h_num_matches));
+  std::allocator_traits<counter_allocator_type>::deallocate(counter_allocator_, d_num_matches, 1);
 
   return output_end;
 }
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index 097b85277..faac19002 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -149,6 +149,8 @@ class static_multimap {
   using allocator_type     = Allocator;
   using slot_allocator_type =
     typename std::allocator_traits<Allocator>::rebind_alloc<pair_atomic_type>;
+  using counter_allocator_type =
+    typename std::allocator_traits<Allocator>::rebind_alloc<atomic_ctr_type>;
 
   static_multimap(static_multimap const&) = delete;
   static_multimap(static_multimap&&)      = delete;
@@ -1369,13 +1371,14 @@ class static_multimap {
   }
 
  private:
-  pair_atomic_type* slots_{nullptr};      ///< Pointer to flat slots storage
-  std::size_t capacity_{};                ///< Total number of slots
-  std::size_t size_{};                    ///< Number of keys in map
-  Key empty_key_sentinel_{};              ///< Key value that represents an empty slot
-  Value empty_value_sentinel_{};          ///< Initial value of empty slot
-  slot_allocator_type slot_allocator_{};  ///< Allocator used to allocate slots
-};                                        // class static_multimap
+  pair_atomic_type* slots_{nullptr};            ///< Pointer to flat slots storage
+  std::size_t capacity_{};                      ///< Total number of slots
+  std::size_t size_{};                          ///< Number of keys in map
+  Key empty_key_sentinel_{};                    ///< Key value that represents an empty slot
+  Value empty_value_sentinel_{};                ///< Initial value of empty slot
+  slot_allocator_type slot_allocator_{};        ///< Allocator used to allocate slots
+  counter_allocator_type counter_allocator_{};  ///< Allocator used to allocate counters
+};                                              // class static_multimap
 }  // namespace cuco
 
 #include <cuco/detail/static_multimap.inl>

From 88fa92bf19764feb130a61e933474617ed32de6f Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Tue, 27 Jul 2021 13:10:13 -0400
Subject: [PATCH 171/267] Add insert_if bulk function

---
 include/cuco/detail/static_multimap.inl       | 20 ++++++++
 .../cuco/detail/static_multimap_kernels.cuh   | 51 +++++++++++++++++--
 include/cuco/static_multimap.cuh              | 22 ++++++--
 3 files changed, 85 insertions(+), 8 deletions(-)

diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index 82dbaa675..cd112dda1 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -75,6 +75,26 @@ void static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::insert(InputI
   CUCO_CUDA_TRY(cudaStreamSynchronize(stream));
 }
 
+template <typename Key,
+          typename Value,
+          class ProbeSequence,
+          cuda::thread_scope Scope,
+          typename Allocator>
+template <typename InputIt, typename Predicate, typename KeyEqual>
+void static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::insert_if(
+  InputIt first, InputIt last, Predicate pred, cudaStream_t stream, KeyEqual key_equal)
+{
+  auto num_keys         = std::distance(first, last);
+  auto const block_size = 128;
+  auto const stride     = 1;
+  auto const grid_size  = (cg_size() * num_keys + stride * block_size - 1) / (stride * block_size);
+  auto view             = get_device_mutable_view();
+
+  detail::insert_if<block_size, cg_size()>
+    <<<grid_size, block_size, 0, stream>>>(first, first + num_keys, view, pred, key_equal);
+  CUCO_CUDA_TRY(cudaStreamSynchronize(stream));
+}
+
 template <typename Key,
           typename Value,
           class ProbeSequence,
diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap_kernels.cuh
index c30a72e16..8ae4af591 100644
--- a/include/cuco/detail/static_multimap_kernels.cuh
+++ b/include/cuco/detail/static_multimap_kernels.cuh
@@ -57,11 +57,9 @@ __global__ void initialize(pair_atomic_type* const slots, Key k, Value v, std::s
 /**
  * @brief Inserts all key/value pairs in the range `[first, last)`.
  *
- * If multiple keys in `[first, last)` compare equal, it is unspecified which
- * element is inserted. Uses the CUDA Cooperative Groups API to leverage groups
- * of multiple threads to perform each key/value insertion. This provides a
- * significant boost in throughput compared to the non Cooperative Group
- * `insert` at moderate to high load factors.
+ * Uses the CUDA Cooperative Groups API to leverage groups of multiple threads to perform each
+ * key/value insertion. This provides a significant boost in throughput compared to the non
+ * Cooperative Group `insert` at moderate to high load factors.
  *
  * @tparam block_size The size of the thread block
  * @tparam tile_size The number of threads in the Cooperative Groups used to perform
@@ -94,6 +92,49 @@ __global__ void insert(InputIt first, InputIt last, viewT view, KeyEqual key_equ
   }
 }
 
+/**
+ * @brief Inserts key/value pairs in the range `[first, last)` if `pred` returns true.
+ *
+ * Uses the CUDA Cooperative Groups API to leverage groups of multiple threads to perform each
+ * key/value insertion. This provides a significant boost in throughput compared to the non
+ * Cooperative Group `insert` at moderate to high load factors.
+ *
+ * @tparam block_size The size of the thread block
+ * @tparam tile_size The number of threads in the Cooperative Groups used to perform
+ * inserts
+ * @tparam InputIt Device accessible input iterator whose `value_type` is
+ * convertible to the map's `value_type`
+ * @tparam viewT Type of device view allowing access of hash map storage
+ * @tparam Predicate Unary predicate function type
+ * @tparam KeyEqual Binary callable type
+ * @param first Beginning of the sequence of key/value pairs
+ * @param last End of the sequence of key/value pairs
+ * @param view Mutable device view used to access the hash map's slot storage
+ * @param key_equal The binary function used to compare two keys for equality
+ */
+template <uint32_t block_size,
+          uint32_t tile_size,
+          typename InputIt,
+          typename viewT,
+          typename Predicate,
+          typename KeyEqual>
+__global__ void insert_if(
+  InputIt first, InputIt last, viewT view, Predicate pred, KeyEqual key_equal)
+{
+  auto tile = cg::tiled_partition<tile_size>(cg::this_thread_block());
+  auto tid  = block_size * blockIdx.x + threadIdx.x;
+  auto it   = first + tid / tile_size;
+
+  while (it < last) {
+    if (pred(*it)) {
+      // force conversion to value_type
+      typename viewT::value_type const insert_pair{*it};
+      view.insert(tile, insert_pair, key_equal);
+    }
+    it += (gridDim.x * block_size) / tile_size;
+  }
+}
+
 /**
  * @brief Indicates whether the keys in the range `[first, last)` are contained in the map.
  *
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index faac19002..a9b5a6494 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -208,9 +208,6 @@ class static_multimap {
   /**
    * @brief Inserts all key/value pairs in the range `[first, last)`.
    *
-   * If multiple keys in `[first, last)` compare equal, it is unspecified which
-   * element is inserted.
-   *
    * @tparam InputIt Device accessible input iterator whose `value_type` is
    * convertible to the map's `value_type`
    * @tparam KeyEqual Binary callable type
@@ -225,6 +222,25 @@ class static_multimap {
               cudaStream_t stream = 0,
               KeyEqual key_equal  = KeyEqual{});
 
+  /**
+   * @brief Inserts key/value pairs in the range `[first, last)` if `pred` returns true.
+   *
+   * @tparam InputIt Device accessible input iterator whose `value_type` is
+   * convertible to the map's `value_type`
+   * @tparam Predicate Unary predicate function type
+   * @tparam KeyEqual Binary callable type
+   * @param first Beginning of the sequence of key/value pairs
+   * @param last End of the sequence of key/value pairs
+   * @param stream CUDA stream used for insert
+   * @param key_equal The binary function to compare two keys for equality
+   */
+  template <typename InputIt, typename Predicate, typename KeyEqual = thrust::equal_to<key_type>>
+  void insert_if(InputIt first,
+                 InputIt last,
+                 Predicate pred,
+                 cudaStream_t stream = 0,
+                 KeyEqual key_equal  = KeyEqual{});
+
   /**
    * @brief Indicates whether the keys in the range `[first, last)` are contained in the map.
    *

From b1adea6b45a3c972a95006e1b55fc43da56a33e3 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Tue, 27 Jul 2021 13:46:55 -0400
Subject: [PATCH 172/267] Add insert_if unit tests

---
 tests/static_multimap/static_multimap_test.cu | 37 +++++++++++++++++++
 1 file changed, 37 insertions(+)

diff --git a/tests/static_multimap/static_multimap_test.cu b/tests/static_multimap/static_multimap_test.cu
index 32c891f1d..d0ae8a7fb 100644
--- a/tests/static_multimap/static_multimap_test.cu
+++ b/tests/static_multimap/static_multimap_test.cu
@@ -54,6 +54,7 @@ struct pair_equal {
     return lhs.first == rhs.first;
   }
 };
+
 }  // namespace
 
 enum class dist_type { UNIQUE, DUAL, UNIFORM, GAUSSIAN };
@@ -249,6 +250,42 @@ TEMPLATE_TEST_CASE_SIG("Handling of non-matches",
   }
 }
 
+TEMPLATE_TEST_CASE_SIG("Test of insert_if",
+                       "",
+                       ((typename Key, typename Value, dist_type Dist), Key, Value, Dist),
+                       (int32_t, int32_t, dist_type::UNIQUE),
+                       (int32_t, int64_t, dist_type::UNIQUE),
+                       (int64_t, int64_t, dist_type::UNIQUE))
+{
+  constexpr std::size_t num_keys{1'000'000};
+  cuco::static_multimap<Key, Value> map{2'000'000, -1, -1};
+
+  auto m_view = map.get_device_mutable_view();
+  auto view   = map.get_device_view();
+
+  std::vector<Key> h_keys(num_keys);
+  std::vector<cuco::pair_type<Key, Value>> h_pairs(num_keys);
+
+  generate_keys<Dist, Key>(h_keys.begin(), h_keys.end());
+
+  for (auto i = 0; i < num_keys; ++i) {
+    h_pairs[i].first  = h_keys[i];
+    h_pairs[i].second = h_keys[i];
+  }
+
+  thrust::device_vector<Key> d_keys(h_keys);
+  thrust::device_vector<cuco::pair_type<Key, Value>> d_pairs(h_pairs);
+
+  auto pred_lambda = [] __device__(cuco::pair_type<Key, Value> pair) {
+    return pair.first % 2 == 0;
+  };
+  map.insert_if(d_pairs.begin(), d_pairs.end(), pred_lambda);
+
+  auto num = map.count(d_keys.begin(), d_keys.end());
+
+  REQUIRE(num * 2 == num_keys);
+}
+
 TEMPLATE_TEST_CASE_SIG("Evaluation of pair functions",
                        "",
                        ((typename Key, typename Value, dist_type Dist), Key, Value, Dist),

From 12dc7a1ff1806fce010283ea5c82c26528f5e2e3 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Wed, 28 Jul 2021 16:30:01 -0400
Subject: [PATCH 173/267] Make all immutable functions constant

---
 include/cuco/detail/static_multimap.inl | 54 +++++++++++++++----------
 include/cuco/static_multimap.cuh        | 14 +++----
 2 files changed, 39 insertions(+), 29 deletions(-)

diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index cd112dda1..df32c3c78 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -102,7 +102,7 @@ template <typename Key,
           typename Allocator>
 template <typename InputIt, typename OutputIt, typename KeyEqual>
 void static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::contains(
-  InputIt first, InputIt last, OutputIt output_begin, cudaStream_t stream, KeyEqual key_equal)
+  InputIt first, InputIt last, OutputIt output_begin, cudaStream_t stream, KeyEqual key_equal) const
 {
   auto num_keys         = std::distance(first, last);
   auto const block_size = 128;
@@ -121,10 +121,8 @@ template <typename Key,
           cuda::thread_scope Scope,
           typename Allocator>
 template <typename InputIt, typename KeyEqual>
-std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::count(InputIt first,
-                                                                                InputIt last,
-                                                                                cudaStream_t stream,
-                                                                                KeyEqual key_equal)
+std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::count(
+  InputIt first, InputIt last, cudaStream_t stream, KeyEqual key_equal) const
 {
   auto num_keys         = std::distance(first, last);
   auto const block_size = 128;
@@ -137,7 +135,8 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::count(
   atomic_ctr_type *h_num_matches, *d_num_matches;
 
   CUCO_CUDA_TRY(cudaMallocHost((void**)&h_num_matches, sizeof(atomic_ctr_type)));
-  d_num_matches = std::allocator_traits<counter_allocator_type>::allocate(counter_allocator_, 1);
+  auto tmp_counter_allocator = counter_allocator_;
+  d_num_matches = std::allocator_traits<counter_allocator_type>::allocate(tmp_counter_allocator, 1);
 
   h_num_matches->store(0ull, cuda::std::memory_order_relaxed);
   CUCO_CUDA_TRY(cudaMemcpyAsync(
@@ -151,7 +150,8 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::count(
 
   auto result = h_num_matches->load(cuda::std::memory_order_relaxed);
   CUCO_CUDA_TRY(cudaFreeHost(h_num_matches));
-  std::allocator_traits<counter_allocator_type>::deallocate(counter_allocator_, d_num_matches, 1);
+  std::allocator_traits<counter_allocator_type>::deallocate(
+    tmp_counter_allocator, d_num_matches, 1);
 
   return result;
 }
@@ -163,7 +163,7 @@ template <typename Key,
           typename Allocator>
 template <typename InputIt, typename KeyEqual>
 std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::count_outer(
-  InputIt first, InputIt last, cudaStream_t stream, KeyEqual key_equal)
+  InputIt first, InputIt last, cudaStream_t stream, KeyEqual key_equal) const
 {
   auto num_keys         = std::distance(first, last);
   auto const block_size = 128;
@@ -176,7 +176,8 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::count_
   atomic_ctr_type *h_num_matches, *d_num_matches;
 
   CUCO_CUDA_TRY(cudaMallocHost((void**)&h_num_matches, sizeof(atomic_ctr_type)));
-  d_num_matches = std::allocator_traits<counter_allocator_type>::allocate(counter_allocator_, 1);
+  auto tmp_counter_allocator = counter_allocator_;
+  d_num_matches = std::allocator_traits<counter_allocator_type>::allocate(tmp_counter_allocator, 1);
 
   h_num_matches->store(0ull, cuda::std::memory_order_relaxed);
   CUCO_CUDA_TRY(cudaMemcpyAsync(
@@ -190,7 +191,8 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::count_
 
   auto result = h_num_matches->load(cuda::std::memory_order_relaxed);
   CUCO_CUDA_TRY(cudaFreeHost(h_num_matches));
-  std::allocator_traits<counter_allocator_type>::deallocate(counter_allocator_, d_num_matches, 1);
+  std::allocator_traits<counter_allocator_type>::deallocate(
+    tmp_counter_allocator, d_num_matches, 1);
 
   return result;
 }
@@ -202,7 +204,7 @@ template <typename Key,
           typename Allocator>
 template <typename InputIt, typename PairEqual>
 std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::pair_count(
-  InputIt first, InputIt last, PairEqual pair_equal, cudaStream_t stream)
+  InputIt first, InputIt last, PairEqual pair_equal, cudaStream_t stream) const
 {
   auto num_keys         = std::distance(first, last);
   auto const block_size = 128;
@@ -215,7 +217,8 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::pair_c
   atomic_ctr_type *h_num_matches, *d_num_matches;
 
   CUCO_CUDA_TRY(cudaMallocHost((void**)&h_num_matches, sizeof(atomic_ctr_type)));
-  d_num_matches = std::allocator_traits<counter_allocator_type>::allocate(counter_allocator_, 1);
+  auto tmp_counter_allocator = counter_allocator_;
+  d_num_matches = std::allocator_traits<counter_allocator_type>::allocate(tmp_counter_allocator, 1);
 
   h_num_matches->store(0ull, cuda::std::memory_order_relaxed);
   CUCO_CUDA_TRY(cudaMemcpyAsync(
@@ -229,7 +232,8 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::pair_c
 
   auto result = h_num_matches->load(cuda::std::memory_order_relaxed);
   CUCO_CUDA_TRY(cudaFreeHost(h_num_matches));
-  std::allocator_traits<counter_allocator_type>::deallocate(counter_allocator_, d_num_matches, 1);
+  std::allocator_traits<counter_allocator_type>::deallocate(
+    tmp_counter_allocator, d_num_matches, 1);
 
   return result;
 }
@@ -241,7 +245,7 @@ template <typename Key,
           typename Allocator>
 template <typename InputIt, typename PairEqual>
 std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::pair_count_outer(
-  InputIt first, InputIt last, PairEqual pair_equal, cudaStream_t stream)
+  InputIt first, InputIt last, PairEqual pair_equal, cudaStream_t stream) const
 {
   auto num_keys         = std::distance(first, last);
   auto const block_size = 128;
@@ -254,7 +258,8 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::pair_c
   atomic_ctr_type *h_num_matches, *d_num_matches;
 
   CUCO_CUDA_TRY(cudaMallocHost((void**)&h_num_matches, sizeof(atomic_ctr_type)));
-  d_num_matches = std::allocator_traits<counter_allocator_type>::allocate(counter_allocator_, 1);
+  auto tmp_counter_allocator = counter_allocator_;
+  d_num_matches = std::allocator_traits<counter_allocator_type>::allocate(tmp_counter_allocator, 1);
 
   h_num_matches->store(0ull, cuda::std::memory_order_relaxed);
   CUCO_CUDA_TRY(cudaMemcpyAsync(
@@ -268,7 +273,8 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::pair_c
 
   auto result = h_num_matches->load(cuda::std::memory_order_relaxed);
   CUCO_CUDA_TRY(cudaFreeHost(h_num_matches));
-  std::allocator_traits<counter_allocator_type>::deallocate(counter_allocator_, d_num_matches, 1);
+  std::allocator_traits<counter_allocator_type>::deallocate(
+    tmp_counter_allocator, d_num_matches, 1);
 
   return result;
 }
@@ -280,7 +286,7 @@ template <typename Key,
           typename Allocator>
 template <typename InputIt, typename OutputIt, typename KeyEqual>
 OutputIt static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::retrieve(
-  InputIt first, InputIt last, OutputIt output_begin, cudaStream_t stream, KeyEqual key_equal)
+  InputIt first, InputIt last, OutputIt output_begin, cudaStream_t stream, KeyEqual key_equal) const
 {
   auto num_keys         = std::distance(first, last);
   auto const block_size = 128;
@@ -295,7 +301,8 @@ OutputIt static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::retrieve(
   atomic_ctr_type *h_num_matches, *d_num_matches;
 
   CUCO_CUDA_TRY(cudaMallocHost((void**)&h_num_matches, sizeof(atomic_ctr_type)));
-  d_num_matches = std::allocator_traits<counter_allocator_type>::allocate(counter_allocator_, 1);
+  auto tmp_counter_allocator = counter_allocator_;
+  d_num_matches = std::allocator_traits<counter_allocator_type>::allocate(tmp_counter_allocator, 1);
 
   h_num_matches->store(0ull, cuda::std::memory_order_relaxed);
   CUCO_CUDA_TRY(cudaMemcpyAsync(
@@ -317,7 +324,8 @@ OutputIt static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::retrieve(
 
   auto output_end = output_begin + h_num_matches->load(cuda::std::memory_order_relaxed);
   CUCO_CUDA_TRY(cudaFreeHost(h_num_matches));
-  std::allocator_traits<counter_allocator_type>::deallocate(counter_allocator_, d_num_matches, 1);
+  std::allocator_traits<counter_allocator_type>::deallocate(
+    tmp_counter_allocator, d_num_matches, 1);
 
   return output_end;
 }
@@ -329,7 +337,7 @@ template <typename Key,
           typename Allocator>
 template <typename InputIt, typename OutputIt, typename KeyEqual>
 OutputIt static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::retrieve_outer(
-  InputIt first, InputIt last, OutputIt output_begin, cudaStream_t stream, KeyEqual key_equal)
+  InputIt first, InputIt last, OutputIt output_begin, cudaStream_t stream, KeyEqual key_equal) const
 {
   auto num_keys         = std::distance(first, last);
   auto const block_size = 128;
@@ -344,7 +352,8 @@ OutputIt static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::retrieve_
   atomic_ctr_type *h_num_matches, *d_num_matches;
 
   CUCO_CUDA_TRY(cudaMallocHost((void**)&h_num_matches, sizeof(atomic_ctr_type)));
-  d_num_matches = std::allocator_traits<counter_allocator_type>::allocate(counter_allocator_, 1);
+  auto tmp_counter_allocator = counter_allocator_;
+  d_num_matches = std::allocator_traits<counter_allocator_type>::allocate(tmp_counter_allocator, 1);
 
   h_num_matches->store(0ull, cuda::std::memory_order_relaxed);
   CUCO_CUDA_TRY(cudaMemcpyAsync(
@@ -366,7 +375,8 @@ OutputIt static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::retrieve_
 
   auto output_end = output_begin + h_num_matches->load(cuda::std::memory_order_relaxed);
   CUCO_CUDA_TRY(cudaFreeHost(h_num_matches));
-  std::allocator_traits<counter_allocator_type>::deallocate(counter_allocator_, d_num_matches, 1);
+  std::allocator_traits<counter_allocator_type>::deallocate(
+    tmp_counter_allocator, d_num_matches, 1);
 
   return output_end;
 }
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index a9b5a6494..70bbef245 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -262,7 +262,7 @@ class static_multimap {
                 InputIt last,
                 OutputIt output_begin,
                 cudaStream_t stream = 0,
-                KeyEqual key_equal  = KeyEqual{});
+                KeyEqual key_equal  = KeyEqual{}) const;
 
   /**
    * @brief Counts the occurrences of keys in `[first, last)` contained in the multimap.
@@ -279,7 +279,7 @@ class static_multimap {
   std::size_t count(InputIt first,
                     InputIt last,
                     cudaStream_t stream = 0,
-                    KeyEqual key_equal  = KeyEqual{});
+                    KeyEqual key_equal  = KeyEqual{}) const;
 
   /**
    * @brief Counts the occurrences of keys in `[first, last)` contained in the multimap. If no
@@ -297,7 +297,7 @@ class static_multimap {
   std::size_t count_outer(InputIt first,
                           InputIt last,
                           cudaStream_t stream = 0,
-                          KeyEqual key_equal  = KeyEqual{});
+                          KeyEqual key_equal  = KeyEqual{}) const;
 
   /**
    * @brief Counts the occurrences of key/value pairs in `[first, last)` contained in the multimap.
@@ -314,7 +314,7 @@ class static_multimap {
   std::size_t pair_count(InputIt first,
                          InputIt last,
                          PairEqual pair_equal,
-                         cudaStream_t stream = 0);
+                         cudaStream_t stream = 0) const;
 
   /**
    * @brief Counts the occurrences of key/value pairs in `[first, last)` contained in the multimap.
@@ -332,7 +332,7 @@ class static_multimap {
   std::size_t pair_count_outer(InputIt first,
                                InputIt last,
                                PairEqual pair_equal,
-                               cudaStream_t stream = 0);
+                               cudaStream_t stream = 0) const;
 
   /**
    * @brief Finds all the values corresponding to all keys in the range `[first, last)`.
@@ -360,7 +360,7 @@ class static_multimap {
                     InputIt last,
                     OutputIt output_begin,
                     cudaStream_t stream = 0,
-                    KeyEqual key_equal  = KeyEqual{});
+                    KeyEqual key_equal  = KeyEqual{}) const;
 
   /**
    * @brief Finds all the matches corresponding to all keys in the range `[first, last)`.
@@ -389,7 +389,7 @@ class static_multimap {
                           InputIt last,
                           OutputIt output_begin,
                           cudaStream_t stream = 0,
-                          KeyEqual key_equal  = KeyEqual{});
+                          KeyEqual key_equal  = KeyEqual{}) const;
 
  private:
   /**

From 8cb913b0891e2b64c62f12565e5700f2a98d4b63 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Thu, 29 Jul 2021 12:14:58 -0400
Subject: [PATCH 174/267] Add pair_retrieve API in multimap header

---
 include/cuco/static_multimap.cuh | 66 +++++++++++++++++++++++++++++++-
 1 file changed, 65 insertions(+), 1 deletion(-)

diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index 70bbef245..dc4c85230 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -370,7 +370,7 @@ class static_multimap {
    * sentinel.
    *
    * Behavior is undefined if the total number of matching keys exceeds `std::distance(output_begin,
-   * output_end)`. Use `count()` to determine the number of matching keys.
+   * output_end)`. Use `count_outer()` to determine the number of matching keys.
    *
    * @tparam InputIt Device accessible input iterator whose `value_type` is
    * convertible to the map's `key_type`
@@ -391,6 +391,70 @@ class static_multimap {
                           cudaStream_t stream = 0,
                           KeyEqual key_equal  = KeyEqual{}) const;
 
+  /**
+   * @brief Finds all pairs matching the input probe pair in the range `[first, last)`.
+   *
+   * if pair_equal(*(first + i), slot[j]) returns true, then *(first+i) is stored to
+   * `probe_output_begin`, and slot[j] is stored to `contained_output_begin`.
+   *
+   * Behavior is undefined if the total number of matching pairs exceeds
+   * `std::distance(probe_output_begin, probe_output_end)` (or
+   * `std::distance(contained_output_begin, contained_output_end)`). Use
+   * `pair_count()` to determine the number of matching pairs.
+   *
+   * @tparam InputIt Device accessible input iterator for probe pairs
+   * @tparam OutputIt1 Device accessible output iterator for probe matches
+   * @tparam OutputIt2 Device accessible output iterator for contained matches
+   * @tparam PairEqual Binary callable type
+   * @param first Beginning of the sequence of pairs
+   * @param last End of the sequence of pairs
+   * @param probe_output_begin Beginning of the sequence of the matched probe pairs
+   * @param probe_output_begin Beginning of the sequence of the matched contained pairs
+   * @param pair_equal The binary function to compare two pairs for equality
+   * @param stream CUDA stream used for retrieve_outer
+   * @return The total number of matches
+   */
+  template <typename InputIt, typename OutputIt1, typename OutputIt2, typename PairEqual>
+  std::size_t pair_retrieve(InputIt first,
+                            InputIt last,
+                            OutputIt1 probe_output_begin,
+                            OutputIt2 contained_output_begin,
+                            PairEqual pair_equal,
+                            cudaStream_t stream = 0) const;
+
+  /**
+   * @brief Finds all pairs matching the input probe pair in the range `[first, last)`.
+   *
+   * if pair_equal(*(first + i), slot[j]) returns true, then *(first+i) is stored to
+   * `probe_output_begin`, and slot[j] is stored to `contained_output_begin`. If *(first+i) doesn't
+   * have matches in the map, copies *(first + i) in `probe_output_begin` and a pair of
+   * `empty_key_sentinel` and `empty_value_sentinel` in `contained_output_begin`.
+   *
+   * Behavior is undefined if the total number of matching pairs exceeds
+   * `std::distance(probe_output_begin, probe_output_end)` (or
+   * `std::distance(contained_output_begin, contained_output_end)`). Use
+   * `pair_count()` to determine the number of matching pairs.
+   *
+   * @tparam InputIt Device accessible input iterator for probe pairs
+   * @tparam OutputIt1 Device accessible output iterator for probe matches
+   * @tparam OutputIt2 Device accessible output iterator for contained matches
+   * @tparam PairEqual Binary callable type
+   * @param first Beginning of the sequence of pairs
+   * @param last End of the sequence of pairs
+   * @param probe_output_begin Beginning of the sequence of the matched probe pairs
+   * @param probe_output_begin Beginning of the sequence of the matched contained pairs
+   * @param pair_equal The binary function to compare two pairs for equality
+   * @param stream CUDA stream used for retrieve_outer
+   * @return The total number of matches
+   */
+  template <typename InputIt, typename OutputIt1, typename Output2, typename PairEqual>
+  std::size_t pair_retrieve_outer(InputIt first,
+                                  InputIt last,
+                                  OutputIt1 probe_output_begin,
+                                  OutputIt2 contained_output_begin,
+                                  PairEqual pair_equal,
+                                  cudaStream_t stream = 0) const;
+
  private:
   /**
    * @brief Indicate if vector load is used.

From ef249a5ecda3a1610ac32dd0f6b834ab0f8a49fe Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Thu, 29 Jul 2021 12:39:49 -0400
Subject: [PATCH 175/267] Fix a typo

---
 include/cuco/static_multimap.cuh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index dc4c85230..2fb8ae395 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -447,7 +447,7 @@ class static_multimap {
    * @param stream CUDA stream used for retrieve_outer
    * @return The total number of matches
    */
-  template <typename InputIt, typename OutputIt1, typename Output2, typename PairEqual>
+  template <typename InputIt, typename OutputIt1, typename OutputIt2, typename PairEqual>
   std::size_t pair_retrieve_outer(InputIt first,
                                   InputIt last,
                                   OutputIt1 probe_output_begin,

From 9631ea05ca40f2161b9b25d527c9dc56f0b7da23 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Thu, 29 Jul 2021 12:40:58 -0400
Subject: [PATCH 176/267] Add pair_retrieve member functions

---
 include/cuco/detail/static_multimap.inl       | 132 +++++++++++++++++-
 .../cuco/detail/static_multimap_kernels.cuh   |  46 ++++++
 2 files changed, 172 insertions(+), 6 deletions(-)

diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index df32c3c78..2b8a6f4cc 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -138,7 +138,7 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::count(
   auto tmp_counter_allocator = counter_allocator_;
   d_num_matches = std::allocator_traits<counter_allocator_type>::allocate(tmp_counter_allocator, 1);
 
-  h_num_matches->store(0ull, cuda::std::memory_order_relaxed);
+  h_num_matches->store(static_cast<std::size_t>(0), cuda::std::memory_order_relaxed);
   CUCO_CUDA_TRY(cudaMemcpyAsync(
     d_num_matches, h_num_matches, sizeof(atomic_ctr_type), cudaMemcpyHostToDevice, stream));
 
@@ -179,7 +179,7 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::count_
   auto tmp_counter_allocator = counter_allocator_;
   d_num_matches = std::allocator_traits<counter_allocator_type>::allocate(tmp_counter_allocator, 1);
 
-  h_num_matches->store(0ull, cuda::std::memory_order_relaxed);
+  h_num_matches->store(static_cast<std::size_t>(0), cuda::std::memory_order_relaxed);
   CUCO_CUDA_TRY(cudaMemcpyAsync(
     d_num_matches, h_num_matches, sizeof(atomic_ctr_type), cudaMemcpyHostToDevice, stream));
 
@@ -220,7 +220,7 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::pair_c
   auto tmp_counter_allocator = counter_allocator_;
   d_num_matches = std::allocator_traits<counter_allocator_type>::allocate(tmp_counter_allocator, 1);
 
-  h_num_matches->store(0ull, cuda::std::memory_order_relaxed);
+  h_num_matches->store(static_cast<std::size_t>(0), cuda::std::memory_order_relaxed);
   CUCO_CUDA_TRY(cudaMemcpyAsync(
     d_num_matches, h_num_matches, sizeof(atomic_ctr_type), cudaMemcpyHostToDevice, stream));
 
@@ -261,7 +261,7 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::pair_c
   auto tmp_counter_allocator = counter_allocator_;
   d_num_matches = std::allocator_traits<counter_allocator_type>::allocate(tmp_counter_allocator, 1);
 
-  h_num_matches->store(0ull, cuda::std::memory_order_relaxed);
+  h_num_matches->store(static_cast<std::size_t>(0), cuda::std::memory_order_relaxed);
   CUCO_CUDA_TRY(cudaMemcpyAsync(
     d_num_matches, h_num_matches, sizeof(atomic_ctr_type), cudaMemcpyHostToDevice, stream));
 
@@ -304,7 +304,7 @@ OutputIt static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::retrieve(
   auto tmp_counter_allocator = counter_allocator_;
   d_num_matches = std::allocator_traits<counter_allocator_type>::allocate(tmp_counter_allocator, 1);
 
-  h_num_matches->store(0ull, cuda::std::memory_order_relaxed);
+  h_num_matches->store(static_cast<std::size_t>(0), cuda::std::memory_order_relaxed);
   CUCO_CUDA_TRY(cudaMemcpyAsync(
     d_num_matches, h_num_matches, sizeof(atomic_ctr_type), cudaMemcpyHostToDevice, stream));
 
@@ -355,7 +355,7 @@ OutputIt static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::retrieve_
   auto tmp_counter_allocator = counter_allocator_;
   d_num_matches = std::allocator_traits<counter_allocator_type>::allocate(tmp_counter_allocator, 1);
 
-  h_num_matches->store(0ull, cuda::std::memory_order_relaxed);
+  h_num_matches->store(static_cast<std::size_t>(0), cuda::std::memory_order_relaxed);
   CUCO_CUDA_TRY(cudaMemcpyAsync(
     d_num_matches, h_num_matches, sizeof(atomic_ctr_type), cudaMemcpyHostToDevice, stream));
 
@@ -381,6 +381,126 @@ OutputIt static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::retrieve_
   return output_end;
 }
 
+template <typename Key,
+          typename Value,
+          class ProbeSequence,
+          cuda::thread_scope Scope,
+          typename Allocator>
+template <typename InputIt, typename OutputIt1, typename OutputIt2, typename PairEqual>
+std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::pair_retrieve(
+  InputIt first,
+  InputIt last,
+  OutputIt1 probe_output_begin,
+  OutputIt2 contained_output_begin,
+  PairEqual pair_equal,
+  cudaStream_t stream) const
+{
+  auto num_pairs        = std::distance(first, last);
+  auto const block_size = 128;
+  // Using per-warp buffer for vector loads and per-CG buffer for scalar loads
+  auto const buffer_size = uses_vector_load() ? (warp_size() * 3u) : (cg_size() * 3u);
+  auto const stride      = 1;
+  auto const grid_size = (cg_size() * num_pairs + stride * block_size - 1) / (stride * block_size);
+  auto view            = get_device_view();
+
+  constexpr bool is_outer = false;
+
+  atomic_ctr_type *h_num_matches, *d_num_matches;
+
+  CUCO_CUDA_TRY(cudaMallocHost((void**)&h_num_matches, sizeof(atomic_ctr_type)));
+  auto tmp_counter_allocator = counter_allocator_;
+  d_num_matches = std::allocator_traits<counter_allocator_type>::allocate(tmp_counter_allocator, 1);
+
+  h_num_matches->store(static_cast<std::size_t>(0), cuda::std::memory_order_relaxed);
+  CUCO_CUDA_TRY(cudaMemcpyAsync(
+    d_num_matches, h_num_matches, sizeof(atomic_ctr_type), cudaMemcpyHostToDevice, stream));
+
+  if constexpr (uses_vector_load()) {
+    detail::vectorized_pair_retrieve<block_size,
+                                     warp_size(),
+                                     cg_size(),
+                                     buffer_size,
+                                     Key,
+                                     Value,
+                                     is_outer><<<grid_size, block_size, 0, stream>>>(
+      first, last, probe_output_begin, contained_output_begin, d_num_matches, view, pair_equal);
+  } else {
+    detail::pair_retrieve<block_size, warp_size(), cg_size(), buffer_size, Key, Value, is_outer>
+      <<<grid_size, block_size, 0, stream>>>(
+        first, last, probe_output_begin, contained_output_begin, d_num_matches, view, pair_equal);
+  }
+  CUCO_CUDA_TRY(cudaMemcpyAsync(
+    h_num_matches, d_num_matches, sizeof(atomic_ctr_type), cudaMemcpyDeviceToHost, stream));
+  CUCO_CUDA_TRY(cudaStreamSynchronize(stream));
+
+  auto res = h_num_matches->load(cuda::std::memory_order_relaxed);
+  CUCO_CUDA_TRY(cudaFreeHost(h_num_matches));
+  std::allocator_traits<counter_allocator_type>::deallocate(
+    tmp_counter_allocator, d_num_matches, 1);
+
+  return res;
+}
+
+template <typename Key,
+          typename Value,
+          class ProbeSequence,
+          cuda::thread_scope Scope,
+          typename Allocator>
+template <typename InputIt, typename OutputIt1, typename OutputIt2, typename PairEqual>
+std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::pair_retrieve_outer(
+  InputIt first,
+  InputIt last,
+  OutputIt1 probe_output_begin,
+  OutputIt2 contained_output_begin,
+  PairEqual pair_equal,
+  cudaStream_t stream) const
+{
+  auto num_pairs        = std::distance(first, last);
+  auto const block_size = 128;
+  // Using per-warp buffer for vector loads and per-CG buffer for scalar loads
+  auto const buffer_size = uses_vector_load() ? (warp_size() * 3u) : (cg_size() * 3u);
+  auto const stride      = 1;
+  auto const grid_size = (cg_size() * num_pairs + stride * block_size - 1) / (stride * block_size);
+  auto view            = get_device_view();
+
+  constexpr bool is_outer = true;
+
+  atomic_ctr_type *h_num_matches, *d_num_matches;
+
+  CUCO_CUDA_TRY(cudaMallocHost((void**)&h_num_matches, sizeof(atomic_ctr_type)));
+  auto tmp_counter_allocator = counter_allocator_;
+  d_num_matches = std::allocator_traits<counter_allocator_type>::allocate(tmp_counter_allocator, 1);
+
+  h_num_matches->store(static_cast<std::size_t>(0), cuda::std::memory_order_relaxed);
+  CUCO_CUDA_TRY(cudaMemcpyAsync(
+    d_num_matches, h_num_matches, sizeof(atomic_ctr_type), cudaMemcpyHostToDevice, stream));
+
+  if constexpr (uses_vector_load()) {
+    detail::vectorized_pair_retrieve<block_size,
+                                     warp_size(),
+                                     cg_size(),
+                                     buffer_size,
+                                     Key,
+                                     Value,
+                                     is_outer><<<grid_size, block_size, 0, stream>>>(
+      first, last, probe_output_begin, contained_output_begin, d_num_matches, view, pair_equal);
+  } else {
+    detail::pair_retrieve<block_size, warp_size(), cg_size(), buffer_size, Key, Value, is_outer>
+      <<<grid_size, block_size, 0, stream>>>(
+        first, last, probe_output_begin, contained_output_begin, d_num_matches, view, pair_equal);
+  }
+  CUCO_CUDA_TRY(cudaMemcpyAsync(
+    h_num_matches, d_num_matches, sizeof(atomic_ctr_type), cudaMemcpyDeviceToHost, stream));
+  CUCO_CUDA_TRY(cudaStreamSynchronize(stream));
+
+  auto res = h_num_matches->load(cuda::std::memory_order_relaxed);
+  CUCO_CUDA_TRY(cudaFreeHost(h_num_matches));
+  std::allocator_traits<counter_allocator_type>::deallocate(
+    tmp_counter_allocator, d_num_matches, 1);
+
+  return res;
+}
+
 template <typename Key,
           typename Value,
           class ProbeSequence,
diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap_kernels.cuh
index 8ae4af591..f3096a336 100644
--- a/include/cuco/detail/static_multimap_kernels.cuh
+++ b/include/cuco/detail/static_multimap_kernels.cuh
@@ -492,5 +492,51 @@ __global__ void retrieve(InputIt first,
   }
 }
 
+template <uint32_t block_size,
+          uint32_t warp_size,
+          uint32_t tile_size,
+          uint32_t buffer_size,
+          typename Key,
+          typename Value,
+          bool is_outer,
+          typename InputIt,
+          typename OutputIt1,
+          typename OutputIt2,
+          typename atomicT,
+          typename viewT,
+          typename PairEqual>
+__global__ void vectorized_pair_retrieve(InputIt first,
+                                         InputIt last,
+                                         OutputIt1 probe_output_begin,
+                                         OutputIt1 contained_output_begin,
+                                         atomicT* num_matches,
+                                         viewT view,
+                                         PairEqual pair_equal)
+{
+}
+
+template <uint32_t block_size,
+          uint32_t warp_size,
+          uint32_t tile_size,
+          uint32_t buffer_size,
+          typename Key,
+          typename Value,
+          bool is_outer,
+          typename InputIt,
+          typename OutputIt1,
+          typename OutputIt2,
+          typename atomicT,
+          typename viewT,
+          typename PairEqual>
+__global__ void pair_retrieve(InputIt first,
+                              InputIt last,
+                              OutputIt1 probe_output_begin,
+                              OutputIt1 contained_output_begin,
+                              atomicT* num_matches,
+                              viewT view,
+                              PairEqual pair_equal)
+{
+}
+
 }  // namespace detail
 }  // namespace cuco

From 0d46747dca2a9680475ce339f3e6fb7cb5a7b465 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Thu, 29 Jul 2021 17:39:41 -0400
Subject: [PATCH 177/267] Fix typos

---
 include/cuco/detail/static_multimap_kernels.cuh | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap_kernels.cuh
index f3096a336..d2ca5a49f 100644
--- a/include/cuco/detail/static_multimap_kernels.cuh
+++ b/include/cuco/detail/static_multimap_kernels.cuh
@@ -508,7 +508,7 @@ template <uint32_t block_size,
 __global__ void vectorized_pair_retrieve(InputIt first,
                                          InputIt last,
                                          OutputIt1 probe_output_begin,
-                                         OutputIt1 contained_output_begin,
+                                         OutputIt2 contained_output_begin,
                                          atomicT* num_matches,
                                          viewT view,
                                          PairEqual pair_equal)
@@ -531,7 +531,7 @@ template <uint32_t block_size,
 __global__ void pair_retrieve(InputIt first,
                               InputIt last,
                               OutputIt1 probe_output_begin,
-                              OutputIt1 contained_output_begin,
+                              OutputIt2 contained_output_begin,
                               atomicT* num_matches,
                               viewT view,
                               PairEqual pair_equal)

From 6d2025ddced9b19437697b03bfa67f385be6a119 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Thu, 29 Jul 2021 19:04:01 -0400
Subject: [PATCH 178/267] Add a new flush_output_buffer API + minor doc
 corrections

---
 include/cuco/static_multimap.cuh | 28 ++++++++++++++++++++++++++--
 1 file changed, 26 insertions(+), 2 deletions(-)

diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index 2fb8ae395..6eb1dad78 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -409,7 +409,7 @@ class static_multimap {
    * @param first Beginning of the sequence of pairs
    * @param last End of the sequence of pairs
    * @param probe_output_begin Beginning of the sequence of the matched probe pairs
-   * @param probe_output_begin Beginning of the sequence of the matched contained pairs
+   * @param contained_output_begin Beginning of the sequence of the matched contained pairs
    * @param pair_equal The binary function to compare two pairs for equality
    * @param stream CUDA stream used for retrieve_outer
    * @return The total number of matches
@@ -442,7 +442,7 @@ class static_multimap {
    * @param first Beginning of the sequence of pairs
    * @param last End of the sequence of pairs
    * @param probe_output_begin Beginning of the sequence of the matched probe pairs
-   * @param probe_output_begin Beginning of the sequence of the matched contained pairs
+   * @param contained_output_begin Beginning of the sequence of the matched contained pairs
    * @param pair_equal The binary function to compare two pairs for equality
    * @param stream CUDA stream used for retrieve_outer
    * @return The total number of matches
@@ -1147,6 +1147,30 @@ class static_multimap {
                         atomicT* num_matches,
                         OutputIt output_begin) noexcept;
 
+    /**
+     * @brief Flushes per-CG buffer into the output sequence.
+     *
+     * @tparam CG Cooperative Group type
+     * @tparam atomicT Type of atomic storage
+     * @tparam OutputIt1 Device accessible output iterator for probe pairs
+     * @tparam OutputIt2 Device accessible output iterator for contained pairs
+     * @param g The Cooperative Group used to flush output buffer
+     * @param num_outputs Number of valid output in the buffer
+     * @param probe_output_buffer Buffer of the matched probe pair sequence
+     * @param contained_output_buffer Buffer of the matched contained pair sequence
+     * @param num_matches Size of the output sequence
+     * @param probe_output_begin Beginning of the output sequence of the matched probe pairs
+     * @param contained_output_begin Beginning of the output sequence of the matched contained pairs
+     */
+    template <typename CG, typename atomicT, typename OutputIt1, typename OutputIt2>
+    __inline__ __device__ void flush_output_buffer(CG const& g,
+                                                   uint32_t const num_outputs,
+                                                   value_type* probe_output_buffer,
+                                                   value_type* contained_output_buffer,
+                                                   atomicT* num_matches,
+                                                   OutputIt1 probe_output_begin,
+                                                   OutputIt2 contained_output_begin) noexcept;
+
     /**
      * @brief Indicates whether the key `k` was inserted into the map.
      *

From 652cbe13c4bde503dcefcb82d3f3783b885a15ef Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Thu, 29 Jul 2021 20:50:45 -0400
Subject: [PATCH 179/267] Add pair_retrieve device functions

---
 include/cuco/detail/static_multimap.inl       | 366 ++++++++++++++++++
 .../cuco/detail/static_multimap_kernels.cuh   | 104 +++++
 include/cuco/static_multimap.cuh              | 269 ++++++++++++-
 3 files changed, 738 insertions(+), 1 deletion(-)

diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index 2b8a6f4cc..2165456f3 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -1138,6 +1138,195 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::retri
   }  // while running
 }
 
+template <typename Key,
+          typename Value,
+          class ProbeSequence,
+          cuda::thread_scope Scope,
+          typename Allocator>
+template <uint32_t buffer_size,
+          bool is_outer,
+          typename warpT,
+          typename CG,
+          typename atomicT,
+          typename OutputIt1,
+          typename OutputIt2,
+          typename PairEqual>
+__device__ void
+static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_retrieve_impl(
+  warpT const& warp,
+  CG const& g,
+  value_type const& pair,
+  uint32_t* warp_counter,
+  value_type* probe_output_buffer,
+  value_type* contained_output_buffer,
+  atomicT* num_matches,
+  OutputIt1 probe_output_begin,
+  OutputIt2 contained_output_begin,
+  PairEqual pair_equal) noexcept
+{
+  const uint32_t cg_lane_id = g.thread_rank();
+
+  auto key          = pair.first;
+  auto current_slot = initial_slot(g, key);
+
+  bool running                      = true;
+  [[maybe_unused]] bool found_match = false;
+
+  while (warp.any(running)) {
+    if (running) {
+      value_type arr[2];
+      load_pair_array(&arr[0], current_slot);
+
+      auto const first_slot_is_empty =
+        detail::bitwise_compare(arr[0].first, this->get_empty_key_sentinel());
+      auto const second_slot_is_empty =
+        detail::bitwise_compare(arr[1].first, this->get_empty_key_sentinel());
+      auto const first_equals  = (not first_slot_is_empty and pair_equal(arr[0], pair));
+      auto const second_equals = (not second_slot_is_empty and pair_equal(arr[1], pair));
+      auto const first_exists  = g.ballot(first_equals);
+      auto const second_exists = g.ballot(second_equals);
+
+      if (first_exists or second_exists) {
+        if constexpr (is_outer) { found_match = true; }
+
+        auto num_first_matches  = __popc(first_exists);
+        auto num_second_matches = __popc(second_exists);
+
+        uint32_t output_idx;
+        if (0 == cg_lane_id) {
+          output_idx = atomicAdd(warp_counter, (num_first_matches + num_second_matches));
+        }
+        output_idx = g.shfl(output_idx, 0);
+
+        if (first_equals) {
+          auto lane_offset = __popc(first_exists & ((1 << cg_lane_id) - 1));
+          probe_output_buffer[output_idx + lane_offset]     = pair;
+          contained_output_buffer[output_idx + lane_offset] = arr[0];
+        }
+        if (second_equals) {
+          auto lane_offset = __popc(second_exists & ((1 << cg_lane_id) - 1));
+          probe_output_buffer[output_idx + lane_offset]     = pair;
+          contained_output_buffer[output_idx + lane_offset] = arr[1];
+        }
+      }
+      if (g.any(first_slot_is_empty or second_slot_is_empty)) {
+        running = false;
+        if constexpr (is_outer) {
+          if ((not found_match) && (cg_lane_id == 0)) {
+            auto output_idx                 = atomicAdd(warp_counter, 1);
+            probe_output_buffer[output_idx] = pair;
+            contained_output_buffer[output_idx] =
+              cuco::make_pair<Key, Value>(std::move(this->get_empty_key_sentinel()),
+                                          std::move(this->get_empty_value_sentinel()));
+          }
+        }
+      }
+    }  // if running
+
+    warp.sync();
+    if (*warp_counter + warp.size() * vector_width() > buffer_size) {
+      flush_output_buffer(warp,
+                          *warp_counter,
+                          probe_output_buffer,
+                          contained_output_buffer,
+                          num_matches,
+                          probe_output_begin,
+                          contained_output_begin);
+      // First lane reset warp-level counter
+      if (warp.thread_rank() == 0) { *warp_counter = 0; }
+    }
+
+    current_slot = next_slot(current_slot);
+  }  // while running
+}
+
+template <typename Key,
+          typename Value,
+          class ProbeSequence,
+          cuda::thread_scope Scope,
+          typename Allocator>
+template <uint32_t cg_size,
+          uint32_t buffer_size,
+          bool is_outer,
+          typename CG,
+          typename atomicT,
+          typename OutputIt1,
+          typename OutputIt2,
+          typename PairEqual>
+__device__ void
+static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_retrieve_impl(
+  CG const& g,
+  value_type const& pair,
+  uint32_t* cg_counter,
+  value_type* probe_output_buffer,
+  value_type* contained_output_buffer,
+  atomicT* num_matches,
+  OutputIt1 probe_output_begin,
+  OutputIt2 contained_output_begin,
+  PairEqual pair_equal) noexcept
+{
+  const uint32_t lane_id = g.thread_rank();
+
+  auto key          = pair.first;
+  auto current_slot = initial_slot(g, key);
+
+  bool running                      = true;
+  [[maybe_unused]] bool found_match = false;
+
+  while (running) {
+    // TODO: Replace reinterpret_cast with atomic ref when possible. The current implementation
+    // is unsafe!
+    static_assert(sizeof(Key) == sizeof(cuda::atomic<Key>));
+    static_assert(sizeof(Value) == sizeof(cuda::atomic<Value>));
+    value_type slot_contents = *reinterpret_cast<value_type const*>(current_slot);
+
+    auto const slot_is_empty =
+      detail::bitwise_compare(slot_contents.first, this->get_empty_key_sentinel());
+    auto const equals = (not slot_is_empty and pair_equal(slot_contents, pair));
+    auto const exists = g.ballot(equals);
+
+    if (exists) {
+      if constexpr (is_outer) { found_match = true; }
+      auto num_matches    = __popc(exists);
+      uint32_t output_idx = *cg_counter;
+      if (equals) {
+        // Each match computes its lane-level offset
+        auto lane_offset                                  = __popc(exists & ((1 << lane_id) - 1));
+        probe_output_buffer[output_idx + lane_offset]     = pair;
+        contained_output_buffer[output_idx + lane_offset] = slot_contents;
+      }
+      if (0 == lane_id) { (*cg_counter) += num_matches; }
+    }
+    if (g.any(slot_is_empty)) {
+      running = false;
+      if constexpr (is_outer) {
+        if ((not found_match) && (lane_id == 0)) {
+          auto output_idx                     = (*cg_counter)++;
+          probe_output_buffer[output_idx]     = pair;
+          contained_output_buffer[output_idx] = cuco::make_pair<Key, Value>(
+            std::move(this->get_empty_key_sentinel()), std::move(this->get_empty_value_sentinel()));
+        }
+      }
+    }
+
+    g.sync();
+
+    // Flush if the next iteration won't fit into buffer
+    if ((*cg_counter + cg_size) > buffer_size) {
+      flush_output_buffer(g,
+                          *cg_counter,
+                          probe_output_buffer,
+                          contained_output_buffer,
+                          num_matches,
+                          probe_output_begin,
+                          contained_output_begin);
+      // First lane reset CG-level counter
+      if (lane_id == 0) { *cg_counter = 0; }
+    }
+    current_slot = next_slot(current_slot);
+  }  // while running
+}
+
 // public APIs
 
 template <typename Key,
@@ -1203,6 +1392,35 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::flush
   }
 }
 
+template <typename Key,
+          typename Value,
+          class ProbeSequence,
+          cuda::thread_scope Scope,
+          typename Allocator>
+template <typename CG, typename atomicT, typename OutputIt1, typename OutputIt2>
+__inline__ __device__ void
+static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::flush_output_buffer(
+  CG const& g,
+  uint32_t const num_outputs,
+  value_type* probe_output_buffer,
+  value_type* contained_output_buffer,
+  atomicT* num_matches,
+  OutputIt1 probe_output_begin,
+  OutputIt2 contained_output_begin) noexcept
+{
+  std::size_t offset;
+  const auto lane_id = g.thread_rank();
+  if (0 == lane_id) {
+    offset = num_matches->fetch_add(num_outputs, cuda::std::memory_order_relaxed);
+  }
+  offset = g.shfl(offset, 0);
+
+  for (auto index = lane_id; index < num_outputs; index += g.size()) {
+    *(probe_output_begin + offset + index)     = probe_output_buffer[index];
+    *(contained_output_begin + offset + index) = contained_output_buffer[index];
+  }
+}
+
 template <typename Key,
           typename Value,
           class ProbeSequence,
@@ -1380,4 +1598,152 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::retri
     g, k, cg_counter, output_buffer, num_matches, output_begin);
 }
 
+template <typename Key,
+          typename Value,
+          class ProbeSequence,
+          cuda::thread_scope Scope,
+          typename Allocator>
+template <uint32_t buffer_size,
+          typename warpT,
+          typename CG,
+          typename atomicT,
+          typename OutputIt1,
+          typename OutputIt2,
+          typename PairEqual>
+__device__ void
+static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_retrieve(
+  warpT const& warp,
+  CG const& g,
+  value_type const& pair,
+  uint32_t* warp_counter,
+  value_type* probe_output_buffer,
+  value_type* contained_output_buffer,
+  atomicT* num_matches,
+  OutputIt1 probe_output_begin,
+  OutputIt2 contained_output_begin,
+  PairEqual pair_equal) noexcept
+{
+  constexpr bool is_outer = false;
+  pair_retrieve_impl<buffer_size, is_outer>(warp,
+                                            g,
+                                            pair,
+                                            warp_counter,
+                                            probe_output_buffer,
+                                            contained_output_buffer,
+                                            num_matches,
+                                            probe_output_begin,
+                                            contained_output_begin,
+                                            pair_equal);
+}
+
+template <typename Key,
+          typename Value,
+          class ProbeSequence,
+          cuda::thread_scope Scope,
+          typename Allocator>
+template <uint32_t buffer_size,
+          typename warpT,
+          typename CG,
+          typename atomicT,
+          typename OutputIt1,
+          typename OutputIt2,
+          typename PairEqual>
+__device__ void
+static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_retrieve_outer(
+  warpT const& warp,
+  CG const& g,
+  value_type const& pair,
+  uint32_t* warp_counter,
+  value_type* probe_output_buffer,
+  value_type* contained_output_buffer,
+  atomicT* num_matches,
+  OutputIt1 probe_output_begin,
+  OutputIt2 contained_output_begin,
+  PairEqual pair_equal) noexcept
+{
+  constexpr bool is_outer = true;
+  pair_retrieve_impl<buffer_size, is_outer>(warp,
+                                            g,
+                                            pair,
+                                            warp_counter,
+                                            probe_output_buffer,
+                                            contained_output_buffer,
+                                            num_matches,
+                                            probe_output_begin,
+                                            contained_output_begin,
+                                            pair_equal);
+}
+
+template <typename Key,
+          typename Value,
+          class ProbeSequence,
+          cuda::thread_scope Scope,
+          typename Allocator>
+template <uint32_t cg_size,
+          uint32_t buffer_size,
+          typename CG,
+          typename atomicT,
+          typename OutputIt1,
+          typename OutputIt2,
+          typename PairEqual>
+__device__ void
+static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_retrieve(
+  CG const& g,
+  value_type const& pair,
+  uint32_t* cg_counter,
+  value_type* probe_output_buffer,
+  value_type* contained_output_buffer,
+  atomicT* num_matches,
+  OutputIt1 probe_output_begin,
+  OutputIt2 contained_output_begin,
+  PairEqual pair_equal) noexcept
+{
+  constexpr bool is_outer = false;
+  pair_retrieve_impl<cg_size, buffer_size, is_outer>(g,
+                                                     pair,
+                                                     cg_counter,
+                                                     probe_output_buffer,
+                                                     contained_output_buffer,
+                                                     num_matches,
+                                                     probe_output_begin,
+                                                     contained_output_begin,
+                                                     pair_equal);
+}
+
+template <typename Key,
+          typename Value,
+          class ProbeSequence,
+          cuda::thread_scope Scope,
+          typename Allocator>
+template <uint32_t cg_size,
+          uint32_t buffer_size,
+          typename CG,
+          typename atomicT,
+          typename OutputIt1,
+          typename OutputIt2,
+          typename PairEqual>
+__device__ void
+static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_retrieve_outer(
+  CG const& g,
+  value_type const& pair,
+  uint32_t* cg_counter,
+  value_type* probe_output_buffer,
+  value_type* contained_output_buffer,
+  atomicT* num_matches,
+  OutputIt1 probe_output_begin,
+  OutputIt2 contained_output_begin,
+  PairEqual pair_equal) noexcept
+{
+  constexpr bool is_outer = true;
+  pair_retrieve_impl<cg_size, buffer_size, is_outer>(g,
+                                                     pair,
+                                                     cg_counter,
+                                                     probe_output_buffer,
+                                                     contained_output_buffer,
+                                                     num_matches,
+                                                     probe_output_begin,
+                                                     contained_output_begin,
+                                                     pair_equal);
+}
+
 }  // namespace cuco
diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap_kernels.cuh
index d2ca5a49f..8b77fece7 100644
--- a/include/cuco/detail/static_multimap_kernels.cuh
+++ b/include/cuco/detail/static_multimap_kernels.cuh
@@ -513,6 +513,60 @@ __global__ void vectorized_pair_retrieve(InputIt first,
                                          viewT view,
                                          PairEqual pair_equal)
 {
+  constexpr uint32_t num_warps = block_size / warp_size;
+  const uint32_t warp_id       = threadIdx.x / warp_size;
+
+  auto warp     = cg::tiled_partition<warp_size>(cg::this_thread_block());
+  auto tile     = cg::tiled_partition<tile_size>(cg::this_thread_block());
+  auto tid      = block_size * blockIdx.x + threadIdx.x;
+  auto pair_idx = tid / tile_size;
+
+  __shared__ cuco::pair_type<Key, Value> probe_output_buffer[num_warps][buffer_size];
+  __shared__ cuco::pair_type<Key, Value> contained_output_buffer[num_warps][buffer_size];
+  // TODO: replace this with shared memory cuda::atomic variables once the dynamiic initialization
+  // warning issue is solved __shared__ atomicT toto_countter[num_warps];
+  __shared__ uint32_t warp_counter[num_warps];
+
+  if (warp.thread_rank() == 0) { warp_counter[warp_id] = 0; }
+
+  while (warp.any(first + pair_idx < last)) {
+    typename viewT::value_type pair = *(first + pair_idx);
+    if constexpr (is_outer) {
+      view.pair_retrieve_outer<buffer_size>(warp,
+                                            tile,
+                                            pair,
+                                            &warp_counter[warp_id],
+                                            probe_output_buffer[warp_id],
+                                            contained_output_buffer[warp_id],
+                                            num_matches,
+                                            probe_output_begin,
+                                            contained_output_begin,
+                                            pair_equal);
+    } else {
+      view.pair_retrieve<buffer_size>(warp,
+                                      tile,
+                                      pair,
+                                      &warp_counter[warp_id],
+                                      probe_output_buffer[warp_id],
+                                      contained_output_buffer[warp_id],
+                                      num_matches,
+                                      probe_output_begin,
+                                      contained_output_begin,
+                                      pair_equal);
+    }
+    pair_idx += (gridDim.x * block_size) / tile_size;
+  }
+
+  // Final flush of output buffer
+  if (warp_counter[warp_id] > 0) {
+    view.flush_output_buffer(warp,
+                             warp_counter[warp_id],
+                             probe_output_buffer[warp_id],
+                             contained_output_buffer[warp_id],
+                             num_matches,
+                             probe_output_begin,
+                             contained_output_begin);
+  }
 }
 
 template <uint32_t block_size,
@@ -536,6 +590,56 @@ __global__ void pair_retrieve(InputIt first,
                               viewT view,
                               PairEqual pair_equal)
 {
+  auto tile     = cg::tiled_partition<tile_size>(cg::this_thread_block());
+  auto tid      = block_size * blockIdx.x + threadIdx.x;
+  auto pair_idx = tid / tile_size;
+
+  constexpr uint32_t num_cgs = block_size / tile_size;
+  const uint32_t cg_id       = threadIdx.x / tile_size;
+  const uint32_t lane_id     = tile.thread_rank();
+
+  __shared__ cuco::pair_type<Key, Value> probe_output_buffer[num_cgs][buffer_size];
+  __shared__ cuco::pair_type<Key, Value> contained_output_buffer[num_cgs][buffer_size];
+  __shared__ uint32_t cg_counter[num_cgs];
+
+  if (lane_id == 0) { cg_counter[cg_id] = 0; }
+
+  while (first + pair_idx < last) {
+    typename viewT::value_type pair = *(first + pair_idx);
+    if constexpr (is_outer) {
+      view.pair_retrieve_outer<tile_size, buffer_size>(tile,
+                                                       pair,
+                                                       &cg_counter[cg_id],
+                                                       probe_output_buffer[cg_id],
+                                                       contained_output_buffer[cg_id],
+                                                       num_matches,
+                                                       probe_output_begin,
+                                                       contained_output_begin,
+                                                       pair_equal);
+    } else {
+      view.pair_retrieve<tile_size, buffer_size>(tile,
+                                                 pair,
+                                                 &cg_counter[cg_id],
+                                                 probe_output_buffer[cg_id],
+                                                 contained_output_buffer[cg_id],
+                                                 num_matches,
+                                                 probe_output_begin,
+                                                 contained_output_begin,
+                                                 pair_equal);
+    }
+    pair_idx += (gridDim.x * block_size) / tile_size;
+  }
+
+  // Final flush of output buffer
+  if (cg_counter[cg_id] > 0) {
+    view.flush_output_buffer(tile,
+                             cg_counter[cg_id],
+                             probe_output_begin[cg_id],
+                             contained_output_buffer[cg_id],
+                             num_matches,
+                             probe_output_begin,
+                             contained_output_begin);
+  }
 }
 
 }  // namespace detail
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index 6eb1dad78..1911d618a 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -1100,6 +1100,98 @@ class static_multimap {
                                   OutputIt output_begin,
                                   KeyEqual key_equal = KeyEqual{}) noexcept;
 
+    /**
+     * @brief Find all the matches of a given pair contained in multimap using vector
+     * loads with per-warp shared memory buffer.
+     *
+     * For pair `p`, if pair_equal(p, slot[j]) returns true, copies `p` to unspecified locations in
+     * `[probe_output_begin, probe_output_end)` and copies slot[j] to unspecified locations in
+     * `[contained_output_begin, contained_output_end)`. In case of non-matches, copies `p` and the
+     * empty value sentinels into the output only if `is_outer` is true.
+     *
+     * @tparam buffer_size Size of the output buffer
+     * @tparam is_outer Boolean flag indicating whether outer join is peformed or not
+     * @tparam warpT Warp (Cooperative Group) type
+     * @tparam CG Cooperative Group type
+     * @tparam atomicT Type of atomic storage
+     * @tparam OutputIt1 Device accessible output iterator for probe matches
+     * @tparam OutputIt2 Device accessible output iterator for contained matches
+     * @tparam PairEqual Binary callable type
+     * @param warp The Cooperative Group (or warp) used to flush output buffer
+     * @param g The Cooperative Group used to retrieve
+     * @param pair The pair to search for
+     * @param warp_counter Pointer to the warp counter
+     * @param probe_output_buffer Buffer of the matched probe pair sequence
+     * @param contained_output_buffer Buffer of the matched contained pair sequence
+     * @param num_matches Size of the output sequence
+     * @param probe_output_begin Beginning of the output sequence of the matched probe pairs
+     * @param contained_output_begin Beginning of the output sequence of the matched contained pairs
+     * @param pair_equal The binary callable used to compare two pairs for equality
+     */
+    template <uint32_t buffer_size,
+              bool is_outer,
+              typename warpT,
+              typename CG,
+              typename atomicT,
+              typename OutputIt1,
+              typename OutputIt2,
+              typename PairEqual>
+    __device__ void pair_retrieve_impl(warpT const& warp,
+                                       CG const& g,
+                                       value_type const& pair,
+                                       uint32_t* warp_counter,
+                                       value_type* probe_output_buffer,
+                                       value_type* contained_output_buffer,
+                                       atomicT* num_matches,
+                                       OutputIt1 probe_output_begin,
+                                       OutputIt2 contained_output_begin,
+                                       PairEqual pair_equal) noexcept;
+
+    /**
+     * @brief Find all the matches of a given pair contained in multimap using scalar
+     * loads with per-cg shared memory buffer.
+     *
+     * For pair `p`, if pair_equal(p, slot[j]) returns true, copies `p` to unspecified locations in
+     * `[probe_output_begin, probe_output_end)` and copies slot[j] to unspecified locations in
+     * `[contained_output_begin, contained_output_end)`. In case of non-matches, copies `p` and the
+     * empty value sentinels into the output only if `is_outer` is true.
+     *
+     * @tparam cg_size The number of threads in CUDA Cooperative Groups
+     * @tparam buffer_size Size of the output buffer
+     * @tparam is_outer Boolean flag indicating whether outer join is peformed or not
+     * @tparam CG Cooperative Group type
+     * @tparam atomicT Type of atomic storage
+     * @tparam OutputIt1 Device accessible output iterator for probe matches
+     * @tparam OutputIt2 Device accessible output iterator for contained matches
+     * @tparam PairEqual Binary callable type
+     * @param g The Cooperative Group used to retrieve
+     * @param pair The pair to search for
+     * @param cg_counter Pointer to the CG counter
+     * @param probe_output_buffer Buffer of the matched probe pair sequence
+     * @param contained_output_buffer Buffer of the matched contained pair sequence
+     * @param num_matches Size of the output sequence
+     * @param probe_output_begin Beginning of the output sequence of the matched probe pairs
+     * @param contained_output_begin Beginning of the output sequence of the matched contained pairs
+     * @param pair_equal The binary callable used to compare two pairs for equality
+     */
+    template <uint32_t cg_size,
+              uint32_t buffer_size,
+              bool is_outer,
+              typename CG,
+              typename atomicT,
+              typename OutputIt1,
+              typename OutputIt2,
+              typename PairEqual>
+    __device__ void pair_retrieve_impl(CG const& g,
+                                       value_type const& pair,
+                                       uint32_t* cg_counter,
+                                       value_type* probe_output_buffer,
+                                       value_type* contained_output_buffer,
+                                       atomicT* num_matches,
+                                       OutputIt1 probe_output_begin,
+                                       OutputIt2 contained_output_begin,
+                                       PairEqual pair_equal) noexcept;
+
    public:
     /**
      * @brief Flushes per-CG buffer into the output sequence using CG memcpy_async.
@@ -1148,7 +1240,7 @@ class static_multimap {
                         OutputIt output_begin) noexcept;
 
     /**
-     * @brief Flushes per-CG buffer into the output sequence.
+     * @brief Flushes per-CG buffer into the output sequences.
      *
      * @tparam CG Cooperative Group type
      * @tparam atomicT Type of atomic storage
@@ -1416,6 +1508,181 @@ class static_multimap {
                                    atomicT* num_matches,
                                    OutputIt output_begin,
                                    KeyEqual key_equal = KeyEqual{}) noexcept;
+
+    /**
+     * @brief Find all the matches of a given pair contained in multimap using vector
+     * loads with per-warp shared memory buffer.
+     *
+     * For pair `p`, if pair_equal(p, slot[j]) returns true, copies `p` to unspecified locations in
+     * `[probe_output_begin, probe_output_end)` and copies slot[j] to unspecified locations in
+     * `[contained_output_begin, contained_output_end)`.
+     *
+     * @tparam buffer_size Size of the output buffer
+     * @tparam warpT Warp (Cooperative Group) type
+     * @tparam CG Cooperative Group type
+     * @tparam atomicT Type of atomic storage
+     * @tparam OutputIt1 Device accessible output iterator for probe matches
+     * @tparam OutputIt2 Device accessible output iterator for contained matches
+     * @tparam PairEqual Binary callable type
+     * @param warp The Cooperative Group (or warp) used to flush output buffer
+     * @param g The Cooperative Group used to retrieve
+     * @param pair The pair to search for
+     * @param warp_counter Pointer to the warp counter
+     * @param probe_output_buffer Buffer of the matched probe pair sequence
+     * @param contained_output_buffer Buffer of the matched contained pair sequence
+     * @param num_matches Size of the output sequence
+     * @param probe_output_begin Beginning of the output sequence of the matched probe pairs
+     * @param contained_output_begin Beginning of the output sequence of the matched contained pairs
+     * @param pair_equal The binary callable used to compare two pairs for equality
+     */
+    template <uint32_t buffer_size,
+              typename warpT,
+              typename CG,
+              typename atomicT,
+              typename OutputIt1,
+              typename OutputIt2,
+              typename PairEqual>
+    __device__ void pair_retrieve(warpT const& warp,
+                                  CG const& g,
+                                  value_type const& pair,
+                                  uint32_t* warp_counter,
+                                  value_type* probe_output_buffer,
+                                  value_type* contained_output_buffer,
+                                  atomicT* num_matches,
+                                  OutputIt1 probe_output_begin,
+                                  OutputIt2 contained_output_begin,
+                                  PairEqual pair_equal) noexcept;
+
+    /**
+     * @brief Find all the matches of a given pair contained in multimap using vector
+     * loads with per-warp shared memory buffer.
+     *
+     * For pair `p`, if pair_equal(p, slot[j]) returns true, copies `p` to unspecified locations in
+     * `[probe_output_begin, probe_output_end)` and copies slot[j] to unspecified locations in
+     * `[contained_output_begin, contained_output_end)`. In case of non-matches, copies `p` and the
+     * empty value sentinels into the output.
+     *
+     * @tparam buffer_size Size of the output buffer
+     * @tparam warpT Warp (Cooperative Group) type
+     * @tparam CG Cooperative Group type
+     * @tparam atomicT Type of atomic storage
+     * @tparam OutputIt1 Device accessible output iterator for probe matches
+     * @tparam OutputIt2 Device accessible output iterator for contained matches
+     * @tparam PairEqual Binary callable type
+     * @param warp The Cooperative Group (or warp) used to flush output buffer
+     * @param g The Cooperative Group used to retrieve
+     * @param pair The pair to search for
+     * @param warp_counter Pointer to the warp counter
+     * @param probe_output_buffer Buffer of the matched probe pair sequence
+     * @param contained_output_buffer Buffer of the matched contained pair sequence
+     * @param num_matches Size of the output sequence
+     * @param probe_output_begin Beginning of the output sequence of the matched probe pairs
+     * @param contained_output_begin Beginning of the output sequence of the matched contained pairs
+     * @param pair_equal The binary callable used to compare two pairs for equality
+     */
+    template <uint32_t buffer_size,
+              typename warpT,
+              typename CG,
+              typename atomicT,
+              typename OutputIt1,
+              typename OutputIt2,
+              typename PairEqual>
+    __device__ void pair_retrieve_outer(warpT const& warp,
+                                        CG const& g,
+                                        value_type const& pair,
+                                        uint32_t* warp_counter,
+                                        value_type* probe_output_buffer,
+                                        value_type* contained_output_buffer,
+                                        atomicT* num_matches,
+                                        OutputIt1 probe_output_begin,
+                                        OutputIt2 contained_output_begin,
+                                        PairEqual pair_equal) noexcept;
+
+    /**
+     * @brief Find all the matches of a given pair contained in multimap using scalar
+     * loads with per-cg shared memory buffer.
+     *
+     * For pair `p`, if pair_equal(p, slot[j]) returns true, copies `p` to unspecified locations in
+     * `[probe_output_begin, probe_output_end)` and copies slot[j] to unspecified locations in
+     * `[contained_output_begin, contained_output_end)`.
+     *
+     * @tparam cg_size The number of threads in CUDA Cooperative Groups
+     * @tparam buffer_size Size of the output buffer
+     * @tparam CG Cooperative Group type
+     * @tparam atomicT Type of atomic storage
+     * @tparam OutputIt1 Device accessible output iterator for probe matches
+     * @tparam OutputIt2 Device accessible output iterator for contained matches
+     * @tparam PairEqual Binary callable type
+     * @param g The Cooperative Group used to retrieve
+     * @param pair The pair to search for
+     * @param cg_counter Pointer to the CG counter
+     * @param probe_output_buffer Buffer of the matched probe pair sequence
+     * @param contained_output_buffer Buffer of the matched contained pair sequence
+     * @param num_matches Size of the output sequence
+     * @param probe_output_begin Beginning of the output sequence of the matched probe pairs
+     * @param contained_output_begin Beginning of the output sequence of the matched contained pairs
+     * @param pair_equal The binary callable used to compare two pairs for equality
+     */
+    template <uint32_t cg_size,
+              uint32_t buffer_size,
+              typename CG,
+              typename atomicT,
+              typename OutputIt1,
+              typename OutputIt2,
+              typename PairEqual>
+    __device__ void pair_retrieve(CG const& g,
+                                  value_type const& pair,
+                                  uint32_t* cg_counter,
+                                  value_type* probe_output_buffer,
+                                  value_type* contained_output_buffer,
+                                  atomicT* num_matches,
+                                  OutputIt1 probe_output_begin,
+                                  OutputIt2 contained_output_begin,
+                                  PairEqual pair_equal) noexcept;
+
+    /**
+     * @brief Find all the matches of a given pair contained in multimap using scalar
+     * loads with per-cg shared memory buffer.
+     *
+     * For pair `p`, if pair_equal(p, slot[j]) returns true, copies `p` to unspecified locations in
+     * `[probe_output_begin, probe_output_end)` and copies slot[j] to unspecified locations in
+     * `[contained_output_begin, contained_output_end)`. In case of non-matches, copies `p` and the
+     * empty value sentinels into the output.
+     *
+     * @tparam cg_size The number of threads in CUDA Cooperative Groups
+     * @tparam buffer_size Size of the output buffer
+     * @tparam CG Cooperative Group type
+     * @tparam atomicT Type of atomic storage
+     * @tparam OutputIt1 Device accessible output iterator for probe matches
+     * @tparam OutputIt2 Device accessible output iterator for contained matches
+     * @tparam PairEqual Binary callable type
+     * @param g The Cooperative Group used to retrieve
+     * @param pair The pair to search for
+     * @param cg_counter Pointer to the CG counter
+     * @param probe_output_buffer Buffer of the matched probe pair sequence
+     * @param contained_output_buffer Buffer of the matched contained pair sequence
+     * @param num_matches Size of the output sequence
+     * @param probe_output_begin Beginning of the output sequence of the matched probe pairs
+     * @param contained_output_begin Beginning of the output sequence of the matched contained pairs
+     * @param pair_equal The binary callable used to compare two pairs for equality
+     */
+    template <uint32_t cg_size,
+              uint32_t buffer_size,
+              typename CG,
+              typename atomicT,
+              typename OutputIt1,
+              typename OutputIt2,
+              typename PairEqual>
+    __device__ void pair_retrieve_outer(CG const& g,
+                                        value_type const& pair,
+                                        uint32_t* cg_counter,
+                                        value_type* probe_output_buffer,
+                                        value_type* contained_output_buffer,
+                                        atomicT* num_matches,
+                                        OutputIt1 probe_output_begin,
+                                        OutputIt2 contained_output_begin,
+                                        PairEqual pair_equal) noexcept;
+
   };  // class device_view
 
   /**

From f1b94a96c3acc1becdf76bcb79d431f22fb09a3a Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Fri, 30 Jul 2021 13:06:18 -0400
Subject: [PATCH 180/267] Use zip iterator for pair_retrieve functions

---
 include/cuco/detail/static_multimap.inl |  74 +++++++++--------
 include/cuco/static_multimap.cuh        | 102 ++++++++++++------------
 2 files changed, 90 insertions(+), 86 deletions(-)

diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index 2165456f3..42b7460c7 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -386,12 +386,12 @@ template <typename Key,
           class ProbeSequence,
           cuda::thread_scope Scope,
           typename Allocator>
-template <typename InputIt, typename OutputIt1, typename OutputIt2, typename PairEqual>
+template <typename InputIt, typename OutputZipIt1, typename OutputZipIt2, typename PairEqual>
 std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::pair_retrieve(
   InputIt first,
   InputIt last,
-  OutputIt1 probe_output_begin,
-  OutputIt2 contained_output_begin,
+  OutputZipIt1 probe_output_begin,
+  OutputZipIt2 contained_output_begin,
   PairEqual pair_equal,
   cudaStream_t stream) const
 {
@@ -446,12 +446,12 @@ template <typename Key,
           class ProbeSequence,
           cuda::thread_scope Scope,
           typename Allocator>
-template <typename InputIt, typename OutputIt1, typename OutputIt2, typename PairEqual>
+template <typename InputIt, typename OutputZipIt1, typename OutputZipIt2, typename PairEqual>
 std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::pair_retrieve_outer(
   InputIt first,
   InputIt last,
-  OutputIt1 probe_output_begin,
-  OutputIt2 contained_output_begin,
+  OutputZipIt1 probe_output_begin,
+  OutputZipIt2 contained_output_begin,
   PairEqual pair_equal,
   cudaStream_t stream) const
 {
@@ -1148,8 +1148,8 @@ template <uint32_t buffer_size,
           typename warpT,
           typename CG,
           typename atomicT,
-          typename OutputIt1,
-          typename OutputIt2,
+          typename OutputZipIt1,
+          typename OutputZipIt2,
           typename PairEqual>
 __device__ void
 static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_retrieve_impl(
@@ -1160,8 +1160,8 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_
   value_type* probe_output_buffer,
   value_type* contained_output_buffer,
   atomicT* num_matches,
-  OutputIt1 probe_output_begin,
-  OutputIt2 contained_output_begin,
+  OutputZipIt1 probe_output_begin,
+  OutputZipIt2 contained_output_begin,
   PairEqual pair_equal) noexcept
 {
   const uint32_t cg_lane_id = g.thread_rank();
@@ -1250,8 +1250,8 @@ template <uint32_t cg_size,
           bool is_outer,
           typename CG,
           typename atomicT,
-          typename OutputIt1,
-          typename OutputIt2,
+          typename OutputZipIt1,
+          typename OutputZipIt2,
           typename PairEqual>
 __device__ void
 static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_retrieve_impl(
@@ -1261,8 +1261,8 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_
   value_type* probe_output_buffer,
   value_type* contained_output_buffer,
   atomicT* num_matches,
-  OutputIt1 probe_output_begin,
-  OutputIt2 contained_output_begin,
+  OutputZipIt1 probe_output_begin,
+  OutputZipIt2 contained_output_begin,
   PairEqual pair_equal) noexcept
 {
   const uint32_t lane_id = g.thread_rank();
@@ -1397,7 +1397,7 @@ template <typename Key,
           class ProbeSequence,
           cuda::thread_scope Scope,
           typename Allocator>
-template <typename CG, typename atomicT, typename OutputIt1, typename OutputIt2>
+template <typename CG, typename atomicT, typename OutputZipIt1, typename OutputZipIt2>
 __inline__ __device__ void
 static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::flush_output_buffer(
   CG const& g,
@@ -1405,8 +1405,8 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::flush
   value_type* probe_output_buffer,
   value_type* contained_output_buffer,
   atomicT* num_matches,
-  OutputIt1 probe_output_begin,
-  OutputIt2 contained_output_begin) noexcept
+  OutputZipIt1 probe_output_begin,
+  OutputZipIt2 contained_output_begin) noexcept
 {
   std::size_t offset;
   const auto lane_id = g.thread_rank();
@@ -1416,8 +1416,12 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::flush
   offset = g.shfl(offset, 0);
 
   for (auto index = lane_id; index < num_outputs; index += g.size()) {
-    *(probe_output_begin + offset + index)     = probe_output_buffer[index];
-    *(contained_output_begin + offset + index) = contained_output_buffer[index];
+    auto& probe_pair                                        = probe_output_buffer[index];
+    auto& contained_pair                                    = contained_output_buffer[index];
+    thrust::get<0>(probe_output_begin + offset + index)     = probe_pair.first;
+    thrust::get<1>(probe_output_begin + offset + index)     = probe_pair.second;
+    thrust::get<0>(contained_output_begin + offset + index) = contained_pair.first;
+    thrust::get<1>(contained_output_begin + offset + index) = contained_pair.second;
   }
 }
 
@@ -1607,8 +1611,8 @@ template <uint32_t buffer_size,
           typename warpT,
           typename CG,
           typename atomicT,
-          typename OutputIt1,
-          typename OutputIt2,
+          typename OutputZipIt1,
+          typename OutputZipIt2,
           typename PairEqual>
 __device__ void
 static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_retrieve(
@@ -1619,8 +1623,8 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_
   value_type* probe_output_buffer,
   value_type* contained_output_buffer,
   atomicT* num_matches,
-  OutputIt1 probe_output_begin,
-  OutputIt2 contained_output_begin,
+  OutputZipIt1 probe_output_begin,
+  OutputZipIt2 contained_output_begin,
   PairEqual pair_equal) noexcept
 {
   constexpr bool is_outer = false;
@@ -1645,8 +1649,8 @@ template <uint32_t buffer_size,
           typename warpT,
           typename CG,
           typename atomicT,
-          typename OutputIt1,
-          typename OutputIt2,
+          typename OutputZipIt1,
+          typename OutputZipIt2,
           typename PairEqual>
 __device__ void
 static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_retrieve_outer(
@@ -1657,8 +1661,8 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_
   value_type* probe_output_buffer,
   value_type* contained_output_buffer,
   atomicT* num_matches,
-  OutputIt1 probe_output_begin,
-  OutputIt2 contained_output_begin,
+  OutputZipIt1 probe_output_begin,
+  OutputZipIt2 contained_output_begin,
   PairEqual pair_equal) noexcept
 {
   constexpr bool is_outer = true;
@@ -1683,8 +1687,8 @@ template <uint32_t cg_size,
           uint32_t buffer_size,
           typename CG,
           typename atomicT,
-          typename OutputIt1,
-          typename OutputIt2,
+          typename OutputZipIt1,
+          typename OutputZipIt2,
           typename PairEqual>
 __device__ void
 static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_retrieve(
@@ -1694,8 +1698,8 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_
   value_type* probe_output_buffer,
   value_type* contained_output_buffer,
   atomicT* num_matches,
-  OutputIt1 probe_output_begin,
-  OutputIt2 contained_output_begin,
+  OutputZipIt1 probe_output_begin,
+  OutputZipIt2 contained_output_begin,
   PairEqual pair_equal) noexcept
 {
   constexpr bool is_outer = false;
@@ -1719,8 +1723,8 @@ template <uint32_t cg_size,
           uint32_t buffer_size,
           typename CG,
           typename atomicT,
-          typename OutputIt1,
-          typename OutputIt2,
+          typename OutputZipIt1,
+          typename OutputZipIt2,
           typename PairEqual>
 __device__ void
 static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_retrieve_outer(
@@ -1730,8 +1734,8 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_
   value_type* probe_output_buffer,
   value_type* contained_output_buffer,
   atomicT* num_matches,
-  OutputIt1 probe_output_begin,
-  OutputIt2 contained_output_begin,
+  OutputZipIt1 probe_output_begin,
+  OutputZipIt2 contained_output_begin,
   PairEqual pair_equal) noexcept
 {
   constexpr bool is_outer = true;
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index 1911d618a..4694c780b 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -403,8 +403,8 @@ class static_multimap {
    * `pair_count()` to determine the number of matching pairs.
    *
    * @tparam InputIt Device accessible input iterator for probe pairs
-   * @tparam OutputIt1 Device accessible output iterator for probe matches
-   * @tparam OutputIt2 Device accessible output iterator for contained matches
+   * @tparam OutputZipIt1 Device accessible output zip iterator for probe matches
+   * @tparam OutputZipIt2 Device accessible output zip iterator for contained matches
    * @tparam PairEqual Binary callable type
    * @param first Beginning of the sequence of pairs
    * @param last End of the sequence of pairs
@@ -414,11 +414,11 @@ class static_multimap {
    * @param stream CUDA stream used for retrieve_outer
    * @return The total number of matches
    */
-  template <typename InputIt, typename OutputIt1, typename OutputIt2, typename PairEqual>
+  template <typename InputIt, typename OutputZipIt1, typename OutputZipIt2, typename PairEqual>
   std::size_t pair_retrieve(InputIt first,
                             InputIt last,
-                            OutputIt1 probe_output_begin,
-                            OutputIt2 contained_output_begin,
+                            OutputZipIt1 probe_output_begin,
+                            OutputZipIt2 contained_output_begin,
                             PairEqual pair_equal,
                             cudaStream_t stream = 0) const;
 
@@ -436,8 +436,8 @@ class static_multimap {
    * `pair_count()` to determine the number of matching pairs.
    *
    * @tparam InputIt Device accessible input iterator for probe pairs
-   * @tparam OutputIt1 Device accessible output iterator for probe matches
-   * @tparam OutputIt2 Device accessible output iterator for contained matches
+   * @tparam OutputZipIt1 Device accessible output zip iterator for probe matches
+   * @tparam OutputZipIt2 Device accessible output zip iterator for contained matches
    * @tparam PairEqual Binary callable type
    * @param first Beginning of the sequence of pairs
    * @param last End of the sequence of pairs
@@ -447,11 +447,11 @@ class static_multimap {
    * @param stream CUDA stream used for retrieve_outer
    * @return The total number of matches
    */
-  template <typename InputIt, typename OutputIt1, typename OutputIt2, typename PairEqual>
+  template <typename InputIt, typename OutputZipIt1, typename OutputZipIt2, typename PairEqual>
   std::size_t pair_retrieve_outer(InputIt first,
                                   InputIt last,
-                                  OutputIt1 probe_output_begin,
-                                  OutputIt2 contained_output_begin,
+                                  OutputZipIt1 probe_output_begin,
+                                  OutputZipIt2 contained_output_begin,
                                   PairEqual pair_equal,
                                   cudaStream_t stream = 0) const;
 
@@ -1114,8 +1114,8 @@ class static_multimap {
      * @tparam warpT Warp (Cooperative Group) type
      * @tparam CG Cooperative Group type
      * @tparam atomicT Type of atomic storage
-     * @tparam OutputIt1 Device accessible output iterator for probe matches
-     * @tparam OutputIt2 Device accessible output iterator for contained matches
+     * @tparam OutputZipIt1 Device accessible output zip iterator for probe matches
+     * @tparam OutputZipIt2 Device accessible output zip iterator for contained matches
      * @tparam PairEqual Binary callable type
      * @param warp The Cooperative Group (or warp) used to flush output buffer
      * @param g The Cooperative Group used to retrieve
@@ -1133,8 +1133,8 @@ class static_multimap {
               typename warpT,
               typename CG,
               typename atomicT,
-              typename OutputIt1,
-              typename OutputIt2,
+              typename OutputZipIt1,
+              typename OutputZipIt2,
               typename PairEqual>
     __device__ void pair_retrieve_impl(warpT const& warp,
                                        CG const& g,
@@ -1143,8 +1143,8 @@ class static_multimap {
                                        value_type* probe_output_buffer,
                                        value_type* contained_output_buffer,
                                        atomicT* num_matches,
-                                       OutputIt1 probe_output_begin,
-                                       OutputIt2 contained_output_begin,
+                                       OutputZipIt1 probe_output_begin,
+                                       OutputZipIt2 contained_output_begin,
                                        PairEqual pair_equal) noexcept;
 
     /**
@@ -1161,8 +1161,8 @@ class static_multimap {
      * @tparam is_outer Boolean flag indicating whether outer join is peformed or not
      * @tparam CG Cooperative Group type
      * @tparam atomicT Type of atomic storage
-     * @tparam OutputIt1 Device accessible output iterator for probe matches
-     * @tparam OutputIt2 Device accessible output iterator for contained matches
+     * @tparam OutputZipIt1 Device accessible output zip iterator for probe matches
+     * @tparam OutputZipIt2 Device accessible output zip iterator for contained matches
      * @tparam PairEqual Binary callable type
      * @param g The Cooperative Group used to retrieve
      * @param pair The pair to search for
@@ -1179,8 +1179,8 @@ class static_multimap {
               bool is_outer,
               typename CG,
               typename atomicT,
-              typename OutputIt1,
-              typename OutputIt2,
+              typename OutputZipIt1,
+              typename OutputZipIt2,
               typename PairEqual>
     __device__ void pair_retrieve_impl(CG const& g,
                                        value_type const& pair,
@@ -1188,8 +1188,8 @@ class static_multimap {
                                        value_type* probe_output_buffer,
                                        value_type* contained_output_buffer,
                                        atomicT* num_matches,
-                                       OutputIt1 probe_output_begin,
-                                       OutputIt2 contained_output_begin,
+                                       OutputZipIt1 probe_output_begin,
+                                       OutputZipIt2 contained_output_begin,
                                        PairEqual pair_equal) noexcept;
 
    public:
@@ -1244,8 +1244,8 @@ class static_multimap {
      *
      * @tparam CG Cooperative Group type
      * @tparam atomicT Type of atomic storage
-     * @tparam OutputIt1 Device accessible output iterator for probe pairs
-     * @tparam OutputIt2 Device accessible output iterator for contained pairs
+     * @tparam OutputZipIt1 Device accessible output zip iterator for probe pairs
+     * @tparam OutputZipIt2 Device accessible output zip iterator for contained pairs
      * @param g The Cooperative Group used to flush output buffer
      * @param num_outputs Number of valid output in the buffer
      * @param probe_output_buffer Buffer of the matched probe pair sequence
@@ -1254,14 +1254,14 @@ class static_multimap {
      * @param probe_output_begin Beginning of the output sequence of the matched probe pairs
      * @param contained_output_begin Beginning of the output sequence of the matched contained pairs
      */
-    template <typename CG, typename atomicT, typename OutputIt1, typename OutputIt2>
+    template <typename CG, typename atomicT, typename OutputZipIt1, typename OutputZipIt2>
     __inline__ __device__ void flush_output_buffer(CG const& g,
                                                    uint32_t const num_outputs,
                                                    value_type* probe_output_buffer,
                                                    value_type* contained_output_buffer,
                                                    atomicT* num_matches,
-                                                   OutputIt1 probe_output_begin,
-                                                   OutputIt2 contained_output_begin) noexcept;
+                                                   OutputZipIt1 probe_output_begin,
+                                                   OutputZipIt2 contained_output_begin) noexcept;
 
     /**
      * @brief Indicates whether the key `k` was inserted into the map.
@@ -1521,8 +1521,8 @@ class static_multimap {
      * @tparam warpT Warp (Cooperative Group) type
      * @tparam CG Cooperative Group type
      * @tparam atomicT Type of atomic storage
-     * @tparam OutputIt1 Device accessible output iterator for probe matches
-     * @tparam OutputIt2 Device accessible output iterator for contained matches
+     * @tparam OutputZipIt1 Device accessible output zip iterator for probe matches
+     * @tparam OutputZipIt2 Device accessible output zip iterator for contained matches
      * @tparam PairEqual Binary callable type
      * @param warp The Cooperative Group (or warp) used to flush output buffer
      * @param g The Cooperative Group used to retrieve
@@ -1539,8 +1539,8 @@ class static_multimap {
               typename warpT,
               typename CG,
               typename atomicT,
-              typename OutputIt1,
-              typename OutputIt2,
+              typename OutputZipIt1,
+              typename OutputZipIt2,
               typename PairEqual>
     __device__ void pair_retrieve(warpT const& warp,
                                   CG const& g,
@@ -1549,8 +1549,8 @@ class static_multimap {
                                   value_type* probe_output_buffer,
                                   value_type* contained_output_buffer,
                                   atomicT* num_matches,
-                                  OutputIt1 probe_output_begin,
-                                  OutputIt2 contained_output_begin,
+                                  OutputZipIt1 probe_output_begin,
+                                  OutputZipIt2 contained_output_begin,
                                   PairEqual pair_equal) noexcept;
 
     /**
@@ -1566,8 +1566,8 @@ class static_multimap {
      * @tparam warpT Warp (Cooperative Group) type
      * @tparam CG Cooperative Group type
      * @tparam atomicT Type of atomic storage
-     * @tparam OutputIt1 Device accessible output iterator for probe matches
-     * @tparam OutputIt2 Device accessible output iterator for contained matches
+     * @tparam OutputZipIt1 Device accessible output zip iterator for probe matches
+     * @tparam OutputZipIt2 Device accessible output zip iterator for contained matches
      * @tparam PairEqual Binary callable type
      * @param warp The Cooperative Group (or warp) used to flush output buffer
      * @param g The Cooperative Group used to retrieve
@@ -1584,8 +1584,8 @@ class static_multimap {
               typename warpT,
               typename CG,
               typename atomicT,
-              typename OutputIt1,
-              typename OutputIt2,
+              typename OutputZipIt1,
+              typename OutputZipIt2,
               typename PairEqual>
     __device__ void pair_retrieve_outer(warpT const& warp,
                                         CG const& g,
@@ -1594,8 +1594,8 @@ class static_multimap {
                                         value_type* probe_output_buffer,
                                         value_type* contained_output_buffer,
                                         atomicT* num_matches,
-                                        OutputIt1 probe_output_begin,
-                                        OutputIt2 contained_output_begin,
+                                        OutputZipIt1 probe_output_begin,
+                                        OutputZipIt2 contained_output_begin,
                                         PairEqual pair_equal) noexcept;
 
     /**
@@ -1610,8 +1610,8 @@ class static_multimap {
      * @tparam buffer_size Size of the output buffer
      * @tparam CG Cooperative Group type
      * @tparam atomicT Type of atomic storage
-     * @tparam OutputIt1 Device accessible output iterator for probe matches
-     * @tparam OutputIt2 Device accessible output iterator for contained matches
+     * @tparam OutputZipIt1 Device accessible output zip iterator for probe matches
+     * @tparam OutputZipIt2 Device accessible output zip iterator for contained matches
      * @tparam PairEqual Binary callable type
      * @param g The Cooperative Group used to retrieve
      * @param pair The pair to search for
@@ -1627,8 +1627,8 @@ class static_multimap {
               uint32_t buffer_size,
               typename CG,
               typename atomicT,
-              typename OutputIt1,
-              typename OutputIt2,
+              typename OutputZipIt1,
+              typename OutputZipIt2,
               typename PairEqual>
     __device__ void pair_retrieve(CG const& g,
                                   value_type const& pair,
@@ -1636,8 +1636,8 @@ class static_multimap {
                                   value_type* probe_output_buffer,
                                   value_type* contained_output_buffer,
                                   atomicT* num_matches,
-                                  OutputIt1 probe_output_begin,
-                                  OutputIt2 contained_output_begin,
+                                  OutputZipIt1 probe_output_begin,
+                                  OutputZipIt2 contained_output_begin,
                                   PairEqual pair_equal) noexcept;
 
     /**
@@ -1653,8 +1653,8 @@ class static_multimap {
      * @tparam buffer_size Size of the output buffer
      * @tparam CG Cooperative Group type
      * @tparam atomicT Type of atomic storage
-     * @tparam OutputIt1 Device accessible output iterator for probe matches
-     * @tparam OutputIt2 Device accessible output iterator for contained matches
+     * @tparam OutputZipIt1 Device accessible output zip iterator for probe matches
+     * @tparam OutputZipIt2 Device accessible output zip iterator for contained matches
      * @tparam PairEqual Binary callable type
      * @param g The Cooperative Group used to retrieve
      * @param pair The pair to search for
@@ -1670,8 +1670,8 @@ class static_multimap {
               uint32_t buffer_size,
               typename CG,
               typename atomicT,
-              typename OutputIt1,
-              typename OutputIt2,
+              typename OutputZipIt1,
+              typename OutputZipIt2,
               typename PairEqual>
     __device__ void pair_retrieve_outer(CG const& g,
                                         value_type const& pair,
@@ -1679,8 +1679,8 @@ class static_multimap {
                                         value_type* probe_output_buffer,
                                         value_type* contained_output_buffer,
                                         atomicT* num_matches,
-                                        OutputIt1 probe_output_begin,
-                                        OutputIt2 contained_output_begin,
+                                        OutputZipIt1 probe_output_begin,
+                                        OutputZipIt2 contained_output_begin,
                                         PairEqual pair_equal) noexcept;
 
   };  // class device_view

From 62dc120e2f309f63684e7dca171df398e12525b3 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Fri, 30 Jul 2021 13:16:59 -0400
Subject: [PATCH 181/267] Include thrust tuple header

---
 include/cuco/detail/static_multimap.inl | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index 42b7460c7..6f4a4a1ce 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -14,6 +14,8 @@
  * limitations under the License.
  */
 
+#include <thrust/tuple.h>
+
 namespace cuco {
 
 template <typename Key,

From b149744add5dfc94f6d1ead9c1b1123503e7e4ee Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Fri, 30 Jul 2021 13:22:05 -0400
Subject: [PATCH 182/267] Dereference zip iterator

---
 include/cuco/detail/static_multimap.inl | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index 6f4a4a1ce..6e76395ee 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -1420,10 +1420,10 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::flush
   for (auto index = lane_id; index < num_outputs; index += g.size()) {
     auto& probe_pair                                        = probe_output_buffer[index];
     auto& contained_pair                                    = contained_output_buffer[index];
-    thrust::get<0>(probe_output_begin + offset + index)     = probe_pair.first;
-    thrust::get<1>(probe_output_begin + offset + index)     = probe_pair.second;
-    thrust::get<0>(contained_output_begin + offset + index) = contained_pair.first;
-    thrust::get<1>(contained_output_begin + offset + index) = contained_pair.second;
+    thrust::get<0>(*(probe_output_begin + offset + index))     = probe_pair.first;
+    thrust::get<1>(*(probe_output_begin + offset + index))     = probe_pair.second;
+    thrust::get<0>(*(contained_output_begin + offset + index)) = contained_pair.first;
+    thrust::get<1>(*(contained_output_begin + offset + index)) = contained_pair.second;
   }
 }
 

From 438f8131a92fd66e99c2d5de1a790b57c4adbee6 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Wed, 4 Aug 2021 11:36:13 -0400
Subject: [PATCH 183/267] Minor cleanups

---
 include/cuco/detail/static_multimap_kernels.cuh | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap_kernels.cuh
index 8b77fece7..9dac5cfdd 100644
--- a/include/cuco/detail/static_multimap_kernels.cuh
+++ b/include/cuco/detail/static_multimap_kernels.cuh
@@ -126,9 +126,9 @@ __global__ void insert_if(
   auto it   = first + tid / tile_size;
 
   while (it < last) {
-    if (pred(*it)) {
+    typename viewT::value_type const insert_pair{*it};
+    if (pred(insert_pair)) {
       // force conversion to value_type
-      typename viewT::value_type const insert_pair{*it};
       view.insert(tile, insert_pair, key_equal);
     }
     it += (gridDim.x * block_size) / tile_size;

From 4909e7963c33af7d40f897737e9ba91402062f07 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Thu, 5 Aug 2021 10:05:57 -0400
Subject: [PATCH 184/267] Fix a bug in pair retrieve: offset for second equals

---
 include/cuco/detail/static_multimap.inl | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index 6e76395ee..e2dfe31fd 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -1207,8 +1207,8 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_
         }
         if (second_equals) {
           auto lane_offset = __popc(second_exists & ((1 << cg_lane_id) - 1));
-          probe_output_buffer[output_idx + lane_offset]     = pair;
-          contained_output_buffer[output_idx + lane_offset] = arr[1];
+          probe_output_buffer[output_idx + num_first_matches + lane_offset]     = pair;
+          contained_output_buffer[output_idx + num_first_matches + lane_offset] = arr[1];
         }
       }
       if (g.any(first_slot_is_empty or second_slot_is_empty)) {
@@ -1418,8 +1418,8 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::flush
   offset = g.shfl(offset, 0);
 
   for (auto index = lane_id; index < num_outputs; index += g.size()) {
-    auto& probe_pair                                        = probe_output_buffer[index];
-    auto& contained_pair                                    = contained_output_buffer[index];
+    auto& probe_pair                                           = probe_output_buffer[index];
+    auto& contained_pair                                       = contained_output_buffer[index];
     thrust::get<0>(*(probe_output_begin + offset + index))     = probe_pair.first;
     thrust::get<1>(*(probe_output_begin + offset + index))     = probe_pair.second;
     thrust::get<0>(*(contained_output_begin + offset + index)) = contained_pair.first;

From fe309f54106dedc147c3c0201958b7811a36d122 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Thu, 5 Aug 2021 10:28:48 -0400
Subject: [PATCH 185/267] Add unit tests for pair retrieve function + fix a
 typo

---
 .../cuco/detail/static_multimap_kernels.cuh   |  2 +-
 tests/static_multimap/static_multimap_test.cu | 35 +++++++++++++++++++
 2 files changed, 36 insertions(+), 1 deletion(-)

diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap_kernels.cuh
index 9dac5cfdd..95f5962fd 100644
--- a/include/cuco/detail/static_multimap_kernels.cuh
+++ b/include/cuco/detail/static_multimap_kernels.cuh
@@ -634,7 +634,7 @@ __global__ void pair_retrieve(InputIt first,
   if (cg_counter[cg_id] > 0) {
     view.flush_output_buffer(tile,
                              cg_counter[cg_id],
-                             probe_output_begin[cg_id],
+                             probe_output_buffer[cg_id],
                              contained_output_buffer[cg_id],
                              num_matches,
                              probe_output_begin,
diff --git a/tests/static_multimap/static_multimap_test.cu b/tests/static_multimap/static_multimap_test.cu
index d0ae8a7fb..bfbfb8819 100644
--- a/tests/static_multimap/static_multimap_test.cu
+++ b/tests/static_multimap/static_multimap_test.cu
@@ -17,6 +17,7 @@
 #include <thrust/count.h>
 #include <thrust/device_vector.h>
 #include <thrust/for_each.h>
+#include <thrust/iterator/discard_iterator.h>
 #include <algorithm>
 #include <catch2/catch.hpp>
 #include <cuco/static_multimap.cuh>
@@ -326,4 +327,38 @@ TEMPLATE_TEST_CASE_SIG("Evaluation of pair functions",
 
     REQUIRE(num_outer == (num + num_pairs / 2));
   }
+
+  SECTION("Output of pair_count and pair_retrieve should be coherent.")
+  {
+    auto num = map.pair_count(d_pairs.begin(), d_pairs.end(), pair_equal<Key, Value>{});
+
+    auto out1_zip = thrust::make_zip_iterator(
+      thrust::make_tuple(thrust::make_discard_iterator(), thrust::make_discard_iterator()));
+    auto out2_zip = thrust::make_zip_iterator(
+      thrust::make_tuple(thrust::make_discard_iterator(), thrust::make_discard_iterator()));
+
+    REQUIRE(num == num_pairs);
+
+    auto size = map.pair_retrieve(
+      d_pairs.begin(), d_pairs.end(), out1_zip, out2_zip, pair_equal<Key, Value>{});
+
+    REQUIRE(num == size);
+  }
+
+  SECTION("Output of pair_count_outer and pair_retrieve_outer should be coherent.")
+  {
+    auto num = map.pair_count_outer(d_pairs.begin(), d_pairs.end(), pair_equal<Key, Value>{});
+
+    auto out1_zip = thrust::make_zip_iterator(
+      thrust::make_tuple(thrust::make_discard_iterator(), thrust::make_discard_iterator()));
+    auto out2_zip = thrust::make_zip_iterator(
+      thrust::make_tuple(thrust::make_discard_iterator(), thrust::make_discard_iterator()));
+
+    REQUIRE(num == num_pairs * 1.5);
+
+    auto size = map.pair_retrieve_outer(
+      d_pairs.begin(), d_pairs.end(), out1_zip, out2_zip, pair_equal<Key, Value>{});
+
+    REQUIRE(num == size);
+  }
 }

From 32e551bdaaa1943dd97b4ea928280175d03270c6 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Thu, 5 Aug 2021 12:46:39 -0400
Subject: [PATCH 186/267] Add unit tests for small test cases

---
 tests/static_multimap/static_multimap_test.cu | 107 +++++++++++++++---
 1 file changed, 94 insertions(+), 13 deletions(-)

diff --git a/tests/static_multimap/static_multimap_test.cu b/tests/static_multimap/static_multimap_test.cu
index bfbfb8819..6291ac506 100644
--- a/tests/static_multimap/static_multimap_test.cu
+++ b/tests/static_multimap/static_multimap_test.cu
@@ -94,9 +94,6 @@ TEMPLATE_TEST_CASE_SIG("Each key appears twice",
   constexpr std::size_t num_items{400};
   cuco::static_multimap<Key, Value> map{500, -1, -1};
 
-  auto m_view = map.get_device_mutable_view();
-  auto view   = map.get_device_view();
-
   std::vector<Key> h_keys(num_items);
   std::vector<cuco::pair_type<Key, Value>> h_pairs(num_items);
 
@@ -181,9 +178,6 @@ TEMPLATE_TEST_CASE_SIG("Handling of non-matches",
   constexpr std::size_t num_keys{1'000'000};
   cuco::static_multimap<Key, Value> map{2'000'000, -1, -1};
 
-  auto m_view = map.get_device_mutable_view();
-  auto view   = map.get_device_view();
-
   std::vector<Key> h_keys(num_keys);
   std::vector<cuco::pair_type<Key, Value>> h_pairs(num_keys);
 
@@ -251,7 +245,7 @@ TEMPLATE_TEST_CASE_SIG("Handling of non-matches",
   }
 }
 
-TEMPLATE_TEST_CASE_SIG("Test of insert_if",
+TEMPLATE_TEST_CASE_SIG("Tests of insert_if",
                        "",
                        ((typename Key, typename Value, dist_type Dist), Key, Value, Dist),
                        (int32_t, int32_t, dist_type::UNIQUE),
@@ -261,9 +255,6 @@ TEMPLATE_TEST_CASE_SIG("Test of insert_if",
   constexpr std::size_t num_keys{1'000'000};
   cuco::static_multimap<Key, Value> map{2'000'000, -1, -1};
 
-  auto m_view = map.get_device_mutable_view();
-  auto view   = map.get_device_view();
-
   std::vector<Key> h_keys(num_keys);
   std::vector<cuco::pair_type<Key, Value>> h_pairs(num_keys);
 
@@ -297,9 +288,6 @@ TEMPLATE_TEST_CASE_SIG("Evaluation of pair functions",
   constexpr std::size_t num_pairs{5'000'000};
   cuco::static_multimap<Key, Value> map{10'000'000, -1, -1};
 
-  auto m_view = map.get_device_mutable_view();
-  auto view   = map.get_device_view();
-
   std::vector<Key> h_keys(num_pairs);
   std::vector<cuco::pair_type<Key, Value>> h_pairs(num_pairs);
 
@@ -362,3 +350,96 @@ TEMPLATE_TEST_CASE_SIG("Evaluation of pair functions",
     REQUIRE(num == size);
   }
 }
+
+TEMPLATE_TEST_CASE_SIG("Evaluation of small test cases",
+                       "",
+                       ((typename Key, typename Value, dist_type Dist), Key, Value, Dist),
+                       (int32_t, int32_t, dist_type::UNIQUE),
+                       (int32_t, int64_t, dist_type::UNIQUE),
+                       (int64_t, int64_t, dist_type::UNIQUE))
+{
+  constexpr std::size_t num_items{3};
+  cuco::static_multimap<Key, Value> map{2 * num_items, -1, -1};
+
+  std::vector<Key> h_keys(num_items);
+  std::vector<cuco::pair_type<Key, Value>> h_pairs(num_items);
+
+  generate_keys<Dist, Key>(h_keys.begin(), h_keys.end());
+
+  for (auto i = 0; i < num_items; ++i) {
+    h_pairs[i].first  = h_keys[i] / 2;
+    h_pairs[i].second = h_keys[i];
+  }
+
+  thrust::device_vector<Key> d_keys(h_keys);
+  thrust::device_vector<cuco::pair_type<Key, Value>> d_pairs(h_pairs);
+
+  map.insert(d_pairs.begin(), d_pairs.end());
+
+  for (auto i = 0; i < num_items; ++i) {
+    h_pairs[i].first = h_keys[i];
+  }
+  d_pairs = h_pairs;
+
+  SECTION("Output of count and retrieve should be coherent.")
+  {
+    auto num = map.count(d_keys.begin(), d_keys.end());
+    thrust::device_vector<cuco::pair_type<Key, Value>> d_results(num);
+
+    REQUIRE(num == num_items);
+
+    auto output_begin = d_results.data().get();
+    auto output_end   = map.retrieve(d_keys.begin(), d_keys.end(), output_begin);
+    auto size         = thrust::distance(output_begin, output_end);
+
+    REQUIRE(num == size);
+  }
+
+  SECTION("Output of count_outer and retrieve_outer should be coherent.")
+  {
+    auto num = map.count_outer(d_keys.begin(), d_keys.end());
+    thrust::device_vector<cuco::pair_type<Key, Value>> d_results(num);
+
+    REQUIRE(num == num_items + 1);
+
+    auto output_begin = d_results.data().get();
+    auto output_end   = map.retrieve_outer(d_keys.begin(), d_keys.end(), output_begin);
+    auto size         = thrust::distance(output_begin, output_end);
+
+    REQUIRE(num == size);
+  }
+
+  SECTION("Output of pair_count and pair_retrieve should be coherent.")
+  {
+    auto num = map.pair_count(d_pairs.begin(), d_pairs.end(), pair_equal<Key, Value>{});
+
+    auto out1_zip = thrust::make_zip_iterator(
+      thrust::make_tuple(thrust::make_discard_iterator(), thrust::make_discard_iterator()));
+    auto out2_zip = thrust::make_zip_iterator(
+      thrust::make_tuple(thrust::make_discard_iterator(), thrust::make_discard_iterator()));
+
+    REQUIRE(num == num_items);
+
+    auto size = map.pair_retrieve(
+      d_pairs.begin(), d_pairs.end(), out1_zip, out2_zip, pair_equal<Key, Value>{});
+
+    REQUIRE(num == size);
+  }
+
+  SECTION("Output of pair_count_outer and pair_retrieve_outer should be coherent.")
+  {
+    auto num = map.pair_count_outer(d_pairs.begin(), d_pairs.end(), pair_equal<Key, Value>{});
+
+    auto out1_zip = thrust::make_zip_iterator(
+      thrust::make_tuple(thrust::make_discard_iterator(), thrust::make_discard_iterator()));
+    auto out2_zip = thrust::make_zip_iterator(
+      thrust::make_tuple(thrust::make_discard_iterator(), thrust::make_discard_iterator()));
+
+    REQUIRE(num == num_items + 1);
+
+    auto size = map.pair_retrieve_outer(
+      d_pairs.begin(), d_pairs.end(), out1_zip, out2_zip, pair_equal<Key, Value>{});
+
+    REQUIRE(num == size);
+  }
+}

From b1e2cd95c0081219035fc0d806ecf0d5a82c57c1 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Thu, 5 Aug 2021 14:25:47 -0400
Subject: [PATCH 187/267] Fix a warp cg synchronization bug

---
 .../cuco/detail/static_multimap_kernels.cuh   | 94 ++++++++++---------
 1 file changed, 52 insertions(+), 42 deletions(-)

diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap_kernels.cuh
index 95f5962fd..8b1d6af79 100644
--- a/include/cuco/detail/static_multimap_kernels.cuh
+++ b/include/cuco/detail/static_multimap_kernels.cuh
@@ -378,25 +378,30 @@ __global__ void vectorized_retrieve(InputIt first,
   if (warp.thread_rank() == 0) { warp_counter[warp_id] = 0; }
 
   while (warp.any(first + key_idx < last)) {
-    auto key = *(first + key_idx);
-    if constexpr (is_outer) {
-      view.retrieve_outer<buffer_size>(warp,
-                                       tile,
-                                       key,
-                                       &warp_counter[warp_id],
-                                       output_buffer[warp_id],
-                                       num_matches,
-                                       output_begin,
-                                       key_equal);
-    } else {
-      view.retrieve<buffer_size>(warp,
-                                 tile,
-                                 key,
-                                 &warp_counter[warp_id],
-                                 output_buffer[warp_id],
-                                 num_matches,
-                                 output_begin,
-                                 key_equal);
+    bool active_flag = first + key_idx < last;
+    auto active_warp = cg::binary_partition<warp_size>(warp, active_flag);
+
+    if (active_flag) {
+      auto key = *(first + key_idx);
+      if constexpr (is_outer) {
+        view.retrieve_outer<buffer_size>(active_warp,
+                                         tile,
+                                         key,
+                                         &warp_counter[warp_id],
+                                         output_buffer[warp_id],
+                                         num_matches,
+                                         output_begin,
+                                         key_equal);
+      } else {
+        view.retrieve<buffer_size>(active_warp,
+                                   tile,
+                                   key,
+                                   &warp_counter[warp_id],
+                                   output_buffer[warp_id],
+                                   num_matches,
+                                   output_begin,
+                                   key_equal);
+      }
     }
     key_idx += (gridDim.x * block_size) / tile_size;
   }
@@ -530,29 +535,34 @@ __global__ void vectorized_pair_retrieve(InputIt first,
   if (warp.thread_rank() == 0) { warp_counter[warp_id] = 0; }
 
   while (warp.any(first + pair_idx < last)) {
-    typename viewT::value_type pair = *(first + pair_idx);
-    if constexpr (is_outer) {
-      view.pair_retrieve_outer<buffer_size>(warp,
-                                            tile,
-                                            pair,
-                                            &warp_counter[warp_id],
-                                            probe_output_buffer[warp_id],
-                                            contained_output_buffer[warp_id],
-                                            num_matches,
-                                            probe_output_begin,
-                                            contained_output_begin,
-                                            pair_equal);
-    } else {
-      view.pair_retrieve<buffer_size>(warp,
-                                      tile,
-                                      pair,
-                                      &warp_counter[warp_id],
-                                      probe_output_buffer[warp_id],
-                                      contained_output_buffer[warp_id],
-                                      num_matches,
-                                      probe_output_begin,
-                                      contained_output_begin,
-                                      pair_equal);
+    bool active_flag = first + pair_idx < last;
+    auto active_warp = cg::binary_partition<warp_size>(warp, active_flag);
+
+    if (active_flag) {
+      typename viewT::value_type pair = *(first + pair_idx);
+      if constexpr (is_outer) {
+        view.pair_retrieve_outer<buffer_size>(active_warp,
+                                              tile,
+                                              pair,
+                                              &warp_counter[warp_id],
+                                              probe_output_buffer[warp_id],
+                                              contained_output_buffer[warp_id],
+                                              num_matches,
+                                              probe_output_begin,
+                                              contained_output_begin,
+                                              pair_equal);
+      } else {
+        view.pair_retrieve<buffer_size>(active_warp,
+                                        tile,
+                                        pair,
+                                        &warp_counter[warp_id],
+                                        probe_output_buffer[warp_id],
+                                        contained_output_buffer[warp_id],
+                                        num_matches,
+                                        probe_output_begin,
+                                        contained_output_begin,
+                                        pair_equal);
+      }
     }
     pair_idx += (gridDim.x * block_size) / tile_size;
   }

From ec28dd0623cc2faabe6a1c67ab55da9b50ebf2cc Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Mon, 9 Aug 2021 19:05:41 -0400
Subject: [PATCH 188/267] Add stream argument to multimap constructor

---
 include/cuco/detail/static_multimap.inl | 10 +++++++---
 include/cuco/static_multimap.cuh        |  2 ++
 2 files changed, 9 insertions(+), 3 deletions(-)

diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index e2dfe31fd..7337af4aa 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -24,7 +24,11 @@ template <typename Key,
           cuda::thread_scope Scope,
           typename Allocator>
 static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::static_multimap(
-  std::size_t capacity, Key empty_key_sentinel, Value empty_value_sentinel, Allocator const& alloc)
+  std::size_t capacity,
+  Key empty_key_sentinel,
+  Value empty_value_sentinel,
+  cudaStream_t stream,
+  Allocator const& alloc)
   : empty_key_sentinel_{empty_key_sentinel},
     empty_value_sentinel_{empty_value_sentinel},
     slot_allocator_{alloc},
@@ -41,8 +45,8 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::static_multimap(
   auto constexpr block_size = 256;
   auto constexpr stride     = 4;
   auto const grid_size      = (get_capacity() + stride * block_size - 1) / (stride * block_size);
-  detail::initialize<atomic_key_type, atomic_mapped_type>
-    <<<grid_size, block_size>>>(slots_, empty_key_sentinel, empty_value_sentinel, get_capacity());
+  detail::initialize<atomic_key_type, atomic_mapped_type><<<grid_size, block_size, 0, stream>>>(
+    slots_, empty_key_sentinel, empty_value_sentinel, get_capacity());
 }
 
 template <typename Key,
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index 4694c780b..0de4ae0c3 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -192,11 +192,13 @@ class static_multimap {
    * @param capacity The total number of slots in the map
    * @param empty_key_sentinel The reserved key value for empty slots
    * @param empty_value_sentinel The reserved mapped value for empty slots
+   * @param stream CUDA stream used to initialize the map
    * @param alloc Allocator used for allocating device storage
    */
   static_multimap(std::size_t capacity,
                   Key empty_key_sentinel,
                   Value empty_value_sentinel,
+                  cudaStream_t stream    = 0,
                   Allocator const& alloc = Allocator{});
 
   /**

From 04343dade4571e4b8ddc56751a85b76987a27515 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Wed, 11 Aug 2021 17:53:47 -0400
Subject: [PATCH 189/267] Add optional precomputed hash argument

---
 include/cuco/detail/probe_sequences.cuh       |  10 +-
 include/cuco/detail/static_multimap.inl       | 111 ++++++++++++-----
 .../cuco/detail/static_multimap_kernels.cuh   |  40 +++++--
 include/cuco/static_multimap.cuh              | 112 ++++++++++++++++--
 4 files changed, 218 insertions(+), 55 deletions(-)

diff --git a/include/cuco/detail/probe_sequences.cuh b/include/cuco/detail/probe_sequences.cuh
index bc71bfceb..906133d99 100644
--- a/include/cuco/detail/probe_sequences.cuh
+++ b/include/cuco/detail/probe_sequences.cuh
@@ -84,17 +84,21 @@ class double_hashing : public probe_sequence_base<Key, Value, CGSize, Scope> {
   }
 
   template <typename CG>
-  __device__ iterator initial_slot(CG const& g, Key const k) noexcept
+  __device__ iterator initial_slot(
+    CG const& g,
+    Key const k,
+    thrust::optional<typename detail::MurmurHash3_32<Key>::result_type> precomputed_hash) noexcept
   {
     std::size_t index;
+    auto const hash_value = precomputed_hash.value_or(hash1_(k));
     if constexpr (uses_vector_load()) {
       step_size_ = (hash2_(k + 1) % (capacity_ / (cg_size() * vector_width()) - 1) + 1) *
                    cg_size() * vector_width();
-      index = hash1_(k) % (capacity_ / (cg_size() * vector_width())) * cg_size() * vector_width() +
+      index = hash_value % (capacity_ / (cg_size() * vector_width())) * cg_size() * vector_width() +
               g.thread_rank() * vector_width();
     } else {
       step_size_ = (hash2_(k + 1) % (capacity_ / cg_size() - 1) + 1) * cg_size();
-      index      = (hash1_(k) + g.thread_rank()) % capacity_;
+      index      = (hash_value + g.thread_rank()) % capacity_;
     }
     return slots_ + index;
   }
diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index 7337af4aa..4e58dc673 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -634,9 +634,12 @@ template <typename Key,
 template <bool uses_vector_load, typename CG, typename KeyEqual>
 __device__ std::enable_if_t<uses_vector_load, void>
 static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_mutable_view::insert_impl(
-  CG g, value_type const& insert_pair, KeyEqual key_equal) noexcept
+  CG g,
+  value_type const& insert_pair,
+  optional_hash_type precomputed_hash,
+  KeyEqual key_equal) noexcept
 {
-  auto current_slot = initial_slot(g, insert_pair.first);
+  auto current_slot = initial_slot(g, insert_pair.first, precomputed_hash);
   while (true) {
     value_type arr[2];
     load_pair_array(&arr[0], current_slot);
@@ -681,9 +684,12 @@ template <typename Key,
 template <bool uses_vector_load, typename CG, typename KeyEqual>
 __device__ std::enable_if_t<not uses_vector_load, void>
 static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_mutable_view::insert_impl(
-  CG g, value_type const& insert_pair, KeyEqual key_equal) noexcept
+  CG g,
+  value_type const& insert_pair,
+  optional_hash_type precomputed_hash,
+  KeyEqual key_equal) noexcept
 {
-  auto current_slot = initial_slot(g, insert_pair.first);
+  auto current_slot = initial_slot(g, insert_pair.first, precomputed_hash);
 
   while (true) {
     key_type const existing_key = current_slot->first.load(cuda::memory_order_relaxed);
@@ -729,9 +735,12 @@ template <typename Key,
 template <typename CG, typename KeyEqual>
 __device__ void
 static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_mutable_view::insert(
-  CG g, value_type const& insert_pair, KeyEqual key_equal) noexcept
+  CG g,
+  value_type const& insert_pair,
+  optional_hash_type precomputed_hash,
+  KeyEqual key_equal) noexcept
 {
-  insert_impl<uses_vector_load()>(g, insert_pair, key_equal);
+  insert_impl<uses_vector_load()>(g, insert_pair, precomputed_hash, key_equal);
 }
 
 template <typename Key,
@@ -742,9 +751,9 @@ template <typename Key,
 template <bool uses_vector_load, typename CG, typename KeyEqual>
 __device__ std::enable_if_t<uses_vector_load, bool>
 static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::contains_impl(
-  CG g, Key const& k, KeyEqual key_equal) noexcept
+  CG g, Key const& k, optional_hash_type precomputed_hash, KeyEqual key_equal) noexcept
 {
-  auto current_slot = initial_slot(g, k);
+  auto current_slot = initial_slot(g, k, precomputed_hash);
 
   while (true) {
     value_type arr[2];
@@ -777,9 +786,9 @@ template <typename Key,
 template <bool uses_vector_load, typename CG, typename KeyEqual>
 __device__ std::enable_if_t<not uses_vector_load, bool>
 static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::contains_impl(
-  CG g, Key const& k, KeyEqual key_equal) noexcept
+  CG g, Key const& k, optional_hash_type precomputed_hash, KeyEqual key_equal) noexcept
 {
-  auto current_slot = initial_slot(g, k);
+  auto current_slot = initial_slot(g, k, precomputed_hash);
 
   while (true) {
     key_type const existing_key = current_slot->first.load(cuda::std::memory_order_relaxed);
@@ -811,9 +820,13 @@ template <typename Key,
 template <bool uses_vector_load, bool is_outer, typename CG, typename KeyEqual>
 __device__ std::enable_if_t<uses_vector_load, void>
 static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::count_impl(
-  CG const& g, Key const& k, std::size_t& thread_num_matches, KeyEqual key_equal) noexcept
+  CG const& g,
+  Key const& k,
+  optional_hash_type precomputed_hash,
+  std::size_t& thread_num_matches,
+  KeyEqual key_equal) noexcept
 {
-  auto current_slot = initial_slot(g, k);
+  auto current_slot = initial_slot(g, k, precomputed_hash);
 
   [[maybe_unused]] bool found_match = false;
 
@@ -853,9 +866,13 @@ template <typename Key,
 template <bool uses_vector_load, bool is_outer, typename CG, typename KeyEqual>
 __device__ std::enable_if_t<not uses_vector_load, void>
 static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::count_impl(
-  CG const& g, Key const& k, std::size_t& thread_num_matches, KeyEqual key_equal) noexcept
+  CG const& g,
+  Key const& k,
+  optional_hash_type precomputed_hash,
+  std::size_t& thread_num_matches,
+  KeyEqual key_equal) noexcept
 {
-  auto current_slot = initial_slot(g, k);
+  auto current_slot = initial_slot(g, k, precomputed_hash);
 
   [[maybe_unused]] bool found_match = false;
 
@@ -894,11 +911,12 @@ __device__ std::enable_if_t<uses_vector_load, void>
 static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_count_impl(
   CG const& g,
   value_type const& pair,
+  optional_hash_type precomputed_hash,
   std::size_t& thread_num_matches,
   PairEqual pair_equal) noexcept
 {
   auto key          = pair.first;
-  auto current_slot = initial_slot(g, key);
+  auto current_slot = initial_slot(g, key, precomputed_hash);
 
   [[maybe_unused]] bool found_match = false;
 
@@ -941,11 +959,12 @@ __device__ std::enable_if_t<not uses_vector_load, void>
 static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_count_impl(
   CG const& g,
   value_type const& pair,
+  optional_hash_type precomputed_hash,
   std::size_t& thread_num_matches,
   PairEqual pair_equal) noexcept
 {
   auto key          = pair.first;
-  auto current_slot = initial_slot(g, key);
+  auto current_slot = initial_slot(g, key, precomputed_hash);
 
   [[maybe_unused]] bool found_match = false;
 
@@ -990,6 +1009,7 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::retri
   warpT const& warp,
   CG const& g,
   Key const& k,
+  optional_hash_type precomputed_hash,
   uint32_t* warp_counter,
   value_type* output_buffer,
   atomicT* num_matches,
@@ -998,7 +1018,7 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::retri
 {
   const uint32_t cg_lane_id = g.thread_rank();
 
-  auto current_slot = initial_slot(g, k);
+  auto current_slot = initial_slot(g, k, precomputed_hash);
 
   bool running                      = true;
   [[maybe_unused]] bool found_match = false;
@@ -1082,6 +1102,7 @@ __device__ void
 static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::retrieve_impl(
   CG const& g,
   Key const& k,
+  optional_hash_type precomputed_hash,
   uint32_t* cg_counter,
   value_type* output_buffer,
   atomicT* num_matches,
@@ -1090,7 +1111,7 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::retri
 {
   const uint32_t lane_id = g.thread_rank();
 
-  auto current_slot = initial_slot(g, k);
+  auto current_slot = initial_slot(g, k, precomputed_hash);
 
   bool running                      = true;
   [[maybe_unused]] bool found_match = false;
@@ -1162,6 +1183,7 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_
   warpT const& warp,
   CG const& g,
   value_type const& pair,
+  optional_hash_type precomputed_hash,
   uint32_t* warp_counter,
   value_type* probe_output_buffer,
   value_type* contained_output_buffer,
@@ -1173,7 +1195,7 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_
   const uint32_t cg_lane_id = g.thread_rank();
 
   auto key          = pair.first;
-  auto current_slot = initial_slot(g, key);
+  auto current_slot = initial_slot(g, key, precomputed_hash);
 
   bool running                      = true;
   [[maybe_unused]] bool found_match = false;
@@ -1263,6 +1285,7 @@ __device__ void
 static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_retrieve_impl(
   CG const& g,
   value_type const& pair,
+  optional_hash_type precomputed_hash,
   uint32_t* cg_counter,
   value_type* probe_output_buffer,
   value_type* contained_output_buffer,
@@ -1274,7 +1297,7 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_
   const uint32_t lane_id = g.thread_rank();
 
   auto key          = pair.first;
-  auto current_slot = initial_slot(g, key);
+  auto current_slot = initial_slot(g, key, precomputed_hash);
 
   bool running                      = true;
   [[maybe_unused]] bool found_match = false;
@@ -1438,9 +1461,9 @@ template <typename Key,
           typename Allocator>
 template <typename CG, typename KeyEqual>
 __device__ bool static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::contains(
-  CG g, Key const& k, KeyEqual key_equal) noexcept
+  CG g, Key const& k, optional_hash_type precomputed_hash, KeyEqual key_equal) noexcept
 {
-  return contains_impl<uses_vector_load()>(g, k, key_equal);
+  return contains_impl<uses_vector_load()>(g, k, precomputed_hash, key_equal);
 }
 
 template <typename Key,
@@ -1450,10 +1473,14 @@ template <typename Key,
           typename Allocator>
 template <typename CG, typename KeyEqual>
 __device__ void static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::count(
-  CG const& g, Key const& k, std::size_t& thread_num_matches, KeyEqual key_equal) noexcept
+  CG const& g,
+  Key const& k,
+  optional_hash_type precomputed_hash,
+  std::size_t& thread_num_matches,
+  KeyEqual key_equal) noexcept
 {
   constexpr bool is_outer = false;
-  count_impl<uses_vector_load(), is_outer>(g, k, thread_num_matches, key_equal);
+  count_impl<uses_vector_load(), is_outer>(g, k, precomputed_hash, thread_num_matches, key_equal);
 }
 
 template <typename Key,
@@ -1464,10 +1491,14 @@ template <typename Key,
 template <typename CG, typename KeyEqual>
 __device__ void
 static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::count_outer(
-  CG const& g, Key const& k, std::size_t& thread_num_matches, KeyEqual key_equal) noexcept
+  CG const& g,
+  Key const& k,
+  optional_hash_type precomputed_hash,
+  std::size_t& thread_num_matches,
+  KeyEqual key_equal) noexcept
 {
   constexpr bool is_outer = true;
-  count_impl<uses_vector_load(), is_outer>(g, k, thread_num_matches, key_equal);
+  count_impl<uses_vector_load(), is_outer>(g, k, precomputed_hash, thread_num_matches, key_equal);
 }
 
 template <typename Key,
@@ -1480,11 +1511,13 @@ __device__ void
 static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_count(
   CG const& g,
   value_type const& pair,
+  optional_hash_type precomputed_hash,
   std::size_t& thread_num_matches,
   PairEqual pair_equal) noexcept
 {
   constexpr bool is_outer = false;
-  pair_count_impl<uses_vector_load(), is_outer>(g, pair, thread_num_matches, pair_equal);
+  pair_count_impl<uses_vector_load(), is_outer>(
+    g, pair, precomputed_hash, thread_num_matches, pair_equal);
 }
 
 template <typename Key,
@@ -1497,11 +1530,13 @@ __device__ void
 static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_count_outer(
   CG const& g,
   value_type const& pair,
+  optional_hash_type precomputed_hash,
   std::size_t& thread_num_matches,
   PairEqual pair_equal) noexcept
 {
   constexpr bool is_outer = true;
-  pair_count_impl<uses_vector_load(), is_outer>(g, pair, thread_num_matches, pair_equal);
+  pair_count_impl<uses_vector_load(), is_outer>(
+    g, pair, precomputed_hash, thread_num_matches, pair_equal);
 }
 
 template <typename Key,
@@ -1519,6 +1554,7 @@ __device__ void static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::de
   warpT const& warp,
   CG const& g,
   Key const& k,
+  optional_hash_type precomputed_hash,
   uint32_t* warp_counter,
   value_type* output_buffer,
   atomicT* num_matches,
@@ -1527,7 +1563,7 @@ __device__ void static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::de
 {
   constexpr bool is_outer = false;
   retrieve_impl<buffer_size, is_outer>(
-    warp, g, k, warp_counter, output_buffer, num_matches, output_begin);
+    warp, g, k, precomputed_hash, warp_counter, output_buffer, num_matches, output_begin);
 }
 
 template <typename Key,
@@ -1546,6 +1582,7 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::retri
   warpT const& warp,
   CG const& g,
   Key const& k,
+  optional_hash_type precomputed_hash,
   uint32_t* warp_counter,
   value_type* output_buffer,
   atomicT* num_matches,
@@ -1554,7 +1591,7 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::retri
 {
   constexpr bool is_outer = true;
   retrieve_impl<buffer_size, is_outer>(
-    warp, g, k, warp_counter, output_buffer, num_matches, output_begin);
+    warp, g, k, precomputed_hash, warp_counter, output_buffer, num_matches, output_begin);
 }
 
 template <typename Key,
@@ -1571,6 +1608,7 @@ template <uint32_t cg_size,
 __device__ void static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::retrieve(
   CG const& g,
   Key const& k,
+  optional_hash_type precomputed_hash,
   uint32_t* cg_counter,
   value_type* output_buffer,
   atomicT* num_matches,
@@ -1579,7 +1617,7 @@ __device__ void static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::de
 {
   constexpr bool is_outer = false;
   retrieve_impl<cg_size, buffer_size, is_outer>(
-    g, k, cg_counter, output_buffer, num_matches, output_begin);
+    g, k, precomputed_hash, cg_counter, output_buffer, num_matches, output_begin);
 }
 
 template <typename Key,
@@ -1597,6 +1635,7 @@ __device__ void
 static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::retrieve_outer(
   CG const& g,
   Key const& k,
+  optional_hash_type precomputed_hash,
   uint32_t* cg_counter,
   value_type* output_buffer,
   atomicT* num_matches,
@@ -1605,7 +1644,7 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::retri
 {
   constexpr bool is_outer = true;
   retrieve_impl<cg_size, buffer_size, is_outer>(
-    g, k, cg_counter, output_buffer, num_matches, output_begin);
+    g, k, precomputed_hash, cg_counter, output_buffer, num_matches, output_begin);
 }
 
 template <typename Key,
@@ -1625,6 +1664,7 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_
   warpT const& warp,
   CG const& g,
   value_type const& pair,
+  optional_hash_type precomputed_hash,
   uint32_t* warp_counter,
   value_type* probe_output_buffer,
   value_type* contained_output_buffer,
@@ -1637,6 +1677,7 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_
   pair_retrieve_impl<buffer_size, is_outer>(warp,
                                             g,
                                             pair,
+                                            precomputed_hash,
                                             warp_counter,
                                             probe_output_buffer,
                                             contained_output_buffer,
@@ -1663,6 +1704,7 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_
   warpT const& warp,
   CG const& g,
   value_type const& pair,
+  optional_hash_type precomputed_hash,
   uint32_t* warp_counter,
   value_type* probe_output_buffer,
   value_type* contained_output_buffer,
@@ -1675,6 +1717,7 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_
   pair_retrieve_impl<buffer_size, is_outer>(warp,
                                             g,
                                             pair,
+                                            precomputed_hash,
                                             warp_counter,
                                             probe_output_buffer,
                                             contained_output_buffer,
@@ -1700,6 +1743,7 @@ __device__ void
 static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_retrieve(
   CG const& g,
   value_type const& pair,
+  optional_hash_type precomputed_hash,
   uint32_t* cg_counter,
   value_type* probe_output_buffer,
   value_type* contained_output_buffer,
@@ -1711,6 +1755,7 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_
   constexpr bool is_outer = false;
   pair_retrieve_impl<cg_size, buffer_size, is_outer>(g,
                                                      pair,
+                                                     precomputed_hash,
                                                      cg_counter,
                                                      probe_output_buffer,
                                                      contained_output_buffer,
@@ -1736,6 +1781,7 @@ __device__ void
 static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_retrieve_outer(
   CG const& g,
   value_type const& pair,
+  optional_hash_type precomputed_hash,
   uint32_t* cg_counter,
   value_type* probe_output_buffer,
   value_type* contained_output_buffer,
@@ -1747,6 +1793,7 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_
   constexpr bool is_outer = true;
   pair_retrieve_impl<cg_size, buffer_size, is_outer>(g,
                                                      pair,
+                                                     precomputed_hash,
                                                      cg_counter,
                                                      probe_output_buffer,
                                                      contained_output_buffer,
diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap_kernels.cuh
index 8b1d6af79..d92ab91fc 100644
--- a/include/cuco/detail/static_multimap_kernels.cuh
+++ b/include/cuco/detail/static_multimap_kernels.cuh
@@ -87,7 +87,7 @@ __global__ void insert(InputIt first, InputIt last, viewT view, KeyEqual key_equ
   while (it < last) {
     // force conversion to value_type
     typename viewT::value_type const insert_pair{*it};
-    view.insert(tile, insert_pair, key_equal);
+    view.insert(tile, insert_pair, thrust::nullopt, key_equal);
     it += (gridDim.x * block_size) / tile_size;
   }
 }
@@ -129,7 +129,7 @@ __global__ void insert_if(
     typename viewT::value_type const insert_pair{*it};
     if (pred(insert_pair)) {
       // force conversion to value_type
-      view.insert(tile, insert_pair, key_equal);
+      view.insert(tile, insert_pair, thrust::nullopt, key_equal);
     }
     it += (gridDim.x * block_size) / tile_size;
   }
@@ -173,7 +173,7 @@ __global__ void contains(
 
   while (first + key_idx < last) {
     auto key   = *(first + key_idx);
-    auto found = view.contains(tile, key, key_equal);
+    auto found = view.contains(tile, key, thrust::nullopt, key_equal);
 
     /*
      * The ld.relaxed.gpu instruction used in view.find causes L1 to
@@ -236,9 +236,9 @@ __global__ void count(
   while (first + key_idx < last) {
     auto key = *(first + key_idx);
     if constexpr (is_outer) {
-      view.count_outer(tile, key, thread_num_matches, key_equal);
+      view.count_outer(tile, key, thrust::nullopt, thread_num_matches, key_equal);
     } else {
-      view.count(tile, key, thread_num_matches, key_equal);
+      view.count(tile, key, thrust::nullopt, thread_num_matches, key_equal);
     }
     key_idx += (gridDim.x * block_size) / tile_size;
   }
@@ -295,9 +295,9 @@ __global__ void pair_count(
   while (first + pair_idx < last) {
     typename viewT::value_type const pair = *(first + pair_idx);
     if constexpr (is_outer) {
-      view.pair_count_outer(tile, pair, thread_num_matches, pair_equal);
+      view.pair_count_outer(tile, pair, thrust::nullopt, thread_num_matches, pair_equal);
     } else {
-      view.pair_count(tile, pair, thread_num_matches, pair_equal);
+      view.pair_count(tile, pair, thrust::nullopt, thread_num_matches, pair_equal);
     }
     pair_idx += (gridDim.x * block_size) / tile_size;
   }
@@ -387,6 +387,7 @@ __global__ void vectorized_retrieve(InputIt first,
         view.retrieve_outer<buffer_size>(active_warp,
                                          tile,
                                          key,
+                                         thrust::nullopt,
                                          &warp_counter[warp_id],
                                          output_buffer[warp_id],
                                          num_matches,
@@ -396,6 +397,7 @@ __global__ void vectorized_retrieve(InputIt first,
         view.retrieve<buffer_size>(active_warp,
                                    tile,
                                    key,
+                                   thrust::nullopt,
                                    &warp_counter[warp_id],
                                    output_buffer[warp_id],
                                    num_matches,
@@ -481,11 +483,23 @@ __global__ void retrieve(InputIt first,
   while (first + key_idx < last) {
     auto key = *(first + key_idx);
     if constexpr (is_outer) {
-      view.retrieve_outer<tile_size, buffer_size>(
-        tile, key, &cg_counter[cg_id], output_buffer[cg_id], num_matches, output_begin, key_equal);
+      view.retrieve_outer<tile_size, buffer_size>(tile,
+                                                  key,
+                                                  thrust::nullopt,
+                                                  &cg_counter[cg_id],
+                                                  output_buffer[cg_id],
+                                                  num_matches,
+                                                  output_begin,
+                                                  key_equal);
     } else {
-      view.retrieve<tile_size, buffer_size>(
-        tile, key, &cg_counter[cg_id], output_buffer[cg_id], num_matches, output_begin, key_equal);
+      view.retrieve<tile_size, buffer_size>(tile,
+                                            key,
+                                            thrust::nullopt,
+                                            &cg_counter[cg_id],
+                                            output_buffer[cg_id],
+                                            num_matches,
+                                            output_begin,
+                                            key_equal);
     }
     key_idx += (gridDim.x * block_size) / tile_size;
   }
@@ -544,6 +558,7 @@ __global__ void vectorized_pair_retrieve(InputIt first,
         view.pair_retrieve_outer<buffer_size>(active_warp,
                                               tile,
                                               pair,
+                                              thrust::nullopt,
                                               &warp_counter[warp_id],
                                               probe_output_buffer[warp_id],
                                               contained_output_buffer[warp_id],
@@ -555,6 +570,7 @@ __global__ void vectorized_pair_retrieve(InputIt first,
         view.pair_retrieve<buffer_size>(active_warp,
                                         tile,
                                         pair,
+                                        thrust::nullopt,
                                         &warp_counter[warp_id],
                                         probe_output_buffer[warp_id],
                                         contained_output_buffer[warp_id],
@@ -619,6 +635,7 @@ __global__ void pair_retrieve(InputIt first,
     if constexpr (is_outer) {
       view.pair_retrieve_outer<tile_size, buffer_size>(tile,
                                                        pair,
+                                                       thrust::nullopt,
                                                        &cg_counter[cg_id],
                                                        probe_output_buffer[cg_id],
                                                        contained_output_buffer[cg_id],
@@ -629,6 +646,7 @@ __global__ void pair_retrieve(InputIt first,
     } else {
       view.pair_retrieve<tile_size, buffer_size>(tile,
                                                  pair,
+                                                 thrust::nullopt,
                                                  &cg_counter[cg_id],
                                                  probe_output_buffer[cg_id],
                                                  contained_output_buffer[cg_id],
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index 0de4ae0c3..cf0e9e973 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -19,6 +19,7 @@
 #include <cooperative_groups.h>
 #include <thrust/distance.h>
 #include <thrust/functional.h>
+#include <thrust/optional.h>
 #include <cub/cub.cuh>
 #include <memory>
 
@@ -151,6 +152,7 @@ class static_multimap {
     typename std::allocator_traits<Allocator>::rebind_alloc<pair_atomic_type>;
   using counter_allocator_type =
     typename std::allocator_traits<Allocator>::rebind_alloc<atomic_ctr_type>;
+  using optional_hash_type = thrust::optional<typename detail::MurmurHash3_32<Key>::result_type>;
 
   static_multimap(static_multimap const&) = delete;
   static_multimap(static_multimap&&)      = delete;
@@ -547,9 +549,11 @@ class static_multimap {
      * @return Pointer to the initial slot for `k`
      */
     template <typename CG>
-    __device__ iterator initial_slot(CG const& g, Key const& k) noexcept
+    __device__ iterator initial_slot(CG const& g,
+                                     Key const& k,
+                                     optional_hash_type precomputed_hash) noexcept
     {
-      return probe_sequence_.initial_slot(g, k);
+      return probe_sequence_.initial_slot(g, k, precomputed_hash);
     }
 
     /**
@@ -563,9 +567,11 @@ class static_multimap {
      * @return Pointer to the initial slot for `k`
      */
     template <typename CG>
-    __device__ const_iterator initial_slot(CG g, Key const& k) const noexcept
+    __device__ const_iterator initial_slot(CG g,
+                                           Key const& k,
+                                           optional_hash_type precomputed_hash) const noexcept
     {
-      return probe_sequence_.initial_slot(g, k);
+      return probe_sequence_.initial_slot(g, k, precomputed_hash);
     }
 
     /**
@@ -735,13 +741,18 @@ class static_multimap {
      *
      * @param g The Cooperative Group that performs the insert
      * @param insert_pair The pair to insert
+     * @param precomputed_hash Optional value which allows users to specify the precomputed hash
+     * value
      * @param key_equal The binary callable used to compare two keys for
      * equality
      * @return void.
      */
     template <bool uses_vector_load, typename CG, typename KeyEqual = thrust::equal_to<key_type>>
     __device__ std::enable_if_t<uses_vector_load, void> insert_impl(
-      CG g, value_type const& insert_pair, KeyEqual key_equal = KeyEqual{}) noexcept;
+      CG g,
+      value_type const& insert_pair,
+      optional_hash_type precomputed_hash,
+      KeyEqual key_equal = KeyEqual{}) noexcept;
 
     /**
      * @brief Inserts the specified key/value pair into the map using scalar loads.
@@ -752,13 +763,18 @@ class static_multimap {
      *
      * @param g The Cooperative Group that performs the insert
      * @param insert_pair The pair to insert
+     * @param precomputed_hash Optional value which allows users to specify the precomputed hash
+     * value
      * @param key_equal The binary callable used to compare two keys for
      * equality
      * @return void.
      */
     template <bool uses_vector_load, typename CG, typename KeyEqual = thrust::equal_to<key_type>>
     __device__ std::enable_if_t<not uses_vector_load, void> insert_impl(
-      CG g, value_type const& insert_pair, KeyEqual key_equal = KeyEqual{}) noexcept;
+      CG g,
+      value_type const& insert_pair,
+      optional_hash_type precomputed_hash,
+      KeyEqual key_equal = KeyEqual{}) noexcept;
 
    public:
     /**
@@ -769,6 +785,8 @@ class static_multimap {
      *
      * @param g The Cooperative Group that performs the insert
      * @param insert_pair The pair to insert
+     * @param precomputed_hash Optional value which allows users to specify the precomputed hash
+     * value
      * @param key_equal The binary callable used to compare two keys for
      * equality
      * @return void.
@@ -776,6 +794,7 @@ class static_multimap {
     template <typename CG, typename KeyEqual = thrust::equal_to<key_type>>
     __device__ void insert(CG g,
                            value_type const& insert_pair,
+                           optional_hash_type precomputed_hash,
                            KeyEqual key_equal = KeyEqual{}) noexcept;
   };  // class device mutable view
 
@@ -900,6 +919,8 @@ class static_multimap {
      * @tparam KeyEqual Binary callable type
      * @param g The Cooperative Group used to perform the contains operation
      * @param k The key to search for
+     * @param precomputed_hash Optional value which allows users to specify the precomputed hash
+     * value
      * @param key_equal The binary callable used to compare two keys
      * for equality
      * @return A boolean indicating whether the key/value pair
@@ -907,7 +928,10 @@ class static_multimap {
      */
     template <bool uses_vector_load, typename CG, typename KeyEqual = thrust::equal_to<key_type>>
     __device__ std::enable_if_t<uses_vector_load, bool> contains_impl(
-      CG g, Key const& k, KeyEqual key_equal = KeyEqual{}) noexcept;
+      CG g,
+      Key const& k,
+      optional_hash_type precomputed_hash,
+      KeyEqual key_equal = KeyEqual{}) noexcept;
 
     /**
      * @brief Indicates whether the key `k` was inserted into the map using scalar loads.
@@ -923,6 +947,8 @@ class static_multimap {
      * @tparam KeyEqual Binary callable type
      * @param g The Cooperative Group used to perform the contains operation
      * @param k The key to search for
+     * @param precomputed_hash Optional value which allows users to specify the precomputed hash
+     * value
      * @param key_equal The binary callable used to compare two keys
      * for equality
      * @return A boolean indicating whether the key/value pair
@@ -930,7 +956,10 @@ class static_multimap {
      */
     template <bool uses_vector_load, typename CG, typename KeyEqual = thrust::equal_to<key_type>>
     __device__ std::enable_if_t<not uses_vector_load, bool> contains_impl(
-      CG g, Key const& k, KeyEqual key_equal = KeyEqual{}) noexcept;
+      CG g,
+      Key const& k,
+      optional_hash_type precomputed_hash,
+      KeyEqual key_equal = KeyEqual{}) noexcept;
 
     /**
      * @brief Counts the occurrence of a given key contained in multimap using vector loads.
@@ -941,6 +970,8 @@ class static_multimap {
      * @tparam KeyEqual Binary callable type
      * @param g The Cooperative Group used to perform the count operation
      * @param k The key to search for
+     * @param precomputed_hash Optional value which allows users to specify the precomputed hash
+     * value
      * @param thread_num_matches Number of matches found by the current thread
      * @param key_equal The binary callable used to compare two keys
      * for equality
@@ -952,6 +983,7 @@ class static_multimap {
     __device__ std::enable_if_t<uses_vector_load, void> count_impl(
       CG const& g,
       Key const& k,
+      optional_hash_type precomputed_hash,
       std::size_t& thread_num_matches,
       KeyEqual key_equal = KeyEqual{}) noexcept;
 
@@ -964,6 +996,8 @@ class static_multimap {
      * @tparam KeyEqual Binary callable type
      * @param g The Cooperative Group used to perform the count operation
      * @param k The key to search for
+     * @param precomputed_hash Optional value which allows users to specify the precomputed hash
+     * value
      * @param thread_num_matches Number of matches found by the current thread
      * @param key_equal The binary callable used to compare two keys
      * for equality
@@ -975,6 +1009,7 @@ class static_multimap {
     __device__ std::enable_if_t<not uses_vector_load, void> count_impl(
       CG const& g,
       Key const& k,
+      optional_hash_type precomputed_hash,
       std::size_t& thread_num_matches,
       KeyEqual key_equal = KeyEqual{}) noexcept;
 
@@ -988,6 +1023,8 @@ class static_multimap {
      * @tparam PairEqual Binary callable type
      * @param g The Cooperative Group used to perform the pair_count operation
      * @param pair The pair to search for
+     * @param precomputed_hash Optional value which allows users to specify the precomputed hash
+     * value
      * @param thread_num_matches Number of matches found by the current thread
      * @param pair_equal The binary callable used to compare two pairs
      * for equality
@@ -996,6 +1033,7 @@ class static_multimap {
     __device__ std::enable_if_t<uses_vector_load, void> pair_count_impl(
       CG const& g,
       value_type const& pair,
+      optional_hash_type precomputed_hash,
       std::size_t& thread_num_matches,
       PairEqual pair_equal) noexcept;
 
@@ -1009,6 +1047,8 @@ class static_multimap {
      * @tparam PairEqual Binary callable type
      * @param g The Cooperative Group used to perform the pair_count operation
      * @param pair The pair to search for
+     * @param precomputed_hash Optional value which allows users to specify the precomputed hash
+     * value
      * @param thread_num_matches Number of matches found by the current thread
      * @param pair_equal The binary callable used to compare two pairs
      * for equality
@@ -1017,6 +1057,7 @@ class static_multimap {
     __device__ std::enable_if_t<not uses_vector_load, void> pair_count_impl(
       CG const& g,
       value_type const& pair,
+      optional_hash_type precomputed_hash,
       std::size_t& thread_num_matches,
       PairEqual pair_equal) noexcept;
 
@@ -1039,6 +1080,8 @@ class static_multimap {
      * @param warp The Cooperative Group (or warp) used to flush output buffer
      * @param g The Cooperative Group used to retrieve
      * @param k The key to search for
+     * @param precomputed_hash Optional value which allows users to specify the precomputed hash
+     * value
      * @param warp_counter Pointer to the warp counter
      * @param output_buffer Shared memory buffer of the key/value pair sequence
      * @param num_matches Size of the output sequence
@@ -1056,6 +1099,7 @@ class static_multimap {
     __device__ void retrieve_impl(warpT const& warp,
                                   CG const& g,
                                   Key const& k,
+                                  optional_hash_type precomputed_hash,
                                   uint32_t* warp_counter,
                                   value_type* output_buffer,
                                   atomicT* num_matches,
@@ -1080,6 +1124,8 @@ class static_multimap {
      * @tparam KeyEqual Binary callable type
      * @param g The Cooperative Group used to retrieve
      * @param k The key to search for
+     * @param precomputed_hash Optional value which allows users to specify the precomputed hash
+     * value
      * @param cg_counter Pointer to the CG counter
      * @param output_buffer Shared memory buffer of the key/value pair sequence
      * @param num_matches Size of the output sequence
@@ -1096,6 +1142,7 @@ class static_multimap {
               typename KeyEqual = thrust::equal_to<key_type>>
     __device__ void retrieve_impl(CG const& g,
                                   Key const& k,
+                                  optional_hash_type precomputed_hash,
                                   uint32_t* cg_counter,
                                   value_type* output_buffer,
                                   atomicT* num_matches,
@@ -1122,6 +1169,8 @@ class static_multimap {
      * @param warp The Cooperative Group (or warp) used to flush output buffer
      * @param g The Cooperative Group used to retrieve
      * @param pair The pair to search for
+     * @param precomputed_hash Optional value which allows users to specify the precomputed hash
+     * value
      * @param warp_counter Pointer to the warp counter
      * @param probe_output_buffer Buffer of the matched probe pair sequence
      * @param contained_output_buffer Buffer of the matched contained pair sequence
@@ -1141,6 +1190,7 @@ class static_multimap {
     __device__ void pair_retrieve_impl(warpT const& warp,
                                        CG const& g,
                                        value_type const& pair,
+                                       optional_hash_type precomputed_hash,
                                        uint32_t* warp_counter,
                                        value_type* probe_output_buffer,
                                        value_type* contained_output_buffer,
@@ -1168,6 +1218,8 @@ class static_multimap {
      * @tparam PairEqual Binary callable type
      * @param g The Cooperative Group used to retrieve
      * @param pair The pair to search for
+     * @param precomputed_hash Optional value which allows users to specify the precomputed hash
+     * value
      * @param cg_counter Pointer to the CG counter
      * @param probe_output_buffer Buffer of the matched probe pair sequence
      * @param contained_output_buffer Buffer of the matched contained pair sequence
@@ -1186,6 +1238,7 @@ class static_multimap {
               typename PairEqual>
     __device__ void pair_retrieve_impl(CG const& g,
                                        value_type const& pair,
+                                       optional_hash_type precomputed_hash,
                                        uint32_t* cg_counter,
                                        value_type* probe_output_buffer,
                                        value_type* contained_output_buffer,
@@ -1278,13 +1331,18 @@ class static_multimap {
      * @tparam KeyEqual Binary callable type
      * @param g The Cooperative Group used to perform the contains operation
      * @param k The key to search for
+     * @param precomputed_hash Optional value which allows users to specify the precomputed hash
+     * value
      * @param key_equal The binary callable used to compare two keys
      * for equality
      * @return A boolean indicating whether the key/value pair
      * containing `k` was inserted
      */
     template <typename CG, typename KeyEqual = thrust::equal_to<key_type>>
-    __device__ bool contains(CG g, Key const& k, KeyEqual key_equal = KeyEqual{}) noexcept;
+    __device__ bool contains(CG g,
+                             Key const& k,
+                             optional_hash_type precomputed_hash,
+                             KeyEqual key_equal = KeyEqual{}) noexcept;
 
     /**
      * @brief Counts the occurrence of a given key contained in multimap.
@@ -1293,6 +1351,8 @@ class static_multimap {
      * @tparam KeyEqual Binary callable type
      * @param g The Cooperative Group used to perform the count operation
      * @param k The key to search for
+     * @param precomputed_hash Optional value which allows users to specify the precomputed hash
+     * value
      * @param thread_num_matches Number of matches found by the current thread
      * @param key_equal The binary callable used to compare two keys
      * for equality
@@ -1300,6 +1360,7 @@ class static_multimap {
     template <typename CG, typename KeyEqual = thrust::equal_to<key_type>>
     __device__ void count(CG const& g,
                           Key const& k,
+                          optional_hash_type precomputed_hash,
                           std::size_t& thread_num_matches,
                           KeyEqual key_equal = KeyEqual{}) noexcept;
 
@@ -1311,6 +1372,8 @@ class static_multimap {
      * @tparam KeyEqual Binary callable type
      * @param g The Cooperative Group used to perform the count operation
      * @param k The key to search for
+     * @param precomputed_hash Optional value which allows users to specify the precomputed hash
+     * value
      * @param thread_num_matches Number of matches found by the current thread
      * @param key_equal The binary callable used to compare two keys
      * for equality
@@ -1318,6 +1381,7 @@ class static_multimap {
     template <typename CG, typename KeyEqual = thrust::equal_to<key_type>>
     __device__ void count_outer(CG const& g,
                                 Key const& k,
+                                optional_hash_type precomputed_hash,
                                 std::size_t& thread_num_matches,
                                 KeyEqual key_equal = KeyEqual{}) noexcept;
 
@@ -1328,6 +1392,8 @@ class static_multimap {
      * @tparam PairEqual Binary callable type
      * @param g The Cooperative Group used to perform the pair_count operation
      * @param pair The pair to search for
+     * @param precomputed_hash Optional value which allows users to specify the precomputed hash
+     * value
      * @param thread_num_matches Number of matches found by the current thread
      * @param pair_equal The binary callable used to compare two pairs
      * for equality
@@ -1335,6 +1401,7 @@ class static_multimap {
     template <typename CG, typename PairEqual>
     __device__ void pair_count(CG const& g,
                                value_type const& pair,
+                               optional_hash_type precomputed_hash,
                                std::size_t& thread_num_matches,
                                PairEqual pair_equal) noexcept;
 
@@ -1346,6 +1413,8 @@ class static_multimap {
      * @tparam PairEqual Binary callable type
      * @param g The Cooperative Group used to perform the pair_count operation
      * @param pair The pair to search for
+     * @param precomputed_hash Optional value which allows users to specify the precomputed hash
+     * value
      * @param thread_num_matches Number of matches found by the current thread
      * @param pair_equal The binary callable used to compare two pairs
      * for equality
@@ -1353,6 +1422,7 @@ class static_multimap {
     template <typename CG, typename PairEqual>
     __device__ void pair_count_outer(CG const& g,
                                      value_type const& pair,
+                                     optional_hash_type precomputed_hash,
                                      std::size_t& thread_num_matches,
                                      PairEqual pair_equal) noexcept;
 
@@ -1374,6 +1444,8 @@ class static_multimap {
      * @param warp The Cooperative Group (or warp) used to flush output buffer
      * @param g The Cooperative Group used to retrieve
      * @param k The key to search for
+     * @param precomputed_hash Optional value which allows users to specify the precomputed hash
+     * value
      * @param warp_counter Pointer to the warp counter
      * @param output_buffer Shared memory buffer of the key/value pair sequence
      * @param num_matches Size of the output sequence
@@ -1390,6 +1462,7 @@ class static_multimap {
     __device__ void retrieve(warpT const& warp,
                              CG const& g,
                              Key const& k,
+                             optional_hash_type precomputed_hash,
                              uint32_t* warp_counter,
                              value_type* output_buffer,
                              atomicT* num_matches,
@@ -1414,6 +1487,8 @@ class static_multimap {
      * @param warp The Cooperative Group (or warp) used to flush output buffer
      * @param g The Cooperative Group used to retrieve
      * @param k The key to search for
+     * @param precomputed_hash Optional value which allows users to specify the precomputed hash
+     * value
      * @param warp_counter Pointer to the warp counter
      * @param output_buffer Shared memory buffer of the key/value pair sequence
      * @param num_matches Size of the output sequence
@@ -1430,6 +1505,7 @@ class static_multimap {
     __device__ void retrieve_outer(warpT const& warp,
                                    CG const& g,
                                    Key const& k,
+                                   optional_hash_type precomputed_hash,
                                    uint32_t* warp_counter,
                                    value_type* output_buffer,
                                    atomicT* num_matches,
@@ -1452,6 +1528,8 @@ class static_multimap {
      * @tparam KeyEqual Binary callable type
      * @param g The Cooperative Group used to retrieve
      * @param k The key to search for
+     * @param precomputed_hash Optional value which allows users to specify the precomputed hash
+     * value
      * @param cg_counter Pointer to the CG counter
      * @param output_buffer Shared memory buffer of the key/value pair sequence
      * @param num_matches Size of the output sequence
@@ -1467,6 +1545,7 @@ class static_multimap {
               typename KeyEqual = thrust::equal_to<key_type>>
     __device__ void retrieve(CG const& g,
                              Key const& k,
+                             optional_hash_type precomputed_hash,
                              uint32_t* cg_counter,
                              value_type* output_buffer,
                              atomicT* num_matches,
@@ -1490,6 +1569,8 @@ class static_multimap {
      * @tparam KeyEqual Binary callable type
      * @param g The Cooperative Group used to retrieve
      * @param k The key to search for
+     * @param precomputed_hash Optional value which allows users to specify the precomputed hash
+     * value
      * @param cg_counter Pointer to the CG counter
      * @param output_buffer Shared memory buffer of the key/value pair sequence
      * @param num_matches Size of the output sequence
@@ -1505,6 +1586,7 @@ class static_multimap {
               typename KeyEqual = thrust::equal_to<key_type>>
     __device__ void retrieve_outer(CG const& g,
                                    Key const& k,
+                                   optional_hash_type precomputed_hash,
                                    uint32_t* cg_counter,
                                    value_type* output_buffer,
                                    atomicT* num_matches,
@@ -1529,6 +1611,8 @@ class static_multimap {
      * @param warp The Cooperative Group (or warp) used to flush output buffer
      * @param g The Cooperative Group used to retrieve
      * @param pair The pair to search for
+     * @param precomputed_hash Optional value which allows users to specify the precomputed hash
+     * value
      * @param warp_counter Pointer to the warp counter
      * @param probe_output_buffer Buffer of the matched probe pair sequence
      * @param contained_output_buffer Buffer of the matched contained pair sequence
@@ -1547,6 +1631,7 @@ class static_multimap {
     __device__ void pair_retrieve(warpT const& warp,
                                   CG const& g,
                                   value_type const& pair,
+                                  optional_hash_type precomputed_hash,
                                   uint32_t* warp_counter,
                                   value_type* probe_output_buffer,
                                   value_type* contained_output_buffer,
@@ -1574,6 +1659,8 @@ class static_multimap {
      * @param warp The Cooperative Group (or warp) used to flush output buffer
      * @param g The Cooperative Group used to retrieve
      * @param pair The pair to search for
+     * @param precomputed_hash Optional value which allows users to specify the precomputed hash
+     * value
      * @param warp_counter Pointer to the warp counter
      * @param probe_output_buffer Buffer of the matched probe pair sequence
      * @param contained_output_buffer Buffer of the matched contained pair sequence
@@ -1592,6 +1679,7 @@ class static_multimap {
     __device__ void pair_retrieve_outer(warpT const& warp,
                                         CG const& g,
                                         value_type const& pair,
+                                        optional_hash_type precomputed_hash,
                                         uint32_t* warp_counter,
                                         value_type* probe_output_buffer,
                                         value_type* contained_output_buffer,
@@ -1617,6 +1705,8 @@ class static_multimap {
      * @tparam PairEqual Binary callable type
      * @param g The Cooperative Group used to retrieve
      * @param pair The pair to search for
+     * @param precomputed_hash Optional value which allows users to specify the precomputed hash
+     * value
      * @param cg_counter Pointer to the CG counter
      * @param probe_output_buffer Buffer of the matched probe pair sequence
      * @param contained_output_buffer Buffer of the matched contained pair sequence
@@ -1634,6 +1724,7 @@ class static_multimap {
               typename PairEqual>
     __device__ void pair_retrieve(CG const& g,
                                   value_type const& pair,
+                                  optional_hash_type precomputed_hash,
                                   uint32_t* cg_counter,
                                   value_type* probe_output_buffer,
                                   value_type* contained_output_buffer,
@@ -1660,6 +1751,8 @@ class static_multimap {
      * @tparam PairEqual Binary callable type
      * @param g The Cooperative Group used to retrieve
      * @param pair The pair to search for
+     * @param precomputed_hash Optional value which allows users to specify the precomputed hash
+     * value
      * @param cg_counter Pointer to the CG counter
      * @param probe_output_buffer Buffer of the matched probe pair sequence
      * @param contained_output_buffer Buffer of the matched contained pair sequence
@@ -1677,6 +1770,7 @@ class static_multimap {
               typename PairEqual>
     __device__ void pair_retrieve_outer(CG const& g,
                                         value_type const& pair,
+                                        optional_hash_type precomputed_hash,
                                         uint32_t* cg_counter,
                                         value_type* probe_output_buffer,
                                         value_type* contained_output_buffer,

From 12c3701fe198b663eacb5aa6a382ad4507306035 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Fri, 13 Aug 2021 12:11:49 -0400
Subject: [PATCH 190/267] Cleanups: add d_counter as a memeber variable

---
 include/cuco/detail/static_multimap.inl | 189 ++++++------------------
 include/cuco/static_multimap.cuh        |   3 +-
 2 files changed, 49 insertions(+), 143 deletions(-)

diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index 4e58dc673..b25d72ac0 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -34,6 +34,8 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::static_multimap(
     slot_allocator_{alloc},
     counter_allocator_{alloc}
 {
+  d_counter = std::allocator_traits<counter_allocator_type>::allocate(counter_allocator_, 1);
+
   if constexpr (uses_vector_load()) {
     capacity_ = cuco::detail::get_valid_capacity<cg_size() * vector_width()>(capacity);
   } else {
@@ -56,6 +58,7 @@ template <typename Key,
           typename Allocator>
 static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::~static_multimap()
 {
+  std::allocator_traits<counter_allocator_type>::deallocate(counter_allocator_, d_counter, 1);
   std::allocator_traits<slot_allocator_type>::deallocate(slot_allocator_, slots_, capacity_);
 }
 
@@ -138,28 +141,16 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::count(
 
   constexpr bool is_outer = false;
 
-  atomic_ctr_type *h_num_matches, *d_num_matches;
-
-  CUCO_CUDA_TRY(cudaMallocHost((void**)&h_num_matches, sizeof(atomic_ctr_type)));
-  auto tmp_counter_allocator = counter_allocator_;
-  d_num_matches = std::allocator_traits<counter_allocator_type>::allocate(tmp_counter_allocator, 1);
-
-  h_num_matches->store(static_cast<std::size_t>(0), cuda::std::memory_order_relaxed);
-  CUCO_CUDA_TRY(cudaMemcpyAsync(
-    d_num_matches, h_num_matches, sizeof(atomic_ctr_type), cudaMemcpyHostToDevice, stream));
+  cudaMemsetAsync(d_counter, 0, sizeof(atomic_ctr_type), stream);
+  std::size_t h_counter;
 
   detail::count<block_size, cg_size(), Key, Value, is_outer>
-    <<<grid_size, block_size, 0, stream>>>(first, last, d_num_matches, view, key_equal);
+    <<<grid_size, block_size, 0, stream>>>(first, last, d_counter, view, key_equal);
   CUCO_CUDA_TRY(cudaMemcpyAsync(
-    h_num_matches, d_num_matches, sizeof(atomic_ctr_type), cudaMemcpyDeviceToHost, stream));
+    &h_counter, d_counter, sizeof(atomic_ctr_type), cudaMemcpyDeviceToHost, stream));
   CUCO_CUDA_TRY(cudaStreamSynchronize(stream));
 
-  auto result = h_num_matches->load(cuda::std::memory_order_relaxed);
-  CUCO_CUDA_TRY(cudaFreeHost(h_num_matches));
-  std::allocator_traits<counter_allocator_type>::deallocate(
-    tmp_counter_allocator, d_num_matches, 1);
-
-  return result;
+  return h_counter;
 }
 
 template <typename Key,
@@ -179,28 +170,16 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::count_
 
   constexpr bool is_outer = true;
 
-  atomic_ctr_type *h_num_matches, *d_num_matches;
-
-  CUCO_CUDA_TRY(cudaMallocHost((void**)&h_num_matches, sizeof(atomic_ctr_type)));
-  auto tmp_counter_allocator = counter_allocator_;
-  d_num_matches = std::allocator_traits<counter_allocator_type>::allocate(tmp_counter_allocator, 1);
-
-  h_num_matches->store(static_cast<std::size_t>(0), cuda::std::memory_order_relaxed);
-  CUCO_CUDA_TRY(cudaMemcpyAsync(
-    d_num_matches, h_num_matches, sizeof(atomic_ctr_type), cudaMemcpyHostToDevice, stream));
+  cudaMemsetAsync(d_counter, 0, sizeof(atomic_ctr_type), stream);
+  std::size_t h_counter;
 
   detail::count<block_size, cg_size(), Key, Value, is_outer>
-    <<<grid_size, block_size, 0, stream>>>(first, last, d_num_matches, view, key_equal);
+    <<<grid_size, block_size, 0, stream>>>(first, last, d_counter, view, key_equal);
   CUCO_CUDA_TRY(cudaMemcpyAsync(
-    h_num_matches, d_num_matches, sizeof(atomic_ctr_type), cudaMemcpyDeviceToHost, stream));
+    &h_counter, d_counter, sizeof(atomic_ctr_type), cudaMemcpyDeviceToHost, stream));
   CUCO_CUDA_TRY(cudaStreamSynchronize(stream));
 
-  auto result = h_num_matches->load(cuda::std::memory_order_relaxed);
-  CUCO_CUDA_TRY(cudaFreeHost(h_num_matches));
-  std::allocator_traits<counter_allocator_type>::deallocate(
-    tmp_counter_allocator, d_num_matches, 1);
-
-  return result;
+  return h_counter;
 }
 
 template <typename Key,
@@ -220,28 +199,16 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::pair_c
 
   constexpr bool is_outer = false;
 
-  atomic_ctr_type *h_num_matches, *d_num_matches;
-
-  CUCO_CUDA_TRY(cudaMallocHost((void**)&h_num_matches, sizeof(atomic_ctr_type)));
-  auto tmp_counter_allocator = counter_allocator_;
-  d_num_matches = std::allocator_traits<counter_allocator_type>::allocate(tmp_counter_allocator, 1);
-
-  h_num_matches->store(static_cast<std::size_t>(0), cuda::std::memory_order_relaxed);
-  CUCO_CUDA_TRY(cudaMemcpyAsync(
-    d_num_matches, h_num_matches, sizeof(atomic_ctr_type), cudaMemcpyHostToDevice, stream));
+  cudaMemsetAsync(d_counter, 0, sizeof(atomic_ctr_type), stream);
+  std::size_t h_counter;
 
   detail::pair_count<block_size, cg_size(), Key, Value, is_outer>
-    <<<grid_size, block_size, 0, stream>>>(first, last, d_num_matches, view, pair_equal);
+    <<<grid_size, block_size, 0, stream>>>(first, last, d_counter, view, pair_equal);
   CUCO_CUDA_TRY(cudaMemcpyAsync(
-    h_num_matches, d_num_matches, sizeof(atomic_ctr_type), cudaMemcpyDeviceToHost, stream));
+    &h_counter, d_counter, sizeof(atomic_ctr_type), cudaMemcpyDeviceToHost, stream));
   CUCO_CUDA_TRY(cudaStreamSynchronize(stream));
 
-  auto result = h_num_matches->load(cuda::std::memory_order_relaxed);
-  CUCO_CUDA_TRY(cudaFreeHost(h_num_matches));
-  std::allocator_traits<counter_allocator_type>::deallocate(
-    tmp_counter_allocator, d_num_matches, 1);
-
-  return result;
+  return h_counter;
 }
 
 template <typename Key,
@@ -261,28 +228,16 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::pair_c
 
   constexpr bool is_outer = true;
 
-  atomic_ctr_type *h_num_matches, *d_num_matches;
-
-  CUCO_CUDA_TRY(cudaMallocHost((void**)&h_num_matches, sizeof(atomic_ctr_type)));
-  auto tmp_counter_allocator = counter_allocator_;
-  d_num_matches = std::allocator_traits<counter_allocator_type>::allocate(tmp_counter_allocator, 1);
-
-  h_num_matches->store(static_cast<std::size_t>(0), cuda::std::memory_order_relaxed);
-  CUCO_CUDA_TRY(cudaMemcpyAsync(
-    d_num_matches, h_num_matches, sizeof(atomic_ctr_type), cudaMemcpyHostToDevice, stream));
+  cudaMemsetAsync(d_counter, 0, sizeof(atomic_ctr_type), stream);
+  std::size_t h_counter;
 
   detail::pair_count<block_size, cg_size(), Key, Value, is_outer>
-    <<<grid_size, block_size, 0, stream>>>(first, last, d_num_matches, view, pair_equal);
+    <<<grid_size, block_size, 0, stream>>>(first, last, d_counter, view, pair_equal);
   CUCO_CUDA_TRY(cudaMemcpyAsync(
-    h_num_matches, d_num_matches, sizeof(atomic_ctr_type), cudaMemcpyDeviceToHost, stream));
+    &h_counter, d_counter, sizeof(atomic_ctr_type), cudaMemcpyDeviceToHost, stream));
   CUCO_CUDA_TRY(cudaStreamSynchronize(stream));
 
-  auto result = h_num_matches->load(cuda::std::memory_order_relaxed);
-  CUCO_CUDA_TRY(cudaFreeHost(h_num_matches));
-  std::allocator_traits<counter_allocator_type>::deallocate(
-    tmp_counter_allocator, d_num_matches, 1);
-
-  return result;
+  return h_counter;
 }
 
 template <typename Key,
@@ -304,35 +259,22 @@ OutputIt static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::retrieve(
 
   constexpr bool is_outer = false;
 
-  atomic_ctr_type *h_num_matches, *d_num_matches;
-
-  CUCO_CUDA_TRY(cudaMallocHost((void**)&h_num_matches, sizeof(atomic_ctr_type)));
-  auto tmp_counter_allocator = counter_allocator_;
-  d_num_matches = std::allocator_traits<counter_allocator_type>::allocate(tmp_counter_allocator, 1);
-
-  h_num_matches->store(static_cast<std::size_t>(0), cuda::std::memory_order_relaxed);
-  CUCO_CUDA_TRY(cudaMemcpyAsync(
-    d_num_matches, h_num_matches, sizeof(atomic_ctr_type), cudaMemcpyHostToDevice, stream));
+  cudaMemsetAsync(d_counter, 0, sizeof(atomic_ctr_type), stream);
+  std::size_t h_counter;
 
   if constexpr (uses_vector_load()) {
     detail::
       vectorized_retrieve<block_size, warp_size(), cg_size(), buffer_size, Key, Value, is_outer>
-      <<<grid_size, block_size, 0, stream>>>(
-        first, last, output_begin, d_num_matches, view, key_equal);
+      <<<grid_size, block_size, 0, stream>>>(first, last, output_begin, d_counter, view, key_equal);
   } else {
     detail::retrieve<block_size, warp_size(), cg_size(), buffer_size, Key, Value, is_outer>
-      <<<grid_size, block_size, 0, stream>>>(
-        first, last, output_begin, d_num_matches, view, key_equal);
+      <<<grid_size, block_size, 0, stream>>>(first, last, output_begin, d_counter, view, key_equal);
   }
   CUCO_CUDA_TRY(cudaMemcpyAsync(
-    h_num_matches, d_num_matches, sizeof(atomic_ctr_type), cudaMemcpyDeviceToHost, stream));
+    &h_counter, d_counter, sizeof(atomic_ctr_type), cudaMemcpyDeviceToHost, stream));
   CUCO_CUDA_TRY(cudaStreamSynchronize(stream));
 
-  auto output_end = output_begin + h_num_matches->load(cuda::std::memory_order_relaxed);
-  CUCO_CUDA_TRY(cudaFreeHost(h_num_matches));
-  std::allocator_traits<counter_allocator_type>::deallocate(
-    tmp_counter_allocator, d_num_matches, 1);
-
+  auto output_end = output_begin + h_counter;
   return output_end;
 }
 
@@ -355,35 +297,22 @@ OutputIt static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::retrieve_
 
   constexpr bool is_outer = true;
 
-  atomic_ctr_type *h_num_matches, *d_num_matches;
-
-  CUCO_CUDA_TRY(cudaMallocHost((void**)&h_num_matches, sizeof(atomic_ctr_type)));
-  auto tmp_counter_allocator = counter_allocator_;
-  d_num_matches = std::allocator_traits<counter_allocator_type>::allocate(tmp_counter_allocator, 1);
-
-  h_num_matches->store(static_cast<std::size_t>(0), cuda::std::memory_order_relaxed);
-  CUCO_CUDA_TRY(cudaMemcpyAsync(
-    d_num_matches, h_num_matches, sizeof(atomic_ctr_type), cudaMemcpyHostToDevice, stream));
+  cudaMemsetAsync(d_counter, 0, sizeof(atomic_ctr_type), stream);
+  std::size_t h_counter;
 
   if constexpr (uses_vector_load()) {
     detail::
       vectorized_retrieve<block_size, warp_size(), cg_size(), buffer_size, Key, Value, is_outer>
-      <<<grid_size, block_size, 0, stream>>>(
-        first, last, output_begin, d_num_matches, view, key_equal);
+      <<<grid_size, block_size, 0, stream>>>(first, last, output_begin, d_counter, view, key_equal);
   } else {
     detail::retrieve<block_size, warp_size(), cg_size(), buffer_size, Key, Value, is_outer>
-      <<<grid_size, block_size, 0, stream>>>(
-        first, last, output_begin, d_num_matches, view, key_equal);
+      <<<grid_size, block_size, 0, stream>>>(first, last, output_begin, d_counter, view, key_equal);
   }
   CUCO_CUDA_TRY(cudaMemcpyAsync(
-    h_num_matches, d_num_matches, sizeof(atomic_ctr_type), cudaMemcpyDeviceToHost, stream));
+    &h_counter, d_counter, sizeof(atomic_ctr_type), cudaMemcpyDeviceToHost, stream));
   CUCO_CUDA_TRY(cudaStreamSynchronize(stream));
 
-  auto output_end = output_begin + h_num_matches->load(cuda::std::memory_order_relaxed);
-  CUCO_CUDA_TRY(cudaFreeHost(h_num_matches));
-  std::allocator_traits<counter_allocator_type>::deallocate(
-    tmp_counter_allocator, d_num_matches, 1);
-
+  auto output_end = output_begin + h_counter;
   return output_end;
 }
 
@@ -411,15 +340,8 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::pair_r
 
   constexpr bool is_outer = false;
 
-  atomic_ctr_type *h_num_matches, *d_num_matches;
-
-  CUCO_CUDA_TRY(cudaMallocHost((void**)&h_num_matches, sizeof(atomic_ctr_type)));
-  auto tmp_counter_allocator = counter_allocator_;
-  d_num_matches = std::allocator_traits<counter_allocator_type>::allocate(tmp_counter_allocator, 1);
-
-  h_num_matches->store(static_cast<std::size_t>(0), cuda::std::memory_order_relaxed);
-  CUCO_CUDA_TRY(cudaMemcpyAsync(
-    d_num_matches, h_num_matches, sizeof(atomic_ctr_type), cudaMemcpyHostToDevice, stream));
+  cudaMemsetAsync(d_counter, 0, sizeof(atomic_ctr_type), stream);
+  std::size_t h_counter;
 
   if constexpr (uses_vector_load()) {
     detail::vectorized_pair_retrieve<block_size,
@@ -429,22 +351,17 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::pair_r
                                      Key,
                                      Value,
                                      is_outer><<<grid_size, block_size, 0, stream>>>(
-      first, last, probe_output_begin, contained_output_begin, d_num_matches, view, pair_equal);
+      first, last, probe_output_begin, contained_output_begin, d_counter, view, pair_equal);
   } else {
     detail::pair_retrieve<block_size, warp_size(), cg_size(), buffer_size, Key, Value, is_outer>
       <<<grid_size, block_size, 0, stream>>>(
-        first, last, probe_output_begin, contained_output_begin, d_num_matches, view, pair_equal);
+        first, last, probe_output_begin, contained_output_begin, d_counter, view, pair_equal);
   }
   CUCO_CUDA_TRY(cudaMemcpyAsync(
-    h_num_matches, d_num_matches, sizeof(atomic_ctr_type), cudaMemcpyDeviceToHost, stream));
+    &h_counter, d_counter, sizeof(atomic_ctr_type), cudaMemcpyDeviceToHost, stream));
   CUCO_CUDA_TRY(cudaStreamSynchronize(stream));
 
-  auto res = h_num_matches->load(cuda::std::memory_order_relaxed);
-  CUCO_CUDA_TRY(cudaFreeHost(h_num_matches));
-  std::allocator_traits<counter_allocator_type>::deallocate(
-    tmp_counter_allocator, d_num_matches, 1);
-
-  return res;
+  return h_counter;
 }
 
 template <typename Key,
@@ -471,15 +388,8 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::pair_r
 
   constexpr bool is_outer = true;
 
-  atomic_ctr_type *h_num_matches, *d_num_matches;
-
-  CUCO_CUDA_TRY(cudaMallocHost((void**)&h_num_matches, sizeof(atomic_ctr_type)));
-  auto tmp_counter_allocator = counter_allocator_;
-  d_num_matches = std::allocator_traits<counter_allocator_type>::allocate(tmp_counter_allocator, 1);
-
-  h_num_matches->store(static_cast<std::size_t>(0), cuda::std::memory_order_relaxed);
-  CUCO_CUDA_TRY(cudaMemcpyAsync(
-    d_num_matches, h_num_matches, sizeof(atomic_ctr_type), cudaMemcpyHostToDevice, stream));
+  cudaMemsetAsync(d_counter, 0, sizeof(atomic_ctr_type), stream);
+  std::size_t h_counter;
 
   if constexpr (uses_vector_load()) {
     detail::vectorized_pair_retrieve<block_size,
@@ -489,22 +399,17 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::pair_r
                                      Key,
                                      Value,
                                      is_outer><<<grid_size, block_size, 0, stream>>>(
-      first, last, probe_output_begin, contained_output_begin, d_num_matches, view, pair_equal);
+      first, last, probe_output_begin, contained_output_begin, d_counter, view, pair_equal);
   } else {
     detail::pair_retrieve<block_size, warp_size(), cg_size(), buffer_size, Key, Value, is_outer>
       <<<grid_size, block_size, 0, stream>>>(
-        first, last, probe_output_begin, contained_output_begin, d_num_matches, view, pair_equal);
+        first, last, probe_output_begin, contained_output_begin, d_counter, view, pair_equal);
   }
   CUCO_CUDA_TRY(cudaMemcpyAsync(
-    h_num_matches, d_num_matches, sizeof(atomic_ctr_type), cudaMemcpyDeviceToHost, stream));
+    &h_counter, d_counter, sizeof(atomic_ctr_type), cudaMemcpyDeviceToHost, stream));
   CUCO_CUDA_TRY(cudaStreamSynchronize(stream));
 
-  auto res = h_num_matches->load(cuda::std::memory_order_relaxed);
-  CUCO_CUDA_TRY(cudaFreeHost(h_num_matches));
-  std::allocator_traits<counter_allocator_type>::deallocate(
-    tmp_counter_allocator, d_num_matches, 1);
-
-  return res;
+  return h_counter;
 }
 
 template <typename Key,
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index cf0e9e973..db606bfd2 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -1845,7 +1845,8 @@ class static_multimap {
   Value empty_value_sentinel_{};                ///< Initial value of empty slot
   slot_allocator_type slot_allocator_{};        ///< Allocator used to allocate slots
   counter_allocator_type counter_allocator_{};  ///< Allocator used to allocate counters
-};                                              // class static_multimap
+  atomic_ctr_type* d_counter;
+};  // class static_multimap
 }  // namespace cuco
 
 #include <cuco/detail/static_multimap.inl>

From be58a9ac7f43be023fb0b0e13c87ba6d2fc06eeb Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Tue, 17 Aug 2021 12:33:32 -0400
Subject: [PATCH 191/267] Cleanups: remove unused template parameters

---
 .../static_multimap/find_all_bench.cu         |  3 +-
 include/cuco/detail/static_multimap.inl       | 44 +++++++------------
 .../cuco/detail/static_multimap_kernels.cuh   | 42 +++++++-----------
 3 files changed, 32 insertions(+), 57 deletions(-)

diff --git a/benchmarks/hash_table/static_multimap/find_all_bench.cu b/benchmarks/hash_table/static_multimap/find_all_bench.cu
index e030f4cc6..8a6332ae9 100644
--- a/benchmarks/hash_table/static_multimap/find_all_bench.cu
+++ b/benchmarks/hash_table/static_multimap/find_all_bench.cu
@@ -105,8 +105,7 @@ std::enable_if_t<(sizeof(Key) == sizeof(Value)), void> nvbench_find_all(
       CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_items, sizeof(atomic_ctr_type), device_id));
 
       // Use timers to explicitly mark the target region
-      cuco::detail::
-        vectorized_retrieve<block_size, warp_size, CGSize, buffer_size, Key, Value, is_outer>
+      cuco::detail::vectorized_retrieve<block_size, warp_size, CGSize, buffer_size, is_outer>
         <<<grid_size, block_size, 0, launch.get_stream()>>>(d_unique_keys.begin(),
                                                             d_unique_keys.end(),
                                                             d_results.data().get(),
diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index b25d72ac0..de49fca49 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -144,7 +144,7 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::count(
   cudaMemsetAsync(d_counter, 0, sizeof(atomic_ctr_type), stream);
   std::size_t h_counter;
 
-  detail::count<block_size, cg_size(), Key, Value, is_outer>
+  detail::count<block_size, cg_size(), is_outer>
     <<<grid_size, block_size, 0, stream>>>(first, last, d_counter, view, key_equal);
   CUCO_CUDA_TRY(cudaMemcpyAsync(
     &h_counter, d_counter, sizeof(atomic_ctr_type), cudaMemcpyDeviceToHost, stream));
@@ -173,7 +173,7 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::count_
   cudaMemsetAsync(d_counter, 0, sizeof(atomic_ctr_type), stream);
   std::size_t h_counter;
 
-  detail::count<block_size, cg_size(), Key, Value, is_outer>
+  detail::count<block_size, cg_size(), is_outer>
     <<<grid_size, block_size, 0, stream>>>(first, last, d_counter, view, key_equal);
   CUCO_CUDA_TRY(cudaMemcpyAsync(
     &h_counter, d_counter, sizeof(atomic_ctr_type), cudaMemcpyDeviceToHost, stream));
@@ -202,7 +202,7 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::pair_c
   cudaMemsetAsync(d_counter, 0, sizeof(atomic_ctr_type), stream);
   std::size_t h_counter;
 
-  detail::pair_count<block_size, cg_size(), Key, Value, is_outer>
+  detail::pair_count<block_size, cg_size(), is_outer>
     <<<grid_size, block_size, 0, stream>>>(first, last, d_counter, view, pair_equal);
   CUCO_CUDA_TRY(cudaMemcpyAsync(
     &h_counter, d_counter, sizeof(atomic_ctr_type), cudaMemcpyDeviceToHost, stream));
@@ -231,7 +231,7 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::pair_c
   cudaMemsetAsync(d_counter, 0, sizeof(atomic_ctr_type), stream);
   std::size_t h_counter;
 
-  detail::pair_count<block_size, cg_size(), Key, Value, is_outer>
+  detail::pair_count<block_size, cg_size(), is_outer>
     <<<grid_size, block_size, 0, stream>>>(first, last, d_counter, view, pair_equal);
   CUCO_CUDA_TRY(cudaMemcpyAsync(
     &h_counter, d_counter, sizeof(atomic_ctr_type), cudaMemcpyDeviceToHost, stream));
@@ -263,11 +263,10 @@ OutputIt static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::retrieve(
   std::size_t h_counter;
 
   if constexpr (uses_vector_load()) {
-    detail::
-      vectorized_retrieve<block_size, warp_size(), cg_size(), buffer_size, Key, Value, is_outer>
+    detail::vectorized_retrieve<block_size, warp_size(), cg_size(), buffer_size, is_outer>
       <<<grid_size, block_size, 0, stream>>>(first, last, output_begin, d_counter, view, key_equal);
   } else {
-    detail::retrieve<block_size, warp_size(), cg_size(), buffer_size, Key, Value, is_outer>
+    detail::retrieve<block_size, warp_size(), cg_size(), buffer_size, is_outer>
       <<<grid_size, block_size, 0, stream>>>(first, last, output_begin, d_counter, view, key_equal);
   }
   CUCO_CUDA_TRY(cudaMemcpyAsync(
@@ -301,11 +300,10 @@ OutputIt static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::retrieve_
   std::size_t h_counter;
 
   if constexpr (uses_vector_load()) {
-    detail::
-      vectorized_retrieve<block_size, warp_size(), cg_size(), buffer_size, Key, Value, is_outer>
+    detail::vectorized_retrieve<block_size, warp_size(), cg_size(), buffer_size, is_outer>
       <<<grid_size, block_size, 0, stream>>>(first, last, output_begin, d_counter, view, key_equal);
   } else {
-    detail::retrieve<block_size, warp_size(), cg_size(), buffer_size, Key, Value, is_outer>
+    detail::retrieve<block_size, warp_size(), cg_size(), buffer_size, is_outer>
       <<<grid_size, block_size, 0, stream>>>(first, last, output_begin, d_counter, view, key_equal);
   }
   CUCO_CUDA_TRY(cudaMemcpyAsync(
@@ -344,16 +342,11 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::pair_r
   std::size_t h_counter;
 
   if constexpr (uses_vector_load()) {
-    detail::vectorized_pair_retrieve<block_size,
-                                     warp_size(),
-                                     cg_size(),
-                                     buffer_size,
-                                     Key,
-                                     Value,
-                                     is_outer><<<grid_size, block_size, 0, stream>>>(
-      first, last, probe_output_begin, contained_output_begin, d_counter, view, pair_equal);
+    detail::vectorized_pair_retrieve<block_size, warp_size(), cg_size(), buffer_size, is_outer>
+      <<<grid_size, block_size, 0, stream>>>(
+        first, last, probe_output_begin, contained_output_begin, d_counter, view, pair_equal);
   } else {
-    detail::pair_retrieve<block_size, warp_size(), cg_size(), buffer_size, Key, Value, is_outer>
+    detail::pair_retrieve<block_size, warp_size(), cg_size(), buffer_size, is_outer>
       <<<grid_size, block_size, 0, stream>>>(
         first, last, probe_output_begin, contained_output_begin, d_counter, view, pair_equal);
   }
@@ -392,16 +385,11 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::pair_r
   std::size_t h_counter;
 
   if constexpr (uses_vector_load()) {
-    detail::vectorized_pair_retrieve<block_size,
-                                     warp_size(),
-                                     cg_size(),
-                                     buffer_size,
-                                     Key,
-                                     Value,
-                                     is_outer><<<grid_size, block_size, 0, stream>>>(
-      first, last, probe_output_begin, contained_output_begin, d_counter, view, pair_equal);
+    detail::vectorized_pair_retrieve<block_size, warp_size(), cg_size(), buffer_size, is_outer>
+      <<<grid_size, block_size, 0, stream>>>(
+        first, last, probe_output_begin, contained_output_begin, d_counter, view, pair_equal);
   } else {
-    detail::pair_retrieve<block_size, warp_size(), cg_size(), buffer_size, Key, Value, is_outer>
+    detail::pair_retrieve<block_size, warp_size(), cg_size(), buffer_size, is_outer>
       <<<grid_size, block_size, 0, stream>>>(
         first, last, probe_output_begin, contained_output_begin, d_counter, view, pair_equal);
   }
diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap_kernels.cuh
index d92ab91fc..2629abdfc 100644
--- a/include/cuco/detail/static_multimap_kernels.cuh
+++ b/include/cuco/detail/static_multimap_kernels.cuh
@@ -197,8 +197,6 @@ __global__ void contains(
  *
  * @tparam block_size The size of the thread block
  * @tparam tile_size The number of threads in the Cooperative Groups used to perform counts
- * @tparam Key key type
- * @tparam Value The type of the mapped value for the map
  * @tparam uses_vector_load Boolean flag indicating whether vector loads are used or not
  * @tparam is_outer Boolean flag indicating whether the current functions is used for outer join
  * operations or not
@@ -215,8 +213,6 @@ __global__ void contains(
  */
 template <uint32_t block_size,
           uint32_t tile_size,
-          typename Key,
-          typename Value,
           bool is_outer,
           typename InputIt,
           typename atomicT,
@@ -258,8 +254,6 @@ __global__ void count(
  *
  * @tparam block_size The size of the thread block
  * @tparam tile_size The number of threads in the Cooperative Groups used to perform counts
- * @tparam Key key type
- * @tparam Value The type of the mapped value for the map
  * @tparam is_outer Boolean flag indicating whether the current functions is used for outer join
  * operations or not
  * @tparam Input Device accesible input iterator of key/value pairs
@@ -274,8 +268,6 @@ __global__ void count(
  */
 template <uint32_t block_size,
           uint32_t tile_size,
-          typename Key,
-          typename Value,
           bool is_outer,
           typename InputIt,
           typename atomicT,
@@ -325,8 +317,6 @@ __global__ void pair_count(
  * @tparam warp_size The size of the warp
  * @tparam tile_size The number of threads in the Cooperative Groups
  * @tparam buffer_size Size of the output buffer
- * @tparam Key key type
- * @tparam Value The type of the mapped value for the map
  * @tparam is_outer Boolean flag indicating whether the current functions is used for outer join
  * operations or not
  * @tparam InputIt Device accessible input iterator whose `value_type` is
@@ -347,8 +337,6 @@ template <uint32_t block_size,
           uint32_t warp_size,
           uint32_t tile_size,
           uint32_t buffer_size,
-          typename Key,
-          typename Value,
           bool is_outer,
           typename InputIt,
           typename OutputIt,
@@ -362,6 +350,8 @@ __global__ void vectorized_retrieve(InputIt first,
                                     viewT view,
                                     KeyEqual key_equal)
 {
+  using pair_type = typename viewT::value_type;
+
   constexpr uint32_t num_warps = block_size / warp_size;
   const uint32_t warp_id       = threadIdx.x / warp_size;
 
@@ -370,7 +360,7 @@ __global__ void vectorized_retrieve(InputIt first,
   auto tid     = block_size * blockIdx.x + threadIdx.x;
   auto key_idx = tid / tile_size;
 
-  __shared__ cuco::pair_type<Key, Value> output_buffer[num_warps][buffer_size];
+  __shared__ pair_type output_buffer[num_warps][buffer_size];
   // TODO: replace this with shared memory cuda::atomic variables once the dynamiic initialization
   // warning issue is solved __shared__ atomicT toto_countter[num_warps];
   __shared__ uint32_t warp_counter[num_warps];
@@ -430,8 +420,6 @@ __global__ void vectorized_retrieve(InputIt first,
  * @tparam warp_size The size of the warp
  * @tparam tile_size The number of threads in the Cooperative Groups
  * @tparam buffer_size Size of the output buffer
- * @tparam Key key type
- * @tparam Value The type of the mapped value for the map
  * @tparam is_outer Boolean flag indicating whether the current functions is used for outer join
  * operations or not
  * @tparam InputIt Device accessible input iterator whose `value_type` is
@@ -452,8 +440,6 @@ template <uint32_t block_size,
           uint32_t warp_size,
           uint32_t tile_size,
           uint32_t buffer_size,
-          typename Key,
-          typename Value,
           bool is_outer,
           typename InputIt,
           typename OutputIt,
@@ -467,6 +453,8 @@ __global__ void retrieve(InputIt first,
                          viewT view,
                          KeyEqual key_equal)
 {
+  using pair_type = typename viewT::value_type;
+
   auto tile    = cg::tiled_partition<tile_size>(cg::this_thread_block());
   auto tid     = block_size * blockIdx.x + threadIdx.x;
   auto key_idx = tid / tile_size;
@@ -475,7 +463,7 @@ __global__ void retrieve(InputIt first,
   const uint32_t cg_id       = threadIdx.x / tile_size;
   const uint32_t lane_id     = tile.thread_rank();
 
-  __shared__ cuco::pair_type<Key, Value> output_buffer[num_cgs][buffer_size];
+  __shared__ pair_type output_buffer[num_cgs][buffer_size];
   __shared__ uint32_t cg_counter[num_cgs];
 
   if (lane_id == 0) { cg_counter[cg_id] = 0; }
@@ -515,8 +503,6 @@ template <uint32_t block_size,
           uint32_t warp_size,
           uint32_t tile_size,
           uint32_t buffer_size,
-          typename Key,
-          typename Value,
           bool is_outer,
           typename InputIt,
           typename OutputIt1,
@@ -532,6 +518,8 @@ __global__ void vectorized_pair_retrieve(InputIt first,
                                          viewT view,
                                          PairEqual pair_equal)
 {
+  using pair_type = typename viewT::value_type;
+
   constexpr uint32_t num_warps = block_size / warp_size;
   const uint32_t warp_id       = threadIdx.x / warp_size;
 
@@ -540,8 +528,8 @@ __global__ void vectorized_pair_retrieve(InputIt first,
   auto tid      = block_size * blockIdx.x + threadIdx.x;
   auto pair_idx = tid / tile_size;
 
-  __shared__ cuco::pair_type<Key, Value> probe_output_buffer[num_warps][buffer_size];
-  __shared__ cuco::pair_type<Key, Value> contained_output_buffer[num_warps][buffer_size];
+  __shared__ pair_type probe_output_buffer[num_warps][buffer_size];
+  __shared__ pair_type contained_output_buffer[num_warps][buffer_size];
   // TODO: replace this with shared memory cuda::atomic variables once the dynamiic initialization
   // warning issue is solved __shared__ atomicT toto_countter[num_warps];
   __shared__ uint32_t warp_counter[num_warps];
@@ -553,7 +541,7 @@ __global__ void vectorized_pair_retrieve(InputIt first,
     auto active_warp = cg::binary_partition<warp_size>(warp, active_flag);
 
     if (active_flag) {
-      typename viewT::value_type pair = *(first + pair_idx);
+      pair_type pair = *(first + pair_idx);
       if constexpr (is_outer) {
         view.pair_retrieve_outer<buffer_size>(active_warp,
                                               tile,
@@ -599,8 +587,6 @@ template <uint32_t block_size,
           uint32_t warp_size,
           uint32_t tile_size,
           uint32_t buffer_size,
-          typename Key,
-          typename Value,
           bool is_outer,
           typename InputIt,
           typename OutputIt1,
@@ -616,6 +602,8 @@ __global__ void pair_retrieve(InputIt first,
                               viewT view,
                               PairEqual pair_equal)
 {
+  using pair_type = typename viewT::value_type;
+
   auto tile     = cg::tiled_partition<tile_size>(cg::this_thread_block());
   auto tid      = block_size * blockIdx.x + threadIdx.x;
   auto pair_idx = tid / tile_size;
@@ -624,8 +612,8 @@ __global__ void pair_retrieve(InputIt first,
   const uint32_t cg_id       = threadIdx.x / tile_size;
   const uint32_t lane_id     = tile.thread_rank();
 
-  __shared__ cuco::pair_type<Key, Value> probe_output_buffer[num_cgs][buffer_size];
-  __shared__ cuco::pair_type<Key, Value> contained_output_buffer[num_cgs][buffer_size];
+  __shared__ pair_type probe_output_buffer[num_cgs][buffer_size];
+  __shared__ pair_type contained_output_buffer[num_cgs][buffer_size];
   __shared__ uint32_t cg_counter[num_cgs];
 
   if (lane_id == 0) { cg_counter[cg_id] = 0; }

From 0735c19d2fcab47e854b38d2d0561ab399673b7b Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Tue, 17 Aug 2021 13:40:27 -0400
Subject: [PATCH 192/267] Add stream as allocator argument + cleanups

---
 include/cuco/allocator.hpp              |  7 ++-
 include/cuco/detail/static_multimap.inl | 72 +++++++++++++------------
 include/cuco/static_multimap.cuh        |  3 +-
 3 files changed, 45 insertions(+), 37 deletions(-)

diff --git a/include/cuco/allocator.hpp b/include/cuco/allocator.hpp
index 71d590384..634e90423 100644
--- a/include/cuco/allocator.hpp
+++ b/include/cuco/allocator.hpp
@@ -32,14 +32,17 @@ class cuda_allocator {
   {
   }
 
-  value_type* allocate(std::size_t n)
+  value_type* allocate(std::size_t n, cudaStream_t stream = 0)
   {
     value_type* p;
     CUCO_CUDA_TRY(cudaMalloc(&p, sizeof(value_type) * n));
     return p;
   }
 
-  void deallocate(value_type* p, std::size_t) { CUCO_CUDA_TRY(cudaFree(p)); }
+  void deallocate(value_type* p, std::size_t, cudaStream_t stream = 0)
+  {
+    CUCO_CUDA_TRY(cudaFree(p));
+  }
 };
 
 template <typename T, typename U>
diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index de49fca49..0b043b9db 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -32,17 +32,17 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::static_multimap(
   : empty_key_sentinel_{empty_key_sentinel},
     empty_value_sentinel_{empty_value_sentinel},
     slot_allocator_{alloc},
-    counter_allocator_{alloc}
+    counter_allocator_{alloc},
+    stream_{stream}
 {
-  d_counter = std::allocator_traits<counter_allocator_type>::allocate(counter_allocator_, 1);
-
   if constexpr (uses_vector_load()) {
     capacity_ = cuco::detail::get_valid_capacity<cg_size() * vector_width()>(capacity);
   } else {
     capacity_ = cuco::detail::get_valid_capacity<cg_size()>(capacity);
   }
 
-  slots_ = std::allocator_traits<slot_allocator_type>::allocate(slot_allocator_, get_capacity());
+  d_counter_ = counter_allocator_.allocate(1, stream_);
+  slots_     = slot_allocator_.allocate(get_capacity(), stream_);
 
   auto constexpr block_size = 256;
   auto constexpr stride     = 4;
@@ -58,8 +58,8 @@ template <typename Key,
           typename Allocator>
 static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::~static_multimap()
 {
-  std::allocator_traits<counter_allocator_type>::deallocate(counter_allocator_, d_counter, 1);
-  std::allocator_traits<slot_allocator_type>::deallocate(slot_allocator_, slots_, capacity_);
+  counter_allocator_.deallocate(d_counter_, 1, stream_);
+  slot_allocator_.deallocate(slots_, get_capacity(), stream_);
 }
 
 template <typename Key,
@@ -141,13 +141,13 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::count(
 
   constexpr bool is_outer = false;
 
-  cudaMemsetAsync(d_counter, 0, sizeof(atomic_ctr_type), stream);
+  cudaMemsetAsync(d_counter_, 0, sizeof(atomic_ctr_type), stream);
   std::size_t h_counter;
 
   detail::count<block_size, cg_size(), is_outer>
-    <<<grid_size, block_size, 0, stream>>>(first, last, d_counter, view, key_equal);
+    <<<grid_size, block_size, 0, stream>>>(first, last, d_counter_, view, key_equal);
   CUCO_CUDA_TRY(cudaMemcpyAsync(
-    &h_counter, d_counter, sizeof(atomic_ctr_type), cudaMemcpyDeviceToHost, stream));
+    &h_counter, d_counter_, sizeof(atomic_ctr_type), cudaMemcpyDeviceToHost, stream));
   CUCO_CUDA_TRY(cudaStreamSynchronize(stream));
 
   return h_counter;
@@ -170,13 +170,13 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::count_
 
   constexpr bool is_outer = true;
 
-  cudaMemsetAsync(d_counter, 0, sizeof(atomic_ctr_type), stream);
+  cudaMemsetAsync(d_counter_, 0, sizeof(atomic_ctr_type), stream);
   std::size_t h_counter;
 
   detail::count<block_size, cg_size(), is_outer>
-    <<<grid_size, block_size, 0, stream>>>(first, last, d_counter, view, key_equal);
+    <<<grid_size, block_size, 0, stream>>>(first, last, d_counter_, view, key_equal);
   CUCO_CUDA_TRY(cudaMemcpyAsync(
-    &h_counter, d_counter, sizeof(atomic_ctr_type), cudaMemcpyDeviceToHost, stream));
+    &h_counter, d_counter_, sizeof(atomic_ctr_type), cudaMemcpyDeviceToHost, stream));
   CUCO_CUDA_TRY(cudaStreamSynchronize(stream));
 
   return h_counter;
@@ -199,13 +199,13 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::pair_c
 
   constexpr bool is_outer = false;
 
-  cudaMemsetAsync(d_counter, 0, sizeof(atomic_ctr_type), stream);
+  cudaMemsetAsync(d_counter_, 0, sizeof(atomic_ctr_type), stream);
   std::size_t h_counter;
 
   detail::pair_count<block_size, cg_size(), is_outer>
-    <<<grid_size, block_size, 0, stream>>>(first, last, d_counter, view, pair_equal);
+    <<<grid_size, block_size, 0, stream>>>(first, last, d_counter_, view, pair_equal);
   CUCO_CUDA_TRY(cudaMemcpyAsync(
-    &h_counter, d_counter, sizeof(atomic_ctr_type), cudaMemcpyDeviceToHost, stream));
+    &h_counter, d_counter_, sizeof(atomic_ctr_type), cudaMemcpyDeviceToHost, stream));
   CUCO_CUDA_TRY(cudaStreamSynchronize(stream));
 
   return h_counter;
@@ -228,13 +228,13 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::pair_c
 
   constexpr bool is_outer = true;
 
-  cudaMemsetAsync(d_counter, 0, sizeof(atomic_ctr_type), stream);
+  cudaMemsetAsync(d_counter_, 0, sizeof(atomic_ctr_type), stream);
   std::size_t h_counter;
 
   detail::pair_count<block_size, cg_size(), is_outer>
-    <<<grid_size, block_size, 0, stream>>>(first, last, d_counter, view, pair_equal);
+    <<<grid_size, block_size, 0, stream>>>(first, last, d_counter_, view, pair_equal);
   CUCO_CUDA_TRY(cudaMemcpyAsync(
-    &h_counter, d_counter, sizeof(atomic_ctr_type), cudaMemcpyDeviceToHost, stream));
+    &h_counter, d_counter_, sizeof(atomic_ctr_type), cudaMemcpyDeviceToHost, stream));
   CUCO_CUDA_TRY(cudaStreamSynchronize(stream));
 
   return h_counter;
@@ -259,18 +259,20 @@ OutputIt static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::retrieve(
 
   constexpr bool is_outer = false;
 
-  cudaMemsetAsync(d_counter, 0, sizeof(atomic_ctr_type), stream);
+  cudaMemsetAsync(d_counter_, 0, sizeof(atomic_ctr_type), stream);
   std::size_t h_counter;
 
   if constexpr (uses_vector_load()) {
     detail::vectorized_retrieve<block_size, warp_size(), cg_size(), buffer_size, is_outer>
-      <<<grid_size, block_size, 0, stream>>>(first, last, output_begin, d_counter, view, key_equal);
+      <<<grid_size, block_size, 0, stream>>>(
+        first, last, output_begin, d_counter_, view, key_equal);
   } else {
     detail::retrieve<block_size, warp_size(), cg_size(), buffer_size, is_outer>
-      <<<grid_size, block_size, 0, stream>>>(first, last, output_begin, d_counter, view, key_equal);
+      <<<grid_size, block_size, 0, stream>>>(
+        first, last, output_begin, d_counter_, view, key_equal);
   }
   CUCO_CUDA_TRY(cudaMemcpyAsync(
-    &h_counter, d_counter, sizeof(atomic_ctr_type), cudaMemcpyDeviceToHost, stream));
+    &h_counter, d_counter_, sizeof(atomic_ctr_type), cudaMemcpyDeviceToHost, stream));
   CUCO_CUDA_TRY(cudaStreamSynchronize(stream));
 
   auto output_end = output_begin + h_counter;
@@ -296,18 +298,20 @@ OutputIt static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::retrieve_
 
   constexpr bool is_outer = true;
 
-  cudaMemsetAsync(d_counter, 0, sizeof(atomic_ctr_type), stream);
+  cudaMemsetAsync(d_counter_, 0, sizeof(atomic_ctr_type), stream);
   std::size_t h_counter;
 
   if constexpr (uses_vector_load()) {
     detail::vectorized_retrieve<block_size, warp_size(), cg_size(), buffer_size, is_outer>
-      <<<grid_size, block_size, 0, stream>>>(first, last, output_begin, d_counter, view, key_equal);
+      <<<grid_size, block_size, 0, stream>>>(
+        first, last, output_begin, d_counter_, view, key_equal);
   } else {
     detail::retrieve<block_size, warp_size(), cg_size(), buffer_size, is_outer>
-      <<<grid_size, block_size, 0, stream>>>(first, last, output_begin, d_counter, view, key_equal);
+      <<<grid_size, block_size, 0, stream>>>(
+        first, last, output_begin, d_counter_, view, key_equal);
   }
   CUCO_CUDA_TRY(cudaMemcpyAsync(
-    &h_counter, d_counter, sizeof(atomic_ctr_type), cudaMemcpyDeviceToHost, stream));
+    &h_counter, d_counter_, sizeof(atomic_ctr_type), cudaMemcpyDeviceToHost, stream));
   CUCO_CUDA_TRY(cudaStreamSynchronize(stream));
 
   auto output_end = output_begin + h_counter;
@@ -338,20 +342,20 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::pair_r
 
   constexpr bool is_outer = false;
 
-  cudaMemsetAsync(d_counter, 0, sizeof(atomic_ctr_type), stream);
+  cudaMemsetAsync(d_counter_, 0, sizeof(atomic_ctr_type), stream);
   std::size_t h_counter;
 
   if constexpr (uses_vector_load()) {
     detail::vectorized_pair_retrieve<block_size, warp_size(), cg_size(), buffer_size, is_outer>
       <<<grid_size, block_size, 0, stream>>>(
-        first, last, probe_output_begin, contained_output_begin, d_counter, view, pair_equal);
+        first, last, probe_output_begin, contained_output_begin, d_counter_, view, pair_equal);
   } else {
     detail::pair_retrieve<block_size, warp_size(), cg_size(), buffer_size, is_outer>
       <<<grid_size, block_size, 0, stream>>>(
-        first, last, probe_output_begin, contained_output_begin, d_counter, view, pair_equal);
+        first, last, probe_output_begin, contained_output_begin, d_counter_, view, pair_equal);
   }
   CUCO_CUDA_TRY(cudaMemcpyAsync(
-    &h_counter, d_counter, sizeof(atomic_ctr_type), cudaMemcpyDeviceToHost, stream));
+    &h_counter, d_counter_, sizeof(atomic_ctr_type), cudaMemcpyDeviceToHost, stream));
   CUCO_CUDA_TRY(cudaStreamSynchronize(stream));
 
   return h_counter;
@@ -381,20 +385,20 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::pair_r
 
   constexpr bool is_outer = true;
 
-  cudaMemsetAsync(d_counter, 0, sizeof(atomic_ctr_type), stream);
+  cudaMemsetAsync(d_counter_, 0, sizeof(atomic_ctr_type), stream);
   std::size_t h_counter;
 
   if constexpr (uses_vector_load()) {
     detail::vectorized_pair_retrieve<block_size, warp_size(), cg_size(), buffer_size, is_outer>
       <<<grid_size, block_size, 0, stream>>>(
-        first, last, probe_output_begin, contained_output_begin, d_counter, view, pair_equal);
+        first, last, probe_output_begin, contained_output_begin, d_counter_, view, pair_equal);
   } else {
     detail::pair_retrieve<block_size, warp_size(), cg_size(), buffer_size, is_outer>
       <<<grid_size, block_size, 0, stream>>>(
-        first, last, probe_output_begin, contained_output_begin, d_counter, view, pair_equal);
+        first, last, probe_output_begin, contained_output_begin, d_counter_, view, pair_equal);
   }
   CUCO_CUDA_TRY(cudaMemcpyAsync(
-    &h_counter, d_counter, sizeof(atomic_ctr_type), cudaMemcpyDeviceToHost, stream));
+    &h_counter, d_counter_, sizeof(atomic_ctr_type), cudaMemcpyDeviceToHost, stream));
   CUCO_CUDA_TRY(cudaStreamSynchronize(stream));
 
   return h_counter;
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index db606bfd2..221e634cd 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -1845,7 +1845,8 @@ class static_multimap {
   Value empty_value_sentinel_{};                ///< Initial value of empty slot
   slot_allocator_type slot_allocator_{};        ///< Allocator used to allocate slots
   counter_allocator_type counter_allocator_{};  ///< Allocator used to allocate counters
-  atomic_ctr_type* d_counter;
+  atomic_ctr_type* d_counter_;
+  cudaStream_t const& stream_;
 };  // class static_multimap
 }  // namespace cuco
 

From 7476735c583f0d920a5ab780c4ca03eed79ed730 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Tue, 17 Aug 2021 16:59:57 -0400
Subject: [PATCH 193/267] Member variable stream: const value instead of const
 reference

---
 include/cuco/static_multimap.cuh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index 221e634cd..1092a3411 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -1846,7 +1846,7 @@ class static_multimap {
   slot_allocator_type slot_allocator_{};        ///< Allocator used to allocate slots
   counter_allocator_type counter_allocator_{};  ///< Allocator used to allocate counters
   atomic_ctr_type* d_counter_;
-  cudaStream_t const& stream_;
+  cudaStream_t const stream_;
 };  // class static_multimap
 }  // namespace cuco
 

From 0b4eeeb8ab443bf2829b20ffa4848a8064a5eef6 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Mon, 23 Aug 2021 13:36:42 -0400
Subject: [PATCH 194/267] Use cudaOccupancyMaxActiveBlocksPerMultiprocessor to
 determine grid size

---
 include/cuco/detail/static_multimap.inl | 354 +++++++++++++++++++-----
 1 file changed, 288 insertions(+), 66 deletions(-)

diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index 0b043b9db..d04a98342 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -44,9 +44,19 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::static_multimap(
   d_counter_ = counter_allocator_.allocate(1, stream_);
   slots_     = slot_allocator_.allocate(get_capacity(), stream_);
 
-  auto constexpr block_size = 256;
-  auto constexpr stride     = 4;
-  auto const grid_size      = (get_capacity() + stride * block_size - 1) / (stride * block_size);
+  auto constexpr block_size = 128;
+  int grid_size{-1};
+  cudaOccupancyMaxActiveBlocksPerMultiprocessor(
+    &grid_size,
+    detail::initialize<atomic_key_type, atomic_mapped_type, Key, Value, pair_atomic_type>,
+    block_size,
+    0);
+  int dev_id{-1};
+  cudaGetDevice(&dev_id);
+  int num_sms{-1};
+  cudaDeviceGetAttribute(&num_sms, cudaDevAttrMultiProcessorCount, dev_id);
+  grid_size *= num_sms;
+
   detail::initialize<atomic_key_type, atomic_mapped_type><<<grid_size, block_size, 0, stream>>>(
     slots_, empty_key_sentinel, empty_value_sentinel, get_capacity());
 }
@@ -73,11 +83,21 @@ void static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::insert(InputI
                                                                           cudaStream_t stream,
                                                                           KeyEqual key_equal)
 {
-  auto num_keys         = std::distance(first, last);
-  auto const block_size = 128;
-  auto const stride     = 1;
-  auto const grid_size  = (cg_size() * num_keys + stride * block_size - 1) / (stride * block_size);
-  auto view             = get_device_mutable_view();
+  auto num_keys = std::distance(first, last);
+  auto view     = get_device_mutable_view();
+
+  auto constexpr block_size = 128;
+  int grid_size{-1};
+  cudaOccupancyMaxActiveBlocksPerMultiprocessor(
+    &grid_size,
+    detail::insert<block_size, cg_size(), InputIt, device_mutable_view, KeyEqual>,
+    block_size,
+    0);
+  int dev_id{-1};
+  cudaGetDevice(&dev_id);
+  int num_sms{-1};
+  cudaDeviceGetAttribute(&num_sms, cudaDevAttrMultiProcessorCount, dev_id);
+  grid_size *= num_sms;
 
   detail::insert<block_size, cg_size()>
     <<<grid_size, block_size, 0, stream>>>(first, first + num_keys, view, key_equal);
@@ -93,11 +113,21 @@ template <typename InputIt, typename Predicate, typename KeyEqual>
 void static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::insert_if(
   InputIt first, InputIt last, Predicate pred, cudaStream_t stream, KeyEqual key_equal)
 {
-  auto num_keys         = std::distance(first, last);
-  auto const block_size = 128;
-  auto const stride     = 1;
-  auto const grid_size  = (cg_size() * num_keys + stride * block_size - 1) / (stride * block_size);
-  auto view             = get_device_mutable_view();
+  auto num_keys = std::distance(first, last);
+  auto view     = get_device_mutable_view();
+
+  auto constexpr block_size = 128;
+  int grid_size{-1};
+  cudaOccupancyMaxActiveBlocksPerMultiprocessor(
+    &grid_size,
+    detail::insert_if<block_size, cg_size(), InputIt, device_mutable_view, Predicate, KeyEqual>,
+    block_size,
+    0);
+  int dev_id{-1};
+  cudaGetDevice(&dev_id);
+  int num_sms{-1};
+  cudaDeviceGetAttribute(&num_sms, cudaDevAttrMultiProcessorCount, dev_id);
+  grid_size *= num_sms;
 
   detail::insert_if<block_size, cg_size()>
     <<<grid_size, block_size, 0, stream>>>(first, first + num_keys, view, pred, key_equal);
@@ -113,11 +143,21 @@ template <typename InputIt, typename OutputIt, typename KeyEqual>
 void static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::contains(
   InputIt first, InputIt last, OutputIt output_begin, cudaStream_t stream, KeyEqual key_equal) const
 {
-  auto num_keys         = std::distance(first, last);
-  auto const block_size = 128;
-  auto const stride     = 1;
-  auto const grid_size  = (cg_size() * num_keys + stride * block_size - 1) / (stride * block_size);
-  auto view             = get_device_view();
+  auto num_keys = std::distance(first, last);
+  auto view     = get_device_view();
+
+  auto constexpr block_size = 128;
+  int grid_size{-1};
+  cudaOccupancyMaxActiveBlocksPerMultiprocessor(
+    &grid_size,
+    detail::contains<block_size, cg_size(), InputIt, OutputIt, device_view, KeyEqual>,
+    block_size,
+    0);
+  int dev_id{-1};
+  cudaGetDevice(&dev_id);
+  int num_sms{-1};
+  cudaDeviceGetAttribute(&num_sms, cudaDevAttrMultiProcessorCount, dev_id);
+  grid_size *= num_sms;
 
   detail::contains<block_size, cg_size()>
     <<<grid_size, block_size, 0, stream>>>(first, last, output_begin, view, key_equal);
@@ -133,14 +173,24 @@ template <typename InputIt, typename KeyEqual>
 std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::count(
   InputIt first, InputIt last, cudaStream_t stream, KeyEqual key_equal) const
 {
-  auto num_keys         = std::distance(first, last);
-  auto const block_size = 128;
-  auto const stride     = 1;
-  auto const grid_size  = (cg_size() * num_keys + stride * block_size - 1) / (stride * block_size);
-  auto view             = get_device_view();
+  auto num_keys = std::distance(first, last);
+  auto view     = get_device_view();
 
   constexpr bool is_outer = false;
 
+  auto constexpr block_size = 128;
+  int grid_size{-1};
+  cudaOccupancyMaxActiveBlocksPerMultiprocessor(
+    &grid_size,
+    detail::count<block_size, cg_size(), is_outer, InputIt, atomic_ctr_type, device_view, KeyEqual>,
+    block_size,
+    0);
+  int dev_id{-1};
+  cudaGetDevice(&dev_id);
+  int num_sms{-1};
+  cudaDeviceGetAttribute(&num_sms, cudaDevAttrMultiProcessorCount, dev_id);
+  grid_size *= num_sms;
+
   cudaMemsetAsync(d_counter_, 0, sizeof(atomic_ctr_type), stream);
   std::size_t h_counter;
 
@@ -162,14 +212,24 @@ template <typename InputIt, typename KeyEqual>
 std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::count_outer(
   InputIt first, InputIt last, cudaStream_t stream, KeyEqual key_equal) const
 {
-  auto num_keys         = std::distance(first, last);
-  auto const block_size = 128;
-  auto const stride     = 1;
-  auto const grid_size  = (cg_size() * num_keys + stride * block_size - 1) / (stride * block_size);
-  auto view             = get_device_view();
+  auto num_keys = std::distance(first, last);
+  auto view     = get_device_view();
 
   constexpr bool is_outer = true;
 
+  auto constexpr block_size = 128;
+  int grid_size{-1};
+  cudaOccupancyMaxActiveBlocksPerMultiprocessor(
+    &grid_size,
+    detail::count<block_size, cg_size(), is_outer, InputIt, atomic_ctr_type, device_view, KeyEqual>,
+    block_size,
+    0);
+  int dev_id{-1};
+  cudaGetDevice(&dev_id);
+  int num_sms{-1};
+  cudaDeviceGetAttribute(&num_sms, cudaDevAttrMultiProcessorCount, dev_id);
+  grid_size *= num_sms;
+
   cudaMemsetAsync(d_counter_, 0, sizeof(atomic_ctr_type), stream);
   std::size_t h_counter;
 
@@ -191,14 +251,25 @@ template <typename InputIt, typename PairEqual>
 std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::pair_count(
   InputIt first, InputIt last, PairEqual pair_equal, cudaStream_t stream) const
 {
-  auto num_keys         = std::distance(first, last);
-  auto const block_size = 128;
-  auto const stride     = 1;
-  auto const grid_size  = (cg_size() * num_keys + stride * block_size - 1) / (stride * block_size);
-  auto view             = get_device_view();
+  auto num_keys = std::distance(first, last);
+  auto view     = get_device_view();
 
   constexpr bool is_outer = false;
 
+  auto constexpr block_size = 128;
+  int grid_size{-1};
+  cudaOccupancyMaxActiveBlocksPerMultiprocessor(
+    &grid_size,
+    detail::
+      pair_count<block_size, cg_size(), is_outer, InputIt, atomic_ctr_type, device_view, PairEqual>,
+    block_size,
+    0);
+  int dev_id{-1};
+  cudaGetDevice(&dev_id);
+  int num_sms{-1};
+  cudaDeviceGetAttribute(&num_sms, cudaDevAttrMultiProcessorCount, dev_id);
+  grid_size *= num_sms;
+
   cudaMemsetAsync(d_counter_, 0, sizeof(atomic_ctr_type), stream);
   std::size_t h_counter;
 
@@ -220,14 +291,25 @@ template <typename InputIt, typename PairEqual>
 std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::pair_count_outer(
   InputIt first, InputIt last, PairEqual pair_equal, cudaStream_t stream) const
 {
-  auto num_keys         = std::distance(first, last);
-  auto const block_size = 128;
-  auto const stride     = 1;
-  auto const grid_size  = (cg_size() * num_keys + stride * block_size - 1) / (stride * block_size);
-  auto view             = get_device_view();
+  auto num_keys = std::distance(first, last);
+  auto view     = get_device_view();
 
   constexpr bool is_outer = true;
 
+  auto constexpr block_size = 128;
+  int grid_size{-1};
+  cudaOccupancyMaxActiveBlocksPerMultiprocessor(
+    &grid_size,
+    detail::
+      pair_count<block_size, cg_size(), is_outer, InputIt, atomic_ctr_type, device_view, PairEqual>,
+    block_size,
+    0);
+  int dev_id{-1};
+  cudaGetDevice(&dev_id);
+  int num_sms{-1};
+  cudaDeviceGetAttribute(&num_sms, cudaDevAttrMultiProcessorCount, dev_id);
+  grid_size *= num_sms;
+
   cudaMemsetAsync(d_counter_, 0, sizeof(atomic_ctr_type), stream);
   std::size_t h_counter;
 
@@ -249,15 +331,49 @@ template <typename InputIt, typename OutputIt, typename KeyEqual>
 OutputIt static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::retrieve(
   InputIt first, InputIt last, OutputIt output_begin, cudaStream_t stream, KeyEqual key_equal) const
 {
-  auto num_keys         = std::distance(first, last);
-  auto const block_size = 128;
+  auto num_keys = std::distance(first, last);
+  auto view     = get_device_view();
+
   // Using per-warp buffer for vector loads and per-CG buffer for scalar loads
-  auto const buffer_size = uses_vector_load() ? (warp_size() * 3u) : (cg_size() * 3u);
-  auto const stride      = 1;
-  auto const grid_size   = (cg_size() * num_keys + stride * block_size - 1) / (stride * block_size);
-  auto view              = get_device_view();
+  constexpr auto buffer_size = uses_vector_load() ? (warp_size() * 3u) : (cg_size() * 3u);
+  constexpr bool is_outer    = false;
 
-  constexpr bool is_outer = false;
+  auto constexpr block_size = 128;
+  int grid_size{-1};
+  if constexpr (uses_vector_load()) {
+    cudaOccupancyMaxActiveBlocksPerMultiprocessor(&grid_size,
+                                                  detail::vectorized_retrieve<block_size,
+                                                                              warp_size(),
+                                                                              cg_size(),
+                                                                              buffer_size,
+                                                                              is_outer,
+                                                                              InputIt,
+                                                                              OutputIt,
+                                                                              atomic_ctr_type,
+                                                                              device_view,
+                                                                              KeyEqual>,
+                                                  block_size,
+                                                  0);
+  } else {
+    cudaOccupancyMaxActiveBlocksPerMultiprocessor(&grid_size,
+                                                  detail::retrieve<block_size,
+                                                                   warp_size(),
+                                                                   cg_size(),
+                                                                   buffer_size,
+                                                                   is_outer,
+                                                                   InputIt,
+                                                                   OutputIt,
+                                                                   atomic_ctr_type,
+                                                                   device_view,
+                                                                   KeyEqual>,
+                                                  block_size,
+                                                  0);
+  }
+  int dev_id{-1};
+  cudaGetDevice(&dev_id);
+  int num_sms{-1};
+  cudaDeviceGetAttribute(&num_sms, cudaDevAttrMultiProcessorCount, dev_id);
+  grid_size *= num_sms;
 
   cudaMemsetAsync(d_counter_, 0, sizeof(atomic_ctr_type), stream);
   std::size_t h_counter;
@@ -288,15 +404,49 @@ template <typename InputIt, typename OutputIt, typename KeyEqual>
 OutputIt static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::retrieve_outer(
   InputIt first, InputIt last, OutputIt output_begin, cudaStream_t stream, KeyEqual key_equal) const
 {
-  auto num_keys         = std::distance(first, last);
-  auto const block_size = 128;
+  auto num_keys = std::distance(first, last);
+  auto view     = get_device_view();
+
   // Using per-warp buffer for vector loads and per-CG buffer for scalar loads
-  auto const buffer_size = uses_vector_load() ? (warp_size() * 3u) : (cg_size() * 3u);
-  auto const stride      = 1;
-  auto const grid_size   = (cg_size() * num_keys + stride * block_size - 1) / (stride * block_size);
-  auto view              = get_device_view();
+  constexpr auto buffer_size = uses_vector_load() ? (warp_size() * 3u) : (cg_size() * 3u);
+  constexpr bool is_outer    = true;
 
-  constexpr bool is_outer = true;
+  auto constexpr block_size = 128;
+  int grid_size{-1};
+  if constexpr (uses_vector_load()) {
+    cudaOccupancyMaxActiveBlocksPerMultiprocessor(&grid_size,
+                                                  detail::vectorized_retrieve<block_size,
+                                                                              warp_size(),
+                                                                              cg_size(),
+                                                                              buffer_size,
+                                                                              is_outer,
+                                                                              InputIt,
+                                                                              OutputIt,
+                                                                              atomic_ctr_type,
+                                                                              device_view,
+                                                                              KeyEqual>,
+                                                  block_size,
+                                                  0);
+  } else {
+    cudaOccupancyMaxActiveBlocksPerMultiprocessor(&grid_size,
+                                                  detail::retrieve<block_size,
+                                                                   warp_size(),
+                                                                   cg_size(),
+                                                                   buffer_size,
+                                                                   is_outer,
+                                                                   InputIt,
+                                                                   OutputIt,
+                                                                   atomic_ctr_type,
+                                                                   device_view,
+                                                                   KeyEqual>,
+                                                  block_size,
+                                                  0);
+  }
+  int dev_id{-1};
+  cudaGetDevice(&dev_id);
+  int num_sms{-1};
+  cudaDeviceGetAttribute(&num_sms, cudaDevAttrMultiProcessorCount, dev_id);
+  grid_size *= num_sms;
 
   cudaMemsetAsync(d_counter_, 0, sizeof(atomic_ctr_type), stream);
   std::size_t h_counter;
@@ -332,15 +482,51 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::pair_r
   PairEqual pair_equal,
   cudaStream_t stream) const
 {
-  auto num_pairs        = std::distance(first, last);
-  auto const block_size = 128;
+  auto num_pairs = std::distance(first, last);
+  auto view      = get_device_view();
+
   // Using per-warp buffer for vector loads and per-CG buffer for scalar loads
-  auto const buffer_size = uses_vector_load() ? (warp_size() * 3u) : (cg_size() * 3u);
-  auto const stride      = 1;
-  auto const grid_size = (cg_size() * num_pairs + stride * block_size - 1) / (stride * block_size);
-  auto view            = get_device_view();
+  constexpr auto buffer_size = uses_vector_load() ? (warp_size() * 3u) : (cg_size() * 3u);
+  constexpr bool is_outer    = false;
 
-  constexpr bool is_outer = false;
+  auto constexpr block_size = 128;
+  int grid_size{-1};
+  if constexpr (uses_vector_load()) {
+    cudaOccupancyMaxActiveBlocksPerMultiprocessor(&grid_size,
+                                                  detail::vectorized_pair_retrieve<block_size,
+                                                                                   warp_size(),
+                                                                                   cg_size(),
+                                                                                   buffer_size,
+                                                                                   is_outer,
+                                                                                   InputIt,
+                                                                                   OutputZipIt1,
+                                                                                   OutputZipIt2,
+                                                                                   atomic_ctr_type,
+                                                                                   device_view,
+                                                                                   PairEqual>,
+                                                  block_size,
+                                                  0);
+  } else {
+    cudaOccupancyMaxActiveBlocksPerMultiprocessor(&grid_size,
+                                                  detail::pair_retrieve<block_size,
+                                                                        warp_size(),
+                                                                        cg_size(),
+                                                                        buffer_size,
+                                                                        is_outer,
+                                                                        InputIt,
+                                                                        OutputZipIt1,
+                                                                        OutputZipIt2,
+                                                                        atomic_ctr_type,
+                                                                        device_view,
+                                                                        PairEqual>,
+                                                  block_size,
+                                                  0);
+  }
+  int dev_id{-1};
+  cudaGetDevice(&dev_id);
+  int num_sms{-1};
+  cudaDeviceGetAttribute(&num_sms, cudaDevAttrMultiProcessorCount, dev_id);
+  grid_size *= num_sms;
 
   cudaMemsetAsync(d_counter_, 0, sizeof(atomic_ctr_type), stream);
   std::size_t h_counter;
@@ -375,15 +561,51 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::pair_r
   PairEqual pair_equal,
   cudaStream_t stream) const
 {
-  auto num_pairs        = std::distance(first, last);
-  auto const block_size = 128;
+  auto num_pairs = std::distance(first, last);
+  auto view      = get_device_view();
+
   // Using per-warp buffer for vector loads and per-CG buffer for scalar loads
-  auto const buffer_size = uses_vector_load() ? (warp_size() * 3u) : (cg_size() * 3u);
-  auto const stride      = 1;
-  auto const grid_size = (cg_size() * num_pairs + stride * block_size - 1) / (stride * block_size);
-  auto view            = get_device_view();
+  constexpr auto buffer_size = uses_vector_load() ? (warp_size() * 3u) : (cg_size() * 3u);
+  constexpr bool is_outer    = true;
 
-  constexpr bool is_outer = true;
+  auto constexpr block_size = 128;
+  int grid_size{-1};
+  if constexpr (uses_vector_load()) {
+    cudaOccupancyMaxActiveBlocksPerMultiprocessor(&grid_size,
+                                                  detail::vectorized_pair_retrieve<block_size,
+                                                                                   warp_size(),
+                                                                                   cg_size(),
+                                                                                   buffer_size,
+                                                                                   is_outer,
+                                                                                   InputIt,
+                                                                                   OutputZipIt1,
+                                                                                   OutputZipIt2,
+                                                                                   atomic_ctr_type,
+                                                                                   device_view,
+                                                                                   PairEqual>,
+                                                  block_size,
+                                                  0);
+  } else {
+    cudaOccupancyMaxActiveBlocksPerMultiprocessor(&grid_size,
+                                                  detail::pair_retrieve<block_size,
+                                                                        warp_size(),
+                                                                        cg_size(),
+                                                                        buffer_size,
+                                                                        is_outer,
+                                                                        InputIt,
+                                                                        OutputZipIt1,
+                                                                        OutputZipIt2,
+                                                                        atomic_ctr_type,
+                                                                        device_view,
+                                                                        PairEqual>,
+                                                  block_size,
+                                                  0);
+  }
+  int dev_id{-1};
+  cudaGetDevice(&dev_id);
+  int num_sms{-1};
+  cudaDeviceGetAttribute(&num_sms, cudaDevAttrMultiProcessorCount, dev_id);
+  grid_size *= num_sms;
 
   cudaMemsetAsync(d_counter_, 0, sizeof(atomic_ctr_type), stream);
   std::size_t h_counter;

From d9d92ee3e3fd09a0e60a772a7a7c71d5ce47dff5 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Mon, 23 Aug 2021 13:40:33 -0400
Subject: [PATCH 195/267] Fix a minor bug: use bitwise_compare to compare
 against sentinel

---
 include/cuco/detail/static_multimap.inl | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index d04a98342..af791dfdc 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -1090,7 +1090,8 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_
   while (true) {
     auto slot_contents = *reinterpret_cast<cuco::pair_type<Key, Value> const*>(current_slot);
 
-    auto const slot_is_empty = (slot_contents.first == this->get_empty_key_sentinel());
+    auto const slot_is_empty =
+      detail::bitwise_compare(slot_contents.first, this->get_empty_key_sentinel());
 
     auto const equals = not slot_is_empty and pair_equal(slot_contents, pair);
 

From e152871936254fb22121bdfad6e06988d9de6cbf Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Mon, 23 Aug 2021 13:54:49 -0400
Subject: [PATCH 196/267] Remove unused key_equal in insert functions

---
 include/cuco/detail/static_multimap.inl       | 66 +++++++------------
 .../cuco/detail/static_multimap_kernels.cuh   | 23 ++-----
 include/cuco/static_multimap.cuh              | 64 ++++--------------
 3 files changed, 44 insertions(+), 109 deletions(-)

diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index af791dfdc..1487a71cb 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -77,11 +77,10 @@ template <typename Key,
           class ProbeSequence,
           cuda::thread_scope Scope,
           typename Allocator>
-template <typename InputIt, typename KeyEqual>
+template <typename InputIt>
 void static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::insert(InputIt first,
                                                                           InputIt last,
-                                                                          cudaStream_t stream,
-                                                                          KeyEqual key_equal)
+                                                                          cudaStream_t stream)
 {
   auto num_keys = std::distance(first, last);
   auto view     = get_device_mutable_view();
@@ -89,10 +88,7 @@ void static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::insert(InputI
   auto constexpr block_size = 128;
   int grid_size{-1};
   cudaOccupancyMaxActiveBlocksPerMultiprocessor(
-    &grid_size,
-    detail::insert<block_size, cg_size(), InputIt, device_mutable_view, KeyEqual>,
-    block_size,
-    0);
+    &grid_size, detail::insert<block_size, cg_size(), InputIt, device_mutable_view>, block_size, 0);
   int dev_id{-1};
   cudaGetDevice(&dev_id);
   int num_sms{-1};
@@ -100,7 +96,7 @@ void static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::insert(InputI
   grid_size *= num_sms;
 
   detail::insert<block_size, cg_size()>
-    <<<grid_size, block_size, 0, stream>>>(first, first + num_keys, view, key_equal);
+    <<<grid_size, block_size, 0, stream>>>(first, first + num_keys, view);
   CUCO_CUDA_TRY(cudaStreamSynchronize(stream));
 }
 
@@ -109,9 +105,11 @@ template <typename Key,
           class ProbeSequence,
           cuda::thread_scope Scope,
           typename Allocator>
-template <typename InputIt, typename Predicate, typename KeyEqual>
-void static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::insert_if(
-  InputIt first, InputIt last, Predicate pred, cudaStream_t stream, KeyEqual key_equal)
+template <typename InputIt, typename Predicate>
+void static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::insert_if(InputIt first,
+                                                                             InputIt last,
+                                                                             Predicate pred,
+                                                                             cudaStream_t stream)
 {
   auto num_keys = std::distance(first, last);
   auto view     = get_device_mutable_view();
@@ -120,7 +118,7 @@ void static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::insert_if(
   int grid_size{-1};
   cudaOccupancyMaxActiveBlocksPerMultiprocessor(
     &grid_size,
-    detail::insert_if<block_size, cg_size(), InputIt, device_mutable_view, Predicate, KeyEqual>,
+    detail::insert_if<block_size, cg_size(), InputIt, device_mutable_view, Predicate>,
     block_size,
     0);
   int dev_id{-1};
@@ -130,7 +128,7 @@ void static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::insert_if(
   grid_size *= num_sms;
 
   detail::insert_if<block_size, cg_size()>
-    <<<grid_size, block_size, 0, stream>>>(first, first + num_keys, view, pred, key_equal);
+    <<<grid_size, block_size, 0, stream>>>(first, first + num_keys, view, pred);
   CUCO_CUDA_TRY(cudaStreamSynchronize(stream));
 }
 
@@ -649,11 +647,10 @@ template <typename Key,
           class ProbeSequence,
           cuda::thread_scope Scope,
           typename Allocator>
-template <typename KeyEqual>
 __device__
   static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_mutable_view::insert_result
   static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_mutable_view::packed_cas(
-    iterator current_slot, value_type const& insert_pair, KeyEqual key_equal) noexcept
+    iterator current_slot, value_type const& insert_pair) noexcept
 {
   auto expected_key   = this->get_empty_key_sentinel();
   auto expected_value = this->get_empty_value_sentinel();
@@ -678,13 +675,10 @@ template <typename Key,
           class ProbeSequence,
           cuda::thread_scope Scope,
           typename Allocator>
-template <typename KeyEqual>
 __device__
   static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_mutable_view::insert_result
   static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_mutable_view::
-    back_to_back_cas(iterator current_slot,
-                     value_type const& insert_pair,
-                     KeyEqual key_equal) noexcept
+    back_to_back_cas(iterator current_slot, value_type const& insert_pair) noexcept
 {
   using cuda::std::memory_order_relaxed;
 
@@ -720,13 +714,10 @@ template <typename Key,
           class ProbeSequence,
           cuda::thread_scope Scope,
           typename Allocator>
-template <typename KeyEqual>
 __device__
   static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_mutable_view::insert_result
   static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_mutable_view::
-    cas_dependent_write(iterator current_slot,
-                        value_type const& insert_pair,
-                        KeyEqual key_equal) noexcept
+    cas_dependent_write(iterator current_slot, value_type const& insert_pair) noexcept
 {
   using cuda::std::memory_order_relaxed;
   auto expected_key = this->get_empty_key_sentinel();
@@ -750,13 +741,10 @@ template <typename Key,
           class ProbeSequence,
           cuda::thread_scope Scope,
           typename Allocator>
-template <bool uses_vector_load, typename CG, typename KeyEqual>
+template <bool uses_vector_load, typename CG>
 __device__ std::enable_if_t<uses_vector_load, void>
 static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_mutable_view::insert_impl(
-  CG g,
-  value_type const& insert_pair,
-  optional_hash_type precomputed_hash,
-  KeyEqual key_equal) noexcept
+  CG g, value_type const& insert_pair, optional_hash_type precomputed_hash) noexcept
 {
   auto current_slot = initial_slot(g, insert_pair.first, precomputed_hash);
   while (true) {
@@ -778,7 +766,7 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_mutable_vie
       if (g.thread_rank() == src_lane) {
         auto insert_location = first_slot_is_empty ? current_slot : current_slot + 1;
         // One single CAS operation since vector loads are dedicated to packable pairs
-        status = packed_cas(insert_location, insert_pair, key_equal);
+        status = packed_cas(insert_location, insert_pair);
       }
 
       // successful insert
@@ -800,13 +788,10 @@ template <typename Key,
           class ProbeSequence,
           cuda::thread_scope Scope,
           typename Allocator>
-template <bool uses_vector_load, typename CG, typename KeyEqual>
+template <bool uses_vector_load, typename CG>
 __device__ std::enable_if_t<not uses_vector_load, void>
 static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_mutable_view::insert_impl(
-  CG g,
-  value_type const& insert_pair,
-  optional_hash_type precomputed_hash,
-  KeyEqual key_equal) noexcept
+  CG g, value_type const& insert_pair, optional_hash_type precomputed_hash) noexcept
 {
   auto current_slot = initial_slot(g, insert_pair.first, precomputed_hash);
 
@@ -826,9 +811,9 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_mutable_vie
 
       if (g.thread_rank() == src_lane) {
 #if __CUDA_ARCH__ < 700
-        status = cas_dependent_write(current_slot, insert_pair, key_equal);
+        status = cas_dependent_write(current_slot, insert_pair);
 #else
-        status = back_to_back_cas(current_slot, insert_pair, key_equal);
+        status = back_to_back_cas(current_slot, insert_pair);
 #endif
       }
 
@@ -851,15 +836,12 @@ template <typename Key,
           class ProbeSequence,
           cuda::thread_scope Scope,
           typename Allocator>
-template <typename CG, typename KeyEqual>
+template <typename CG>
 __device__ void
 static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_mutable_view::insert(
-  CG g,
-  value_type const& insert_pair,
-  optional_hash_type precomputed_hash,
-  KeyEqual key_equal) noexcept
+  CG g, value_type const& insert_pair, optional_hash_type precomputed_hash) noexcept
 {
-  insert_impl<uses_vector_load()>(g, insert_pair, precomputed_hash, key_equal);
+  insert_impl<uses_vector_load()>(g, insert_pair, precomputed_hash);
 }
 
 template <typename Key,
diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap_kernels.cuh
index 2629abdfc..a95117f64 100644
--- a/include/cuco/detail/static_multimap_kernels.cuh
+++ b/include/cuco/detail/static_multimap_kernels.cuh
@@ -67,18 +67,13 @@ __global__ void initialize(pair_atomic_type* const slots, Key k, Value v, std::s
  * @tparam InputIt Device accessible input iterator whose `value_type` is
  * convertible to the map's `value_type`
  * @tparam viewT Type of device view allowing access of hash map storage
- * @tparam KeyEqual Binary callable type
+ *
  * @param first Beginning of the sequence of key/value pairs
  * @param last End of the sequence of key/value pairs
  * @param view Mutable device view used to access the hash map's slot storage
- * @param key_equal The binary function used to compare two keys for equality
  */
-template <uint32_t block_size,
-          uint32_t tile_size,
-          typename InputIt,
-          typename viewT,
-          typename KeyEqual>
-__global__ void insert(InputIt first, InputIt last, viewT view, KeyEqual key_equal)
+template <uint32_t block_size, uint32_t tile_size, typename InputIt, typename viewT>
+__global__ void insert(InputIt first, InputIt last, viewT view)
 {
   auto tile = cg::tiled_partition<tile_size>(cg::this_thread_block());
   auto tid  = block_size * blockIdx.x + threadIdx.x;
@@ -87,7 +82,7 @@ __global__ void insert(InputIt first, InputIt last, viewT view, KeyEqual key_equ
   while (it < last) {
     // force conversion to value_type
     typename viewT::value_type const insert_pair{*it};
-    view.insert(tile, insert_pair, thrust::nullopt, key_equal);
+    view.insert(tile, insert_pair, thrust::nullopt);
     it += (gridDim.x * block_size) / tile_size;
   }
 }
@@ -106,20 +101,16 @@ __global__ void insert(InputIt first, InputIt last, viewT view, KeyEqual key_equ
  * convertible to the map's `value_type`
  * @tparam viewT Type of device view allowing access of hash map storage
  * @tparam Predicate Unary predicate function type
- * @tparam KeyEqual Binary callable type
  * @param first Beginning of the sequence of key/value pairs
  * @param last End of the sequence of key/value pairs
  * @param view Mutable device view used to access the hash map's slot storage
- * @param key_equal The binary function used to compare two keys for equality
  */
 template <uint32_t block_size,
           uint32_t tile_size,
           typename InputIt,
           typename viewT,
-          typename Predicate,
-          typename KeyEqual>
-__global__ void insert_if(
-  InputIt first, InputIt last, viewT view, Predicate pred, KeyEqual key_equal)
+          typename Predicate>
+__global__ void insert_if(InputIt first, InputIt last, viewT view, Predicate pred)
 {
   auto tile = cg::tiled_partition<tile_size>(cg::this_thread_block());
   auto tid  = block_size * blockIdx.x + threadIdx.x;
@@ -129,7 +120,7 @@ __global__ void insert_if(
     typename viewT::value_type const insert_pair{*it};
     if (pred(insert_pair)) {
       // force conversion to value_type
-      view.insert(tile, insert_pair, thrust::nullopt, key_equal);
+      view.insert(tile, insert_pair, thrust::nullopt);
     }
     it += (gridDim.x * block_size) / tile_size;
   }
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index 1092a3411..85df36500 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -214,17 +214,12 @@ class static_multimap {
    *
    * @tparam InputIt Device accessible input iterator whose `value_type` is
    * convertible to the map's `value_type`
-   * @tparam KeyEqual Binary callable type
    * @param first Beginning of the sequence of key/value pairs
    * @param last End of the sequence of key/value pairs
    * @param stream CUDA stream used for insert
-   * @param key_equal The binary function to compare two keys for equality
    */
-  template <typename InputIt, typename KeyEqual = thrust::equal_to<key_type>>
-  void insert(InputIt first,
-              InputIt last,
-              cudaStream_t stream = 0,
-              KeyEqual key_equal  = KeyEqual{});
+  template <typename InputIt>
+  void insert(InputIt first, InputIt last, cudaStream_t stream = 0);
 
   /**
    * @brief Inserts key/value pairs in the range `[first, last)` if `pred` returns true.
@@ -238,12 +233,8 @@ class static_multimap {
    * @param stream CUDA stream used for insert
    * @param key_equal The binary function to compare two keys for equality
    */
-  template <typename InputIt, typename Predicate, typename KeyEqual = thrust::equal_to<key_type>>
-  void insert_if(InputIt first,
-                 InputIt last,
-                 Predicate pred,
-                 cudaStream_t stream = 0,
-                 KeyEqual key_equal  = KeyEqual{});
+  template <typename InputIt, typename Predicate>
+  void insert_if(InputIt first, InputIt last, Predicate pred, cudaStream_t stream = 0);
 
   /**
    * @brief Indicates whether the keys in the range `[first, last)` are contained in the map.
@@ -689,113 +680,84 @@ class static_multimap {
     /**
      * @brief Inserts the specified key/value pair with one single CAS operation.
      *
-     * @tparam KeyEqual Binary callable type
      * @param current_slot The slot to insert
      * @param insert_pair The pair to insert
      * @param key_equal The binary callable used to compare two keys for
      * equality
      * @return An insert result from the `insert_resullt` enumeration.
      */
-    template <typename KeyEqual>
     __device__ insert_result packed_cas(iterator current_slot,
-                                        value_type const& insert_pair,
-                                        KeyEqual key_equal) noexcept;
+                                        value_type const& insert_pair) noexcept;
 
     /**
      * @brief Inserts the specified key/value pair with two back-to-back CAS operations.
      *
-     * @tparam KeyEqual Binary callable type
      * @param current_slot The slot to insert
      * @param insert_pair The pair to insert
-     * @param key_equal The binary callable used to compare two keys for
-     * equality
      * @return An insert result from the `insert_resullt` enumeration.
      */
-    template <typename KeyEqual>
     __device__ insert_result back_to_back_cas(iterator current_slot,
-                                              value_type const& insert_pair,
-                                              KeyEqual key_equal) noexcept;
+                                              value_type const& insert_pair) noexcept;
 
     /**
      * @brief Inserts the specified key/value pair with a CAS of the key and a dependent write of
      * the value.
      *
-     * @tparam KeyEqual Binary callable type
      * @param current_slot The slot to insert
      * @param insert_pair The pair to insert
-     * @param key_equal The binary callable used to compare two keys for
-     * equality
      * @return An insert result from the `insert_resullt` enumeration.
      */
-    template <typename KeyEqual>
     __device__ insert_result cas_dependent_write(iterator current_slot,
-                                                 value_type const& insert_pair,
-                                                 KeyEqual key_equal) noexcept;
+                                                 value_type const& insert_pair) noexcept;
 
     /**
      * @brief Inserts the specified key/value pair into the map using vector loads.
      *
      * @tparam uses_vector_load Boolean flag indicating whether vector loads are used or not
      * @tparam CG Cooperative Group type
-     * @tparam KeyEqual Binary callable type
      *
      * @param g The Cooperative Group that performs the insert
      * @param insert_pair The pair to insert
      * @param precomputed_hash Optional value which allows users to specify the precomputed hash
      * value
-     * @param key_equal The binary callable used to compare two keys for
-     * equality
      * @return void.
      */
-    template <bool uses_vector_load, typename CG, typename KeyEqual = thrust::equal_to<key_type>>
+    template <bool uses_vector_load, typename CG>
     __device__ std::enable_if_t<uses_vector_load, void> insert_impl(
-      CG g,
-      value_type const& insert_pair,
-      optional_hash_type precomputed_hash,
-      KeyEqual key_equal = KeyEqual{}) noexcept;
+      CG g, value_type const& insert_pair, optional_hash_type precomputed_hash) noexcept;
 
     /**
      * @brief Inserts the specified key/value pair into the map using scalar loads.
      *
      * @tparam uses_vector_load Boolean flag indicating whether vector loads are used or not
      * @tparam CG Cooperative Group type
-     * @tparam KeyEqual Binary callable type
      *
      * @param g The Cooperative Group that performs the insert
      * @param insert_pair The pair to insert
      * @param precomputed_hash Optional value which allows users to specify the precomputed hash
      * value
-     * @param key_equal The binary callable used to compare two keys for
-     * equality
      * @return void.
      */
-    template <bool uses_vector_load, typename CG, typename KeyEqual = thrust::equal_to<key_type>>
+    template <bool uses_vector_load, typename CG>
     __device__ std::enable_if_t<not uses_vector_load, void> insert_impl(
-      CG g,
-      value_type const& insert_pair,
-      optional_hash_type precomputed_hash,
-      KeyEqual key_equal = KeyEqual{}) noexcept;
+      CG g, value_type const& insert_pair, optional_hash_type precomputed_hash) noexcept;
 
    public:
     /**
      * @brief Inserts the specified key/value pair into the map.
      *
      * @tparam CG Cooperative Group type
-     * @tparam KeyEqual Binary callable type
      *
      * @param g The Cooperative Group that performs the insert
      * @param insert_pair The pair to insert
      * @param precomputed_hash Optional value which allows users to specify the precomputed hash
      * value
-     * @param key_equal The binary callable used to compare two keys for
-     * equality
      * @return void.
      */
-    template <typename CG, typename KeyEqual = thrust::equal_to<key_type>>
+    template <typename CG>
     __device__ void insert(CG g,
                            value_type const& insert_pair,
-                           optional_hash_type precomputed_hash,
-                           KeyEqual key_equal = KeyEqual{}) noexcept;
+                           optional_hash_type precomputed_hash) noexcept;
   };  // class device mutable view
 
   /**

From 72e95b4ddbcaa19b687a80b86edde9078882e54a Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Mon, 23 Aug 2021 14:17:24 -0400
Subject: [PATCH 197/267] Remove precomputed_hash logic + fix a bug in
 vectorized linear probing

---
 include/cuco/detail/probe_sequences.cuh       |  22 ++--
 include/cuco/detail/static_multimap.inl       | 102 +++++-----------
 .../cuco/detail/static_multimap_kernels.cuh   |  40 ++-----
 include/cuco/static_multimap.cuh              | 109 ++----------------
 4 files changed, 66 insertions(+), 207 deletions(-)

diff --git a/include/cuco/detail/probe_sequences.cuh b/include/cuco/detail/probe_sequences.cuh
index 906133d99..f83a8c2a3 100644
--- a/include/cuco/detail/probe_sequences.cuh
+++ b/include/cuco/detail/probe_sequences.cuh
@@ -84,13 +84,10 @@ class double_hashing : public probe_sequence_base<Key, Value, CGSize, Scope> {
   }
 
   template <typename CG>
-  __device__ iterator initial_slot(
-    CG const& g,
-    Key const k,
-    thrust::optional<typename detail::MurmurHash3_32<Key>::result_type> precomputed_hash) noexcept
+  __device__ iterator initial_slot(CG const& g, Key const k) noexcept
   {
     std::size_t index;
-    auto const hash_value = precomputed_hash.value_or(hash1_(k));
+    auto const hash_value = hash1_(k);
     if constexpr (uses_vector_load()) {
       step_size_ = (hash2_(k + 1) % (capacity_ / (cg_size() * vector_width()) - 1) + 1) *
                    cg_size() * vector_width();
@@ -137,21 +134,28 @@ class linear_probing : public probe_sequence_base<Key, Value, CGSize, Scope> {
   template <typename CG>
   __device__ iterator initial_slot(CG const& g, Key const k) noexcept
   {
+    auto hash_value = hash_(k);
+    hash_value      = hash_value % 2 ? hash_value + 1 : hash_value;
+
+    std::size_t offset;
     if constexpr (uses_vector_load()) {
-      return &slots_[(hash_(k) + g.thread_rank() * vector_width()) % capacity_];
+      offset = g.thread_rank() * vector_width();
     } else {
-      return &slots_[(hash_(k) + g.thread_rank()) % capacity_];
+      offset = g.thread_rank();
     }
+    return &slots_[(hash_value + offset) % capacity_];
   }
 
   __device__ iterator next_slot(iterator s) noexcept
   {
     std::size_t index = s - slots_;
+    std::size_t offset;
     if constexpr (uses_vector_load()) {
-      return &slots_[(index + cg_size() * vector_width()) % capacity_];
+      offset = cg_size() * vector_width();
     } else {
-      return &slots_[(index + cg_size()) % capacity_];
+      offset = cg_size();
     }
+    return &slots_[(index + offset) % capacity_];
   }
 
  private:
diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index 1487a71cb..47df44f60 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -744,9 +744,9 @@ template <typename Key,
 template <bool uses_vector_load, typename CG>
 __device__ std::enable_if_t<uses_vector_load, void>
 static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_mutable_view::insert_impl(
-  CG g, value_type const& insert_pair, optional_hash_type precomputed_hash) noexcept
+  CG g, value_type const& insert_pair) noexcept
 {
-  auto current_slot = initial_slot(g, insert_pair.first, precomputed_hash);
+  auto current_slot = initial_slot(g, insert_pair.first);
   while (true) {
     value_type arr[2];
     load_pair_array(&arr[0], current_slot);
@@ -791,9 +791,9 @@ template <typename Key,
 template <bool uses_vector_load, typename CG>
 __device__ std::enable_if_t<not uses_vector_load, void>
 static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_mutable_view::insert_impl(
-  CG g, value_type const& insert_pair, optional_hash_type precomputed_hash) noexcept
+  CG g, value_type const& insert_pair) noexcept
 {
-  auto current_slot = initial_slot(g, insert_pair.first, precomputed_hash);
+  auto current_slot = initial_slot(g, insert_pair.first);
 
   while (true) {
     key_type const existing_key = current_slot->first.load(cuda::memory_order_relaxed);
@@ -839,9 +839,9 @@ template <typename Key,
 template <typename CG>
 __device__ void
 static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_mutable_view::insert(
-  CG g, value_type const& insert_pair, optional_hash_type precomputed_hash) noexcept
+  CG g, value_type const& insert_pair) noexcept
 {
-  insert_impl<uses_vector_load()>(g, insert_pair, precomputed_hash);
+  insert_impl<uses_vector_load()>(g, insert_pair);
 }
 
 template <typename Key,
@@ -852,9 +852,9 @@ template <typename Key,
 template <bool uses_vector_load, typename CG, typename KeyEqual>
 __device__ std::enable_if_t<uses_vector_load, bool>
 static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::contains_impl(
-  CG g, Key const& k, optional_hash_type precomputed_hash, KeyEqual key_equal) noexcept
+  CG g, Key const& k, KeyEqual key_equal) noexcept
 {
-  auto current_slot = initial_slot(g, k, precomputed_hash);
+  auto current_slot = initial_slot(g, k);
 
   while (true) {
     value_type arr[2];
@@ -887,9 +887,9 @@ template <typename Key,
 template <bool uses_vector_load, typename CG, typename KeyEqual>
 __device__ std::enable_if_t<not uses_vector_load, bool>
 static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::contains_impl(
-  CG g, Key const& k, optional_hash_type precomputed_hash, KeyEqual key_equal) noexcept
+  CG g, Key const& k, KeyEqual key_equal) noexcept
 {
-  auto current_slot = initial_slot(g, k, precomputed_hash);
+  auto current_slot = initial_slot(g, k);
 
   while (true) {
     key_type const existing_key = current_slot->first.load(cuda::std::memory_order_relaxed);
@@ -921,13 +921,9 @@ template <typename Key,
 template <bool uses_vector_load, bool is_outer, typename CG, typename KeyEqual>
 __device__ std::enable_if_t<uses_vector_load, void>
 static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::count_impl(
-  CG const& g,
-  Key const& k,
-  optional_hash_type precomputed_hash,
-  std::size_t& thread_num_matches,
-  KeyEqual key_equal) noexcept
+  CG const& g, Key const& k, std::size_t& thread_num_matches, KeyEqual key_equal) noexcept
 {
-  auto current_slot = initial_slot(g, k, precomputed_hash);
+  auto current_slot = initial_slot(g, k);
 
   [[maybe_unused]] bool found_match = false;
 
@@ -967,13 +963,9 @@ template <typename Key,
 template <bool uses_vector_load, bool is_outer, typename CG, typename KeyEqual>
 __device__ std::enable_if_t<not uses_vector_load, void>
 static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::count_impl(
-  CG const& g,
-  Key const& k,
-  optional_hash_type precomputed_hash,
-  std::size_t& thread_num_matches,
-  KeyEqual key_equal) noexcept
+  CG const& g, Key const& k, std::size_t& thread_num_matches, KeyEqual key_equal) noexcept
 {
-  auto current_slot = initial_slot(g, k, precomputed_hash);
+  auto current_slot = initial_slot(g, k);
 
   [[maybe_unused]] bool found_match = false;
 
@@ -1012,12 +1004,11 @@ __device__ std::enable_if_t<uses_vector_load, void>
 static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_count_impl(
   CG const& g,
   value_type const& pair,
-  optional_hash_type precomputed_hash,
   std::size_t& thread_num_matches,
   PairEqual pair_equal) noexcept
 {
   auto key          = pair.first;
-  auto current_slot = initial_slot(g, key, precomputed_hash);
+  auto current_slot = initial_slot(g, key);
 
   [[maybe_unused]] bool found_match = false;
 
@@ -1060,12 +1051,11 @@ __device__ std::enable_if_t<not uses_vector_load, void>
 static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_count_impl(
   CG const& g,
   value_type const& pair,
-  optional_hash_type precomputed_hash,
   std::size_t& thread_num_matches,
   PairEqual pair_equal) noexcept
 {
   auto key          = pair.first;
-  auto current_slot = initial_slot(g, key, precomputed_hash);
+  auto current_slot = initial_slot(g, key);
 
   [[maybe_unused]] bool found_match = false;
 
@@ -1111,7 +1101,6 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::retri
   warpT const& warp,
   CG const& g,
   Key const& k,
-  optional_hash_type precomputed_hash,
   uint32_t* warp_counter,
   value_type* output_buffer,
   atomicT* num_matches,
@@ -1120,7 +1109,7 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::retri
 {
   const uint32_t cg_lane_id = g.thread_rank();
 
-  auto current_slot = initial_slot(g, k, precomputed_hash);
+  auto current_slot = initial_slot(g, k);
 
   bool running                      = true;
   [[maybe_unused]] bool found_match = false;
@@ -1204,7 +1193,6 @@ __device__ void
 static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::retrieve_impl(
   CG const& g,
   Key const& k,
-  optional_hash_type precomputed_hash,
   uint32_t* cg_counter,
   value_type* output_buffer,
   atomicT* num_matches,
@@ -1213,7 +1201,7 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::retri
 {
   const uint32_t lane_id = g.thread_rank();
 
-  auto current_slot = initial_slot(g, k, precomputed_hash);
+  auto current_slot = initial_slot(g, k);
 
   bool running                      = true;
   [[maybe_unused]] bool found_match = false;
@@ -1285,7 +1273,6 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_
   warpT const& warp,
   CG const& g,
   value_type const& pair,
-  optional_hash_type precomputed_hash,
   uint32_t* warp_counter,
   value_type* probe_output_buffer,
   value_type* contained_output_buffer,
@@ -1297,7 +1284,7 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_
   const uint32_t cg_lane_id = g.thread_rank();
 
   auto key          = pair.first;
-  auto current_slot = initial_slot(g, key, precomputed_hash);
+  auto current_slot = initial_slot(g, key);
 
   bool running                      = true;
   [[maybe_unused]] bool found_match = false;
@@ -1387,7 +1374,6 @@ __device__ void
 static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_retrieve_impl(
   CG const& g,
   value_type const& pair,
-  optional_hash_type precomputed_hash,
   uint32_t* cg_counter,
   value_type* probe_output_buffer,
   value_type* contained_output_buffer,
@@ -1399,7 +1385,7 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_
   const uint32_t lane_id = g.thread_rank();
 
   auto key          = pair.first;
-  auto current_slot = initial_slot(g, key, precomputed_hash);
+  auto current_slot = initial_slot(g, key);
 
   bool running                      = true;
   [[maybe_unused]] bool found_match = false;
@@ -1563,9 +1549,9 @@ template <typename Key,
           typename Allocator>
 template <typename CG, typename KeyEqual>
 __device__ bool static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::contains(
-  CG g, Key const& k, optional_hash_type precomputed_hash, KeyEqual key_equal) noexcept
+  CG g, Key const& k, KeyEqual key_equal) noexcept
 {
-  return contains_impl<uses_vector_load()>(g, k, precomputed_hash, key_equal);
+  return contains_impl<uses_vector_load()>(g, k, key_equal);
 }
 
 template <typename Key,
@@ -1575,14 +1561,10 @@ template <typename Key,
           typename Allocator>
 template <typename CG, typename KeyEqual>
 __device__ void static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::count(
-  CG const& g,
-  Key const& k,
-  optional_hash_type precomputed_hash,
-  std::size_t& thread_num_matches,
-  KeyEqual key_equal) noexcept
+  CG const& g, Key const& k, std::size_t& thread_num_matches, KeyEqual key_equal) noexcept
 {
   constexpr bool is_outer = false;
-  count_impl<uses_vector_load(), is_outer>(g, k, precomputed_hash, thread_num_matches, key_equal);
+  count_impl<uses_vector_load(), is_outer>(g, k, thread_num_matches, key_equal);
 }
 
 template <typename Key,
@@ -1593,14 +1575,10 @@ template <typename Key,
 template <typename CG, typename KeyEqual>
 __device__ void
 static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::count_outer(
-  CG const& g,
-  Key const& k,
-  optional_hash_type precomputed_hash,
-  std::size_t& thread_num_matches,
-  KeyEqual key_equal) noexcept
+  CG const& g, Key const& k, std::size_t& thread_num_matches, KeyEqual key_equal) noexcept
 {
   constexpr bool is_outer = true;
-  count_impl<uses_vector_load(), is_outer>(g, k, precomputed_hash, thread_num_matches, key_equal);
+  count_impl<uses_vector_load(), is_outer>(g, k, thread_num_matches, key_equal);
 }
 
 template <typename Key,
@@ -1613,13 +1591,11 @@ __device__ void
 static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_count(
   CG const& g,
   value_type const& pair,
-  optional_hash_type precomputed_hash,
   std::size_t& thread_num_matches,
   PairEqual pair_equal) noexcept
 {
   constexpr bool is_outer = false;
-  pair_count_impl<uses_vector_load(), is_outer>(
-    g, pair, precomputed_hash, thread_num_matches, pair_equal);
+  pair_count_impl<uses_vector_load(), is_outer>(g, pair, thread_num_matches, pair_equal);
 }
 
 template <typename Key,
@@ -1632,13 +1608,11 @@ __device__ void
 static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_count_outer(
   CG const& g,
   value_type const& pair,
-  optional_hash_type precomputed_hash,
   std::size_t& thread_num_matches,
   PairEqual pair_equal) noexcept
 {
   constexpr bool is_outer = true;
-  pair_count_impl<uses_vector_load(), is_outer>(
-    g, pair, precomputed_hash, thread_num_matches, pair_equal);
+  pair_count_impl<uses_vector_load(), is_outer>(g, pair, thread_num_matches, pair_equal);
 }
 
 template <typename Key,
@@ -1656,7 +1630,6 @@ __device__ void static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::de
   warpT const& warp,
   CG const& g,
   Key const& k,
-  optional_hash_type precomputed_hash,
   uint32_t* warp_counter,
   value_type* output_buffer,
   atomicT* num_matches,
@@ -1665,7 +1638,7 @@ __device__ void static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::de
 {
   constexpr bool is_outer = false;
   retrieve_impl<buffer_size, is_outer>(
-    warp, g, k, precomputed_hash, warp_counter, output_buffer, num_matches, output_begin);
+    warp, g, k, warp_counter, output_buffer, num_matches, output_begin);
 }
 
 template <typename Key,
@@ -1684,7 +1657,6 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::retri
   warpT const& warp,
   CG const& g,
   Key const& k,
-  optional_hash_type precomputed_hash,
   uint32_t* warp_counter,
   value_type* output_buffer,
   atomicT* num_matches,
@@ -1693,7 +1665,7 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::retri
 {
   constexpr bool is_outer = true;
   retrieve_impl<buffer_size, is_outer>(
-    warp, g, k, precomputed_hash, warp_counter, output_buffer, num_matches, output_begin);
+    warp, g, k, warp_counter, output_buffer, num_matches, output_begin);
 }
 
 template <typename Key,
@@ -1710,7 +1682,6 @@ template <uint32_t cg_size,
 __device__ void static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::retrieve(
   CG const& g,
   Key const& k,
-  optional_hash_type precomputed_hash,
   uint32_t* cg_counter,
   value_type* output_buffer,
   atomicT* num_matches,
@@ -1719,7 +1690,7 @@ __device__ void static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::de
 {
   constexpr bool is_outer = false;
   retrieve_impl<cg_size, buffer_size, is_outer>(
-    g, k, precomputed_hash, cg_counter, output_buffer, num_matches, output_begin);
+    g, k, cg_counter, output_buffer, num_matches, output_begin);
 }
 
 template <typename Key,
@@ -1737,7 +1708,6 @@ __device__ void
 static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::retrieve_outer(
   CG const& g,
   Key const& k,
-  optional_hash_type precomputed_hash,
   uint32_t* cg_counter,
   value_type* output_buffer,
   atomicT* num_matches,
@@ -1746,7 +1716,7 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::retri
 {
   constexpr bool is_outer = true;
   retrieve_impl<cg_size, buffer_size, is_outer>(
-    g, k, precomputed_hash, cg_counter, output_buffer, num_matches, output_begin);
+    g, k, cg_counter, output_buffer, num_matches, output_begin);
 }
 
 template <typename Key,
@@ -1766,7 +1736,6 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_
   warpT const& warp,
   CG const& g,
   value_type const& pair,
-  optional_hash_type precomputed_hash,
   uint32_t* warp_counter,
   value_type* probe_output_buffer,
   value_type* contained_output_buffer,
@@ -1779,7 +1748,6 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_
   pair_retrieve_impl<buffer_size, is_outer>(warp,
                                             g,
                                             pair,
-                                            precomputed_hash,
                                             warp_counter,
                                             probe_output_buffer,
                                             contained_output_buffer,
@@ -1806,7 +1774,6 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_
   warpT const& warp,
   CG const& g,
   value_type const& pair,
-  optional_hash_type precomputed_hash,
   uint32_t* warp_counter,
   value_type* probe_output_buffer,
   value_type* contained_output_buffer,
@@ -1819,7 +1786,6 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_
   pair_retrieve_impl<buffer_size, is_outer>(warp,
                                             g,
                                             pair,
-                                            precomputed_hash,
                                             warp_counter,
                                             probe_output_buffer,
                                             contained_output_buffer,
@@ -1845,7 +1811,6 @@ __device__ void
 static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_retrieve(
   CG const& g,
   value_type const& pair,
-  optional_hash_type precomputed_hash,
   uint32_t* cg_counter,
   value_type* probe_output_buffer,
   value_type* contained_output_buffer,
@@ -1857,7 +1822,6 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_
   constexpr bool is_outer = false;
   pair_retrieve_impl<cg_size, buffer_size, is_outer>(g,
                                                      pair,
-                                                     precomputed_hash,
                                                      cg_counter,
                                                      probe_output_buffer,
                                                      contained_output_buffer,
@@ -1883,7 +1847,6 @@ __device__ void
 static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_retrieve_outer(
   CG const& g,
   value_type const& pair,
-  optional_hash_type precomputed_hash,
   uint32_t* cg_counter,
   value_type* probe_output_buffer,
   value_type* contained_output_buffer,
@@ -1895,7 +1858,6 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_
   constexpr bool is_outer = true;
   pair_retrieve_impl<cg_size, buffer_size, is_outer>(g,
                                                      pair,
-                                                     precomputed_hash,
                                                      cg_counter,
                                                      probe_output_buffer,
                                                      contained_output_buffer,
diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap_kernels.cuh
index a95117f64..ce656a5a0 100644
--- a/include/cuco/detail/static_multimap_kernels.cuh
+++ b/include/cuco/detail/static_multimap_kernels.cuh
@@ -82,7 +82,7 @@ __global__ void insert(InputIt first, InputIt last, viewT view)
   while (it < last) {
     // force conversion to value_type
     typename viewT::value_type const insert_pair{*it};
-    view.insert(tile, insert_pair, thrust::nullopt);
+    view.insert(tile, insert_pair);
     it += (gridDim.x * block_size) / tile_size;
   }
 }
@@ -120,7 +120,7 @@ __global__ void insert_if(InputIt first, InputIt last, viewT view, Predicate pre
     typename viewT::value_type const insert_pair{*it};
     if (pred(insert_pair)) {
       // force conversion to value_type
-      view.insert(tile, insert_pair, thrust::nullopt);
+      view.insert(tile, insert_pair);
     }
     it += (gridDim.x * block_size) / tile_size;
   }
@@ -164,7 +164,7 @@ __global__ void contains(
 
   while (first + key_idx < last) {
     auto key   = *(first + key_idx);
-    auto found = view.contains(tile, key, thrust::nullopt, key_equal);
+    auto found = view.contains(tile, key, key_equal);
 
     /*
      * The ld.relaxed.gpu instruction used in view.find causes L1 to
@@ -223,9 +223,9 @@ __global__ void count(
   while (first + key_idx < last) {
     auto key = *(first + key_idx);
     if constexpr (is_outer) {
-      view.count_outer(tile, key, thrust::nullopt, thread_num_matches, key_equal);
+      view.count_outer(tile, key, thread_num_matches, key_equal);
     } else {
-      view.count(tile, key, thrust::nullopt, thread_num_matches, key_equal);
+      view.count(tile, key, thread_num_matches, key_equal);
     }
     key_idx += (gridDim.x * block_size) / tile_size;
   }
@@ -278,9 +278,9 @@ __global__ void pair_count(
   while (first + pair_idx < last) {
     typename viewT::value_type const pair = *(first + pair_idx);
     if constexpr (is_outer) {
-      view.pair_count_outer(tile, pair, thrust::nullopt, thread_num_matches, pair_equal);
+      view.pair_count_outer(tile, pair, thread_num_matches, pair_equal);
     } else {
-      view.pair_count(tile, pair, thrust::nullopt, thread_num_matches, pair_equal);
+      view.pair_count(tile, pair, thread_num_matches, pair_equal);
     }
     pair_idx += (gridDim.x * block_size) / tile_size;
   }
@@ -368,7 +368,6 @@ __global__ void vectorized_retrieve(InputIt first,
         view.retrieve_outer<buffer_size>(active_warp,
                                          tile,
                                          key,
-                                         thrust::nullopt,
                                          &warp_counter[warp_id],
                                          output_buffer[warp_id],
                                          num_matches,
@@ -378,7 +377,6 @@ __global__ void vectorized_retrieve(InputIt first,
         view.retrieve<buffer_size>(active_warp,
                                    tile,
                                    key,
-                                   thrust::nullopt,
                                    &warp_counter[warp_id],
                                    output_buffer[warp_id],
                                    num_matches,
@@ -462,23 +460,11 @@ __global__ void retrieve(InputIt first,
   while (first + key_idx < last) {
     auto key = *(first + key_idx);
     if constexpr (is_outer) {
-      view.retrieve_outer<tile_size, buffer_size>(tile,
-                                                  key,
-                                                  thrust::nullopt,
-                                                  &cg_counter[cg_id],
-                                                  output_buffer[cg_id],
-                                                  num_matches,
-                                                  output_begin,
-                                                  key_equal);
+      view.retrieve_outer<tile_size, buffer_size>(
+        tile, key, &cg_counter[cg_id], output_buffer[cg_id], num_matches, output_begin, key_equal);
     } else {
-      view.retrieve<tile_size, buffer_size>(tile,
-                                            key,
-                                            thrust::nullopt,
-                                            &cg_counter[cg_id],
-                                            output_buffer[cg_id],
-                                            num_matches,
-                                            output_begin,
-                                            key_equal);
+      view.retrieve<tile_size, buffer_size>(
+        tile, key, &cg_counter[cg_id], output_buffer[cg_id], num_matches, output_begin, key_equal);
     }
     key_idx += (gridDim.x * block_size) / tile_size;
   }
@@ -537,7 +523,6 @@ __global__ void vectorized_pair_retrieve(InputIt first,
         view.pair_retrieve_outer<buffer_size>(active_warp,
                                               tile,
                                               pair,
-                                              thrust::nullopt,
                                               &warp_counter[warp_id],
                                               probe_output_buffer[warp_id],
                                               contained_output_buffer[warp_id],
@@ -549,7 +534,6 @@ __global__ void vectorized_pair_retrieve(InputIt first,
         view.pair_retrieve<buffer_size>(active_warp,
                                         tile,
                                         pair,
-                                        thrust::nullopt,
                                         &warp_counter[warp_id],
                                         probe_output_buffer[warp_id],
                                         contained_output_buffer[warp_id],
@@ -614,7 +598,6 @@ __global__ void pair_retrieve(InputIt first,
     if constexpr (is_outer) {
       view.pair_retrieve_outer<tile_size, buffer_size>(tile,
                                                        pair,
-                                                       thrust::nullopt,
                                                        &cg_counter[cg_id],
                                                        probe_output_buffer[cg_id],
                                                        contained_output_buffer[cg_id],
@@ -625,7 +608,6 @@ __global__ void pair_retrieve(InputIt first,
     } else {
       view.pair_retrieve<tile_size, buffer_size>(tile,
                                                  pair,
-                                                 thrust::nullopt,
                                                  &cg_counter[cg_id],
                                                  probe_output_buffer[cg_id],
                                                  contained_output_buffer[cg_id],
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index 85df36500..d93fa6f13 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -19,7 +19,6 @@
 #include <cooperative_groups.h>
 #include <thrust/distance.h>
 #include <thrust/functional.h>
-#include <thrust/optional.h>
 #include <cub/cub.cuh>
 #include <memory>
 
@@ -152,7 +151,6 @@ class static_multimap {
     typename std::allocator_traits<Allocator>::rebind_alloc<pair_atomic_type>;
   using counter_allocator_type =
     typename std::allocator_traits<Allocator>::rebind_alloc<atomic_ctr_type>;
-  using optional_hash_type = thrust::optional<typename detail::MurmurHash3_32<Key>::result_type>;
 
   static_multimap(static_multimap const&) = delete;
   static_multimap(static_multimap&&)      = delete;
@@ -540,11 +538,9 @@ class static_multimap {
      * @return Pointer to the initial slot for `k`
      */
     template <typename CG>
-    __device__ iterator initial_slot(CG const& g,
-                                     Key const& k,
-                                     optional_hash_type precomputed_hash) noexcept
+    __device__ iterator initial_slot(CG const& g, Key const& k) noexcept
     {
-      return probe_sequence_.initial_slot(g, k, precomputed_hash);
+      return probe_sequence_.initial_slot(g, k);
     }
 
     /**
@@ -558,11 +554,9 @@ class static_multimap {
      * @return Pointer to the initial slot for `k`
      */
     template <typename CG>
-    __device__ const_iterator initial_slot(CG g,
-                                           Key const& k,
-                                           optional_hash_type precomputed_hash) const noexcept
+    __device__ const_iterator initial_slot(CG g, Key const& k) const noexcept
     {
-      return probe_sequence_.initial_slot(g, k, precomputed_hash);
+      return probe_sequence_.initial_slot(g, k);
     }
 
     /**
@@ -718,13 +712,11 @@ class static_multimap {
      *
      * @param g The Cooperative Group that performs the insert
      * @param insert_pair The pair to insert
-     * @param precomputed_hash Optional value which allows users to specify the precomputed hash
-     * value
      * @return void.
      */
     template <bool uses_vector_load, typename CG>
     __device__ std::enable_if_t<uses_vector_load, void> insert_impl(
-      CG g, value_type const& insert_pair, optional_hash_type precomputed_hash) noexcept;
+      CG g, value_type const& insert_pair) noexcept;
 
     /**
      * @brief Inserts the specified key/value pair into the map using scalar loads.
@@ -734,13 +726,11 @@ class static_multimap {
      *
      * @param g The Cooperative Group that performs the insert
      * @param insert_pair The pair to insert
-     * @param precomputed_hash Optional value which allows users to specify the precomputed hash
-     * value
      * @return void.
      */
     template <bool uses_vector_load, typename CG>
     __device__ std::enable_if_t<not uses_vector_load, void> insert_impl(
-      CG g, value_type const& insert_pair, optional_hash_type precomputed_hash) noexcept;
+      CG g, value_type const& insert_pair) noexcept;
 
    public:
     /**
@@ -750,14 +740,10 @@ class static_multimap {
      *
      * @param g The Cooperative Group that performs the insert
      * @param insert_pair The pair to insert
-     * @param precomputed_hash Optional value which allows users to specify the precomputed hash
-     * value
      * @return void.
      */
     template <typename CG>
-    __device__ void insert(CG g,
-                           value_type const& insert_pair,
-                           optional_hash_type precomputed_hash) noexcept;
+    __device__ void insert(CG g, value_type const& insert_pair) noexcept;
   };  // class device mutable view
 
   /**
@@ -881,8 +867,6 @@ class static_multimap {
      * @tparam KeyEqual Binary callable type
      * @param g The Cooperative Group used to perform the contains operation
      * @param k The key to search for
-     * @param precomputed_hash Optional value which allows users to specify the precomputed hash
-     * value
      * @param key_equal The binary callable used to compare two keys
      * for equality
      * @return A boolean indicating whether the key/value pair
@@ -890,10 +874,7 @@ class static_multimap {
      */
     template <bool uses_vector_load, typename CG, typename KeyEqual = thrust::equal_to<key_type>>
     __device__ std::enable_if_t<uses_vector_load, bool> contains_impl(
-      CG g,
-      Key const& k,
-      optional_hash_type precomputed_hash,
-      KeyEqual key_equal = KeyEqual{}) noexcept;
+      CG g, Key const& k, KeyEqual key_equal = KeyEqual{}) noexcept;
 
     /**
      * @brief Indicates whether the key `k` was inserted into the map using scalar loads.
@@ -909,8 +890,6 @@ class static_multimap {
      * @tparam KeyEqual Binary callable type
      * @param g The Cooperative Group used to perform the contains operation
      * @param k The key to search for
-     * @param precomputed_hash Optional value which allows users to specify the precomputed hash
-     * value
      * @param key_equal The binary callable used to compare two keys
      * for equality
      * @return A boolean indicating whether the key/value pair
@@ -918,10 +897,7 @@ class static_multimap {
      */
     template <bool uses_vector_load, typename CG, typename KeyEqual = thrust::equal_to<key_type>>
     __device__ std::enable_if_t<not uses_vector_load, bool> contains_impl(
-      CG g,
-      Key const& k,
-      optional_hash_type precomputed_hash,
-      KeyEqual key_equal = KeyEqual{}) noexcept;
+      CG g, Key const& k, KeyEqual key_equal = KeyEqual{}) noexcept;
 
     /**
      * @brief Counts the occurrence of a given key contained in multimap using vector loads.
@@ -932,8 +908,6 @@ class static_multimap {
      * @tparam KeyEqual Binary callable type
      * @param g The Cooperative Group used to perform the count operation
      * @param k The key to search for
-     * @param precomputed_hash Optional value which allows users to specify the precomputed hash
-     * value
      * @param thread_num_matches Number of matches found by the current thread
      * @param key_equal The binary callable used to compare two keys
      * for equality
@@ -945,7 +919,6 @@ class static_multimap {
     __device__ std::enable_if_t<uses_vector_load, void> count_impl(
       CG const& g,
       Key const& k,
-      optional_hash_type precomputed_hash,
       std::size_t& thread_num_matches,
       KeyEqual key_equal = KeyEqual{}) noexcept;
 
@@ -958,8 +931,6 @@ class static_multimap {
      * @tparam KeyEqual Binary callable type
      * @param g The Cooperative Group used to perform the count operation
      * @param k The key to search for
-     * @param precomputed_hash Optional value which allows users to specify the precomputed hash
-     * value
      * @param thread_num_matches Number of matches found by the current thread
      * @param key_equal The binary callable used to compare two keys
      * for equality
@@ -971,7 +942,6 @@ class static_multimap {
     __device__ std::enable_if_t<not uses_vector_load, void> count_impl(
       CG const& g,
       Key const& k,
-      optional_hash_type precomputed_hash,
       std::size_t& thread_num_matches,
       KeyEqual key_equal = KeyEqual{}) noexcept;
 
@@ -985,8 +955,6 @@ class static_multimap {
      * @tparam PairEqual Binary callable type
      * @param g The Cooperative Group used to perform the pair_count operation
      * @param pair The pair to search for
-     * @param precomputed_hash Optional value which allows users to specify the precomputed hash
-     * value
      * @param thread_num_matches Number of matches found by the current thread
      * @param pair_equal The binary callable used to compare two pairs
      * for equality
@@ -995,7 +963,6 @@ class static_multimap {
     __device__ std::enable_if_t<uses_vector_load, void> pair_count_impl(
       CG const& g,
       value_type const& pair,
-      optional_hash_type precomputed_hash,
       std::size_t& thread_num_matches,
       PairEqual pair_equal) noexcept;
 
@@ -1009,8 +976,6 @@ class static_multimap {
      * @tparam PairEqual Binary callable type
      * @param g The Cooperative Group used to perform the pair_count operation
      * @param pair The pair to search for
-     * @param precomputed_hash Optional value which allows users to specify the precomputed hash
-     * value
      * @param thread_num_matches Number of matches found by the current thread
      * @param pair_equal The binary callable used to compare two pairs
      * for equality
@@ -1019,7 +984,6 @@ class static_multimap {
     __device__ std::enable_if_t<not uses_vector_load, void> pair_count_impl(
       CG const& g,
       value_type const& pair,
-      optional_hash_type precomputed_hash,
       std::size_t& thread_num_matches,
       PairEqual pair_equal) noexcept;
 
@@ -1042,8 +1006,6 @@ class static_multimap {
      * @param warp The Cooperative Group (or warp) used to flush output buffer
      * @param g The Cooperative Group used to retrieve
      * @param k The key to search for
-     * @param precomputed_hash Optional value which allows users to specify the precomputed hash
-     * value
      * @param warp_counter Pointer to the warp counter
      * @param output_buffer Shared memory buffer of the key/value pair sequence
      * @param num_matches Size of the output sequence
@@ -1061,7 +1023,6 @@ class static_multimap {
     __device__ void retrieve_impl(warpT const& warp,
                                   CG const& g,
                                   Key const& k,
-                                  optional_hash_type precomputed_hash,
                                   uint32_t* warp_counter,
                                   value_type* output_buffer,
                                   atomicT* num_matches,
@@ -1086,8 +1047,6 @@ class static_multimap {
      * @tparam KeyEqual Binary callable type
      * @param g The Cooperative Group used to retrieve
      * @param k The key to search for
-     * @param precomputed_hash Optional value which allows users to specify the precomputed hash
-     * value
      * @param cg_counter Pointer to the CG counter
      * @param output_buffer Shared memory buffer of the key/value pair sequence
      * @param num_matches Size of the output sequence
@@ -1104,7 +1063,6 @@ class static_multimap {
               typename KeyEqual = thrust::equal_to<key_type>>
     __device__ void retrieve_impl(CG const& g,
                                   Key const& k,
-                                  optional_hash_type precomputed_hash,
                                   uint32_t* cg_counter,
                                   value_type* output_buffer,
                                   atomicT* num_matches,
@@ -1131,8 +1089,6 @@ class static_multimap {
      * @param warp The Cooperative Group (or warp) used to flush output buffer
      * @param g The Cooperative Group used to retrieve
      * @param pair The pair to search for
-     * @param precomputed_hash Optional value which allows users to specify the precomputed hash
-     * value
      * @param warp_counter Pointer to the warp counter
      * @param probe_output_buffer Buffer of the matched probe pair sequence
      * @param contained_output_buffer Buffer of the matched contained pair sequence
@@ -1152,7 +1108,6 @@ class static_multimap {
     __device__ void pair_retrieve_impl(warpT const& warp,
                                        CG const& g,
                                        value_type const& pair,
-                                       optional_hash_type precomputed_hash,
                                        uint32_t* warp_counter,
                                        value_type* probe_output_buffer,
                                        value_type* contained_output_buffer,
@@ -1180,8 +1135,6 @@ class static_multimap {
      * @tparam PairEqual Binary callable type
      * @param g The Cooperative Group used to retrieve
      * @param pair The pair to search for
-     * @param precomputed_hash Optional value which allows users to specify the precomputed hash
-     * value
      * @param cg_counter Pointer to the CG counter
      * @param probe_output_buffer Buffer of the matched probe pair sequence
      * @param contained_output_buffer Buffer of the matched contained pair sequence
@@ -1200,7 +1153,6 @@ class static_multimap {
               typename PairEqual>
     __device__ void pair_retrieve_impl(CG const& g,
                                        value_type const& pair,
-                                       optional_hash_type precomputed_hash,
                                        uint32_t* cg_counter,
                                        value_type* probe_output_buffer,
                                        value_type* contained_output_buffer,
@@ -1293,18 +1245,13 @@ class static_multimap {
      * @tparam KeyEqual Binary callable type
      * @param g The Cooperative Group used to perform the contains operation
      * @param k The key to search for
-     * @param precomputed_hash Optional value which allows users to specify the precomputed hash
-     * value
      * @param key_equal The binary callable used to compare two keys
      * for equality
      * @return A boolean indicating whether the key/value pair
      * containing `k` was inserted
      */
     template <typename CG, typename KeyEqual = thrust::equal_to<key_type>>
-    __device__ bool contains(CG g,
-                             Key const& k,
-                             optional_hash_type precomputed_hash,
-                             KeyEqual key_equal = KeyEqual{}) noexcept;
+    __device__ bool contains(CG g, Key const& k, KeyEqual key_equal = KeyEqual{}) noexcept;
 
     /**
      * @brief Counts the occurrence of a given key contained in multimap.
@@ -1313,8 +1260,6 @@ class static_multimap {
      * @tparam KeyEqual Binary callable type
      * @param g The Cooperative Group used to perform the count operation
      * @param k The key to search for
-     * @param precomputed_hash Optional value which allows users to specify the precomputed hash
-     * value
      * @param thread_num_matches Number of matches found by the current thread
      * @param key_equal The binary callable used to compare two keys
      * for equality
@@ -1322,7 +1267,6 @@ class static_multimap {
     template <typename CG, typename KeyEqual = thrust::equal_to<key_type>>
     __device__ void count(CG const& g,
                           Key const& k,
-                          optional_hash_type precomputed_hash,
                           std::size_t& thread_num_matches,
                           KeyEqual key_equal = KeyEqual{}) noexcept;
 
@@ -1334,8 +1278,6 @@ class static_multimap {
      * @tparam KeyEqual Binary callable type
      * @param g The Cooperative Group used to perform the count operation
      * @param k The key to search for
-     * @param precomputed_hash Optional value which allows users to specify the precomputed hash
-     * value
      * @param thread_num_matches Number of matches found by the current thread
      * @param key_equal The binary callable used to compare two keys
      * for equality
@@ -1343,7 +1285,6 @@ class static_multimap {
     template <typename CG, typename KeyEqual = thrust::equal_to<key_type>>
     __device__ void count_outer(CG const& g,
                                 Key const& k,
-                                optional_hash_type precomputed_hash,
                                 std::size_t& thread_num_matches,
                                 KeyEqual key_equal = KeyEqual{}) noexcept;
 
@@ -1354,8 +1295,6 @@ class static_multimap {
      * @tparam PairEqual Binary callable type
      * @param g The Cooperative Group used to perform the pair_count operation
      * @param pair The pair to search for
-     * @param precomputed_hash Optional value which allows users to specify the precomputed hash
-     * value
      * @param thread_num_matches Number of matches found by the current thread
      * @param pair_equal The binary callable used to compare two pairs
      * for equality
@@ -1363,7 +1302,6 @@ class static_multimap {
     template <typename CG, typename PairEqual>
     __device__ void pair_count(CG const& g,
                                value_type const& pair,
-                               optional_hash_type precomputed_hash,
                                std::size_t& thread_num_matches,
                                PairEqual pair_equal) noexcept;
 
@@ -1375,8 +1313,6 @@ class static_multimap {
      * @tparam PairEqual Binary callable type
      * @param g The Cooperative Group used to perform the pair_count operation
      * @param pair The pair to search for
-     * @param precomputed_hash Optional value which allows users to specify the precomputed hash
-     * value
      * @param thread_num_matches Number of matches found by the current thread
      * @param pair_equal The binary callable used to compare two pairs
      * for equality
@@ -1384,7 +1320,6 @@ class static_multimap {
     template <typename CG, typename PairEqual>
     __device__ void pair_count_outer(CG const& g,
                                      value_type const& pair,
-                                     optional_hash_type precomputed_hash,
                                      std::size_t& thread_num_matches,
                                      PairEqual pair_equal) noexcept;
 
@@ -1406,8 +1341,6 @@ class static_multimap {
      * @param warp The Cooperative Group (or warp) used to flush output buffer
      * @param g The Cooperative Group used to retrieve
      * @param k The key to search for
-     * @param precomputed_hash Optional value which allows users to specify the precomputed hash
-     * value
      * @param warp_counter Pointer to the warp counter
      * @param output_buffer Shared memory buffer of the key/value pair sequence
      * @param num_matches Size of the output sequence
@@ -1424,7 +1357,6 @@ class static_multimap {
     __device__ void retrieve(warpT const& warp,
                              CG const& g,
                              Key const& k,
-                             optional_hash_type precomputed_hash,
                              uint32_t* warp_counter,
                              value_type* output_buffer,
                              atomicT* num_matches,
@@ -1449,8 +1381,6 @@ class static_multimap {
      * @param warp The Cooperative Group (or warp) used to flush output buffer
      * @param g The Cooperative Group used to retrieve
      * @param k The key to search for
-     * @param precomputed_hash Optional value which allows users to specify the precomputed hash
-     * value
      * @param warp_counter Pointer to the warp counter
      * @param output_buffer Shared memory buffer of the key/value pair sequence
      * @param num_matches Size of the output sequence
@@ -1467,7 +1397,6 @@ class static_multimap {
     __device__ void retrieve_outer(warpT const& warp,
                                    CG const& g,
                                    Key const& k,
-                                   optional_hash_type precomputed_hash,
                                    uint32_t* warp_counter,
                                    value_type* output_buffer,
                                    atomicT* num_matches,
@@ -1490,8 +1419,6 @@ class static_multimap {
      * @tparam KeyEqual Binary callable type
      * @param g The Cooperative Group used to retrieve
      * @param k The key to search for
-     * @param precomputed_hash Optional value which allows users to specify the precomputed hash
-     * value
      * @param cg_counter Pointer to the CG counter
      * @param output_buffer Shared memory buffer of the key/value pair sequence
      * @param num_matches Size of the output sequence
@@ -1507,7 +1434,6 @@ class static_multimap {
               typename KeyEqual = thrust::equal_to<key_type>>
     __device__ void retrieve(CG const& g,
                              Key const& k,
-                             optional_hash_type precomputed_hash,
                              uint32_t* cg_counter,
                              value_type* output_buffer,
                              atomicT* num_matches,
@@ -1531,8 +1457,6 @@ class static_multimap {
      * @tparam KeyEqual Binary callable type
      * @param g The Cooperative Group used to retrieve
      * @param k The key to search for
-     * @param precomputed_hash Optional value which allows users to specify the precomputed hash
-     * value
      * @param cg_counter Pointer to the CG counter
      * @param output_buffer Shared memory buffer of the key/value pair sequence
      * @param num_matches Size of the output sequence
@@ -1548,7 +1472,6 @@ class static_multimap {
               typename KeyEqual = thrust::equal_to<key_type>>
     __device__ void retrieve_outer(CG const& g,
                                    Key const& k,
-                                   optional_hash_type precomputed_hash,
                                    uint32_t* cg_counter,
                                    value_type* output_buffer,
                                    atomicT* num_matches,
@@ -1573,8 +1496,6 @@ class static_multimap {
      * @param warp The Cooperative Group (or warp) used to flush output buffer
      * @param g The Cooperative Group used to retrieve
      * @param pair The pair to search for
-     * @param precomputed_hash Optional value which allows users to specify the precomputed hash
-     * value
      * @param warp_counter Pointer to the warp counter
      * @param probe_output_buffer Buffer of the matched probe pair sequence
      * @param contained_output_buffer Buffer of the matched contained pair sequence
@@ -1593,7 +1514,6 @@ class static_multimap {
     __device__ void pair_retrieve(warpT const& warp,
                                   CG const& g,
                                   value_type const& pair,
-                                  optional_hash_type precomputed_hash,
                                   uint32_t* warp_counter,
                                   value_type* probe_output_buffer,
                                   value_type* contained_output_buffer,
@@ -1621,8 +1541,6 @@ class static_multimap {
      * @param warp The Cooperative Group (or warp) used to flush output buffer
      * @param g The Cooperative Group used to retrieve
      * @param pair The pair to search for
-     * @param precomputed_hash Optional value which allows users to specify the precomputed hash
-     * value
      * @param warp_counter Pointer to the warp counter
      * @param probe_output_buffer Buffer of the matched probe pair sequence
      * @param contained_output_buffer Buffer of the matched contained pair sequence
@@ -1641,7 +1559,6 @@ class static_multimap {
     __device__ void pair_retrieve_outer(warpT const& warp,
                                         CG const& g,
                                         value_type const& pair,
-                                        optional_hash_type precomputed_hash,
                                         uint32_t* warp_counter,
                                         value_type* probe_output_buffer,
                                         value_type* contained_output_buffer,
@@ -1667,8 +1584,6 @@ class static_multimap {
      * @tparam PairEqual Binary callable type
      * @param g The Cooperative Group used to retrieve
      * @param pair The pair to search for
-     * @param precomputed_hash Optional value which allows users to specify the precomputed hash
-     * value
      * @param cg_counter Pointer to the CG counter
      * @param probe_output_buffer Buffer of the matched probe pair sequence
      * @param contained_output_buffer Buffer of the matched contained pair sequence
@@ -1686,7 +1601,6 @@ class static_multimap {
               typename PairEqual>
     __device__ void pair_retrieve(CG const& g,
                                   value_type const& pair,
-                                  optional_hash_type precomputed_hash,
                                   uint32_t* cg_counter,
                                   value_type* probe_output_buffer,
                                   value_type* contained_output_buffer,
@@ -1713,8 +1627,6 @@ class static_multimap {
      * @tparam PairEqual Binary callable type
      * @param g The Cooperative Group used to retrieve
      * @param pair The pair to search for
-     * @param precomputed_hash Optional value which allows users to specify the precomputed hash
-     * value
      * @param cg_counter Pointer to the CG counter
      * @param probe_output_buffer Buffer of the matched probe pair sequence
      * @param contained_output_buffer Buffer of the matched contained pair sequence
@@ -1732,7 +1644,6 @@ class static_multimap {
               typename PairEqual>
     __device__ void pair_retrieve_outer(CG const& g,
                                         value_type const& pair,
-                                        optional_hash_type precomputed_hash,
                                         uint32_t* cg_counter,
                                         value_type* probe_output_buffer,
                                         value_type* contained_output_buffer,

From f4565c07ebd14ac96ab71ad8c0a388fe645f9a5f Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Mon, 23 Aug 2021 16:47:33 -0400
Subject: [PATCH 198/267] Use precomputed prime instead of on-the-fly
 calculation

---
 include/cuco/detail/prime.hpp | 20119 +++++++++++++++++++++++++++++++-
 1 file changed, 20115 insertions(+), 4 deletions(-)

diff --git a/include/cuco/detail/prime.hpp b/include/cuco/detail/prime.hpp
index 5042fabbd..6452b4a02 100644
--- a/include/cuco/detail/prime.hpp
+++ b/include/cuco/detail/prime.hpp
@@ -17,10 +17,20117 @@
 #pragma once
 
 namespace cuco {
+
 namespace detail {
 
-// safe division
-#define SDIV(x, y) (((x) + (y)-1) / (y))
+// prime numbers with ~131072 offset
+constexpr std::array<std::uint64_t, 140746> primes = {
+  2,           3,           5,           7,           13,          19,          29,
+  37,          43,          53,          59,          67,          73,          79,
+  89,          97,          103,         109,         127,         137,         149,
+  157,         163,         173,         179,         191,         197,         211,
+  223,         229,         239,         251,         257,         263,         269,
+  277,         283,         293,         307,         313,         331,         337,
+  347,         353,         359,         367,         373,         379,         389,
+  397,         409,         419,         431,         439,         449,         457,
+  463,         479,         487,         499,         509,         521,         541,
+  547,         557,         563,         569,         577,         587,         593,
+  599,         607,         613,         619,         631,         641,         647,
+  653,         659,         673,         683,         691,         701,         709,
+  719,         727,         733,         739,         751,         757,         769,
+  787,         797,         809,         821,         827,         839,         853,
+  859,         877,         883,         907,         919,         929,         937,
+  947,         953,         967,         977,         983,         991,         997,
+  1009,        1019,        1031,        1039,        1049,        1061,        1069,
+  1087,        1093,        1103,        1109,        1117,        1123,        1129,
+  1151,        1163,        1171,        1181,        1187,        1193,        1201,
+  1213,        1223,        1229,        1237,        1249,        1259,        1277,
+  1283,        1289,        1297,        1303,        1319,        1327,        1361,
+  1367,        1373,        1381,        1399,        1409,        1423,        1429,
+  1439,        1447,        1453,        1459,        1471,        1481,        1487,
+  1493,        1499,        1511,        1523,        1531,        1543,        1549,
+  1559,        1567,        1579,        1597,        1607,        1613,        1619,
+  1627,        1637,        1657,        1663,        1669,        1693,        1699,
+  1709,        1721,        1733,        1741,        1747,        1753,        1759,
+  1777,        1783,        1789,        1801,        1811,        1823,        1831,
+  1847,        1861,        1867,        1873,        1879,        1889,        1901,
+  1907,        1913,        1931,        1949,        1973,        1979,        1987,
+  1993,        1999,        2011,        2017,        2027,        2039,        2053,
+  2063,        2069,        2081,        2087,        2099,        2111,        2129,
+  2137,        2143,        2153,        2161,        2179,        2203,        2213,
+  2221,        2237,        2243,        2251,        2267,        2273,        2281,
+  2287,        2293,        2309,        2333,        2339,        2347,        2357,
+  2371,        2377,        2383,        2389,        2399,        2411,        2417,
+  2423,        2437,        2447,        2459,        2467,        2473,        2503,
+  2521,        2531,        2539,        2549,        2557,        2579,        2591,
+  2609,        2617,        2633,        2647,        2657,        2663,        2671,
+  2677,        2683,        2689,        2699,        2707,        2713,        2719,
+  2729,        2741,        2749,        2767,        2777,        2789,        2797,
+  2803,        2819,        2833,        2843,        2851,        2857,        2879,
+  2887,        2897,        2903,        2909,        2917,        2927,        2939,
+  2953,        2963,        2969,        2999,        3011,        3019,        3037,
+  3049,        3061,        3067,        3079,        3089,        3109,        3119,
+  3137,        3163,        3169,        3181,        3187,        3203,        3209,
+  3217,        3229,        3251,        3257,        3271,        3299,        3307,
+  3313,        3319,        3329,        3343,        3359,        3371,        3389,
+  3407,        3413,        3433,        3449,        3457,        3463,        3469,
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+  17088353039, 17088484121, 17088615211, 17088746291, 17088877363, 17089008451, 17089139533,
+  17089270609, 17089401731, 17089532809, 17089663897, 17089794977, 17089926053, 17090057141,
+  17090188309, 17090319413, 17090450497, 17090581591, 17090712689, 17090843827, 17090974907,
+  17091106001, 17091237089, 17091368171, 17091499289, 17091630383, 17091761513, 17091892619,
+  17092023709, 17092154809, 17092285909, 17092417033, 17092548169, 17092679249, 17092810393,
+  17092941527, 17093072611, 17093203697, 17093334799, 17093465941, 17093597021, 17093728153,
+  17093859239, 17093990311, 17094121411, 17094252503, 17094383591, 17094514669, 17094645791,
+  17094776947, 17094908099, 17095039201, 17095170331, 17095301413, 17095432537, 17095563629,
+  17095694701, 17095825807, 17095956901, 17096087989, 17096219093, 17096350189, 17096481317,
+  17096612399, 17096743471, 17096874569, 17097005671, 17097136783, 17097267871, 17097398951,
+  17097530027, 17097661121, 17097792223, 17097923321, 17098054411, 17098185491, 17098316579,
+  17098447657, 17098578737, 17098709809, 17098840883, 17098971991, 17099103071, 17099234161,
+  17099365247, 17099496343, 17099627431, 17099758513, 17099889601, 17100020683, 17100151771,
+  17100282917, 17100414007, 17100545087, 17100676163, 17100807239, 17100938339, 17101069417,
+  17101200503, 17101331579, 17101462657, 17101593731, 17101724803, 17101855889, 17101986971,
+  17102118049, 17102249137, 17102380259, 17102511337, 17102642419, 17102773507, 17102904593,
+  17103035669, 17103166753, 17103297847, 17103428933, 17103560029, 17103691103, 17103822181,
+  17103953261, 17104084339, 17104215437, 17104346513, 17104477589, 17104608683, 17104739761,
+  17104870873, 17105001961, 17105133061, 17105264171, 17105395247, 17105526323, 17105657419,
+  17105788507, 17105919581, 17106050657, 17106181753, 17106312893, 17106443969, 17106575053,
+  17106706129, 17106837269, 17106968353, 17107099519, 17107230601, 17107361701, 17107492811,
+  17107623901, 17107754987, 17107886069, 17108017153, 17108148227, 17108279329, 17108410447,
+  17108541533, 17108672621, 17108803697, 17108934781, 17109065861, 17109196957, 17109328073,
+  17109459149, 17109590227, 17109721303, 17109852401, 17109983533, 17110114613, 17110245689,
+  17110376761, 17110507849, 17110638949, 17110770059, 17110901147, 17111032231, 17111163313,
+  17111294399, 17111425493, 17111556571, 17111687657, 17111818751, 17111949857, 17112080929,
+  17112212059, 17112343133, 17112474209, 17112605281, 17112736357, 17112867467, 17112998543,
+  17113129619, 17113260721, 17113391837, 17113522913, 17113654021, 17113785103, 17113916179,
+  17114047259, 17114178331, 17114309441, 17114440529, 17114571629, 17114702701, 17114833777,
+  17114964859, 17115095951, 17115227027, 17115358121, 17115489203, 17115620297, 17115751423,
+  17115882521, 17116013597, 17116144687, 17116275773, 17116406893, 17116537993, 17116669081,
+  17116800187, 17116931281, 17117062363, 17117193437, 17117324509, 17117455591, 17117586667,
+  17117717789, 17117848861, 17117979941, 17118111029, 17118242101, 17118373187, 17118504259,
+  17118635341, 17118766481, 17118897583, 17119028737, 17119159829, 17119290919, 17119421993,
+  17119553069, 17119684147, 17119815227, 17119946309, 17120077399, 17120208481, 17120339603,
+  17120470681, 17120601761, 17120732869, 17120863987, 17120995061, 17121126137, 17121257213,
+  17121388289, 17121519377, 17121650473, 17121781549, 17121912659, 17122043747, 17122174837,
+  17122305911, 17122437001, 17122568081, 17122699157, 17122830271, 17122961377, 17123092457,
+  17123223539, 17123354657, 17123485759, 17123616889, 17123747983, 17123879083, 17124010157,
+  17124141271, 17124272377, 17124403477, 17124534569, 17124665651, 17124796747, 17124927821,
+  17125058911, 17125189987, 17125321063, 17125452151, 17125583227, 17125714321, 17125845403,
+  17125976507, 17126107597, 17126238709, 17126369807, 17126500883, 17126631961, 17126763047,
+  17126894123, 17127025219, 17127156317, 17127287389, 17127418489, 17127549629, 17127680741,
+  17127811817, 17127942941, 17128074013, 17128205123, 17128336219, 17128467311, 17128598429,
+  17128729507, 17128860583, 17128991707, 17129122793, 17129253877, 17129384999, 17129516071,
+  17129647189, 17129778313, 17129909411, 17130040489, 17130171581, 17130302681, 17130433769,
+  17130564851, 17130695947, 17130827029, 17130958139, 17131089253, 17131220329, 17131351427,
+  17131482581, 17131613659, 17131744739, 17131875811, 17132006923, 17132137999, 17132269079,
+  17132400157, 17132531281, 17132662361, 17132793479, 17132924551, 17133055633, 17133186731,
+  17133317867, 17133448939, 17133580073, 17133711197, 17133842291, 17133973391, 17134104473,
+  17134235603, 17134366679, 17134497769, 17134628873, 17134759973, 17134891057, 17135022137,
+  17135153219, 17135284321, 17135415481, 17135546557, 17135677631, 17135808719, 17135939801,
+  17136070879, 17136201961, 17136333041, 17136464141, 17136595213, 17136726293, 17136857377,
+  17136988471, 17137119563, 17137250651, 17137381733, 17137512839, 17137643911, 17137774999,
+  17137906109, 17138037181, 17138168291, 17138299379, 17138430467, 17138561563, 17138692651,
+  17138823739, 17138954813, 17139085937, 17139217021, 17139348101, 17139479221, 17139610321,
+  17139741431, 17139872509, 17140003591, 17140134677, 17140265767, 17140396879, 17140527973,
+  17140659047, 17140790167, 17140921247, 17141052323, 17141183401, 17141314483, 17141445569,
+  17141576653, 17141707747, 17141838853, 17141969939, 17142101021, 17142232109, 17142363223,
+  17142494311, 17142625387, 17142756467, 17142887539, 17143018621, 17143149721, 17143280867,
+  17143411963, 17143543049, 17143674127, 17143805209, 17143936283, 17144067383, 17144198521,
+  17144329597, 17144460677, 17144591773, 17144722849, 17144853997, 17144985083, 17145116161,
+  17145247253, 17145378343, 17145509453, 17145640571, 17145771647, 17145902723, 17146033799,
+  17146164881, 17146295983, 17146427057, 17146558129, 17146689223, 17146820329, 17146951463,
+  17147082541, 17147213711, 17147344889, 17147475977, 17147607049, 17147738123, 17147869199,
+  17148000271, 17148131347, 17148262441, 17148393523, 17148524641, 17148655781, 17148786923,
+  17148918041, 17149049143, 17149180229, 17149311331, 17149442417, 17149573501, 17149704599,
+  17149835683, 17149966781, 17150097863, 17150228941, 17150360027, 17150491153, 17150622253,
+  17150753353, 17150884427, 17151015559, 17151146641, 17151277723, 17151408823, 17151539929,
+  17151671027, 17151802139, 17151933259, 17152064339, 17152195457, 17152326533, 17152457611,
+  17152588709, 17152719791, 17152850881, 17152982009, 17153113127, 17153244233, 17153375311,
+  17153506411, 17153637499, 17153768591, 17153899667, 17154030743, 17154161821, 17154292921,
+  17154424003, 17154555113, 17154686203, 17154817279, 17154948367, 17155079441, 17155210517,
+  17155341589, 17155472669, 17155603741, 17155734823, 17155865897, 17155996973, 17156128063,
+  17156259137, 17156390219, 17156521321, 17156652427, 17156783549, 17156914657, 17157045749,
+  17157176837, 17157307943, 17157439037, 17157570119, 17157701191, 17157832271, 17157963349,
+  17158094431, 17158225523, 17158356617, 17158487779, 17158618879, 17158749973, 17158881049,
+  17159012149, 17159143229, 17159274311, 17159405383, 17159536463, 17159667649, 17159798723,
+  17159929811, 17160060889, 17160191963, 17160323053, 17160454151, 17160585227, 17160716327,
+  17160847399, 17160978493, 17161109573, 17161240721, 17161371833, 17161502921, 17161634029,
+  17161765109, 17161896197, 17162027269, 17162158357, 17162289437, 17162420519, 17162551613,
+  17162682709, 17162813813, 17162944907, 17163075989, 17163207077, 17163338149, 17163469223,
+  17163600331, 17163731411, 17163862501, 17163993599, 17164124699, 17164255789, 17164386937,
+  17164518047, 17164649119, 17164780207, 17164911299, 17165042381, 17165173453, 17165304551,
+  17165435659, 17165566771, 17165697869, 17165828957, 17165960033, 17166091157, 17166222251,
+  17166353333, 17166484411, 17166615491, 17166746567, 17166877691, 17167008767, 17167139867,
+  17167270997, 17167402123, 17167533203, 17167664287, 17167795361, 17167926439, 17168057521,
+  17168188601, 17168319677, 17168450779, 17168581873, 17168712959, 17168844047, 17168975147,
+  17169106271, 17169237353, 17169368429, 17169499519, 17169630619, 17169761723, 17169892853,
+  17170023961, 17170155061, 17170286137, 17170417213, 17170548289, 17170679437, 17170810513,
+  17170941587, 17171072669, 17171203747, 17171334833, 17171465917, 17171596993, 17171728069,
+  17171859149, 17171990237, 17172121327, 17172252413, 17172383489, 17172514561, 17172645643,
+  17172776717, 17172907813, 17173038893, 17173169987, 17173301059, 17173432163, 17173563259,
+  17173694333, 17173825421, 17173956517, 17174087597, 17174218681, 17174349779, 17174480867,
+  17174611943, 17174743031, 17174874103, 17175005201, 17175136273, 17175267353, 17175398473,
+  17175529577, 17175660743, 17175791821, 17175922903, 17176053997, 17176185083, 17176316167,
+  17176447243, 17176578343, 17176709449, 17176840529, 17176971601, 17177102693, 17177233783,
+  17177364857, 17177495953, 17177627053, 17177758133};
 
 /**
  * @brief Indicates whether the input `num` is a prime number.
@@ -59,6 +20166,9 @@ constexpr std::size_t compute_prime(std::size_t num) noexcept
   return num;
 }
 
+// safe division
+#define SDIV(x, y) (((x) + (y)-1) / (y))
+
 /**
  * @brief Calculates the adjusted/valid capacity based on `CGSize` and the initial `capacity`.
  *
@@ -69,8 +20179,9 @@ constexpr std::size_t compute_prime(std::size_t num) noexcept
 template <std::size_t CGSize>
 constexpr std::size_t get_valid_capacity(std::size_t capacity) noexcept
 {
-  auto const min_prime = compute_prime(SDIV(capacity, CGSize));
-  return min_prime * CGSize;
+  auto const c         = SDIV(capacity, CGSize);
+  auto const min_prime = std::lower_bound(primes.begin(), primes.end(), c);
+  return *min_prime * CGSize;
 }
 
 }  // namespace detail

From da10ebae8a2ec02ddd3505eb4c02ba663d2f2bb2 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Fri, 27 Aug 2021 20:11:37 -0400
Subject: [PATCH 199/267] Fix several typos

---
 include/cuco/static_multimap.cuh | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index d93fa6f13..6ebae3215 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -77,9 +77,9 @@ namespace cuco {
  * The singular device-side operations allow individual threads to perform
  * independent operations (e.g. `insert`, etc.) from device code. These
  * operations are accessed through non-owning, trivially copyable "view" types:
- * `device_view` and `mutable_device_view`. The `device_view` class is an
+ * `device_view` and `device_mutable_view`. The `device_view` class is an
  * immutable view that allows only non-modifying operations such as `count` or
- * `contains`. The `mutable_device_view` class only allows `insert` operations.
+ * `contains`. The `device_mutable_view` class only allows `insert` operations.
  * The two types are separate to prevent erroneous concurrent insert/find
  * operations.
  *
@@ -628,7 +628,7 @@ class static_multimap {
    * // Inserts a sequence of pairs {{0,0}, {1,1}, ... {i,i}}
    * thrust::for_each(thrust::make_counting_iterator(0),
    *                  thrust::make_counting_iterator(50'000),
-   *                  [map = m.get_mutable_device_view()]
+   *                  [map = m.get_device_mutable_view()]
    *                  __device__ (auto i) mutable {
    *                     map.insert(thrust::make_pair(i,i));
    *                  });

From f249c443dbcd6ba68ee5c6b79cd2b7bb7104ae13 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Tue, 31 Aug 2021 11:59:53 -0400
Subject: [PATCH 200/267] Fix a bug: initialize output_idx before ballot

---
 include/cuco/detail/static_multimap.inl | 20 ++++++++++----------
 1 file changed, 10 insertions(+), 10 deletions(-)

diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index 47df44f60..82bcf7558 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -1215,13 +1215,13 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::retri
 
     auto const slot_is_empty =
       detail::bitwise_compare(slot_contents.first, this->get_empty_key_sentinel());
-    auto const equals = (not slot_is_empty and key_equal(slot_contents.first, k));
-    auto const exists = g.ballot(equals);
+    auto const equals   = (not slot_is_empty and key_equal(slot_contents.first, k));
+    uint32_t output_idx = *cg_counter;
+    auto const exists   = g.ballot(equals);
 
     if (exists) {
       if constexpr (is_outer) { found_match = true; }
-      auto num_matches    = __popc(exists);
-      uint32_t output_idx = *cg_counter;
+      auto num_matches = __popc(exists);
       if (equals) {
         // Each match computes its lane-level offset
         auto lane_offset = __popc(exists & ((1 << lane_id) - 1));
@@ -1235,7 +1235,7 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::retri
       running = false;
       if constexpr (is_outer) {
         if ((not found_match) && (lane_id == 0)) {
-          auto output_idx           = (*cg_counter)++;
+          output_idx                = (*cg_counter)++;
           Key key                   = k;
           output_buffer[output_idx] = cuco::make_pair<Key, Value>(
             std::move(key), std::move(this->get_empty_value_sentinel()));
@@ -1399,13 +1399,13 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_
 
     auto const slot_is_empty =
       detail::bitwise_compare(slot_contents.first, this->get_empty_key_sentinel());
-    auto const equals = (not slot_is_empty and pair_equal(slot_contents, pair));
-    auto const exists = g.ballot(equals);
+    auto const equals   = (not slot_is_empty and pair_equal(slot_contents, pair));
+    uint32_t output_idx = *cg_counter;
+    auto const exists   = g.ballot(equals);
 
     if (exists) {
       if constexpr (is_outer) { found_match = true; }
-      auto num_matches    = __popc(exists);
-      uint32_t output_idx = *cg_counter;
+      auto num_matches = __popc(exists);
       if (equals) {
         // Each match computes its lane-level offset
         auto lane_offset                                  = __popc(exists & ((1 << lane_id) - 1));
@@ -1418,7 +1418,7 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_
       running = false;
       if constexpr (is_outer) {
         if ((not found_match) && (lane_id == 0)) {
-          auto output_idx                     = (*cg_counter)++;
+          output_idx                          = (*cg_counter)++;
           probe_output_buffer[output_idx]     = pair;
           contained_output_buffer[output_idx] = cuco::make_pair<Key, Value>(
             std::move(this->get_empty_key_sentinel()), std::move(this->get_empty_value_sentinel()));

From 1fec3cbe253da6f6861b9d7f826c704058a27fa4 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Thu, 2 Sep 2021 22:19:49 -0400
Subject: [PATCH 201/267] Refactor insert_if to take a stencil sequence

---
 include/cuco/detail/static_multimap.inl       | 17 ++++++-------
 .../cuco/detail/static_multimap_kernels.cuh   | 25 +++++++++++--------
 include/cuco/static_multimap.cuh              | 15 ++++++-----
 tests/static_multimap/static_multimap_test.cu |  6 ++---
 4 files changed, 33 insertions(+), 30 deletions(-)

diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index 82bcf7558..d9cb6c0e9 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -105,20 +105,17 @@ template <typename Key,
           class ProbeSequence,
           cuda::thread_scope Scope,
           typename Allocator>
-template <typename InputIt, typename Predicate>
-void static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::insert_if(InputIt first,
-                                                                             InputIt last,
-                                                                             Predicate pred,
-                                                                             cudaStream_t stream)
+template <typename InputIt, typename StencilIt, typename Predicate>
+void static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::insert_if_n(
+  InputIt first, StencilIt stencil, std::size_t n, Predicate pred, cudaStream_t stream)
 {
-  auto num_keys = std::distance(first, last);
-  auto view     = get_device_mutable_view();
+  auto view = get_device_mutable_view();
 
   auto constexpr block_size = 128;
   int grid_size{-1};
   cudaOccupancyMaxActiveBlocksPerMultiprocessor(
     &grid_size,
-    detail::insert_if<block_size, cg_size(), InputIt, device_mutable_view, Predicate>,
+    detail::insert_if_n<block_size, cg_size(), InputIt, StencilIt, device_mutable_view, Predicate>,
     block_size,
     0);
   int dev_id{-1};
@@ -127,8 +124,8 @@ void static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::insert_if(Inp
   cudaDeviceGetAttribute(&num_sms, cudaDevAttrMultiProcessorCount, dev_id);
   grid_size *= num_sms;
 
-  detail::insert_if<block_size, cg_size()>
-    <<<grid_size, block_size, 0, stream>>>(first, first + num_keys, view, pred);
+  detail::insert_if_n<block_size, cg_size()>
+    <<<grid_size, block_size, 0, stream>>>(first, stencil, n, view, pred);
   CUCO_CUDA_TRY(cudaStreamSynchronize(stream));
 }
 
diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap_kernels.cuh
index ce656a5a0..8bbf96616 100644
--- a/include/cuco/detail/static_multimap_kernels.cuh
+++ b/include/cuco/detail/static_multimap_kernels.cuh
@@ -88,7 +88,8 @@ __global__ void insert(InputIt first, InputIt last, viewT view)
 }
 
 /**
- * @brief Inserts key/value pairs in the range `[first, last)` if `pred` returns true.
+ * @brief Inserts key/value pairs in the range `[first, first + n)` if `pred` of the
+ * corresponding stencil returns true.
  *
  * Uses the CUDA Cooperative Groups API to leverage groups of multiple threads to perform each
  * key/value insertion. This provides a significant boost in throughput compared to the non
@@ -99,30 +100,34 @@ __global__ void insert(InputIt first, InputIt last, viewT view)
  * inserts
  * @tparam InputIt Device accessible input iterator whose `value_type` is
  * convertible to the map's `value_type`
+ * @tparam StencilIt Device accessible stencil iterator
  * @tparam viewT Type of device view allowing access of hash map storage
  * @tparam Predicate Unary predicate function type
  * @param first Beginning of the sequence of key/value pairs
- * @param last End of the sequence of key/value pairs
+ * @param s Beginning of the stencil sequence
+ * @param n Number of elements to insert
  * @param view Mutable device view used to access the hash map's slot storage
+ * @param pred Predicate to test on the given stencil sequence
  */
 template <uint32_t block_size,
           uint32_t tile_size,
           typename InputIt,
+          typename StencilIt,
           typename viewT,
           typename Predicate>
-__global__ void insert_if(InputIt first, InputIt last, viewT view, Predicate pred)
+__global__ void insert_if_n(InputIt first, StencilIt s, std::size_t n, viewT view, Predicate pred)
 {
-  auto tile = cg::tiled_partition<tile_size>(cg::this_thread_block());
-  auto tid  = block_size * blockIdx.x + threadIdx.x;
-  auto it   = first + tid / tile_size;
+  auto tile      = cg::tiled_partition<tile_size>(cg::this_thread_block());
+  auto const tid = block_size * blockIdx.x + threadIdx.x;
+  auto i         = tid / tile_size;
 
-  while (it < last) {
-    typename viewT::value_type const insert_pair{*it};
-    if (pred(insert_pair)) {
+  while (i < n) {
+    if (pred(*(s + i))) {
+      typename viewT::value_type const insert_pair{*(first + i)};
       // force conversion to value_type
       view.insert(tile, insert_pair);
     }
-    it += (gridDim.x * block_size) / tile_size;
+    i += (gridDim.x * block_size) / tile_size;
   }
 }
 
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index 6ebae3215..72fbd0636 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -220,19 +220,22 @@ class static_multimap {
   void insert(InputIt first, InputIt last, cudaStream_t stream = 0);
 
   /**
-   * @brief Inserts key/value pairs in the range `[first, last)` if `pred` returns true.
+   * @brief Inserts key/value pairs in the range `[first, first + n)` if `pred`
+   * of the corresponding stencil returns true.
    *
    * @tparam InputIt Device accessible input iterator whose `value_type` is
    * convertible to the map's `value_type`
+   * @tparam StencilIt Device accessible stencil iterator
    * @tparam Predicate Unary predicate function type
-   * @tparam KeyEqual Binary callable type
    * @param first Beginning of the sequence of key/value pairs
-   * @param last End of the sequence of key/value pairs
+   * @param stencil Beginning of the stencil sequence
+   * @param n Number of elements to insert
+   * @param pred Predicate to test on the given stencil sequence
    * @param stream CUDA stream used for insert
-   * @param key_equal The binary function to compare two keys for equality
    */
-  template <typename InputIt, typename Predicate>
-  void insert_if(InputIt first, InputIt last, Predicate pred, cudaStream_t stream = 0);
+  template <typename InputIt, typename StencilIt, typename Predicate>
+  void insert_if_n(
+    InputIt first, StencilIt stencil, std::size_t n, Predicate pred, cudaStream_t stream = 0);
 
   /**
    * @brief Indicates whether the keys in the range `[first, last)` are contained in the map.
diff --git a/tests/static_multimap/static_multimap_test.cu b/tests/static_multimap/static_multimap_test.cu
index 6291ac506..b0a874eda 100644
--- a/tests/static_multimap/static_multimap_test.cu
+++ b/tests/static_multimap/static_multimap_test.cu
@@ -268,10 +268,8 @@ TEMPLATE_TEST_CASE_SIG("Tests of insert_if",
   thrust::device_vector<Key> d_keys(h_keys);
   thrust::device_vector<cuco::pair_type<Key, Value>> d_pairs(h_pairs);
 
-  auto pred_lambda = [] __device__(cuco::pair_type<Key, Value> pair) {
-    return pair.first % 2 == 0;
-  };
-  map.insert_if(d_pairs.begin(), d_pairs.end(), pred_lambda);
+  auto pred_lambda = [] __device__(Key k) { return k % 2 == 0; };
+  map.insert_if_n(d_pairs.begin(), d_keys.begin(), d_pairs.size(), pred_lambda);
 
   auto num = map.count(d_keys.begin(), d_keys.end());
 

From e917e3db45054e1f0c3f8eb357a639d6881f53c1 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Fri, 3 Sep 2021 08:01:23 -0400
Subject: [PATCH 202/267] Cleanups: get rid of atomic loads + use value_type

---
 include/cuco/detail/static_multimap.inl | 27 +++++++++++++------------
 1 file changed, 14 insertions(+), 13 deletions(-)

diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index d9cb6c0e9..e5bd02b44 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -793,7 +793,8 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_mutable_vie
   auto current_slot = initial_slot(g, insert_pair.first);
 
   while (true) {
-    key_type const existing_key = current_slot->first.load(cuda::memory_order_relaxed);
+    value_type slot_contents = *reinterpret_cast<value_type const*>(current_slot);
+    auto const& existing_key = slot_contents.first;
 
     // The user provide `key_equal` can never be used to compare against `empty_key_sentinel` as the
     // sentinel is not a valid key value. Therefore, first check for the sentinel
@@ -889,7 +890,8 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::conta
   auto current_slot = initial_slot(g, k);
 
   while (true) {
-    key_type const existing_key = current_slot->first.load(cuda::std::memory_order_relaxed);
+    value_type slot_contents = *reinterpret_cast<value_type const*>(current_slot);
+    auto const& existing_key = slot_contents.first;
 
     // The user provide `key_equal` can never be used to compare against `empty_key_sentinel` as the
     // sentinel is not a valid key value. Therefore, first check for the sentinel
@@ -967,9 +969,8 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::count
   [[maybe_unused]] bool found_match = false;
 
   while (true) {
-    pair<Key, Value> slot_contents =
-      *reinterpret_cast<cuco::pair_type<Key, Value> const*>(current_slot);
-    auto const& current_key = slot_contents.first;
+    value_type slot_contents = *reinterpret_cast<value_type const*>(current_slot);
+    auto const& current_key  = slot_contents.first;
 
     auto const slot_is_empty = detail::bitwise_compare(current_key, this->get_empty_key_sentinel());
     auto const equals        = not slot_is_empty and key_equal(current_key, k);
@@ -1057,7 +1058,7 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_
   [[maybe_unused]] bool found_match = false;
 
   while (true) {
-    auto slot_contents = *reinterpret_cast<cuco::pair_type<Key, Value> const*>(current_slot);
+    auto slot_contents = *reinterpret_cast<value_type const*>(current_slot);
 
     auto const slot_is_empty =
       detail::bitwise_compare(slot_contents.first, this->get_empty_key_sentinel());
@@ -1208,7 +1209,7 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::retri
     // is unsafe!
     static_assert(sizeof(Key) == sizeof(cuda::atomic<Key>));
     static_assert(sizeof(Value) == sizeof(cuda::atomic<Value>));
-    pair<Key, Value> slot_contents = *reinterpret_cast<pair<Key, Value> const*>(current_slot);
+    value_type slot_contents = *reinterpret_cast<value_type const*>(current_slot);
 
     auto const slot_is_empty =
       detail::bitwise_compare(slot_contents.first, this->get_empty_key_sentinel());
@@ -1467,14 +1468,14 @@ __inline__ __device__
   offset = g.shfl(offset, 0);
 
 #if defined(CUCO_HAS_CUDA_BARRIER)
-  cooperative_groups::memcpy_async(g,
-                                   output_begin + offset,
-                                   output_buffer,
-                                   cuda::aligned_size_t<alignof(cuco::pair_type<Key, Value>)>(
-                                     sizeof(cuco::pair_type<Key, Value>) * num_outputs));
+  cooperative_groups::memcpy_async(
+    g,
+    output_begin + offset,
+    output_buffer,
+    cuda::aligned_size_t<alignof(value_type)>(sizeof(value_type) * num_outputs));
 #else
   cooperative_groups::memcpy_async(
-    g, output_begin + offset, output_buffer, sizeof(cuco::pair_type<Key, Value>) * num_outputs);
+    g, output_begin + offset, output_buffer, sizeof(value_type) * num_outputs);
 #endif
 }
 

From 5752e34db11e2189c6cb49d09441a75b75144bb8 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Fri, 3 Sep 2021 08:47:38 -0400
Subject: [PATCH 203/267] Refactor count/pair_count APIs

---
 include/cuco/detail/static_multimap.inl       | 81 +++++++++----------
 .../cuco/detail/static_multimap_kernels.cuh   |  8 +-
 include/cuco/static_multimap.cuh              | 78 +++++++-----------
 3 files changed, 72 insertions(+), 95 deletions(-)

diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index e5bd02b44..5a2fe5e6e 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -918,10 +918,11 @@ template <typename Key,
           cuda::thread_scope Scope,
           typename Allocator>
 template <bool uses_vector_load, bool is_outer, typename CG, typename KeyEqual>
-__device__ std::enable_if_t<uses_vector_load, void>
+__device__ std::enable_if_t<uses_vector_load, std::size_t>
 static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::count_impl(
-  CG const& g, Key const& k, std::size_t& thread_num_matches, KeyEqual key_equal) noexcept
+  CG const& g, Key const& k, KeyEqual key_equal) noexcept
 {
+  std::size_t count = 0;
   auto current_slot = initial_slot(g, k);
 
   [[maybe_unused]] bool found_match = false;
@@ -941,13 +942,13 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::count
       if (g.any(first_equals or second_equals)) { found_match = true; }
     }
 
-    thread_num_matches += (first_equals + second_equals);
+    count += (first_equals + second_equals);
 
     if (g.any(first_slot_is_empty or second_slot_is_empty)) {
       if constexpr (is_outer) {
-        if ((not found_match) && (g.thread_rank() == 0)) { thread_num_matches++; }
+        if ((not found_match) && (g.thread_rank() == 0)) { count++; }
       }
-      break;
+      return count;
     }
 
     current_slot = next_slot(current_slot);
@@ -960,10 +961,11 @@ template <typename Key,
           cuda::thread_scope Scope,
           typename Allocator>
 template <bool uses_vector_load, bool is_outer, typename CG, typename KeyEqual>
-__device__ std::enable_if_t<not uses_vector_load, void>
+__device__ std::enable_if_t<not uses_vector_load, std::size_t>
 static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::count_impl(
-  CG const& g, Key const& k, std::size_t& thread_num_matches, KeyEqual key_equal) noexcept
+  CG const& g, Key const& k, KeyEqual key_equal) noexcept
 {
+  std::size_t count = 0;
   auto current_slot = initial_slot(g, k);
 
   [[maybe_unused]] bool found_match = false;
@@ -979,13 +981,13 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::count
       if (g.any(equals)) { found_match = true; }
     }
 
-    thread_num_matches += equals;
+    count += equals;
 
     if (g.any(slot_is_empty)) {
       if constexpr (is_outer) {
-        if ((not found_match) && (g.thread_rank() == 0)) { thread_num_matches++; }
+        if ((not found_match) && (g.thread_rank() == 0)) { count++; }
       }
-      break;
+      return count;
     }
 
     current_slot = next_slot(current_slot);
@@ -998,13 +1000,11 @@ template <typename Key,
           cuda::thread_scope Scope,
           typename Allocator>
 template <bool uses_vector_load, bool is_outer, typename CG, typename PairEqual>
-__device__ std::enable_if_t<uses_vector_load, void>
+__device__ std::enable_if_t<uses_vector_load, std::size_t>
 static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_count_impl(
-  CG const& g,
-  value_type const& pair,
-  std::size_t& thread_num_matches,
-  PairEqual pair_equal) noexcept
+  CG const& g, value_type const& pair, PairEqual pair_equal) noexcept
 {
+  std::size_t count = 0;
   auto key          = pair.first;
   auto current_slot = initial_slot(g, key);
 
@@ -1026,13 +1026,13 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_
       if (g.any(first_slot_equals or second_slot_equals)) { found_match = true; }
     }
 
-    thread_num_matches += (first_slot_equals + second_slot_equals);
+    count += (first_slot_equals + second_slot_equals);
 
     if (g.any(first_slot_is_empty or second_slot_is_empty)) {
       if constexpr (is_outer) {
-        if ((not found_match) && (g.thread_rank() == 0)) { thread_num_matches++; }
+        if ((not found_match) && (g.thread_rank() == 0)) { count++; }
       }
-      break;
+      return count;
     }
 
     current_slot = next_slot(current_slot);
@@ -1045,13 +1045,11 @@ template <typename Key,
           cuda::thread_scope Scope,
           typename Allocator>
 template <bool uses_vector_load, bool is_outer, typename CG, typename PairEqual>
-__device__ std::enable_if_t<not uses_vector_load, void>
+__device__ std::enable_if_t<not uses_vector_load, std::size_t>
 static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_count_impl(
-  CG const& g,
-  value_type const& pair,
-  std::size_t& thread_num_matches,
-  PairEqual pair_equal) noexcept
+  CG const& g, value_type const& pair, PairEqual pair_equal) noexcept
 {
+  std::size_t count = 0;
   auto key          = pair.first;
   auto current_slot = initial_slot(g, key);
 
@@ -1069,13 +1067,13 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_
       if (g.any(equals)) { found_match = true; }
     }
 
-    thread_num_matches += equals;
+    count += equals;
 
     if (g.any(slot_is_empty)) {
       if constexpr (is_outer) {
-        if ((not found_match) && (g.thread_rank() == 0)) { thread_num_matches++; }
+        if ((not found_match) && (g.thread_rank() == 0)) { count++; }
       }
-      break;
+      return count;
     }
 
     current_slot = next_slot(current_slot);
@@ -1558,11 +1556,12 @@ template <typename Key,
           cuda::thread_scope Scope,
           typename Allocator>
 template <typename CG, typename KeyEqual>
-__device__ void static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::count(
-  CG const& g, Key const& k, std::size_t& thread_num_matches, KeyEqual key_equal) noexcept
+__device__ std::size_t
+static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::count(
+  CG const& g, Key const& k, KeyEqual key_equal) noexcept
 {
   constexpr bool is_outer = false;
-  count_impl<uses_vector_load(), is_outer>(g, k, thread_num_matches, key_equal);
+  return count_impl<uses_vector_load(), is_outer>(g, k, key_equal);
 }
 
 template <typename Key,
@@ -1571,12 +1570,12 @@ template <typename Key,
           cuda::thread_scope Scope,
           typename Allocator>
 template <typename CG, typename KeyEqual>
-__device__ void
+__device__ std::size_t
 static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::count_outer(
-  CG const& g, Key const& k, std::size_t& thread_num_matches, KeyEqual key_equal) noexcept
+  CG const& g, Key const& k, KeyEqual key_equal) noexcept
 {
   constexpr bool is_outer = true;
-  count_impl<uses_vector_load(), is_outer>(g, k, thread_num_matches, key_equal);
+  return count_impl<uses_vector_load(), is_outer>(g, k, key_equal);
 }
 
 template <typename Key,
@@ -1585,15 +1584,12 @@ template <typename Key,
           cuda::thread_scope Scope,
           typename Allocator>
 template <typename CG, typename PairEqual>
-__device__ void
+__device__ std::size_t
 static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_count(
-  CG const& g,
-  value_type const& pair,
-  std::size_t& thread_num_matches,
-  PairEqual pair_equal) noexcept
+  CG const& g, value_type const& pair, PairEqual pair_equal) noexcept
 {
   constexpr bool is_outer = false;
-  pair_count_impl<uses_vector_load(), is_outer>(g, pair, thread_num_matches, pair_equal);
+  return pair_count_impl<uses_vector_load(), is_outer>(g, pair, pair_equal);
 }
 
 template <typename Key,
@@ -1602,15 +1598,12 @@ template <typename Key,
           cuda::thread_scope Scope,
           typename Allocator>
 template <typename CG, typename PairEqual>
-__device__ void
+__device__ std::size_t
 static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_count_outer(
-  CG const& g,
-  value_type const& pair,
-  std::size_t& thread_num_matches,
-  PairEqual pair_equal) noexcept
+  CG const& g, value_type const& pair, PairEqual pair_equal) noexcept
 {
   constexpr bool is_outer = true;
-  pair_count_impl<uses_vector_load(), is_outer>(g, pair, thread_num_matches, pair_equal);
+  return pair_count_impl<uses_vector_load(), is_outer>(g, pair, pair_equal);
 }
 
 template <typename Key,
diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap_kernels.cuh
index 8bbf96616..21c2aff45 100644
--- a/include/cuco/detail/static_multimap_kernels.cuh
+++ b/include/cuco/detail/static_multimap_kernels.cuh
@@ -228,9 +228,9 @@ __global__ void count(
   while (first + key_idx < last) {
     auto key = *(first + key_idx);
     if constexpr (is_outer) {
-      view.count_outer(tile, key, thread_num_matches, key_equal);
+      thread_num_matches += view.count_outer(tile, key, key_equal);
     } else {
-      view.count(tile, key, thread_num_matches, key_equal);
+      thread_num_matches += view.count(tile, key, key_equal);
     }
     key_idx += (gridDim.x * block_size) / tile_size;
   }
@@ -283,9 +283,9 @@ __global__ void pair_count(
   while (first + pair_idx < last) {
     typename viewT::value_type const pair = *(first + pair_idx);
     if constexpr (is_outer) {
-      view.pair_count_outer(tile, pair, thread_num_matches, pair_equal);
+      thread_num_matches += view.pair_count_outer(tile, pair, pair_equal);
     } else {
-      view.pair_count(tile, pair, thread_num_matches, pair_equal);
+      thread_num_matches += view.pair_count(tile, pair, pair_equal);
     }
     pair_idx += (gridDim.x * block_size) / tile_size;
   }
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index 72fbd0636..d03589591 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -911,19 +911,16 @@ class static_multimap {
      * @tparam KeyEqual Binary callable type
      * @param g The Cooperative Group used to perform the count operation
      * @param k The key to search for
-     * @param thread_num_matches Number of matches found by the current thread
      * @param key_equal The binary callable used to compare two keys
      * for equality
+     * @return Number of matches found by the current thread
      */
     template <bool uses_vector_load,
               bool is_outer,
               typename CG,
               typename KeyEqual = thrust::equal_to<key_type>>
-    __device__ std::enable_if_t<uses_vector_load, void> count_impl(
-      CG const& g,
-      Key const& k,
-      std::size_t& thread_num_matches,
-      KeyEqual key_equal = KeyEqual{}) noexcept;
+    __device__ std::enable_if_t<uses_vector_load, std::size_t> count_impl(
+      CG const& g, Key const& k, KeyEqual key_equal = KeyEqual{}) noexcept;
 
     /**
      * @brief Counts the occurrence of a given key contained in multimap using scalar loads.
@@ -934,19 +931,16 @@ class static_multimap {
      * @tparam KeyEqual Binary callable type
      * @param g The Cooperative Group used to perform the count operation
      * @param k The key to search for
-     * @param thread_num_matches Number of matches found by the current thread
      * @param key_equal The binary callable used to compare two keys
      * for equality
+     * @return Number of matches found by the current thread
      */
     template <bool uses_vector_load,
               bool is_outer,
               typename CG,
               typename KeyEqual = thrust::equal_to<key_type>>
-    __device__ std::enable_if_t<not uses_vector_load, void> count_impl(
-      CG const& g,
-      Key const& k,
-      std::size_t& thread_num_matches,
-      KeyEqual key_equal = KeyEqual{}) noexcept;
+    __device__ std::enable_if_t<not uses_vector_load, std::size_t> count_impl(
+      CG const& g, Key const& k, KeyEqual key_equal = KeyEqual{}) noexcept;
 
     /**
      * @brief Counts the occurrence of a given key/value pair contained in multimap using vector
@@ -958,16 +952,13 @@ class static_multimap {
      * @tparam PairEqual Binary callable type
      * @param g The Cooperative Group used to perform the pair_count operation
      * @param pair The pair to search for
-     * @param thread_num_matches Number of matches found by the current thread
      * @param pair_equal The binary callable used to compare two pairs
      * for equality
+     * @return Number of matches found by the current thread
      */
     template <bool uses_vector_load, bool is_outer, typename CG, typename PairEqual>
-    __device__ std::enable_if_t<uses_vector_load, void> pair_count_impl(
-      CG const& g,
-      value_type const& pair,
-      std::size_t& thread_num_matches,
-      PairEqual pair_equal) noexcept;
+    __device__ std::enable_if_t<uses_vector_load, std::size_t> pair_count_impl(
+      CG const& g, value_type const& pair, PairEqual pair_equal) noexcept;
 
     /**
      * @brief Counts the occurrence of a given key/value pair contained in multimap using scalar
@@ -979,16 +970,13 @@ class static_multimap {
      * @tparam PairEqual Binary callable type
      * @param g The Cooperative Group used to perform the pair_count operation
      * @param pair The pair to search for
-     * @param thread_num_matches Number of matches found by the current thread
      * @param pair_equal The binary callable used to compare two pairs
      * for equality
+     * @return Number of matches found by the current thread
      */
     template <bool uses_vector_load, bool is_outer, typename CG, typename PairEqual>
-    __device__ std::enable_if_t<not uses_vector_load, void> pair_count_impl(
-      CG const& g,
-      value_type const& pair,
-      std::size_t& thread_num_matches,
-      PairEqual pair_equal) noexcept;
+    __device__ std::enable_if_t<not uses_vector_load, std::size_t> pair_count_impl(
+      CG const& g, value_type const& pair, PairEqual pair_equal) noexcept;
 
     /**
      * @brief Find all the matches of a given key contained in multimap using vector
@@ -1263,15 +1251,14 @@ class static_multimap {
      * @tparam KeyEqual Binary callable type
      * @param g The Cooperative Group used to perform the count operation
      * @param k The key to search for
-     * @param thread_num_matches Number of matches found by the current thread
      * @param key_equal The binary callable used to compare two keys
      * for equality
+     * @return Number of matches found by the current thread
      */
     template <typename CG, typename KeyEqual = thrust::equal_to<key_type>>
-    __device__ void count(CG const& g,
-                          Key const& k,
-                          std::size_t& thread_num_matches,
-                          KeyEqual key_equal = KeyEqual{}) noexcept;
+    __device__ std::size_t count(CG const& g,
+                                 Key const& k,
+                                 KeyEqual key_equal = KeyEqual{}) noexcept;
 
     /**
      * @brief Counts the occurrence of a given key contained in multimap. If no
@@ -1281,15 +1268,14 @@ class static_multimap {
      * @tparam KeyEqual Binary callable type
      * @param g The Cooperative Group used to perform the count operation
      * @param k The key to search for
-     * @param thread_num_matches Number of matches found by the current thread
      * @param key_equal The binary callable used to compare two keys
      * for equality
+     * @return Number of matches found by the current thread
      */
     template <typename CG, typename KeyEqual = thrust::equal_to<key_type>>
-    __device__ void count_outer(CG const& g,
-                                Key const& k,
-                                std::size_t& thread_num_matches,
-                                KeyEqual key_equal = KeyEqual{}) noexcept;
+    __device__ std::size_t count_outer(CG const& g,
+                                       Key const& k,
+                                       KeyEqual key_equal = KeyEqual{}) noexcept;
 
     /**
      * @brief Counts the occurrence of a given key/value pair contained in multimap.
@@ -1298,15 +1284,14 @@ class static_multimap {
      * @tparam PairEqual Binary callable type
      * @param g The Cooperative Group used to perform the pair_count operation
      * @param pair The pair to search for
-     * @param thread_num_matches Number of matches found by the current thread
      * @param pair_equal The binary callable used to compare two pairs
      * for equality
+     * @return Number of matches found by the current thread
      */
     template <typename CG, typename PairEqual>
-    __device__ void pair_count(CG const& g,
-                               value_type const& pair,
-                               std::size_t& thread_num_matches,
-                               PairEqual pair_equal) noexcept;
+    __device__ std::size_t pair_count(CG const& g,
+                                      value_type const& pair,
+                                      PairEqual pair_equal) noexcept;
 
     /**
      * @brief Counts the occurrence of a given key/value pair contained in multimap.
@@ -1316,15 +1301,14 @@ class static_multimap {
      * @tparam PairEqual Binary callable type
      * @param g The Cooperative Group used to perform the pair_count operation
      * @param pair The pair to search for
-     * @param thread_num_matches Number of matches found by the current thread
      * @param pair_equal The binary callable used to compare two pairs
      * for equality
+     * @return Number of matches found by the current thread
      */
     template <typename CG, typename PairEqual>
-    __device__ void pair_count_outer(CG const& g,
-                                     value_type const& pair,
-                                     std::size_t& thread_num_matches,
-                                     PairEqual pair_equal) noexcept;
+    __device__ std::size_t pair_count_outer(CG const& g,
+                                            value_type const& pair,
+                                            PairEqual pair_equal) noexcept;
 
     /**
      * @brief Find all the matches of a given key contained in multimap using vector
@@ -1721,9 +1705,9 @@ class static_multimap {
   Value empty_value_sentinel_{};                ///< Initial value of empty slot
   slot_allocator_type slot_allocator_{};        ///< Allocator used to allocate slots
   counter_allocator_type counter_allocator_{};  ///< Allocator used to allocate counters
-  atomic_ctr_type* d_counter_;
-  cudaStream_t const stream_;
-};  // class static_multimap
+  atomic_ctr_type* d_counter_;                  ///< Preallocated device counter
+  cudaStream_t const stream_;                   ///< CUDA stream used for initialization
+};                                              // class static_multimap
 }  // namespace cuco
 
 #include <cuco/detail/static_multimap.inl>

From ed2155626ad9f43e297aff33b4728f9dc6b9bdf7 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Fri, 3 Sep 2021 10:45:00 -0400
Subject: [PATCH 204/267] Refactor host bulk insert_if API

---
 include/cuco/detail/static_multimap.inl       | 9 +++++----
 include/cuco/static_multimap.cuh              | 5 +++--
 tests/static_multimap/static_multimap_test.cu | 2 +-
 3 files changed, 9 insertions(+), 7 deletions(-)

diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index 5a2fe5e6e..1ab2aaf88 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -106,10 +106,11 @@ template <typename Key,
           cuda::thread_scope Scope,
           typename Allocator>
 template <typename InputIt, typename StencilIt, typename Predicate>
-void static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::insert_if_n(
-  InputIt first, StencilIt stencil, std::size_t n, Predicate pred, cudaStream_t stream)
+void static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::insert_if(
+  InputIt first, InputIt last, StencilIt stencil, Predicate pred, cudaStream_t stream)
 {
-  auto view = get_device_mutable_view();
+  auto num_elements = std::distance(first, last);
+  auto view         = get_device_mutable_view();
 
   auto constexpr block_size = 128;
   int grid_size{-1};
@@ -125,7 +126,7 @@ void static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::insert_if_n(
   grid_size *= num_sms;
 
   detail::insert_if_n<block_size, cg_size()>
-    <<<grid_size, block_size, 0, stream>>>(first, stencil, n, view, pred);
+    <<<grid_size, block_size, 0, stream>>>(first, stencil, num_elements, view, pred);
   CUCO_CUDA_TRY(cudaStreamSynchronize(stream));
 }
 
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index d03589591..7fdde2d8a 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -228,14 +228,15 @@ class static_multimap {
    * @tparam StencilIt Device accessible stencil iterator
    * @tparam Predicate Unary predicate function type
    * @param first Beginning of the sequence of key/value pairs
+   * @param last End of the sequence of key/value pairs
    * @param stencil Beginning of the stencil sequence
    * @param n Number of elements to insert
    * @param pred Predicate to test on the given stencil sequence
    * @param stream CUDA stream used for insert
    */
   template <typename InputIt, typename StencilIt, typename Predicate>
-  void insert_if_n(
-    InputIt first, StencilIt stencil, std::size_t n, Predicate pred, cudaStream_t stream = 0);
+  void insert_if(
+    InputIt first, InputIt last, StencilIt stencil, Predicate pred, cudaStream_t stream = 0);
 
   /**
    * @brief Indicates whether the keys in the range `[first, last)` are contained in the map.
diff --git a/tests/static_multimap/static_multimap_test.cu b/tests/static_multimap/static_multimap_test.cu
index b0a874eda..870ae13c4 100644
--- a/tests/static_multimap/static_multimap_test.cu
+++ b/tests/static_multimap/static_multimap_test.cu
@@ -269,7 +269,7 @@ TEMPLATE_TEST_CASE_SIG("Tests of insert_if",
   thrust::device_vector<cuco::pair_type<Key, Value>> d_pairs(h_pairs);
 
   auto pred_lambda = [] __device__(Key k) { return k % 2 == 0; };
-  map.insert_if_n(d_pairs.begin(), d_keys.begin(), d_pairs.size(), pred_lambda);
+  map.insert_if(d_pairs.begin(), d_pairs.begin() + d_pairs.size(), d_keys.begin(), pred_lambda);
 
   auto num = map.count(d_keys.begin(), d_keys.end());
 

From fa1a8e68652282976dc074a9fad37777458ac9a3 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Tue, 7 Sep 2021 14:29:01 -0400
Subject: [PATCH 205/267] Fix a bug: cuda::atomic taking Scope template
 parameter

---
 include/cuco/detail/static_multimap.inl | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index 1ab2aaf88..794e21e54 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -657,9 +657,9 @@ __device__
     cuco::make_pair<Key, Value>(std::move(expected_key), std::move(expected_value))};
   cuco::detail::pair_converter<value_type> new_pair{insert_pair};
 
-  auto slot =
-    reinterpret_cast<cuda::atomic<typename cuco::detail::pair_converter<value_type>::packed_type>*>(
-      current_slot);
+  auto slot = reinterpret_cast<
+    cuda::atomic<typename cuco::detail::pair_converter<value_type>::packed_type, Scope>*>(
+    current_slot);
 
   bool success = slot->compare_exchange_strong(
     expected_pair.packed, new_pair.packed, cuda::std::memory_order_relaxed);

From 60c28e61db07149049106983921b18dc29f237f7 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Thu, 9 Sep 2021 17:48:38 -0400
Subject: [PATCH 206/267] Add multimap move constructor

---
 include/cuco/static_multimap.cuh | 20 +++++++++++++++++++-
 1 file changed, 19 insertions(+), 1 deletion(-)

diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index 7fdde2d8a..e3cc9d2a1 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -153,7 +153,6 @@ class static_multimap {
     typename std::allocator_traits<Allocator>::rebind_alloc<atomic_ctr_type>;
 
   static_multimap(static_multimap const&) = delete;
-  static_multimap(static_multimap&&)      = delete;
   static_multimap& operator=(static_multimap const&) = delete;
   static_multimap& operator=(static_multimap&&) = delete;
 
@@ -201,6 +200,25 @@ class static_multimap {
                   cudaStream_t stream    = 0,
                   Allocator const& alloc = Allocator{});
 
+  /**
+   * @brief Move-constructor
+   * @param other Object to be moved
+   */
+  static_multimap(static_multimap&& other)
+    : slots_(std::move(other.slots_)),
+      capacity_(std::move(other.capacity_)),
+      size_(std::move(other.size_)),
+      empty_key_sentinel_(std::move(other.empty_key_sentinel_)),
+      empty_value_sentinel_(std::move(other.empty_value_sentinel_)),
+      slot_allocator_(std::move(other.slot_allocator_)),
+      counter_allocator_(std::move(other.counter_allocator_)),
+      d_counter_(std::move(other.d_counter_)),
+      stream_(std::move(other.stream_))
+  {
+    other.slots_     = nullptr;
+    other.d_counter_ = nullptr;
+  }
+
   /**
    * @brief Destroys the map and frees its contents.
    *

From d741b995666bd8b28fcdd0796c6799e2021a0797 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Fri, 10 Sep 2021 19:13:27 -0400
Subject: [PATCH 207/267] Use default move ctor & assignment operator

---
 include/cuco/detail/static_multimap.inl |  2 +-
 include/cuco/static_multimap.cuh        | 28 ++++++-------------------
 2 files changed, 7 insertions(+), 23 deletions(-)

diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index 794e21e54..201ad3d41 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -57,7 +57,7 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::static_multimap(
   cudaDeviceGetAttribute(&num_sms, cudaDevAttrMultiProcessorCount, dev_id);
   grid_size *= num_sms;
 
-  detail::initialize<atomic_key_type, atomic_mapped_type><<<grid_size, block_size, 0, stream>>>(
+  detail::initialize<atomic_key_type, atomic_mapped_type><<<grid_size, block_size, 0, stream_>>>(
     slots_, empty_key_sentinel, empty_value_sentinel, get_capacity());
 }
 
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index e3cc9d2a1..0bf9d82ba 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -154,7 +154,10 @@ class static_multimap {
 
   static_multimap(static_multimap const&) = delete;
   static_multimap& operator=(static_multimap const&) = delete;
-  static_multimap& operator=(static_multimap&&) = delete;
+
+  static_multimap()                  = default;
+  static_multimap(static_multimap&&) = default;
+  static_multimap& operator=(static_multimap&&) = default;
 
   /**
    * @brief Indicate the warp size.
@@ -200,25 +203,6 @@ class static_multimap {
                   cudaStream_t stream    = 0,
                   Allocator const& alloc = Allocator{});
 
-  /**
-   * @brief Move-constructor
-   * @param other Object to be moved
-   */
-  static_multimap(static_multimap&& other)
-    : slots_(std::move(other.slots_)),
-      capacity_(std::move(other.capacity_)),
-      size_(std::move(other.size_)),
-      empty_key_sentinel_(std::move(other.empty_key_sentinel_)),
-      empty_value_sentinel_(std::move(other.empty_value_sentinel_)),
-      slot_allocator_(std::move(other.slot_allocator_)),
-      counter_allocator_(std::move(other.counter_allocator_)),
-      d_counter_(std::move(other.d_counter_)),
-      stream_(std::move(other.stream_))
-  {
-    other.slots_     = nullptr;
-    other.d_counter_ = nullptr;
-  }
-
   /**
    * @brief Destroys the map and frees its contents.
    *
@@ -1718,14 +1702,14 @@ class static_multimap {
 
  private:
   pair_atomic_type* slots_{nullptr};            ///< Pointer to flat slots storage
+  atomic_ctr_type* d_counter_{nullptr};         ///< Preallocated device counter
   std::size_t capacity_{};                      ///< Total number of slots
   std::size_t size_{};                          ///< Number of keys in map
   Key empty_key_sentinel_{};                    ///< Key value that represents an empty slot
   Value empty_value_sentinel_{};                ///< Initial value of empty slot
   slot_allocator_type slot_allocator_{};        ///< Allocator used to allocate slots
   counter_allocator_type counter_allocator_{};  ///< Allocator used to allocate counters
-  atomic_ctr_type* d_counter_;                  ///< Preallocated device counter
-  cudaStream_t const stream_;                   ///< CUDA stream used for initialization
+  cudaStream_t stream_{};                       ///< CUDA stream used for initialization
 };                                              // class static_multimap
 }  // namespace cuco
 

From 62bee532046f92cfaa53a4ef5ff4ca6246794a84 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Mon, 13 Sep 2021 13:52:39 -0400
Subject: [PATCH 208/267] Update benchmarks: remove unused timer, rename
 variables and benchmark host-bulk API

---
 .../static_multimap/find_all_bench.cu         | 80 +++++--------------
 .../static_multimap/static_multimap_bench.cu  | 29 +++----
 2 files changed, 32 insertions(+), 77 deletions(-)

diff --git a/benchmarks/hash_table/static_multimap/find_all_bench.cu b/benchmarks/hash_table/static_multimap/find_all_bench.cu
index 8a6332ae9..18c8c8860 100644
--- a/benchmarks/hash_table/static_multimap/find_all_bench.cu
+++ b/benchmarks/hash_table/static_multimap/find_all_bench.cu
@@ -26,12 +26,14 @@
  *
  */
 template <typename Key, typename OutputIt>
-static void generate_multikeys(OutputIt output_begin, OutputIt output_end, size_t const num_reps)
+static void generate_multikeys(OutputIt output_begin,
+                               OutputIt output_end,
+                               size_t const multiplicity)
 {
   auto num_keys = std::distance(output_begin, output_end);
 
   for (auto i = 0; i < num_keys; ++i) {
-    output_begin[i] = (i % (num_keys / num_reps)) + 1;
+    output_begin[i] = (i % (num_keys / multiplicity)) + 1;
   }
 }
 
@@ -48,18 +50,18 @@ std::enable_if_t<(sizeof(Key) == sizeof(Value)), void> nvbench_find_all(
   nvbench::state& state,
   nvbench::type_list<Key, Value, nvbench::enum_type<CGSize>, nvbench::enum_type<BufferSize>>)
 {
-  std::size_t const num_keys = state.get_int64("NumInputs");
-  auto const occupancy       = state.get_float64("Occupancy");
-  std::size_t const size     = num_keys / occupancy;
-  std::size_t const num_reps = state.get_int64("NumReps");
+  std::size_t const num_keys     = state.get_int64("NumInputs");
+  auto const occupancy           = state.get_float64("Occupancy");
+  std::size_t const size         = num_keys / occupancy;
+  std::size_t const multiplicity = state.get_int64("Multiplicity");
 
-  constexpr bool is_outer = true;
+  state.add_element_count(num_keys, "NumKeys");
+  state.add_global_memory_writes<Key>(num_keys * 2);
 
   std::vector<Key> h_keys(num_keys);
   std::vector<cuco::pair_type<Key, Value>> h_pairs(num_keys);
 
-  generate_multikeys<Key>(h_keys.begin(), h_keys.end(), num_reps);
-
+  generate_multikeys<Key>(h_keys.begin(), h_keys.end(), multiplicity);
   for (auto i = 0; i < num_keys; ++i) {
     Key key           = h_keys[i];
     Value val         = h_keys[i];
@@ -67,56 +69,18 @@ std::enable_if_t<(sizeof(Key) == sizeof(Value)), void> nvbench_find_all(
     h_pairs[i].second = val;
   }
 
-  // Get an array of unique keys
-  std::set<Key> key_set(h_keys.begin(), h_keys.end());
-  std::vector<Key> h_unique_keys(key_set.begin(), key_set.end());
-  thrust::device_vector<Key> d_unique_keys(h_unique_keys);
-
-  thrust::device_vector<cuco::pair_type<Key, Value>> d_results(2 * num_keys);
+  thrust::device_vector<Key> d_keys(h_keys);
   thrust::device_vector<cuco::pair_type<Key, Value>> d_pairs(h_pairs);
 
-  state.add_element_count(num_keys, "NumKeys");
-  state.add_global_memory_writes<Key>(num_keys * 2);
+  cuco::static_multimap<Key, Value, cuco::double_hashing<Key, Value, CGSize>> map{size, -1, -1};
+  map.insert(d_pairs.begin(), d_pairs.end());
+
+  auto const output_size = map.count_outer(d_keys.begin(), d_keys.end());
+  thrust::device_vector<cuco::pair_type<Key, Value>> d_results(output_size);
 
-  state.exec(
-    nvbench::exec_tag::sync | nvbench::exec_tag::timer, [&](nvbench::launch& launch, auto& timer) {
-      cuco::static_multimap<Key, Value, cuco::double_hashing<Key, Value, CGSize>> map{size, -1, -1};
-      map.insert(d_pairs.begin(), d_pairs.end());
-
-      timer.start();
-      auto view = map.get_device_view();
-
-      auto const block_size  = 128;
-      auto const warp_size   = 32;
-      auto const buffer_size = CGSize * BufferSize;
-      auto const stride      = 1;
-      auto const grid_size = (CGSize * num_keys + stride * block_size - 1) / (stride * block_size);
-
-      using KeyEqual = thrust::equal_to<Key>;
-
-      KeyEqual key_equal;
-
-      using atomic_ctr_type = typename cuco::static_multimap<Key, Value>::atomic_ctr_type;
-      atomic_ctr_type* num_items;
-      CUCO_CUDA_TRY(cudaMallocManaged(&num_items, sizeof(atomic_ctr_type)));
-      *num_items = 0;
-      int device_id;
-      CUCO_CUDA_TRY(cudaGetDevice(&device_id));
-      CUCO_CUDA_TRY(cudaMemPrefetchAsync(num_items, sizeof(atomic_ctr_type), device_id));
-
-      // Use timers to explicitly mark the target region
-      cuco::detail::vectorized_retrieve<block_size, warp_size, CGSize, buffer_size, is_outer>
-        <<<grid_size, block_size, 0, launch.get_stream()>>>(d_unique_keys.begin(),
-                                                            d_unique_keys.end(),
-                                                            d_results.data().get(),
-                                                            num_items,
-                                                            view,
-                                                            key_equal);
-      CUCO_CUDA_TRY(cudaDeviceSynchronize());
-      timer.stop();
-
-      CUCO_CUDA_TRY(cudaFree(num_items));
-    });
+  state.exec(nvbench::exec_tag::sync, [&](nvbench::launch& launch) {
+    map.retrieve_outer(d_keys.begin(), d_keys.end(), d_results.data().get(), launch.get_stream());
+  });
 }
 
 template <typename Key, typename Value, nvbench::int32_t CGSize, nvbench::int32_t BufferSize>
@@ -139,7 +103,7 @@ NVBENCH_BENCH_TYPES(nvbench_find_all,
   .set_max_noise(3)                            // Custom noise: 3%. By default: 0.5%.
   .add_int64_axis("NumInputs", {100'000'000})  // Total number of key/value pairs: 100'000'000
   .add_float64_axis("Occupancy", {0.4})
-  .add_int64_power_of_two_axis("NumReps", nvbench::range(0, 8, 1));
+  .add_int64_power_of_two_axis("Multiplicity", nvbench::range(0, 8, 1));
 
 NVBENCH_BENCH_TYPES(
   nvbench_find_all,
@@ -149,4 +113,4 @@ NVBENCH_BENCH_TYPES(
   .set_max_noise(3)                            // Custom noise: 3%. By default: 0.5%.
   .add_int64_axis("NumInputs", {100'000'000})  // Total number of key/value pairs: 100'000'000
   .add_float64_axis("Occupancy", {0.4})
-  .add_int64_power_of_two_axis("NumReps", nvbench::range(0, 8, 1));
+  .add_int64_power_of_two_axis("Multiplicity", nvbench::range(0, 8, 1));
diff --git a/benchmarks/hash_table/static_multimap/static_multimap_bench.cu b/benchmarks/hash_table/static_multimap/static_multimap_bench.cu
index 47fd4c81b..c6d3fd58a 100644
--- a/benchmarks/hash_table/static_multimap/static_multimap_bench.cu
+++ b/benchmarks/hash_table/static_multimap/static_multimap_bench.cu
@@ -110,12 +110,9 @@ std::enable_if_t<(sizeof(Key) == sizeof(Value)), void> nvbench_static_multimap_c
   cuco::static_multimap<Key, Value> map{size, -1, -1};
   map.insert(d_pairs.begin(), d_pairs.end());
 
-  state.exec(nvbench::exec_tag::sync | nvbench::exec_tag::timer,
-             [&](nvbench::launch& launch, auto& timer) {
-               timer.start();
-               auto count = map.count(d_keys.begin(), d_keys.end(), launch.get_stream());
-               timer.stop();
-             });
+  state.exec(nvbench::exec_tag::sync, [&](nvbench::launch& launch) {
+    auto count = map.count(d_keys.begin(), d_keys.end(), launch.get_stream());
+  });
 }
 
 template <typename Key, typename Value, dist_type Dist, nvbench::int32_t Multiplicity>
@@ -167,12 +164,9 @@ std::enable_if_t<(sizeof(Key) == sizeof(Value)), void> nvbench_static_multimap_f
   auto const output_size = map.count_outer(d_keys.begin(), d_keys.end());
   thrust::device_vector<cuco::pair_type<Key, Value>> d_results(output_size);
 
-  state.exec(
-    nvbench::exec_tag::sync | nvbench::exec_tag::timer, [&](nvbench::launch& launch, auto& timer) {
-      timer.start();
-      map.retrieve_outer(d_keys.begin(), d_keys.end(), d_results.data().get(), launch.get_stream());
-      timer.stop();
-    });
+  state.exec(nvbench::exec_tag::sync, [&](nvbench::launch& launch) {
+    map.retrieve_outer(d_keys.begin(), d_keys.end(), d_results.data().get(), launch.get_stream());
+  });
 }
 
 template <typename Key, typename Value, dist_type Dist, nvbench::int32_t Multiplicity>
@@ -224,13 +218,10 @@ std::enable_if_t<(sizeof(Key) == sizeof(Value)), void> nvbench_static_multimap_r
   auto const output_size = map.count_outer(d_keys.begin(), d_keys.end());
   thrust::device_vector<cuco::pair_type<Key, Value>> d_results(output_size);
 
-  state.exec(
-    nvbench::exec_tag::sync | nvbench::exec_tag::timer, [&](nvbench::launch& launch, auto& timer) {
-      timer.start();
-      auto count = map.count_outer(d_keys.begin(), d_keys.end(), launch.get_stream());
-      map.retrieve_outer(d_keys.begin(), d_keys.end(), d_results.data().get(), launch.get_stream());
-      timer.stop();
-    });
+  state.exec(nvbench::exec_tag::sync, [&](nvbench::launch& launch) {
+    auto count = map.count_outer(d_keys.begin(), d_keys.end(), launch.get_stream());
+    map.retrieve_outer(d_keys.begin(), d_keys.end(), d_results.data().get(), launch.get_stream());
+  });
 }
 
 template <typename Key, typename Value, dist_type Dist, nvbench::int32_t Multiplicity>

From 4174391d19091cd475ead0f8b0724668cdc0cfcb Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Mon, 13 Sep 2021 14:25:20 -0400
Subject: [PATCH 209/267] Update multimap example: add comments, show outer use
 case

---
 .../static_multimap_example.cu                | 26 ++++++++++++-------
 1 file changed, 17 insertions(+), 9 deletions(-)

diff --git a/examples/static_multimap/static_multimap_example.cu b/examples/static_multimap/static_multimap_example.cu
index 430dba292..2ef03298d 100644
--- a/examples/static_multimap/static_multimap_example.cu
+++ b/examples/static_multimap/static_multimap_example.cu
@@ -27,35 +27,43 @@ int main(void)
   int empty_key_sentinel   = -1;
   int empty_value_sentinel = -1;
 
+  constexpr std::size_t N = 50'000;
+
   // Constructs a multimap with 100,000 slots using -1 and -1 as the empty key/value
   // sentinels. Note the capacity is chosen knowing we will insert 50,000 keys,
   // for an load factor of 50%.
-  cuco::static_multimap<int, int> map{100'000, empty_key_sentinel, empty_value_sentinel};
+  cuco::static_multimap<int, int> map{N * 2, empty_key_sentinel, empty_value_sentinel};
 
-  thrust::device_vector<thrust::pair<int, int>> pairs(50'000);
+  thrust::device_vector<thrust::pair<int, int>> pairs(N);
 
-  // Create a sequence of pairs. Eeach key corresponds to two pairs.
+  // Create a sequence of pairs. Eeach key has two matches.
   // E.g., {{0,0}, {1,1}, ... {0,25'000}, {1, 25'001}, ...}
   thrust::transform(thrust::make_counting_iterator<int>(0),
                     thrust::make_counting_iterator<int>(pairs.size()),
                     pairs.begin(),
-                    [] __device__(auto i) { return thrust::make_pair(i % (50'000 / 2), i); });
+                    [] __device__(auto i) { return thrust::make_pair(i % (N / 2), i); });
 
   // Inserts all pairs into the map
   map.insert(pairs.begin(), pairs.end());
 
-  // Sequence of keys {0, 1, 2, ...}
-  thrust::device_vector<int> keys_to_find(50'000);
+  // Sequence of probe keys {0, 1, 2, ... 49'999}
+  thrust::device_vector<int> keys_to_find(N);
   thrust::sequence(keys_to_find.begin(), keys_to_find.end(), 0);
 
-  // Counts number of matches for all keys {0, 1, 2, ... 50'000}
-  auto const output_size = map.count(keys_to_find.begin(), keys_to_find.end());
+  // Counts the occurrences of keys in [0, 50'000) contained in the multimap.
+  // The `_outer` suffix indicates that the occurrence of a non-match is 1.
+  auto const output_size = map.count_outer(keys_to_find.begin(), keys_to_find.end());
 
   thrust::device_vector<cuco::pair_type<int, int>> d_results(output_size);
 
   // Finds all keys {0, 1, 2, ...} and stores associated key/value pairs into `d_results`
   // If a key `keys_to_find[i]` doesn't exist, `d_results[i].second == empty_value_sentinel`
-  map.retrieve(keys_to_find.begin(), keys_to_find.end(), d_results.data().get());
+  auto output_end =
+    map.retrieve_outer(keys_to_find.begin(), keys_to_find.end(), d_results.data().get());
+  auto retrieve_size = output_end - d_results.data().get();
+
+  // The total number of outer matches should be `N + N / 2`
+  assert(not(output_size == retrieve_size == N + N / 2));
 
   return 0;
 }

From 65c32ed64af06e075d4d4e4adced4303d717ce8d Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Mon, 13 Sep 2021 14:36:27 -0400
Subject: [PATCH 210/267] Move probe sequence classes into cuco::detail
 namespace

---
 benchmarks/hash_table/static_multimap/find_all_bench.cu | 3 ++-
 include/cuco/detail/probe_sequences.cuh                 | 2 ++
 include/cuco/static_multimap.cuh                        | 2 +-
 3 files changed, 5 insertions(+), 2 deletions(-)

diff --git a/benchmarks/hash_table/static_multimap/find_all_bench.cu b/benchmarks/hash_table/static_multimap/find_all_bench.cu
index 18c8c8860..3447aa1c2 100644
--- a/benchmarks/hash_table/static_multimap/find_all_bench.cu
+++ b/benchmarks/hash_table/static_multimap/find_all_bench.cu
@@ -72,7 +72,8 @@ std::enable_if_t<(sizeof(Key) == sizeof(Value)), void> nvbench_find_all(
   thrust::device_vector<Key> d_keys(h_keys);
   thrust::device_vector<cuco::pair_type<Key, Value>> d_pairs(h_pairs);
 
-  cuco::static_multimap<Key, Value, cuco::double_hashing<Key, Value, CGSize>> map{size, -1, -1};
+  cuco::static_multimap<Key, Value, cuco::detail::double_hashing<Key, Value, CGSize>> map{
+    size, -1, -1};
   map.insert(d_pairs.begin(), d_pairs.end());
 
   auto const output_size = map.count_outer(d_keys.begin(), d_keys.end());
diff --git a/include/cuco/detail/probe_sequences.cuh b/include/cuco/detail/probe_sequences.cuh
index f83a8c2a3..37d9d0beb 100644
--- a/include/cuco/detail/probe_sequences.cuh
+++ b/include/cuco/detail/probe_sequences.cuh
@@ -22,6 +22,7 @@
 #include <cuco/detail/pair.cuh>
 
 namespace cuco {
+namespace detail {
 
 template <typename Key,
           typename Value,
@@ -162,4 +163,5 @@ class linear_probing : public probe_sequence_base<Key, Value, CGSize, Scope> {
   Hash hash_{};
 };  // class linear_probing
 
+}  // namespace detail
 }  // namespace cuco
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index 0bf9d82ba..e07e96733 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -124,7 +124,7 @@ namespace cuco {
  */
 template <typename Key,
           typename Value,
-          class ProbeSequence      = cuco::double_hashing<Key, Value>,
+          class ProbeSequence      = cuco::detail::double_hashing<Key, Value>,
           cuda::thread_scope Scope = cuda::thread_scope_device,
           typename Allocator       = cuco::cuda_allocator<char>>
 class static_multimap {

From c66372397203a7600de1f559d39ce3127e7dedca Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Mon, 13 Sep 2021 15:33:32 -0400
Subject: [PATCH 211/267] Update probe sequence constructors: taking hash
 object instead of default constructible

---
 include/cuco/detail/probe_sequences.cuh | 28 ++++++++++++++++---------
 1 file changed, 18 insertions(+), 10 deletions(-)

diff --git a/include/cuco/detail/probe_sequences.cuh b/include/cuco/detail/probe_sequences.cuh
index 37d9d0beb..c3fc0268a 100644
--- a/include/cuco/detail/probe_sequences.cuh
+++ b/include/cuco/detail/probe_sequences.cuh
@@ -26,7 +26,7 @@ namespace detail {
 
 template <typename Key,
           typename Value,
-          uint32_t CGSize          = 8,
+          uint32_t CGSize,
           cuda::thread_scope Scope = cuda::thread_scope_device>
 class probe_sequence_base {
  protected:
@@ -79,8 +79,12 @@ class double_hashing : public probe_sequence_base<Key, Value, CGSize, Scope> {
   using probe_sequence_base<Key, Value, CGSize, Scope>::vector_width;
   using probe_sequence_base<Key, Value, CGSize, Scope>::uses_vector_load;
 
-  __host__ __device__ explicit double_hashing(iterator slots, std::size_t capacity) noexcept
-    : probe_sequence_base<Key, Value, CGSize, Scope>{slots, capacity}
+  __host__ __device__ explicit double_hashing(
+    iterator slots,
+    std::size_t capacity,
+    Hash1 hash1 = cuco::detail::MurmurHash3_32<Key>{},
+    Hash2 hash2 = cuco::detail::MurmurHash3_32<Key>{}) noexcept
+    : probe_sequence_base<Key, Value, CGSize, Scope>{slots, capacity}, hash1_{hash1}, hash2_{hash2}
   {
   }
 
@@ -109,8 +113,8 @@ class double_hashing : public probe_sequence_base<Key, Value, CGSize, Scope> {
 
  private:
   std::size_t step_size_;
-  Hash1 hash1_{};
-  Hash2 hash2_{};
+  Hash1 hash1_;
+  Hash2 hash2_;
 };  // class double_hashing
 
 template <typename Key,
@@ -127,16 +131,20 @@ class linear_probing : public probe_sequence_base<Key, Value, CGSize, Scope> {
   using probe_sequence_base<Key, Value, CGSize, Scope>::vector_width;
   using probe_sequence_base<Key, Value, CGSize, Scope>::uses_vector_load;
 
-  __host__ __device__ explicit linear_probing(iterator slots, std::size_t capacity)
-    : probe_sequence_base<Key, Value, CGSize, Scope>{slots, capacity}
+  __host__ __device__ explicit linear_probing(iterator slots,
+                                              std::size_t capacity,
+                                              Hash hash = cuco::detail::MurmurHash3_32<Key>{})
+    : probe_sequence_base<Key, Value, CGSize, Scope>{slots, capacity}, hash_(hash)
   {
   }
 
   template <typename CG>
   __device__ iterator initial_slot(CG const& g, Key const k) noexcept
   {
-    auto hash_value = hash_(k);
-    hash_value      = hash_value % 2 ? hash_value + 1 : hash_value;
+    auto const hash_value = [&]() {
+      auto const tmp = hash_(k);
+      return tmp + tmp % 2;  // ensure initial hash value is always even
+    }();
 
     std::size_t offset;
     if constexpr (uses_vector_load()) {
@@ -160,7 +168,7 @@ class linear_probing : public probe_sequence_base<Key, Value, CGSize, Scope> {
   }
 
  private:
-  Hash hash_{};
+  Hash hash_;
 };  // class linear_probing
 
 }  // namespace detail

From 93dc6452b642957f25b20593077fa5432a13e65f Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Mon, 13 Sep 2021 15:35:15 -0400
Subject: [PATCH 212/267] Minor doc updates

---
 include/cuco/detail/bitwise_compare.cuh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/include/cuco/detail/bitwise_compare.cuh b/include/cuco/detail/bitwise_compare.cuh
index afa2c25ef..66c798123 100644
--- a/include/cuco/detail/bitwise_compare.cuh
+++ b/include/cuco/detail/bitwise_compare.cuh
@@ -77,7 +77,7 @@ __host__ __device__ bool bitwise_compare(T const& lhs, T const& rhs)
 
 /**
  * @brief Customization point that can be specialized to indicate that it is safe to perform bitwise
- * equality comparisons on objects of type `T`.
+ * equality comparisons on the object-representation of objects of type `T`.
  *
  * By default, only types where `std::has_unique_object_representations_v<T>` is true are safe for
  * bitwise equality. However, this can be too restrictive for some types, e.g., floating point

From ee191366e243204bbdcda0e42618623f7cb070a1 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Mon, 13 Sep 2021 16:16:42 -0400
Subject: [PATCH 213/267] Move is_bitwise_comparable to traits header +
 formatting

---
 include/cuco/detail/bitwise_compare.cuh    |  34 ---
 include/cuco/detail/static_map.inl         |   2 +
 include/cuco/detail/static_map_kernels.cuh | 308 ++++++++++-----------
 include/cuco/detail/static_multimap.inl    |   2 +
 include/cuco/static_map.cuh                |  12 +-
 include/cuco/static_multimap.cuh           |  12 +-
 include/cuco/traits.hpp                    |  58 ++++
 7 files changed, 217 insertions(+), 211 deletions(-)
 create mode 100644 include/cuco/traits.hpp

diff --git a/include/cuco/detail/bitwise_compare.cuh b/include/cuco/detail/bitwise_compare.cuh
index 66c798123..03efb5fc4 100644
--- a/include/cuco/detail/bitwise_compare.cuh
+++ b/include/cuco/detail/bitwise_compare.cuh
@@ -75,39 +75,5 @@ __host__ __device__ bool bitwise_compare(T const& lhs, T const& rhs)
                                                           reinterpret_cast<char const*>(&rhs));
 }
 
-/**
- * @brief Customization point that can be specialized to indicate that it is safe to perform bitwise
- * equality comparisons on the object-representation of objects of type `T`.
- *
- * By default, only types where `std::has_unique_object_representations_v<T>` is true are safe for
- * bitwise equality. However, this can be too restrictive for some types, e.g., floating point
- * types.
- *
- * User-defined specializations of `is_bitwise_comparable` are allowed, but it is the users
- * responsibility to ensure values do not occur that would lead to unexpected behavior. For example,
- * if a `NaN` bit pattern were used as the empty sentinel value, it may not compare bitwise equal to
- * other `NaN` bit patterns.
- *
- */
-template <typename T, typename = void>
-struct is_bitwise_comparable : std::false_type {
-};
-
-/// By default, only types with unique object representations are allowed
-template <typename T>
-struct is_bitwise_comparable<T, std::enable_if_t<std::has_unique_object_representations_v<T>>>
-  : std::true_type {
-};
-
-/**
- * @brief Declares that a type `Type` is bitwise comparable.
- *
- */
-#define CUCO_DECLARE_BITWISE_COMPARABLE(Type)           \
-  namespace cuco {                                      \
-  template <>                                           \
-  struct is_bitwise_comparable<Type> : std::true_type { \
-  };                                                    \
-  }
 }  // namespace detail
 }  // namespace cuco
diff --git a/include/cuco/detail/static_map.inl b/include/cuco/detail/static_map.inl
index e572eb6b0..53a14696d 100644
--- a/include/cuco/detail/static_map.inl
+++ b/include/cuco/detail/static_map.inl
@@ -14,6 +14,8 @@
  * limitations under the License.
  */
 
+#include <cuco/detail/bitwise_compare.cuh>
+
 namespace cuco {
 
 template <typename Key, typename Value, cuda::thread_scope Scope, typename Allocator>
diff --git a/include/cuco/detail/static_map_kernels.cuh b/include/cuco/detail/static_map_kernels.cuh
index aa4606aa6..e166de3c6 100644
--- a/include/cuco/detail/static_map_kernels.cuh
+++ b/include/cuco/detail/static_map_kernels.cuh
@@ -12,9 +12,9 @@
  * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  * See the License for the specific language governing permissions and
  * limitations under the License.
- */ 
+ */
 
-namespace cuco{
+namespace cuco {
 namespace detail {
 namespace cg = cooperative_groups;
 
@@ -23,7 +23,7 @@ namespace cg = cooperative_groups;
  *
  * Each space in `slots` that can hold a key value pair is initialized to a
  * `pair_atomic_type` containing the key `k` and the value `v`.
- * 
+ *
  * @tparam atomic_key_type Type of the `Key` atomic container
  * @tparam atomic_mapped_type Type of the `Value` atomic container
  * @tparam Key key type
@@ -34,16 +34,14 @@ namespace cg = cooperative_groups;
  * @param v Value to which all values in `slots` are initialized
  * @param size Size of the storage pointed to by `slots`
  */
-template<std::size_t block_size,
-         typename atomic_key_type,
-         typename atomic_mapped_type,
-         typename Key,
-         typename Value,
-         typename pair_atomic_type>
-__global__ void initialize(
-  pair_atomic_type* const slots, Key k,
-  Value v, std::size_t size) {
-  
+template <std::size_t block_size,
+          typename atomic_key_type,
+          typename atomic_mapped_type,
+          typename Key,
+          typename Value,
+          typename pair_atomic_type>
+__global__ void initialize(pair_atomic_type* const slots, Key k, Value v, std::size_t size)
+{
   auto tid = block_size * blockIdx.x + threadIdx.x;
   while (tid < size) {
     new (&slots[tid].first) atomic_key_type{k};
@@ -53,14 +51,14 @@ __global__ void initialize(
 }
 
 /**
- * @brief Inserts all key/value pairs in the range `[first, last)`. 
- *  
+ * @brief Inserts all key/value pairs in the range `[first, last)`.
+ *
  * If multiple keys in `[first, last)` compare equal, it is unspecified which
  * element is inserted.
- * 
- * @tparam block_size 
+ *
+ * @tparam block_size
  * @tparam InputIt Device accessible input iterator whose `value_type` is
-* convertible to the map's `value_type`
+ * convertible to the map's `value_type`
  * @tparam atomicT Type of atomic storage
  * @tparam viewT Type of device view allowing access of hash map storage
  * @tparam Hash Unary callable type
@@ -72,25 +70,22 @@ __global__ void initialize(
  * @param hash The unary function to apply to hash each key
  * @param key_equal The binary function used to compare two keys for equality
  */
-template<std::size_t block_size,
-         typename InputIt,
-         typename atomicT,
-         typename viewT,
-         typename Hash, 
-         typename KeyEqual>
-__global__ void insert(InputIt first,
-                       InputIt last,
-                       atomicT* num_successes,
-                       viewT view,
-                       Hash hash,
-                       KeyEqual key_equal) {
+template <std::size_t block_size,
+          typename InputIt,
+          typename atomicT,
+          typename viewT,
+          typename Hash,
+          typename KeyEqual>
+__global__ void insert(
+  InputIt first, InputIt last, atomicT* num_successes, viewT view, Hash hash, KeyEqual key_equal)
+{
   typedef cub::BlockReduce<std::size_t, block_size> BlockReduce;
   __shared__ typename BlockReduce::TempStorage temp_storage;
   std::size_t thread_num_successes = 0;
-  
+
   auto tid = block_size * blockIdx.x + threadIdx.x;
-  auto it = first + tid;
-  
+  auto it  = first + tid;
+
   while (it < last) {
     typename viewT::value_type const insert_pair{*it};
     if (view.insert(insert_pair, hash, key_equal)) { thread_num_successes++; }
@@ -100,25 +95,23 @@ __global__ void insert(InputIt first,
   // compute number of successfully inserted elements for each block
   // and atomically add to the grand total
   std::size_t block_num_successes = BlockReduce(temp_storage).Sum(thread_num_successes);
-  if(threadIdx.x == 0) {
-    *num_successes += block_num_successes;
-  }
+  if (threadIdx.x == 0) { *num_successes += block_num_successes; }
 }
 
 /**
- * @brief Inserts all key/value pairs in the range `[first, last)`. 
- *  
+ * @brief Inserts all key/value pairs in the range `[first, last)`.
+ *
  * If multiple keys in `[first, last)` compare equal, it is unspecified which
  * element is inserted. Uses the CUDA Cooperative Groups API to leverage groups
- * of multiple threads to perform each key/value insertion. This provides a 
+ * of multiple threads to perform each key/value insertion. This provides a
  * significant boost in throughput compared to the non Cooperative Group
  * `insert` at moderate to high load factors.
- * 
- * @tparam block_size 
+ *
+ * @tparam block_size
  * @tparam tile_size The number of threads in the Cooperative Groups used to perform
  * inserts
  * @tparam InputIt Device accessible input iterator whose `value_type` is
-* convertible to the map's `value_type`
+ * convertible to the map's `value_type`
  * @tparam atomicT Type of atomic storage
  * @tparam viewT Type of device view allowing access of hash map storage
  * @tparam Hash Unary callable type
@@ -130,27 +123,24 @@ __global__ void insert(InputIt first,
  * @param hash The unary function to apply to hash each key
  * @param key_equal The binary function used to compare two keys for equality
  */
-template<std::size_t block_size,
-         uint32_t tile_size,
-         typename InputIt,
-         typename atomicT,
-         typename viewT,
-         typename Hash, 
-         typename KeyEqual>
-__global__ void insert(InputIt first,
-                       InputIt last,
-                       atomicT* num_successes,
-                       viewT view,
-                       Hash hash,
-                       KeyEqual key_equal) {
+template <std::size_t block_size,
+          uint32_t tile_size,
+          typename InputIt,
+          typename atomicT,
+          typename viewT,
+          typename Hash,
+          typename KeyEqual>
+__global__ void insert(
+  InputIt first, InputIt last, atomicT* num_successes, viewT view, Hash hash, KeyEqual key_equal)
+{
   typedef cub::BlockReduce<std::size_t, block_size> BlockReduce;
   __shared__ typename BlockReduce::TempStorage temp_storage;
   std::size_t thread_num_successes = 0;
 
   auto tile = cg::tiled_partition<tile_size>(cg::this_thread_block());
-  auto tid = block_size * blockIdx.x + threadIdx.x;
-  auto it = first + tid / tile_size;
-  
+  auto tid  = block_size * blockIdx.x + threadIdx.x;
+  auto it   = first + tid / tile_size;
+
   while (it < last) {
     // force conversion to value_type
     typename viewT::value_type const insert_pair{*it};
@@ -159,25 +149,23 @@ __global__ void insert(InputIt first,
     }
     it += (gridDim.x * block_size) / tile_size;
   }
-  
+
   // compute number of successfully inserted elements for each block
   // and atomically add to the grand total
   std::size_t block_num_successes = BlockReduce(temp_storage).Sum(thread_num_successes);
-  if(threadIdx.x == 0) {
-    *num_successes += block_num_successes;
-  }
+  if (threadIdx.x == 0) { *num_successes += block_num_successes; }
 }
 
 /**
  * @brief Finds the values corresponding to all keys in the range `[first, last)`.
- * 
- * If the key `*(first + i)` exists in the map, copies its associated value to `(output_begin + i)`. 
- * Else, copies the empty value sentinel. 
+ *
+ * If the key `*(first + i)` exists in the map, copies its associated value to `(output_begin + i)`.
+ * Else, copies the empty value sentinel.
  * @tparam block_size The size of the thread block
  * @tparam Value The type of the mapped value for the map
  * @tparam InputIt Device accessible input iterator whose `value_type` is
  * convertible to the map's `key_type`
- * @tparam OutputIt Device accessible output iterator whose `value_type` is 
+ * @tparam OutputIt Device accessible output iterator whose `value_type` is
  * convertible to the map's `mapped_type`
  * @tparam viewT Type of device view allowing access of hash map storage
  * @tparam Hash Unary callable type
@@ -189,37 +177,34 @@ __global__ void insert(InputIt first,
  * @param hash The unary function to apply to hash each key
  * @param key_equal The binary function to compare two keys for equality
  */
-template<std::size_t block_size,
-         typename Value,
-         typename InputIt, typename OutputIt, 
-         typename viewT,
-         typename Hash, 
-         typename KeyEqual>
-__global__ void find(InputIt first,
-                     InputIt last,
-                     OutputIt output_begin,
-                     viewT view,
-                     Hash hash,
-                     KeyEqual key_equal) {
-  auto tid = block_size * blockIdx.x + threadIdx.x;
+template <std::size_t block_size,
+          typename Value,
+          typename InputIt,
+          typename OutputIt,
+          typename viewT,
+          typename Hash,
+          typename KeyEqual>
+__global__ void find(
+  InputIt first, InputIt last, OutputIt output_begin, viewT view, Hash hash, KeyEqual key_equal)
+{
+  auto tid     = block_size * blockIdx.x + threadIdx.x;
   auto key_idx = tid;
   __shared__ Value writeBuffer[block_size];
-  
-  while(first + key_idx < last) {
-    auto key = *(first + key_idx);
+
+  while (first + key_idx < last) {
+    auto key   = *(first + key_idx);
     auto found = view.find(key, hash, key_equal);
 
-    /* 
-     * The ld.relaxed.gpu instruction used in view.find causes L1 to 
+    /*
+     * The ld.relaxed.gpu instruction used in view.find causes L1 to
      * flush more frequently, causing increased sector stores from L2 to global memory.
      * By writing results to shared memory and then synchronizing before writing back
      * to global, we no longer rely on L1, preventing the increase in sector stores from
      * L2 to global and improving performance.
      */
-    writeBuffer[threadIdx.x] =
-      found == view.end()
-        ? view.get_empty_value_sentinel()
-        : found->second.load(cuda::std::memory_order_relaxed);
+    writeBuffer[threadIdx.x] = found == view.end()
+                                 ? view.get_empty_value_sentinel()
+                                 : found->second.load(cuda::std::memory_order_relaxed);
     __syncthreads();
     *(output_begin + key_idx) = writeBuffer[threadIdx.x];
     key_idx += gridDim.x * block_size;
@@ -228,10 +213,10 @@ __global__ void find(InputIt first,
 
 /**
  * @brief Finds the values corresponding to all keys in the range `[first, last)`.
- * 
- * If the key `*(first + i)` exists in the map, copies its associated value to `(output_begin + i)`. 
+ *
+ * If the key `*(first + i)` exists in the map, copies its associated value to `(output_begin + i)`.
  * Else, copies the empty value sentinel. Uses the CUDA Cooperative Groups API to leverage groups
- * of multiple threads to find each key. This provides a significant boost in throughput compared 
+ * of multiple threads to find each key. This provides a significant boost in throughput compared
  * to the non Cooperative Group `find` at moderate to high load factors.
  *
  * @tparam block_size The size of the thread block
@@ -240,7 +225,7 @@ __global__ void find(InputIt first,
  * @tparam Value The type of the mapped value for the map
  * @tparam InputIt Device accessible input iterator whose `value_type` is
  * convertible to the map's `key_type`
- * @tparam OutputIt Device accessible output iterator whose `value_type` is 
+ * @tparam OutputIt Device accessible output iterator whose `value_type` is
  * convertible to the map's `mapped_type`
  * @tparam viewT Type of device view allowing access of hash map storage
  * @tparam Hash Unary callable type
@@ -252,42 +237,40 @@ __global__ void find(InputIt first,
  * @param hash The unary function to apply to hash each key
  * @param key_equal The binary function to compare two keys for equality
  */
-template<std::size_t block_size, uint32_t tile_size,
-         typename Value,
-         typename InputIt, typename OutputIt, 
-         typename viewT,
-         typename Hash, 
-         typename KeyEqual>
-__global__ void find(InputIt first,
-                     InputIt last,
-                     OutputIt output_begin,
-                     viewT view,
-                     Hash hash,
-                     KeyEqual key_equal) {
-  auto tile = cg::tiled_partition<tile_size>(cg::this_thread_block());
-  auto tid = block_size * blockIdx.x + threadIdx.x;
+template <std::size_t block_size,
+          uint32_t tile_size,
+          typename Value,
+          typename InputIt,
+          typename OutputIt,
+          typename viewT,
+          typename Hash,
+          typename KeyEqual>
+__global__ void find(
+  InputIt first, InputIt last, OutputIt output_begin, viewT view, Hash hash, KeyEqual key_equal)
+{
+  auto tile    = cg::tiled_partition<tile_size>(cg::this_thread_block());
+  auto tid     = block_size * blockIdx.x + threadIdx.x;
   auto key_idx = tid / tile_size;
   __shared__ Value writeBuffer[block_size];
-  
-  while(first + key_idx < last) {
-    auto key = *(first + key_idx);
+
+  while (first + key_idx < last) {
+    auto key   = *(first + key_idx);
     auto found = view.find(tile, key, hash, key_equal);
-    
-    /* 
-     * The ld.relaxed.gpu instruction used in view.find causes L1 to 
+
+    /*
+     * The ld.relaxed.gpu instruction used in view.find causes L1 to
      * flush more frequently, causing increased sector stores from L2 to global memory.
      * By writing results to shared memory and then synchronizing before writing back
      * to global, we no longer rely on L1, preventing the increase in sector stores from
      * L2 to global and improving performance.
      */
-    if(tile.thread_rank() == 0) {
+    if (tile.thread_rank() == 0) {
       writeBuffer[threadIdx.x / tile_size] =
-        found == view.end()
-          ? view.get_empty_value_sentinel()
-          : found->second.load(cuda::std::memory_order_relaxed);
+        found == view.end() ? view.get_empty_value_sentinel()
+                            : found->second.load(cuda::std::memory_order_relaxed);
     }
     __syncthreads();
-    if(tile.thread_rank() == 0) {
+    if (tile.thread_rank() == 0) {
       *(output_begin + key_idx) = writeBuffer[threadIdx.x / tile_size];
     }
     key_idx += (gridDim.x * block_size) / tile_size;
@@ -296,7 +279,7 @@ __global__ void find(InputIt first,
 
 /**
  * @brief Indicates whether the keys in the range `[first, last)` are contained in the map.
- * 
+ *
  * Writes a `bool` to `(output + i)` indicating if the key `*(first + i)` exists in the map.
  *
  * @tparam block_size The size of the thread block
@@ -306,7 +289,7 @@ __global__ void find(InputIt first,
  * convertible to the map's `mapped_type`
  * @tparam viewT Type of device view allowing access of hash map storage
  * @tparam Hash Unary callable type
- * @tparam KeyEqual Binary callable type 
+ * @tparam KeyEqual Binary callable type
  * @param first Beginning of the sequence of keys
  * @param last End of the sequence of keys
  * @param output_begin Beginning of the sequence of booleans for the presence of each key
@@ -314,26 +297,24 @@ __global__ void find(InputIt first,
  * @param hash The unary function to apply to hash each key
  * @param key_equal The binary function to compare two keys for equality
  */
-template<std::size_t block_size,
-         typename InputIt, typename OutputIt, 
-         typename viewT,
-         typename Hash, 
-         typename KeyEqual>
-__global__ void contains(InputIt first,
-                         InputIt last,
-                         OutputIt output_begin,
-                         viewT view,
-                         Hash hash,
-                         KeyEqual key_equal) {
-  auto tid = block_size * blockIdx.x + threadIdx.x;
+template <std::size_t block_size,
+          typename InputIt,
+          typename OutputIt,
+          typename viewT,
+          typename Hash,
+          typename KeyEqual>
+__global__ void contains(
+  InputIt first, InputIt last, OutputIt output_begin, viewT view, Hash hash, KeyEqual key_equal)
+{
+  auto tid     = block_size * blockIdx.x + threadIdx.x;
   auto key_idx = tid;
   __shared__ bool writeBuffer[block_size];
-  
-  while(first + key_idx < last) {
+
+  while (first + key_idx < last) {
     auto key = *(first + key_idx);
-    
-    /* 
-     * The ld.relaxed.gpu instruction used in view.find causes L1 to 
+
+    /*
+     * The ld.relaxed.gpu instruction used in view.find causes L1 to
      * flush more frequently, causing increased sector stores from L2 to global memory.
      * By writing results to shared memory and then synchronizing before writing back
      * to global, we no longer rely on L1, preventing the increase in sector stores from
@@ -348,10 +329,10 @@ __global__ void contains(InputIt first,
 
 /**
  * @brief Indicates whether the keys in the range `[first, last)` are contained in the map.
- * 
+ *
  * Writes a `bool` to `(output + i)` indicating if the key `*(first + i)` exists in the map.
- * Uses the CUDA Cooperative Groups API to leverage groups of multiple threads to perform the 
- * contains operation for each key. This provides a significant boost in throughput compared 
+ * Uses the CUDA Cooperative Groups API to leverage groups of multiple threads to perform the
+ * contains operation for each key. This provides a significant boost in throughput compared
  * to the non Cooperative Group `contains` at moderate to high load factors.
  *
  * @tparam block_size The size of the thread block
@@ -363,7 +344,7 @@ __global__ void contains(InputIt first,
  * convertible to the map's `mapped_type`
  * @tparam viewT Type of device view allowing access of hash map storage
  * @tparam Hash Unary callable type
- * @tparam KeyEqual Binary callable type 
+ * @tparam KeyEqual Binary callable type
  * @param first Beginning of the sequence of keys
  * @param last End of the sequence of keys
  * @param output_begin Beginning of the sequence of booleans for the presence of each key
@@ -371,43 +352,40 @@ __global__ void contains(InputIt first,
  * @param hash The unary function to apply to hash each key
  * @param key_equal The binary function to compare two keys for equality
  */
-template<std::size_t block_size, uint32_t tile_size,
-         typename InputIt, typename OutputIt, 
-         typename viewT,
-         typename Hash, 
-         typename KeyEqual>
-__global__ void contains(InputIt first,
-                         InputIt last,
-                         OutputIt output_begin,
-                         viewT view,
-                         Hash hash,
-                         KeyEqual key_equal) {
-  auto tile = cg::tiled_partition<tile_size>(cg::this_thread_block());
-  auto tid = block_size * blockIdx.x + threadIdx.x;
+template <std::size_t block_size,
+          uint32_t tile_size,
+          typename InputIt,
+          typename OutputIt,
+          typename viewT,
+          typename Hash,
+          typename KeyEqual>
+__global__ void contains(
+  InputIt first, InputIt last, OutputIt output_begin, viewT view, Hash hash, KeyEqual key_equal)
+{
+  auto tile    = cg::tiled_partition<tile_size>(cg::this_thread_block());
+  auto tid     = block_size * blockIdx.x + threadIdx.x;
   auto key_idx = tid / tile_size;
   __shared__ bool writeBuffer[block_size];
-  
-  while(first + key_idx < last) {
-    auto key = *(first + key_idx);
+
+  while (first + key_idx < last) {
+    auto key   = *(first + key_idx);
     auto found = view.contains(tile, key, hash, key_equal);
-    
-    /* 
-     * The ld.relaxed.gpu instruction used in view.find causes L1 to 
+
+    /*
+     * The ld.relaxed.gpu instruction used in view.find causes L1 to
      * flush more frequently, causing increased sector stores from L2 to global memory.
      * By writing results to shared memory and then synchronizing before writing back
      * to global, we no longer rely on L1, preventing the increase in sector stores from
      * L2 to global and improving performance.
      */
-    if(tile.thread_rank() == 0) {
-      writeBuffer[threadIdx.x / tile_size] = found;
-    }
+    if (tile.thread_rank() == 0) { writeBuffer[threadIdx.x / tile_size] = found; }
     __syncthreads();
-    if(tile.thread_rank() == 0) {
+    if (tile.thread_rank() == 0) {
       *(output_begin + key_idx) = writeBuffer[threadIdx.x / tile_size];
     }
     key_idx += (gridDim.x * block_size) / tile_size;
   }
 }
 
-} // namespace detail
-} // namespace cuco
+}  // namespace detail
+}  // namespace cuco
diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index 201ad3d41..54de77221 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -16,6 +16,8 @@
 
 #include <thrust/tuple.h>
 
+#include <cuco/detail/bitwise_compare.cuh>
+
 namespace cuco {
 
 template <typename Key,
diff --git a/include/cuco/static_map.cuh b/include/cuco/static_map.cuh
index 885356872..ab9bf1350 100644
--- a/include/cuco/static_map.cuh
+++ b/include/cuco/static_map.cuh
@@ -24,6 +24,7 @@
 #include <memory>
 
 #include <cuco/allocator.hpp>
+#include <cuco/traits.hpp>
 
 #if defined(CUDART_VERSION) && (CUDART_VERSION >= 11000) && defined(__CUDA_ARCH__) && \
   (__CUDA_ARCH__ >= 700)
@@ -34,7 +35,6 @@
 #include <cuda/barrier>
 #endif
 
-#include <cuco/detail/bitwise_compare.cuh>
 #include <cuco/detail/error.hpp>
 #include <cuco/detail/hash_functions.cuh>
 #include <cuco/detail/pair.cuh>
@@ -54,7 +54,7 @@ class dynamic_map;
  * `cuco::detail::is_packable` constexpr).
  *
  * Current limitations:
- * - Requires keys and values that where `cuco::detail::is_bitwise_comparable<T>::value` is true
+ * - Requires keys and values that where `cuco::is_bitwise_comparable<T>::value` is true
  *    - Comparisons against the "sentinel" values will always be done with bitwise comparisons.
  * - Does not support erasing keys
  * - Capacity is fixed and will not grow automatically
@@ -119,14 +119,14 @@ template <typename Key,
           typename Allocator       = cuco::cuda_allocator<char>>
 class static_map {
   static_assert(
-    detail::is_bitwise_comparable<Key>::value,
+    cuco::is_bitwise_comparable<Key>::value,
     "Key type must have unique object representations or have been explicitly declared as safe for "
-    "bitwise comparison via specialization of cuco::detail::is_bitwise_comparable<Key>.");
+    "bitwise comparison via specialization of cuco::is_bitwise_comparable<Key>.");
 
-  static_assert(detail::is_bitwise_comparable<Value>::value,
+  static_assert(cuco::is_bitwise_comparable<Value>::value,
                 "Value type must have unique object representations or have been explicitly "
                 "declared as safe for bitwise comparison via specialization of "
-                "cuco::detail::is_bitwise_comparable<Value>.");
+                "cuco::is_bitwise_comparable<Value>.");
 
   friend class dynamic_map<Key, Value, Scope, Allocator>;
 
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index e07e96733..a3058c850 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -23,6 +23,7 @@
 #include <memory>
 
 #include <cuco/allocator.hpp>
+#include <cuco/traits.hpp>
 
 #if defined(CUDART_VERSION) && (CUDART_VERSION >= 11000) && defined(__CUDA_ARCH__) && \
   (__CUDA_ARCH__ >= 700)
@@ -40,7 +41,6 @@
 #include <cuda/barrier>
 #endif
 
-#include <cuco/detail/bitwise_compare.cuh>
 #include <cuco/detail/error.hpp>
 #include <cuco/detail/prime.hpp>
 #include <cuco/detail/probe_sequences.cuh>
@@ -56,7 +56,7 @@ namespace cuco {
  * concurrent insert and find) from threads in device code.
  *
  * Current limitations:
- * - Requires keys and values that where `cuco::detail::is_bitwise_comparable<T>::value` is true
+ * - Requires keys and values that where `cuco::is_bitwise_comparable<T>::value` is true
  * - Comparisons against the "sentinel" values will always be done with bitwise comparisons
  * Therefore, the objects must have unique, bitwise object representations (e.g., no padding bits).
  * - Does not support erasing keys
@@ -129,14 +129,14 @@ template <typename Key,
           typename Allocator       = cuco::cuda_allocator<char>>
 class static_multimap {
   static_assert(
-    detail::is_bitwise_comparable<Key>::value,
+    cuco::is_bitwise_comparable<Key>::value,
     "Key type must have unique object representations or have been explicitly declared as safe for "
-    "bitwise comparison via specialization of cuco::detail::is_bitwise_comparable<Key>.");
+    "bitwise comparison via specialization of cuco::is_bitwise_comparable<Key>.");
 
   static_assert(
-    detail::is_bitwise_comparable<Value>::value,
+    cuco::is_bitwise_comparable<Value>::value,
     "Value type must have unique object representations or have been explicitly declared as safe "
-    "for bitwise comparison via specialization of cuco::detail::is_bitwise_comparable<Value>.");
+    "for bitwise comparison via specialization of cuco::is_bitwise_comparable<Value>.");
 
  public:
   using value_type         = cuco::pair_type<Key, Value>;
diff --git a/include/cuco/traits.hpp b/include/cuco/traits.hpp
new file mode 100644
index 000000000..19d0bce9d
--- /dev/null
+++ b/include/cuco/traits.hpp
@@ -0,0 +1,58 @@
+/*
+ * Copyright (c) 2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <type_traits>
+
+namespace cuco {
+
+/**
+ * @brief Customization point that can be specialized to indicate that it is safe to perform bitwise
+ * equality comparisons on the object-representation of objects of type `T`.
+ *
+ * By default, only types where `std::has_unique_object_representations_v<T>` is true are safe for
+ * bitwise equality. However, this can be too restrictive for some types, e.g., floating point
+ * types.
+ *
+ * User-defined specializations of `is_bitwise_comparable` are allowed, but it is the users
+ * responsibility to ensure values do not occur that would lead to unexpected behavior. For example,
+ * if a `NaN` bit pattern were used as the empty sentinel value, it may not compare bitwise equal to
+ * other `NaN` bit patterns.
+ *
+ */
+template <typename T, typename = void>
+struct is_bitwise_comparable : std::false_type {
+};
+
+/// By default, only types with unique object representations are allowed
+template <typename T>
+struct is_bitwise_comparable<T, std::enable_if_t<std::has_unique_object_representations_v<T>>>
+  : std::true_type {
+};
+
+/**
+ * @brief Declares that a type `Type` is bitwise comparable.
+ *
+ */
+#define CUCO_DECLARE_BITWISE_COMPARABLE(Type)           \
+  namespace cuco {                                      \
+  template <>                                           \
+  struct is_bitwise_comparable<Type> : std::true_type { \
+  };                                                    \
+  }
+
+}  // namespace cuco

From 42532f4914398d9d6c5ddbcb8ea46e6aaf0f25d8 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Mon, 13 Sep 2021 17:27:22 -0400
Subject: [PATCH 214/267] Update docs

---
 include/cuco/static_multimap.cuh | 72 ++++++++++++++++++++------------
 1 file changed, 45 insertions(+), 27 deletions(-)

diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index a3058c850..c634cb147 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -52,18 +52,20 @@ namespace cuco {
  * @brief A GPU-accelerated, unordered, associative container of key-value
  * pairs that supports equivalent keys.
  *
- * Allows constant time concurrent inserts or concurrent find operations (not
- * concurrent insert and find) from threads in device code.
+ * Allows constant time concurrent inserts or concurrent find operations from threads in device
+ * code. Concurrent insert/find is allowed only when
+ * `static_multimap<Key, Value>::supports_concurrent_insert_find()` is true.
  *
  * Current limitations:
- * - Requires keys and values that where `cuco::is_bitwise_comparable<T>::value` is true
+ * - Requires keys and values where `cuco::is_bitwise_comparable<T>::value` is true
  * - Comparisons against the "sentinel" values will always be done with bitwise comparisons
  * Therefore, the objects must have unique, bitwise object representations (e.g., no padding bits).
  * - Does not support erasing keys
  * - Capacity is fixed and will not grow automatically
  * - Requires the user to specify sentinel values for both key and mapped value
  * to indicate empty slots
- * - Does not support concurrent insert and find operations
+ * - Concurrent insert/find is only supported when `static_multimap<Key,
+ * Value>::supports_concurrent_insert_find()` is true`
  *
  * The `static_multimap` supports two types of operations:
  * - Host-side "bulk" operations
@@ -85,8 +87,8 @@ namespace cuco {
  *
  * Loading two consecutive slots instead of one for small pairs can improve cache utilization since
  * 16B memory loads are natively supported at the SASS/hardware level. This `vector load` method is
- * implicitly applied to all involved operations (e.g. `insert`, `count`, and `retrieve`, etc.) if
- * the pair is packable (see `multimap::uses_vector_load` logic).
+ * implicitly applied to all involved operations (e.g. `insert`, `count`, and `retrieve`, etc.) when
+ * pairs are packable (see `multimap::uses_vector_load` logic).
  *
  * Example:
  * \code{.cpp}
@@ -115,7 +117,7 @@ namespace cuco {
  * \endcode
  *
  *
- * @tparam Key Arithmetic type used for key
+ * @tparam Key Type used for keys
  * @tparam Value Type of the mapped values
  * @tparam ProbeSequence Probe sequence used in multimap
  * @tparam Scope The scope in which insert/find operations will be performed by
@@ -160,16 +162,19 @@ class static_multimap {
   static_multimap& operator=(static_multimap&&) = default;
 
   /**
-   * @brief Indicate the warp size.
+   * @brief Indicate if concurrent insert/find is supported for the key/value types.
    *
-   * @return The warp size.
+   * @return Boolean indicating if concurrent insert/find is supported.
    */
-  static constexpr uint32_t warp_size() noexcept { return 32u; }
+  __host__ __device__ static constexpr bool supports_concurrent_insert_find() noexcept
+  {
+    return cuco::detail::is_packable<value_type>();
+  }
 
   /**
    * @brief The size of the CUDA cooperative thread group.
    *
-   * @return Boolean indicating if concurrent insert/find is supported.
+   * @return The CG size.
    */
   static constexpr uint32_t cg_size() noexcept { return ProbeSequence::cg_size(); }
 
@@ -212,8 +217,9 @@ class static_multimap {
   /**
    * @brief Inserts all key/value pairs in the range `[first, last)`.
    *
-   * @tparam InputIt Device accessible input iterator whose `value_type` is
-   * convertible to the map's `value_type`
+   * @tparam InputIt Device accessible random access input iterator where
+   * `std::is_converitble<std::iterator_traits<InputIt>::value_type,
+   * static_multimap<K,V>::value_type>` is `true`
    * @param first Beginning of the sequence of key/value pairs
    * @param last End of the sequence of key/value pairs
    * @param stream CUDA stream used for insert
@@ -225,15 +231,19 @@ class static_multimap {
    * @brief Inserts key/value pairs in the range `[first, first + n)` if `pred`
    * of the corresponding stencil returns true.
    *
-   * @tparam InputIt Device accessible input iterator whose `value_type` is
-   * convertible to the map's `value_type`
-   * @tparam StencilIt Device accessible stencil iterator
-   * @tparam Predicate Unary predicate function type
+   * The key/value pair `*(first + i)` is inserted if `pred( *(stencil+i) )` returns true.
+   *
+   * @tparam InputIt Device accessible random access input iterator where
+   * `std::is_converitble<std::iterator_traits<InputIt>::value_type,
+   * static_multimap<K,V>::value_type>` is `true`
+   * @tparam StencilIt Device accessible random access iterator whose value_type is convertible to
+   * Predicate's argument type
+   * @tparam Predicate Unary predicate callable
    * @param first Beginning of the sequence of key/value pairs
    * @param last End of the sequence of key/value pairs
    * @param stencil Beginning of the stencil sequence
-   * @param n Number of elements to insert
-   * @param pred Predicate to test on the given stencil sequence
+   * @param pred Predicate to test on every element in the range `[stencil, stencil +
+   * std::distance(first, last))`
    * @param stream CUDA stream used for insert
    */
   template <typename InputIt, typename StencilIt, typename Predicate>
@@ -243,16 +253,17 @@ class static_multimap {
   /**
    * @brief Indicates whether the keys in the range `[first, last)` are contained in the map.
    *
-   * Writes a `bool` to `(output + i)` indicating if the key `*(first + i)` exists in the map.
+   * Stores `true` or `false` to `(output + i)` indicating if the key `*(first + i)` exists in the
+   * map.
    *
    * @tparam InputIt Device accessible input iterator whose `value_type` is
    * convertible to the map's `key_type`
    * @tparam OutputIt Device accessible output iterator whose `value_type` is
-   * convertible to the map's `mapped_type`
-   * @tparam KeyEqual Binary callable type
+   * convertible from `bool`
+   * @tparam KeyEqual Binary callable type used to compare two keys for equality
    * @param first Beginning of the sequence of keys
    * @param last End of the sequence of keys
-   * @param output_begin Beginning of the sequence of booleans for the presence of each key
+   * @param output_begin Beginning of the output sequence indicating whether each key is present
    * @param stream CUDA stream used for contains
    * @param key_equal The binary function to compare two keys for equality
    */
@@ -334,7 +345,7 @@ class static_multimap {
                                cudaStream_t stream = 0) const;
 
   /**
-   * @brief Finds all the values corresponding to all keys in the range `[first, last)`.
+   * @brief Retrieves all the values corresponding to all keys in the range `[first, last)`.
    *
    * If the key `k = *(first + i)` exists in the map, copies `k` and all associated values to
    * unspecified locations in `[output_begin, output_end)`. Else, does nothing.
@@ -362,7 +373,7 @@ class static_multimap {
                     KeyEqual key_equal  = KeyEqual{}) const;
 
   /**
-   * @brief Finds all the matches corresponding to all keys in the range `[first, last)`.
+   * @brief Retrieves all the matches corresponding to all keys in the range `[first, last)`.
    *
    * If the key `k = *(first + i)` exists in the map, copies `k` and all associated values to
    * unspecified locations in `[output_begin, output_end)`. Else, copies `k` and the empty value
@@ -391,7 +402,7 @@ class static_multimap {
                           KeyEqual key_equal  = KeyEqual{}) const;
 
   /**
-   * @brief Finds all pairs matching the input probe pair in the range `[first, last)`.
+   * @brief Retrieves all pairs matching the input probe pair in the range `[first, last)`.
    *
    * if pair_equal(*(first + i), slot[j]) returns true, then *(first+i) is stored to
    * `probe_output_begin`, and slot[j] is stored to `contained_output_begin`.
@@ -422,7 +433,7 @@ class static_multimap {
                             cudaStream_t stream = 0) const;
 
   /**
-   * @brief Finds all pairs matching the input probe pair in the range `[first, last)`.
+   * @brief Retrieves all pairs matching the input probe pair in the range `[first, last)`.
    *
    * if pair_equal(*(first + i), slot[j]) returns true, then *(first+i) is stored to
    * `probe_output_begin`, and slot[j] is stored to `contained_output_begin`. If *(first+i) doesn't
@@ -469,6 +480,13 @@ class static_multimap {
    */
   static constexpr uint32_t vector_width() noexcept { return ProbeSequence::vector_width(); }
 
+  /**
+   * @brief Indicate the warp size.
+   *
+   * @return The warp size.
+   */
+  static constexpr uint32_t warp_size() noexcept { return 32u; }
+
   class device_view_base {
    protected:
     // Import member type definitions from `static_multimap`

From ac10e1283eaa778790b51a753a3fedfe5242acf7 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Wed, 15 Sep 2021 12:00:53 -0400
Subject: [PATCH 215/267] Updates: allocated memory using unique pointer +
 cleanups

---
 include/cuco/detail/prime.hpp           |  23 ++--
 include/cuco/detail/static_multimap.inl | 118 ++++++++++----------
 include/cuco/static_multimap.cuh        | 137 +++++++++++++++---------
 3 files changed, 163 insertions(+), 115 deletions(-)

diff --git a/include/cuco/detail/prime.hpp b/include/cuco/detail/prime.hpp
index 6452b4a02..8d6bdb75c 100644
--- a/include/cuco/detail/prime.hpp
+++ b/include/cuco/detail/prime.hpp
@@ -17,10 +17,8 @@
 #pragma once
 
 namespace cuco {
-
 namespace detail {
 
-// prime numbers with ~131072 offset
 constexpr std::array<std::uint64_t, 140746> primes = {
   2,           3,           5,           7,           13,          19,          29,
   37,          43,          53,          59,          67,          73,          79,
@@ -20170,18 +20168,27 @@ constexpr std::size_t compute_prime(std::size_t num) noexcept
 #define SDIV(x, y) (((x) + (y)-1) / (y))
 
 /**
- * @brief Calculates the adjusted/valid capacity based on `CGSize` and the initial `capacity`.
+ * @brief Calculates the valid capacity based on `cg_size` , `vector_width`
+ * and the initial `capacity`.
+ *
+ * @tparam cg_size Cooperative Group size
+ * @tparam vector_width Length of vector load
+ * @tparam uses_vector_load If vector load is used
  *
- * @tparam CGSize Cooperative Group size
  * @param capacity The initially requested capacity
- * @return An adjusted capacity greater than or equal to `capacity`
+ * @return A valid capacity no smaller than the requested `capacity`
  */
-template <std::size_t CGSize>
+template <uint32_t cg_size, uint32_t vector_width, bool uses_vector_load>
 constexpr std::size_t get_valid_capacity(std::size_t capacity) noexcept
 {
-  auto const c         = SDIV(capacity, CGSize);
+  auto const stride = [&]() {
+    if constexpr (uses_vector_load) { return cg_size * vector_width; }
+    if constexpr (not uses_vector_load) { return cg_size; }
+  }();
+
+  auto const c         = SDIV(capacity, stride);
   auto const min_prime = std::lower_bound(primes.begin(), primes.end(), c);
-  return *min_prime * CGSize;
+  return *min_prime * stride;
 }
 
 }  // namespace detail
diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index 54de77221..b46100ba9 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -31,21 +31,18 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::static_multimap(
   Value empty_value_sentinel,
   cudaStream_t stream,
   Allocator const& alloc)
-  : empty_key_sentinel_{empty_key_sentinel},
+  : capacity_{cuco::detail::get_valid_capacity<cg_size(), vector_width(), uses_vector_load()>(
+      capacity)},
+    empty_key_sentinel_{empty_key_sentinel},
     empty_value_sentinel_{empty_value_sentinel},
-    slot_allocator_{alloc},
     counter_allocator_{alloc},
-    stream_{stream}
+    slot_allocator_{alloc},
+    stream_{stream},
+    delete_counter_{counter_allocator_, stream_},
+    delete_slots_{slot_allocator_, capacity_, stream_},
+    d_counter_{counter_allocator_.allocate(1, stream_), delete_counter_},
+    slots_{slot_allocator_.allocate(capacity_, stream_), delete_slots_}
 {
-  if constexpr (uses_vector_load()) {
-    capacity_ = cuco::detail::get_valid_capacity<cg_size() * vector_width()>(capacity);
-  } else {
-    capacity_ = cuco::detail::get_valid_capacity<cg_size()>(capacity);
-  }
-
-  d_counter_ = counter_allocator_.allocate(1, stream_);
-  slots_     = slot_allocator_.allocate(get_capacity(), stream_);
-
   auto constexpr block_size = 128;
   int grid_size{-1};
   cudaOccupancyMaxActiveBlocksPerMultiprocessor(
@@ -60,18 +57,7 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::static_multimap(
   grid_size *= num_sms;
 
   detail::initialize<atomic_key_type, atomic_mapped_type><<<grid_size, block_size, 0, stream_>>>(
-    slots_, empty_key_sentinel, empty_value_sentinel, get_capacity());
-}
-
-template <typename Key,
-          typename Value,
-          class ProbeSequence,
-          cuda::thread_scope Scope,
-          typename Allocator>
-static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::~static_multimap()
-{
-  counter_allocator_.deallocate(d_counter_, 1, stream_);
-  slot_allocator_.deallocate(slots_, get_capacity(), stream_);
+    slots_.get(), empty_key_sentinel, empty_value_sentinel, get_capacity());
 }
 
 template <typename Key,
@@ -189,13 +175,13 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::count(
   cudaDeviceGetAttribute(&num_sms, cudaDevAttrMultiProcessorCount, dev_id);
   grid_size *= num_sms;
 
-  cudaMemsetAsync(d_counter_, 0, sizeof(atomic_ctr_type), stream);
+  cudaMemsetAsync(d_counter_.get(), 0, sizeof(atomic_ctr_type), stream);
   std::size_t h_counter;
 
   detail::count<block_size, cg_size(), is_outer>
-    <<<grid_size, block_size, 0, stream>>>(first, last, d_counter_, view, key_equal);
+    <<<grid_size, block_size, 0, stream>>>(first, last, d_counter_.get(), view, key_equal);
   CUCO_CUDA_TRY(cudaMemcpyAsync(
-    &h_counter, d_counter_, sizeof(atomic_ctr_type), cudaMemcpyDeviceToHost, stream));
+    &h_counter, d_counter_.get(), sizeof(atomic_ctr_type), cudaMemcpyDeviceToHost, stream));
   CUCO_CUDA_TRY(cudaStreamSynchronize(stream));
 
   return h_counter;
@@ -228,13 +214,13 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::count_
   cudaDeviceGetAttribute(&num_sms, cudaDevAttrMultiProcessorCount, dev_id);
   grid_size *= num_sms;
 
-  cudaMemsetAsync(d_counter_, 0, sizeof(atomic_ctr_type), stream);
+  cudaMemsetAsync(d_counter_.get(), 0, sizeof(atomic_ctr_type), stream);
   std::size_t h_counter;
 
   detail::count<block_size, cg_size(), is_outer>
-    <<<grid_size, block_size, 0, stream>>>(first, last, d_counter_, view, key_equal);
+    <<<grid_size, block_size, 0, stream>>>(first, last, d_counter_.get(), view, key_equal);
   CUCO_CUDA_TRY(cudaMemcpyAsync(
-    &h_counter, d_counter_, sizeof(atomic_ctr_type), cudaMemcpyDeviceToHost, stream));
+    &h_counter, d_counter_.get(), sizeof(atomic_ctr_type), cudaMemcpyDeviceToHost, stream));
   CUCO_CUDA_TRY(cudaStreamSynchronize(stream));
 
   return h_counter;
@@ -268,13 +254,13 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::pair_c
   cudaDeviceGetAttribute(&num_sms, cudaDevAttrMultiProcessorCount, dev_id);
   grid_size *= num_sms;
 
-  cudaMemsetAsync(d_counter_, 0, sizeof(atomic_ctr_type), stream);
+  cudaMemsetAsync(d_counter_.get(), 0, sizeof(atomic_ctr_type), stream);
   std::size_t h_counter;
 
   detail::pair_count<block_size, cg_size(), is_outer>
-    <<<grid_size, block_size, 0, stream>>>(first, last, d_counter_, view, pair_equal);
+    <<<grid_size, block_size, 0, stream>>>(first, last, d_counter_.get(), view, pair_equal);
   CUCO_CUDA_TRY(cudaMemcpyAsync(
-    &h_counter, d_counter_, sizeof(atomic_ctr_type), cudaMemcpyDeviceToHost, stream));
+    &h_counter, d_counter_.get(), sizeof(atomic_ctr_type), cudaMemcpyDeviceToHost, stream));
   CUCO_CUDA_TRY(cudaStreamSynchronize(stream));
 
   return h_counter;
@@ -308,13 +294,13 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::pair_c
   cudaDeviceGetAttribute(&num_sms, cudaDevAttrMultiProcessorCount, dev_id);
   grid_size *= num_sms;
 
-  cudaMemsetAsync(d_counter_, 0, sizeof(atomic_ctr_type), stream);
+  cudaMemsetAsync(d_counter_.get(), 0, sizeof(atomic_ctr_type), stream);
   std::size_t h_counter;
 
   detail::pair_count<block_size, cg_size(), is_outer>
-    <<<grid_size, block_size, 0, stream>>>(first, last, d_counter_, view, pair_equal);
+    <<<grid_size, block_size, 0, stream>>>(first, last, d_counter_.get(), view, pair_equal);
   CUCO_CUDA_TRY(cudaMemcpyAsync(
-    &h_counter, d_counter_, sizeof(atomic_ctr_type), cudaMemcpyDeviceToHost, stream));
+    &h_counter, d_counter_.get(), sizeof(atomic_ctr_type), cudaMemcpyDeviceToHost, stream));
   CUCO_CUDA_TRY(cudaStreamSynchronize(stream));
 
   return h_counter;
@@ -373,20 +359,20 @@ OutputIt static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::retrieve(
   cudaDeviceGetAttribute(&num_sms, cudaDevAttrMultiProcessorCount, dev_id);
   grid_size *= num_sms;
 
-  cudaMemsetAsync(d_counter_, 0, sizeof(atomic_ctr_type), stream);
+  cudaMemsetAsync(d_counter_.get(), 0, sizeof(atomic_ctr_type), stream);
   std::size_t h_counter;
 
   if constexpr (uses_vector_load()) {
     detail::vectorized_retrieve<block_size, warp_size(), cg_size(), buffer_size, is_outer>
       <<<grid_size, block_size, 0, stream>>>(
-        first, last, output_begin, d_counter_, view, key_equal);
+        first, last, output_begin, d_counter_.get(), view, key_equal);
   } else {
     detail::retrieve<block_size, warp_size(), cg_size(), buffer_size, is_outer>
       <<<grid_size, block_size, 0, stream>>>(
-        first, last, output_begin, d_counter_, view, key_equal);
+        first, last, output_begin, d_counter_.get(), view, key_equal);
   }
   CUCO_CUDA_TRY(cudaMemcpyAsync(
-    &h_counter, d_counter_, sizeof(atomic_ctr_type), cudaMemcpyDeviceToHost, stream));
+    &h_counter, d_counter_.get(), sizeof(atomic_ctr_type), cudaMemcpyDeviceToHost, stream));
   CUCO_CUDA_TRY(cudaStreamSynchronize(stream));
 
   auto output_end = output_begin + h_counter;
@@ -446,20 +432,20 @@ OutputIt static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::retrieve_
   cudaDeviceGetAttribute(&num_sms, cudaDevAttrMultiProcessorCount, dev_id);
   grid_size *= num_sms;
 
-  cudaMemsetAsync(d_counter_, 0, sizeof(atomic_ctr_type), stream);
+  cudaMemsetAsync(d_counter_.get(), 0, sizeof(atomic_ctr_type), stream);
   std::size_t h_counter;
 
   if constexpr (uses_vector_load()) {
     detail::vectorized_retrieve<block_size, warp_size(), cg_size(), buffer_size, is_outer>
       <<<grid_size, block_size, 0, stream>>>(
-        first, last, output_begin, d_counter_, view, key_equal);
+        first, last, output_begin, d_counter_.get(), view, key_equal);
   } else {
     detail::retrieve<block_size, warp_size(), cg_size(), buffer_size, is_outer>
       <<<grid_size, block_size, 0, stream>>>(
-        first, last, output_begin, d_counter_, view, key_equal);
+        first, last, output_begin, d_counter_.get(), view, key_equal);
   }
   CUCO_CUDA_TRY(cudaMemcpyAsync(
-    &h_counter, d_counter_, sizeof(atomic_ctr_type), cudaMemcpyDeviceToHost, stream));
+    &h_counter, d_counter_.get(), sizeof(atomic_ctr_type), cudaMemcpyDeviceToHost, stream));
   CUCO_CUDA_TRY(cudaStreamSynchronize(stream));
 
   auto output_end = output_begin + h_counter;
@@ -526,20 +512,30 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::pair_r
   cudaDeviceGetAttribute(&num_sms, cudaDevAttrMultiProcessorCount, dev_id);
   grid_size *= num_sms;
 
-  cudaMemsetAsync(d_counter_, 0, sizeof(atomic_ctr_type), stream);
+  cudaMemsetAsync(d_counter_.get(), 0, sizeof(atomic_ctr_type), stream);
   std::size_t h_counter;
 
   if constexpr (uses_vector_load()) {
     detail::vectorized_pair_retrieve<block_size, warp_size(), cg_size(), buffer_size, is_outer>
-      <<<grid_size, block_size, 0, stream>>>(
-        first, last, probe_output_begin, contained_output_begin, d_counter_, view, pair_equal);
+      <<<grid_size, block_size, 0, stream>>>(first,
+                                             last,
+                                             probe_output_begin,
+                                             contained_output_begin,
+                                             d_counter_.get(),
+                                             view,
+                                             pair_equal);
   } else {
     detail::pair_retrieve<block_size, warp_size(), cg_size(), buffer_size, is_outer>
-      <<<grid_size, block_size, 0, stream>>>(
-        first, last, probe_output_begin, contained_output_begin, d_counter_, view, pair_equal);
+      <<<grid_size, block_size, 0, stream>>>(first,
+                                             last,
+                                             probe_output_begin,
+                                             contained_output_begin,
+                                             d_counter_.get(),
+                                             view,
+                                             pair_equal);
   }
   CUCO_CUDA_TRY(cudaMemcpyAsync(
-    &h_counter, d_counter_, sizeof(atomic_ctr_type), cudaMemcpyDeviceToHost, stream));
+    &h_counter, d_counter_.get(), sizeof(atomic_ctr_type), cudaMemcpyDeviceToHost, stream));
   CUCO_CUDA_TRY(cudaStreamSynchronize(stream));
 
   return h_counter;
@@ -605,20 +601,30 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::pair_r
   cudaDeviceGetAttribute(&num_sms, cudaDevAttrMultiProcessorCount, dev_id);
   grid_size *= num_sms;
 
-  cudaMemsetAsync(d_counter_, 0, sizeof(atomic_ctr_type), stream);
+  cudaMemsetAsync(d_counter_.get(), 0, sizeof(atomic_ctr_type), stream);
   std::size_t h_counter;
 
   if constexpr (uses_vector_load()) {
     detail::vectorized_pair_retrieve<block_size, warp_size(), cg_size(), buffer_size, is_outer>
-      <<<grid_size, block_size, 0, stream>>>(
-        first, last, probe_output_begin, contained_output_begin, d_counter_, view, pair_equal);
+      <<<grid_size, block_size, 0, stream>>>(first,
+                                             last,
+                                             probe_output_begin,
+                                             contained_output_begin,
+                                             d_counter_.get(),
+                                             view,
+                                             pair_equal);
   } else {
     detail::pair_retrieve<block_size, warp_size(), cg_size(), buffer_size, is_outer>
-      <<<grid_size, block_size, 0, stream>>>(
-        first, last, probe_output_begin, contained_output_begin, d_counter_, view, pair_equal);
+      <<<grid_size, block_size, 0, stream>>>(first,
+                                             last,
+                                             probe_output_begin,
+                                             contained_output_begin,
+                                             d_counter_.get(),
+                                             view,
+                                             pair_equal);
   }
   CUCO_CUDA_TRY(cudaMemcpyAsync(
-    &h_counter, d_counter_, sizeof(atomic_ctr_type), cudaMemcpyDeviceToHost, stream));
+    &h_counter, d_counter_.get(), sizeof(atomic_ctr_type), cudaMemcpyDeviceToHost, stream));
   CUCO_CUDA_TRY(cudaStreamSynchronize(stream));
 
   return h_counter;
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index c634cb147..d0e60d179 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -160,6 +160,7 @@ class static_multimap {
   static_multimap()                  = default;
   static_multimap(static_multimap&&) = default;
   static_multimap& operator=(static_multimap&&) = default;
+  ~static_multimap()                            = default;
 
   /**
    * @brief Indicate if concurrent insert/find is supported for the key/value types.
@@ -208,12 +209,6 @@ class static_multimap {
                   cudaStream_t stream    = 0,
                   Allocator const& alloc = Allocator{});
 
-  /**
-   * @brief Destroys the map and frees its contents.
-   *
-   */
-  ~static_multimap();
-
   /**
    * @brief Inserts all key/value pairs in the range `[first, last)`.
    *
@@ -474,9 +469,9 @@ class static_multimap {
   static constexpr bool uses_vector_load() noexcept { return ProbeSequence::uses_vector_load(); }
 
   /**
-   * @brief The number of elements for each vector load
+   * @brief The number of elements in each vector load
    *
-   * @return The number of elements for each vector load.
+   * @return The number of elements in each vector load.
    */
   static constexpr uint32_t vector_width() noexcept { return ProbeSequence::vector_width(); }
 
@@ -487,6 +482,28 @@ class static_multimap {
    */
   static constexpr uint32_t warp_size() noexcept { return 32u; }
 
+  struct counter_deleter {
+    counter_deleter(counter_allocator_type& a, cudaStream_t& s) : allocator{a}, stream{s} {}
+
+    void operator()(atomic_ctr_type* ptr) { allocator.deallocate(ptr, 1, stream); }
+
+    counter_allocator_type& allocator;
+    cudaStream_t& stream;
+  };
+
+  struct slot_deleter {
+    slot_deleter(slot_allocator_type& a, size_t& c, cudaStream_t& s)
+      : allocator{a}, capacity{c}, stream{s}
+    {
+    }
+
+    void operator()(pair_atomic_type* ptr) { allocator.deallocate(ptr, capacity, stream); }
+
+    slot_allocator_type& allocator;
+    size_t& capacity;
+    cudaStream_t& stream;
+  };
+
   class device_view_base {
    protected:
     // Import member type definitions from `static_multimap`
@@ -718,8 +735,8 @@ class static_multimap {
                                               value_type const& insert_pair) noexcept;
 
     /**
-     * @brief Inserts the specified key/value pair with a CAS of the key and a dependent write of
-     * the value.
+     * @brief Inserts the specified key/value pair with a CAS of the key and a dependent write
+     * of the value.
      *
      * @param current_slot The slot to insert
      * @param insert_pair The pair to insert
@@ -838,8 +855,8 @@ class static_multimap {
      * @tparam CG The type of the cooperative thread group
      * @param g The cooperative thread group used to copy the slots
      * @param source_device_view `device_view` to copy from
-     * @param memory_to_use Array large enough to support `capacity` elements. Object does not take
-     * the ownership of the memory
+     * @param memory_to_use Array large enough to support `capacity` elements. Object does not
+     * take the ownership of the memory
      * @return Copy of passed `device_view`
      */
     template <typename CG>
@@ -1004,8 +1021,8 @@ class static_multimap {
      * loads with per-warp shared memory buffer.
      *
      * For keys `k = *(first + i)` existing in the map, copies `k` and all associated values to
-     * unspecified locations in `[output_begin, output_end)`. In case of non-matches, copies `k` and
-     * the empty value sentinel into the output only if `is_outer` is true.
+     * unspecified locations in `[output_begin, output_end)`. In case of non-matches, copies `k`
+     * and the empty value sentinel into the output only if `is_outer` is true.
      *
      * @tparam buffer_size Size of the output buffer
      * @tparam is_outer Boolean flag indicating whether outer join is peformed or not
@@ -1046,8 +1063,8 @@ class static_multimap {
      * loads with per-cg shared memory buffer.
      *
      * For keys `k = *(first + i)` existing in the map, copies `k` and all associated values to
-     * unspecified locations in `[output_begin, output_end)`. In case of non-matches, copies `k` and
-     * the empty value sentinel into the output only if `is_outer` is true.
+     * unspecified locations in `[output_begin, output_end)`. In case of non-matches, copies `k`
+     * and the empty value sentinel into the output only if `is_outer` is true.
      *
      * @tparam cg_size The number of threads in CUDA Cooperative Groups
      * @tparam buffer_size Size of the output buffer
@@ -1085,10 +1102,11 @@ class static_multimap {
      * @brief Find all the matches of a given pair contained in multimap using vector
      * loads with per-warp shared memory buffer.
      *
-     * For pair `p`, if pair_equal(p, slot[j]) returns true, copies `p` to unspecified locations in
+     * For pair `p`, if pair_equal(p, slot[j]) returns true, copies `p` to unspecified locations
+     * in
      * `[probe_output_begin, probe_output_end)` and copies slot[j] to unspecified locations in
-     * `[contained_output_begin, contained_output_end)`. In case of non-matches, copies `p` and the
-     * empty value sentinels into the output only if `is_outer` is true.
+     * `[contained_output_begin, contained_output_end)`. In case of non-matches, copies `p` and
+     * the empty value sentinels into the output only if `is_outer` is true.
      *
      * @tparam buffer_size Size of the output buffer
      * @tparam is_outer Boolean flag indicating whether outer join is peformed or not
@@ -1106,7 +1124,8 @@ class static_multimap {
      * @param contained_output_buffer Buffer of the matched contained pair sequence
      * @param num_matches Size of the output sequence
      * @param probe_output_begin Beginning of the output sequence of the matched probe pairs
-     * @param contained_output_begin Beginning of the output sequence of the matched contained pairs
+     * @param contained_output_begin Beginning of the output sequence of the matched contained
+     * pairs
      * @param pair_equal The binary callable used to compare two pairs for equality
      */
     template <uint32_t buffer_size,
@@ -1132,10 +1151,11 @@ class static_multimap {
      * @brief Find all the matches of a given pair contained in multimap using scalar
      * loads with per-cg shared memory buffer.
      *
-     * For pair `p`, if pair_equal(p, slot[j]) returns true, copies `p` to unspecified locations in
+     * For pair `p`, if pair_equal(p, slot[j]) returns true, copies `p` to unspecified locations
+     * in
      * `[probe_output_begin, probe_output_end)` and copies slot[j] to unspecified locations in
-     * `[contained_output_begin, contained_output_end)`. In case of non-matches, copies `p` and the
-     * empty value sentinels into the output only if `is_outer` is true.
+     * `[contained_output_begin, contained_output_end)`. In case of non-matches, copies `p` and
+     * the empty value sentinels into the output only if `is_outer` is true.
      *
      * @tparam cg_size The number of threads in CUDA Cooperative Groups
      * @tparam buffer_size Size of the output buffer
@@ -1152,7 +1172,8 @@ class static_multimap {
      * @param contained_output_buffer Buffer of the matched contained pair sequence
      * @param num_matches Size of the output sequence
      * @param probe_output_begin Beginning of the output sequence of the matched probe pairs
-     * @param contained_output_begin Beginning of the output sequence of the matched contained pairs
+     * @param contained_output_begin Beginning of the output sequence of the matched contained
+     * pairs
      * @param pair_equal The binary callable used to compare two pairs for equality
      */
     template <uint32_t cg_size,
@@ -1233,7 +1254,8 @@ class static_multimap {
      * @param contained_output_buffer Buffer of the matched contained pair sequence
      * @param num_matches Size of the output sequence
      * @param probe_output_begin Beginning of the output sequence of the matched probe pairs
-     * @param contained_output_begin Beginning of the output sequence of the matched contained pairs
+     * @param contained_output_begin Beginning of the output sequence of the matched contained
+     * pairs
      */
     template <typename CG, typename atomicT, typename OutputZipIt1, typename OutputZipIt2>
     __inline__ __device__ void flush_output_buffer(CG const& g,
@@ -1336,8 +1358,8 @@ class static_multimap {
      * loads with per-warp shared memory buffer.
      *
      * For keys `k = *(first + i)` existing in the map, copies `k` and all associated values to
-     * unspecified locations in `[output_begin, output_end)`. In case of non-matches, copies `k` and
-     * the empty value sentinel into the output only if `is_outer` is true.
+     * unspecified locations in `[output_begin, output_end)`. In case of non-matches, copies `k`
+     * and the empty value sentinel into the output only if `is_outer` is true.
      *
      * @tparam buffer_size Size of the output buffer
      * @tparam warpT Warp (Cooperative Group) type
@@ -1376,8 +1398,8 @@ class static_multimap {
      * loads with per-warp shared memory buffer.
      *
      * For keys `k = *(first + i)` existing in the map, copies `k` and all associated values to
-     * unspecified locations in `[output_begin, output_end)`. In case of non-matches, copies `k` and
-     * the empty value sentinel into the output only if `is_outer` is true.
+     * unspecified locations in `[output_begin, output_end)`. In case of non-matches, copies `k`
+     * and the empty value sentinel into the output only if `is_outer` is true.
      *
      * @tparam buffer_size Size of the output buffer
      * @tparam warpT Warp (Cooperative Group) type
@@ -1453,8 +1475,8 @@ class static_multimap {
      * loads with per-cg shared memory buffer.
      *
      * For keys `k = *(first + i)` existing in the map, copies `k` and all associated values to
-     * unspecified locations in `[output_begin, output_end)`. In case of non-matches, copies `k` and
-     * the empty value sentinel into the output.
+     * unspecified locations in `[output_begin, output_end)`. In case of non-matches, copies `k`
+     * and the empty value sentinel into the output.
      *
      * @tparam cg_size The number of threads in CUDA Cooperative Groups
      * @tparam buffer_size Size of the output buffer
@@ -1490,7 +1512,8 @@ class static_multimap {
      * @brief Find all the matches of a given pair contained in multimap using vector
      * loads with per-warp shared memory buffer.
      *
-     * For pair `p`, if pair_equal(p, slot[j]) returns true, copies `p` to unspecified locations in
+     * For pair `p`, if pair_equal(p, slot[j]) returns true, copies `p` to unspecified locations
+     * in
      * `[probe_output_begin, probe_output_end)` and copies slot[j] to unspecified locations in
      * `[contained_output_begin, contained_output_end)`.
      *
@@ -1509,7 +1532,8 @@ class static_multimap {
      * @param contained_output_buffer Buffer of the matched contained pair sequence
      * @param num_matches Size of the output sequence
      * @param probe_output_begin Beginning of the output sequence of the matched probe pairs
-     * @param contained_output_begin Beginning of the output sequence of the matched contained pairs
+     * @param contained_output_begin Beginning of the output sequence of the matched contained
+     * pairs
      * @param pair_equal The binary callable used to compare two pairs for equality
      */
     template <uint32_t buffer_size,
@@ -1534,10 +1558,11 @@ class static_multimap {
      * @brief Find all the matches of a given pair contained in multimap using vector
      * loads with per-warp shared memory buffer.
      *
-     * For pair `p`, if pair_equal(p, slot[j]) returns true, copies `p` to unspecified locations in
+     * For pair `p`, if pair_equal(p, slot[j]) returns true, copies `p` to unspecified locations
+     * in
      * `[probe_output_begin, probe_output_end)` and copies slot[j] to unspecified locations in
-     * `[contained_output_begin, contained_output_end)`. In case of non-matches, copies `p` and the
-     * empty value sentinels into the output.
+     * `[contained_output_begin, contained_output_end)`. In case of non-matches, copies `p` and
+     * the empty value sentinels into the output.
      *
      * @tparam buffer_size Size of the output buffer
      * @tparam warpT Warp (Cooperative Group) type
@@ -1554,7 +1579,8 @@ class static_multimap {
      * @param contained_output_buffer Buffer of the matched contained pair sequence
      * @param num_matches Size of the output sequence
      * @param probe_output_begin Beginning of the output sequence of the matched probe pairs
-     * @param contained_output_begin Beginning of the output sequence of the matched contained pairs
+     * @param contained_output_begin Beginning of the output sequence of the matched contained
+     * pairs
      * @param pair_equal The binary callable used to compare two pairs for equality
      */
     template <uint32_t buffer_size,
@@ -1579,7 +1605,8 @@ class static_multimap {
      * @brief Find all the matches of a given pair contained in multimap using scalar
      * loads with per-cg shared memory buffer.
      *
-     * For pair `p`, if pair_equal(p, slot[j]) returns true, copies `p` to unspecified locations in
+     * For pair `p`, if pair_equal(p, slot[j]) returns true, copies `p` to unspecified locations
+     * in
      * `[probe_output_begin, probe_output_end)` and copies slot[j] to unspecified locations in
      * `[contained_output_begin, contained_output_end)`.
      *
@@ -1597,7 +1624,8 @@ class static_multimap {
      * @param contained_output_buffer Buffer of the matched contained pair sequence
      * @param num_matches Size of the output sequence
      * @param probe_output_begin Beginning of the output sequence of the matched probe pairs
-     * @param contained_output_begin Beginning of the output sequence of the matched contained pairs
+     * @param contained_output_begin Beginning of the output sequence of the matched contained
+     * pairs
      * @param pair_equal The binary callable used to compare two pairs for equality
      */
     template <uint32_t cg_size,
@@ -1621,10 +1649,11 @@ class static_multimap {
      * @brief Find all the matches of a given pair contained in multimap using scalar
      * loads with per-cg shared memory buffer.
      *
-     * For pair `p`, if pair_equal(p, slot[j]) returns true, copies `p` to unspecified locations in
+     * For pair `p`, if pair_equal(p, slot[j]) returns true, copies `p` to unspecified locations
+     * in
      * `[probe_output_begin, probe_output_end)` and copies slot[j] to unspecified locations in
-     * `[contained_output_begin, contained_output_end)`. In case of non-matches, copies `p` and the
-     * empty value sentinels into the output.
+     * `[contained_output_begin, contained_output_end)`. In case of non-matches, copies `p` and
+     * the empty value sentinels into the output.
      *
      * @tparam cg_size The number of threads in CUDA Cooperative Groups
      * @tparam buffer_size Size of the output buffer
@@ -1640,7 +1669,8 @@ class static_multimap {
      * @param contained_output_buffer Buffer of the matched contained pair sequence
      * @param num_matches Size of the output sequence
      * @param probe_output_begin Beginning of the output sequence of the matched probe pairs
-     * @param contained_output_begin Beginning of the output sequence of the matched contained pairs
+     * @param contained_output_begin Beginning of the output sequence of the matched contained
+     * pairs
      * @param pair_equal The binary callable used to compare two pairs for equality
      */
     template <uint32_t cg_size,
@@ -1698,29 +1728,28 @@ class static_multimap {
   Value get_empty_value_sentinel() const noexcept { return empty_value_sentinel_; }
 
   /**
-   * @brief Constructs a device_view object based on the members of the `static_multimap` object.
+   * @brief Constructs a device_view object based on the members of the `static_multimap`
+   * object.
    *
    * @return A device_view object based on the members of the `static_multimap` object
    */
   device_view get_device_view() const noexcept
   {
-    return device_view(slots_, capacity_, empty_key_sentinel_, empty_value_sentinel_);
+    return device_view(slots_.get(), capacity_, empty_key_sentinel_, empty_value_sentinel_);
   }
 
   /**
-   * @brief Constructs a device_mutable_view object based on the members of the `static_multimap`
-   * object
+   * @brief Constructs a device_mutable_view object based on the members of the
+   * `static_multimap` object
    *
    * @return A device_mutable_view object based on the members of the `static_multimap` object
    */
   device_mutable_view get_device_mutable_view() const noexcept
   {
-    return device_mutable_view(slots_, capacity_, empty_key_sentinel_, empty_value_sentinel_);
+    return device_mutable_view(slots_.get(), capacity_, empty_key_sentinel_, empty_value_sentinel_);
   }
 
  private:
-  pair_atomic_type* slots_{nullptr};            ///< Pointer to flat slots storage
-  atomic_ctr_type* d_counter_{nullptr};         ///< Preallocated device counter
   std::size_t capacity_{};                      ///< Total number of slots
   std::size_t size_{};                          ///< Number of keys in map
   Key empty_key_sentinel_{};                    ///< Key value that represents an empty slot
@@ -1728,7 +1757,13 @@ class static_multimap {
   slot_allocator_type slot_allocator_{};        ///< Allocator used to allocate slots
   counter_allocator_type counter_allocator_{};  ///< Allocator used to allocate counters
   cudaStream_t stream_{};                       ///< CUDA stream used for initialization
-};                                              // class static_multimap
+  counter_deleter delete_counter_{};            ///< Custom counter deleter
+  slot_deleter delete_slots_{};                 ///< Custom slot deleter
+  std::unique_ptr<atomic_ctr_type, counter_deleter> d_counter_;  ///< Preallocated device counter
+  std::unique_ptr<pair_atomic_type, slot_deleter> slots_;        ///< Pointer to flat slots storage
+
+};  // class static_multimap
+
 }  // namespace cuco
 
 #include <cuco/detail/static_multimap.inl>

From 27aafc3dbd067863d4498f484463c7b4361bfa77 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Wed, 15 Sep 2021 12:26:24 -0400
Subject: [PATCH 216/267] Cleanups using util header

---
 include/cuco/detail/prime.hpp           |   6 +-
 include/cuco/detail/static_multimap.inl | 356 +++++++++---------------
 include/cuco/detail/util.hpp            |  53 ++++
 3 files changed, 188 insertions(+), 227 deletions(-)
 create mode 100644 include/cuco/detail/util.hpp

diff --git a/include/cuco/detail/prime.hpp b/include/cuco/detail/prime.hpp
index 8d6bdb75c..2dceba653 100644
--- a/include/cuco/detail/prime.hpp
+++ b/include/cuco/detail/prime.hpp
@@ -16,6 +16,8 @@
 
 #pragma once
 
+#include <cuco/detail/util.hpp>
+
 namespace cuco {
 namespace detail {
 
@@ -20164,9 +20166,6 @@ constexpr std::size_t compute_prime(std::size_t num) noexcept
   return num;
 }
 
-// safe division
-#define SDIV(x, y) (((x) + (y)-1) / (y))
-
 /**
  * @brief Calculates the valid capacity based on `cg_size` , `vector_width`
  * and the initial `capacity`.
@@ -20192,5 +20191,4 @@ constexpr std::size_t get_valid_capacity(std::size_t capacity) noexcept
 }
 
 }  // namespace detail
-
 }  // namespace cuco
diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index b46100ba9..5a64ab3f8 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -17,6 +17,7 @@
 #include <thrust/tuple.h>
 
 #include <cuco/detail/bitwise_compare.cuh>
+#include <cuco/detail/util.hpp>
 
 namespace cuco {
 
@@ -44,17 +45,9 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::static_multimap(
     slots_{slot_allocator_.allocate(capacity_, stream_), delete_slots_}
 {
   auto constexpr block_size = 128;
-  int grid_size{-1};
-  cudaOccupancyMaxActiveBlocksPerMultiprocessor(
-    &grid_size,
+  auto const grid_size      = cuco::detail::get_grid_size(
     detail::initialize<atomic_key_type, atomic_mapped_type, Key, Value, pair_atomic_type>,
-    block_size,
-    0);
-  int dev_id{-1};
-  cudaGetDevice(&dev_id);
-  int num_sms{-1};
-  cudaDeviceGetAttribute(&num_sms, cudaDevAttrMultiProcessorCount, dev_id);
-  grid_size *= num_sms;
+    block_size);
 
   detail::initialize<atomic_key_type, atomic_mapped_type><<<grid_size, block_size, 0, stream_>>>(
     slots_.get(), empty_key_sentinel, empty_value_sentinel, get_capacity());
@@ -74,14 +67,8 @@ void static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::insert(InputI
   auto view     = get_device_mutable_view();
 
   auto constexpr block_size = 128;
-  int grid_size{-1};
-  cudaOccupancyMaxActiveBlocksPerMultiprocessor(
-    &grid_size, detail::insert<block_size, cg_size(), InputIt, device_mutable_view>, block_size, 0);
-  int dev_id{-1};
-  cudaGetDevice(&dev_id);
-  int num_sms{-1};
-  cudaDeviceGetAttribute(&num_sms, cudaDevAttrMultiProcessorCount, dev_id);
-  grid_size *= num_sms;
+  auto const grid_size      = cuco::detail::get_grid_size(
+    detail::insert<block_size, cg_size(), InputIt, device_mutable_view>, block_size);
 
   detail::insert<block_size, cg_size()>
     <<<grid_size, block_size, 0, stream>>>(first, first + num_keys, view);
@@ -101,17 +88,9 @@ void static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::insert_if(
   auto view         = get_device_mutable_view();
 
   auto constexpr block_size = 128;
-  int grid_size{-1};
-  cudaOccupancyMaxActiveBlocksPerMultiprocessor(
-    &grid_size,
+  auto const grid_size      = cuco::detail::get_grid_size(
     detail::insert_if_n<block_size, cg_size(), InputIt, StencilIt, device_mutable_view, Predicate>,
-    block_size,
-    0);
-  int dev_id{-1};
-  cudaGetDevice(&dev_id);
-  int num_sms{-1};
-  cudaDeviceGetAttribute(&num_sms, cudaDevAttrMultiProcessorCount, dev_id);
-  grid_size *= num_sms;
+    block_size);
 
   detail::insert_if_n<block_size, cg_size()>
     <<<grid_size, block_size, 0, stream>>>(first, stencil, num_elements, view, pred);
@@ -131,17 +110,8 @@ void static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::contains(
   auto view     = get_device_view();
 
   auto constexpr block_size = 128;
-  int grid_size{-1};
-  cudaOccupancyMaxActiveBlocksPerMultiprocessor(
-    &grid_size,
-    detail::contains<block_size, cg_size(), InputIt, OutputIt, device_view, KeyEqual>,
-    block_size,
-    0);
-  int dev_id{-1};
-  cudaGetDevice(&dev_id);
-  int num_sms{-1};
-  cudaDeviceGetAttribute(&num_sms, cudaDevAttrMultiProcessorCount, dev_id);
-  grid_size *= num_sms;
+  auto const grid_size      = cuco::detail::get_grid_size(
+    detail::contains<block_size, cg_size(), InputIt, OutputIt, device_view, KeyEqual>, block_size);
 
   detail::contains<block_size, cg_size()>
     <<<grid_size, block_size, 0, stream>>>(first, last, output_begin, view, key_equal);
@@ -163,17 +133,9 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::count(
   constexpr bool is_outer = false;
 
   auto constexpr block_size = 128;
-  int grid_size{-1};
-  cudaOccupancyMaxActiveBlocksPerMultiprocessor(
-    &grid_size,
+  auto const grid_size      = cuco::detail::get_grid_size(
     detail::count<block_size, cg_size(), is_outer, InputIt, atomic_ctr_type, device_view, KeyEqual>,
-    block_size,
-    0);
-  int dev_id{-1};
-  cudaGetDevice(&dev_id);
-  int num_sms{-1};
-  cudaDeviceGetAttribute(&num_sms, cudaDevAttrMultiProcessorCount, dev_id);
-  grid_size *= num_sms;
+    block_size);
 
   cudaMemsetAsync(d_counter_.get(), 0, sizeof(atomic_ctr_type), stream);
   std::size_t h_counter;
@@ -202,17 +164,9 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::count_
   constexpr bool is_outer = true;
 
   auto constexpr block_size = 128;
-  int grid_size{-1};
-  cudaOccupancyMaxActiveBlocksPerMultiprocessor(
-    &grid_size,
+  auto const grid_size      = cuco::detail::get_grid_size(
     detail::count<block_size, cg_size(), is_outer, InputIt, atomic_ctr_type, device_view, KeyEqual>,
-    block_size,
-    0);
-  int dev_id{-1};
-  cudaGetDevice(&dev_id);
-  int num_sms{-1};
-  cudaDeviceGetAttribute(&num_sms, cudaDevAttrMultiProcessorCount, dev_id);
-  grid_size *= num_sms;
+    block_size);
 
   cudaMemsetAsync(d_counter_.get(), 0, sizeof(atomic_ctr_type), stream);
   std::size_t h_counter;
@@ -241,18 +195,10 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::pair_c
   constexpr bool is_outer = false;
 
   auto constexpr block_size = 128;
-  int grid_size{-1};
-  cudaOccupancyMaxActiveBlocksPerMultiprocessor(
-    &grid_size,
+  auto const grid_size      = cuco::detail::get_grid_size(
     detail::
       pair_count<block_size, cg_size(), is_outer, InputIt, atomic_ctr_type, device_view, PairEqual>,
-    block_size,
-    0);
-  int dev_id{-1};
-  cudaGetDevice(&dev_id);
-  int num_sms{-1};
-  cudaDeviceGetAttribute(&num_sms, cudaDevAttrMultiProcessorCount, dev_id);
-  grid_size *= num_sms;
+    block_size);
 
   cudaMemsetAsync(d_counter_.get(), 0, sizeof(atomic_ctr_type), stream);
   std::size_t h_counter;
@@ -281,18 +227,10 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::pair_c
   constexpr bool is_outer = true;
 
   auto constexpr block_size = 128;
-  int grid_size{-1};
-  cudaOccupancyMaxActiveBlocksPerMultiprocessor(
-    &grid_size,
+  auto const grid_size      = cuco::detail::get_grid_size(
     detail::
       pair_count<block_size, cg_size(), is_outer, InputIt, atomic_ctr_type, device_view, PairEqual>,
-    block_size,
-    0);
-  int dev_id{-1};
-  cudaGetDevice(&dev_id);
-  int num_sms{-1};
-  cudaDeviceGetAttribute(&num_sms, cudaDevAttrMultiProcessorCount, dev_id);
-  grid_size *= num_sms;
+    block_size);
 
   cudaMemsetAsync(d_counter_.get(), 0, sizeof(atomic_ctr_type), stream);
   std::size_t h_counter;
@@ -323,41 +261,34 @@ OutputIt static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::retrieve(
   constexpr bool is_outer    = false;
 
   auto constexpr block_size = 128;
-  int grid_size{-1};
-  if constexpr (uses_vector_load()) {
-    cudaOccupancyMaxActiveBlocksPerMultiprocessor(&grid_size,
-                                                  detail::vectorized_retrieve<block_size,
-                                                                              warp_size(),
-                                                                              cg_size(),
-                                                                              buffer_size,
-                                                                              is_outer,
-                                                                              InputIt,
-                                                                              OutputIt,
-                                                                              atomic_ctr_type,
-                                                                              device_view,
-                                                                              KeyEqual>,
-                                                  block_size,
-                                                  0);
-  } else {
-    cudaOccupancyMaxActiveBlocksPerMultiprocessor(&grid_size,
-                                                  detail::retrieve<block_size,
-                                                                   warp_size(),
-                                                                   cg_size(),
-                                                                   buffer_size,
-                                                                   is_outer,
-                                                                   InputIt,
-                                                                   OutputIt,
-                                                                   atomic_ctr_type,
-                                                                   device_view,
-                                                                   KeyEqual>,
-                                                  block_size,
-                                                  0);
-  }
-  int dev_id{-1};
-  cudaGetDevice(&dev_id);
-  int num_sms{-1};
-  cudaDeviceGetAttribute(&num_sms, cudaDevAttrMultiProcessorCount, dev_id);
-  grid_size *= num_sms;
+  auto const grid_size      = [&]() {
+    if constexpr (uses_vector_load()) {
+      return detail::get_grid_size(detail::vectorized_retrieve<block_size,
+                                                               warp_size(),
+                                                               cg_size(),
+                                                               buffer_size,
+                                                               is_outer,
+                                                               InputIt,
+                                                               OutputIt,
+                                                               atomic_ctr_type,
+                                                               device_view,
+                                                               KeyEqual>,
+                                   block_size);
+    }
+    if constexpr (not uses_vector_load()) {
+      return detail::get_grid_size(detail::retrieve<block_size,
+                                                    warp_size(),
+                                                    cg_size(),
+                                                    buffer_size,
+                                                    is_outer,
+                                                    InputIt,
+                                                    OutputIt,
+                                                    atomic_ctr_type,
+                                                    device_view,
+                                                    KeyEqual>,
+                                   block_size);
+    }
+  }();
 
   cudaMemsetAsync(d_counter_.get(), 0, sizeof(atomic_ctr_type), stream);
   std::size_t h_counter;
@@ -396,41 +327,34 @@ OutputIt static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::retrieve_
   constexpr bool is_outer    = true;
 
   auto constexpr block_size = 128;
-  int grid_size{-1};
-  if constexpr (uses_vector_load()) {
-    cudaOccupancyMaxActiveBlocksPerMultiprocessor(&grid_size,
-                                                  detail::vectorized_retrieve<block_size,
-                                                                              warp_size(),
-                                                                              cg_size(),
-                                                                              buffer_size,
-                                                                              is_outer,
-                                                                              InputIt,
-                                                                              OutputIt,
-                                                                              atomic_ctr_type,
-                                                                              device_view,
-                                                                              KeyEqual>,
-                                                  block_size,
-                                                  0);
-  } else {
-    cudaOccupancyMaxActiveBlocksPerMultiprocessor(&grid_size,
-                                                  detail::retrieve<block_size,
-                                                                   warp_size(),
-                                                                   cg_size(),
-                                                                   buffer_size,
-                                                                   is_outer,
-                                                                   InputIt,
-                                                                   OutputIt,
-                                                                   atomic_ctr_type,
-                                                                   device_view,
-                                                                   KeyEqual>,
-                                                  block_size,
-                                                  0);
-  }
-  int dev_id{-1};
-  cudaGetDevice(&dev_id);
-  int num_sms{-1};
-  cudaDeviceGetAttribute(&num_sms, cudaDevAttrMultiProcessorCount, dev_id);
-  grid_size *= num_sms;
+  auto const grid_size      = [&]() {
+    if constexpr (uses_vector_load()) {
+      return detail::get_grid_size(detail::vectorized_retrieve<block_size,
+                                                               warp_size(),
+                                                               cg_size(),
+                                                               buffer_size,
+                                                               is_outer,
+                                                               InputIt,
+                                                               OutputIt,
+                                                               atomic_ctr_type,
+                                                               device_view,
+                                                               KeyEqual>,
+                                   block_size);
+    }
+    if constexpr (not uses_vector_load()) {
+      return detail::get_grid_size(detail::retrieve<block_size,
+                                                    warp_size(),
+                                                    cg_size(),
+                                                    buffer_size,
+                                                    is_outer,
+                                                    InputIt,
+                                                    OutputIt,
+                                                    atomic_ctr_type,
+                                                    device_view,
+                                                    KeyEqual>,
+                                   block_size);
+    }
+  }();
 
   cudaMemsetAsync(d_counter_.get(), 0, sizeof(atomic_ctr_type), stream);
   std::size_t h_counter;
@@ -474,43 +398,36 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::pair_r
   constexpr bool is_outer    = false;
 
   auto constexpr block_size = 128;
-  int grid_size{-1};
-  if constexpr (uses_vector_load()) {
-    cudaOccupancyMaxActiveBlocksPerMultiprocessor(&grid_size,
-                                                  detail::vectorized_pair_retrieve<block_size,
-                                                                                   warp_size(),
-                                                                                   cg_size(),
-                                                                                   buffer_size,
-                                                                                   is_outer,
-                                                                                   InputIt,
-                                                                                   OutputZipIt1,
-                                                                                   OutputZipIt2,
-                                                                                   atomic_ctr_type,
-                                                                                   device_view,
-                                                                                   PairEqual>,
-                                                  block_size,
-                                                  0);
-  } else {
-    cudaOccupancyMaxActiveBlocksPerMultiprocessor(&grid_size,
-                                                  detail::pair_retrieve<block_size,
-                                                                        warp_size(),
-                                                                        cg_size(),
-                                                                        buffer_size,
-                                                                        is_outer,
-                                                                        InputIt,
-                                                                        OutputZipIt1,
-                                                                        OutputZipIt2,
-                                                                        atomic_ctr_type,
-                                                                        device_view,
-                                                                        PairEqual>,
-                                                  block_size,
-                                                  0);
-  }
-  int dev_id{-1};
-  cudaGetDevice(&dev_id);
-  int num_sms{-1};
-  cudaDeviceGetAttribute(&num_sms, cudaDevAttrMultiProcessorCount, dev_id);
-  grid_size *= num_sms;
+  auto const grid_size      = [&]() {
+    if constexpr (uses_vector_load()) {
+      return detail::get_grid_size(detail::vectorized_pair_retrieve<block_size,
+                                                                    warp_size(),
+                                                                    cg_size(),
+                                                                    buffer_size,
+                                                                    is_outer,
+                                                                    InputIt,
+                                                                    OutputZipIt1,
+                                                                    OutputZipIt2,
+                                                                    atomic_ctr_type,
+                                                                    device_view,
+                                                                    PairEqual>,
+                                   block_size);
+    }
+    if constexpr (not uses_vector_load()) {
+      return detail::get_grid_size(detail::pair_retrieve<block_size,
+                                                         warp_size(),
+                                                         cg_size(),
+                                                         buffer_size,
+                                                         is_outer,
+                                                         InputIt,
+                                                         OutputZipIt1,
+                                                         OutputZipIt2,
+                                                         atomic_ctr_type,
+                                                         device_view,
+                                                         PairEqual>,
+                                   block_size);
+    }
+  }();
 
   cudaMemsetAsync(d_counter_.get(), 0, sizeof(atomic_ctr_type), stream);
   std::size_t h_counter;
@@ -563,43 +480,36 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::pair_r
   constexpr bool is_outer    = true;
 
   auto constexpr block_size = 128;
-  int grid_size{-1};
-  if constexpr (uses_vector_load()) {
-    cudaOccupancyMaxActiveBlocksPerMultiprocessor(&grid_size,
-                                                  detail::vectorized_pair_retrieve<block_size,
-                                                                                   warp_size(),
-                                                                                   cg_size(),
-                                                                                   buffer_size,
-                                                                                   is_outer,
-                                                                                   InputIt,
-                                                                                   OutputZipIt1,
-                                                                                   OutputZipIt2,
-                                                                                   atomic_ctr_type,
-                                                                                   device_view,
-                                                                                   PairEqual>,
-                                                  block_size,
-                                                  0);
-  } else {
-    cudaOccupancyMaxActiveBlocksPerMultiprocessor(&grid_size,
-                                                  detail::pair_retrieve<block_size,
-                                                                        warp_size(),
-                                                                        cg_size(),
-                                                                        buffer_size,
-                                                                        is_outer,
-                                                                        InputIt,
-                                                                        OutputZipIt1,
-                                                                        OutputZipIt2,
-                                                                        atomic_ctr_type,
-                                                                        device_view,
-                                                                        PairEqual>,
-                                                  block_size,
-                                                  0);
-  }
-  int dev_id{-1};
-  cudaGetDevice(&dev_id);
-  int num_sms{-1};
-  cudaDeviceGetAttribute(&num_sms, cudaDevAttrMultiProcessorCount, dev_id);
-  grid_size *= num_sms;
+  auto const grid_size      = [&]() {
+    if constexpr (uses_vector_load()) {
+      return detail::get_grid_size(detail::vectorized_pair_retrieve<block_size,
+                                                                    warp_size(),
+                                                                    cg_size(),
+                                                                    buffer_size,
+                                                                    is_outer,
+                                                                    InputIt,
+                                                                    OutputZipIt1,
+                                                                    OutputZipIt2,
+                                                                    atomic_ctr_type,
+                                                                    device_view,
+                                                                    PairEqual>,
+                                   block_size);
+    }
+    if constexpr (not uses_vector_load()) {
+      return detail::get_grid_size(detail::pair_retrieve<block_size,
+                                                         warp_size(),
+                                                         cg_size(),
+                                                         buffer_size,
+                                                         is_outer,
+                                                         InputIt,
+                                                         OutputZipIt1,
+                                                         OutputZipIt2,
+                                                         atomic_ctr_type,
+                                                         device_view,
+                                                         PairEqual>,
+                                   block_size);
+    }
+  }();
 
   cudaMemsetAsync(d_counter_.get(), 0, sizeof(atomic_ctr_type), stream);
   std::size_t h_counter;
diff --git a/include/cuco/detail/util.hpp b/include/cuco/detail/util.hpp
new file mode 100644
index 000000000..40697ff5c
--- /dev/null
+++ b/include/cuco/detail/util.hpp
@@ -0,0 +1,53 @@
+/*
+ * Copyright (c) 2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ */
+
+#pragma once
+
+namespace cuco {
+namespace detail {
+
+/**
+ * @brief Compute the number of bits of a simple type.
+ *
+ * @tparam T The type we want to infer its size in bits
+ *
+ * @return Size of type T in bits
+ */
+template <typename T>
+static constexpr std::size_t type_bits() noexcept
+{
+  return sizeof(T) * CHAR_BIT;
+}
+
+// safe division
+#ifndef SDIV
+#define SDIV(x, y) (((x) + (y)-1) / (y))
+#endif
+
+template <typename Kernel>
+auto get_grid_size(Kernel kernel, std::size_t block_size, std::size_t dynamic_smem_bytes = 0)
+{
+  int grid_size{-1};
+  cudaOccupancyMaxActiveBlocksPerMultiprocessor(&grid_size, kernel, block_size, dynamic_smem_bytes);
+  int dev_id{-1};
+  cudaGetDevice(&dev_id);
+  int num_sms{-1};
+  cudaDeviceGetAttribute(&num_sms, cudaDevAttrMultiProcessorCount, dev_id);
+  grid_size *= num_sms;
+  return grid_size;
+}
+
+}  // namespace detail
+}  // namespace cuco

From 08c6ceaf2cd5c72d94d158e3a051d7f4337785ac Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Wed, 15 Sep 2021 20:35:44 -0400
Subject: [PATCH 217/267] Update docs + code cleanups

---
 include/cuco/detail/probe_sequences.cuh | 270 ++++++++++++++++++------
 include/cuco/static_multimap.cuh        |  62 +++---
 2 files changed, 233 insertions(+), 99 deletions(-)

diff --git a/include/cuco/detail/probe_sequences.cuh b/include/cuco/detail/probe_sequences.cuh
index c3fc0268a..91b47a255 100644
--- a/include/cuco/detail/probe_sequences.cuh
+++ b/include/cuco/detail/probe_sequences.cuh
@@ -24,10 +24,22 @@
 namespace cuco {
 namespace detail {
 
-template <typename Key,
-          typename Value,
-          uint32_t CGSize,
-          cuda::thread_scope Scope = cuda::thread_scope_device>
+/**
+ * @brief Base class for a hash map probe sequence. This class should not be used directly.
+ *
+ * Hash map operations are generally memory-bandwidth bound. A vector-load loads two consecutive
+ * slots instead of one to fully utilize the 16B memory load supported by SASS/hardware thus
+ * improve memory throughput. This method (flagged by `uses_vector_load` logic) is implicitly
+ * applied to all hash map operations (e.g. `insert`, `count`, and `retrieve`, etc.) when pairs
+ * are packable (see `cuco::detail::is_packable` logic).
+ *
+ * @tparam Key Type used for keys
+ * @tparam Value Type of the mapped values
+ * @tparam CGSize Number of threads in CUDA Cooperative Groups
+ * @tparam Scope The scope in which multimap operations will be performed by
+ * individual threads
+ */
+template <typename Key, typename Value, uint32_t CGSize, cuda::thread_scope Scope>
 class probe_sequence_base {
  protected:
   using value_type         = cuco::pair_type<Key, Value>;
@@ -40,136 +52,256 @@ class probe_sequence_base {
   using const_iterator     = pair_atomic_type const*;
 
  public:
+  /**
+   * @brief Returns the size of the CUDA cooperative thread group.
+   */
   __host__ __device__ static constexpr uint32_t cg_size() noexcept { return CGSize; }
 
+  /**
+   * @brief Returns the number of elements loaded with each vector-load.
+   */
   __host__ __device__ static constexpr uint32_t vector_width() noexcept { return 2u; }
 
+  /**
+   * @brief Indicates if vector-load is used.
+   *
+   * Users have no explicit control on whether vector-load is used.
+   *
+   * @return Boolean indicating if vector-load is used.
+   */
   __host__ __device__ static constexpr bool uses_vector_load() noexcept
   {
     return cuco::detail::is_packable<value_type>();
   }
 
+  /**
+   * @brief Constructs a probe sequence based on the given hash map features.
+   *
+   * @param slots Pointer to beginning of the hash map slots
+   * @param capacity Capacity of the hash map
+   */
   __host__ __device__ explicit probe_sequence_base(iterator slots, std::size_t capacity)
     : slots_{slots}, capacity_{capacity}
   {
   }
 
+  /**
+   * @brief Returns the capacity of the hash map.
+   */
   __host__ __device__ std::size_t get_capacity() const noexcept { return capacity_; }
 
+  /**
+   * @brief Returns slots array.
+   */
   __device__ iterator get_slots() noexcept { return slots_; }
+
+  /**
+   * @brief Returns slots array.
+   */
   __device__ const_iterator get_slots() const noexcept { return slots_; }
 
  protected:
-  iterator slots_;
-  const std::size_t capacity_;
-};  // class probe_sequence_base
+  iterator slots_;              ///< Pointer to beginning of the hash map slots
+  const std::size_t capacity_;  ///< Total number of slots
+};                              // class probe_sequence_base
 
+/**
+ * @brief Cooperative Groups based Linear probing scheme.
+ *
+ * Linear probing is efficient only when few collisions are present. Performance hints:
+ * - Use linear probing only when collisions are rare. e.g. low occupancy or low multiplicity.
+ * - `CGSize` = 1 or 2 when hash map is small (10'000'000 or less), 4 or 8 otherwise.
+ *
+ * @tparam Key Type used for keys
+ * @tparam Value Type of the mapped values
+ * @tparam CGSize Number of threads in CUDA Cooperative Groups
+ * @tparam Hash Unary callable type
+ * @tparam Scope The scope in which multimap operations will be performed by
+ * individual threads
+ */
 template <typename Key,
           typename Value,
           uint32_t CGSize          = 8,
-          typename Hash1           = cuco::detail::MurmurHash3_32<Key>,
-          typename Hash2           = cuco::detail::MurmurHash3_32<Key>,
+          typename Hash            = cuco::detail::MurmurHash3_32<Key>,
           cuda::thread_scope Scope = cuda::thread_scope_device>
-class double_hashing : public probe_sequence_base<Key, Value, CGSize, Scope> {
+class linear_probing : public probe_sequence_base<Key, Value, CGSize, Scope> {
  public:
-  using iterator = typename probe_sequence_base<Key, Value, CGSize, Scope>::iterator;
-  using probe_sequence_base<Key, Value, CGSize, Scope>::capacity_;
-  using probe_sequence_base<Key, Value, CGSize, Scope>::slots_;
-  using probe_sequence_base<Key, Value, CGSize, Scope>::cg_size;
-  using probe_sequence_base<Key, Value, CGSize, Scope>::vector_width;
-  using probe_sequence_base<Key, Value, CGSize, Scope>::uses_vector_load;
+  using probe_sequence_base_type = probe_sequence_base<Key, Value, CGSize, Scope>;
+  using iterator                 = typename probe_sequence_base_type::iterator;
+  using probe_sequence_base_type::capacity_;
+  using probe_sequence_base_type::cg_size;
+  using probe_sequence_base_type::slots_;
+  using probe_sequence_base_type::uses_vector_load;
+  using probe_sequence_base_type::vector_width;
 
-  __host__ __device__ explicit double_hashing(
-    iterator slots,
-    std::size_t capacity,
-    Hash1 hash1 = cuco::detail::MurmurHash3_32<Key>{},
-    Hash2 hash2 = cuco::detail::MurmurHash3_32<Key>{}) noexcept
-    : probe_sequence_base<Key, Value, CGSize, Scope>{slots, capacity}, hash1_{hash1}, hash2_{hash2}
+  /**
+   * @brief Constructs a linear probing scheme based on the given hash map features.
+   *
+   * @param slots Pointer to beginning of the hash map slots
+   * @param capacity Capacity of the hash map
+   * @param hash Unary function to hash each key
+   */
+  __host__ __device__ explicit linear_probing(iterator slots,
+                                              std::size_t capacity,
+                                              Hash hash = cuco::detail::MurmurHash3_32<Key>{})
+    : probe_sequence_base<Key, Value, CGSize, Scope>{slots, capacity}, hash_(hash)
   {
   }
 
+  /**
+   * @brief Returns the initial slot for a given key `k`.
+   *
+   * If vector-load is enabled, the return slot is always even to avoid illegal memory access.
+   *
+   * @tparam CG CUDA Cooperative Groups type
+   * @param g the Cooperative Group for which the initial slot is needed
+   * @param k The key to get the slot for
+   * @return Pointer to the initial slot for `k`
+   */
   template <typename CG>
   __device__ iterator initial_slot(CG const& g, Key const k) noexcept
   {
-    std::size_t index;
-    auto const hash_value = hash1_(k);
-    if constexpr (uses_vector_load()) {
-      step_size_ = (hash2_(k + 1) % (capacity_ / (cg_size() * vector_width()) - 1) + 1) *
-                   cg_size() * vector_width();
-      index = hash_value % (capacity_ / (cg_size() * vector_width())) * cg_size() * vector_width() +
-              g.thread_rank() * vector_width();
-    } else {
-      step_size_ = (hash2_(k + 1) % (capacity_ / cg_size() - 1) + 1) * cg_size();
-      index      = (hash_value + g.thread_rank()) % capacity_;
-    }
-    return slots_ + index;
+    auto const hash_value = [&]() {
+      auto const tmp = hash_(k);
+      if constexpr (uses_vector_load()) {
+        // ensure initial hash value is always even
+        return tmp + tmp % 2;
+      }
+      if constexpr (not uses_vector_load()) { return tmp; }
+    }();
+
+    auto const offset = [&]() {
+      if constexpr (uses_vector_load()) { return g.thread_rank() * vector_width(); }
+      if constexpr (not uses_vector_load()) { return g.thread_rank(); }
+    }();
+
+    // Each CG accesses to a window of (`cg_size` * `vector_width`)
+    // slots if vector-load is used or `cg_size` slots otherwise
+    return &slots_[(hash_value + offset) % capacity_];
   }
 
+  /**
+   * @brief Given a slot `s`, returns the next slot.
+   *
+   * If `s` is the last slot, wraps back around to the first slot.
+   *
+   * @param s The slot to advance
+   * @return The next slot after `s`
+   */
   __device__ iterator next_slot(iterator s) noexcept
   {
     std::size_t index = s - slots_;
-    return &slots_[(index + step_size_) % capacity_];
+    std::size_t offset;
+    if constexpr (uses_vector_load()) {
+      offset = cg_size() * vector_width();
+    } else {
+      offset = cg_size();
+    }
+    return &slots_[(index + offset) % capacity_];
   }
 
  private:
-  std::size_t step_size_;
-  Hash1 hash1_;
-  Hash2 hash2_;
-};  // class double_hashing
+  Hash hash_;  ///< The unary callable used to hash the key
+};             // class linear_probing
 
+/**
+ * @brief Cooperative Groups based double hashing scheme.
+ *
+ * Default probe sequence for `cuco::static_multimap`. Double hashing shows superior
+ * performance when dealing with high multiplicty and/or high occupancy use cases. Performance
+ * hints:
+ * - `CGSize` = 1 or 2 when hash map is small (10'000'000 or less), 4 or 8 otherwise.
+ *
+ * @tparam Key Type used for keys
+ * @tparam Value Type of the mapped values
+ * @tparam CGSize Number of threads in CUDA Cooperative Groups
+ * @tparam Hash1 Unary callable type
+ * @tparam Hash2 Unary callable type
+ * @tparam Scope The scope in which multimap operations will be performed by
+ * individual threads
+ */
 template <typename Key,
           typename Value,
           uint32_t CGSize          = 8,
-          typename Hash            = cuco::detail::MurmurHash3_32<Key>,
+          typename Hash1           = cuco::detail::MurmurHash3_32<Key>,
+          typename Hash2           = cuco::detail::MurmurHash3_32<Key>,
           cuda::thread_scope Scope = cuda::thread_scope_device>
-class linear_probing : public probe_sequence_base<Key, Value, CGSize, Scope> {
+class double_hashing : public probe_sequence_base<Key, Value, CGSize, Scope> {
  public:
-  using iterator = typename probe_sequence_base<Key, Value, CGSize, Scope>::iterator;
-  using probe_sequence_base<Key, Value, CGSize, Scope>::capacity_;
-  using probe_sequence_base<Key, Value, CGSize, Scope>::slots_;
-  using probe_sequence_base<Key, Value, CGSize, Scope>::cg_size;
-  using probe_sequence_base<Key, Value, CGSize, Scope>::vector_width;
-  using probe_sequence_base<Key, Value, CGSize, Scope>::uses_vector_load;
+  using probe_sequence_base_type = probe_sequence_base<Key, Value, CGSize, Scope>;
+  using iterator                 = typename probe_sequence_base_type::iterator;
+  using probe_sequence_base_type::capacity_;
+  using probe_sequence_base_type::cg_size;
+  using probe_sequence_base_type::slots_;
+  using probe_sequence_base_type::uses_vector_load;
+  using probe_sequence_base_type::vector_width;
 
-  __host__ __device__ explicit linear_probing(iterator slots,
-                                              std::size_t capacity,
-                                              Hash hash = cuco::detail::MurmurHash3_32<Key>{})
-    : probe_sequence_base<Key, Value, CGSize, Scope>{slots, capacity}, hash_(hash)
+  /**
+   * @brief Constructs a double hashing scheme based on the given hash map features.
+   *
+   * @param slots Pointer to beginning of the hash map slots
+   * @param capacity Capacity of the hash map
+   * @param hash1 First hasher to hash each key
+   * @param hash2 Second hasher to determine step size
+   */
+  __host__ __device__ explicit double_hashing(
+    iterator slots,
+    std::size_t capacity,
+    Hash1 hash1 = cuco::detail::MurmurHash3_32<Key>{},
+    Hash2 hash2 = cuco::detail::MurmurHash3_32<Key>{}) noexcept
+    : probe_sequence_base<Key, Value, CGSize, Scope>{slots, capacity}, hash1_{hash1}, hash2_{hash2}
   {
   }
 
+  /**
+   * @brief Returns the initial slot for a given key `k`.
+   *
+   * If vector-load is enabled, the return slot is always a multiple of (`cg_size` * `vector_width`)
+   * to avoid illegal memory access.
+   *
+   * @tparam CG CUDA Cooperative Groups type
+   * @param g the Cooperative Group for which the initial slot is needed
+   * @param k The key to get the slot for
+   * @return Pointer to the initial slot for `k`
+   */
   template <typename CG>
   __device__ iterator initial_slot(CG const& g, Key const k) noexcept
   {
-    auto const hash_value = [&]() {
-      auto const tmp = hash_(k);
-      return tmp + tmp % 2;  // ensure initial hash value is always even
-    }();
-
-    std::size_t offset;
+    std::size_t index;
+    auto const hash_value = hash1_(k);
     if constexpr (uses_vector_load()) {
-      offset = g.thread_rank() * vector_width();
+      // step size in range [1, capacity-1] * cg_size * vector_width
+      step_size_ = (hash2_(k + 1) % (capacity_ / (cg_size() * vector_width()) - 1) + 1) *
+                   cg_size() * vector_width();
+      index = hash_value % (capacity_ / (cg_size() * vector_width())) * cg_size() * vector_width() +
+              g.thread_rank() * vector_width();
     } else {
-      offset = g.thread_rank();
+      // step size in range [1, capacity-1] * cg_size
+      step_size_ = (hash2_(k + 1) % (capacity_ / cg_size() - 1) + 1) * cg_size();
+      index      = (hash_value + g.thread_rank()) % capacity_;
     }
-    return &slots_[(hash_value + offset) % capacity_];
+    return slots_ + index;
   }
 
+  /**
+   * @brief Given a slot `s`, returns the next slot.
+   *
+   * If `s` is the last slot, wraps back around to the first slot.
+   *
+   * @param s The slot to advance
+   * @return The next slot after `s`
+   */
   __device__ iterator next_slot(iterator s) noexcept
   {
     std::size_t index = s - slots_;
-    std::size_t offset;
-    if constexpr (uses_vector_load()) {
-      offset = cg_size() * vector_width();
-    } else {
-      offset = cg_size();
-    }
-    return &slots_[(index + offset) % capacity_];
+    return &slots_[(index + step_size_) % capacity_];
   }
 
  private:
-  Hash hash_;
-};  // class linear_probing
+  Hash1 hash1_;            ///< The first unary callable used to hash the key
+  Hash2 hash2_;            ///< The second unary callable used to determine step size
+  std::size_t step_size_;  ///< The step stride when searching for the next slot
+};                         // class double_hashing
 
 }  // namespace detail
 }  // namespace cuco
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index d0e60d179..a42bd3021 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -85,10 +85,10 @@ namespace cuco {
  * The two types are separate to prevent erroneous concurrent insert/find
  * operations.
  *
- * Loading two consecutive slots instead of one for small pairs can improve cache utilization since
- * 16B memory loads are natively supported at the SASS/hardware level. This `vector load` method is
- * implicitly applied to all involved operations (e.g. `insert`, `count`, and `retrieve`, etc.) when
- * pairs are packable (see `multimap::uses_vector_load` logic).
+ * By default, query operations (e.g. `count` and `retrieve`) take `Key` as input and do not include
+ * non-matches in the output. APIs with `_outer` suffix should be used if non-match handling is
+ * desired. The `pair_` prefix indicates that the input data are key-value pairs whose type can be
+ * converted to multimap's `value_type`.
  *
  * Example:
  * \code{.cpp}
@@ -119,9 +119,9 @@ namespace cuco {
  *
  * @tparam Key Type used for keys
  * @tparam Value Type of the mapped values
- * @tparam ProbeSequence Probe sequence used in multimap
- * @tparam Scope The scope in which insert/find operations will be performed by
- * individual threads.
+ * @tparam ProbeSequence Probe sequence defined in `detail/probe_sequence.cuh`
+ * @tparam Scope The scope in which multimap operations will be performed by
+ * individual threads
  * @tparam Allocator Type of allocator used for device storage
  */
 template <typename Key,
@@ -214,7 +214,7 @@ class static_multimap {
    *
    * @tparam InputIt Device accessible random access input iterator where
    * `std::is_converitble<std::iterator_traits<InputIt>::value_type,
-   * static_multimap<K,V>::value_type>` is `true`
+   * static_multimap<K, V>::value_type>` is `true`
    * @param first Beginning of the sequence of key/value pairs
    * @param last End of the sequence of key/value pairs
    * @param stream CUDA stream used for insert
@@ -230,7 +230,7 @@ class static_multimap {
    *
    * @tparam InputIt Device accessible random access input iterator where
    * `std::is_converitble<std::iterator_traits<InputIt>::value_type,
-   * static_multimap<K,V>::value_type>` is `true`
+   * static_multimap<K, V>::value_type>` is `true`
    * @tparam StencilIt Device accessible random access iterator whose value_type is convertible to
    * Predicate's argument type
    * @tparam Predicate Unary predicate callable
@@ -462,26 +462,27 @@ class static_multimap {
 
  private:
   /**
-   * @brief Indicate if vector load is used.
+   * @brief Indicates if vector-load is used.
    *
-   * @return Boolean indicating if vector load is used.
+   * Users have no explicit control on whether vector-load is used.
+   *
+   * @return Boolean indicating if vector-load is used.
    */
   static constexpr bool uses_vector_load() noexcept { return ProbeSequence::uses_vector_load(); }
 
   /**
-   * @brief The number of elements in each vector load
-   *
-   * @return The number of elements in each vector load.
+   * @brief Returns the number of pairs loaded with each vector-load
    */
   static constexpr uint32_t vector_width() noexcept { return ProbeSequence::vector_width(); }
 
   /**
-   * @brief Indicate the warp size.
-   *
-   * @return The warp size.
+   * @brief Returns the warp size.
    */
   static constexpr uint32_t warp_size() noexcept { return 32u; }
 
+  /**
+   * @brief Custom deleter for unique pointer of device counter.
+   */
   struct counter_deleter {
     counter_deleter(counter_allocator_type& a, cudaStream_t& s) : allocator{a}, stream{s} {}
 
@@ -491,6 +492,9 @@ class static_multimap {
     cudaStream_t& stream;
   };
 
+  /**
+   * @brief Custom deleter for unique pointer of slots.
+   */
   struct slot_deleter {
     slot_deleter(slot_allocator_type& a, size_t& c, cudaStream_t& s)
       : allocator{a}, capacity{c}, stream{s}
@@ -514,16 +518,16 @@ class static_multimap {
     using const_iterator = pair_atomic_type const*;
 
     /**
-     * @brief Indicate if vector load is used.
+     * @brief Indicates if vector-load is used.
+     *
+     * Users have no explicit control on whether vector-load is used.
      *
-     * @return Boolean indicating if vector load is used.
+     * @return Boolean indicating if vector-load is used.
      */
     static constexpr bool uses_vector_load() noexcept { return ProbeSequence::uses_vector_load(); }
 
     /**
-     * @brief The number of elements for each vector load
-     *
-     * @return The number of elements for each vector load.
+     * @brief Returns the number of pairs loaded with each vector-load
      */
     static constexpr uint32_t vector_width() noexcept { return ProbeSequence::vector_width(); }
 
@@ -536,7 +540,7 @@ class static_multimap {
     __device__ void load_pair_array(value_type* arr, const_iterator current_slot) noexcept;
 
    private:
-    ProbeSequence probe_sequence_;
+    ProbeSequence probe_sequence_;  ///< Probe sequence used to probe the hash map
     Key empty_key_sentinel_{};      ///< Key value that represents an empty slot
     Value empty_value_sentinel_{};  ///< Initial Value of empty slot
 
@@ -1689,7 +1693,6 @@ class static_multimap {
                                         OutputZipIt1 probe_output_begin,
                                         OutputZipIt2 contained_output_begin,
                                         PairEqual pair_equal) noexcept;
-
   };  // class device_view
 
   /**
@@ -1756,13 +1759,12 @@ class static_multimap {
   Value empty_value_sentinel_{};                ///< Initial value of empty slot
   slot_allocator_type slot_allocator_{};        ///< Allocator used to allocate slots
   counter_allocator_type counter_allocator_{};  ///< Allocator used to allocate counters
-  cudaStream_t stream_{};                       ///< CUDA stream used for initialization
+  cudaStream_t stream_{};                       ///< CUDA stream used for ctor/dtor
   counter_deleter delete_counter_{};            ///< Custom counter deleter
-  slot_deleter delete_slots_{};                 ///< Custom slot deleter
-  std::unique_ptr<atomic_ctr_type, counter_deleter> d_counter_;  ///< Preallocated device counter
-  std::unique_ptr<pair_atomic_type, slot_deleter> slots_;        ///< Pointer to flat slots storage
-
-};  // class static_multimap
+  slot_deleter delete_slots_{};                 ///< Custom slots deleter
+  std::unique_ptr<atomic_ctr_type, counter_deleter> d_counter_{};  ///< Preallocated device counter
+  std::unique_ptr<pair_atomic_type, slot_deleter> slots_{};  ///< Pointer to flat slots storage
+};                                                           // class static_multimap
 
 }  // namespace cuco
 

From c2af6fd3448facd59f2c421ed81508e1a2cad4d7 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Wed, 15 Sep 2021 20:50:52 -0400
Subject: [PATCH 218/267] Add manual default constructor

---
 include/cuco/detail/static_multimap.inl | 10 ++++++++++
 include/cuco/static_multimap.cuh        | 10 +++++++---
 2 files changed, 17 insertions(+), 3 deletions(-)

diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index 5a64ab3f8..d80096067 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -21,6 +21,16 @@
 
 namespace cuco {
 
+template <typename Key,
+          typename Value,
+          class ProbeSequence,
+          cuda::thread_scope Scope,
+          typename Allocator>
+static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::static_multimap()
+  : delete_counter_{counter_allocator_, stream_}, delete_slots_{slot_allocator_, capacity_, stream_}
+{
+}
+
 template <typename Key,
           typename Value,
           class ProbeSequence,
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index a42bd3021..3c128b551 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -157,7 +157,6 @@ class static_multimap {
   static_multimap(static_multimap const&) = delete;
   static_multimap& operator=(static_multimap const&) = delete;
 
-  static_multimap()                  = default;
   static_multimap(static_multimap&&) = default;
   static_multimap& operator=(static_multimap&&) = default;
   ~static_multimap()                            = default;
@@ -179,6 +178,11 @@ class static_multimap {
    */
   static constexpr uint32_t cg_size() noexcept { return ProbeSequence::cg_size(); }
 
+  /**
+   * @brief Default constructor.
+   */
+  static_multimap();
+
   /**
    * @brief Construct a fixed-size map with the specified capacity and sentinel values.
    * @brief Construct a statically sized map with the specified number of slots
@@ -1760,8 +1764,8 @@ class static_multimap {
   slot_allocator_type slot_allocator_{};        ///< Allocator used to allocate slots
   counter_allocator_type counter_allocator_{};  ///< Allocator used to allocate counters
   cudaStream_t stream_{};                       ///< CUDA stream used for ctor/dtor
-  counter_deleter delete_counter_{};            ///< Custom counter deleter
-  slot_deleter delete_slots_{};                 ///< Custom slots deleter
+  counter_deleter delete_counter_;              ///< Custom counter deleter
+  slot_deleter delete_slots_;                   ///< Custom slots deleter
   std::unique_ptr<atomic_ctr_type, counter_deleter> d_counter_{};  ///< Preallocated device counter
   std::unique_ptr<pair_atomic_type, slot_deleter> slots_{};  ///< Pointer to flat slots storage
 };                                                           // class static_multimap

From 3d7a6c19cd72ccfd0401067196e35a42940b1de3 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Wed, 15 Sep 2021 20:59:27 -0400
Subject: [PATCH 219/267] Default constructed unique pointers

---
 include/cuco/detail/static_multimap.inl | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index d80096067..86148b717 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -27,7 +27,10 @@ template <typename Key,
           cuda::thread_scope Scope,
           typename Allocator>
 static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::static_multimap()
-  : delete_counter_{counter_allocator_, stream_}, delete_slots_{slot_allocator_, capacity_, stream_}
+  : delete_counter_{counter_allocator_, stream_},
+    delete_slots_{slot_allocator_, capacity_, stream_},
+    d_counter_{nullptr, delete_counter_},
+    slots_{nullptr, delete_slots_}
 {
 }
 

From aedc8284ef1ef5725b911226d45000122eb2d544 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Thu, 16 Sep 2021 12:54:51 -0400
Subject: [PATCH 220/267] Add move assignment for custom deleters

---
 include/cuco/static_multimap.cuh | 23 +++++++++++++++++++++++
 1 file changed, 23 insertions(+)

diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index 3c128b551..0f6ca83f3 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -490,8 +490,19 @@ class static_multimap {
   struct counter_deleter {
     counter_deleter(counter_allocator_type& a, cudaStream_t& s) : allocator{a}, stream{s} {}
 
+    counter_deleter(counter_deleter const&) = default;
+
     void operator()(atomic_ctr_type* ptr) { allocator.deallocate(ptr, 1, stream); }
 
+    counter_deleter& operator=(counter_deleter&& other)
+    {
+      if (this != &other) {
+        allocator = other.allocator;
+        stream    = other.stream;
+      }
+      return *this;
+    }
+
     counter_allocator_type& allocator;
     cudaStream_t& stream;
   };
@@ -505,8 +516,20 @@ class static_multimap {
     {
     }
 
+    slot_deleter(slot_deleter const&) = default;
+
     void operator()(pair_atomic_type* ptr) { allocator.deallocate(ptr, capacity, stream); }
 
+    slot_deleter& operator=(slot_deleter&& other)
+    {
+      if (this != &other) {
+        allocator = other.allocator;
+        capacity  = other.capacity;
+        stream    = other.stream;
+      }
+      return *this;
+    }
+
     slot_allocator_type& allocator;
     size_t& capacity;
     cudaStream_t& stream;

From 14384d7e162bbea55c05a5e557c5e564a8616875 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Thu, 16 Sep 2021 13:41:34 -0400
Subject: [PATCH 221/267] Code cleanups: use CUCO_HAS_CG_MEMCPY_ASYNC macro

---
 include/cuco/detail/static_multimap.inl | 62 +++++++++----------------
 include/cuco/static_multimap.cuh        | 40 +++-------------
 2 files changed, 28 insertions(+), 74 deletions(-)

diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index 86148b717..ca89ec89c 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -1380,44 +1380,7 @@ template <typename Key,
           cuda::thread_scope Scope,
           typename Allocator>
 template <typename CG, typename atomicT, typename OutputIt>
-__inline__ __device__
-  std::enable_if_t<thrust::is_contiguous_iterator<OutputIt>::value and SUPPORTS_CG_MEMCPY_ASYNC,
-                   void>
-  static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::flush_output_buffer(
-    CG const& g,
-    uint32_t const num_outputs,
-    value_type* output_buffer,
-    atomicT* num_matches,
-    OutputIt output_begin) noexcept
-{
-  std::size_t offset;
-  const auto lane_id = g.thread_rank();
-  if (0 == lane_id) {
-    offset = num_matches->fetch_add(num_outputs, cuda::std::memory_order_relaxed);
-  }
-  offset = g.shfl(offset, 0);
-
-#if defined(CUCO_HAS_CUDA_BARRIER)
-  cooperative_groups::memcpy_async(
-    g,
-    output_begin + offset,
-    output_buffer,
-    cuda::aligned_size_t<alignof(value_type)>(sizeof(value_type) * num_outputs));
-#else
-  cooperative_groups::memcpy_async(
-    g, output_begin + offset, output_buffer, sizeof(value_type) * num_outputs);
-#endif
-}
-
-template <typename Key,
-          typename Value,
-          class ProbeSequence,
-          cuda::thread_scope Scope,
-          typename Allocator>
-template <typename CG, typename atomicT, typename OutputIt>
-__inline__ __device__ std::enable_if_t<not(thrust::is_contiguous_iterator<OutputIt>::value and
-                                           SUPPORTS_CG_MEMCPY_ASYNC),
-                                       void>
+__inline__ __device__ void
 static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::flush_output_buffer(
   CG const& g,
   uint32_t const num_outputs,
@@ -1432,8 +1395,27 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::flush
   }
   offset = g.shfl(offset, 0);
 
-  for (auto index = lane_id; index < num_outputs; index += g.size()) {
-    *(output_begin + offset + index) = output_buffer[index];
+  if constexpr (thrust::is_contiguous_iterator_v<OutputIt>) {
+#if defined(CUCO_HAS_CG_MEMCPY_ASYNC)
+#if defined(CUCO_HAS_CUDA_BARRIER)
+    cooperative_groups::memcpy_async(
+      g,
+      output_begin + offset,
+      output_buffer,
+      cuda::aligned_size_t<alignof(value_type)>(sizeof(value_type) * num_outputs));
+#else
+    cooperative_groups::memcpy_async(
+      g, output_begin + offset, output_buffer, sizeof(value_type) * num_outputs);
+#endif  // end CUCO_HAS_CUDA_BARRIER
+#else
+    for (auto index = lane_id; index < num_outputs; index += g.size()) {
+      *(output_begin + offset + index) = output_buffer[index];
+    }
+#endif  // end CUCO_HAS_CG_MEMCPY_ASYNC
+  } else {
+    for (auto index = lane_id; index < num_outputs; index += g.size()) {
+      *(output_begin + offset + index) = output_buffer[index];
+    }
   }
 }
 
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index 0f6ca83f3..82ab28c08 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -32,9 +32,7 @@
 
 // cg::memcpy_aysnc is supported for CUDA 11.1 and up
 #if defined(CUDART_VERSION) && (CUDART_VERSION >= 11100)
-#define SUPPORTS_CG_MEMCPY_ASYNC 1
-#else
-#define SUPPORTS_CG_MEMCPY_ASYNC 0
+#define CUCO_HAS_CG_MEMCPY_ASYNC
 #endif
 
 #if defined(CUCO_HAS_CUDA_BARRIER)
@@ -1226,29 +1224,6 @@ class static_multimap {
                                        PairEqual pair_equal) noexcept;
 
    public:
-    /**
-     * @brief Flushes per-CG buffer into the output sequence using CG memcpy_async.
-     *
-     * @tparam CG Cooperative Group type
-     * @tparam atomicT Type of atomic storage
-     * @tparam OutputIt Device accessible output iterator whose `value_type` is
-     * convertible to the map's `mapped_type`
-     * @param g The Cooperative Group used to flush output buffer
-     * @param num_outputs Number of valid output in the buffer
-     * @param output_buffer Buffer of the key/value pair sequence
-     * @param num_matches Size of the output sequence
-     * @param output_begin Beginning of the output sequence of key/value pairs
-     */
-    template <typename CG, typename atomicT, typename OutputIt>
-    __inline__ __device__
-      std::enable_if_t<thrust::is_contiguous_iterator<OutputIt>::value and SUPPORTS_CG_MEMCPY_ASYNC,
-                       void>
-      flush_output_buffer(CG const& g,
-                          uint32_t const num_outputs,
-                          value_type* output_buffer,
-                          atomicT* num_matches,
-                          OutputIt output_begin) noexcept;
-
     /**
      * @brief Flushes per-CG buffer into the output sequence.
      *
@@ -1263,14 +1238,11 @@ class static_multimap {
      * @param output_begin Beginning of the output sequence of key/value pairs
      */
     template <typename CG, typename atomicT, typename OutputIt>
-    __inline__ __device__ std::enable_if_t<not(thrust::is_contiguous_iterator<OutputIt>::value and
-                                               SUPPORTS_CG_MEMCPY_ASYNC),
-                                           void>
-    flush_output_buffer(CG const& g,
-                        uint32_t const num_outputs,
-                        value_type* output_buffer,
-                        atomicT* num_matches,
-                        OutputIt output_begin) noexcept;
+    __inline__ __device__ void flush_output_buffer(CG const& g,
+                                                   uint32_t const num_outputs,
+                                                   value_type* output_buffer,
+                                                   atomicT* num_matches,
+                                                   OutputIt output_begin) noexcept;
 
     /**
      * @brief Flushes per-CG buffer into the output sequences.

From d8b3036e6a8a62c43eae86dd294ffd85f9386b4f Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Thu, 16 Sep 2021 14:05:30 -0400
Subject: [PATCH 222/267] CMake file cleanups

---
 benchmarks/CMakeLists.txt | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/benchmarks/CMakeLists.txt b/benchmarks/CMakeLists.txt
index 6220f908a..7b02316d9 100644
--- a/benchmarks/CMakeLists.txt
+++ b/benchmarks/CMakeLists.txt
@@ -19,10 +19,9 @@ CPMAddPackage(
   GIT_SHALLOW TRUE
 )
 
-set_target_properties(benchmark PROPERTIES CXX_STANDARD 17)
-
 ###################################################################################################
-# - compiler function -----------------------------------------------------------------------------
+### compiler function #############################################################################
+###################################################################################################
 
 ###################################################################################################
 function(ConfigureBench BENCH_NAME BENCH_SRC)

From a6fec80aea209b65bfcbdd2ce609f7e4af850c00 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Thu, 16 Sep 2021 16:49:27 -0400
Subject: [PATCH 223/267] Add linear probing unit tests

---
 tests/static_multimap/static_multimap_test.cu | 359 ++++++++----------
 1 file changed, 158 insertions(+), 201 deletions(-)

diff --git a/tests/static_multimap/static_multimap_test.cu b/tests/static_multimap/static_multimap_test.cu
index 870ae13c4..3796bf202 100644
--- a/tests/static_multimap/static_multimap_test.cu
+++ b/tests/static_multimap/static_multimap_test.cu
@@ -14,14 +14,17 @@
  * limitations under the License.
  */
 
+#include <algorithm>
+#include <limits>
+
+#include <catch2/catch.hpp>
+
 #include <thrust/count.h>
 #include <thrust/device_vector.h>
 #include <thrust/for_each.h>
 #include <thrust/iterator/discard_iterator.h>
-#include <algorithm>
-#include <catch2/catch.hpp>
+
 #include <cuco/static_multimap.cuh>
-#include <limits>
 
 namespace {
 namespace cg = cooperative_groups;
@@ -59,6 +62,7 @@ struct pair_equal {
 }  // namespace
 
 enum class dist_type { UNIQUE, DUAL, UNIFORM, GAUSSIAN };
+enum class probe_sequence { linear_probing, double_hashing };
 
 template <dist_type Dist, typename Key, typename OutputIt>
 static void generate_keys(OutputIt output_begin, OutputIt output_end)
@@ -84,50 +88,26 @@ static void generate_keys(OutputIt output_begin, OutputIt output_end)
   }
 }
 
-TEMPLATE_TEST_CASE_SIG("Each key appears twice",
-                       "",
-                       ((typename Key, typename Value, dist_type Dist), Key, Value, Dist),
-                       (int32_t, int32_t, dist_type::DUAL),
-                       (int32_t, int64_t, dist_type::DUAL),
-                       (int64_t, int64_t, dist_type::DUAL))
+template <typename Map, typename PairIt, typename KeyIt, typename ResultIt>
+__inline__ void test_multiplicity_two(
+  Map& map, PairIt pair_begin, KeyIt key_begin, ResultIt result_begin, std::size_t num_items)
 {
-  constexpr std::size_t num_items{400};
-  cuco::static_multimap<Key, Value> map{500, -1, -1};
-
-  std::vector<Key> h_keys(num_items);
-  std::vector<cuco::pair_type<Key, Value>> h_pairs(num_items);
-
-  generate_keys<Dist, Key>(h_keys.begin(), h_keys.end());
-
-  for (auto i = 0; i < num_items; ++i) {
-    Key key           = h_keys[i];
-    Value val         = i;
-    h_pairs[i].first  = key;
-    h_pairs[i].second = val;
-  }
-  thrust::device_vector<cuco::pair_type<Key, Value>> d_pairs(h_pairs);
-  thrust::device_vector<cuco::pair_type<Key, Value>> d_results(num_items);
-
-  // Get unique keys
-  std::set<Key> key_set(h_keys.begin(), h_keys.end());
-  std::vector<Key> h_unique_keys(key_set.begin(), key_set.end());
-  thrust::device_vector<Key> d_unique_keys(h_unique_keys);
-
-  thrust::device_vector<bool> d_contained(num_items / 2);
+  auto num_keys = num_items / 2;
+  thrust::device_vector<bool> d_contained(num_keys);
 
   SECTION("Non-inserted key/value pairs should not be contained.")
   {
-    map.contains(d_unique_keys.begin(), d_unique_keys.end(), d_contained.begin());
+    map.contains(key_begin, key_begin + num_keys, d_contained.begin());
 
     REQUIRE(
       none_of(d_contained.begin(), d_contained.end(), [] __device__(bool const& b) { return b; }));
   }
 
-  map.insert(d_pairs.begin(), d_pairs.end());
+  map.insert(pair_begin, pair_begin + num_items);
 
   SECTION("All inserted key/value pairs should be contained.")
   {
-    map.contains(d_unique_keys.begin(), d_unique_keys.end(), d_contained.begin());
+    map.contains(key_begin, key_begin + num_keys, d_contained.begin());
 
     REQUIRE(
       all_of(d_contained.begin(), d_contained.end(), [] __device__(bool const& b) { return b; }));
@@ -136,12 +116,12 @@ TEMPLATE_TEST_CASE_SIG("Each key appears twice",
   SECTION("Total count should be equal to the number of inserted pairs.")
   {
     // Count matching keys
-    auto num = map.count(d_unique_keys.begin(), d_unique_keys.end());
+    auto num = map.count(key_begin, key_begin + num_keys);
 
     REQUIRE(num == num_items);
 
-    auto output_begin = d_results.data().get();
-    auto output_end   = map.retrieve(d_unique_keys.begin(), d_unique_keys.end(), output_begin);
+    auto output_begin = result_begin;
+    auto output_end   = map.retrieve(key_begin, key_begin + num_keys, output_begin);
     auto size         = thrust::distance(output_begin, output_end);
 
     REQUIRE(size == num_items);
@@ -149,111 +129,116 @@ TEMPLATE_TEST_CASE_SIG("Each key appears twice",
 
   SECTION("count and count_outer should return the same value.")
   {
-    auto num       = map.count(d_unique_keys.begin(), d_unique_keys.end());
-    auto num_outer = map.count_outer(d_unique_keys.begin(), d_unique_keys.end());
+    auto num       = map.count(key_begin, key_begin + num_keys);
+    auto num_outer = map.count_outer(key_begin, key_begin + num_keys);
 
     REQUIRE(num == num_outer);
   }
 
-  SECTION("Output size of retrieve and retrieve_outer should be the same.")
+  SECTION("Output of retrieve and retrieve_outer should be the same.")
   {
-    auto output_begin = d_results.data().get();
-    auto output_end   = map.retrieve(d_unique_keys.begin(), d_unique_keys.end(), output_begin);
+    auto output_begin = result_begin;
+    auto output_end   = map.retrieve(key_begin, key_begin + num_keys, output_begin);
     auto size         = thrust::distance(output_begin, output_end);
 
-    output_end      = map.retrieve_outer(d_unique_keys.begin(), d_unique_keys.end(), output_begin);
+    output_end      = map.retrieve_outer(key_begin, key_begin + num_keys, output_begin);
     auto size_outer = thrust::distance(output_begin, output_end);
 
     REQUIRE(size == size_outer);
   }
 }
 
-TEMPLATE_TEST_CASE_SIG("Handling of non-matches",
-                       "",
-                       ((typename Key, typename Value, dist_type Dist), Key, Value, Dist),
-                       (int32_t, int32_t, dist_type::UNIQUE),
-                       (int32_t, int64_t, dist_type::UNIQUE),
-                       (int64_t, int64_t, dist_type::UNIQUE))
+TEMPLATE_TEST_CASE_SIG(
+  "Multiplicity equals two",
+  "",
+  ((typename Key, typename Value, dist_type Dist, probe_sequence Probe), Key, Value, Dist, Probe),
+  (int32_t, int32_t, dist_type::DUAL, probe_sequence::linear_probing),
+  (int32_t, int64_t, dist_type::DUAL, probe_sequence::linear_probing),
+  (int64_t, int64_t, dist_type::DUAL, probe_sequence::linear_probing),
+  (int32_t, int32_t, dist_type::DUAL, probe_sequence::double_hashing),
+  (int32_t, int64_t, dist_type::DUAL, probe_sequence::double_hashing),
+  (int64_t, int64_t, dist_type::DUAL, probe_sequence::double_hashing))
 {
-  constexpr std::size_t num_keys{1'000'000};
-  cuco::static_multimap<Key, Value> map{2'000'000, -1, -1};
+  constexpr std::size_t num_items{4};
 
-  std::vector<Key> h_keys(num_keys);
-  std::vector<cuco::pair_type<Key, Value>> h_pairs(num_keys);
+  std::vector<Key> h_keys(num_items);
+  std::vector<cuco::pair_type<Key, Value>> h_pairs(num_items);
 
   generate_keys<Dist, Key>(h_keys.begin(), h_keys.end());
 
-  for (auto i = 0; i < num_keys; ++i) {
-    h_pairs[i].first  = h_keys[i] / 2;
-    h_pairs[i].second = h_keys[i];
+  for (auto i = 0; i < num_items; ++i) {
+    Key key           = h_keys[i];
+    Value val         = i;
+    h_pairs[i].first  = key;
+    h_pairs[i].second = val;
   }
-
-  thrust::device_vector<Key> d_keys(h_keys);
   thrust::device_vector<cuco::pair_type<Key, Value>> d_pairs(h_pairs);
 
-  map.insert(d_pairs.begin(), d_pairs.end());
+  // Get unique keys
+  std::set<Key> key_set(h_keys.begin(), h_keys.end());
+  std::vector<Key> h_unique_keys(key_set.begin(), key_set.end());
+  thrust::device_vector<Key> d_unique_keys(h_unique_keys);
+
+  thrust::device_vector<cuco::pair_type<Key, Value>> d_results(num_items);
+
+  if constexpr (Probe == probe_sequence::linear_probing) {
+    cuco::static_multimap<Key, Value, cuco::detail::linear_probing<Key, Value, 1>> map{5, -1, -1};
+    test_multiplicity_two(
+      map, d_pairs.begin(), d_unique_keys.begin(), d_results.begin(), num_items);
+  }
+  if constexpr (Probe == probe_sequence::double_hashing) {
+    cuco::static_multimap<Key, Value> map{5, -1, -1};
+    test_multiplicity_two(
+      map, d_pairs.begin(), d_unique_keys.begin(), d_results.begin(), num_items);
+  }
+}
+
+template <typename Key, typename Value, typename Map, typename PairIt, typename KeyIt>
+__inline__ void test_non_matches(Map& map, PairIt pair_begin, KeyIt key_begin, std::size_t num_keys)
+{
+  map.insert(pair_begin, pair_begin + num_keys);
 
   SECTION("Output of count and retrieve should be coherent.")
   {
-    auto num = map.count(d_keys.begin(), d_keys.end());
+    auto num = map.count(key_begin, key_begin + num_keys);
     thrust::device_vector<cuco::pair_type<Key, Value>> d_results(num);
 
     REQUIRE(num == num_keys);
 
     auto output_begin = d_results.data().get();
-    auto output_end   = map.retrieve(d_keys.begin(), d_keys.end(), output_begin);
+    auto output_end   = map.retrieve(key_begin, key_begin + num_keys, output_begin);
     auto size         = thrust::distance(output_begin, output_end);
 
-    REQUIRE(num == size);
+    REQUIRE(size == num_keys);
   }
 
   SECTION("Output of count_outer and retrieve_outer should be coherent.")
   {
-    auto num = map.count_outer(d_keys.begin(), d_keys.end());
+    auto num = map.count_outer(key_begin, key_begin + num_keys);
     thrust::device_vector<cuco::pair_type<Key, Value>> d_results(num);
 
     REQUIRE(num == (num_keys + num_keys / 2));
 
     auto output_begin = d_results.data().get();
-    auto output_end   = map.retrieve_outer(d_keys.begin(), d_keys.end(), output_begin);
+    auto output_end   = map.retrieve_outer(key_begin, key_begin + num_keys, output_begin);
     auto size         = thrust::distance(output_begin, output_end);
 
-    REQUIRE(num == size);
-  }
-
-  SECTION("count_outer handles non-matches wile count doesn't.")
-  {
-    auto num_outer = map.count_outer(d_keys.begin(), d_keys.end());
-    auto num       = map.count(d_keys.begin(), d_keys.end());
-
-    REQUIRE(num_outer == (num + num_keys / 2));
-  }
-
-  SECTION("retrieve_outer handles non-matches wile retrieve doesn't.")
-  {
-    auto num_outer = map.count_outer(d_keys.begin(), d_keys.end());
-    thrust::device_vector<cuco::pair_type<Key, Value>> d_results(num_outer);
-
-    auto output_begin = d_results.data().get();
-    auto output_end   = map.retrieve(d_keys.begin(), d_keys.end(), output_begin);
-    auto size         = thrust::distance(output_begin, output_end);
-
-    output_end      = map.retrieve_outer(d_keys.begin(), d_keys.end(), output_begin);
-    auto size_outer = thrust::distance(output_begin, output_end);
-
-    REQUIRE(size_outer == (size + num_keys / 2));
+    REQUIRE(size == (num_keys + num_keys / 2));
   }
 }
 
-TEMPLATE_TEST_CASE_SIG("Tests of insert_if",
-                       "",
-                       ((typename Key, typename Value, dist_type Dist), Key, Value, Dist),
-                       (int32_t, int32_t, dist_type::UNIQUE),
-                       (int32_t, int64_t, dist_type::UNIQUE),
-                       (int64_t, int64_t, dist_type::UNIQUE))
+TEMPLATE_TEST_CASE_SIG(
+  "Tests of non-matches",
+  "",
+  ((typename Key, typename Value, dist_type Dist, probe_sequence Probe), Key, Value, Dist, Probe),
+  (int32_t, int32_t, dist_type::UNIQUE, probe_sequence::linear_probing),
+  (int32_t, int64_t, dist_type::UNIQUE, probe_sequence::linear_probing),
+  (int64_t, int64_t, dist_type::UNIQUE, probe_sequence::linear_probing),
+  (int32_t, int32_t, dist_type::UNIQUE, probe_sequence::double_hashing),
+  (int32_t, int64_t, dist_type::UNIQUE, probe_sequence::double_hashing),
+  (int64_t, int64_t, dist_type::UNIQUE, probe_sequence::double_hashing))
 {
   constexpr std::size_t num_keys{1'000'000};
-  cuco::static_multimap<Key, Value> map{2'000'000, -1, -1};
 
   std::vector<Key> h_keys(num_keys);
   std::vector<cuco::pair_type<Key, Value>> h_pairs(num_keys);
@@ -261,59 +246,87 @@ TEMPLATE_TEST_CASE_SIG("Tests of insert_if",
   generate_keys<Dist, Key>(h_keys.begin(), h_keys.end());
 
   for (auto i = 0; i < num_keys; ++i) {
-    h_pairs[i].first  = h_keys[i];
+    h_pairs[i].first  = h_keys[i] / 2;
     h_pairs[i].second = h_keys[i];
   }
 
   thrust::device_vector<Key> d_keys(h_keys);
   thrust::device_vector<cuco::pair_type<Key, Value>> d_pairs(h_pairs);
 
+  if constexpr (Probe == probe_sequence::linear_probing) {
+    cuco::static_multimap<Key, Value, cuco::detail::linear_probing<Key, Value, 1>> map{
+      num_keys * 2, -1, -1};
+    test_non_matches<Key, Value>(map, d_pairs.begin(), d_keys.begin(), num_keys);
+  }
+  if constexpr (Probe == probe_sequence::double_hashing) {
+    cuco::static_multimap<Key, Value> map{num_keys * 2, -1, -1};
+    test_non_matches<Key, Value>(map, d_pairs.begin(), d_keys.begin(), num_keys);
+  }
+}
+
+template <typename Key, typename Map, typename PairIt, typename KeyIt>
+__inline__ void test_insert_if(Map& map, PairIt pair_begin, KeyIt key_begin, std::size_t size)
+{
+  // 50% insertion
   auto pred_lambda = [] __device__(Key k) { return k % 2 == 0; };
-  map.insert_if(d_pairs.begin(), d_pairs.begin() + d_pairs.size(), d_keys.begin(), pred_lambda);
 
-  auto num = map.count(d_keys.begin(), d_keys.end());
+  map.insert_if(pair_begin, pair_begin + size, key_begin, pred_lambda);
 
-  REQUIRE(num * 2 == num_keys);
+  auto num = map.count(key_begin, key_begin + size);
+
+  REQUIRE(num * 2 == size);
 }
 
-TEMPLATE_TEST_CASE_SIG("Evaluation of pair functions",
-                       "",
-                       ((typename Key, typename Value, dist_type Dist), Key, Value, Dist),
-                       (int32_t, int32_t, dist_type::UNIQUE),
-                       (int32_t, int64_t, dist_type::UNIQUE),
-                       (int64_t, int64_t, dist_type::UNIQUE))
+TEMPLATE_TEST_CASE_SIG(
+  "Tests of insert_if",
+  "",
+  ((typename Key, typename Value, dist_type Dist, probe_sequence Probe), Key, Value, Dist, Probe),
+  (int32_t, int32_t, dist_type::UNIQUE, probe_sequence::linear_probing),
+  (int32_t, int64_t, dist_type::UNIQUE, probe_sequence::linear_probing),
+  (int64_t, int64_t, dist_type::UNIQUE, probe_sequence::linear_probing),
+  (int32_t, int32_t, dist_type::UNIQUE, probe_sequence::double_hashing),
+  (int32_t, int64_t, dist_type::UNIQUE, probe_sequence::double_hashing),
+  (int64_t, int64_t, dist_type::UNIQUE, probe_sequence::double_hashing))
 {
-  constexpr std::size_t num_pairs{5'000'000};
-  cuco::static_multimap<Key, Value> map{10'000'000, -1, -1};
+  constexpr std::size_t num_keys{1'000};
 
-  std::vector<Key> h_keys(num_pairs);
-  std::vector<cuco::pair_type<Key, Value>> h_pairs(num_pairs);
+  std::vector<Key> h_keys(num_keys);
+  std::vector<cuco::pair_type<Key, Value>> h_pairs(num_keys);
 
   generate_keys<Dist, Key>(h_keys.begin(), h_keys.end());
 
-  for (auto i = 0; i < num_pairs; ++i) {
-    h_pairs[i].first  = h_keys[i] / 2;
+  for (auto i = 0; i < num_keys; ++i) {
+    h_pairs[i].first  = h_keys[i];
     h_pairs[i].second = h_keys[i];
   }
 
   thrust::device_vector<Key> d_keys(h_keys);
   thrust::device_vector<cuco::pair_type<Key, Value>> d_pairs(h_pairs);
 
+  if constexpr (Probe == probe_sequence::linear_probing) {
+    cuco::static_multimap<Key, Value, cuco::detail::linear_probing<Key, Value, 1>> map{
+      num_keys * 2, -1, -1};
+    test_insert_if<Key>(map, d_pairs.begin(), d_keys.begin(), num_keys);
+  }
+  if constexpr (Probe == probe_sequence::double_hashing) {
+    cuco::static_multimap<Key, Value> map{num_keys * 2, -1, -1};
+    test_insert_if<Key>(map, d_pairs.begin(), d_keys.begin(), num_keys);
+  }
+}
+
+template <typename Map, typename Key, typename Value>
+__inline__ void test_pair_functions(Map& map,
+                                    std::vector<cuco::pair_type<Key, Value>>& h_pairs,
+                                    thrust::device_vector<cuco::pair_type<Key, Value>>& d_pairs,
+                                    std::size_t num_pairs)
+{
   map.insert(d_pairs.begin(), d_pairs.end());
 
   for (auto i = 0; i < num_pairs; ++i) {
-    h_pairs[i].first = h_keys[i];
+    h_pairs[i].first = i;
   }
   d_pairs = h_pairs;
 
-  SECTION("pair_count_outer handles non-matches wile pair_count doesn't.")
-  {
-    auto num_outer = map.pair_count_outer(d_pairs.begin(), d_pairs.end(), pair_equal<Key, Value>{});
-    auto num       = map.pair_count(d_pairs.begin(), d_pairs.end(), pair_equal<Key, Value>{});
-
-    REQUIRE(num_outer == (num + num_pairs / 2));
-  }
-
   SECTION("Output of pair_count and pair_retrieve should be coherent.")
   {
     auto num = map.pair_count(d_pairs.begin(), d_pairs.end(), pair_equal<Key, Value>{});
@@ -328,7 +341,7 @@ TEMPLATE_TEST_CASE_SIG("Evaluation of pair functions",
     auto size = map.pair_retrieve(
       d_pairs.begin(), d_pairs.end(), out1_zip, out2_zip, pair_equal<Key, Value>{});
 
-    REQUIRE(num == size);
+    REQUIRE(size == num_pairs);
   }
 
   SECTION("Output of pair_count_outer and pair_retrieve_outer should be coherent.")
@@ -340,31 +353,34 @@ TEMPLATE_TEST_CASE_SIG("Evaluation of pair functions",
     auto out2_zip = thrust::make_zip_iterator(
       thrust::make_tuple(thrust::make_discard_iterator(), thrust::make_discard_iterator()));
 
-    REQUIRE(num == num_pairs * 1.5);
+    REQUIRE(num == (num_pairs + num_pairs / 2));
 
     auto size = map.pair_retrieve_outer(
       d_pairs.begin(), d_pairs.end(), out1_zip, out2_zip, pair_equal<Key, Value>{});
 
-    REQUIRE(num == size);
+    REQUIRE(size == (num_pairs + num_pairs / 2));
   }
 }
 
-TEMPLATE_TEST_CASE_SIG("Evaluation of small test cases",
-                       "",
-                       ((typename Key, typename Value, dist_type Dist), Key, Value, Dist),
-                       (int32_t, int32_t, dist_type::UNIQUE),
-                       (int32_t, int64_t, dist_type::UNIQUE),
-                       (int64_t, int64_t, dist_type::UNIQUE))
+TEMPLATE_TEST_CASE_SIG(
+  "Tests of pair functions",
+  "",
+  ((typename Key, typename Value, dist_type Dist, probe_sequence Probe), Key, Value, Dist, Probe),
+  (int32_t, int32_t, dist_type::UNIQUE, probe_sequence::linear_probing),
+  (int32_t, int64_t, dist_type::UNIQUE, probe_sequence::linear_probing),
+  (int64_t, int64_t, dist_type::UNIQUE, probe_sequence::linear_probing),
+  (int32_t, int32_t, dist_type::UNIQUE, probe_sequence::double_hashing),
+  (int32_t, int64_t, dist_type::UNIQUE, probe_sequence::double_hashing),
+  (int64_t, int64_t, dist_type::UNIQUE, probe_sequence::double_hashing))
 {
-  constexpr std::size_t num_items{3};
-  cuco::static_multimap<Key, Value> map{2 * num_items, -1, -1};
+  constexpr std::size_t num_pairs{4};
 
-  std::vector<Key> h_keys(num_items);
-  std::vector<cuco::pair_type<Key, Value>> h_pairs(num_items);
+  std::vector<Key> h_keys(num_pairs);
+  std::vector<cuco::pair_type<Key, Value>> h_pairs(num_pairs);
 
   generate_keys<Dist, Key>(h_keys.begin(), h_keys.end());
 
-  for (auto i = 0; i < num_items; ++i) {
+  for (auto i = 0; i < num_pairs; ++i) {
     h_pairs[i].first  = h_keys[i] / 2;
     h_pairs[i].second = h_keys[i];
   }
@@ -372,72 +388,13 @@ TEMPLATE_TEST_CASE_SIG("Evaluation of small test cases",
   thrust::device_vector<Key> d_keys(h_keys);
   thrust::device_vector<cuco::pair_type<Key, Value>> d_pairs(h_pairs);
 
-  map.insert(d_pairs.begin(), d_pairs.end());
-
-  for (auto i = 0; i < num_items; ++i) {
-    h_pairs[i].first = h_keys[i];
-  }
-  d_pairs = h_pairs;
-
-  SECTION("Output of count and retrieve should be coherent.")
-  {
-    auto num = map.count(d_keys.begin(), d_keys.end());
-    thrust::device_vector<cuco::pair_type<Key, Value>> d_results(num);
-
-    REQUIRE(num == num_items);
-
-    auto output_begin = d_results.data().get();
-    auto output_end   = map.retrieve(d_keys.begin(), d_keys.end(), output_begin);
-    auto size         = thrust::distance(output_begin, output_end);
-
-    REQUIRE(num == size);
+  if constexpr (Probe == probe_sequence::linear_probing) {
+    cuco::static_multimap<Key, Value, cuco::detail::linear_probing<Key, Value, 1>> map{
+      num_pairs * 2, -1, -1};
+    test_pair_functions(map, h_pairs, d_pairs, num_pairs);
   }
-
-  SECTION("Output of count_outer and retrieve_outer should be coherent.")
-  {
-    auto num = map.count_outer(d_keys.begin(), d_keys.end());
-    thrust::device_vector<cuco::pair_type<Key, Value>> d_results(num);
-
-    REQUIRE(num == num_items + 1);
-
-    auto output_begin = d_results.data().get();
-    auto output_end   = map.retrieve_outer(d_keys.begin(), d_keys.end(), output_begin);
-    auto size         = thrust::distance(output_begin, output_end);
-
-    REQUIRE(num == size);
-  }
-
-  SECTION("Output of pair_count and pair_retrieve should be coherent.")
-  {
-    auto num = map.pair_count(d_pairs.begin(), d_pairs.end(), pair_equal<Key, Value>{});
-
-    auto out1_zip = thrust::make_zip_iterator(
-      thrust::make_tuple(thrust::make_discard_iterator(), thrust::make_discard_iterator()));
-    auto out2_zip = thrust::make_zip_iterator(
-      thrust::make_tuple(thrust::make_discard_iterator(), thrust::make_discard_iterator()));
-
-    REQUIRE(num == num_items);
-
-    auto size = map.pair_retrieve(
-      d_pairs.begin(), d_pairs.end(), out1_zip, out2_zip, pair_equal<Key, Value>{});
-
-    REQUIRE(num == size);
-  }
-
-  SECTION("Output of pair_count_outer and pair_retrieve_outer should be coherent.")
-  {
-    auto num = map.pair_count_outer(d_pairs.begin(), d_pairs.end(), pair_equal<Key, Value>{});
-
-    auto out1_zip = thrust::make_zip_iterator(
-      thrust::make_tuple(thrust::make_discard_iterator(), thrust::make_discard_iterator()));
-    auto out2_zip = thrust::make_zip_iterator(
-      thrust::make_tuple(thrust::make_discard_iterator(), thrust::make_discard_iterator()));
-
-    REQUIRE(num == num_items + 1);
-
-    auto size = map.pair_retrieve_outer(
-      d_pairs.begin(), d_pairs.end(), out1_zip, out2_zip, pair_equal<Key, Value>{});
-
-    REQUIRE(num == size);
+  if constexpr (Probe == probe_sequence::double_hashing) {
+    cuco::static_multimap<Key, Value> map{num_pairs * 2, -1, -1};
+    test_pair_functions(map, h_pairs, d_pairs, num_pairs);
   }
 }

From 3db65221e4735e2ccdeaf1d86ee071f7904a17f2 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Thu, 16 Sep 2021 18:07:44 -0400
Subject: [PATCH 224/267] Use thrust algorithms instead of naive for loops

---
 tests/static_multimap/static_multimap_test.cu | 235 ++++++++----------
 1 file changed, 99 insertions(+), 136 deletions(-)

diff --git a/tests/static_multimap/static_multimap_test.cu b/tests/static_multimap/static_multimap_test.cu
index 3796bf202..41203e7c0 100644
--- a/tests/static_multimap/static_multimap_test.cu
+++ b/tests/static_multimap/static_multimap_test.cu
@@ -61,33 +61,8 @@ struct pair_equal {
 
 }  // namespace
 
-enum class dist_type { UNIQUE, DUAL, UNIFORM, GAUSSIAN };
 enum class probe_sequence { linear_probing, double_hashing };
 
-template <dist_type Dist, typename Key, typename OutputIt>
-static void generate_keys(OutputIt output_begin, OutputIt output_end)
-{
-  auto num_items = std::distance(output_begin, output_end);
-
-  std::random_device rd;
-  std::mt19937 gen{rd()};
-
-  switch (Dist) {
-    case dist_type::UNIQUE: {
-      for (auto i = 0; i < num_items; ++i) {
-        output_begin[i] = i;
-      }
-      break;
-    }
-    case dist_type::DUAL: {
-      for (auto i = 0; i < num_items; ++i) {
-        output_begin[i] = i % (num_items / 2);
-      }
-      break;
-    }
-  }
-}
-
 template <typename Map, typename PairIt, typename KeyIt, typename ResultIt>
 __inline__ void test_multiplicity_two(
   Map& map, PairIt pair_begin, KeyIt key_begin, ResultIt result_begin, std::size_t num_items)
@@ -148,48 +123,40 @@ __inline__ void test_multiplicity_two(
   }
 }
 
-TEMPLATE_TEST_CASE_SIG(
-  "Multiplicity equals two",
-  "",
-  ((typename Key, typename Value, dist_type Dist, probe_sequence Probe), Key, Value, Dist, Probe),
-  (int32_t, int32_t, dist_type::DUAL, probe_sequence::linear_probing),
-  (int32_t, int64_t, dist_type::DUAL, probe_sequence::linear_probing),
-  (int64_t, int64_t, dist_type::DUAL, probe_sequence::linear_probing),
-  (int32_t, int32_t, dist_type::DUAL, probe_sequence::double_hashing),
-  (int32_t, int64_t, dist_type::DUAL, probe_sequence::double_hashing),
-  (int64_t, int64_t, dist_type::DUAL, probe_sequence::double_hashing))
+TEMPLATE_TEST_CASE_SIG("Multiplicity equals two",
+                       "",
+                       ((typename Key, typename Value, probe_sequence Probe), Key, Value, Probe),
+                       (int32_t, int32_t, probe_sequence::linear_probing),
+                       (int32_t, int64_t, probe_sequence::linear_probing),
+                       (int64_t, int64_t, probe_sequence::linear_probing),
+                       (int32_t, int32_t, probe_sequence::double_hashing),
+                       (int32_t, int64_t, probe_sequence::double_hashing),
+                       (int64_t, int64_t, probe_sequence::double_hashing))
 {
   constexpr std::size_t num_items{4};
 
-  std::vector<Key> h_keys(num_items);
-  std::vector<cuco::pair_type<Key, Value>> h_pairs(num_items);
-
-  generate_keys<Dist, Key>(h_keys.begin(), h_keys.end());
+  thrust::device_vector<Key> d_keys(num_items / 2);
+  thrust::device_vector<cuco::pair_type<Key, Value>> d_pairs(num_items);
 
-  for (auto i = 0; i < num_items; ++i) {
-    Key key           = h_keys[i];
-    Value val         = i;
-    h_pairs[i].first  = key;
-    h_pairs[i].second = val;
-  }
-  thrust::device_vector<cuco::pair_type<Key, Value>> d_pairs(h_pairs);
-
-  // Get unique keys
-  std::set<Key> key_set(h_keys.begin(), h_keys.end());
-  std::vector<Key> h_unique_keys(key_set.begin(), key_set.end());
-  thrust::device_vector<Key> d_unique_keys(h_unique_keys);
+  thrust::sequence(thrust::device, d_keys.begin(), d_keys.end());
+  // multiplicity = 2
+  thrust::transform(thrust::device,
+                    thrust::counting_iterator<int>(0),
+                    thrust::counting_iterator<int>(num_items),
+                    d_pairs.begin(),
+                    [] __device__(auto i) {
+                      return cuco::pair_type<Key, Value>{i / 2, i};
+                    });
 
   thrust::device_vector<cuco::pair_type<Key, Value>> d_results(num_items);
 
   if constexpr (Probe == probe_sequence::linear_probing) {
     cuco::static_multimap<Key, Value, cuco::detail::linear_probing<Key, Value, 1>> map{5, -1, -1};
-    test_multiplicity_two(
-      map, d_pairs.begin(), d_unique_keys.begin(), d_results.begin(), num_items);
+    test_multiplicity_two(map, d_pairs.begin(), d_keys.begin(), d_results.begin(), num_items);
   }
   if constexpr (Probe == probe_sequence::double_hashing) {
     cuco::static_multimap<Key, Value> map{5, -1, -1};
-    test_multiplicity_two(
-      map, d_pairs.begin(), d_unique_keys.begin(), d_results.begin(), num_items);
+    test_multiplicity_two(map, d_pairs.begin(), d_keys.begin(), d_results.begin(), num_items);
   }
 }
 
@@ -227,31 +194,30 @@ __inline__ void test_non_matches(Map& map, PairIt pair_begin, KeyIt key_begin, s
   }
 }
 
-TEMPLATE_TEST_CASE_SIG(
-  "Tests of non-matches",
-  "",
-  ((typename Key, typename Value, dist_type Dist, probe_sequence Probe), Key, Value, Dist, Probe),
-  (int32_t, int32_t, dist_type::UNIQUE, probe_sequence::linear_probing),
-  (int32_t, int64_t, dist_type::UNIQUE, probe_sequence::linear_probing),
-  (int64_t, int64_t, dist_type::UNIQUE, probe_sequence::linear_probing),
-  (int32_t, int32_t, dist_type::UNIQUE, probe_sequence::double_hashing),
-  (int32_t, int64_t, dist_type::UNIQUE, probe_sequence::double_hashing),
-  (int64_t, int64_t, dist_type::UNIQUE, probe_sequence::double_hashing))
+TEMPLATE_TEST_CASE_SIG("Tests of non-matches",
+                       "",
+                       ((typename Key, typename Value, probe_sequence Probe), Key, Value, Probe),
+                       (int32_t, int32_t, probe_sequence::linear_probing),
+                       (int32_t, int64_t, probe_sequence::linear_probing),
+                       (int64_t, int64_t, probe_sequence::linear_probing),
+                       (int32_t, int32_t, probe_sequence::double_hashing),
+                       (int32_t, int64_t, probe_sequence::double_hashing),
+                       (int64_t, int64_t, probe_sequence::double_hashing))
 {
   constexpr std::size_t num_keys{1'000'000};
 
-  std::vector<Key> h_keys(num_keys);
-  std::vector<cuco::pair_type<Key, Value>> h_pairs(num_keys);
-
-  generate_keys<Dist, Key>(h_keys.begin(), h_keys.end());
-
-  for (auto i = 0; i < num_keys; ++i) {
-    h_pairs[i].first  = h_keys[i] / 2;
-    h_pairs[i].second = h_keys[i];
-  }
+  thrust::device_vector<Key> d_keys(num_keys);
+  thrust::device_vector<cuco::pair_type<Key, Value>> d_pairs(num_keys);
 
-  thrust::device_vector<Key> d_keys(h_keys);
-  thrust::device_vector<cuco::pair_type<Key, Value>> d_pairs(h_pairs);
+  thrust::sequence(thrust::device, d_keys.begin(), d_keys.end());
+  // multiplicity = 2
+  thrust::transform(thrust::device,
+                    thrust::counting_iterator<int>(0),
+                    thrust::counting_iterator<int>(num_keys),
+                    d_pairs.begin(),
+                    [] __device__(auto i) {
+                      return cuco::pair_type<Key, Value>{i / 2, i};
+                    });
 
   if constexpr (Probe == probe_sequence::linear_probing) {
     cuco::static_multimap<Key, Value, cuco::detail::linear_probing<Key, Value, 1>> map{
@@ -277,31 +243,30 @@ __inline__ void test_insert_if(Map& map, PairIt pair_begin, KeyIt key_begin, std
   REQUIRE(num * 2 == size);
 }
 
-TEMPLATE_TEST_CASE_SIG(
-  "Tests of insert_if",
-  "",
-  ((typename Key, typename Value, dist_type Dist, probe_sequence Probe), Key, Value, Dist, Probe),
-  (int32_t, int32_t, dist_type::UNIQUE, probe_sequence::linear_probing),
-  (int32_t, int64_t, dist_type::UNIQUE, probe_sequence::linear_probing),
-  (int64_t, int64_t, dist_type::UNIQUE, probe_sequence::linear_probing),
-  (int32_t, int32_t, dist_type::UNIQUE, probe_sequence::double_hashing),
-  (int32_t, int64_t, dist_type::UNIQUE, probe_sequence::double_hashing),
-  (int64_t, int64_t, dist_type::UNIQUE, probe_sequence::double_hashing))
+TEMPLATE_TEST_CASE_SIG("Tests of insert_if",
+                       "",
+                       ((typename Key, typename Value, probe_sequence Probe), Key, Value, Probe),
+                       (int32_t, int32_t, probe_sequence::linear_probing),
+                       (int32_t, int64_t, probe_sequence::linear_probing),
+                       (int64_t, int64_t, probe_sequence::linear_probing),
+                       (int32_t, int32_t, probe_sequence::double_hashing),
+                       (int32_t, int64_t, probe_sequence::double_hashing),
+                       (int64_t, int64_t, probe_sequence::double_hashing))
 {
   constexpr std::size_t num_keys{1'000};
 
-  std::vector<Key> h_keys(num_keys);
-  std::vector<cuco::pair_type<Key, Value>> h_pairs(num_keys);
+  thrust::device_vector<Key> d_keys(num_keys);
+  thrust::device_vector<cuco::pair_type<Key, Value>> d_pairs(num_keys);
 
-  generate_keys<Dist, Key>(h_keys.begin(), h_keys.end());
-
-  for (auto i = 0; i < num_keys; ++i) {
-    h_pairs[i].first  = h_keys[i];
-    h_pairs[i].second = h_keys[i];
-  }
-
-  thrust::device_vector<Key> d_keys(h_keys);
-  thrust::device_vector<cuco::pair_type<Key, Value>> d_pairs(h_pairs);
+  thrust::sequence(thrust::device, d_keys.begin(), d_keys.end());
+  // multiplicity = 1
+  thrust::transform(thrust::device,
+                    thrust::counting_iterator<int>(0),
+                    thrust::counting_iterator<int>(num_keys),
+                    d_pairs.begin(),
+                    [] __device__(auto i) {
+                      return cuco::pair_type<Key, Value>{i, i};
+                    });
 
   if constexpr (Probe == probe_sequence::linear_probing) {
     cuco::static_multimap<Key, Value, cuco::detail::linear_probing<Key, Value, 1>> map{
@@ -314,22 +279,24 @@ TEMPLATE_TEST_CASE_SIG(
   }
 }
 
-template <typename Map, typename Key, typename Value>
-__inline__ void test_pair_functions(Map& map,
-                                    std::vector<cuco::pair_type<Key, Value>>& h_pairs,
-                                    thrust::device_vector<cuco::pair_type<Key, Value>>& d_pairs,
-                                    std::size_t num_pairs)
+template <typename Key, typename Value, typename Map, typename PairIt>
+__inline__ void test_pair_functions(Map& map, PairIt pair_begin, std::size_t num_pairs)
 {
-  map.insert(d_pairs.begin(), d_pairs.end());
-
-  for (auto i = 0; i < num_pairs; ++i) {
-    h_pairs[i].first = i;
-  }
-  d_pairs = h_pairs;
+  map.insert(pair_begin, pair_begin + num_pairs);
+  cudaStreamSynchronize(0);
+
+  // query pair matching rate = 50%
+  thrust::transform(thrust::device,
+                    thrust::counting_iterator<int>(0),
+                    thrust::counting_iterator<int>(num_pairs),
+                    pair_begin,
+                    [] __device__(auto i) {
+                      return cuco::pair_type<Key, Value>{i, i};
+                    });
 
   SECTION("Output of pair_count and pair_retrieve should be coherent.")
   {
-    auto num = map.pair_count(d_pairs.begin(), d_pairs.end(), pair_equal<Key, Value>{});
+    auto num = map.pair_count(pair_begin, pair_begin + num_pairs, pair_equal<Key, Value>{});
 
     auto out1_zip = thrust::make_zip_iterator(
       thrust::make_tuple(thrust::make_discard_iterator(), thrust::make_discard_iterator()));
@@ -339,14 +306,14 @@ __inline__ void test_pair_functions(Map& map,
     REQUIRE(num == num_pairs);
 
     auto size = map.pair_retrieve(
-      d_pairs.begin(), d_pairs.end(), out1_zip, out2_zip, pair_equal<Key, Value>{});
+      pair_begin, pair_begin + num_pairs, out1_zip, out2_zip, pair_equal<Key, Value>{});
 
     REQUIRE(size == num_pairs);
   }
 
   SECTION("Output of pair_count_outer and pair_retrieve_outer should be coherent.")
   {
-    auto num = map.pair_count_outer(d_pairs.begin(), d_pairs.end(), pair_equal<Key, Value>{});
+    auto num = map.pair_count_outer(pair_begin, pair_begin + num_pairs, pair_equal<Key, Value>{});
 
     auto out1_zip = thrust::make_zip_iterator(
       thrust::make_tuple(thrust::make_discard_iterator(), thrust::make_discard_iterator()));
@@ -356,45 +323,41 @@ __inline__ void test_pair_functions(Map& map,
     REQUIRE(num == (num_pairs + num_pairs / 2));
 
     auto size = map.pair_retrieve_outer(
-      d_pairs.begin(), d_pairs.end(), out1_zip, out2_zip, pair_equal<Key, Value>{});
+      pair_begin, pair_begin + num_pairs, out1_zip, out2_zip, pair_equal<Key, Value>{});
 
     REQUIRE(size == (num_pairs + num_pairs / 2));
   }
 }
 
-TEMPLATE_TEST_CASE_SIG(
-  "Tests of pair functions",
-  "",
-  ((typename Key, typename Value, dist_type Dist, probe_sequence Probe), Key, Value, Dist, Probe),
-  (int32_t, int32_t, dist_type::UNIQUE, probe_sequence::linear_probing),
-  (int32_t, int64_t, dist_type::UNIQUE, probe_sequence::linear_probing),
-  (int64_t, int64_t, dist_type::UNIQUE, probe_sequence::linear_probing),
-  (int32_t, int32_t, dist_type::UNIQUE, probe_sequence::double_hashing),
-  (int32_t, int64_t, dist_type::UNIQUE, probe_sequence::double_hashing),
-  (int64_t, int64_t, dist_type::UNIQUE, probe_sequence::double_hashing))
+TEMPLATE_TEST_CASE_SIG("Tests of pair functions",
+                       "",
+                       ((typename Key, typename Value, probe_sequence Probe), Key, Value, Probe),
+                       (int32_t, int32_t, probe_sequence::linear_probing),
+                       (int32_t, int64_t, probe_sequence::linear_probing),
+                       (int64_t, int64_t, probe_sequence::linear_probing),
+                       (int32_t, int32_t, probe_sequence::double_hashing),
+                       (int32_t, int64_t, probe_sequence::double_hashing),
+                       (int64_t, int64_t, probe_sequence::double_hashing))
 {
   constexpr std::size_t num_pairs{4};
+  thrust::device_vector<cuco::pair_type<Key, Value>> d_pairs(num_pairs);
 
-  std::vector<Key> h_keys(num_pairs);
-  std::vector<cuco::pair_type<Key, Value>> h_pairs(num_pairs);
-
-  generate_keys<Dist, Key>(h_keys.begin(), h_keys.end());
-
-  for (auto i = 0; i < num_pairs; ++i) {
-    h_pairs[i].first  = h_keys[i] / 2;
-    h_pairs[i].second = h_keys[i];
-  }
-
-  thrust::device_vector<Key> d_keys(h_keys);
-  thrust::device_vector<cuco::pair_type<Key, Value>> d_pairs(h_pairs);
+  // pair multiplicity = 2
+  thrust::transform(thrust::device,
+                    thrust::counting_iterator<int>(0),
+                    thrust::counting_iterator<int>(num_pairs),
+                    d_pairs.begin(),
+                    [] __device__(auto i) {
+                      return cuco::pair_type<Key, Value>{i / 2, i};
+                    });
 
   if constexpr (Probe == probe_sequence::linear_probing) {
     cuco::static_multimap<Key, Value, cuco::detail::linear_probing<Key, Value, 1>> map{
       num_pairs * 2, -1, -1};
-    test_pair_functions(map, h_pairs, d_pairs, num_pairs);
+    test_pair_functions<Key, Value>(map, d_pairs.begin(), num_pairs);
   }
   if constexpr (Probe == probe_sequence::double_hashing) {
     cuco::static_multimap<Key, Value> map{num_pairs * 2, -1, -1};
-    test_pair_functions(map, h_pairs, d_pairs, num_pairs);
+    test_pair_functions<Key, Value>(map, d_pairs.begin(), num_pairs);
   }
 }

From be9def10b13ffecf858ded061493b128a8b2bfb0 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Thu, 16 Sep 2021 19:09:40 -0400
Subject: [PATCH 225/267] Minor correction in probe sequence

---
 include/cuco/detail/probe_sequences.cuh | 11 +++++------
 1 file changed, 5 insertions(+), 6 deletions(-)

diff --git a/include/cuco/detail/probe_sequences.cuh b/include/cuco/detail/probe_sequences.cuh
index 91b47a255..6e76b49e3 100644
--- a/include/cuco/detail/probe_sequences.cuh
+++ b/include/cuco/detail/probe_sequences.cuh
@@ -143,7 +143,7 @@ class linear_probing : public probe_sequence_base<Key, Value, CGSize, Scope> {
    */
   __host__ __device__ explicit linear_probing(iterator slots,
                                               std::size_t capacity,
-                                              Hash hash = cuco::detail::MurmurHash3_32<Key>{})
+                                              Hash hash = Hash{})
     : probe_sequence_base<Key, Value, CGSize, Scope>{slots, capacity}, hash_(hash)
   {
   }
@@ -244,11 +244,10 @@ class double_hashing : public probe_sequence_base<Key, Value, CGSize, Scope> {
    * @param hash1 First hasher to hash each key
    * @param hash2 Second hasher to determine step size
    */
-  __host__ __device__ explicit double_hashing(
-    iterator slots,
-    std::size_t capacity,
-    Hash1 hash1 = cuco::detail::MurmurHash3_32<Key>{},
-    Hash2 hash2 = cuco::detail::MurmurHash3_32<Key>{}) noexcept
+  __host__ __device__ explicit double_hashing(iterator slots,
+                                              std::size_t capacity,
+                                              Hash1 hash1 = Hash1{},
+                                              Hash2 hash2 = Hash2{}) noexcept
     : probe_sequence_base<Key, Value, CGSize, Scope>{slots, capacity}, hash1_{hash1}, hash2_{hash2}
   {
   }

From f7adf961bff8b69f9f90af8dbead0ce70a488fac Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Thu, 16 Sep 2021 20:00:50 -0400
Subject: [PATCH 226/267] Fix a bug: retrieve_impl takes key_equal callable

---
 include/cuco/detail/static_multimap.inl |  8 +++---
 include/cuco/static_multimap.cuh        | 33 +++++++++++--------------
 2 files changed, 18 insertions(+), 23 deletions(-)

diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index ca89ec89c..41fb05ceb 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -1543,7 +1543,7 @@ __device__ void static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::de
 {
   constexpr bool is_outer = false;
   retrieve_impl<buffer_size, is_outer>(
-    warp, g, k, warp_counter, output_buffer, num_matches, output_begin);
+    warp, g, k, warp_counter, output_buffer, num_matches, output_begin, key_equal);
 }
 
 template <typename Key,
@@ -1570,7 +1570,7 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::retri
 {
   constexpr bool is_outer = true;
   retrieve_impl<buffer_size, is_outer>(
-    warp, g, k, warp_counter, output_buffer, num_matches, output_begin);
+    warp, g, k, warp_counter, output_buffer, num_matches, output_begin, key_equal);
 }
 
 template <typename Key,
@@ -1595,7 +1595,7 @@ __device__ void static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::de
 {
   constexpr bool is_outer = false;
   retrieve_impl<cg_size, buffer_size, is_outer>(
-    g, k, cg_counter, output_buffer, num_matches, output_begin);
+    g, k, cg_counter, output_buffer, num_matches, output_begin, key_equal);
 }
 
 template <typename Key,
@@ -1621,7 +1621,7 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::retri
 {
   constexpr bool is_outer = true;
   retrieve_impl<cg_size, buffer_size, is_outer>(
-    g, k, cg_counter, output_buffer, num_matches, output_begin);
+    g, k, cg_counter, output_buffer, num_matches, output_begin, key_equal);
 }
 
 template <typename Key,
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index 82ab28c08..ead4ca325 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -942,9 +942,10 @@ class static_multimap {
      * @return A boolean indicating whether the key/value pair
      * containing `k` was inserted
      */
-    template <bool uses_vector_load, typename CG, typename KeyEqual = thrust::equal_to<key_type>>
-    __device__ std::enable_if_t<uses_vector_load, bool> contains_impl(
-      CG g, Key const& k, KeyEqual key_equal = KeyEqual{}) noexcept;
+    template <bool uses_vector_load, typename CG, typename KeyEqual>
+    __device__ std::enable_if_t<uses_vector_load, bool> contains_impl(CG g,
+                                                                      Key const& k,
+                                                                      KeyEqual key_equal) noexcept;
 
     /**
      * @brief Indicates whether the key `k` was inserted into the map using scalar loads.
@@ -965,9 +966,9 @@ class static_multimap {
      * @return A boolean indicating whether the key/value pair
      * containing `k` was inserted
      */
-    template <bool uses_vector_load, typename CG, typename KeyEqual = thrust::equal_to<key_type>>
+    template <bool uses_vector_load, typename CG, typename KeyEqual>
     __device__ std::enable_if_t<not uses_vector_load, bool> contains_impl(
-      CG g, Key const& k, KeyEqual key_equal = KeyEqual{}) noexcept;
+      CG g, Key const& k, KeyEqual key_equal) noexcept;
 
     /**
      * @brief Counts the occurrence of a given key contained in multimap using vector loads.
@@ -982,12 +983,9 @@ class static_multimap {
      * for equality
      * @return Number of matches found by the current thread
      */
-    template <bool uses_vector_load,
-              bool is_outer,
-              typename CG,
-              typename KeyEqual = thrust::equal_to<key_type>>
+    template <bool uses_vector_load, bool is_outer, typename CG, typename KeyEqual>
     __device__ std::enable_if_t<uses_vector_load, std::size_t> count_impl(
-      CG const& g, Key const& k, KeyEqual key_equal = KeyEqual{}) noexcept;
+      CG const& g, Key const& k, KeyEqual key_equal) noexcept;
 
     /**
      * @brief Counts the occurrence of a given key contained in multimap using scalar loads.
@@ -1002,12 +1000,9 @@ class static_multimap {
      * for equality
      * @return Number of matches found by the current thread
      */
-    template <bool uses_vector_load,
-              bool is_outer,
-              typename CG,
-              typename KeyEqual = thrust::equal_to<key_type>>
+    template <bool uses_vector_load, bool is_outer, typename CG, typename KeyEqual>
     __device__ std::enable_if_t<not uses_vector_load, std::size_t> count_impl(
-      CG const& g, Key const& k, KeyEqual key_equal = KeyEqual{}) noexcept;
+      CG const& g, Key const& k, KeyEqual key_equal) noexcept;
 
     /**
      * @brief Counts the occurrence of a given key/value pair contained in multimap using vector
@@ -1077,7 +1072,7 @@ class static_multimap {
               typename CG,
               typename atomicT,
               typename OutputIt,
-              typename KeyEqual = thrust::equal_to<key_type>>
+              typename KeyEqual>
     __device__ void retrieve_impl(warpT const& warp,
                                   CG const& g,
                                   Key const& k,
@@ -1085,7 +1080,7 @@ class static_multimap {
                                   value_type* output_buffer,
                                   atomicT* num_matches,
                                   OutputIt output_begin,
-                                  KeyEqual key_equal = KeyEqual{}) noexcept;
+                                  KeyEqual key_equal) noexcept;
 
     /**
      * @brief Find all the matches of a given key contained in multimap using scalar
@@ -1118,14 +1113,14 @@ class static_multimap {
               typename CG,
               typename atomicT,
               typename OutputIt,
-              typename KeyEqual = thrust::equal_to<key_type>>
+              typename KeyEqual>
     __device__ void retrieve_impl(CG const& g,
                                   Key const& k,
                                   uint32_t* cg_counter,
                                   value_type* output_buffer,
                                   atomicT* num_matches,
                                   OutputIt output_begin,
-                                  KeyEqual key_equal = KeyEqual{}) noexcept;
+                                  KeyEqual key_equal) noexcept;
 
     /**
      * @brief Find all the matches of a given pair contained in multimap using vector

From 4ec7f69eb27672ce1a65953e52360234bf4c9051 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Thu, 16 Sep 2021 22:09:16 -0400
Subject: [PATCH 227/267] Minor correction in double hashing: avoid using key +
 operator

---
 include/cuco/detail/probe_sequences.cuh | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/include/cuco/detail/probe_sequences.cuh b/include/cuco/detail/probe_sequences.cuh
index 6e76b49e3..6bdc326a6 100644
--- a/include/cuco/detail/probe_sequences.cuh
+++ b/include/cuco/detail/probe_sequences.cuh
@@ -269,14 +269,14 @@ class double_hashing : public probe_sequence_base<Key, Value, CGSize, Scope> {
     std::size_t index;
     auto const hash_value = hash1_(k);
     if constexpr (uses_vector_load()) {
-      // step size in range [1, capacity-1] * cg_size * vector_width
-      step_size_ = (hash2_(k + 1) % (capacity_ / (cg_size() * vector_width()) - 1) + 1) *
-                   cg_size() * vector_width();
+      // step size in range [1, prime - 1] * cg_size * vector_width
+      step_size_ = (hash2_(k) % (capacity_ / (cg_size() * vector_width()) - 1) + 1) * cg_size() *
+                   vector_width();
       index = hash_value % (capacity_ / (cg_size() * vector_width())) * cg_size() * vector_width() +
               g.thread_rank() * vector_width();
     } else {
-      // step size in range [1, capacity-1] * cg_size
-      step_size_ = (hash2_(k + 1) % (capacity_ / cg_size() - 1) + 1) * cg_size();
+      // step size in range [1, prime - 1] * cg_size
+      step_size_ = (hash2_(k) % (capacity_ / cg_size() - 1) + 1) * cg_size();
       index      = (hash_value + g.thread_rank()) % capacity_;
     }
     return slots_ + index;

From c4799ccfadc2c1387fa72a17a47d6d185cec00c0 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Thu, 16 Sep 2021 22:11:41 -0400
Subject: [PATCH 228/267] Add tests for custom key and value types

---
 tests/static_multimap/static_multimap_test.cu | 212 +++++++++++++++++-
 1 file changed, 205 insertions(+), 7 deletions(-)

diff --git a/tests/static_multimap/static_multimap_test.cu b/tests/static_multimap/static_multimap_test.cu
index 41203e7c0..bc4f27f3e 100644
--- a/tests/static_multimap/static_multimap_test.cu
+++ b/tests/static_multimap/static_multimap_test.cu
@@ -50,18 +50,206 @@ bool none_of(Iterator begin, Iterator end, Predicate p)
   return not all_of(begin, end, p);
 }
 
-template <typename Key, typename Value>
-struct pair_equal {
-  __host__ __device__ bool operator()(const cuco::pair_type<Key, Value>& lhs,
-                                      const cuco::pair_type<Key, Value>& rhs) const
+}  // namespace
+
+enum class probe_sequence { linear_probing, double_hashing };
+
+// User-defined key type
+// Need to specify alignment to WAR libcu++ bug where cuda::atomic fails for underaligned types:
+// https://github.com/NVIDIA/libcudacxx/issues/160
+struct alignas(8) key_pair {
+  int32_t a;
+  int32_t b;
+};
+
+struct hash_key_pair {
+  __device__ uint32_t operator()(key_pair k) { return k.a; };
+};
+
+struct key_pair_equals {
+  __device__ bool operator()(const key_pair& lhs, const key_pair& rhs)
   {
-    return lhs.first == rhs.first;
+    return std::tie(lhs.a, lhs.b) == std::tie(rhs.a, rhs.b);
   }
 };
 
-}  // namespace
+struct alignas(8) value_pair {
+  int32_t f;
+  int32_t s;
+};
 
-enum class probe_sequence { linear_probing, double_hashing };
+#define SIZE 10
+__device__ int A[SIZE];
+
+template <typename T>
+struct custom_equals {
+  __device__ bool operator()(T lhs, T rhs) { return A[lhs] == A[rhs]; }
+};
+
+template <typename Key, typename Value, typename Map, typename PairIt, typename KeyIt>
+__inline__ void test_custom_key_value_type(Map& map,
+                                           PairIt pair_begin,
+                                           KeyIt key_begin,
+                                           size_t num_pairs)
+{
+  constexpr cudaStream_t stream = 0;
+
+  SECTION("All inserted keys-value pairs should be correctly recovered during find")
+  {
+    map.insert(pair_begin, pair_begin + num_pairs);
+
+    auto count = map.count(key_begin, key_begin + num_pairs, stream, key_pair_equals{});
+    REQUIRE(count == num_pairs);
+
+    thrust::device_vector<cuco::pair_type<Key, Value>> found_pairs(num_pairs);
+    auto output_end = map.retrieve(
+      key_begin, key_begin + num_pairs, found_pairs.begin(), stream, key_pair_equals{});
+    auto size = output_end - found_pairs.begin();
+
+    REQUIRE(size == num_pairs);
+
+    // sort before compare
+    thrust::sort(
+      thrust::device,
+      found_pairs.begin(),
+      found_pairs.end(),
+      [] __device__(const cuco::pair_type<Key, Value>& lhs,
+                    const cuco::pair_type<Key, Value>& rhs) { return lhs.first.a < rhs.first.a; });
+
+    REQUIRE(thrust::equal(
+      thrust::device,
+      pair_begin,
+      pair_begin + num_pairs,
+      found_pairs.begin(),
+      [] __device__(cuco::pair_type<Key, Value> lhs, cuco::pair_type<Key, Value> rhs) {
+        return lhs.first.a == rhs.first.a;
+      }));
+  }
+
+  SECTION("Outer functions include non-matches in the output while normal functions don't")
+  {
+    map.insert(pair_begin, pair_begin + num_pairs);
+
+    auto const num = num_pairs * 2;
+    thrust::device_vector<Key> query_keys(num);
+    auto query_key_begin = query_keys.begin();
+    thrust::transform(thrust::device,
+                      thrust::counting_iterator<int>(0),
+                      thrust::counting_iterator<int>(num),
+                      query_key_begin,
+                      [] __device__(auto i) {
+                        return Key{i, i};
+                      });
+
+    auto count = map.count(query_key_begin, query_key_begin + num, stream, key_pair_equals{});
+    REQUIRE(count == num_pairs);
+
+    auto count_outer =
+      map.count_outer(query_key_begin, query_key_begin + num, stream, key_pair_equals{});
+    REQUIRE(count_outer == num);
+
+    ////////////////////// tests of retreive
+    thrust::device_vector<cuco::pair_type<Key, Value>> found_pairs(num_pairs);
+    auto output_end = map.retrieve(
+      query_key_begin, query_key_begin + num, found_pairs.begin(), stream, key_pair_equals{});
+    auto size = output_end - found_pairs.begin();
+
+    REQUIRE(size == num_pairs);
+
+    // sort before compare
+    thrust::sort(
+      thrust::device,
+      found_pairs.begin(),
+      found_pairs.end(),
+      [] __device__(const cuco::pair_type<Key, Value>& lhs,
+                    const cuco::pair_type<Key, Value>& rhs) { return lhs.first.a < rhs.first.a; });
+
+    REQUIRE(thrust::equal(
+      thrust::device,
+      pair_begin,
+      pair_begin + num_pairs,
+      found_pairs.begin(),
+      [] __device__(cuco::pair_type<Key, Value> lhs, cuco::pair_type<Key, Value> rhs) {
+        return lhs.first.a == rhs.first.a;
+      }));
+
+    ////////////////////// tests of retreive_outer
+    found_pairs.resize(num);
+    output_end = map.retrieve_outer(
+      query_key_begin, query_key_begin + num, found_pairs.begin(), stream, key_pair_equals{});
+    auto size_outer = output_end - found_pairs.begin();
+
+    REQUIRE(size_outer == num);
+  }
+
+  SECTION("All inserted keys-value pairs should be contained")
+  {
+    thrust::device_vector<bool> contained(num_pairs);
+    map.insert(pair_begin, pair_begin + num_pairs);
+    map.contains(key_begin, key_begin + num_pairs, contained.begin(), stream, key_pair_equals{});
+    REQUIRE(all_of(contained.begin(), contained.end(), [] __device__(bool const& b) { return b; }));
+  }
+
+  SECTION("Non-inserted keys-value pairs should not be contained")
+  {
+    thrust::device_vector<bool> contained(num_pairs);
+    map.contains(key_begin, key_begin + num_pairs, contained.begin(), stream, key_pair_equals{});
+    REQUIRE(
+      none_of(contained.begin(), contained.end(), [] __device__(bool const& b) { return b; }));
+  }
+}
+
+TEMPLATE_TEST_CASE_SIG("User defined key and value type",
+                       "",
+                       ((probe_sequence Probe), Probe),
+                       (probe_sequence::linear_probing),
+                       (probe_sequence::double_hashing))
+{
+  using Key   = key_pair;
+  using Value = value_pair;
+
+  auto constexpr sentinel_key   = Key{-1, -1};
+  auto constexpr sentinel_value = Value{-1, -1};
+
+  constexpr std::size_t num_pairs = 100;
+  constexpr std::size_t capacity  = num_pairs * 2;
+
+  thrust::device_vector<Key> insert_keys(num_pairs);
+  thrust::device_vector<Value> insert_values(num_pairs);
+
+  thrust::transform(thrust::device,
+                    thrust::counting_iterator<int>(0),
+                    thrust::counting_iterator<int>(num_pairs),
+                    insert_keys.begin(),
+                    [] __device__(auto i) {
+                      return Key{i, i};
+                    });
+
+  thrust::transform(thrust::device,
+                    thrust::counting_iterator<int>(0),
+                    thrust::counting_iterator<int>(num_pairs),
+                    insert_values.begin(),
+                    [] __device__(auto i) {
+                      return Value{i, i};
+                    });
+  auto insert_pairs =
+    thrust::make_zip_iterator(thrust::make_tuple(insert_keys.begin(), insert_values.begin()));
+
+  if constexpr (Probe == probe_sequence::linear_probing) {
+    cuco::static_multimap<key_pair,
+                          value_pair,
+                          cuco::detail::linear_probing<key_pair, value_pair, 1, hash_key_pair>>
+      map{capacity, sentinel_key, sentinel_value};
+    test_custom_key_value_type<Key, Value>(map, insert_pairs, insert_keys.begin(), num_pairs);
+  }
+  if constexpr (Probe == probe_sequence::double_hashing) {
+    cuco::static_multimap<Key,
+                          Value,
+                          cuco::detail::double_hashing<Key, Value, 2, hash_key_pair, hash_key_pair>>
+      map{capacity, sentinel_key, sentinel_value};
+    test_custom_key_value_type<Key, Value>(map, insert_pairs, insert_keys.begin(), num_pairs);
+  }
+}
 
 template <typename Map, typename PairIt, typename KeyIt, typename ResultIt>
 __inline__ void test_multiplicity_two(
@@ -279,6 +467,16 @@ TEMPLATE_TEST_CASE_SIG("Tests of insert_if",
   }
 }
 
+// Custom pair equal
+template <typename Key, typename Value>
+struct pair_equal {
+  __device__ bool operator()(const cuco::pair_type<Key, Value>& lhs,
+                             const cuco::pair_type<Key, Value>& rhs) const
+  {
+    return lhs.first == rhs.first;
+  }
+};
+
 template <typename Key, typename Value, typename Map, typename PairIt>
 __inline__ void test_pair_functions(Map& map, PairIt pair_begin, std::size_t num_pairs)
 {

From 9ec49c33218fca2fd8d3bcc70dee97c3c5b80d95 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Fri, 17 Sep 2021 14:01:25 -0400
Subject: [PATCH 229/267] Update docs

---
 .../cuco/detail/static_multimap_kernels.cuh   | 127 +++++++++++++----
 include/cuco/static_multimap.cuh              | 130 ++++++++++--------
 2 files changed, 176 insertions(+), 81 deletions(-)

diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap_kernels.cuh
index 21c2aff45..8ad623d31 100644
--- a/include/cuco/detail/static_multimap_kernels.cuh
+++ b/include/cuco/detail/static_multimap_kernels.cuh
@@ -64,8 +64,9 @@ __global__ void initialize(pair_atomic_type* const slots, Key k, Value v, std::s
  * @tparam block_size The size of the thread block
  * @tparam tile_size The number of threads in the Cooperative Groups used to perform
  * inserts
- * @tparam InputIt Device accessible input iterator whose `value_type` is
- * convertible to the map's `value_type`
+ * @tparam InputIt Device accessible random access input iterator where
+ * `std::is_converitble<std::iterator_traits<InputIt>::value_type,
+ * static_multimap<K, V>::value_type>` is `true`
  * @tparam viewT Type of device view allowing access of hash map storage
  *
  * @param first Beginning of the sequence of key/value pairs
@@ -91,6 +92,8 @@ __global__ void insert(InputIt first, InputIt last, viewT view)
  * @brief Inserts key/value pairs in the range `[first, first + n)` if `pred` of the
  * corresponding stencil returns true.
  *
+ * The key/value pair `*(first + i)` is inserted if `pred( *(stencil + i) )` returns true.
+ *
  * Uses the CUDA Cooperative Groups API to leverage groups of multiple threads to perform each
  * key/value insertion. This provides a significant boost in throughput compared to the non
  * Cooperative Group `insert` at moderate to high load factors.
@@ -98,8 +101,9 @@ __global__ void insert(InputIt first, InputIt last, viewT view)
  * @tparam block_size The size of the thread block
  * @tparam tile_size The number of threads in the Cooperative Groups used to perform
  * inserts
- * @tparam InputIt Device accessible input iterator whose `value_type` is
- * convertible to the map's `value_type`
+ * @tparam InputIt Device accessible random access input iterator where
+ * `std::is_converitble<std::iterator_traits<InputIt>::value_type,
+ * static_multimap<K, V>::value_type>` is `true`
  * @tparam StencilIt Device accessible stencil iterator
  * @tparam viewT Type of device view allowing access of hash map storage
  * @tparam Predicate Unary predicate function type
@@ -134,7 +138,9 @@ __global__ void insert_if_n(InputIt first, StencilIt s, std::size_t n, viewT vie
 /**
  * @brief Indicates whether the keys in the range `[first, last)` are contained in the map.
  *
- * Writes a `bool` to `(output + i)` indicating if the key `*(first + i)` exists in the map.
+ * Stores `true` or `false` to `(output + i)` indicating if the key `*(first + i)` exists in the
+ * map.
+ *
  * Uses the CUDA Cooperative Groups API to leverage groups of multiple threads to perform the
  * contains operation for each key. This provides a significant boost in throughput compared
  * to the non Cooperative Group `contains` at moderate to high load factors.
@@ -194,8 +200,7 @@ __global__ void contains(
  * @tparam block_size The size of the thread block
  * @tparam tile_size The number of threads in the Cooperative Groups used to perform counts
  * @tparam uses_vector_load Boolean flag indicating whether vector loads are used or not
- * @tparam is_outer Boolean flag indicating whether the current functions is used for outer join
- * operations or not
+ * @tparam is_outer Boolean flag indicating whether non-matches are counted
  * @tparam InputIt Device accessible input iterator whose `value_type` is convertible to the map's
  * `key_type`
  * @tparam atomicT Type of atomic storage
@@ -245,14 +250,14 @@ __global__ void count(
 
 /**
  * @brief Counts the occurrences of key/value pairs in `[first, last)` contained in the multimap.
- * If no matches can be found for a given key/value pair and `is_outer` is true, the corresponding
- * occurrence is 1.
+ * The occurrence of non-matches is `1` if `is_outer` is `true`. Otherwise it's `0`.
  *
  * @tparam block_size The size of the thread block
  * @tparam tile_size The number of threads in the Cooperative Groups used to perform counts
- * @tparam is_outer Boolean flag indicating whether the current functions is used for outer join
- * operations or not
- * @tparam Input Device accesible input iterator of key/value pairs
+ * @tparam is_outer Boolean flag indicating whether non-matches are counted
+ * @tparam InputIt Device accessible random access input iterator where
+ * `std::is_converitble<std::iterator_traits<InputIt>::value_type,
+ * static_multimap<K, V>::value_type>` is `true`
  * @tparam atomicT Type of atomic storage
  * @tparam viewT Type of device view allowing access of hash map storage
  * @tparam PairEqual Binary callable
@@ -299,12 +304,14 @@ __global__ void pair_count(
 }
 
 /**
- * @brief Finds all the values corresponding to all keys in the range `[first, last)` using vector
- * oads combined with per-block shared memory buffer.
+ * @brief Retrieves all the values corresponding to all keys in the range `[first, last)`
+ * using vector loads combined with per-block shared memory buffer.
+ *
+ * The `vectorized_` prefix indicates that the vector load method is used.
  *
  * If the key `k = *(first + i)` exists in the map, copies `k` and all associated values to
- * unspecified locations in `[output_begin, output_begin + *num_matches - 1)`. Else, copies `k` and
- * the empty value sentinel.
+ * unspecified locations in `[output_begin, output_begin + *num_matches - 1)`. Copies `k` and
+ * the empty value sentinel into the output only when `is_outer` is `true`.
  *
  * Behavior is undefined if the total number of matching keys exceeds `std::distance(output_begin,
  * output_begin + *num_matches - 1)`. Use `count()` to determine the number of matching keys.
@@ -313,8 +320,7 @@ __global__ void pair_count(
  * @tparam warp_size The size of the warp
  * @tparam tile_size The number of threads in the Cooperative Groups
  * @tparam buffer_size Size of the output buffer
- * @tparam is_outer Boolean flag indicating whether the current functions is used for outer join
- * operations or not
+ * @tparam is_outer Boolean flag indicating whether non-matches are included in the output
  * @tparam InputIt Device accessible input iterator whose `value_type` is
  * convertible to the map's `key_type`
  * @tparam OutputIt Device accessible output iterator whose `value_type` is
@@ -400,12 +406,12 @@ __global__ void vectorized_retrieve(InputIt first,
 }
 
 /**
- * @brief Finds all the values corresponding to all keys in the range `[first, last)` using scalar
- * loads combined with per-CG shared memory buffer.
+ * @brief Retrieves all the values corresponding to all keys in the range `[first, last)` using
+ * scalar loads combined with per-CG shared memory buffer.
  *
  * If the key `k = *(first + i)` exists in the map, copies `k` and all associated values to
- * unspecified locations in `[output_begin, output_begin + *num_matches - 1)`. Else, copies `k` and
- * the empty value sentinel.
+ * unspecified locations in `[output_begin, output_begin + *num_matches - 1)`. Copies `k` and
+ * the empty value sentinel into the output only when `is_outer` is `true`.
  *
  * Behavior is undefined if the total number of matching keys exceeds `std::distance(output_begin,
  * output_begin + *num_matches - 1)`. Use `count()` to determine the number of matching keys.
@@ -414,8 +420,7 @@ __global__ void vectorized_retrieve(InputIt first,
  * @tparam warp_size The size of the warp
  * @tparam tile_size The number of threads in the Cooperative Groups
  * @tparam buffer_size Size of the output buffer
- * @tparam is_outer Boolean flag indicating whether the current functions is used for outer join
- * operations or not
+ * @tparam is_outer Boolean flag indicating whether non-matches are included in the output
  * @tparam InputIt Device accessible input iterator whose `value_type` is
  * convertible to the map's `key_type`
  * @tparam OutputIt Device accessible output iterator whose `value_type` is
@@ -481,6 +486,44 @@ __global__ void retrieve(InputIt first,
   }
 }
 
+/**
+ * @brief Retrieves all pairs matching the input probe pair in the range `[first, last)`
+ * using vector loads combined with per-block shared memory buffer.
+ *
+ * The `vectorized_` prefix indicates that the vector load method is used.
+ *
+ * If pair_equal(*(first + i), slot[j]) returns true, then *(first+i) is stored to unspecified
+ * locations in `probe_output_begin`, and slot[j] is stored to unspecified locations in
+ * `contained_output_begin`. If the given pair has no matches in the map, copies *(first + i) in
+ * `probe_output_begin` and a pair of `empty_key_sentinel` and `empty_value_sentinel` in
+ * `contained_output_begin` only when `is_outer` is `true`.
+ *
+ * Behavior is undefined if the total number of matching pairs exceeds `std::distance(output_begin,
+ * output_begin + *num_matches - 1)`. Use `pair_count()` to determine the number of matching keys.
+ *
+ * @tparam block_size The size of the thread block
+ * @tparam warp_size The size of the warp
+ * @tparam tile_size The number of threads in the Cooperative Groups
+ * @tparam buffer_size Size of the output buffer
+ * @tparam is_outer Boolean flag indicating whether non-matches are included in the output
+ * @tparam InputIt Device accessible random access input iterator where
+ * `std::is_converitble<std::iterator_traits<InputIt>::value_type,
+ * static_multimap<K, V>::value_type>` is `true`
+ * @tparam OutputIt1 Device accessible output iterator whose `value_type` is
+ * convertible to the map's `value_type`
+ * @tparam OutputIt2 Device accessible output iterator whose `value_type` is
+ * convertible to the map's `value_type`
+ * @tparam atomicT Type of atomic storage
+ * @tparam viewT Type of device view allowing access of hash map storage
+ * @tparam PairEqual Binary callable type
+ * @param first Beginning of the sequence of keys
+ * @param last End of the sequence of keys
+ * @param probe_output_begin Beginning of the sequence of the matched probe pairs
+ * @param contained_output_begin Beginning of the sequence of the matched contained pairs
+ * @param num_matches Size of the output sequence
+ * @param view Device view used to access the hash map's slot storage
+ * @param pair_equal The binary function to compare two pairs for equality
+ */
 template <uint32_t block_size,
           uint32_t warp_size,
           uint32_t tile_size,
@@ -563,6 +606,42 @@ __global__ void vectorized_pair_retrieve(InputIt first,
   }
 }
 
+/**
+ * @brief Retrieves all pairs matching the input probe pair in the range `[first, last)`
+ * using scalar loads combined with per-CG shared memory buffer.
+ *
+ * If pair_equal(*(first + i), slot[j]) returns true, then *(first+i) is stored to unspecified
+ * locations in `probe_output_begin`, and slot[j] is stored to unspecified locations in
+ * `contained_output_begin`. If the given pair has no matches in the map, copies *(first + i) in
+ * `probe_output_begin` and a pair of `empty_key_sentinel` and `empty_value_sentinel` in
+ * `contained_output_begin` only when `is_outer` is `true`.
+ *
+ * Behavior is undefined if the total number of matching pairs exceeds `std::distance(output_begin,
+ * output_begin + *num_matches - 1)`. Use `pair_count()` to determine the number of matching keys.
+ *
+ * @tparam block_size The size of the thread block
+ * @tparam warp_size The size of the warp
+ * @tparam tile_size The number of threads in the Cooperative Groups
+ * @tparam buffer_size Size of the output buffer
+ * @tparam is_outer Boolean flag indicating whether non-matches are included in the output
+ * @tparam InputIt Device accessible random access input iterator where
+ * `std::is_converitble<std::iterator_traits<InputIt>::value_type,
+ * static_multimap<K, V>::value_type>` is `true`
+ * @tparam OutputIt1 Device accessible output iterator whose `value_type` is
+ * convertible to the map's `value_type`
+ * @tparam OutputIt2 Device accessible output iterator whose `value_type` is
+ * convertible to the map's `value_type`
+ * @tparam atomicT Type of atomic storage
+ * @tparam viewT Type of device view allowing access of hash map storage
+ * @tparam PairEqual Binary callable type
+ * @param first Beginning of the sequence of keys
+ * @param last End of the sequence of keys
+ * @param probe_output_begin Beginning of the sequence of the matched probe pairs
+ * @param contained_output_begin Beginning of the sequence of the matched contained pairs
+ * @param num_matches Size of the output sequence
+ * @param view Device view used to access the hash map's slot storage
+ * @param pair_equal The binary function to compare two pairs for equality
+ */
 template <uint32_t block_size,
           uint32_t warp_size,
           uint32_t tile_size,
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index ead4ca325..c9b46f813 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -228,13 +228,13 @@ class static_multimap {
    * @brief Inserts key/value pairs in the range `[first, first + n)` if `pred`
    * of the corresponding stencil returns true.
    *
-   * The key/value pair `*(first + i)` is inserted if `pred( *(stencil+i) )` returns true.
+   * The key/value pair `*(first + i)` is inserted if `pred( *(stencil + i) )` returns true.
    *
    * @tparam InputIt Device accessible random access input iterator where
    * `std::is_converitble<std::iterator_traits<InputIt>::value_type,
    * static_multimap<K, V>::value_type>` is `true`
-   * @tparam StencilIt Device accessible random access iterator whose value_type is convertible to
-   * Predicate's argument type
+   * @tparam StencilIt Device accessible random access iterator whose value_type is
+   * convertible to Predicate's argument type
    * @tparam Predicate Unary predicate callable
    * @param first Beginning of the sequence of key/value pairs
    * @param last End of the sequence of key/value pairs
@@ -280,7 +280,7 @@ class static_multimap {
    * @param last End of the sequence of keys to count
    * @param stream CUDA stream used for count
    * @param key_equal Binary function to compare two keys for equality
-   * @return The sum of total occurrences of all keys in `[first,last)`
+   * @return The sum of total occurrences of all keys in `[first, last)`
    */
   template <typename InputIt, typename KeyEqual = thrust::equal_to<key_type>>
   std::size_t count(InputIt first,
@@ -289,8 +289,9 @@ class static_multimap {
                     KeyEqual key_equal  = KeyEqual{}) const;
 
   /**
-   * @brief Counts the occurrences of keys in `[first, last)` contained in the multimap. If no
-   * matches can be found for a given key, the corresponding occurrence is 1.
+   * @brief Counts the occurrences of keys in `[first, last)` contained in the multimap.
+   *
+   * The `_outer` suffix signifies that the occurrence of non-matches is 1.
    *
    * @tparam Input Device accesible input iterator whose `value_type` is convertible to `key_type`
    * @tparam KeyEqual Binary callable
@@ -298,7 +299,7 @@ class static_multimap {
    * @param last End of the sequence of keys to count
    * @param stream CUDA stream used for count_outer
    * @param key_equal Binary function to compare two keys for equality
-   * @return The sum of total occurrences of all keys in `[first,last)`
+   * @return The sum of total occurrences of all keys in `[first, last)`
    */
   template <typename InputIt, typename KeyEqual = thrust::equal_to<key_type>>
   std::size_t count_outer(InputIt first,
@@ -309,13 +310,17 @@ class static_multimap {
   /**
    * @brief Counts the occurrences of key/value pairs in `[first, last)` contained in the multimap.
    *
-   * @tparam Input Device accesible input iterator of key/value pairs
+   * The `pair_` prefix indicates that the input data type is convertible to the map's `value_type`.
+   *
+   * @tparam InputIt Device accessible random access input iterator where
+   * `std::is_converitble<std::iterator_traits<InputIt>::value_type,
+   * static_multimap<K, V>::value_type>` is `true`
    * @tparam PairEqual Binary callable
    * @param first Beginning of the sequence of pairs to count
    * @param last End of the sequence of pairs to count
    * @param pair_equal Binary function to compare two pairs for equality
    * @param stream CUDA stream used for pair_count
-   * @return The sum of total occurrences of all pairs in `[first,last)`
+   * @return The sum of total occurrences of all pairs in `[first, last)`
    */
   template <typename InputIt, typename PairEqual>
   std::size_t pair_count(InputIt first,
@@ -325,15 +330,19 @@ class static_multimap {
 
   /**
    * @brief Counts the occurrences of key/value pairs in `[first, last)` contained in the multimap.
-   * If no matches can be found for a given key/value pair, the corresponding occurrence is 1.
    *
-   * @tparam Input Device accesible input iterator of key/value pairs
+   * The `pair_` prefix indicates that the input data type is convertible to the map's `value_type`.
+   * The `_outer` suffix signifies that the occurrence of non-matches is 1.
+   *
+   * @tparam InputIt Device accessible random access input iterator where
+   * `std::is_converitble<std::iterator_traits<InputIt>::value_type,
+   * static_multimap<K, V>::value_type>` is `true`
    * @tparam PairEqual Binary callable
    * @param first Beginning of the sequence of pairs to count
    * @param last End of the sequence of pairs to count
    * @param pair_equal Binary function to compare two pairs for equality
    * @param stream CUDA stream used for pair_count_outer
-   * @return The sum of total occurrences of all pairs in `[first,last)`
+   * @return The sum of total occurrences of all pairs in `[first, last)`
    */
   template <typename InputIt, typename PairEqual>
   std::size_t pair_count_outer(InputIt first,
@@ -372,9 +381,9 @@ class static_multimap {
   /**
    * @brief Retrieves all the matches corresponding to all keys in the range `[first, last)`.
    *
-   * If the key `k = *(first + i)` exists in the map, copies `k` and all associated values to
-   * unspecified locations in `[output_begin, output_end)`. Else, copies `k` and the empty value
-   * sentinel.
+   * The `_outer` suffix signifies that non-matches are included in the output: If the key
+   * `k = *(first + i)` exists in the map, copies `k` and all associated values to unspecified
+   * locations in `[output_begin, output_end)`. Else, copies `k` and the empty value sentinel.
    *
    * Behavior is undefined if the total number of matching keys exceeds `std::distance(output_begin,
    * output_end)`. Use `count_outer()` to determine the number of matching keys.
@@ -401,15 +410,18 @@ class static_multimap {
   /**
    * @brief Retrieves all pairs matching the input probe pair in the range `[first, last)`.
    *
-   * if pair_equal(*(first + i), slot[j]) returns true, then *(first+i) is stored to
-   * `probe_output_begin`, and slot[j] is stored to `contained_output_begin`.
+   * The `pair_` prefix indicates that the input data type is convertible to the map's
+   * `value_type`. If pair_equal(*(first + i), slot[j]) returns true, then *(first+i) is
+   * stored to `probe_output_begin`, and slot[j] is stored to `contained_output_begin`.
    *
    * Behavior is undefined if the total number of matching pairs exceeds
    * `std::distance(probe_output_begin, probe_output_end)` (or
    * `std::distance(contained_output_begin, contained_output_end)`). Use
    * `pair_count()` to determine the number of matching pairs.
    *
-   * @tparam InputIt Device accessible input iterator for probe pairs
+   * @tparam InputIt Device accessible random access input iterator where
+   * `std::is_converitble<std::iterator_traits<InputIt>::value_type,
+   * static_multimap<K, V>::value_type>` is `true`
    * @tparam OutputZipIt1 Device accessible output zip iterator for probe matches
    * @tparam OutputZipIt2 Device accessible output zip iterator for contained matches
    * @tparam PairEqual Binary callable type
@@ -418,7 +430,7 @@ class static_multimap {
    * @param probe_output_begin Beginning of the sequence of the matched probe pairs
    * @param contained_output_begin Beginning of the sequence of the matched contained pairs
    * @param pair_equal The binary function to compare two pairs for equality
-   * @param stream CUDA stream used for retrieve_outer
+   * @param stream CUDA stream used for pair_retrieve
    * @return The total number of matches
    */
   template <typename InputIt, typename OutputZipIt1, typename OutputZipIt2, typename PairEqual>
@@ -432,7 +444,9 @@ class static_multimap {
   /**
    * @brief Retrieves all pairs matching the input probe pair in the range `[first, last)`.
    *
-   * if pair_equal(*(first + i), slot[j]) returns true, then *(first+i) is stored to
+   * The `pair_` prefix indicates that the input data type is convertible to the map's `value_type`.
+   * The `_outer` suffix signifies that non-matches are included in the output: if
+   * pair_equal(*(first + i), slot[j]) returns true, then *(first+i) is stored to
    * `probe_output_begin`, and slot[j] is stored to `contained_output_begin`. If *(first+i) doesn't
    * have matches in the map, copies *(first + i) in `probe_output_begin` and a pair of
    * `empty_key_sentinel` and `empty_value_sentinel` in `contained_output_begin`.
@@ -442,7 +456,9 @@ class static_multimap {
    * `std::distance(contained_output_begin, contained_output_end)`). Use
    * `pair_count()` to determine the number of matching pairs.
    *
-   * @tparam InputIt Device accessible input iterator for probe pairs
+   * @tparam InputIt Device accessible random access input iterator where
+   * `std::is_converitble<std::iterator_traits<InputIt>::value_type,
+   * static_multimap<K, V>::value_type>` is `true`
    * @tparam OutputZipIt1 Device accessible output zip iterator for probe matches
    * @tparam OutputZipIt2 Device accessible output zip iterator for contained matches
    * @tparam PairEqual Binary callable type
@@ -451,7 +467,7 @@ class static_multimap {
    * @param probe_output_begin Beginning of the sequence of the matched probe pairs
    * @param contained_output_begin Beginning of the sequence of the matched contained pairs
    * @param pair_equal The binary function to compare two pairs for equality
-   * @param stream CUDA stream used for retrieve_outer
+   * @param stream CUDA stream used for pair_retrieve_outer
    * @return The total number of matches
    */
   template <typename InputIt, typename OutputZipIt1, typename OutputZipIt2, typename PairEqual>
@@ -777,7 +793,7 @@ class static_multimap {
     /**
      * @brief Inserts the specified key/value pair into the map using vector loads.
      *
-     * @tparam uses_vector_load Boolean flag indicating whether vector loads are used or not
+     * @tparam uses_vector_load Boolean flag indicating whether vector loads are used
      * @tparam CG Cooperative Group type
      *
      * @param g The Cooperative Group that performs the insert
@@ -791,7 +807,7 @@ class static_multimap {
     /**
      * @brief Inserts the specified key/value pair into the map using scalar loads.
      *
-     * @tparam uses_vector_load Boolean flag indicating whether vector loads are used or not
+     * @tparam uses_vector_load Boolean flag indicating whether vector loads are used
      * @tparam CG Cooperative Group type
      *
      * @param g The Cooperative Group that performs the insert
@@ -926,13 +942,13 @@ class static_multimap {
     /**
      * @brief Indicates whether the key `k` was inserted into the map using vector loads.
      *
-     * If the key `k` was inserted into the map, find returns
+     * If the key `k` was inserted into the map, `contains` returns
      * true. Otherwise, it returns false. Uses the CUDA Cooperative Groups API to
-     * to leverage multiple threads to perform a single contains operation. This provides a
-     * significant boost in throughput compared to the non Cooperative Group
+     * to leverage multiple threads to perform a single `contains` operation. This provides a
+     * significant boost in throughput compared to the non Cooperative Group based
      * `contains` at moderate to high load factors.
      *
-     * @tparam uses_vector_load Boolean flag indicating whether vector loads are used or not
+     * @tparam uses_vector_load Boolean flag indicating whether vector loads are used
      * @tparam CG Cooperative Group type
      * @tparam KeyEqual Binary callable type
      * @param g The Cooperative Group used to perform the contains operation
@@ -950,13 +966,13 @@ class static_multimap {
     /**
      * @brief Indicates whether the key `k` was inserted into the map using scalar loads.
      *
-     * If the key `k` was inserted into the map, find returns
+     * If the key `k` was inserted into the map, `contains` returns
      * true. Otherwise, it returns false. Uses the CUDA Cooperative Groups API to
-     * to leverage multiple threads to perform a single contains operation. This provides a
+     * to leverage multiple threads to perform a single `contains` operation. This provides a
      * significant boost in throughput compared to the non Cooperative Group
      * `contains` at moderate to high load factors.
      *
-     * @tparam uses_vector_load Boolean flag indicating whether vector loads are used or not
+     * @tparam uses_vector_load Boolean flag indicating whether vector loads are used
      * @tparam CG Cooperative Group type
      * @tparam KeyEqual Binary callable type
      * @param g The Cooperative Group used to perform the contains operation
@@ -973,8 +989,8 @@ class static_multimap {
     /**
      * @brief Counts the occurrence of a given key contained in multimap using vector loads.
      *
-     * @tparam uses_vector_load Boolean flag indicating whether vector loads are used or not
-     * @tparam is_outer Boolean flag indicating whether outer join is peformed or not
+     * @tparam uses_vector_load Boolean flag indicating whether vector loads are used
+     * @tparam is_outer Boolean flag indicating whether outer join is peformed
      * @tparam CG Cooperative Group type
      * @tparam KeyEqual Binary callable type
      * @param g The Cooperative Group used to perform the count operation
@@ -990,8 +1006,8 @@ class static_multimap {
     /**
      * @brief Counts the occurrence of a given key contained in multimap using scalar loads.
      *
-     * @tparam uses_vector_load Boolean flag indicating whether vector loads are used or not
-     * @tparam is_outer Boolean flag indicating whether outer join is peformed or not
+     * @tparam uses_vector_load Boolean flag indicating whether vector loads are used
+     * @tparam is_outer Boolean flag indicating whether outer join is peformed
      * @tparam CG Cooperative Group type
      * @tparam KeyEqual Binary callable type
      * @param g The Cooperative Group used to perform the count operation
@@ -1008,8 +1024,8 @@ class static_multimap {
      * @brief Counts the occurrence of a given key/value pair contained in multimap using vector
      * loads.
      *
-     * @tparam uses_vector_load Boolean flag indicating whether vector loads are used or not
-     * @tparam is_outer Boolean flag indicating whether outer join is peformed or not
+     * @tparam uses_vector_load Boolean flag indicating whether vector loads are used
+     * @tparam is_outer Boolean flag indicating whether outer join is peformed
      * @tparam CG Cooperative Group type
      * @tparam PairEqual Binary callable type
      * @param g The Cooperative Group used to perform the pair_count operation
@@ -1026,8 +1042,8 @@ class static_multimap {
      * @brief Counts the occurrence of a given key/value pair contained in multimap using scalar
      * loads.
      *
-     * @tparam uses_vector_load Boolean flag indicating whether vector loads are used or not
-     * @tparam is_outer Boolean flag indicating whether outer join is peformed or not
+     * @tparam uses_vector_load Boolean flag indicating whether vector loads are used
+     * @tparam is_outer Boolean flag indicating whether outer join is peformed
      * @tparam CG Cooperative Group type
      * @tparam PairEqual Binary callable type
      * @param g The Cooperative Group used to perform the pair_count operation
@@ -1041,7 +1057,7 @@ class static_multimap {
       CG const& g, value_type const& pair, PairEqual pair_equal) noexcept;
 
     /**
-     * @brief Find all the matches of a given key contained in multimap using vector
+     * @brief Retrieves all the matches of a given key contained in multimap using vector
      * loads with per-warp shared memory buffer.
      *
      * For keys `k = *(first + i)` existing in the map, copies `k` and all associated values to
@@ -1049,7 +1065,7 @@ class static_multimap {
      * and the empty value sentinel into the output only if `is_outer` is true.
      *
      * @tparam buffer_size Size of the output buffer
-     * @tparam is_outer Boolean flag indicating whether outer join is peformed or not
+     * @tparam is_outer Boolean flag indicating whether outer join is peformed
      * @tparam warpT Warp (Cooperative Group) type
      * @tparam CG Cooperative Group type
      * @tparam atomicT Type of atomic storage
@@ -1083,7 +1099,7 @@ class static_multimap {
                                   KeyEqual key_equal) noexcept;
 
     /**
-     * @brief Find all the matches of a given key contained in multimap using scalar
+     * @brief Retrieves all the matches of a given key contained in multimap using scalar
      * loads with per-cg shared memory buffer.
      *
      * For keys `k = *(first + i)` existing in the map, copies `k` and all associated values to
@@ -1092,7 +1108,7 @@ class static_multimap {
      *
      * @tparam cg_size The number of threads in CUDA Cooperative Groups
      * @tparam buffer_size Size of the output buffer
-     * @tparam is_outer Boolean flag indicating whether outer join is peformed or not
+     * @tparam is_outer Boolean flag indicating whether outer join is peformed
      * @tparam CG Cooperative Group type
      * @tparam atomicT Type of atomic storage
      * @tparam OutputIt Device accessible output iterator whose `value_type` is
@@ -1123,7 +1139,7 @@ class static_multimap {
                                   KeyEqual key_equal) noexcept;
 
     /**
-     * @brief Find all the matches of a given pair contained in multimap using vector
+     * @brief Retrieves all the matches of a given pair contained in multimap using vector
      * loads with per-warp shared memory buffer.
      *
      * For pair `p`, if pair_equal(p, slot[j]) returns true, copies `p` to unspecified locations
@@ -1133,7 +1149,7 @@ class static_multimap {
      * the empty value sentinels into the output only if `is_outer` is true.
      *
      * @tparam buffer_size Size of the output buffer
-     * @tparam is_outer Boolean flag indicating whether outer join is peformed or not
+     * @tparam is_outer Boolean flag indicating whether outer join is peformed
      * @tparam warpT Warp (Cooperative Group) type
      * @tparam CG Cooperative Group type
      * @tparam atomicT Type of atomic storage
@@ -1172,7 +1188,7 @@ class static_multimap {
                                        PairEqual pair_equal) noexcept;
 
     /**
-     * @brief Find all the matches of a given pair contained in multimap using scalar
+     * @brief Retrieves all the matches of a given pair contained in multimap using scalar
      * loads with per-cg shared memory buffer.
      *
      * For pair `p`, if pair_equal(p, slot[j]) returns true, copies `p` to unspecified locations
@@ -1183,7 +1199,7 @@ class static_multimap {
      *
      * @tparam cg_size The number of threads in CUDA Cooperative Groups
      * @tparam buffer_size Size of the output buffer
-     * @tparam is_outer Boolean flag indicating whether outer join is peformed or not
+     * @tparam is_outer Boolean flag indicating whether outer join is peformed
      * @tparam CG Cooperative Group type
      * @tparam atomicT Type of atomic storage
      * @tparam OutputZipIt1 Device accessible output zip iterator for probe matches
@@ -1267,9 +1283,9 @@ class static_multimap {
     /**
      * @brief Indicates whether the key `k` was inserted into the map.
      *
-     * If the key `k` was inserted into the map, find returns
+     * If the key `k` was inserted into the map, `contains` returns
      * true. Otherwise, it returns false. Uses the CUDA Cooperative Groups API to
-     * to leverage multiple threads to perform a single contains operation. This provides a
+     * to leverage multiple threads to perform a single `contains` operation. This provides a
      * significant boost in throughput compared to the non Cooperative Group
      * `contains` at moderate to high load factors.
      *
@@ -1352,7 +1368,7 @@ class static_multimap {
                                             PairEqual pair_equal) noexcept;
 
     /**
-     * @brief Find all the matches of a given key contained in multimap using vector
+     * @brief Retrieves all the matches of a given key contained in multimap using vector
      * loads with per-warp shared memory buffer.
      *
      * For keys `k = *(first + i)` existing in the map, copies `k` and all associated values to
@@ -1392,7 +1408,7 @@ class static_multimap {
                              KeyEqual key_equal = KeyEqual{}) noexcept;
 
     /**
-     * @brief Find all the matches of a given key contained in multimap using vector
+     * @brief Retrieves all the matches of a given key contained in multimap using vector
      * loads with per-warp shared memory buffer.
      *
      * For keys `k = *(first + i)` existing in the map, copies `k` and all associated values to
@@ -1432,7 +1448,7 @@ class static_multimap {
                                    KeyEqual key_equal = KeyEqual{}) noexcept;
 
     /**
-     * @brief Find all the matches of a given key contained in multimap using scalar
+     * @brief Retrieves all the matches of a given key contained in multimap using scalar
      * loads with per-cg shared memory buffer.
      *
      * For keys `k = *(first + i)` existing in the map, copies `k` and all associated values to
@@ -1469,7 +1485,7 @@ class static_multimap {
                              KeyEqual key_equal = KeyEqual{}) noexcept;
 
     /**
-     * @brief Find all the matches of a given key contained in multimap using scalar
+     * @brief Retrieves all the matches of a given key contained in multimap using scalar
      * loads with per-cg shared memory buffer.
      *
      * For keys `k = *(first + i)` existing in the map, copies `k` and all associated values to
@@ -1507,7 +1523,7 @@ class static_multimap {
                                    KeyEqual key_equal = KeyEqual{}) noexcept;
 
     /**
-     * @brief Find all the matches of a given pair contained in multimap using vector
+     * @brief Retrieves all the matches of a given pair contained in multimap using vector
      * loads with per-warp shared memory buffer.
      *
      * For pair `p`, if pair_equal(p, slot[j]) returns true, copies `p` to unspecified locations
@@ -1553,7 +1569,7 @@ class static_multimap {
                                   PairEqual pair_equal) noexcept;
 
     /**
-     * @brief Find all the matches of a given pair contained in multimap using vector
+     * @brief Retrieves all the matches of a given pair contained in multimap using vector
      * loads with per-warp shared memory buffer.
      *
      * For pair `p`, if pair_equal(p, slot[j]) returns true, copies `p` to unspecified locations
@@ -1600,7 +1616,7 @@ class static_multimap {
                                         PairEqual pair_equal) noexcept;
 
     /**
-     * @brief Find all the matches of a given pair contained in multimap using scalar
+     * @brief Retrieves all the matches of a given pair contained in multimap using scalar
      * loads with per-cg shared memory buffer.
      *
      * For pair `p`, if pair_equal(p, slot[j]) returns true, copies `p` to unspecified locations
@@ -1644,7 +1660,7 @@ class static_multimap {
                                   PairEqual pair_equal) noexcept;
 
     /**
-     * @brief Find all the matches of a given pair contained in multimap using scalar
+     * @brief Retrieves all the matches of a given pair contained in multimap using scalar
      * loads with per-cg shared memory buffer.
      *
      * For pair `p`, if pair_equal(p, slot[j]) returns true, copies `p` to unspecified locations

From 17bdffe184ed5d9f5a5a31b9a2899bfacb7d78b7 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Fri, 17 Sep 2021 15:32:33 -0400
Subject: [PATCH 230/267] Updates: docs, examples and probe sequence taking
 map's Scope

---
 .../static_multimap/find_all_bench.cu         |   7 +-
 .../static_multimap_example.cu                |  11 +-
 include/cuco/detail/probe_sequences.cuh       |   4 +
 include/cuco/detail/static_multimap.inl       | 186 +++++++++---------
 include/cuco/static_multimap.cuh              |  10 +-
 tests/static_multimap/static_multimap_test.cu |  33 ++--
 6 files changed, 139 insertions(+), 112 deletions(-)

diff --git a/benchmarks/hash_table/static_multimap/find_all_bench.cu b/benchmarks/hash_table/static_multimap/find_all_bench.cu
index 3447aa1c2..d98aac8d4 100644
--- a/benchmarks/hash_table/static_multimap/find_all_bench.cu
+++ b/benchmarks/hash_table/static_multimap/find_all_bench.cu
@@ -72,8 +72,11 @@ std::enable_if_t<(sizeof(Key) == sizeof(Value)), void> nvbench_find_all(
   thrust::device_vector<Key> d_keys(h_keys);
   thrust::device_vector<cuco::pair_type<Key, Value>> d_pairs(h_pairs);
 
-  cuco::static_multimap<Key, Value, cuco::detail::double_hashing<Key, Value, CGSize>> map{
-    size, -1, -1};
+  cuco::static_multimap<Key,
+                        Value,
+                        cuda::thread_scope_device,
+                        cuco::detail::double_hashing<Key, Value, CGSize>>
+    map{size, -1, -1};
   map.insert(d_pairs.begin(), d_pairs.end());
 
   auto const output_size = map.count_outer(d_keys.begin(), d_keys.end());
diff --git a/examples/static_multimap/static_multimap_example.cu b/examples/static_multimap/static_multimap_example.cu
index 2ef03298d..cda8f2073 100644
--- a/examples/static_multimap/static_multimap_example.cu
+++ b/examples/static_multimap/static_multimap_example.cu
@@ -24,6 +24,9 @@
 
 int main(void)
 {
+  using key_type   = int;
+  using value_type = int;
+
   int empty_key_sentinel   = -1;
   int empty_value_sentinel = -1;
 
@@ -32,9 +35,9 @@ int main(void)
   // Constructs a multimap with 100,000 slots using -1 and -1 as the empty key/value
   // sentinels. Note the capacity is chosen knowing we will insert 50,000 keys,
   // for an load factor of 50%.
-  cuco::static_multimap<int, int> map{N * 2, empty_key_sentinel, empty_value_sentinel};
+  cuco::static_multimap<key_type, value_type> map{N * 2, empty_key_sentinel, empty_value_sentinel};
 
-  thrust::device_vector<thrust::pair<int, int>> pairs(N);
+  thrust::device_vector<thrust::pair<key_type, value_type>> pairs(N);
 
   // Create a sequence of pairs. Eeach key has two matches.
   // E.g., {{0,0}, {1,1}, ... {0,25'000}, {1, 25'001}, ...}
@@ -47,14 +50,14 @@ int main(void)
   map.insert(pairs.begin(), pairs.end());
 
   // Sequence of probe keys {0, 1, 2, ... 49'999}
-  thrust::device_vector<int> keys_to_find(N);
+  thrust::device_vector<key_type> keys_to_find(N);
   thrust::sequence(keys_to_find.begin(), keys_to_find.end(), 0);
 
   // Counts the occurrences of keys in [0, 50'000) contained in the multimap.
   // The `_outer` suffix indicates that the occurrence of a non-match is 1.
   auto const output_size = map.count_outer(keys_to_find.begin(), keys_to_find.end());
 
-  thrust::device_vector<cuco::pair_type<int, int>> d_results(output_size);
+  thrust::device_vector<cuco::pair_type<key_type, value_type>> d_results(output_size);
 
   // Finds all keys {0, 1, 2, ...} and stores associated key/value pairs into `d_results`
   // If a key `keys_to_find[i]` doesn't exist, `d_results[i].second == empty_value_sentinel`
diff --git a/include/cuco/detail/probe_sequences.cuh b/include/cuco/detail/probe_sequences.cuh
index 6bdc326a6..318be4e7d 100644
--- a/include/cuco/detail/probe_sequences.cuh
+++ b/include/cuco/detail/probe_sequences.cuh
@@ -112,6 +112,8 @@ class probe_sequence_base {
  * - Use linear probing only when collisions are rare. e.g. low occupancy or low multiplicity.
  * - `CGSize` = 1 or 2 when hash map is small (10'000'000 or less), 4 or 8 otherwise.
  *
+ * `Hash` should be callable object type.
+ *
  * @tparam Key Type used for keys
  * @tparam Value Type of the mapped values
  * @tparam CGSize Number of threads in CUDA Cooperative Groups
@@ -212,6 +214,8 @@ class linear_probing : public probe_sequence_base<Key, Value, CGSize, Scope> {
  * hints:
  * - `CGSize` = 1 or 2 when hash map is small (10'000'000 or less), 4 or 8 otherwise.
  *
+ * `Hash1` and `Hash2` should be callable object type.
+ *
  * @tparam Key Type used for keys
  * @tparam Value Type of the mapped values
  * @tparam CGSize Number of threads in CUDA Cooperative Groups
diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index 41fb05ceb..d7ea00225 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -23,10 +23,10 @@ namespace cuco {
 
 template <typename Key,
           typename Value,
-          class ProbeSequence,
           cuda::thread_scope Scope,
+          class ProbeSequence,
           typename Allocator>
-static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::static_multimap()
+static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::static_multimap()
   : delete_counter_{counter_allocator_, stream_},
     delete_slots_{slot_allocator_, capacity_, stream_},
     d_counter_{nullptr, delete_counter_},
@@ -36,10 +36,10 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::static_multimap()
 
 template <typename Key,
           typename Value,
-          class ProbeSequence,
           cuda::thread_scope Scope,
+          class ProbeSequence,
           typename Allocator>
-static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::static_multimap(
+static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::static_multimap(
   std::size_t capacity,
   Key empty_key_sentinel,
   Value empty_value_sentinel,
@@ -68,11 +68,11 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::static_multimap(
 
 template <typename Key,
           typename Value,
-          class ProbeSequence,
           cuda::thread_scope Scope,
+          class ProbeSequence,
           typename Allocator>
 template <typename InputIt>
-void static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::insert(InputIt first,
+void static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::insert(InputIt first,
                                                                           InputIt last,
                                                                           cudaStream_t stream)
 {
@@ -90,11 +90,11 @@ void static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::insert(InputI
 
 template <typename Key,
           typename Value,
-          class ProbeSequence,
           cuda::thread_scope Scope,
+          class ProbeSequence,
           typename Allocator>
 template <typename InputIt, typename StencilIt, typename Predicate>
-void static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::insert_if(
+void static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::insert_if(
   InputIt first, InputIt last, StencilIt stencil, Predicate pred, cudaStream_t stream)
 {
   auto num_elements = std::distance(first, last);
@@ -112,11 +112,11 @@ void static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::insert_if(
 
 template <typename Key,
           typename Value,
-          class ProbeSequence,
           cuda::thread_scope Scope,
+          class ProbeSequence,
           typename Allocator>
 template <typename InputIt, typename OutputIt, typename KeyEqual>
-void static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::contains(
+void static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::contains(
   InputIt first, InputIt last, OutputIt output_begin, cudaStream_t stream, KeyEqual key_equal) const
 {
   auto num_keys = std::distance(first, last);
@@ -133,11 +133,11 @@ void static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::contains(
 
 template <typename Key,
           typename Value,
-          class ProbeSequence,
           cuda::thread_scope Scope,
+          class ProbeSequence,
           typename Allocator>
 template <typename InputIt, typename KeyEqual>
-std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::count(
+std::size_t static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::count(
   InputIt first, InputIt last, cudaStream_t stream, KeyEqual key_equal) const
 {
   auto num_keys = std::distance(first, last);
@@ -164,11 +164,11 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::count(
 
 template <typename Key,
           typename Value,
-          class ProbeSequence,
           cuda::thread_scope Scope,
+          class ProbeSequence,
           typename Allocator>
 template <typename InputIt, typename KeyEqual>
-std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::count_outer(
+std::size_t static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::count_outer(
   InputIt first, InputIt last, cudaStream_t stream, KeyEqual key_equal) const
 {
   auto num_keys = std::distance(first, last);
@@ -195,11 +195,11 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::count_
 
 template <typename Key,
           typename Value,
-          class ProbeSequence,
           cuda::thread_scope Scope,
+          class ProbeSequence,
           typename Allocator>
 template <typename InputIt, typename PairEqual>
-std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::pair_count(
+std::size_t static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::pair_count(
   InputIt first, InputIt last, PairEqual pair_equal, cudaStream_t stream) const
 {
   auto num_keys = std::distance(first, last);
@@ -227,11 +227,11 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::pair_c
 
 template <typename Key,
           typename Value,
-          class ProbeSequence,
           cuda::thread_scope Scope,
+          class ProbeSequence,
           typename Allocator>
 template <typename InputIt, typename PairEqual>
-std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::pair_count_outer(
+std::size_t static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::pair_count_outer(
   InputIt first, InputIt last, PairEqual pair_equal, cudaStream_t stream) const
 {
   auto num_keys = std::distance(first, last);
@@ -259,11 +259,11 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::pair_c
 
 template <typename Key,
           typename Value,
-          class ProbeSequence,
           cuda::thread_scope Scope,
+          class ProbeSequence,
           typename Allocator>
 template <typename InputIt, typename OutputIt, typename KeyEqual>
-OutputIt static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::retrieve(
+OutputIt static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::retrieve(
   InputIt first, InputIt last, OutputIt output_begin, cudaStream_t stream, KeyEqual key_equal) const
 {
   auto num_keys = std::distance(first, last);
@@ -325,11 +325,11 @@ OutputIt static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::retrieve(
 
 template <typename Key,
           typename Value,
-          class ProbeSequence,
           cuda::thread_scope Scope,
+          class ProbeSequence,
           typename Allocator>
 template <typename InputIt, typename OutputIt, typename KeyEqual>
-OutputIt static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::retrieve_outer(
+OutputIt static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::retrieve_outer(
   InputIt first, InputIt last, OutputIt output_begin, cudaStream_t stream, KeyEqual key_equal) const
 {
   auto num_keys = std::distance(first, last);
@@ -391,11 +391,11 @@ OutputIt static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::retrieve_
 
 template <typename Key,
           typename Value,
-          class ProbeSequence,
           cuda::thread_scope Scope,
+          class ProbeSequence,
           typename Allocator>
 template <typename InputIt, typename OutputZipIt1, typename OutputZipIt2, typename PairEqual>
-std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::pair_retrieve(
+std::size_t static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::pair_retrieve(
   InputIt first,
   InputIt last,
   OutputZipIt1 probe_output_begin,
@@ -473,11 +473,11 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::pair_r
 
 template <typename Key,
           typename Value,
-          class ProbeSequence,
           cuda::thread_scope Scope,
+          class ProbeSequence,
           typename Allocator>
 template <typename InputIt, typename OutputZipIt1, typename OutputZipIt2, typename PairEqual>
-std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::pair_retrieve_outer(
+std::size_t static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::pair_retrieve_outer(
   InputIt first,
   InputIt last,
   OutputZipIt1 probe_output_begin,
@@ -555,11 +555,11 @@ std::size_t static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::pair_r
 
 template <typename Key,
           typename Value,
-          class ProbeSequence,
           cuda::thread_scope Scope,
+          class ProbeSequence,
           typename Allocator>
 __device__ void
-static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view_base::load_pair_array(
+static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view_base::load_pair_array(
   value_type* arr, const_iterator current_slot) noexcept
 {
   if constexpr (sizeof(value_type) == 4) {
@@ -573,12 +573,12 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view_base::
 
 template <typename Key,
           typename Value,
-          class ProbeSequence,
           cuda::thread_scope Scope,
+          class ProbeSequence,
           typename Allocator>
 __device__
-  static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_mutable_view::insert_result
-  static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_mutable_view::packed_cas(
+  static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_mutable_view::insert_result
+  static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_mutable_view::packed_cas(
     iterator current_slot, value_type const& insert_pair) noexcept
 {
   auto expected_key   = this->get_empty_key_sentinel();
@@ -601,12 +601,12 @@ __device__
 
 template <typename Key,
           typename Value,
-          class ProbeSequence,
           cuda::thread_scope Scope,
+          class ProbeSequence,
           typename Allocator>
 __device__
-  static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_mutable_view::insert_result
-  static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_mutable_view::
+  static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_mutable_view::insert_result
+  static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_mutable_view::
     back_to_back_cas(iterator current_slot, value_type const& insert_pair) noexcept
 {
   using cuda::std::memory_order_relaxed;
@@ -640,12 +640,12 @@ __device__
 
 template <typename Key,
           typename Value,
-          class ProbeSequence,
           cuda::thread_scope Scope,
+          class ProbeSequence,
           typename Allocator>
 __device__
-  static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_mutable_view::insert_result
-  static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_mutable_view::
+  static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_mutable_view::insert_result
+  static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_mutable_view::
     cas_dependent_write(iterator current_slot, value_type const& insert_pair) noexcept
 {
   using cuda::std::memory_order_relaxed;
@@ -667,12 +667,12 @@ __device__
 
 template <typename Key,
           typename Value,
-          class ProbeSequence,
           cuda::thread_scope Scope,
+          class ProbeSequence,
           typename Allocator>
 template <bool uses_vector_load, typename CG>
 __device__ std::enable_if_t<uses_vector_load, void>
-static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_mutable_view::insert_impl(
+static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_mutable_view::insert_impl(
   CG g, value_type const& insert_pair) noexcept
 {
   auto current_slot = initial_slot(g, insert_pair.first);
@@ -714,12 +714,12 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_mutable_vie
 
 template <typename Key,
           typename Value,
-          class ProbeSequence,
           cuda::thread_scope Scope,
+          class ProbeSequence,
           typename Allocator>
 template <bool uses_vector_load, typename CG>
 __device__ std::enable_if_t<not uses_vector_load, void>
-static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_mutable_view::insert_impl(
+static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_mutable_view::insert_impl(
   CG g, value_type const& insert_pair) noexcept
 {
   auto current_slot = initial_slot(g, insert_pair.first);
@@ -763,12 +763,12 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_mutable_vie
 
 template <typename Key,
           typename Value,
-          class ProbeSequence,
           cuda::thread_scope Scope,
+          class ProbeSequence,
           typename Allocator>
 template <typename CG>
 __device__ void
-static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_mutable_view::insert(
+static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_mutable_view::insert(
   CG g, value_type const& insert_pair) noexcept
 {
   insert_impl<uses_vector_load()>(g, insert_pair);
@@ -776,12 +776,12 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_mutable_vie
 
 template <typename Key,
           typename Value,
-          class ProbeSequence,
           cuda::thread_scope Scope,
+          class ProbeSequence,
           typename Allocator>
 template <bool uses_vector_load, typename CG, typename KeyEqual>
 __device__ std::enable_if_t<uses_vector_load, bool>
-static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::contains_impl(
+static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::contains_impl(
   CG g, Key const& k, KeyEqual key_equal) noexcept
 {
   auto current_slot = initial_slot(g, k);
@@ -811,12 +811,12 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::conta
 
 template <typename Key,
           typename Value,
-          class ProbeSequence,
           cuda::thread_scope Scope,
+          class ProbeSequence,
           typename Allocator>
 template <bool uses_vector_load, typename CG, typename KeyEqual>
 __device__ std::enable_if_t<not uses_vector_load, bool>
-static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::contains_impl(
+static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::contains_impl(
   CG g, Key const& k, KeyEqual key_equal) noexcept
 {
   auto current_slot = initial_slot(g, k);
@@ -846,12 +846,12 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::conta
 
 template <typename Key,
           typename Value,
-          class ProbeSequence,
           cuda::thread_scope Scope,
+          class ProbeSequence,
           typename Allocator>
 template <bool uses_vector_load, bool is_outer, typename CG, typename KeyEqual>
 __device__ std::enable_if_t<uses_vector_load, std::size_t>
-static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::count_impl(
+static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::count_impl(
   CG const& g, Key const& k, KeyEqual key_equal) noexcept
 {
   std::size_t count = 0;
@@ -889,12 +889,12 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::count
 
 template <typename Key,
           typename Value,
-          class ProbeSequence,
           cuda::thread_scope Scope,
+          class ProbeSequence,
           typename Allocator>
 template <bool uses_vector_load, bool is_outer, typename CG, typename KeyEqual>
 __device__ std::enable_if_t<not uses_vector_load, std::size_t>
-static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::count_impl(
+static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::count_impl(
   CG const& g, Key const& k, KeyEqual key_equal) noexcept
 {
   std::size_t count = 0;
@@ -928,12 +928,12 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::count
 
 template <typename Key,
           typename Value,
-          class ProbeSequence,
           cuda::thread_scope Scope,
+          class ProbeSequence,
           typename Allocator>
 template <bool uses_vector_load, bool is_outer, typename CG, typename PairEqual>
 __device__ std::enable_if_t<uses_vector_load, std::size_t>
-static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_count_impl(
+static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::pair_count_impl(
   CG const& g, value_type const& pair, PairEqual pair_equal) noexcept
 {
   std::size_t count = 0;
@@ -973,12 +973,12 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_
 
 template <typename Key,
           typename Value,
-          class ProbeSequence,
           cuda::thread_scope Scope,
+          class ProbeSequence,
           typename Allocator>
 template <bool uses_vector_load, bool is_outer, typename CG, typename PairEqual>
 __device__ std::enable_if_t<not uses_vector_load, std::size_t>
-static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_count_impl(
+static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::pair_count_impl(
   CG const& g, value_type const& pair, PairEqual pair_equal) noexcept
 {
   std::size_t count = 0;
@@ -1014,8 +1014,8 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_
 
 template <typename Key,
           typename Value,
-          class ProbeSequence,
           cuda::thread_scope Scope,
+          class ProbeSequence,
           typename Allocator>
 template <uint32_t buffer_size,
           bool is_outer,
@@ -1025,7 +1025,7 @@ template <uint32_t buffer_size,
           typename OutputIt,
           typename KeyEqual>
 __device__ void
-static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::retrieve_impl(
+static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::retrieve_impl(
   warpT const& warp,
   CG const& g,
   Key const& k,
@@ -1107,8 +1107,8 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::retri
 
 template <typename Key,
           typename Value,
-          class ProbeSequence,
           cuda::thread_scope Scope,
+          class ProbeSequence,
           typename Allocator>
 template <uint32_t cg_size,
           uint32_t buffer_size,
@@ -1118,7 +1118,7 @@ template <uint32_t cg_size,
           typename OutputIt,
           typename KeyEqual>
 __device__ void
-static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::retrieve_impl(
+static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::retrieve_impl(
   CG const& g,
   Key const& k,
   uint32_t* cg_counter,
@@ -1185,8 +1185,8 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::retri
 
 template <typename Key,
           typename Value,
-          class ProbeSequence,
           cuda::thread_scope Scope,
+          class ProbeSequence,
           typename Allocator>
 template <uint32_t buffer_size,
           bool is_outer,
@@ -1197,7 +1197,7 @@ template <uint32_t buffer_size,
           typename OutputZipIt2,
           typename PairEqual>
 __device__ void
-static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_retrieve_impl(
+static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::pair_retrieve_impl(
   warpT const& warp,
   CG const& g,
   value_type const& pair,
@@ -1287,8 +1287,8 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_
 
 template <typename Key,
           typename Value,
-          class ProbeSequence,
           cuda::thread_scope Scope,
+          class ProbeSequence,
           typename Allocator>
 template <uint32_t cg_size,
           uint32_t buffer_size,
@@ -1299,7 +1299,7 @@ template <uint32_t cg_size,
           typename OutputZipIt2,
           typename PairEqual>
 __device__ void
-static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_retrieve_impl(
+static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::pair_retrieve_impl(
   CG const& g,
   value_type const& pair,
   uint32_t* cg_counter,
@@ -1376,12 +1376,12 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_
 
 template <typename Key,
           typename Value,
-          class ProbeSequence,
           cuda::thread_scope Scope,
+          class ProbeSequence,
           typename Allocator>
 template <typename CG, typename atomicT, typename OutputIt>
 __inline__ __device__ void
-static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::flush_output_buffer(
+static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::flush_output_buffer(
   CG const& g,
   uint32_t const num_outputs,
   value_type* output_buffer,
@@ -1421,12 +1421,12 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::flush
 
 template <typename Key,
           typename Value,
-          class ProbeSequence,
           cuda::thread_scope Scope,
+          class ProbeSequence,
           typename Allocator>
 template <typename CG, typename atomicT, typename OutputZipIt1, typename OutputZipIt2>
 __inline__ __device__ void
-static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::flush_output_buffer(
+static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::flush_output_buffer(
   CG const& g,
   uint32_t const num_outputs,
   value_type* probe_output_buffer,
@@ -1454,11 +1454,11 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::flush
 
 template <typename Key,
           typename Value,
-          class ProbeSequence,
           cuda::thread_scope Scope,
+          class ProbeSequence,
           typename Allocator>
 template <typename CG, typename KeyEqual>
-__device__ bool static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::contains(
+__device__ bool static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::contains(
   CG g, Key const& k, KeyEqual key_equal) noexcept
 {
   return contains_impl<uses_vector_load()>(g, k, key_equal);
@@ -1466,12 +1466,12 @@ __device__ bool static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::de
 
 template <typename Key,
           typename Value,
-          class ProbeSequence,
           cuda::thread_scope Scope,
+          class ProbeSequence,
           typename Allocator>
 template <typename CG, typename KeyEqual>
 __device__ std::size_t
-static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::count(
+static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::count(
   CG const& g, Key const& k, KeyEqual key_equal) noexcept
 {
   constexpr bool is_outer = false;
@@ -1480,12 +1480,12 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::count
 
 template <typename Key,
           typename Value,
-          class ProbeSequence,
           cuda::thread_scope Scope,
+          class ProbeSequence,
           typename Allocator>
 template <typename CG, typename KeyEqual>
 __device__ std::size_t
-static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::count_outer(
+static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::count_outer(
   CG const& g, Key const& k, KeyEqual key_equal) noexcept
 {
   constexpr bool is_outer = true;
@@ -1494,12 +1494,12 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::count
 
 template <typename Key,
           typename Value,
-          class ProbeSequence,
           cuda::thread_scope Scope,
+          class ProbeSequence,
           typename Allocator>
 template <typename CG, typename PairEqual>
 __device__ std::size_t
-static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_count(
+static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::pair_count(
   CG const& g, value_type const& pair, PairEqual pair_equal) noexcept
 {
   constexpr bool is_outer = false;
@@ -1508,12 +1508,12 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_
 
 template <typename Key,
           typename Value,
-          class ProbeSequence,
           cuda::thread_scope Scope,
+          class ProbeSequence,
           typename Allocator>
 template <typename CG, typename PairEqual>
 __device__ std::size_t
-static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_count_outer(
+static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::pair_count_outer(
   CG const& g, value_type const& pair, PairEqual pair_equal) noexcept
 {
   constexpr bool is_outer = true;
@@ -1522,8 +1522,8 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_
 
 template <typename Key,
           typename Value,
-          class ProbeSequence,
           cuda::thread_scope Scope,
+          class ProbeSequence,
           typename Allocator>
 template <uint32_t buffer_size,
           typename warpT,
@@ -1531,7 +1531,7 @@ template <uint32_t buffer_size,
           typename atomicT,
           typename OutputIt,
           typename KeyEqual>
-__device__ void static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::retrieve(
+__device__ void static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::retrieve(
   warpT const& warp,
   CG const& g,
   Key const& k,
@@ -1548,8 +1548,8 @@ __device__ void static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::de
 
 template <typename Key,
           typename Value,
-          class ProbeSequence,
           cuda::thread_scope Scope,
+          class ProbeSequence,
           typename Allocator>
 template <uint32_t buffer_size,
           typename warpT,
@@ -1558,7 +1558,7 @@ template <uint32_t buffer_size,
           typename OutputIt,
           typename KeyEqual>
 __device__ void
-static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::retrieve_outer(
+static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::retrieve_outer(
   warpT const& warp,
   CG const& g,
   Key const& k,
@@ -1575,8 +1575,8 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::retri
 
 template <typename Key,
           typename Value,
-          class ProbeSequence,
           cuda::thread_scope Scope,
+          class ProbeSequence,
           typename Allocator>
 template <uint32_t cg_size,
           uint32_t buffer_size,
@@ -1584,7 +1584,7 @@ template <uint32_t cg_size,
           typename atomicT,
           typename OutputIt,
           typename KeyEqual>
-__device__ void static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::retrieve(
+__device__ void static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::retrieve(
   CG const& g,
   Key const& k,
   uint32_t* cg_counter,
@@ -1600,8 +1600,8 @@ __device__ void static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::de
 
 template <typename Key,
           typename Value,
-          class ProbeSequence,
           cuda::thread_scope Scope,
+          class ProbeSequence,
           typename Allocator>
 template <uint32_t cg_size,
           uint32_t buffer_size,
@@ -1610,7 +1610,7 @@ template <uint32_t cg_size,
           typename OutputIt,
           typename KeyEqual>
 __device__ void
-static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::retrieve_outer(
+static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::retrieve_outer(
   CG const& g,
   Key const& k,
   uint32_t* cg_counter,
@@ -1626,8 +1626,8 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::retri
 
 template <typename Key,
           typename Value,
-          class ProbeSequence,
           cuda::thread_scope Scope,
+          class ProbeSequence,
           typename Allocator>
 template <uint32_t buffer_size,
           typename warpT,
@@ -1637,7 +1637,7 @@ template <uint32_t buffer_size,
           typename OutputZipIt2,
           typename PairEqual>
 __device__ void
-static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_retrieve(
+static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::pair_retrieve(
   warpT const& warp,
   CG const& g,
   value_type const& pair,
@@ -1664,8 +1664,8 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_
 
 template <typename Key,
           typename Value,
-          class ProbeSequence,
           cuda::thread_scope Scope,
+          class ProbeSequence,
           typename Allocator>
 template <uint32_t buffer_size,
           typename warpT,
@@ -1675,7 +1675,7 @@ template <uint32_t buffer_size,
           typename OutputZipIt2,
           typename PairEqual>
 __device__ void
-static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_retrieve_outer(
+static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::pair_retrieve_outer(
   warpT const& warp,
   CG const& g,
   value_type const& pair,
@@ -1702,8 +1702,8 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_
 
 template <typename Key,
           typename Value,
-          class ProbeSequence,
           cuda::thread_scope Scope,
+          class ProbeSequence,
           typename Allocator>
 template <uint32_t cg_size,
           uint32_t buffer_size,
@@ -1713,7 +1713,7 @@ template <uint32_t cg_size,
           typename OutputZipIt2,
           typename PairEqual>
 __device__ void
-static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_retrieve(
+static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::pair_retrieve(
   CG const& g,
   value_type const& pair,
   uint32_t* cg_counter,
@@ -1738,8 +1738,8 @@ static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_
 
 template <typename Key,
           typename Value,
-          class ProbeSequence,
           cuda::thread_scope Scope,
+          class ProbeSequence,
           typename Allocator>
 template <uint32_t cg_size,
           uint32_t buffer_size,
@@ -1749,7 +1749,7 @@ template <uint32_t cg_size,
           typename OutputZipIt2,
           typename PairEqual>
 __device__ void
-static_multimap<Key, Value, ProbeSequence, Scope, Allocator>::device_view::pair_retrieve_outer(
+static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::pair_retrieve_outer(
   CG const& g,
   value_type const& pair,
   uint32_t* cg_counter,
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index c9b46f813..caa6fa6e7 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -117,15 +117,21 @@ namespace cuco {
  *
  * @tparam Key Type used for keys
  * @tparam Value Type of the mapped values
- * @tparam ProbeSequence Probe sequence defined in `detail/probe_sequence.cuh`
  * @tparam Scope The scope in which multimap operations will be performed by
  * individual threads
+ * @tparam ProbeSequence Probe sequence chosen between `cuco::detail::linear_probing`
+ * and `cuco::detail::double_hashing`. (see `detail/probe_sequences.cuh`)
  * @tparam Allocator Type of allocator used for device storage
  */
 template <typename Key,
           typename Value,
-          class ProbeSequence      = cuco::detail::double_hashing<Key, Value>,
           cuda::thread_scope Scope = cuda::thread_scope_device,
+          class ProbeSequence      = cuco::detail::double_hashing<Key,
+                                                             Value,
+                                                             2,
+                                                             cuco::detail::MurmurHash3_32<Key>,
+                                                             cuco::detail::MurmurHash3_32<Key>,
+                                                             Scope>,
           typename Allocator       = cuco::cuda_allocator<char>>
 class static_multimap {
   static_assert(
diff --git a/tests/static_multimap/static_multimap_test.cu b/tests/static_multimap/static_multimap_test.cu
index bc4f27f3e..3f5495e2c 100644
--- a/tests/static_multimap/static_multimap_test.cu
+++ b/tests/static_multimap/static_multimap_test.cu
@@ -238,15 +238,13 @@ TEMPLATE_TEST_CASE_SIG("User defined key and value type",
   if constexpr (Probe == probe_sequence::linear_probing) {
     cuco::static_multimap<key_pair,
                           value_pair,
+                          cuda::thread_scope_device,
                           cuco::detail::linear_probing<key_pair, value_pair, 1, hash_key_pair>>
       map{capacity, sentinel_key, sentinel_value};
     test_custom_key_value_type<Key, Value>(map, insert_pairs, insert_keys.begin(), num_pairs);
   }
   if constexpr (Probe == probe_sequence::double_hashing) {
-    cuco::static_multimap<Key,
-                          Value,
-                          cuco::detail::double_hashing<Key, Value, 2, hash_key_pair, hash_key_pair>>
-      map{capacity, sentinel_key, sentinel_value};
+    cuco::static_multimap<Key, Value> map{capacity, sentinel_key, sentinel_value};
     test_custom_key_value_type<Key, Value>(map, insert_pairs, insert_keys.begin(), num_pairs);
   }
 }
@@ -339,7 +337,11 @@ TEMPLATE_TEST_CASE_SIG("Multiplicity equals two",
   thrust::device_vector<cuco::pair_type<Key, Value>> d_results(num_items);
 
   if constexpr (Probe == probe_sequence::linear_probing) {
-    cuco::static_multimap<Key, Value, cuco::detail::linear_probing<Key, Value, 1>> map{5, -1, -1};
+    cuco::static_multimap<Key,
+                          Value,
+                          cuda::thread_scope_device,
+                          cuco::detail::linear_probing<Key, Value, 1>>
+      map{5, -1, -1};
     test_multiplicity_two(map, d_pairs.begin(), d_keys.begin(), d_results.begin(), num_items);
   }
   if constexpr (Probe == probe_sequence::double_hashing) {
@@ -408,8 +410,11 @@ TEMPLATE_TEST_CASE_SIG("Tests of non-matches",
                     });
 
   if constexpr (Probe == probe_sequence::linear_probing) {
-    cuco::static_multimap<Key, Value, cuco::detail::linear_probing<Key, Value, 1>> map{
-      num_keys * 2, -1, -1};
+    cuco::static_multimap<Key,
+                          Value,
+                          cuda::thread_scope_device,
+                          cuco::detail::linear_probing<Key, Value, 1>>
+      map{num_keys * 2, -1, -1};
     test_non_matches<Key, Value>(map, d_pairs.begin(), d_keys.begin(), num_keys);
   }
   if constexpr (Probe == probe_sequence::double_hashing) {
@@ -457,8 +462,11 @@ TEMPLATE_TEST_CASE_SIG("Tests of insert_if",
                     });
 
   if constexpr (Probe == probe_sequence::linear_probing) {
-    cuco::static_multimap<Key, Value, cuco::detail::linear_probing<Key, Value, 1>> map{
-      num_keys * 2, -1, -1};
+    cuco::static_multimap<Key,
+                          Value,
+                          cuda::thread_scope_device,
+                          cuco::detail::linear_probing<Key, Value, 1>>
+      map{num_keys * 2, -1, -1};
     test_insert_if<Key>(map, d_pairs.begin(), d_keys.begin(), num_keys);
   }
   if constexpr (Probe == probe_sequence::double_hashing) {
@@ -550,8 +558,11 @@ TEMPLATE_TEST_CASE_SIG("Tests of pair functions",
                     });
 
   if constexpr (Probe == probe_sequence::linear_probing) {
-    cuco::static_multimap<Key, Value, cuco::detail::linear_probing<Key, Value, 1>> map{
-      num_pairs * 2, -1, -1};
+    cuco::static_multimap<Key,
+                          Value,
+                          cuda::thread_scope_device,
+                          cuco::detail::linear_probing<Key, Value, 1>>
+      map{num_pairs * 2, -1, -1};
     test_pair_functions<Key, Value>(map, d_pairs.begin(), num_pairs);
   }
   if constexpr (Probe == probe_sequence::double_hashing) {

From c26366acbef5dbae09d6e8e323a6014aae91fad6 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Fri, 17 Sep 2021 17:22:01 -0400
Subject: [PATCH 231/267] Add thorough tests for retrieve functions

---
 tests/static_multimap/static_multimap_test.cu | 138 +++++++++++++++---
 1 file changed, 121 insertions(+), 17 deletions(-)

diff --git a/tests/static_multimap/static_multimap_test.cu b/tests/static_multimap/static_multimap_test.cu
index 3f5495e2c..d43650a54 100644
--- a/tests/static_multimap/static_multimap_test.cu
+++ b/tests/static_multimap/static_multimap_test.cu
@@ -81,11 +81,6 @@ struct alignas(8) value_pair {
 #define SIZE 10
 __device__ int A[SIZE];
 
-template <typename T>
-struct custom_equals {
-  __device__ bool operator()(T lhs, T rhs) { return A[lhs] == A[rhs]; }
-};
-
 template <typename Key, typename Value, typename Map, typename PairIt, typename KeyIt>
 __inline__ void test_custom_key_value_type(Map& map,
                                            PairIt pair_begin,
@@ -126,7 +121,7 @@ __inline__ void test_custom_key_value_type(Map& map,
       }));
   }
 
-  SECTION("Outer functions include non-matches in the output while normal functions don't")
+  SECTION("Non-matches are not included in the output")
   {
     map.insert(pair_begin, pair_begin + num_pairs);
 
@@ -144,11 +139,6 @@ __inline__ void test_custom_key_value_type(Map& map,
     auto count = map.count(query_key_begin, query_key_begin + num, stream, key_pair_equals{});
     REQUIRE(count == num_pairs);
 
-    auto count_outer =
-      map.count_outer(query_key_begin, query_key_begin + num, stream, key_pair_equals{});
-    REQUIRE(count_outer == num);
-
-    ////////////////////// tests of retreive
     thrust::device_vector<cuco::pair_type<Key, Value>> found_pairs(num_pairs);
     auto output_end = map.retrieve(
       query_key_begin, query_key_begin + num, found_pairs.begin(), stream, key_pair_equals{});
@@ -172,10 +162,29 @@ __inline__ void test_custom_key_value_type(Map& map,
       [] __device__(cuco::pair_type<Key, Value> lhs, cuco::pair_type<Key, Value> rhs) {
         return lhs.first.a == rhs.first.a;
       }));
+  }
+
+  SECTION("Outer functions include non-matches in the output")
+  {
+    map.insert(pair_begin, pair_begin + num_pairs);
+
+    auto const num = num_pairs * 2;
+    thrust::device_vector<Key> query_keys(num);
+    auto query_key_begin = query_keys.begin();
+    thrust::transform(thrust::device,
+                      thrust::counting_iterator<int>(0),
+                      thrust::counting_iterator<int>(num),
+                      query_key_begin,
+                      [] __device__(auto i) {
+                        return Key{i, i};
+                      });
 
-    ////////////////////// tests of retreive_outer
-    found_pairs.resize(num);
-    output_end = map.retrieve_outer(
+    auto count_outer =
+      map.count_outer(query_key_begin, query_key_begin + num, stream, key_pair_equals{});
+    REQUIRE(count_outer == num);
+
+    thrust::device_vector<cuco::pair_type<Key, Value>> found_pairs(num);
+    auto output_end = map.retrieve_outer(
       query_key_begin, query_key_begin + num, found_pairs.begin(), stream, key_pair_equals{});
     auto size_outer = output_end - found_pairs.begin();
 
@@ -249,7 +258,12 @@ TEMPLATE_TEST_CASE_SIG("User defined key and value type",
   }
 }
 
-template <typename Map, typename PairIt, typename KeyIt, typename ResultIt>
+template <typename Key,
+          typename Value,
+          typename Map,
+          typename PairIt,
+          typename KeyIt,
+          typename ResultIt>
 __inline__ void test_multiplicity_two(
   Map& map, PairIt pair_begin, KeyIt key_begin, ResultIt result_begin, std::size_t num_items)
 {
@@ -286,6 +300,25 @@ __inline__ void test_multiplicity_two(
     auto size         = thrust::distance(output_begin, output_end);
 
     REQUIRE(size == num_items);
+
+    // sort before compare
+    thrust::sort(thrust::device,
+                 output_begin,
+                 output_end,
+                 [] __device__(const cuco::pair_type<Key, Value>& lhs,
+                               const cuco::pair_type<Key, Value>& rhs) {
+                   if (lhs.first != rhs.first) { return lhs.first < rhs.first; }
+                   return lhs.second < rhs.second;
+                 });
+
+    REQUIRE(thrust::equal(
+      thrust::device,
+      pair_begin,
+      pair_begin + num_items,
+      output_begin,
+      [] __device__(cuco::pair_type<Key, Value> lhs, cuco::pair_type<Key, Value> rhs) {
+        return lhs.first == rhs.first and lhs.second == rhs.second;
+      }));
   }
 
   SECTION("count and count_outer should return the same value.")
@@ -306,6 +339,25 @@ __inline__ void test_multiplicity_two(
     auto size_outer = thrust::distance(output_begin, output_end);
 
     REQUIRE(size == size_outer);
+
+    // sort before compare
+    thrust::sort(thrust::device,
+                 output_begin,
+                 output_end,
+                 [] __device__(const cuco::pair_type<Key, Value>& lhs,
+                               const cuco::pair_type<Key, Value>& rhs) {
+                   if (lhs.first != rhs.first) { return lhs.first < rhs.first; }
+                   return lhs.second < rhs.second;
+                 });
+
+    REQUIRE(thrust::equal(
+      thrust::device,
+      pair_begin,
+      pair_begin + num_items,
+      output_begin,
+      [] __device__(cuco::pair_type<Key, Value> lhs, cuco::pair_type<Key, Value> rhs) {
+        return lhs.first == rhs.first and lhs.second == rhs.second;
+      }));
   }
 }
 
@@ -342,11 +394,13 @@ TEMPLATE_TEST_CASE_SIG("Multiplicity equals two",
                           cuda::thread_scope_device,
                           cuco::detail::linear_probing<Key, Value, 1>>
       map{5, -1, -1};
-    test_multiplicity_two(map, d_pairs.begin(), d_keys.begin(), d_results.begin(), num_items);
+    test_multiplicity_two<Key, Value>(
+      map, d_pairs.begin(), d_keys.begin(), d_results.begin(), num_items);
   }
   if constexpr (Probe == probe_sequence::double_hashing) {
     cuco::static_multimap<Key, Value> map{5, -1, -1};
-    test_multiplicity_two(map, d_pairs.begin(), d_keys.begin(), d_results.begin(), num_items);
+    test_multiplicity_two<Key, Value>(
+      map, d_pairs.begin(), d_keys.begin(), d_results.begin(), num_items);
   }
 }
 
@@ -367,6 +421,25 @@ __inline__ void test_non_matches(Map& map, PairIt pair_begin, KeyIt key_begin, s
     auto size         = thrust::distance(output_begin, output_end);
 
     REQUIRE(size == num_keys);
+
+    // sort before compare
+    thrust::sort(thrust::device,
+                 output_begin,
+                 output_end,
+                 [] __device__(const cuco::pair_type<Key, Value>& lhs,
+                               const cuco::pair_type<Key, Value>& rhs) {
+                   if (lhs.first != rhs.first) { return lhs.first < rhs.first; }
+                   return lhs.second < rhs.second;
+                 });
+
+    REQUIRE(thrust::equal(
+      thrust::device,
+      pair_begin,
+      pair_begin + num_keys,
+      output_begin,
+      [] __device__(cuco::pair_type<Key, Value> lhs, cuco::pair_type<Key, Value> rhs) {
+        return lhs.first == rhs.first and lhs.second == rhs.second;
+      }));
   }
 
   SECTION("Output of count_outer and retrieve_outer should be coherent.")
@@ -381,6 +454,37 @@ __inline__ void test_non_matches(Map& map, PairIt pair_begin, KeyIt key_begin, s
     auto size         = thrust::distance(output_begin, output_end);
 
     REQUIRE(size == (num_keys + num_keys / 2));
+
+    // sort before compare
+    thrust::sort(thrust::device,
+                 output_begin,
+                 output_end,
+                 [] __device__(const cuco::pair_type<Key, Value>& lhs,
+                               const cuco::pair_type<Key, Value>& rhs) {
+                   if (lhs.first != rhs.first) { return lhs.first < rhs.first; }
+                   return lhs.second < rhs.second;
+                 });
+
+    // create gold reference
+    thrust::device_vector<cuco::pair_type<Key, Value>> gold(size);
+    auto gold_begin = gold.begin();
+    thrust::transform(thrust::device,
+                      thrust::counting_iterator<int>(0),
+                      thrust::counting_iterator<int>(size),
+                      gold_begin,
+                      [num_keys] __device__(auto i) {
+                        if (i < num_keys) { return cuco::pair_type<Key, Value>{i / 2, i}; }
+                        return cuco::pair_type<Key, Value>{i - num_keys / 2, -1};
+                      });
+
+    REQUIRE(thrust::equal(
+      thrust::device,
+      gold_begin,
+      gold_begin + size,
+      output_begin,
+      [] __device__(cuco::pair_type<Key, Value> lhs, cuco::pair_type<Key, Value> rhs) {
+        return lhs.first == rhs.first and lhs.second == rhs.second;
+      }));
   }
 }
 

From b1ff422c538a3c8b495fa36501ced8b3a58b7512 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Mon, 20 Sep 2021 19:17:25 -0400
Subject: [PATCH 232/267] Update size computation & add related unit tests

---
 include/cuco/detail/static_multimap.inl       | 39 +++++++++++++++++++
 include/cuco/static_multimap.cuh              | 27 +++++++++++--
 tests/static_multimap/static_multimap_test.cu | 29 +++++++++++++-
 3 files changed, 90 insertions(+), 5 deletions(-)

diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index d7ea00225..b29dd8038 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -19,6 +19,18 @@
 #include <cuco/detail/bitwise_compare.cuh>
 #include <cuco/detail/util.hpp>
 
+namespace {
+/**
+ * @brief Device functor used to determine if a slot is filled.
+ */
+template <typename Key>
+struct slot_is_filled {
+  slot_is_filled(Key s) : empty_key_sentinel{s} {}
+  __device__ bool operator()(Key const& k) { return k != empty_key_sentinel; }
+  Key empty_key_sentinel;
+};
+}  // anonymous namespace
+
 namespace cuco {
 
 template <typename Key,
@@ -1772,4 +1784,31 @@ static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::pair_
                                                      pair_equal);
 }
 
+template <typename Key,
+          typename Value,
+          cuda::thread_scope Scope,
+          class ProbeSequence,
+          typename Allocator>
+std::size_t static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::get_size(
+  cudaStream_t stream) const noexcept
+{
+  auto begin = thrust::make_transform_iterator(
+    raw_slots(), [] __device__(cuco::pair_type<Key, Value> const& pair) { return pair.first; });
+  slot_is_filled<Key> filled(empty_key_sentinel_);
+
+  return thrust::count_if(thrust::cuda::par.on(stream), begin, begin + capacity_, filled);
+}
+
+template <typename Key,
+          typename Value,
+          cuda::thread_scope Scope,
+          class ProbeSequence,
+          typename Allocator>
+float static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::get_load_factor(
+  cudaStream_t stream) const noexcept
+{
+  auto size = get_size(stream);
+  return static_cast<float>(size) / static_cast<float>(capacity_);
+}
+
 }  // namespace cuco
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index caa6fa6e7..c25002203 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -1711,6 +1711,26 @@ class static_multimap {
                                         PairEqual pair_equal) noexcept;
   };  // class device_view
 
+  /**
+   * @brief Return the raw pointer of the hash map slots.
+   */
+  value_type* raw_slots() noexcept
+  {
+    // Unsafe access to the slots stripping away their atomic-ness to allow non-atomic access.
+    // TODO: to be replace by atomic_ref when it's ready
+    return reinterpret_cast<value_type*>(slots_.get());
+  }
+
+  /**
+   * @brief Return the raw pointer of the hash map slots.
+   */
+  value_type const* raw_slots() const noexcept
+  {
+    // Unsafe access to the slots stripping away their atomic-ness to allow non-atomic access.
+    // TODO: to be replace by atomic_ref when it's ready
+    return reinterpret_cast<value_type const*>(slots_.get());
+  }
+
   /**
    * @brief Gets the maximum number of elements the hash map can hold.
    *
@@ -1721,16 +1741,18 @@ class static_multimap {
   /**
    * @brief Gets the number of elements in the hash map.
    *
+   * @param stream CUDA stream used to get the number of inserted elements
    * @return The number of elements in the map
    */
-  std::size_t get_size() const noexcept { return size_; }
+  std::size_t get_size(cudaStream_t stream = 0) const noexcept;
 
   /**
    * @brief Gets the load factor of the hash map.
    *
+   * @param stream CUDA stream used to get the load factor
    * @return The load factor of the hash map
    */
-  float get_load_factor() const noexcept { return static_cast<float>(size_) / capacity_; }
+  float get_load_factor(cudaStream_t stream = 0) const noexcept;
 
   /**
    * @brief Gets the sentinel value used to represent an empty key slot.
@@ -1770,7 +1792,6 @@ class static_multimap {
 
  private:
   std::size_t capacity_{};                      ///< Total number of slots
-  std::size_t size_{};                          ///< Number of keys in map
   Key empty_key_sentinel_{};                    ///< Key value that represents an empty slot
   Value empty_value_sentinel_{};                ///< Initial value of empty slot
   slot_allocator_type slot_allocator_{};        ///< Allocator used to allocate slots
diff --git a/tests/static_multimap/static_multimap_test.cu b/tests/static_multimap/static_multimap_test.cu
index d43650a54..000382490 100644
--- a/tests/static_multimap/static_multimap_test.cu
+++ b/tests/static_multimap/static_multimap_test.cu
@@ -60,6 +60,7 @@ enum class probe_sequence { linear_probing, double_hashing };
 struct alignas(8) key_pair {
   int32_t a;
   int32_t b;
+  __device__ bool operator!=(key_pair const& other) const { return a != other.a and b != other.b; }
 };
 
 struct hash_key_pair {
@@ -93,6 +94,9 @@ __inline__ void test_custom_key_value_type(Map& map,
   {
     map.insert(pair_begin, pair_begin + num_pairs);
 
+    auto res = map.get_size();
+    REQUIRE(res == num_pairs);
+
     auto count = map.count(key_begin, key_begin + num_pairs, stream, key_pair_equals{});
     REQUIRE(count == num_pairs);
 
@@ -193,14 +197,21 @@ __inline__ void test_custom_key_value_type(Map& map,
 
   SECTION("All inserted keys-value pairs should be contained")
   {
-    thrust::device_vector<bool> contained(num_pairs);
     map.insert(pair_begin, pair_begin + num_pairs);
+
+    auto size = map.get_size();
+    REQUIRE(size == num_pairs);
+
+    thrust::device_vector<bool> contained(num_pairs);
     map.contains(key_begin, key_begin + num_pairs, contained.begin(), stream, key_pair_equals{});
     REQUIRE(all_of(contained.begin(), contained.end(), [] __device__(bool const& b) { return b; }));
   }
 
   SECTION("Non-inserted keys-value pairs should not be contained")
   {
+    auto size = map.get_size();
+    REQUIRE(size == 0);
+
     thrust::device_vector<bool> contained(num_pairs);
     map.contains(key_begin, key_begin + num_pairs, contained.begin(), stream, key_pair_equals{});
     REQUIRE(
@@ -272,6 +283,9 @@ __inline__ void test_multiplicity_two(
 
   SECTION("Non-inserted key/value pairs should not be contained.")
   {
+    auto size = map.get_size();
+    REQUIRE(size == 0);
+
     map.contains(key_begin, key_begin + num_keys, d_contained.begin());
 
     REQUIRE(
@@ -282,6 +296,9 @@ __inline__ void test_multiplicity_two(
 
   SECTION("All inserted key/value pairs should be contained.")
   {
+    auto size = map.get_size();
+    REQUIRE(size == num_items);
+
     map.contains(key_begin, key_begin + num_keys, d_contained.begin());
 
     REQUIRE(
@@ -409,6 +426,9 @@ __inline__ void test_non_matches(Map& map, PairIt pair_begin, KeyIt key_begin, s
 {
   map.insert(pair_begin, pair_begin + num_keys);
 
+  auto res = map.get_size();
+  REQUIRE(res == num_keys);
+
   SECTION("Output of count and retrieve should be coherent.")
   {
     auto num = map.count(key_begin, key_begin + num_keys);
@@ -535,8 +555,10 @@ __inline__ void test_insert_if(Map& map, PairIt pair_begin, KeyIt key_begin, std
 
   map.insert_if(pair_begin, pair_begin + size, key_begin, pred_lambda);
 
-  auto num = map.count(key_begin, key_begin + size);
+  auto res = map.get_size();
+  REQUIRE(res * 2 == size);
 
+  auto num = map.count(key_begin, key_begin + size);
   REQUIRE(num * 2 == size);
 }
 
@@ -595,6 +617,9 @@ __inline__ void test_pair_functions(Map& map, PairIt pair_begin, std::size_t num
   map.insert(pair_begin, pair_begin + num_pairs);
   cudaStreamSynchronize(0);
 
+  auto res = map.get_size();
+  REQUIRE(res == num_pairs);
+
   // query pair matching rate = 50%
   thrust::transform(thrust::device,
                     thrust::counting_iterator<int>(0),

From 0a3e137c5b1740a557a2ec1078b9aaf9093dd17f Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Wed, 22 Sep 2021 16:49:05 -0400
Subject: [PATCH 233/267] Use non-default seed to get rid of the potential
 secondary clustering issue in double hashing

---
 include/cuco/detail/hash_functions.cuh  | 4 +++-
 include/cuco/detail/probe_sequences.cuh | 4 +++-
 2 files changed, 6 insertions(+), 2 deletions(-)

diff --git a/include/cuco/detail/hash_functions.cuh b/include/cuco/detail/hash_functions.cuh
index aec550aba..d5dcd0f64 100644
--- a/include/cuco/detail/hash_functions.cuh
+++ b/include/cuco/detail/hash_functions.cuh
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2017, NVIDIA CORPORATION.
+ * Copyright (c) 2017-2021, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -36,6 +36,8 @@ struct MurmurHash3_32 {
 
   __host__ __device__ constexpr MurmurHash3_32() : m_seed(0) {}
 
+  __host__ __device__ constexpr MurmurHash3_32(uint32_t seed) : m_seed(seed) {}
+
   constexpr result_type __host__ __device__ operator()(Key const& key) const noexcept
   {
     constexpr int len         = sizeof(argument_type);
diff --git a/include/cuco/detail/probe_sequences.cuh b/include/cuco/detail/probe_sequences.cuh
index 318be4e7d..b377398ee 100644
--- a/include/cuco/detail/probe_sequences.cuh
+++ b/include/cuco/detail/probe_sequences.cuh
@@ -243,6 +243,8 @@ class double_hashing : public probe_sequence_base<Key, Value, CGSize, Scope> {
   /**
    * @brief Constructs a double hashing scheme based on the given hash map features.
    *
+   * `hash2` takes a different seed to reduce the chance of secondary clustering.
+   *
    * @param slots Pointer to beginning of the hash map slots
    * @param capacity Capacity of the hash map
    * @param hash1 First hasher to hash each key
@@ -251,7 +253,7 @@ class double_hashing : public probe_sequence_base<Key, Value, CGSize, Scope> {
   __host__ __device__ explicit double_hashing(iterator slots,
                                               std::size_t capacity,
                                               Hash1 hash1 = Hash1{},
-                                              Hash2 hash2 = Hash2{}) noexcept
+                                              Hash2 hash2 = Hash2{1}) noexcept
     : probe_sequence_base<Key, Value, CGSize, Scope>{slots, capacity}, hash1_{hash1}, hash2_{hash2}
   {
   }

From ed6b25360ab296e9a01a0e2d0dd43ec1eabc06a4 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Thu, 7 Oct 2021 16:52:21 -0400
Subject: [PATCH 234/267] Include necessary headers

---
 include/cuco/detail/static_multimap.inl | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index b29dd8038..7cde62d52 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -14,6 +14,8 @@
  * limitations under the License.
  */
 
+#include <thrust/count.h>
+#include <thrust/iterator/transform_iterator.h>
 #include <thrust/tuple.h>
 
 #include <cuco/detail/bitwise_compare.cuh>

From 0dd290eccd6a50517148857e0baa6c45f99d1ae2 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Fri, 8 Oct 2021 11:37:34 -0400
Subject: [PATCH 235/267] Add and use is_bitwise_comparable_v

---
 include/cuco/static_map.cuh      | 10 +++++-----
 include/cuco/static_multimap.cuh | 10 +++++-----
 include/cuco/traits.hpp          |  3 +++
 3 files changed, 13 insertions(+), 10 deletions(-)

diff --git a/include/cuco/static_map.cuh b/include/cuco/static_map.cuh
index ab9bf1350..1ae7ee314 100644
--- a/include/cuco/static_map.cuh
+++ b/include/cuco/static_map.cuh
@@ -54,7 +54,7 @@ class dynamic_map;
  * `cuco::detail::is_packable` constexpr).
  *
  * Current limitations:
- * - Requires keys and values that where `cuco::is_bitwise_comparable<T>::value` is true
+ * - Requires keys and values that where `cuco::is_bitwise_comparable_v<T>` is true
  *    - Comparisons against the "sentinel" values will always be done with bitwise comparisons.
  * - Does not support erasing keys
  * - Capacity is fixed and will not grow automatically
@@ -119,14 +119,14 @@ template <typename Key,
           typename Allocator       = cuco::cuda_allocator<char>>
 class static_map {
   static_assert(
-    cuco::is_bitwise_comparable<Key>::value,
+    cuco::is_bitwise_comparable_v<Key>,
     "Key type must have unique object representations or have been explicitly declared as safe for "
-    "bitwise comparison via specialization of cuco::is_bitwise_comparable<Key>.");
+    "bitwise comparison via specialization of cuco::is_bitwise_comparable_v<Key>.");
 
-  static_assert(cuco::is_bitwise_comparable<Value>::value,
+  static_assert(cuco::is_bitwise_comparable_v<Value>,
                 "Value type must have unique object representations or have been explicitly "
                 "declared as safe for bitwise comparison via specialization of "
-                "cuco::is_bitwise_comparable<Value>.");
+                "cuco::is_bitwise_comparable_v<Value>.");
 
   friend class dynamic_map<Key, Value, Scope, Allocator>;
 
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index c25002203..46990879b 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -55,7 +55,7 @@ namespace cuco {
  * `static_multimap<Key, Value>::supports_concurrent_insert_find()` is true.
  *
  * Current limitations:
- * - Requires keys and values where `cuco::is_bitwise_comparable<T>::value` is true
+ * - Requires keys and values where `cuco::is_bitwise_comparable_v<T>` is true
  * - Comparisons against the "sentinel" values will always be done with bitwise comparisons
  * Therefore, the objects must have unique, bitwise object representations (e.g., no padding bits).
  * - Does not support erasing keys
@@ -135,14 +135,14 @@ template <typename Key,
           typename Allocator       = cuco::cuda_allocator<char>>
 class static_multimap {
   static_assert(
-    cuco::is_bitwise_comparable<Key>::value,
+    cuco::is_bitwise_comparable_v<Key>,
     "Key type must have unique object representations or have been explicitly declared as safe for "
-    "bitwise comparison via specialization of cuco::is_bitwise_comparable<Key>.");
+    "bitwise comparison via specialization of cuco::is_bitwise_comparable_v<Key>.");
 
   static_assert(
-    cuco::is_bitwise_comparable<Value>::value,
+    cuco::is_bitwise_comparable_v<Value>,
     "Value type must have unique object representations or have been explicitly declared as safe "
-    "for bitwise comparison via specialization of cuco::is_bitwise_comparable<Value>.");
+    "for bitwise comparison via specialization of cuco::is_bitwise_comparable_v<Value>.");
 
  public:
   using value_type         = cuco::pair_type<Key, Value>;
diff --git a/include/cuco/traits.hpp b/include/cuco/traits.hpp
index 19d0bce9d..445a40daf 100644
--- a/include/cuco/traits.hpp
+++ b/include/cuco/traits.hpp
@@ -44,6 +44,9 @@ struct is_bitwise_comparable<T, std::enable_if_t<std::has_unique_object_represen
   : std::true_type {
 };
 
+template <typename T>
+inline constexpr bool is_bitwise_comparable_v = is_bitwise_comparable<T>::value;
+
 /**
  * @brief Declares that a type `Type` is bitwise comparable.
  *

From f84216f6fad1ca99b58ac667357a149742ae20d5 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Fri, 8 Oct 2021 16:14:00 -0400
Subject: [PATCH 236/267] Update docs

---
 .../cuco/detail/static_multimap_kernels.cuh   |  69 +++---
 include/cuco/static_multimap.cuh              | 197 ++++++++++--------
 2 files changed, 146 insertions(+), 120 deletions(-)

diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap_kernels.cuh
index 8ad623d31..fa1ef65f2 100644
--- a/include/cuco/detail/static_multimap_kernels.cuh
+++ b/include/cuco/detail/static_multimap_kernels.cuh
@@ -65,7 +65,7 @@ __global__ void initialize(pair_atomic_type* const slots, Key k, Value v, std::s
  * @tparam tile_size The number of threads in the Cooperative Groups used to perform
  * inserts
  * @tparam InputIt Device accessible random access input iterator where
- * `std::is_converitble<std::iterator_traits<InputIt>::value_type,
+ * `std::is_convertible<std::iterator_traits<InputIt>::value_type,
  * static_multimap<K, V>::value_type>` is `true`
  * @tparam viewT Type of device view allowing access of hash map storage
  *
@@ -102,16 +102,18 @@ __global__ void insert(InputIt first, InputIt last, viewT view)
  * @tparam tile_size The number of threads in the Cooperative Groups used to perform
  * inserts
  * @tparam InputIt Device accessible random access input iterator where
- * `std::is_converitble<std::iterator_traits<InputIt>::value_type,
+ * `std::is_convertible<std::iterator_traits<InputIt>::value_type,
  * static_multimap<K, V>::value_type>` is `true`
- * @tparam StencilIt Device accessible stencil iterator
+ * @tparam StencilIt Device accessible random access iterator whose value_type is
+ * convertible to Predicate's argument type
  * @tparam viewT Type of device view allowing access of hash map storage
- * @tparam Predicate Unary predicate function type
+ * @tparam Predicate Unary predicate callable whose return type must be convertible to `bool` and
+ * argument type is convertible from `std::iterator_traits<StencilIt>::value_type`.
  * @param first Beginning of the sequence of key/value pairs
  * @param s Beginning of the stencil sequence
  * @param n Number of elements to insert
  * @param view Mutable device view used to access the hash map's slot storage
- * @param pred Predicate to test on the given stencil sequence
+ * @param pred Predicate to test on every element in the range `[s, s + n)`
  */
 template <uint32_t block_size,
           uint32_t tile_size,
@@ -149,8 +151,7 @@ __global__ void insert_if_n(InputIt first, StencilIt s, std::size_t n, viewT vie
  * @tparam tile_size The number of threads in the Cooperative Groups
  * @tparam InputIt Device accessible input iterator whose `value_type` is
  * convertible to the map's `key_type`
- * @tparam OutputIt Device accessible output iterator whose `value_type` is
- * convertible to the map's `mapped_type`
+ * @tparam OutputIt Device accessible output iterator whose `value_type` is `bool`
  * @tparam viewT Type of device view allowing access of hash map storage
  * @tparam KeyEqual Binary callable type
  * @param first Beginning of the sequence of keys
@@ -194,8 +195,11 @@ __global__ void contains(
 }
 
 /**
- * @brief Counts the occurrences of keys in `[first, last)` contained in the multimap. If `is_outer`
- * is true, the corresponding occurrence for non-matches is 1. Otherwise, it's 0.
+ * @brief Counts the occurrences of keys in `[first, last)` contained in the multimap.
+ *
+ * For each key, `k = *(first + i)`, counts all matching keys, `k'`, as determined by `key_equal(k,
+ * k')` and stores the sum of all matches for all keys to `num_matches`. If `k` does not have any
+ * matches, it contributes 1 to the final sum only if `is_outer` is true.
  *
  * @tparam block_size The size of the thread block
  * @tparam tile_size The number of threads in the Cooperative Groups used to perform counts
@@ -250,13 +254,16 @@ __global__ void count(
 
 /**
  * @brief Counts the occurrences of key/value pairs in `[first, last)` contained in the multimap.
- * The occurrence of non-matches is `1` if `is_outer` is `true`. Otherwise it's `0`.
+ *
+ * For pair, `p = *(first + i)`, counts all matching pairs, `p'`, as determined by `pair_equal(p,
+ * p')` and stores the sum of all matches for all pairs to `num_matches`. If `p` does not have any
+ * matches, it contributes 1 to the final sum only if `is_outer` is true.
  *
  * @tparam block_size The size of the thread block
  * @tparam tile_size The number of threads in the Cooperative Groups used to perform counts
  * @tparam is_outer Boolean flag indicating whether non-matches are counted
  * @tparam InputIt Device accessible random access input iterator where
- * `std::is_converitble<std::iterator_traits<InputIt>::value_type,
+ * `std::is_convertible<std::iterator_traits<InputIt>::value_type,
  * static_multimap<K, V>::value_type>` is `true`
  * @tparam atomicT Type of atomic storage
  * @tparam viewT Type of device view allowing access of hash map storage
@@ -307,14 +314,12 @@ __global__ void pair_count(
  * @brief Retrieves all the values corresponding to all keys in the range `[first, last)`
  * using vector loads combined with per-block shared memory buffer.
  *
- * The `vectorized_` prefix indicates that the vector load method is used.
- *
- * If the key `k = *(first + i)` exists in the map, copies `k` and all associated values to
- * unspecified locations in `[output_begin, output_begin + *num_matches - 1)`. Copies `k` and
- * the empty value sentinel into the output only when `is_outer` is `true`.
+ * For key `k = *(first + i)` existing in the map, copies `k` and all associated values to
+ * unspecified locations in `[output_begin, output_end)`. If `k` does not have any matches, copies
+ * `k` and `empty_value_sentinel()` into the output only if `is_outer` is true.
  *
  * Behavior is undefined if the total number of matching keys exceeds `std::distance(output_begin,
- * output_begin + *num_matches - 1)`. Use `count()` to determine the number of matching keys.
+ * output_begin + *num_matches - 1)`. Use `count()` to determine the size of the output range.
  *
  * @tparam block_size The size of the thread block
  * @tparam warp_size The size of the warp
@@ -324,7 +329,7 @@ __global__ void pair_count(
  * @tparam InputIt Device accessible input iterator whose `value_type` is
  * convertible to the map's `key_type`
  * @tparam OutputIt Device accessible output iterator whose `value_type` is
- * convertible to the map's `mapped_type`
+ * constructible from the map's `value_type`
  * @tparam atomicT Type of atomic storage
  * @tparam viewT Type of device view allowing access of hash map storage
  * @tparam KeyEqual Binary callable type
@@ -409,12 +414,12 @@ __global__ void vectorized_retrieve(InputIt first,
  * @brief Retrieves all the values corresponding to all keys in the range `[first, last)` using
  * scalar loads combined with per-CG shared memory buffer.
  *
- * If the key `k = *(first + i)` exists in the map, copies `k` and all associated values to
- * unspecified locations in `[output_begin, output_begin + *num_matches - 1)`. Copies `k` and
- * the empty value sentinel into the output only when `is_outer` is `true`.
+ * For key `k = *(first + i)` existing in the map, copies `k` and all associated values to
+ * unspecified locations in `[output_begin, output_end)`. If `k` does not have any matches, copies
+ * `k` and `empty_value_sentinel()` into the output only if `is_outer` is true.
  *
  * Behavior is undefined if the total number of matching keys exceeds `std::distance(output_begin,
- * output_begin + *num_matches - 1)`. Use `count()` to determine the number of matching keys.
+ * output_begin + *num_matches - 1)`. Use `count()` to determine the size of the output range.
  *
  * @tparam block_size The size of the thread block
  * @tparam warp_size The size of the warp
@@ -424,7 +429,7 @@ __global__ void vectorized_retrieve(InputIt first,
  * @tparam InputIt Device accessible input iterator whose `value_type` is
  * convertible to the map's `key_type`
  * @tparam OutputIt Device accessible output iterator whose `value_type` is
- * convertible to the map's `mapped_type`
+ * constructible from the map's `value_type`
  * @tparam atomicT Type of atomic storage
  * @tparam viewT Type of device view allowing access of hash map storage
  * @tparam KeyEqual Binary callable type
@@ -490,8 +495,6 @@ __global__ void retrieve(InputIt first,
  * @brief Retrieves all pairs matching the input probe pair in the range `[first, last)`
  * using vector loads combined with per-block shared memory buffer.
  *
- * The `vectorized_` prefix indicates that the vector load method is used.
- *
  * If pair_equal(*(first + i), slot[j]) returns true, then *(first+i) is stored to unspecified
  * locations in `probe_output_begin`, and slot[j] is stored to unspecified locations in
  * `contained_output_begin`. If the given pair has no matches in the map, copies *(first + i) in
@@ -499,7 +502,7 @@ __global__ void retrieve(InputIt first,
  * `contained_output_begin` only when `is_outer` is `true`.
  *
  * Behavior is undefined if the total number of matching pairs exceeds `std::distance(output_begin,
- * output_begin + *num_matches - 1)`. Use `pair_count()` to determine the number of matching keys.
+ * output_begin + *num_matches - 1)`. Use `pair_count()` to determine the size of the output range.
  *
  * @tparam block_size The size of the thread block
  * @tparam warp_size The size of the warp
@@ -507,12 +510,12 @@ __global__ void retrieve(InputIt first,
  * @tparam buffer_size Size of the output buffer
  * @tparam is_outer Boolean flag indicating whether non-matches are included in the output
  * @tparam InputIt Device accessible random access input iterator where
- * `std::is_converitble<std::iterator_traits<InputIt>::value_type,
+ * `std::is_convertible<std::iterator_traits<InputIt>::value_type,
  * static_multimap<K, V>::value_type>` is `true`
  * @tparam OutputIt1 Device accessible output iterator whose `value_type` is
- * convertible to the map's `value_type`
+ * constructible from the map's `value_type`
  * @tparam OutputIt2 Device accessible output iterator whose `value_type` is
- * convertible to the map's `value_type`
+ * constructible from the map's `value_type`
  * @tparam atomicT Type of atomic storage
  * @tparam viewT Type of device view allowing access of hash map storage
  * @tparam PairEqual Binary callable type
@@ -617,7 +620,7 @@ __global__ void vectorized_pair_retrieve(InputIt first,
  * `contained_output_begin` only when `is_outer` is `true`.
  *
  * Behavior is undefined if the total number of matching pairs exceeds `std::distance(output_begin,
- * output_begin + *num_matches - 1)`. Use `pair_count()` to determine the number of matching keys.
+ * output_begin + *num_matches - 1)`. Use `pair_count()` to determine the size of the output range.
  *
  * @tparam block_size The size of the thread block
  * @tparam warp_size The size of the warp
@@ -625,12 +628,12 @@ __global__ void vectorized_pair_retrieve(InputIt first,
  * @tparam buffer_size Size of the output buffer
  * @tparam is_outer Boolean flag indicating whether non-matches are included in the output
  * @tparam InputIt Device accessible random access input iterator where
- * `std::is_converitble<std::iterator_traits<InputIt>::value_type,
+ * `std::is_convertible<std::iterator_traits<InputIt>::value_type,
  * static_multimap<K, V>::value_type>` is `true`
  * @tparam OutputIt1 Device accessible output iterator whose `value_type` is
- * convertible to the map's `value_type`
+ * constructible from the map's `value_type`
  * @tparam OutputIt2 Device accessible output iterator whose `value_type` is
- * convertible to the map's `value_type`
+ * constructible from the map's `value_type`
  * @tparam atomicT Type of atomic storage
  * @tparam viewT Type of device view allowing access of hash map storage
  * @tparam PairEqual Binary callable type
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index 46990879b..e790a05ce 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -83,10 +83,18 @@ namespace cuco {
  * The two types are separate to prevent erroneous concurrent insert/find
  * operations.
  *
- * By default, query operations (e.g. `count` and `retrieve`) take `Key` as input and do not include
- * non-matches in the output. APIs with `_outer` suffix should be used if non-match handling is
- * desired. The `pair_` prefix indicates that the input data are key-value pairs whose type can be
- * converted to multimap's `value_type`.
+ * By default, when querying for a Key `k` in operations like `count` or `retrieve`, if `k` is not
+ * present in the map, it will not contribute to the output. Query APIs with the `_outer` suffix
+ * will include non-matching keys in the output. See the relevant API documentation for more
+ * information.
+ *
+ * Typical associative container query APIs like `retrieve` look up values by solely by key, e.g.,
+ * `count` for a Key `k` will count all values whose associated key `k'` matches `k` as determined
+ * by `key_equal(k, k')`. In some cases, one may want to consider both key _and_ value when
+ * determining if a key-value pair should contribute to the output. `static_multimap` supports this
+ * use case with APIs prefixed with `pair_`, e.g., `pair_count` is given a key-value pair
+ * `{k,v}` and only counts key-value pairs, `{k', v'}`, in the map where `pair_equal({k,v}, {k',
+ * v'})` is true. See the relevant API documentation for more information.
  *
  * Example:
  * \code{.cpp}
@@ -115,8 +123,8 @@ namespace cuco {
  * \endcode
  *
  *
- * @tparam Key Type used for keys
- * @tparam Value Type of the mapped values
+ * @tparam Key Type used for keys. Requires `cuco::is_bitwise_comparable_v<Key>`
+ * @tparam Value Type of the mapped values. Requires `cuco::is_bitwise_comparable_v<Value>`
  * @tparam Scope The scope in which multimap operations will be performed by
  * individual threads
  * @tparam ProbeSequence Probe sequence chosen between `cuco::detail::linear_probing`
@@ -188,9 +196,8 @@ class static_multimap {
   static_multimap();
 
   /**
-   * @brief Construct a fixed-size map with the specified capacity and sentinel values.
-   * @brief Construct a statically sized map with the specified number of slots
-   * and sentinel values.
+   * @brief Construct a statically-sized map with the specified initial capacity,
+   * sentinel values and CUDA stream.
    *
    * The capacity of the map is fixed. Insert operations will not automatically
    * grow the map. Attempting to insert more unique keys than the capacity of
@@ -221,7 +228,7 @@ class static_multimap {
    * @brief Inserts all key/value pairs in the range `[first, last)`.
    *
    * @tparam InputIt Device accessible random access input iterator where
-   * `std::is_converitble<std::iterator_traits<InputIt>::value_type,
+   * `std::is_convertible<std::iterator_traits<InputIt>::value_type,
    * static_multimap<K, V>::value_type>` is `true`
    * @param first Beginning of the sequence of key/value pairs
    * @param last End of the sequence of key/value pairs
@@ -237,11 +244,12 @@ class static_multimap {
    * The key/value pair `*(first + i)` is inserted if `pred( *(stencil + i) )` returns true.
    *
    * @tparam InputIt Device accessible random access input iterator where
-   * `std::is_converitble<std::iterator_traits<InputIt>::value_type,
+   * `std::is_convertible<std::iterator_traits<InputIt>::value_type,
    * static_multimap<K, V>::value_type>` is `true`
    * @tparam StencilIt Device accessible random access iterator whose value_type is
    * convertible to Predicate's argument type
-   * @tparam Predicate Unary predicate callable
+   * @tparam Predicate Unary predicate callable whose return type must be convertible to `bool` and
+   * argument type is convertible from `std::iterator_traits<StencilIt>::value_type`.
    * @param first Beginning of the sequence of key/value pairs
    * @param last End of the sequence of key/value pairs
    * @param stencil Beginning of the stencil sequence
@@ -261,8 +269,8 @@ class static_multimap {
    *
    * @tparam InputIt Device accessible input iterator whose `value_type` is
    * convertible to the map's `key_type`
-   * @tparam OutputIt Device accessible output iterator whose `value_type` is
-   * convertible from `bool`
+   * @tparam OutputIt Device accessible output iterator whose `value_type` is convertible from
+   * `bool`
    * @tparam KeyEqual Binary callable type used to compare two keys for equality
    * @param first Beginning of the sequence of keys
    * @param last End of the sequence of keys
@@ -280,6 +288,9 @@ class static_multimap {
   /**
    * @brief Counts the occurrences of keys in `[first, last)` contained in the multimap.
    *
+   * For each key, `k = *(first + i)`, counts all matching keys, `k'`, as determined by
+   * `key_equal(k, k')` and returns the sum of all matches for all keys.
+   *
    * @tparam Input Device accesible input iterator whose `value_type` is convertible to `key_type`
    * @tparam KeyEqual Binary callable
    * @param first Beginning of the sequence of keys to count
@@ -297,7 +308,9 @@ class static_multimap {
   /**
    * @brief Counts the occurrences of keys in `[first, last)` contained in the multimap.
    *
-   * The `_outer` suffix signifies that the occurrence of non-matches is 1.
+   * For each key, `k = *(first + i)`, counts all matching keys, `k'`, as determined by
+   * `key_equal(k, k')` and returns the sum of all matches for all keys. If `k` does not have any
+   * matches, it contributes 1 to the final sum.
    *
    * @tparam Input Device accesible input iterator whose `value_type` is convertible to `key_type`
    * @tparam KeyEqual Binary callable
@@ -305,7 +318,8 @@ class static_multimap {
    * @param last End of the sequence of keys to count
    * @param stream CUDA stream used for count_outer
    * @param key_equal Binary function to compare two keys for equality
-   * @return The sum of total occurrences of all keys in `[first, last)`
+   * @return The sum of total occurrences of all keys in `[first, last)` where keys without matches
+   * are considered to have a single occurrence.
    */
   template <typename InputIt, typename KeyEqual = thrust::equal_to<key_type>>
   std::size_t count_outer(InputIt first,
@@ -316,10 +330,11 @@ class static_multimap {
   /**
    * @brief Counts the occurrences of key/value pairs in `[first, last)` contained in the multimap.
    *
-   * The `pair_` prefix indicates that the input data type is convertible to the map's `value_type`.
+   * For key-value pair, `kv = *(first + i)`, counts all matching key-value pairs, `kv'`, as
+   * determined by `pair_equal(kv, kv')` and returns the sum of all matches for all key-value pairs.
    *
    * @tparam InputIt Device accessible random access input iterator where
-   * `std::is_converitble<std::iterator_traits<InputIt>::value_type,
+   * `std::is_convertible<std::iterator_traits<InputIt>::value_type,
    * static_multimap<K, V>::value_type>` is `true`
    * @tparam PairEqual Binary callable
    * @param first Beginning of the sequence of pairs to count
@@ -337,18 +352,20 @@ class static_multimap {
   /**
    * @brief Counts the occurrences of key/value pairs in `[first, last)` contained in the multimap.
    *
-   * The `pair_` prefix indicates that the input data type is convertible to the map's `value_type`.
-   * The `_outer` suffix signifies that the occurrence of non-matches is 1.
+   * For key-value pair, `kv = *(first + i)`, counts all matching key-value pairs, `kv'`, as
+   * determined by `pair_equal(kv, kv')` and returns the sum of all matches for all key-value pairs.
+   * if `kv` does not have any matches, it contributes 1 to the final sum.
    *
    * @tparam InputIt Device accessible random access input iterator where
-   * `std::is_converitble<std::iterator_traits<InputIt>::value_type,
+   * `std::is_convertible<std::iterator_traits<InputIt>::value_type,
    * static_multimap<K, V>::value_type>` is `true`
    * @tparam PairEqual Binary callable
    * @param first Beginning of the sequence of pairs to count
    * @param last End of the sequence of pairs to count
    * @param pair_equal Binary function to compare two pairs for equality
    * @param stream CUDA stream used for pair_count_outer
-   * @return The sum of total occurrences of all pairs in `[first, last)`
+   * @return The sum of total occurrences of all pairs in `[first, last)` where a key-value pair
+   * without a match is considered to have a single occurrence
    */
   template <typename InputIt, typename PairEqual>
   std::size_t pair_count_outer(InputIt first,
@@ -359,16 +376,16 @@ class static_multimap {
   /**
    * @brief Retrieves all the values corresponding to all keys in the range `[first, last)`.
    *
-   * If the key `k = *(first + i)` exists in the map, copies `k` and all associated values to
+   * If key `k = *(first + i)` exists in the map, copies `k` and all associated values to
    * unspecified locations in `[output_begin, output_end)`. Else, does nothing.
    *
-   * Behavior is undefined if the total number of matching keys exceeds `std::distance(output_begin,
-   * output_end)`. Use `count()` to determine the number of matching keys.
+   * Behavior is undefined if the size of the output range exceeds `std::distance(output_begin,
+   * output_end)`. Use `count()` to determine the size of the output range.
    *
    * @tparam InputIt Device accessible input iterator whose `value_type` is
    * convertible to the map's `key_type`
    * @tparam OutputIt Device accessible output iterator whose `value_type` is
-   * convertible to the map's `value_type`
+   * constructible from the map's `value_type`
    * @tparam KeyEqual Binary callable type
    * @param first Beginning of the sequence of keys
    * @param last End of the sequence of keys
@@ -387,17 +404,17 @@ class static_multimap {
   /**
    * @brief Retrieves all the matches corresponding to all keys in the range `[first, last)`.
    *
-   * The `_outer` suffix signifies that non-matches are included in the output: If the key
-   * `k = *(first + i)` exists in the map, copies `k` and all associated values to unspecified
-   * locations in `[output_begin, output_end)`. Else, copies `k` and the empty value sentinel.
+   * If key `k = *(first + i)` exists in the map, copies `k` and all associated values to
+   * unspecified locations in `[output_begin, output_end)`. Else, copies `k` and
+   * `empty_value_sentinel`.
    *
-   * Behavior is undefined if the total number of matching keys exceeds `std::distance(output_begin,
-   * output_end)`. Use `count_outer()` to determine the number of matching keys.
+   * Behavior is undefined if the size of the output range exceeds `std::distance(output_begin,
+   * output_end)`. Use `count_outer()` to determine the size of the output range.
    *
    * @tparam InputIt Device accessible input iterator whose `value_type` is
    * convertible to the map's `key_type`
    * @tparam OutputIt Device accessible output iterator whose `value_type` is
-   * convertible to the map's `value_type`
+   * constructible from the map's `value_type`
    * @tparam KeyEqual Binary callable type
    * @param first Beginning of the sequence of keys
    * @param last End of the sequence of keys
@@ -420,13 +437,13 @@ class static_multimap {
    * `value_type`. If pair_equal(*(first + i), slot[j]) returns true, then *(first+i) is
    * stored to `probe_output_begin`, and slot[j] is stored to `contained_output_begin`.
    *
-   * Behavior is undefined if the total number of matching pairs exceeds
+   * Behavior is undefined if the size of the output range exceeds
    * `std::distance(probe_output_begin, probe_output_end)` (or
    * `std::distance(contained_output_begin, contained_output_end)`). Use
-   * `pair_count()` to determine the number of matching pairs.
+   * `pair_count()` to determine the size of the output range.
    *
    * @tparam InputIt Device accessible random access input iterator where
-   * `std::is_converitble<std::iterator_traits<InputIt>::value_type,
+   * `std::is_convertible<std::iterator_traits<InputIt>::value_type,
    * static_multimap<K, V>::value_type>` is `true`
    * @tparam OutputZipIt1 Device accessible output zip iterator for probe matches
    * @tparam OutputZipIt2 Device accessible output zip iterator for contained matches
@@ -451,19 +468,18 @@ class static_multimap {
    * @brief Retrieves all pairs matching the input probe pair in the range `[first, last)`.
    *
    * The `pair_` prefix indicates that the input data type is convertible to the map's `value_type`.
-   * The `_outer` suffix signifies that non-matches are included in the output: if
-   * pair_equal(*(first + i), slot[j]) returns true, then *(first+i) is stored to
+   * If pair_equal(*(first + i), slot[j]) returns true, then *(first+i) is stored to
    * `probe_output_begin`, and slot[j] is stored to `contained_output_begin`. If *(first+i) doesn't
    * have matches in the map, copies *(first + i) in `probe_output_begin` and a pair of
    * `empty_key_sentinel` and `empty_value_sentinel` in `contained_output_begin`.
    *
-   * Behavior is undefined if the total number of matching pairs exceeds
+   * Behavior is undefined if the size of the output range exceeds
    * `std::distance(probe_output_begin, probe_output_end)` (or
    * `std::distance(contained_output_begin, contained_output_end)`). Use
-   * `pair_count()` to determine the number of matching pairs.
+   * `pair_count()` to determine the size of the output range.
    *
    * @tparam InputIt Device accessible random access input iterator where
-   * `std::is_converitble<std::iterator_traits<InputIt>::value_type,
+   * `std::is_convertible<std::iterator_traits<InputIt>::value_type,
    * static_multimap<K, V>::value_type>` is `true`
    * @tparam OutputZipIt1 Device accessible output zip iterator for probe matches
    * @tparam OutputZipIt2 Device accessible output zip iterator for contained matches
@@ -946,7 +962,7 @@ class static_multimap {
 
    private:
     /**
-     * @brief Indicates whether the key `k` was inserted into the map using vector loads.
+     * @brief Indicates whether the key `k` exists in the map using vector loads.
      *
      * If the key `k` was inserted into the map, `contains` returns
      * true. Otherwise, it returns false. Uses the CUDA Cooperative Groups API to
@@ -970,7 +986,7 @@ class static_multimap {
                                                                       KeyEqual key_equal) noexcept;
 
     /**
-     * @brief Indicates whether the key `k` was inserted into the map using scalar loads.
+     * @brief Indicates whether the key `k` exists in the map using scalar loads.
      *
      * If the key `k` was inserted into the map, `contains` returns
      * true. Otherwise, it returns false. Uses the CUDA Cooperative Groups API to
@@ -1066,9 +1082,9 @@ class static_multimap {
      * @brief Retrieves all the matches of a given key contained in multimap using vector
      * loads with per-warp shared memory buffer.
      *
-     * For keys `k = *(first + i)` existing in the map, copies `k` and all associated values to
-     * unspecified locations in `[output_begin, output_end)`. In case of non-matches, copies `k`
-     * and the empty value sentinel into the output only if `is_outer` is true.
+     * For key `k` existing in the map, copies `k` and all associated values to unspecified
+     * locations in `[output_begin, output_end)`. If `k` does not have any matches, copies `k` and
+     * `empty_value_sentinel()` into the output only if `is_outer` is true.
      *
      * @tparam buffer_size Size of the output buffer
      * @tparam is_outer Boolean flag indicating whether outer join is peformed
@@ -1076,7 +1092,7 @@ class static_multimap {
      * @tparam CG Cooperative Group type
      * @tparam atomicT Type of atomic storage
      * @tparam OutputIt Device accessible output iterator whose `value_type` is
-     * convertible to the map's `mapped_type`
+     * constructible from the map's `value_type`
      * @tparam KeyEqual Binary callable type
      * @param warp The Cooperative Group (or warp) used to flush output buffer
      * @param g The Cooperative Group used to retrieve
@@ -1108,9 +1124,9 @@ class static_multimap {
      * @brief Retrieves all the matches of a given key contained in multimap using scalar
      * loads with per-cg shared memory buffer.
      *
-     * For keys `k = *(first + i)` existing in the map, copies `k` and all associated values to
-     * unspecified locations in `[output_begin, output_end)`. In case of non-matches, copies `k`
-     * and the empty value sentinel into the output only if `is_outer` is true.
+     * For key `k` existing in the map, copies `k` and all associated values to unspecified
+     * locations in `[output_begin, output_end)`. If `k` does not have any matches, copies `k` and
+     * `empty_value_sentinel()` into the output only if `is_outer` is true.
      *
      * @tparam cg_size The number of threads in CUDA Cooperative Groups
      * @tparam buffer_size Size of the output buffer
@@ -1118,7 +1134,7 @@ class static_multimap {
      * @tparam CG Cooperative Group type
      * @tparam atomicT Type of atomic storage
      * @tparam OutputIt Device accessible output iterator whose `value_type` is
-     * convertible to the map's `mapped_type`
+     * constructible from the map's `value_type`
      * @tparam KeyEqual Binary callable type
      * @param g The Cooperative Group used to retrieve
      * @param k The key to search for
@@ -1149,10 +1165,10 @@ class static_multimap {
      * loads with per-warp shared memory buffer.
      *
      * For pair `p`, if pair_equal(p, slot[j]) returns true, copies `p` to unspecified locations
-     * in
-     * `[probe_output_begin, probe_output_end)` and copies slot[j] to unspecified locations in
-     * `[contained_output_begin, contained_output_end)`. In case of non-matches, copies `p` and
-     * the empty value sentinels into the output only if `is_outer` is true.
+     * in `[probe_output_begin, probe_output_end)` and copies slot[j] to unspecified locations in
+     * `[contained_output_begin, contained_output_end)`. If `p` does not have any matches, copies
+     * `p` and a pair of `empty_key_sentinel` and `empty_value_sentinel` into the output only if
+     * `is_outer` is true.
      *
      * @tparam buffer_size Size of the output buffer
      * @tparam is_outer Boolean flag indicating whether outer join is peformed
@@ -1198,10 +1214,10 @@ class static_multimap {
      * loads with per-cg shared memory buffer.
      *
      * For pair `p`, if pair_equal(p, slot[j]) returns true, copies `p` to unspecified locations
-     * in
-     * `[probe_output_begin, probe_output_end)` and copies slot[j] to unspecified locations in
-     * `[contained_output_begin, contained_output_end)`. In case of non-matches, copies `p` and
-     * the empty value sentinels into the output only if `is_outer` is true.
+     * in `[probe_output_begin, probe_output_end)` and copies slot[j] to unspecified locations in
+     * `[contained_output_begin, contained_output_end)`. If `p` does not have any matches, copies
+     * `p` and a pair of `empty_key_sentinel` and `empty_value_sentinel` into the output only if
+     * `is_outer` is true.
      *
      * @tparam cg_size The number of threads in CUDA Cooperative Groups
      * @tparam buffer_size Size of the output buffer
@@ -1247,7 +1263,7 @@ class static_multimap {
      * @tparam CG Cooperative Group type
      * @tparam atomicT Type of atomic storage
      * @tparam OutputIt Device accessible output iterator whose `value_type` is
-     * convertible to the map's `mapped_type`
+     * constructible from the map's `value_type`
      * @param g The Cooperative Group used to flush output buffer
      * @param num_outputs Number of valid output in the buffer
      * @param output_buffer Buffer of the key/value pair sequence
@@ -1287,7 +1303,7 @@ class static_multimap {
                                                    OutputZipIt2 contained_output_begin) noexcept;
 
     /**
-     * @brief Indicates whether the key `k` was inserted into the map.
+     * @brief Indicates whether the key `k` exists in the map.
      *
      * If the key `k` was inserted into the map, `contains` returns
      * true. Otherwise, it returns false. Uses the CUDA Cooperative Groups API to
@@ -1310,6 +1326,9 @@ class static_multimap {
     /**
      * @brief Counts the occurrence of a given key contained in multimap.
      *
+     * For a given key, `k`, counts all matching keys, `k'`, as determined by `key_equal(k, k')` and
+     * returns the sum of all matches for `k`.
+     *
      * @tparam CG Cooperative Group type
      * @tparam KeyEqual Binary callable type
      * @param g The Cooperative Group used to perform the count operation
@@ -1327,6 +1346,9 @@ class static_multimap {
      * @brief Counts the occurrence of a given key contained in multimap. If no
      * matches can be found for a given key, the corresponding occurrence is 1.
      *
+     * For a given key, `k`, counts all matching keys, `k'`, as determined by `key_equal(k, k')` and
+     * returns the sum of all matches for `k`. If `k` does not have any matches, returns 1.
+     *
      * @tparam CG Cooperative Group type
      * @tparam KeyEqual Binary callable type
      * @param g The Cooperative Group used to perform the count operation
@@ -1343,6 +1365,9 @@ class static_multimap {
     /**
      * @brief Counts the occurrence of a given key/value pair contained in multimap.
      *
+     * For a given pair, `p`, counts all matching pairs, `p'`, as determined by `pair_equal(p, p')`
+     * and returns the sum of all matches for `p`.
+     *
      * @tparam CG Cooperative Group type
      * @tparam PairEqual Binary callable type
      * @param g The Cooperative Group used to perform the pair_count operation
@@ -1360,6 +1385,9 @@ class static_multimap {
      * @brief Counts the occurrence of a given key/value pair contained in multimap.
      * If no matches can be found for a given key, the corresponding occurrence is 1.
      *
+     * For a given pair, `p`, counts all matching pairs, `p'`, as determined by `pair_equal(p, p')`
+     * and returns the sum of all matches for `p`. If `p` does not have any matches, returns 1.
+     *
      * @tparam CG Cooperative Group type
      * @tparam PairEqual Binary callable type
      * @param g The Cooperative Group used to perform the pair_count operation
@@ -1377,16 +1405,15 @@ class static_multimap {
      * @brief Retrieves all the matches of a given key contained in multimap using vector
      * loads with per-warp shared memory buffer.
      *
-     * For keys `k = *(first + i)` existing in the map, copies `k` and all associated values to
-     * unspecified locations in `[output_begin, output_end)`. In case of non-matches, copies `k`
-     * and the empty value sentinel into the output only if `is_outer` is true.
+     * For key `k` existing in the map, copies `k` and all associated values to unspecified
+     * locations in `[output_begin, output_end)`.
      *
      * @tparam buffer_size Size of the output buffer
      * @tparam warpT Warp (Cooperative Group) type
      * @tparam CG Cooperative Group type
      * @tparam atomicT Type of atomic storage
      * @tparam OutputIt Device accessible output iterator whose `value_type` is
-     * convertible to the map's `mapped_type`
+     * constructible from the map's `value_type`
      * @tparam KeyEqual Binary callable type
      * @param warp The Cooperative Group (or warp) used to flush output buffer
      * @param g The Cooperative Group used to retrieve
@@ -1417,16 +1444,16 @@ class static_multimap {
      * @brief Retrieves all the matches of a given key contained in multimap using vector
      * loads with per-warp shared memory buffer.
      *
-     * For keys `k = *(first + i)` existing in the map, copies `k` and all associated values to
-     * unspecified locations in `[output_begin, output_end)`. In case of non-matches, copies `k`
-     * and the empty value sentinel into the output only if `is_outer` is true.
+     * For key `k` existing in the map, copies `k` and all associated values to unspecified
+     * locations in `[output_begin, output_end)`. If `k` does not have any matches, copies `k` and
+     * `empty_value_sentinel()` into the output.
      *
      * @tparam buffer_size Size of the output buffer
      * @tparam warpT Warp (Cooperative Group) type
      * @tparam CG Cooperative Group type
      * @tparam atomicT Type of atomic storage
      * @tparam OutputIt Device accessible output iterator whose `value_type` is
-     * convertible to the map's `mapped_type`
+     * constructible from the map's `value_type`
      * @tparam KeyEqual Binary callable type
      * @param warp The Cooperative Group (or warp) used to flush output buffer
      * @param g The Cooperative Group used to retrieve
@@ -1457,15 +1484,15 @@ class static_multimap {
      * @brief Retrieves all the matches of a given key contained in multimap using scalar
      * loads with per-cg shared memory buffer.
      *
-     * For keys `k = *(first + i)` existing in the map, copies `k` and all associated values to
-     * unspecified locations in `[output_begin, output_end)`.
+     * For key `k` existing in the map, copies `k` and all associated values to unspecified
+     * locations in `[output_begin, output_end)`.
      *
      * @tparam cg_size The number of threads in CUDA Cooperative Groups
      * @tparam buffer_size Size of the output buffer
      * @tparam CG Cooperative Group type
      * @tparam atomicT Type of atomic storage
      * @tparam OutputIt Device accessible output iterator whose `value_type` is
-     * convertible to the map's `mapped_type`
+     * constructible from the map's `value_type`
      * @tparam KeyEqual Binary callable type
      * @param g The Cooperative Group used to retrieve
      * @param k The key to search for
@@ -1494,16 +1521,16 @@ class static_multimap {
      * @brief Retrieves all the matches of a given key contained in multimap using scalar
      * loads with per-cg shared memory buffer.
      *
-     * For keys `k = *(first + i)` existing in the map, copies `k` and all associated values to
-     * unspecified locations in `[output_begin, output_end)`. In case of non-matches, copies `k`
-     * and the empty value sentinel into the output.
+     * For key `k` existing in the map, copies `k` and all associated values to unspecified
+     * locations in `[output_begin, output_end)`. If `k` does not have any matches, copies `k` and
+     * `empty_value_sentinel` into the output.
      *
      * @tparam cg_size The number of threads in CUDA Cooperative Groups
      * @tparam buffer_size Size of the output buffer
      * @tparam CG Cooperative Group type
      * @tparam atomicT Type of atomic storage
      * @tparam OutputIt Device accessible output iterator whose `value_type` is
-     * convertible to the map's `mapped_type`
+     * constructible from the map's `value_type`
      * @tparam KeyEqual Binary callable type
      * @param g The Cooperative Group used to retrieve
      * @param k The key to search for
@@ -1533,8 +1560,7 @@ class static_multimap {
      * loads with per-warp shared memory buffer.
      *
      * For pair `p`, if pair_equal(p, slot[j]) returns true, copies `p` to unspecified locations
-     * in
-     * `[probe_output_begin, probe_output_end)` and copies slot[j] to unspecified locations in
+     * in `[probe_output_begin, probe_output_end)` and copies slot[j] to unspecified locations in
      * `[contained_output_begin, contained_output_end)`.
      *
      * @tparam buffer_size Size of the output buffer
@@ -1579,10 +1605,9 @@ class static_multimap {
      * loads with per-warp shared memory buffer.
      *
      * For pair `p`, if pair_equal(p, slot[j]) returns true, copies `p` to unspecified locations
-     * in
-     * `[probe_output_begin, probe_output_end)` and copies slot[j] to unspecified locations in
-     * `[contained_output_begin, contained_output_end)`. In case of non-matches, copies `p` and
-     * the empty value sentinels into the output.
+     * in `[probe_output_begin, probe_output_end)` and copies slot[j] to unspecified locations in
+     * `[contained_output_begin, contained_output_end)`. If `p` does not have any matches, copies
+     * `p` and a pair of `empty_key_sentinel` and `empty_value_sentinel` into the output.
      *
      * @tparam buffer_size Size of the output buffer
      * @tparam warpT Warp (Cooperative Group) type
@@ -1626,8 +1651,7 @@ class static_multimap {
      * loads with per-cg shared memory buffer.
      *
      * For pair `p`, if pair_equal(p, slot[j]) returns true, copies `p` to unspecified locations
-     * in
-     * `[probe_output_begin, probe_output_end)` and copies slot[j] to unspecified locations in
+     * in `[probe_output_begin, probe_output_end)` and copies slot[j] to unspecified locations in
      * `[contained_output_begin, contained_output_end)`.
      *
      * @tparam cg_size The number of threads in CUDA Cooperative Groups
@@ -1670,10 +1694,9 @@ class static_multimap {
      * loads with per-cg shared memory buffer.
      *
      * For pair `p`, if pair_equal(p, slot[j]) returns true, copies `p` to unspecified locations
-     * in
-     * `[probe_output_begin, probe_output_end)` and copies slot[j] to unspecified locations in
-     * `[contained_output_begin, contained_output_end)`. In case of non-matches, copies `p` and
-     * the empty value sentinels into the output.
+     * in `[probe_output_begin, probe_output_end)` and copies slot[j] to unspecified locations in
+     * `[contained_output_begin, contained_output_end)`. If `p` does not have any matches, copies
+     * `p` and a pair of `empty_key_sentinel` and `empty_value_sentinel` into the output.
      *
      * @tparam cg_size The number of threads in CUDA Cooperative Groups
      * @tparam buffer_size Size of the output buffer

From 294ffc1b851777ff3cc84ec0e384a05e447c13f7 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Fri, 8 Oct 2021 19:10:57 -0400
Subject: [PATCH 237/267] pair_retrieve returns pair of iterators instead of
 size

---
 include/cuco/detail/static_multimap.inl       |  92 ++++++------
 .../cuco/detail/static_multimap_kernels.cuh   |  18 +--
 include/cuco/static_multimap.cuh              | 140 ++++++++++--------
 tests/static_multimap/static_multimap_test.cu |  20 +--
 4 files changed, 145 insertions(+), 125 deletions(-)

diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index 7cde62d52..38740fe52 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -408,12 +408,13 @@ template <typename Key,
           cuda::thread_scope Scope,
           class ProbeSequence,
           typename Allocator>
-template <typename InputIt, typename OutputZipIt1, typename OutputZipIt2, typename PairEqual>
-std::size_t static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::pair_retrieve(
+template <typename InputIt, typename OutputIt1, typename OutputIt2, typename PairEqual>
+std::pair<OutputIt1, OutputIt2>
+static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::pair_retrieve(
   InputIt first,
   InputIt last,
-  OutputZipIt1 probe_output_begin,
-  OutputZipIt2 contained_output_begin,
+  OutputIt1 probe_output_begin,
+  OutputIt2 contained_output_begin,
   PairEqual pair_equal,
   cudaStream_t stream) const
 {
@@ -433,8 +434,8 @@ std::size_t static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::pair_r
                                                                     buffer_size,
                                                                     is_outer,
                                                                     InputIt,
-                                                                    OutputZipIt1,
-                                                                    OutputZipIt2,
+                                                                    OutputIt1,
+                                                                    OutputIt2,
                                                                     atomic_ctr_type,
                                                                     device_view,
                                                                     PairEqual>,
@@ -447,8 +448,8 @@ std::size_t static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::pair_r
                                                          buffer_size,
                                                          is_outer,
                                                          InputIt,
-                                                         OutputZipIt1,
-                                                         OutputZipIt2,
+                                                         OutputIt1,
+                                                         OutputIt2,
                                                          atomic_ctr_type,
                                                          device_view,
                                                          PairEqual>,
@@ -482,7 +483,7 @@ std::size_t static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::pair_r
     &h_counter, d_counter_.get(), sizeof(atomic_ctr_type), cudaMemcpyDeviceToHost, stream));
   CUCO_CUDA_TRY(cudaStreamSynchronize(stream));
 
-  return h_counter;
+  return std::make_pair(probe_output_begin + h_counter, contained_output_begin + h_counter);
 }
 
 template <typename Key,
@@ -490,12 +491,13 @@ template <typename Key,
           cuda::thread_scope Scope,
           class ProbeSequence,
           typename Allocator>
-template <typename InputIt, typename OutputZipIt1, typename OutputZipIt2, typename PairEqual>
-std::size_t static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::pair_retrieve_outer(
+template <typename InputIt, typename OutputIt1, typename OutputIt2, typename PairEqual>
+std::pair<OutputIt1, OutputIt2>
+static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::pair_retrieve_outer(
   InputIt first,
   InputIt last,
-  OutputZipIt1 probe_output_begin,
-  OutputZipIt2 contained_output_begin,
+  OutputIt1 probe_output_begin,
+  OutputIt2 contained_output_begin,
   PairEqual pair_equal,
   cudaStream_t stream) const
 {
@@ -515,8 +517,8 @@ std::size_t static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::pair_r
                                                                     buffer_size,
                                                                     is_outer,
                                                                     InputIt,
-                                                                    OutputZipIt1,
-                                                                    OutputZipIt2,
+                                                                    OutputIt1,
+                                                                    OutputIt2,
                                                                     atomic_ctr_type,
                                                                     device_view,
                                                                     PairEqual>,
@@ -529,8 +531,8 @@ std::size_t static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::pair_r
                                                          buffer_size,
                                                          is_outer,
                                                          InputIt,
-                                                         OutputZipIt1,
-                                                         OutputZipIt2,
+                                                         OutputIt1,
+                                                         OutputIt2,
                                                          atomic_ctr_type,
                                                          device_view,
                                                          PairEqual>,
@@ -564,7 +566,7 @@ std::size_t static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::pair_r
     &h_counter, d_counter_.get(), sizeof(atomic_ctr_type), cudaMemcpyDeviceToHost, stream));
   CUCO_CUDA_TRY(cudaStreamSynchronize(stream));
 
-  return h_counter;
+  return std::make_pair(probe_output_begin + h_counter, contained_output_begin + h_counter);
 }
 
 template <typename Key,
@@ -1207,8 +1209,8 @@ template <uint32_t buffer_size,
           typename warpT,
           typename CG,
           typename atomicT,
-          typename OutputZipIt1,
-          typename OutputZipIt2,
+          typename OutputIt1,
+          typename OutputIt2,
           typename PairEqual>
 __device__ void
 static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::pair_retrieve_impl(
@@ -1219,8 +1221,8 @@ static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::pair_
   value_type* probe_output_buffer,
   value_type* contained_output_buffer,
   atomicT* num_matches,
-  OutputZipIt1 probe_output_begin,
-  OutputZipIt2 contained_output_begin,
+  OutputIt1 probe_output_begin,
+  OutputIt2 contained_output_begin,
   PairEqual pair_equal) noexcept
 {
   const uint32_t cg_lane_id = g.thread_rank();
@@ -1309,8 +1311,8 @@ template <uint32_t cg_size,
           bool is_outer,
           typename CG,
           typename atomicT,
-          typename OutputZipIt1,
-          typename OutputZipIt2,
+          typename OutputIt1,
+          typename OutputIt2,
           typename PairEqual>
 __device__ void
 static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::pair_retrieve_impl(
@@ -1320,8 +1322,8 @@ static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::pair_
   value_type* probe_output_buffer,
   value_type* contained_output_buffer,
   atomicT* num_matches,
-  OutputZipIt1 probe_output_begin,
-  OutputZipIt2 contained_output_begin,
+  OutputIt1 probe_output_begin,
+  OutputIt2 contained_output_begin,
   PairEqual pair_equal) noexcept
 {
   const uint32_t lane_id = g.thread_rank();
@@ -1438,7 +1440,7 @@ template <typename Key,
           cuda::thread_scope Scope,
           class ProbeSequence,
           typename Allocator>
-template <typename CG, typename atomicT, typename OutputZipIt1, typename OutputZipIt2>
+template <typename CG, typename atomicT, typename OutputIt1, typename OutputIt2>
 __inline__ __device__ void
 static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::flush_output_buffer(
   CG const& g,
@@ -1446,8 +1448,8 @@ static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::flush
   value_type* probe_output_buffer,
   value_type* contained_output_buffer,
   atomicT* num_matches,
-  OutputZipIt1 probe_output_begin,
-  OutputZipIt2 contained_output_begin) noexcept
+  OutputIt1 probe_output_begin,
+  OutputIt2 contained_output_begin) noexcept
 {
   std::size_t offset;
   const auto lane_id = g.thread_rank();
@@ -1647,8 +1649,8 @@ template <uint32_t buffer_size,
           typename warpT,
           typename CG,
           typename atomicT,
-          typename OutputZipIt1,
-          typename OutputZipIt2,
+          typename OutputIt1,
+          typename OutputIt2,
           typename PairEqual>
 __device__ void
 static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::pair_retrieve(
@@ -1659,8 +1661,8 @@ static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::pair_
   value_type* probe_output_buffer,
   value_type* contained_output_buffer,
   atomicT* num_matches,
-  OutputZipIt1 probe_output_begin,
-  OutputZipIt2 contained_output_begin,
+  OutputIt1 probe_output_begin,
+  OutputIt2 contained_output_begin,
   PairEqual pair_equal) noexcept
 {
   constexpr bool is_outer = false;
@@ -1685,8 +1687,8 @@ template <uint32_t buffer_size,
           typename warpT,
           typename CG,
           typename atomicT,
-          typename OutputZipIt1,
-          typename OutputZipIt2,
+          typename OutputIt1,
+          typename OutputIt2,
           typename PairEqual>
 __device__ void
 static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::pair_retrieve_outer(
@@ -1697,8 +1699,8 @@ static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::pair_
   value_type* probe_output_buffer,
   value_type* contained_output_buffer,
   atomicT* num_matches,
-  OutputZipIt1 probe_output_begin,
-  OutputZipIt2 contained_output_begin,
+  OutputIt1 probe_output_begin,
+  OutputIt2 contained_output_begin,
   PairEqual pair_equal) noexcept
 {
   constexpr bool is_outer = true;
@@ -1723,8 +1725,8 @@ template <uint32_t cg_size,
           uint32_t buffer_size,
           typename CG,
           typename atomicT,
-          typename OutputZipIt1,
-          typename OutputZipIt2,
+          typename OutputIt1,
+          typename OutputIt2,
           typename PairEqual>
 __device__ void
 static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::pair_retrieve(
@@ -1734,8 +1736,8 @@ static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::pair_
   value_type* probe_output_buffer,
   value_type* contained_output_buffer,
   atomicT* num_matches,
-  OutputZipIt1 probe_output_begin,
-  OutputZipIt2 contained_output_begin,
+  OutputIt1 probe_output_begin,
+  OutputIt2 contained_output_begin,
   PairEqual pair_equal) noexcept
 {
   constexpr bool is_outer = false;
@@ -1759,8 +1761,8 @@ template <uint32_t cg_size,
           uint32_t buffer_size,
           typename CG,
           typename atomicT,
-          typename OutputZipIt1,
-          typename OutputZipIt2,
+          typename OutputIt1,
+          typename OutputIt2,
           typename PairEqual>
 __device__ void
 static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::pair_retrieve_outer(
@@ -1770,8 +1772,8 @@ static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::pair_
   value_type* probe_output_buffer,
   value_type* contained_output_buffer,
   atomicT* num_matches,
-  OutputZipIt1 probe_output_begin,
-  OutputZipIt2 contained_output_begin,
+  OutputIt1 probe_output_begin,
+  OutputIt2 contained_output_begin,
   PairEqual pair_equal) noexcept
 {
   constexpr bool is_outer = true;
diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap_kernels.cuh
index fa1ef65f2..e098b978e 100644
--- a/include/cuco/detail/static_multimap_kernels.cuh
+++ b/include/cuco/detail/static_multimap_kernels.cuh
@@ -151,7 +151,7 @@ __global__ void insert_if_n(InputIt first, StencilIt s, std::size_t n, viewT vie
  * @tparam tile_size The number of threads in the Cooperative Groups
  * @tparam InputIt Device accessible input iterator whose `value_type` is
  * convertible to the map's `key_type`
- * @tparam OutputIt Device accessible output iterator whose `value_type` is `bool`
+ * @tparam OutputIt Device accessible output iterator whose `value_type` is convertible from `bool`
  * @tparam viewT Type of device view allowing access of hash map storage
  * @tparam KeyEqual Binary callable type
  * @param first Beginning of the sequence of keys
@@ -512,10 +512,10 @@ __global__ void retrieve(InputIt first,
  * @tparam InputIt Device accessible random access input iterator where
  * `std::is_convertible<std::iterator_traits<InputIt>::value_type,
  * static_multimap<K, V>::value_type>` is `true`
- * @tparam OutputIt1 Device accessible output iterator whose `value_type` is
- * constructible from the map's `value_type`
- * @tparam OutputIt2 Device accessible output iterator whose `value_type` is
- * constructible from the map's `value_type`
+ * @tparam OutputIt1 Device accessible output iterator whose `value_type` is constructible from
+ * `InputIt`s `value_type`.
+ * @tparam OutputIt2 Device accessible output iterator whose `value_type` is constructible from
+ * the map's `value_type`.
  * @tparam atomicT Type of atomic storage
  * @tparam viewT Type of device view allowing access of hash map storage
  * @tparam PairEqual Binary callable type
@@ -630,10 +630,10 @@ __global__ void vectorized_pair_retrieve(InputIt first,
  * @tparam InputIt Device accessible random access input iterator where
  * `std::is_convertible<std::iterator_traits<InputIt>::value_type,
  * static_multimap<K, V>::value_type>` is `true`
- * @tparam OutputIt1 Device accessible output iterator whose `value_type` is
- * constructible from the map's `value_type`
- * @tparam OutputIt2 Device accessible output iterator whose `value_type` is
- * constructible from the map's `value_type`
+ * @tparam OutputIt1 Device accessible output iterator whose `value_type` is constructible from
+ * `InputIt`s `value_type`.
+ * @tparam OutputIt2 Device accessible output iterator whose `value_type` is constructible from
+ * the map's `value_type`.
  * @tparam atomicT Type of atomic storage
  * @tparam viewT Type of device view allowing access of hash map storage
  * @tparam PairEqual Binary callable type
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index e790a05ce..fd6466e53 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -445,8 +445,10 @@ class static_multimap {
    * @tparam InputIt Device accessible random access input iterator where
    * `std::is_convertible<std::iterator_traits<InputIt>::value_type,
    * static_multimap<K, V>::value_type>` is `true`
-   * @tparam OutputZipIt1 Device accessible output zip iterator for probe matches
-   * @tparam OutputZipIt2 Device accessible output zip iterator for contained matches
+   * @tparam OutputIt1 Device accessible output iterator whose `value_type` is constructible from
+   * `InputIt`s `value_type`.
+   * @tparam OutputIt2 Device accessible output iterator whose `value_type` is constructible from
+   * the map's `value_type`.
    * @tparam PairEqual Binary callable type
    * @param first Beginning of the sequence of pairs
    * @param last End of the sequence of pairs
@@ -454,15 +456,15 @@ class static_multimap {
    * @param contained_output_begin Beginning of the sequence of the matched contained pairs
    * @param pair_equal The binary function to compare two pairs for equality
    * @param stream CUDA stream used for pair_retrieve
-   * @return The total number of matches
+   * @return Pair of iterators pointing to the last elements in the output
    */
-  template <typename InputIt, typename OutputZipIt1, typename OutputZipIt2, typename PairEqual>
-  std::size_t pair_retrieve(InputIt first,
-                            InputIt last,
-                            OutputZipIt1 probe_output_begin,
-                            OutputZipIt2 contained_output_begin,
-                            PairEqual pair_equal,
-                            cudaStream_t stream = 0) const;
+  template <typename InputIt, typename OutputIt1, typename OutputIt2, typename PairEqual>
+  std::pair<OutputIt1, OutputIt2> pair_retrieve(InputIt first,
+                                                InputIt last,
+                                                OutputIt1 probe_output_begin,
+                                                OutputIt2 contained_output_begin,
+                                                PairEqual pair_equal,
+                                                cudaStream_t stream = 0) const;
 
   /**
    * @brief Retrieves all pairs matching the input probe pair in the range `[first, last)`.
@@ -481,8 +483,10 @@ class static_multimap {
    * @tparam InputIt Device accessible random access input iterator where
    * `std::is_convertible<std::iterator_traits<InputIt>::value_type,
    * static_multimap<K, V>::value_type>` is `true`
-   * @tparam OutputZipIt1 Device accessible output zip iterator for probe matches
-   * @tparam OutputZipIt2 Device accessible output zip iterator for contained matches
+   * @tparam OutputIt1 Device accessible output iterator whose `value_type` is constructible from
+   * `InputIt`s `value_type`.
+   * @tparam OutputIt2 Device accessible output iterator whose `value_type` is constructible from
+   * the map's `value_type`.
    * @tparam PairEqual Binary callable type
    * @param first Beginning of the sequence of pairs
    * @param last End of the sequence of pairs
@@ -490,15 +494,15 @@ class static_multimap {
    * @param contained_output_begin Beginning of the sequence of the matched contained pairs
    * @param pair_equal The binary function to compare two pairs for equality
    * @param stream CUDA stream used for pair_retrieve_outer
-   * @return The total number of matches
+   * @return Pair of iterators pointing to the last elements in the output
    */
-  template <typename InputIt, typename OutputZipIt1, typename OutputZipIt2, typename PairEqual>
-  std::size_t pair_retrieve_outer(InputIt first,
-                                  InputIt last,
-                                  OutputZipIt1 probe_output_begin,
-                                  OutputZipIt2 contained_output_begin,
-                                  PairEqual pair_equal,
-                                  cudaStream_t stream = 0) const;
+  template <typename InputIt, typename OutputIt1, typename OutputIt2, typename PairEqual>
+  std::pair<OutputIt1, OutputIt2> pair_retrieve_outer(InputIt first,
+                                                      InputIt last,
+                                                      OutputIt1 probe_output_begin,
+                                                      OutputIt2 contained_output_begin,
+                                                      PairEqual pair_equal,
+                                                      cudaStream_t stream = 0) const;
 
  private:
   /**
@@ -1175,8 +1179,10 @@ class static_multimap {
      * @tparam warpT Warp (Cooperative Group) type
      * @tparam CG Cooperative Group type
      * @tparam atomicT Type of atomic storage
-     * @tparam OutputZipIt1 Device accessible output zip iterator for probe matches
-     * @tparam OutputZipIt2 Device accessible output zip iterator for contained matches
+     * @tparam OutputIt1 Device accessible output iterator whose `value_type` is constructible from
+     * `InputIt`s `value_type`.
+     * @tparam OutputIt2 Device accessible output iterator whose `value_type` is constructible from
+     * the map's `value_type`.
      * @tparam PairEqual Binary callable type
      * @param warp The Cooperative Group (or warp) used to flush output buffer
      * @param g The Cooperative Group used to retrieve
@@ -1195,8 +1201,8 @@ class static_multimap {
               typename warpT,
               typename CG,
               typename atomicT,
-              typename OutputZipIt1,
-              typename OutputZipIt2,
+              typename OutputIt1,
+              typename OutputIt2,
               typename PairEqual>
     __device__ void pair_retrieve_impl(warpT const& warp,
                                        CG const& g,
@@ -1205,8 +1211,8 @@ class static_multimap {
                                        value_type* probe_output_buffer,
                                        value_type* contained_output_buffer,
                                        atomicT* num_matches,
-                                       OutputZipIt1 probe_output_begin,
-                                       OutputZipIt2 contained_output_begin,
+                                       OutputIt1 probe_output_begin,
+                                       OutputIt2 contained_output_begin,
                                        PairEqual pair_equal) noexcept;
 
     /**
@@ -1224,8 +1230,10 @@ class static_multimap {
      * @tparam is_outer Boolean flag indicating whether outer join is peformed
      * @tparam CG Cooperative Group type
      * @tparam atomicT Type of atomic storage
-     * @tparam OutputZipIt1 Device accessible output zip iterator for probe matches
-     * @tparam OutputZipIt2 Device accessible output zip iterator for contained matches
+     * @tparam OutputIt1 Device accessible output iterator whose `value_type` is constructible from
+     * `InputIt`s `value_type`.
+     * @tparam OutputIt2 Device accessible output iterator whose `value_type` is constructible from
+     * the map's `value_type`.
      * @tparam PairEqual Binary callable type
      * @param g The Cooperative Group used to retrieve
      * @param pair The pair to search for
@@ -1243,8 +1251,8 @@ class static_multimap {
               bool is_outer,
               typename CG,
               typename atomicT,
-              typename OutputZipIt1,
-              typename OutputZipIt2,
+              typename OutputIt1,
+              typename OutputIt2,
               typename PairEqual>
     __device__ void pair_retrieve_impl(CG const& g,
                                        value_type const& pair,
@@ -1252,8 +1260,8 @@ class static_multimap {
                                        value_type* probe_output_buffer,
                                        value_type* contained_output_buffer,
                                        atomicT* num_matches,
-                                       OutputZipIt1 probe_output_begin,
-                                       OutputZipIt2 contained_output_begin,
+                                       OutputIt1 probe_output_begin,
+                                       OutputIt2 contained_output_begin,
                                        PairEqual pair_equal) noexcept;
 
    public:
@@ -1282,8 +1290,10 @@ class static_multimap {
      *
      * @tparam CG Cooperative Group type
      * @tparam atomicT Type of atomic storage
-     * @tparam OutputZipIt1 Device accessible output zip iterator for probe pairs
-     * @tparam OutputZipIt2 Device accessible output zip iterator for contained pairs
+     * @tparam OutputIt1 Device accessible output iterator whose `value_type` is constructible from
+     * `InputIt`s `value_type`.
+     * @tparam OutputIt2 Device accessible output iterator whose `value_type` is constructible from
+     * the map's `value_type`.
      * @param g The Cooperative Group used to flush output buffer
      * @param num_outputs Number of valid output in the buffer
      * @param probe_output_buffer Buffer of the matched probe pair sequence
@@ -1293,14 +1303,14 @@ class static_multimap {
      * @param contained_output_begin Beginning of the output sequence of the matched contained
      * pairs
      */
-    template <typename CG, typename atomicT, typename OutputZipIt1, typename OutputZipIt2>
+    template <typename CG, typename atomicT, typename OutputIt1, typename OutputIt2>
     __inline__ __device__ void flush_output_buffer(CG const& g,
                                                    uint32_t const num_outputs,
                                                    value_type* probe_output_buffer,
                                                    value_type* contained_output_buffer,
                                                    atomicT* num_matches,
-                                                   OutputZipIt1 probe_output_begin,
-                                                   OutputZipIt2 contained_output_begin) noexcept;
+                                                   OutputIt1 probe_output_begin,
+                                                   OutputIt2 contained_output_begin) noexcept;
 
     /**
      * @brief Indicates whether the key `k` exists in the map.
@@ -1567,8 +1577,10 @@ class static_multimap {
      * @tparam warpT Warp (Cooperative Group) type
      * @tparam CG Cooperative Group type
      * @tparam atomicT Type of atomic storage
-     * @tparam OutputZipIt1 Device accessible output zip iterator for probe matches
-     * @tparam OutputZipIt2 Device accessible output zip iterator for contained matches
+     * @tparam OutputIt1 Device accessible output iterator whose `value_type` is constructible from
+     * `InputIt`s `value_type`.
+     * @tparam OutputIt2 Device accessible output iterator whose `value_type` is constructible from
+     * the map's `value_type`.
      * @tparam PairEqual Binary callable type
      * @param warp The Cooperative Group (or warp) used to flush output buffer
      * @param g The Cooperative Group used to retrieve
@@ -1586,8 +1598,8 @@ class static_multimap {
               typename warpT,
               typename CG,
               typename atomicT,
-              typename OutputZipIt1,
-              typename OutputZipIt2,
+              typename OutputIt1,
+              typename OutputIt2,
               typename PairEqual>
     __device__ void pair_retrieve(warpT const& warp,
                                   CG const& g,
@@ -1596,8 +1608,8 @@ class static_multimap {
                                   value_type* probe_output_buffer,
                                   value_type* contained_output_buffer,
                                   atomicT* num_matches,
-                                  OutputZipIt1 probe_output_begin,
-                                  OutputZipIt2 contained_output_begin,
+                                  OutputIt1 probe_output_begin,
+                                  OutputIt2 contained_output_begin,
                                   PairEqual pair_equal) noexcept;
 
     /**
@@ -1613,8 +1625,10 @@ class static_multimap {
      * @tparam warpT Warp (Cooperative Group) type
      * @tparam CG Cooperative Group type
      * @tparam atomicT Type of atomic storage
-     * @tparam OutputZipIt1 Device accessible output zip iterator for probe matches
-     * @tparam OutputZipIt2 Device accessible output zip iterator for contained matches
+     * @tparam OutputIt1 Device accessible output iterator whose `value_type` is constructible from
+     * `InputIt`s `value_type`.
+     * @tparam OutputIt2 Device accessible output iterator whose `value_type` is constructible from
+     * the map's `value_type`.
      * @tparam PairEqual Binary callable type
      * @param warp The Cooperative Group (or warp) used to flush output buffer
      * @param g The Cooperative Group used to retrieve
@@ -1632,8 +1646,8 @@ class static_multimap {
               typename warpT,
               typename CG,
               typename atomicT,
-              typename OutputZipIt1,
-              typename OutputZipIt2,
+              typename OutputIt1,
+              typename OutputIt2,
               typename PairEqual>
     __device__ void pair_retrieve_outer(warpT const& warp,
                                         CG const& g,
@@ -1642,8 +1656,8 @@ class static_multimap {
                                         value_type* probe_output_buffer,
                                         value_type* contained_output_buffer,
                                         atomicT* num_matches,
-                                        OutputZipIt1 probe_output_begin,
-                                        OutputZipIt2 contained_output_begin,
+                                        OutputIt1 probe_output_begin,
+                                        OutputIt2 contained_output_begin,
                                         PairEqual pair_equal) noexcept;
 
     /**
@@ -1658,8 +1672,10 @@ class static_multimap {
      * @tparam buffer_size Size of the output buffer
      * @tparam CG Cooperative Group type
      * @tparam atomicT Type of atomic storage
-     * @tparam OutputZipIt1 Device accessible output zip iterator for probe matches
-     * @tparam OutputZipIt2 Device accessible output zip iterator for contained matches
+     * @tparam OutputIt1 Device accessible output iterator whose `value_type` is constructible from
+     * `InputIt`s `value_type`.
+     * @tparam OutputIt2 Device accessible output iterator whose `value_type` is constructible from
+     * the map's `value_type`.
      * @tparam PairEqual Binary callable type
      * @param g The Cooperative Group used to retrieve
      * @param pair The pair to search for
@@ -1676,8 +1692,8 @@ class static_multimap {
               uint32_t buffer_size,
               typename CG,
               typename atomicT,
-              typename OutputZipIt1,
-              typename OutputZipIt2,
+              typename OutputIt1,
+              typename OutputIt2,
               typename PairEqual>
     __device__ void pair_retrieve(CG const& g,
                                   value_type const& pair,
@@ -1685,8 +1701,8 @@ class static_multimap {
                                   value_type* probe_output_buffer,
                                   value_type* contained_output_buffer,
                                   atomicT* num_matches,
-                                  OutputZipIt1 probe_output_begin,
-                                  OutputZipIt2 contained_output_begin,
+                                  OutputIt1 probe_output_begin,
+                                  OutputIt2 contained_output_begin,
                                   PairEqual pair_equal) noexcept;
 
     /**
@@ -1702,8 +1718,10 @@ class static_multimap {
      * @tparam buffer_size Size of the output buffer
      * @tparam CG Cooperative Group type
      * @tparam atomicT Type of atomic storage
-     * @tparam OutputZipIt1 Device accessible output zip iterator for probe matches
-     * @tparam OutputZipIt2 Device accessible output zip iterator for contained matches
+     * @tparam OutputIt1 Device accessible output iterator whose `value_type` is constructible from
+     * `InputIt`s `value_type`.
+     * @tparam OutputIt2 Device accessible output iterator whose `value_type` is constructible from
+     * the map's `value_type`.
      * @tparam PairEqual Binary callable type
      * @param g The Cooperative Group used to retrieve
      * @param pair The pair to search for
@@ -1720,8 +1738,8 @@ class static_multimap {
               uint32_t buffer_size,
               typename CG,
               typename atomicT,
-              typename OutputZipIt1,
-              typename OutputZipIt2,
+              typename OutputIt1,
+              typename OutputIt2,
               typename PairEqual>
     __device__ void pair_retrieve_outer(CG const& g,
                                         value_type const& pair,
@@ -1729,8 +1747,8 @@ class static_multimap {
                                         value_type* probe_output_buffer,
                                         value_type* contained_output_buffer,
                                         atomicT* num_matches,
-                                        OutputZipIt1 probe_output_begin,
-                                        OutputZipIt2 contained_output_begin,
+                                        OutputIt1 probe_output_begin,
+                                        OutputIt2 contained_output_begin,
                                         PairEqual pair_equal) noexcept;
   };  // class device_view
 
diff --git a/tests/static_multimap/static_multimap_test.cu b/tests/static_multimap/static_multimap_test.cu
index 000382490..6cb31cbf2 100644
--- a/tests/static_multimap/static_multimap_test.cu
+++ b/tests/static_multimap/static_multimap_test.cu
@@ -633,34 +633,34 @@ __inline__ void test_pair_functions(Map& map, PairIt pair_begin, std::size_t num
   {
     auto num = map.pair_count(pair_begin, pair_begin + num_pairs, pair_equal<Key, Value>{});
 
-    auto out1_zip = thrust::make_zip_iterator(
+    auto out1_begin = thrust::make_zip_iterator(
       thrust::make_tuple(thrust::make_discard_iterator(), thrust::make_discard_iterator()));
-    auto out2_zip = thrust::make_zip_iterator(
+    auto out2_begin = thrust::make_zip_iterator(
       thrust::make_tuple(thrust::make_discard_iterator(), thrust::make_discard_iterator()));
 
     REQUIRE(num == num_pairs);
 
-    auto size = map.pair_retrieve(
-      pair_begin, pair_begin + num_pairs, out1_zip, out2_zip, pair_equal<Key, Value>{});
+    auto [out1_end, out2_end] = map.pair_retrieve(
+      pair_begin, pair_begin + num_pairs, out1_begin, out2_begin, pair_equal<Key, Value>{});
 
-    REQUIRE(size == num_pairs);
+    REQUIRE((out1_end - out1_begin) == num_pairs);
   }
 
   SECTION("Output of pair_count_outer and pair_retrieve_outer should be coherent.")
   {
     auto num = map.pair_count_outer(pair_begin, pair_begin + num_pairs, pair_equal<Key, Value>{});
 
-    auto out1_zip = thrust::make_zip_iterator(
+    auto out1_begin = thrust::make_zip_iterator(
       thrust::make_tuple(thrust::make_discard_iterator(), thrust::make_discard_iterator()));
-    auto out2_zip = thrust::make_zip_iterator(
+    auto out2_begin = thrust::make_zip_iterator(
       thrust::make_tuple(thrust::make_discard_iterator(), thrust::make_discard_iterator()));
 
     REQUIRE(num == (num_pairs + num_pairs / 2));
 
-    auto size = map.pair_retrieve_outer(
-      pair_begin, pair_begin + num_pairs, out1_zip, out2_zip, pair_equal<Key, Value>{});
+    auto [out1_end, out2_end] = map.pair_retrieve_outer(
+      pair_begin, pair_begin + num_pairs, out1_begin, out2_begin, pair_equal<Key, Value>{});
 
-    REQUIRE(size == (num_pairs + num_pairs / 2));
+    REQUIRE((out1_end - out1_begin) == (num_pairs + num_pairs / 2));
   }
 }
 

From 5082f8f62f6acff60db11b59857cff71dec63767 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Fri, 8 Oct 2021 19:17:50 -0400
Subject: [PATCH 238/267] Remove default constructor

---
 include/cuco/detail/static_multimap.inl | 13 -------------
 include/cuco/static_multimap.cuh        |  5 -----
 2 files changed, 18 deletions(-)

diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index 38740fe52..72e6ce56b 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -35,19 +35,6 @@ struct slot_is_filled {
 
 namespace cuco {
 
-template <typename Key,
-          typename Value,
-          cuda::thread_scope Scope,
-          class ProbeSequence,
-          typename Allocator>
-static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::static_multimap()
-  : delete_counter_{counter_allocator_, stream_},
-    delete_slots_{slot_allocator_, capacity_, stream_},
-    d_counter_{nullptr, delete_counter_},
-    slots_{nullptr, delete_slots_}
-{
-}
-
 template <typename Key,
           typename Value,
           cuda::thread_scope Scope,
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index fd6466e53..0649ead22 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -190,11 +190,6 @@ class static_multimap {
    */
   static constexpr uint32_t cg_size() noexcept { return ProbeSequence::cg_size(); }
 
-  /**
-   * @brief Default constructor.
-   */
-  static_multimap();
-
   /**
    * @brief Construct a statically-sized map with the specified initial capacity,
    * sentinel values and CUDA stream.

From bf096977d9ea48831e54e132217c4995aec79dc2 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Fri, 8 Oct 2021 19:55:13 -0400
Subject: [PATCH 239/267] Remove move assignment operator for deleters

---
 include/cuco/static_multimap.cuh | 19 -------------------
 1 file changed, 19 deletions(-)

diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index 0649ead22..51514d64d 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -529,15 +529,6 @@ class static_multimap {
 
     void operator()(atomic_ctr_type* ptr) { allocator.deallocate(ptr, 1, stream); }
 
-    counter_deleter& operator=(counter_deleter&& other)
-    {
-      if (this != &other) {
-        allocator = other.allocator;
-        stream    = other.stream;
-      }
-      return *this;
-    }
-
     counter_allocator_type& allocator;
     cudaStream_t& stream;
   };
@@ -555,16 +546,6 @@ class static_multimap {
 
     void operator()(pair_atomic_type* ptr) { allocator.deallocate(ptr, capacity, stream); }
 
-    slot_deleter& operator=(slot_deleter&& other)
-    {
-      if (this != &other) {
-        allocator = other.allocator;
-        capacity  = other.capacity;
-        stream    = other.stream;
-      }
-      return *this;
-    }
-
     slot_allocator_type& allocator;
     size_t& capacity;
     cudaStream_t& stream;

From da040454097549bf669c4e0edf3c9fa0efc45d25 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Tue, 12 Oct 2021 21:21:45 -0400
Subject: [PATCH 240/267] Add docs for flush_output_buffer

---
 include/cuco/static_multimap.cuh | 11 +++++++++++
 1 file changed, 11 insertions(+)

diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index 51514d64d..5dc841aed 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -1244,6 +1244,12 @@ class static_multimap {
     /**
      * @brief Flushes per-CG buffer into the output sequence.
      *
+     * CUDA Cooperative Group, `g`, loads `num_outputs` key-value pairs from `output_buffer` and
+     * writes them into global memory in a coalesced fashion. CG-wide `memcpy_sync` is used if
+     * `CUCO_HAS_CG_MEMCPY_ASYNC` is defined and `thrust::is_contiguous_iterator_v<OutputIt>`
+     * returns true. All threads of `g` must be active due to implicit CG-wide synchronization
+     * during flushing.
+     *
      * @tparam CG Cooperative Group type
      * @tparam atomicT Type of atomic storage
      * @tparam OutputIt Device accessible output iterator whose `value_type` is
@@ -1264,6 +1270,11 @@ class static_multimap {
     /**
      * @brief Flushes per-CG buffer into the output sequences.
      *
+     * CUDA Cooperative Group, `g`, loads `num_outputs` elements from `probe_output_buffer` and
+     * `num_outputs` elements from `contained_output_buffer`, then writes them into global memory
+     * started from `probe_output_begin` and `contained_output_begin` respectively. All threads of
+     * `g` must be active due to implicit CG-wide synchronization during flushing.
+     *
      * @tparam CG Cooperative Group type
      * @tparam atomicT Type of atomic storage
      * @tparam OutputIt1 Device accessible output iterator whose `value_type` is constructible from

From 927b2b42b2c3f9fc18122e17050c1ba86aae50ad Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Tue, 12 Oct 2021 21:22:51 -0400
Subject: [PATCH 241/267] Minor cleanups

---
 include/cuco/detail/static_multimap.inl | 12 ++++--------
 1 file changed, 4 insertions(+), 8 deletions(-)

diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index 72e6ce56b..af9438493 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -1410,15 +1410,11 @@ static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::flush
     cooperative_groups::memcpy_async(
       g, output_begin + offset, output_buffer, sizeof(value_type) * num_outputs);
 #endif  // end CUCO_HAS_CUDA_BARRIER
-#else
-    for (auto index = lane_id; index < num_outputs; index += g.size()) {
-      *(output_begin + offset + index) = output_buffer[index];
-    }
+    return;
 #endif  // end CUCO_HAS_CG_MEMCPY_ASYNC
-  } else {
-    for (auto index = lane_id; index < num_outputs; index += g.size()) {
-      *(output_begin + offset + index) = output_buffer[index];
-    }
+  }
+  for (auto index = lane_id; index < num_outputs; index += g.size()) {
+    *(output_begin + offset + index) = output_buffer[index];
   }
 }
 

From 0be35b781e050c321df47952678a0b09db7ba7a0 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Tue, 12 Oct 2021 21:48:51 -0400
Subject: [PATCH 242/267] Doc cleanups

---
 .../cuco/detail/static_multimap_kernels.cuh   |  4 ++--
 include/cuco/static_multimap.cuh              | 24 +++++++++----------
 2 files changed, 14 insertions(+), 14 deletions(-)

diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap_kernels.cuh
index e098b978e..d3b54af5b 100644
--- a/include/cuco/detail/static_multimap_kernels.cuh
+++ b/include/cuco/detail/static_multimap_kernels.cuh
@@ -312,7 +312,7 @@ __global__ void pair_count(
 
 /**
  * @brief Retrieves all the values corresponding to all keys in the range `[first, last)`
- * using vector loads combined with per-block shared memory buffer.
+ * using vector loads combined with per-warp shared memory buffer.
  *
  * For key `k = *(first + i)` existing in the map, copies `k` and all associated values to
  * unspecified locations in `[output_begin, output_end)`. If `k` does not have any matches, copies
@@ -493,7 +493,7 @@ __global__ void retrieve(InputIt first,
 
 /**
  * @brief Retrieves all pairs matching the input probe pair in the range `[first, last)`
- * using vector loads combined with per-block shared memory buffer.
+ * using vector loads combined with per-warp shared memory buffer.
  *
  * If pair_equal(*(first + i), slot[j]) returns true, then *(first+i) is stored to unspecified
  * locations in `probe_output_begin`, and slot[j] is stored to unspecified locations in
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index 5dc841aed..453790828 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -1102,7 +1102,7 @@ class static_multimap {
 
     /**
      * @brief Retrieves all the matches of a given key contained in multimap using scalar
-     * loads with per-cg shared memory buffer.
+     * loads with per-CG shared memory buffer.
      *
      * For key `k` existing in the map, copies `k` and all associated values to unspecified
      * locations in `[output_begin, output_end)`. If `k` does not have any matches, copies `k` and
@@ -1193,7 +1193,7 @@ class static_multimap {
 
     /**
      * @brief Retrieves all the matches of a given pair contained in multimap using scalar
-     * loads with per-cg shared memory buffer.
+     * loads with per-CG shared memory buffer.
      *
      * For pair `p`, if pair_equal(p, slot[j]) returns true, copies `p` to unspecified locations
      * in `[probe_output_begin, probe_output_end)` and copies slot[j] to unspecified locations in
@@ -1244,8 +1244,8 @@ class static_multimap {
     /**
      * @brief Flushes per-CG buffer into the output sequence.
      *
-     * CUDA Cooperative Group, `g`, loads `num_outputs` key-value pairs from `output_buffer` and
-     * writes them into global memory in a coalesced fashion. CG-wide `memcpy_sync` is used if
+     * A given CUDA Cooperative Group, `g`, loads `num_outputs` key-value pairs from `output_buffer`
+     * and writes them into global memory in a coalesced fashion. CG-wide `memcpy_sync` is used if
      * `CUCO_HAS_CG_MEMCPY_ASYNC` is defined and `thrust::is_contiguous_iterator_v<OutputIt>`
      * returns true. All threads of `g` must be active due to implicit CG-wide synchronization
      * during flushing.
@@ -1270,10 +1270,10 @@ class static_multimap {
     /**
      * @brief Flushes per-CG buffer into the output sequences.
      *
-     * CUDA Cooperative Group, `g`, loads `num_outputs` elements from `probe_output_buffer` and
-     * `num_outputs` elements from `contained_output_buffer`, then writes them into global memory
-     * started from `probe_output_begin` and `contained_output_begin` respectively. All threads of
-     * `g` must be active due to implicit CG-wide synchronization during flushing.
+     * A given CUDA Cooperative Group, `g`, loads `num_outputs` elements from `probe_output_buffer`
+     * and `num_outputs` elements from `contained_output_buffer`, then writes them into global
+     * memory started from `probe_output_begin` and `contained_output_begin` respectively. All
+     * threads of `g` must be active due to implicit CG-wide synchronization during flushing.
      *
      * @tparam CG Cooperative Group type
      * @tparam atomicT Type of atomic storage
@@ -1479,7 +1479,7 @@ class static_multimap {
 
     /**
      * @brief Retrieves all the matches of a given key contained in multimap using scalar
-     * loads with per-cg shared memory buffer.
+     * loads with per-CG shared memory buffer.
      *
      * For key `k` existing in the map, copies `k` and all associated values to unspecified
      * locations in `[output_begin, output_end)`.
@@ -1516,7 +1516,7 @@ class static_multimap {
 
     /**
      * @brief Retrieves all the matches of a given key contained in multimap using scalar
-     * loads with per-cg shared memory buffer.
+     * loads with per-CG shared memory buffer.
      *
      * For key `k` existing in the map, copies `k` and all associated values to unspecified
      * locations in `[output_begin, output_end)`. If `k` does not have any matches, copies `k` and
@@ -1649,7 +1649,7 @@ class static_multimap {
 
     /**
      * @brief Retrieves all the matches of a given pair contained in multimap using scalar
-     * loads with per-cg shared memory buffer.
+     * loads with per-CG shared memory buffer.
      *
      * For pair `p`, if pair_equal(p, slot[j]) returns true, copies `p` to unspecified locations
      * in `[probe_output_begin, probe_output_end)` and copies slot[j] to unspecified locations in
@@ -1694,7 +1694,7 @@ class static_multimap {
 
     /**
      * @brief Retrieves all the matches of a given pair contained in multimap using scalar
-     * loads with per-cg shared memory buffer.
+     * loads with per-CG shared memory buffer.
      *
      * For pair `p`, if pair_equal(p, slot[j]) returns true, copies `p` to unspecified locations
      * in `[probe_output_begin, probe_output_end)` and copies slot[j] to unspecified locations in

From de8603656ae0fa534dc4f3dfe65be368ba0e761a Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Fri, 15 Oct 2021 15:08:40 -0400
Subject: [PATCH 243/267] Test cleanups

---
 tests/static_multimap/static_multimap_test.cu | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/tests/static_multimap/static_multimap_test.cu b/tests/static_multimap/static_multimap_test.cu
index 6cb31cbf2..4e92d7eae 100644
--- a/tests/static_multimap/static_multimap_test.cu
+++ b/tests/static_multimap/static_multimap_test.cu
@@ -79,9 +79,6 @@ struct alignas(8) value_pair {
   int32_t s;
 };
 
-#define SIZE 10
-__device__ int A[SIZE];
-
 template <typename Key, typename Value, typename Map, typename PairIt, typename KeyIt>
 __inline__ void test_custom_key_value_type(Map& map,
                                            PairIt pair_begin,

From 99d26ce7b2249b1eadc80f325415241fa9e8f634 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Fri, 15 Oct 2021 17:53:41 -0400
Subject: [PATCH 244/267] Move _impl functions to a new file

---
 include/cuco/detail/static_multimap_impl.inl | 1291 ++++++++++++++++++
 1 file changed, 1291 insertions(+)
 create mode 100644 include/cuco/detail/static_multimap_impl.inl

diff --git a/include/cuco/detail/static_multimap_impl.inl b/include/cuco/detail/static_multimap_impl.inl
new file mode 100644
index 000000000..601d030aa
--- /dev/null
+++ b/include/cuco/detail/static_multimap_impl.inl
@@ -0,0 +1,1291 @@
+/*
+ * Copyright (c) 2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cuco/static_multimap.cuh>
+
+#include <cuco/detail/bitwise_compare.cuh>
+
+namespace cuco {
+
+template <typename Key,
+          typename Value,
+          cuda::thread_scope Scope,
+          class ProbeSequence,
+          typename Allocator>
+class static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view_impl_base {
+ private:
+  ProbeSequence probe_sequence_;  ///< Probe sequence used to probe the hash map
+  Key empty_key_sentinel_{};      ///< Key value that represents an empty slot
+  Value empty_value_sentinel_{};  ///< Initial Value of empty slot
+
+ protected:
+  // Import member type definitions from `static_multimap`
+  using value_type     = value_type;
+  using key_type       = Key;
+  using mapped_type    = Value;
+  using iterator       = pair_atomic_type*;
+  using const_iterator = pair_atomic_type const*;
+
+  /**
+   * @brief Indicates if vector-load is used.
+   *
+   * Users have no explicit control on whether vector-load is used.
+   *
+   * @return Boolean indicating if vector-load is used.
+   */
+  static constexpr bool uses_vector_load() noexcept { return ProbeSequence::uses_vector_load(); }
+
+  /**
+   * @brief Returns the number of pairs loaded with each vector-load
+   */
+  static constexpr uint32_t vector_width() noexcept { return ProbeSequence::vector_width(); }
+
+  __host__ __device__ device_view_impl_base(pair_atomic_type* slots,
+                                            std::size_t capacity,
+                                            Key empty_key_sentinel,
+                                            Value empty_value_sentinel) noexcept
+    : probe_sequence_{slots, capacity},
+      empty_key_sentinel_{empty_key_sentinel},
+      empty_value_sentinel_{empty_value_sentinel}
+  {
+  }
+
+  /**
+   * @brief Gets slots array.
+   *
+   * @return Slots array
+   */
+  __device__ pair_atomic_type* get_slots() noexcept { return probe_sequence_.get_slots(); }
+
+  /**
+   * @brief Gets slots array.
+   *
+   * @return Slots array
+   */
+  __device__ pair_atomic_type const* get_slots() const noexcept
+  {
+    return probe_sequence_.get_slots();
+  }
+
+  /**
+   * @brief Returns the initial slot for a given key `k`
+   *
+   * To be used for Cooperative Group based probing.
+   *
+   * @tparam CG Cooperative Group type
+   * @param g the Cooperative Group for which the initial slot is needed
+   * @param k The key to get the slot for
+   * @return Pointer to the initial slot for `k`
+   */
+  template <typename CG>
+  __device__ iterator initial_slot(CG const& g, Key const& k) noexcept
+  {
+    return probe_sequence_.initial_slot(g, k);
+  }
+
+  /**
+   * @brief Returns the initial slot for a given key `k`
+   *
+   * To be used for Cooperative Group based probing.
+   *
+   * @tparam CG Cooperative Group type
+   * @param g the Cooperative Group for which the initial slot is needed
+   * @param k The key to get the slot for
+   * @return Pointer to the initial slot for `k`
+   */
+  template <typename CG>
+  __device__ const_iterator initial_slot(CG g, Key const& k) const noexcept
+  {
+    return probe_sequence_.initial_slot(g, k);
+  }
+
+  /**
+   * @brief Given a slot `s`, returns the next slot.
+   *
+   * If `s` is the last slot, wraps back around to the first slot. To
+   * be used for Cooperative Group based probing.
+   *
+   * @param s The slot to advance
+   * @return The next slot after `s`
+   */
+  __device__ iterator next_slot(iterator s) noexcept { return probe_sequence_.next_slot(s); }
+
+  /**
+   * @brief Given a slot `s`, returns the next slot.
+   *
+   * If `s` is the last slot, wraps back around to the first slot. To
+   * be used for Cooperative Group based probing.
+   *
+   * @param s The slot to advance
+   * @return The next slot after `s`
+   */
+  __device__ const_iterator next_slot(const_iterator s) const noexcept
+  {
+    return probe_sequence_.next_slot(s);
+  }
+
+  /**
+   * @brief Gets the maximum number of elements the hash map can hold.
+   *
+   * @return The maximum number of elements the hash map can hold
+   */
+  __host__ __device__ std::size_t get_capacity() const noexcept
+  {
+    return probe_sequence_.get_capacity();
+  }
+
+  /**
+   * @brief Gets the sentinel value used to represent an empty key slot.
+   *
+   * @return The sentinel value used to represent an empty key slot
+   */
+  __host__ __device__ Key get_empty_key_sentinel() const noexcept { return empty_key_sentinel_; }
+
+  /**
+   * @brief Gets the sentinel value used to represent an empty value slot.
+   *
+   * @return The sentinel value used to represent an empty value slot
+   */
+  __host__ __device__ Value get_empty_value_sentinel() const noexcept
+  {
+    return empty_value_sentinel_;
+  }
+
+  /**
+   * @brief Load two key/value pairs from the given slot to the target pair array.
+   *
+   * @param arr The pair array to be loaded
+   * @param current_slot The given slot to load from
+   */
+  __device__ void load_pair_array(value_type* arr, const_iterator current_slot) noexcept
+  {
+    if constexpr (sizeof(value_type) == 4) {
+      auto const tmp = *reinterpret_cast<ushort4 const*>(current_slot);
+      memcpy(&arr[0], &tmp, 2 * sizeof(value_type));
+    } else {
+      auto const tmp = *reinterpret_cast<uint4 const*>(current_slot);
+      memcpy(&arr[0], &tmp, 2 * sizeof(value_type));
+    }
+  }
+
+};  // class device_view_impl_base
+
+template <typename Key,
+          typename Value,
+          cuda::thread_scope Scope,
+          class ProbeSequence,
+          typename Allocator>
+class static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_mutable_view_impl
+  : public device_view_impl_base {
+ public:
+  using value_type     = typename device_view_impl_base::value_type;
+  using key_type       = typename device_view_impl_base::key_type;
+  using mapped_type    = typename device_view_impl_base::mapped_type;
+  using iterator       = typename device_view_impl_base::iterator;
+  using const_iterator = typename device_view_impl_base::const_iterator;
+
+ private:
+  /**
+   * @brief Enumeration of the possible results of attempting to insert into a hash bucket.
+   */
+  enum class insert_result {
+    CONTINUE,  ///< Insert did not succeed, continue trying to insert
+    SUCCESS,   ///< New pair inserted successfully
+    DUPLICATE  ///< Insert did not succeed, key is already present
+  };
+
+  /**
+   * @brief Inserts the specified key/value pair with one single CAS operation.
+   *
+   * @param current_slot The slot to insert
+   * @param insert_pair The pair to insert
+   * @param key_equal The binary callable used to compare two keys for
+   * equality
+   * @return An insert result from the `insert_resullt` enumeration.
+   */
+  __device__ insert_result packed_cas(iterator current_slot, value_type const& insert_pair) noexcept
+  {
+    auto expected_key   = this->get_empty_key_sentinel();
+    auto expected_value = this->get_empty_value_sentinel();
+
+    cuco::detail::pair_converter<value_type> expected_pair{
+      cuco::make_pair<Key, Value>(std::move(expected_key), std::move(expected_value))};
+    cuco::detail::pair_converter<value_type> new_pair{insert_pair};
+
+    auto slot = reinterpret_cast<
+      cuda::atomic<typename cuco::detail::pair_converter<value_type>::packed_type, Scope>*>(
+      current_slot);
+
+    bool success = slot->compare_exchange_strong(
+      expected_pair.packed, new_pair.packed, cuda::std::memory_order_relaxed);
+    if (success) { return insert_result::SUCCESS; }
+
+    return insert_result::CONTINUE;
+  }
+
+  /**
+   * @brief Inserts the specified key/value pair with two back-to-back CAS operations.
+   *
+   * @param current_slot The slot to insert
+   * @param insert_pair The pair to insert
+   * @return An insert result from the `insert_resullt` enumeration.
+   */
+  __device__ insert_result back_to_back_cas(iterator current_slot,
+                                            value_type const& insert_pair) noexcept
+  {
+    using cuda::std::memory_order_relaxed;
+
+    auto expected_key   = this->get_empty_key_sentinel();
+    auto expected_value = this->get_empty_value_sentinel();
+
+    // Back-to-back CAS for 8B/8B key/value pairs
+    auto& slot_key   = current_slot->first;
+    auto& slot_value = current_slot->second;
+
+    bool key_success =
+      slot_key.compare_exchange_strong(expected_key, insert_pair.first, memory_order_relaxed);
+    bool value_success =
+      slot_value.compare_exchange_strong(expected_value, insert_pair.second, memory_order_relaxed);
+
+    if (key_success) {
+      while (not value_success) {
+        value_success =
+          slot_value.compare_exchange_strong(expected_value = this->get_empty_value_sentinel(),
+                                             insert_pair.second,
+                                             memory_order_relaxed);
+      }
+      return insert_result::SUCCESS;
+    } else if (value_success) {
+      slot_value.store(this->get_empty_value_sentinel(), memory_order_relaxed);
+    }
+
+    return insert_result::CONTINUE;
+  }
+
+  /**
+   * @brief Inserts the specified key/value pair with a CAS of the key and a dependent write
+   * of the value.
+   *
+   * @param current_slot The slot to insert
+   * @param insert_pair The pair to insert
+   * @return An insert result from the `insert_resullt` enumeration.
+   */
+  __device__ insert_result cas_dependent_write(iterator current_slot,
+                                               value_type const& insert_pair) noexcept
+  {
+    using cuda::std::memory_order_relaxed;
+    auto expected_key = this->get_empty_key_sentinel();
+
+    auto& slot_key = current_slot->first;
+
+    auto const key_success =
+      slot_key.compare_exchange_strong(expected_key, insert_pair.first, memory_order_relaxed);
+
+    if (key_success) {
+      auto& slot_value = current_slot->second;
+      slot_value.store(insert_pair.second, memory_order_relaxed);
+      return insert_result::SUCCESS;
+    }
+
+    return insert_result::CONTINUE;
+  }
+
+ public:
+  __host__ __device__ device_mutable_view_impl(pair_atomic_type* slots,
+                                               std::size_t capacity,
+                                               Key empty_key_sentinel,
+                                               Value empty_value_sentinel) noexcept
+    : device_view_impl_base{slots, capacity, empty_key_sentinel, empty_value_sentinel}
+  {
+  }
+
+  /**
+   * @brief Inserts the specified key/value pair into the map using vector loads.
+   *
+   * @tparam uses_vector_load Boolean flag indicating whether vector loads are used
+   * @tparam CG Cooperative Group type
+   *
+   * @param g The Cooperative Group that performs the insert
+   * @param insert_pair The pair to insert
+   * @return void.
+   */
+  template <bool uses_vector_load, typename CG>
+  __device__ std::enable_if_t<uses_vector_load, void> insert(CG g,
+                                                             value_type const& insert_pair) noexcept
+  {
+    auto current_slot = initial_slot(g, insert_pair.first);
+    while (true) {
+      value_type arr[2];
+      load_pair_array(&arr[0], current_slot);
+
+      // The user provide `key_equal` can never be used to compare against `empty_key_sentinel` as
+      // the sentinel is not a valid key value. Therefore, first check for the sentinel
+      auto const first_slot_is_empty =
+        (detail::bitwise_compare(arr[0].first, this->get_empty_key_sentinel()));
+      auto const second_slot_is_empty =
+        (detail::bitwise_compare(arr[1].first, this->get_empty_key_sentinel()));
+      auto const window_contains_empty = g.ballot(first_slot_is_empty or second_slot_is_empty);
+
+      if (window_contains_empty) {
+        // the first lane in the group with an empty slot will attempt the insert
+        insert_result status{insert_result::CONTINUE};
+        uint32_t src_lane = __ffs(window_contains_empty) - 1;
+        if (g.thread_rank() == src_lane) {
+          auto insert_location = first_slot_is_empty ? current_slot : current_slot + 1;
+          // One single CAS operation since vector loads are dedicated to packable pairs
+          status = packed_cas(insert_location, insert_pair);
+        }
+
+        // successful insert
+        if (g.any(status == insert_result::SUCCESS)) { return; }
+        // if we've gotten this far, a different key took our spot
+        // before we could insert. We need to retry the insert on the
+        // same window
+      }
+      // if there are no empty slots in the current window,
+      // we move onto the next window
+      else {
+        current_slot = next_slot(current_slot);
+      }
+    }  // while true
+  }
+
+  /**
+   * @brief Inserts the specified key/value pair into the map using scalar loads.
+   *
+   * @tparam uses_vector_load Boolean flag indicating whether vector loads are used
+   * @tparam CG Cooperative Group type
+   *
+   * @param g The Cooperative Group that performs the insert
+   * @param insert_pair The pair to insert
+   * @return void.
+   */
+  template <bool uses_vector_load, typename CG>
+  __device__ std::enable_if_t<not uses_vector_load, void> insert(
+    CG g, value_type const& insert_pair) noexcept
+  {
+    auto current_slot = initial_slot(g, insert_pair.first);
+
+    while (true) {
+      value_type slot_contents = *reinterpret_cast<value_type const*>(current_slot);
+      auto const& existing_key = slot_contents.first;
+
+      // The user provide `key_equal` can never be used to compare against `empty_key_sentinel` as
+      // the sentinel is not a valid key value. Therefore, first check for the sentinel
+      auto const slot_is_empty =
+        detail::bitwise_compare(existing_key, this->get_empty_key_sentinel());
+      auto const window_contains_empty = g.ballot(slot_is_empty);
+
+      if (window_contains_empty) {
+        // the first lane in the group with an empty slot will attempt the insert
+        insert_result status{insert_result::CONTINUE};
+        uint32_t src_lane = __ffs(window_contains_empty) - 1;
+
+        if (g.thread_rank() == src_lane) {
+#if __CUDA_ARCH__ < 700
+          status = cas_dependent_write(current_slot, insert_pair);
+#else
+          status = back_to_back_cas(current_slot, insert_pair);
+#endif
+        }
+
+        // successful insert
+        if (g.any(status == insert_result::SUCCESS)) { return; }
+        // if we've gotten this far, a different key took our spot
+        // before we could insert. We need to retry the insert on the
+        // same window
+      }
+      // if there are no empty slots in the current window,
+      // we move onto the next window
+      else {
+        current_slot = next_slot(current_slot);
+      }
+    }  // while true
+  }
+};  // class device_mutable_view_impl
+
+template <typename Key,
+          typename Value,
+          cuda::thread_scope Scope,
+          class ProbeSequence,
+          typename Allocator>
+class static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view_impl
+  : public device_view_impl_base {
+ public:
+  using value_type     = typename device_view_impl_base::value_type;
+  using key_type       = typename device_view_impl_base::key_type;
+  using mapped_type    = typename device_view_impl_base::mapped_type;
+  using iterator       = typename device_view_impl_base::iterator;
+  using const_iterator = typename device_view_impl_base::const_iterator;
+
+  __host__ __device__ device_view_impl(pair_atomic_type* slots,
+                                       std::size_t capacity,
+                                       Key empty_key_sentinel,
+                                       Value empty_value_sentinel) noexcept
+    : device_view_impl_base{slots, capacity, empty_key_sentinel, empty_value_sentinel}
+  {
+  }
+
+  /**
+   * @brief Flushes per-CG buffer into the output sequence.
+   *
+   * A given CUDA Cooperative Group, `g`, loads `num_outputs` key-value pairs from `output_buffer`
+   * and writes them into global memory in a coalesced fashion. CG-wide `memcpy_sync` is used if
+   * `CUCO_HAS_CG_MEMCPY_ASYNC` is defined and `thrust::is_contiguous_iterator_v<OutputIt>`
+   * returns true. All threads of `g` must be active due to implicit CG-wide synchronization
+   * during flushing.
+   *
+   * @tparam CG Cooperative Group type
+   * @tparam atomicT Type of atomic storage
+   * @tparam OutputIt Device accessible output iterator whose `value_type` is
+   * constructible from the map's `value_type`
+   * @param g The Cooperative Group used to flush output buffer
+   * @param num_outputs Number of valid output in the buffer
+   * @param output_buffer Buffer of the key/value pair sequence
+   * @param num_matches Size of the output sequence
+   * @param output_begin Beginning of the output sequence of key/value pairs
+   */
+  template <typename CG, typename atomicT, typename OutputIt>
+  __inline__ __device__ void flush_output_buffer(CG const& g,
+                                                 uint32_t const num_outputs,
+                                                 value_type* output_buffer,
+                                                 atomicT* num_matches,
+                                                 OutputIt output_begin) noexcept
+  {
+    std::size_t offset;
+    const auto lane_id = g.thread_rank();
+    if (0 == lane_id) {
+      offset = num_matches->fetch_add(num_outputs, cuda::std::memory_order_relaxed);
+    }
+    offset = g.shfl(offset, 0);
+
+    if constexpr (thrust::is_contiguous_iterator_v<OutputIt>) {
+#if defined(CUCO_HAS_CG_MEMCPY_ASYNC)
+#if defined(CUCO_HAS_CUDA_BARRIER)
+      cooperative_groups::memcpy_async(
+        g,
+        output_begin + offset,
+        output_buffer,
+        cuda::aligned_size_t<alignof(value_type)>(sizeof(value_type) * num_outputs));
+#else
+      cooperative_groups::memcpy_async(
+        g, output_begin + offset, output_buffer, sizeof(value_type) * num_outputs);
+#endif  // end CUCO_HAS_CUDA_BARRIER
+      return;
+#endif  // end CUCO_HAS_CG_MEMCPY_ASYNC
+    }
+    for (auto index = lane_id; index < num_outputs; index += g.size()) {
+      *(output_begin + offset + index) = output_buffer[index];
+    }
+  }
+
+  /**
+   * @brief Flushes per-CG buffer into the output sequences.
+   *
+   * A given CUDA Cooperative Group, `g`, loads `num_outputs` elements from `probe_output_buffer`
+   * and `num_outputs` elements from `contained_output_buffer`, then writes them into global
+   * memory started from `probe_output_begin` and `contained_output_begin` respectively. All
+   * threads of `g` must be active due to implicit CG-wide synchronization during flushing.
+   *
+   * @tparam CG Cooperative Group type
+   * @tparam atomicT Type of atomic storage
+   * @tparam OutputIt1 Device accessible output iterator whose `value_type` is constructible from
+   * `InputIt`s `value_type`.
+   * @tparam OutputIt2 Device accessible output iterator whose `value_type` is constructible from
+   * the map's `value_type`.
+   * @param g The Cooperative Group used to flush output buffer
+   * @param num_outputs Number of valid output in the buffer
+   * @param probe_output_buffer Buffer of the matched probe pair sequence
+   * @param contained_output_buffer Buffer of the matched contained pair sequence
+   * @param num_matches Size of the output sequence
+   * @param probe_output_begin Beginning of the output sequence of the matched probe pairs
+   * @param contained_output_begin Beginning of the output sequence of the matched contained
+   * pairs
+   */
+  template <typename CG, typename atomicT, typename OutputIt1, typename OutputIt2>
+  __inline__ __device__ void flush_output_buffer(CG const& g,
+                                                 uint32_t const num_outputs,
+                                                 value_type* probe_output_buffer,
+                                                 value_type* contained_output_buffer,
+                                                 atomicT* num_matches,
+                                                 OutputIt1 probe_output_begin,
+                                                 OutputIt2 contained_output_begin) noexcept
+  {
+    std::size_t offset;
+    const auto lane_id = g.thread_rank();
+    if (0 == lane_id) {
+      offset = num_matches->fetch_add(num_outputs, cuda::std::memory_order_relaxed);
+    }
+    offset = g.shfl(offset, 0);
+
+    for (auto index = lane_id; index < num_outputs; index += g.size()) {
+      auto& probe_pair                                           = probe_output_buffer[index];
+      auto& contained_pair                                       = contained_output_buffer[index];
+      thrust::get<0>(*(probe_output_begin + offset + index))     = probe_pair.first;
+      thrust::get<1>(*(probe_output_begin + offset + index))     = probe_pair.second;
+      thrust::get<0>(*(contained_output_begin + offset + index)) = contained_pair.first;
+      thrust::get<1>(*(contained_output_begin + offset + index)) = contained_pair.second;
+    }
+  }
+
+  /**
+   * @brief Indicates whether the key `k` exists in the map using vector loads.
+   *
+   * If the key `k` was inserted into the map, `contains` returns
+   * true. Otherwise, it returns false. Uses the CUDA Cooperative Groups API to
+   * to leverage multiple threads to perform a single `contains` operation. This provides a
+   * significant boost in throughput compared to the non Cooperative Group based
+   * `contains` at moderate to high load factors.
+   *
+   * @tparam uses_vector_load Boolean flag indicating whether vector loads are used
+   * @tparam CG Cooperative Group type
+   * @tparam KeyEqual Binary callable type
+   * @param g The Cooperative Group used to perform the contains operation
+   * @param k The key to search for
+   * @param key_equal The binary callable used to compare two keys
+   * for equality
+   * @return A boolean indicating whether the key/value pair
+   * containing `k` was inserted
+   */
+  template <bool uses_vector_load, typename CG, typename KeyEqual>
+  __device__ std::enable_if_t<uses_vector_load, bool> contains(CG g,
+                                                               Key const& k,
+                                                               KeyEqual key_equal) noexcept
+  {
+    auto current_slot = initial_slot(g, k);
+
+    while (true) {
+      value_type arr[2];
+      load_pair_array(&arr[0], current_slot);
+
+      auto const first_slot_is_empty =
+        detail::bitwise_compare(arr[0].first, this->get_empty_key_sentinel());
+      auto const second_slot_is_empty =
+        detail::bitwise_compare(arr[1].first, this->get_empty_key_sentinel());
+      auto const first_equals  = (not first_slot_is_empty and key_equal(arr[0].first, k));
+      auto const second_equals = (not second_slot_is_empty and key_equal(arr[1].first, k));
+
+      // the key we were searching for was found by one of the threads, so we return true
+      if (g.any(first_equals or second_equals)) { return true; }
+
+      // we found an empty slot, meaning that the key we're searching for isn't present
+      if (g.any(first_slot_is_empty or second_slot_is_empty)) { return false; }
+
+      // otherwise, all slots in the current window are full with other keys, so we move onto the
+      // next window
+      current_slot = next_slot(current_slot);
+    }
+  }
+
+  /**
+   * @brief Indicates whether the key `k` exists in the map using scalar loads.
+   *
+   * If the key `k` was inserted into the map, `contains` returns
+   * true. Otherwise, it returns false. Uses the CUDA Cooperative Groups API to
+   * to leverage multiple threads to perform a single `contains` operation. This provides a
+   * significant boost in throughput compared to the non Cooperative Group
+   * `contains` at moderate to high load factors.
+   *
+   * @tparam uses_vector_load Boolean flag indicating whether vector loads are used
+   * @tparam CG Cooperative Group type
+   * @tparam KeyEqual Binary callable type
+   * @param g The Cooperative Group used to perform the contains operation
+   * @param k The key to search for
+   * @param key_equal The binary callable used to compare two keys
+   * for equality
+   * @return A boolean indicating whether the key/value pair
+   * containing `k` was inserted
+   */
+  template <bool uses_vector_load, typename CG, typename KeyEqual>
+  __device__ std::enable_if_t<not uses_vector_load, bool> contains(CG g,
+                                                                   Key const& k,
+                                                                   KeyEqual key_equal) noexcept
+  {
+    auto current_slot = initial_slot(g, k);
+
+    while (true) {
+      value_type slot_contents = *reinterpret_cast<value_type const*>(current_slot);
+      auto const& existing_key = slot_contents.first;
+
+      // The user provide `key_equal` can never be used to compare against `empty_key_sentinel` as
+      // the sentinel is not a valid key value. Therefore, first check for the sentinel
+      auto const slot_is_empty =
+        detail::bitwise_compare(existing_key, this->get_empty_key_sentinel());
+
+      auto const equals = (not slot_is_empty and key_equal(existing_key, k));
+
+      // the key we were searching for was found by one of the threads, so we return true
+      if (g.any(equals)) { return true; }
+
+      // we found an empty slot, meaning that the key we're searching for isn't present
+      if (g.any(slot_is_empty)) { return false; }
+
+      // otherwise, all slots in the current window are full with other keys, so we move onto the
+      // next window
+      current_slot = next_slot(current_slot);
+    }
+  }
+
+  /**
+   * @brief Counts the occurrence of a given key contained in multimap using vector loads.
+   *
+   * @tparam uses_vector_load Boolean flag indicating whether vector loads are used
+   * @tparam is_outer Boolean flag indicating whether outer join is peformed
+   * @tparam CG Cooperative Group type
+   * @tparam KeyEqual Binary callable type
+   * @param g The Cooperative Group used to perform the count operation
+   * @param k The key to search for
+   * @param key_equal The binary callable used to compare two keys
+   * for equality
+   * @return Number of matches found by the current thread
+   */
+  template <bool uses_vector_load, bool is_outer, typename CG, typename KeyEqual>
+  __device__ std::enable_if_t<uses_vector_load, std::size_t> count(CG const& g,
+                                                                   Key const& k,
+                                                                   KeyEqual key_equal) noexcept
+  {
+    std::size_t count = 0;
+    auto current_slot = initial_slot(g, k);
+
+    [[maybe_unused]] bool found_match = false;
+
+    while (true) {
+      value_type arr[2];
+      load_pair_array(&arr[0], current_slot);
+
+      auto const first_slot_is_empty =
+        detail::bitwise_compare(arr[0].first, this->get_empty_key_sentinel());
+      auto const second_slot_is_empty =
+        detail::bitwise_compare(arr[1].first, this->get_empty_key_sentinel());
+      auto const first_equals  = (not first_slot_is_empty and key_equal(arr[0].first, k));
+      auto const second_equals = (not second_slot_is_empty and key_equal(arr[1].first, k));
+
+      if constexpr (is_outer) {
+        if (g.any(first_equals or second_equals)) { found_match = true; }
+      }
+
+      count += (first_equals + second_equals);
+
+      if (g.any(first_slot_is_empty or second_slot_is_empty)) {
+        if constexpr (is_outer) {
+          if ((not found_match) && (g.thread_rank() == 0)) { count++; }
+        }
+        return count;
+      }
+
+      current_slot = next_slot(current_slot);
+    }
+  }
+
+  /**
+   * @brief Counts the occurrence of a given key contained in multimap using scalar loads.
+   *
+   * @tparam uses_vector_load Boolean flag indicating whether vector loads are used
+   * @tparam is_outer Boolean flag indicating whether outer join is peformed
+   * @tparam CG Cooperative Group type
+   * @tparam KeyEqual Binary callable type
+   * @param g The Cooperative Group used to perform the count operation
+   * @param k The key to search for
+   * @param key_equal The binary callable used to compare two keys
+   * for equality
+   * @return Number of matches found by the current thread
+   */
+  template <bool uses_vector_load, bool is_outer, typename CG, typename KeyEqual>
+  __device__ std::enable_if_t<not uses_vector_load, std::size_t> count(CG const& g,
+                                                                       Key const& k,
+                                                                       KeyEqual key_equal) noexcept
+  {
+    std::size_t count = 0;
+    auto current_slot = initial_slot(g, k);
+
+    [[maybe_unused]] bool found_match = false;
+
+    while (true) {
+      value_type slot_contents = *reinterpret_cast<value_type const*>(current_slot);
+      auto const& current_key  = slot_contents.first;
+
+      auto const slot_is_empty =
+        detail::bitwise_compare(current_key, this->get_empty_key_sentinel());
+      auto const equals = not slot_is_empty and key_equal(current_key, k);
+
+      if constexpr (is_outer) {
+        if (g.any(equals)) { found_match = true; }
+      }
+
+      count += equals;
+
+      if (g.any(slot_is_empty)) {
+        if constexpr (is_outer) {
+          if ((not found_match) && (g.thread_rank() == 0)) { count++; }
+        }
+        return count;
+      }
+
+      current_slot = next_slot(current_slot);
+    }
+  }
+
+  /**
+   * @brief Counts the occurrence of a given key/value pair contained in multimap using vector
+   * loads.
+   *
+   * @tparam uses_vector_load Boolean flag indicating whether vector loads are used
+   * @tparam is_outer Boolean flag indicating whether outer join is peformed
+   * @tparam CG Cooperative Group type
+   * @tparam PairEqual Binary callable type
+   * @param g The Cooperative Group used to perform the pair_count operation
+   * @param pair The pair to search for
+   * @param pair_equal The binary callable used to compare two pairs
+   * for equality
+   * @return Number of matches found by the current thread
+   */
+  template <bool uses_vector_load, bool is_outer, typename CG, typename PairEqual>
+  __device__ std::enable_if_t<uses_vector_load, std::size_t> pair_count(
+    CG const& g, value_type const& pair, PairEqual pair_equal) noexcept
+  {
+    std::size_t count = 0;
+    auto key          = pair.first;
+    auto current_slot = initial_slot(g, key);
+
+    [[maybe_unused]] bool found_match = false;
+
+    while (true) {
+      value_type arr[2];
+      load_pair_array(&arr[0], current_slot);
+
+      auto const first_slot_is_empty =
+        detail::bitwise_compare(arr[0].first, this->get_empty_key_sentinel());
+      auto const second_slot_is_empty =
+        detail::bitwise_compare(arr[1].first, this->get_empty_key_sentinel());
+
+      auto const first_slot_equals  = (not first_slot_is_empty and pair_equal(arr[0], pair));
+      auto const second_slot_equals = (not second_slot_is_empty and pair_equal(arr[1], pair));
+
+      if constexpr (is_outer) {
+        if (g.any(first_slot_equals or second_slot_equals)) { found_match = true; }
+      }
+
+      count += (first_slot_equals + second_slot_equals);
+
+      if (g.any(first_slot_is_empty or second_slot_is_empty)) {
+        if constexpr (is_outer) {
+          if ((not found_match) && (g.thread_rank() == 0)) { count++; }
+        }
+        return count;
+      }
+
+      current_slot = next_slot(current_slot);
+    }
+  }
+
+  /**
+   * @brief Counts the occurrence of a given key/value pair contained in multimap using scalar
+   * loads.
+   *
+   * @tparam uses_vector_load Boolean flag indicating whether vector loads are used
+   * @tparam is_outer Boolean flag indicating whether outer join is peformed
+   * @tparam CG Cooperative Group type
+   * @tparam PairEqual Binary callable type
+   * @param g The Cooperative Group used to perform the pair_count operation
+   * @param pair The pair to search for
+   * @param pair_equal The binary callable used to compare two pairs
+   * for equality
+   * @return Number of matches found by the current thread
+   */
+  template <bool uses_vector_load, bool is_outer, typename CG, typename PairEqual>
+  __device__ std::enable_if_t<not uses_vector_load, std::size_t> pair_count(
+    CG const& g, value_type const& pair, PairEqual pair_equal) noexcept
+  {
+    std::size_t count = 0;
+    auto key          = pair.first;
+    auto current_slot = initial_slot(g, key);
+
+    [[maybe_unused]] bool found_match = false;
+
+    while (true) {
+      auto slot_contents = *reinterpret_cast<value_type const*>(current_slot);
+
+      auto const slot_is_empty =
+        detail::bitwise_compare(slot_contents.first, this->get_empty_key_sentinel());
+
+      auto const equals = not slot_is_empty and pair_equal(slot_contents, pair);
+
+      if constexpr (is_outer) {
+        if (g.any(equals)) { found_match = true; }
+      }
+
+      count += equals;
+
+      if (g.any(slot_is_empty)) {
+        if constexpr (is_outer) {
+          if ((not found_match) && (g.thread_rank() == 0)) { count++; }
+        }
+        return count;
+      }
+
+      current_slot = next_slot(current_slot);
+    }
+  }
+
+  /**
+   * @brief Retrieves all the matches of a given key contained in multimap using vector
+   * loads with per-warp shared memory buffer.
+   *
+   * For key `k` existing in the map, copies `k` and all associated values to unspecified
+   * locations in `[output_begin, output_end)`. If `k` does not have any matches, copies `k` and
+   * `empty_value_sentinel()` into the output only if `is_outer` is true.
+   *
+   * @tparam buffer_size Size of the output buffer
+   * @tparam is_outer Boolean flag indicating whether outer join is peformed
+   * @tparam warpT Warp (Cooperative Group) type
+   * @tparam CG Cooperative Group type
+   * @tparam atomicT Type of atomic storage
+   * @tparam OutputIt Device accessible output iterator whose `value_type` is
+   * constructible from the map's `value_type`
+   * @tparam KeyEqual Binary callable type
+   * @param warp The Cooperative Group (or warp) used to flush output buffer
+   * @param g The Cooperative Group used to retrieve
+   * @param k The key to search for
+   * @param warp_counter Pointer to the warp counter
+   * @param output_buffer Shared memory buffer of the key/value pair sequence
+   * @param num_matches Size of the output sequence
+   * @param output_begin Beginning of the output sequence of key/value pairs
+   * @param key_equal The binary callable used to compare two keys
+   * for equality
+   */
+  template <uint32_t buffer_size,
+            bool is_outer,
+            typename warpT,
+            typename CG,
+            typename atomicT,
+            typename OutputIt,
+            typename KeyEqual>
+  __device__ void retrieve(warpT const& warp,
+                           CG const& g,
+                           Key const& k,
+                           uint32_t* warp_counter,
+                           value_type* output_buffer,
+                           atomicT* num_matches,
+                           OutputIt output_begin,
+                           KeyEqual key_equal) noexcept
+  {
+    const uint32_t cg_lane_id = g.thread_rank();
+
+    auto current_slot = initial_slot(g, k);
+
+    bool running                      = true;
+    [[maybe_unused]] bool found_match = false;
+
+    while (warp.any(running)) {
+      if (running) {
+        value_type arr[2];
+        load_pair_array(&arr[0], current_slot);
+
+        auto const first_slot_is_empty =
+          detail::bitwise_compare(arr[0].first, this->get_empty_key_sentinel());
+        auto const second_slot_is_empty =
+          detail::bitwise_compare(arr[1].first, this->get_empty_key_sentinel());
+        auto const first_equals  = (not first_slot_is_empty and key_equal(arr[0].first, k));
+        auto const second_equals = (not second_slot_is_empty and key_equal(arr[1].first, k));
+        auto const first_exists  = g.ballot(first_equals);
+        auto const second_exists = g.ballot(second_equals);
+
+        if (first_exists or second_exists) {
+          if constexpr (is_outer) { found_match = true; }
+
+          auto num_first_matches  = __popc(first_exists);
+          auto num_second_matches = __popc(second_exists);
+
+          uint32_t output_idx;
+          if (0 == cg_lane_id) {
+            output_idx = atomicAdd(warp_counter, (num_first_matches + num_second_matches));
+          }
+          output_idx = g.shfl(output_idx, 0);
+
+          if (first_equals) {
+            auto lane_offset = __popc(first_exists & ((1 << cg_lane_id) - 1));
+            Key key          = k;
+            output_buffer[output_idx + lane_offset] =
+              cuco::make_pair<Key, Value>(std::move(key), std::move(arr[0].second));
+          }
+          if (second_equals) {
+            auto lane_offset = __popc(second_exists & ((1 << cg_lane_id) - 1));
+            Key key          = k;
+            output_buffer[output_idx + num_first_matches + lane_offset] =
+              cuco::make_pair<Key, Value>(std::move(key), std::move(arr[1].second));
+          }
+        }
+        if (g.any(first_slot_is_empty or second_slot_is_empty)) {
+          running = false;
+          if constexpr (is_outer) {
+            if ((not found_match) && (cg_lane_id == 0)) {
+              auto output_idx           = atomicAdd(warp_counter, 1);
+              Key key                   = k;
+              output_buffer[output_idx] = cuco::make_pair<Key, Value>(
+                std::move(key), std::move(this->get_empty_value_sentinel()));
+            }
+          }
+        }
+      }  // if running
+
+      warp.sync();
+      if (*warp_counter + warp.size() * vector_width() > buffer_size) {
+        flush_output_buffer(warp, *warp_counter, output_buffer, num_matches, output_begin);
+        // First lane reset warp-level counter
+        if (warp.thread_rank() == 0) { *warp_counter = 0; }
+      }
+
+      current_slot = next_slot(current_slot);
+    }  // while running
+  }
+
+  /**
+   * @brief Retrieves all the matches of a given key contained in multimap using scalar
+   * loads with per-CG shared memory buffer.
+   *
+   * For key `k` existing in the map, copies `k` and all associated values to unspecified
+   * locations in `[output_begin, output_end)`. If `k` does not have any matches, copies `k` and
+   * `empty_value_sentinel()` into the output only if `is_outer` is true.
+   *
+   * @tparam cg_size The number of threads in CUDA Cooperative Groups
+   * @tparam buffer_size Size of the output buffer
+   * @tparam is_outer Boolean flag indicating whether outer join is peformed
+   * @tparam CG Cooperative Group type
+   * @tparam atomicT Type of atomic storage
+   * @tparam OutputIt Device accessible output iterator whose `value_type` is
+   * constructible from the map's `value_type`
+   * @tparam KeyEqual Binary callable type
+   * @param g The Cooperative Group used to retrieve
+   * @param k The key to search for
+   * @param cg_counter Pointer to the CG counter
+   * @param output_buffer Shared memory buffer of the key/value pair sequence
+   * @param num_matches Size of the output sequence
+   * @param output_begin Beginning of the output sequence of key/value pairs
+   * @param key_equal The binary callable used to compare two keys
+   * for equality
+   */
+  template <uint32_t cg_size,
+            uint32_t buffer_size,
+            bool is_outer,
+            typename CG,
+            typename atomicT,
+            typename OutputIt,
+            typename KeyEqual>
+  __device__ void retrieve(CG const& g,
+                           Key const& k,
+                           uint32_t* cg_counter,
+                           value_type* output_buffer,
+                           atomicT* num_matches,
+                           OutputIt output_begin,
+                           KeyEqual key_equal) noexcept
+  {
+    const uint32_t lane_id = g.thread_rank();
+
+    auto current_slot = initial_slot(g, k);
+
+    bool running                      = true;
+    [[maybe_unused]] bool found_match = false;
+
+    while (running) {
+      // TODO: Replace reinterpret_cast with atomic ref when possible. The current implementation
+      // is unsafe!
+      static_assert(sizeof(Key) == sizeof(cuda::atomic<Key>));
+      static_assert(sizeof(Value) == sizeof(cuda::atomic<Value>));
+      value_type slot_contents = *reinterpret_cast<value_type const*>(current_slot);
+
+      auto const slot_is_empty =
+        detail::bitwise_compare(slot_contents.first, this->get_empty_key_sentinel());
+      auto const equals   = (not slot_is_empty and key_equal(slot_contents.first, k));
+      uint32_t output_idx = *cg_counter;
+      auto const exists   = g.ballot(equals);
+
+      if (exists) {
+        if constexpr (is_outer) { found_match = true; }
+        auto num_matches = __popc(exists);
+        if (equals) {
+          // Each match computes its lane-level offset
+          auto lane_offset = __popc(exists & ((1 << lane_id) - 1));
+          Key key          = k;
+          output_buffer[output_idx + lane_offset] =
+            cuco::make_pair<Key, Value>(std::move(key), std::move(slot_contents.second));
+        }
+        if (0 == lane_id) { (*cg_counter) += num_matches; }
+      }
+      if (g.any(slot_is_empty)) {
+        running = false;
+        if constexpr (is_outer) {
+          if ((not found_match) && (lane_id == 0)) {
+            output_idx                = (*cg_counter)++;
+            Key key                   = k;
+            output_buffer[output_idx] = cuco::make_pair<Key, Value>(
+              std::move(key), std::move(this->get_empty_value_sentinel()));
+          }
+        }
+      }
+
+      g.sync();
+
+      // Flush if the next iteration won't fit into buffer
+      if ((*cg_counter + cg_size) > buffer_size) {
+        flush_output_buffer(g, *cg_counter, output_buffer, num_matches, output_begin);
+        // First lane reset CG-level counter
+        if (lane_id == 0) { *cg_counter = 0; }
+      }
+      current_slot = next_slot(current_slot);
+    }  // while running
+  }
+
+  /**
+   * @brief Retrieves all the matches of a given pair contained in multimap using vector
+   * loads with per-warp shared memory buffer.
+   *
+   * For pair `p`, if pair_equal(p, slot[j]) returns true, copies `p` to unspecified locations
+   * in `[probe_output_begin, probe_output_end)` and copies slot[j] to unspecified locations in
+   * `[contained_output_begin, contained_output_end)`. If `p` does not have any matches, copies
+   * `p` and a pair of `empty_key_sentinel` and `empty_value_sentinel` into the output only if
+   * `is_outer` is true.
+   *
+   * @tparam buffer_size Size of the output buffer
+   * @tparam is_outer Boolean flag indicating whether outer join is peformed
+   * @tparam warpT Warp (Cooperative Group) type
+   * @tparam CG Cooperative Group type
+   * @tparam atomicT Type of atomic storage
+   * @tparam OutputIt1 Device accessible output iterator whose `value_type` is constructible from
+   * `InputIt`s `value_type`.
+   * @tparam OutputIt2 Device accessible output iterator whose `value_type` is constructible from
+   * the map's `value_type`.
+   * @tparam PairEqual Binary callable type
+   * @param warp The Cooperative Group (or warp) used to flush output buffer
+   * @param g The Cooperative Group used to retrieve
+   * @param pair The pair to search for
+   * @param warp_counter Pointer to the warp counter
+   * @param probe_output_buffer Buffer of the matched probe pair sequence
+   * @param contained_output_buffer Buffer of the matched contained pair sequence
+   * @param num_matches Size of the output sequence
+   * @param probe_output_begin Beginning of the output sequence of the matched probe pairs
+   * @param contained_output_begin Beginning of the output sequence of the matched contained
+   * pairs
+   * @param pair_equal The binary callable used to compare two pairs for equality
+   */
+  template <uint32_t buffer_size,
+            bool is_outer,
+            typename warpT,
+            typename CG,
+            typename atomicT,
+            typename OutputIt1,
+            typename OutputIt2,
+            typename PairEqual>
+  __device__ void pair_retrieve(warpT const& warp,
+                                CG const& g,
+                                value_type const& pair,
+                                uint32_t* warp_counter,
+                                value_type* probe_output_buffer,
+                                value_type* contained_output_buffer,
+                                atomicT* num_matches,
+                                OutputIt1 probe_output_begin,
+                                OutputIt2 contained_output_begin,
+                                PairEqual pair_equal) noexcept
+  {
+    const uint32_t cg_lane_id = g.thread_rank();
+
+    auto key          = pair.first;
+    auto current_slot = initial_slot(g, key);
+
+    bool running                      = true;
+    [[maybe_unused]] bool found_match = false;
+
+    while (warp.any(running)) {
+      if (running) {
+        value_type arr[2];
+        load_pair_array(&arr[0], current_slot);
+
+        auto const first_slot_is_empty =
+          detail::bitwise_compare(arr[0].first, this->get_empty_key_sentinel());
+        auto const second_slot_is_empty =
+          detail::bitwise_compare(arr[1].first, this->get_empty_key_sentinel());
+        auto const first_equals  = (not first_slot_is_empty and pair_equal(arr[0], pair));
+        auto const second_equals = (not second_slot_is_empty and pair_equal(arr[1], pair));
+        auto const first_exists  = g.ballot(first_equals);
+        auto const second_exists = g.ballot(second_equals);
+
+        if (first_exists or second_exists) {
+          if constexpr (is_outer) { found_match = true; }
+
+          auto num_first_matches  = __popc(first_exists);
+          auto num_second_matches = __popc(second_exists);
+
+          uint32_t output_idx;
+          if (0 == cg_lane_id) {
+            output_idx = atomicAdd(warp_counter, (num_first_matches + num_second_matches));
+          }
+          output_idx = g.shfl(output_idx, 0);
+
+          if (first_equals) {
+            auto lane_offset = __popc(first_exists & ((1 << cg_lane_id) - 1));
+            probe_output_buffer[output_idx + lane_offset]     = pair;
+            contained_output_buffer[output_idx + lane_offset] = arr[0];
+          }
+          if (second_equals) {
+            auto lane_offset = __popc(second_exists & ((1 << cg_lane_id) - 1));
+            probe_output_buffer[output_idx + num_first_matches + lane_offset]     = pair;
+            contained_output_buffer[output_idx + num_first_matches + lane_offset] = arr[1];
+          }
+        }
+        if (g.any(first_slot_is_empty or second_slot_is_empty)) {
+          running = false;
+          if constexpr (is_outer) {
+            if ((not found_match) && (cg_lane_id == 0)) {
+              auto output_idx                 = atomicAdd(warp_counter, 1);
+              probe_output_buffer[output_idx] = pair;
+              contained_output_buffer[output_idx] =
+                cuco::make_pair<Key, Value>(std::move(this->get_empty_key_sentinel()),
+                                            std::move(this->get_empty_value_sentinel()));
+            }
+          }
+        }
+      }  // if running
+
+      warp.sync();
+      if (*warp_counter + warp.size() * vector_width() > buffer_size) {
+        flush_output_buffer(warp,
+                            *warp_counter,
+                            probe_output_buffer,
+                            contained_output_buffer,
+                            num_matches,
+                            probe_output_begin,
+                            contained_output_begin);
+        // First lane reset warp-level counter
+        if (warp.thread_rank() == 0) { *warp_counter = 0; }
+      }
+
+      current_slot = next_slot(current_slot);
+    }  // while running
+  }
+
+  /**
+   * @brief Retrieves all the matches of a given pair contained in multimap using scalar
+   * loads with per-CG shared memory buffer.
+   *
+   * For pair `p`, if pair_equal(p, slot[j]) returns true, copies `p` to unspecified locations
+   * in `[probe_output_begin, probe_output_end)` and copies slot[j] to unspecified locations in
+   * `[contained_output_begin, contained_output_end)`. If `p` does not have any matches, copies
+   * `p` and a pair of `empty_key_sentinel` and `empty_value_sentinel` into the output only if
+   * `is_outer` is true.
+   *
+   * @tparam cg_size The number of threads in CUDA Cooperative Groups
+   * @tparam buffer_size Size of the output buffer
+   * @tparam is_outer Boolean flag indicating whether outer join is peformed
+   * @tparam CG Cooperative Group type
+   * @tparam atomicT Type of atomic storage
+   * @tparam OutputIt1 Device accessible output iterator whose `value_type` is constructible from
+   * `InputIt`s `value_type`.
+   * @tparam OutputIt2 Device accessible output iterator whose `value_type` is constructible from
+   * the map's `value_type`.
+   * @tparam PairEqual Binary callable type
+   * @param g The Cooperative Group used to retrieve
+   * @param pair The pair to search for
+   * @param cg_counter Pointer to the CG counter
+   * @param probe_output_buffer Buffer of the matched probe pair sequence
+   * @param contained_output_buffer Buffer of the matched contained pair sequence
+   * @param num_matches Size of the output sequence
+   * @param probe_output_begin Beginning of the output sequence of the matched probe pairs
+   * @param contained_output_begin Beginning of the output sequence of the matched contained
+   * pairs
+   * @param pair_equal The binary callable used to compare two pairs for equality
+   */
+  template <uint32_t cg_size,
+            uint32_t buffer_size,
+            bool is_outer,
+            typename CG,
+            typename atomicT,
+            typename OutputIt1,
+            typename OutputIt2,
+            typename PairEqual>
+  __device__ void pair_retrieve(CG const& g,
+                                value_type const& pair,
+                                uint32_t* cg_counter,
+                                value_type* probe_output_buffer,
+                                value_type* contained_output_buffer,
+                                atomicT* num_matches,
+                                OutputIt1 probe_output_begin,
+                                OutputIt2 contained_output_begin,
+                                PairEqual pair_equal) noexcept
+  {
+    const uint32_t lane_id = g.thread_rank();
+
+    auto key          = pair.first;
+    auto current_slot = initial_slot(g, key);
+
+    bool running                      = true;
+    [[maybe_unused]] bool found_match = false;
+
+    while (running) {
+      // TODO: Replace reinterpret_cast with atomic ref when possible. The current implementation
+      // is unsafe!
+      static_assert(sizeof(Key) == sizeof(cuda::atomic<Key>));
+      static_assert(sizeof(Value) == sizeof(cuda::atomic<Value>));
+      value_type slot_contents = *reinterpret_cast<value_type const*>(current_slot);
+
+      auto const slot_is_empty =
+        detail::bitwise_compare(slot_contents.first, this->get_empty_key_sentinel());
+      auto const equals   = (not slot_is_empty and pair_equal(slot_contents, pair));
+      uint32_t output_idx = *cg_counter;
+      auto const exists   = g.ballot(equals);
+
+      if (exists) {
+        if constexpr (is_outer) { found_match = true; }
+        auto num_matches = __popc(exists);
+        if (equals) {
+          // Each match computes its lane-level offset
+          auto lane_offset                                  = __popc(exists & ((1 << lane_id) - 1));
+          probe_output_buffer[output_idx + lane_offset]     = pair;
+          contained_output_buffer[output_idx + lane_offset] = slot_contents;
+        }
+        if (0 == lane_id) { (*cg_counter) += num_matches; }
+      }
+      if (g.any(slot_is_empty)) {
+        running = false;
+        if constexpr (is_outer) {
+          if ((not found_match) && (lane_id == 0)) {
+            output_idx                      = (*cg_counter)++;
+            probe_output_buffer[output_idx] = pair;
+            contained_output_buffer[output_idx] =
+              cuco::make_pair<Key, Value>(std::move(this->get_empty_key_sentinel()),
+                                          std::move(this->get_empty_value_sentinel()));
+          }
+        }
+      }
+
+      g.sync();
+
+      // Flush if the next iteration won't fit into buffer
+      if ((*cg_counter + cg_size) > buffer_size) {
+        flush_output_buffer(g,
+                            *cg_counter,
+                            probe_output_buffer,
+                            contained_output_buffer,
+                            num_matches,
+                            probe_output_begin,
+                            contained_output_begin);
+        // First lane reset CG-level counter
+        if (lane_id == 0) { *cg_counter = 0; }
+      }
+      current_slot = next_slot(current_slot);
+    }  // while running
+  }
+};  // class device_view_impl
+
+}  // namespace cuco

From 098073732cfb904ad3e9552151cda020b32df5ab Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Fri, 15 Oct 2021 17:59:43 -0400
Subject: [PATCH 245/267] Updates: move _impl functions out of the public
 header

---
 include/cuco/detail/static_multimap.inl | 968 ++----------------------
 include/cuco/static_multimap.cuh        | 642 ++--------------
 2 files changed, 143 insertions(+), 1467 deletions(-)

diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap.inl
index af9438493..4a4386b48 100644
--- a/include/cuco/detail/static_multimap.inl
+++ b/include/cuco/detail/static_multimap.inl
@@ -18,7 +18,6 @@
 #include <thrust/iterator/transform_iterator.h>
 #include <thrust/tuple.h>
 
-#include <cuco/detail/bitwise_compare.cuh>
 #include <cuco/detail/util.hpp>
 
 namespace {
@@ -556,214 +555,6 @@ static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::pair_retrieve_oute
   return std::make_pair(probe_output_begin + h_counter, contained_output_begin + h_counter);
 }
 
-template <typename Key,
-          typename Value,
-          cuda::thread_scope Scope,
-          class ProbeSequence,
-          typename Allocator>
-__device__ void
-static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view_base::load_pair_array(
-  value_type* arr, const_iterator current_slot) noexcept
-{
-  if constexpr (sizeof(value_type) == 4) {
-    auto const tmp = *reinterpret_cast<ushort4 const*>(current_slot);
-    memcpy(&arr[0], &tmp, 2 * sizeof(value_type));
-  } else {
-    auto const tmp = *reinterpret_cast<uint4 const*>(current_slot);
-    memcpy(&arr[0], &tmp, 2 * sizeof(value_type));
-  }
-}
-
-template <typename Key,
-          typename Value,
-          cuda::thread_scope Scope,
-          class ProbeSequence,
-          typename Allocator>
-__device__
-  static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_mutable_view::insert_result
-  static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_mutable_view::packed_cas(
-    iterator current_slot, value_type const& insert_pair) noexcept
-{
-  auto expected_key   = this->get_empty_key_sentinel();
-  auto expected_value = this->get_empty_value_sentinel();
-
-  cuco::detail::pair_converter<value_type> expected_pair{
-    cuco::make_pair<Key, Value>(std::move(expected_key), std::move(expected_value))};
-  cuco::detail::pair_converter<value_type> new_pair{insert_pair};
-
-  auto slot = reinterpret_cast<
-    cuda::atomic<typename cuco::detail::pair_converter<value_type>::packed_type, Scope>*>(
-    current_slot);
-
-  bool success = slot->compare_exchange_strong(
-    expected_pair.packed, new_pair.packed, cuda::std::memory_order_relaxed);
-  if (success) { return insert_result::SUCCESS; }
-
-  return insert_result::CONTINUE;
-}
-
-template <typename Key,
-          typename Value,
-          cuda::thread_scope Scope,
-          class ProbeSequence,
-          typename Allocator>
-__device__
-  static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_mutable_view::insert_result
-  static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_mutable_view::
-    back_to_back_cas(iterator current_slot, value_type const& insert_pair) noexcept
-{
-  using cuda::std::memory_order_relaxed;
-
-  auto expected_key   = this->get_empty_key_sentinel();
-  auto expected_value = this->get_empty_value_sentinel();
-
-  // Back-to-back CAS for 8B/8B key/value pairs
-  auto& slot_key   = current_slot->first;
-  auto& slot_value = current_slot->second;
-
-  bool key_success =
-    slot_key.compare_exchange_strong(expected_key, insert_pair.first, memory_order_relaxed);
-  bool value_success =
-    slot_value.compare_exchange_strong(expected_value, insert_pair.second, memory_order_relaxed);
-
-  if (key_success) {
-    while (not value_success) {
-      value_success =
-        slot_value.compare_exchange_strong(expected_value = this->get_empty_value_sentinel(),
-                                           insert_pair.second,
-                                           memory_order_relaxed);
-    }
-    return insert_result::SUCCESS;
-  } else if (value_success) {
-    slot_value.store(this->get_empty_value_sentinel(), memory_order_relaxed);
-  }
-
-  return insert_result::CONTINUE;
-}
-
-template <typename Key,
-          typename Value,
-          cuda::thread_scope Scope,
-          class ProbeSequence,
-          typename Allocator>
-__device__
-  static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_mutable_view::insert_result
-  static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_mutable_view::
-    cas_dependent_write(iterator current_slot, value_type const& insert_pair) noexcept
-{
-  using cuda::std::memory_order_relaxed;
-  auto expected_key = this->get_empty_key_sentinel();
-
-  auto& slot_key = current_slot->first;
-
-  auto const key_success =
-    slot_key.compare_exchange_strong(expected_key, insert_pair.first, memory_order_relaxed);
-
-  if (key_success) {
-    auto& slot_value = current_slot->second;
-    slot_value.store(insert_pair.second, memory_order_relaxed);
-    return insert_result::SUCCESS;
-  }
-
-  return insert_result::CONTINUE;
-}
-
-template <typename Key,
-          typename Value,
-          cuda::thread_scope Scope,
-          class ProbeSequence,
-          typename Allocator>
-template <bool uses_vector_load, typename CG>
-__device__ std::enable_if_t<uses_vector_load, void>
-static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_mutable_view::insert_impl(
-  CG g, value_type const& insert_pair) noexcept
-{
-  auto current_slot = initial_slot(g, insert_pair.first);
-  while (true) {
-    value_type arr[2];
-    load_pair_array(&arr[0], current_slot);
-
-    // The user provide `key_equal` can never be used to compare against `empty_key_sentinel` as
-    // the sentinel is not a valid key value. Therefore, first check for the sentinel
-    auto const first_slot_is_empty =
-      (detail::bitwise_compare(arr[0].first, this->get_empty_key_sentinel()));
-    auto const second_slot_is_empty =
-      (detail::bitwise_compare(arr[1].first, this->get_empty_key_sentinel()));
-    auto const window_contains_empty = g.ballot(first_slot_is_empty or second_slot_is_empty);
-
-    if (window_contains_empty) {
-      // the first lane in the group with an empty slot will attempt the insert
-      insert_result status{insert_result::CONTINUE};
-      uint32_t src_lane = __ffs(window_contains_empty) - 1;
-      if (g.thread_rank() == src_lane) {
-        auto insert_location = first_slot_is_empty ? current_slot : current_slot + 1;
-        // One single CAS operation since vector loads are dedicated to packable pairs
-        status = packed_cas(insert_location, insert_pair);
-      }
-
-      // successful insert
-      if (g.any(status == insert_result::SUCCESS)) { return; }
-      // if we've gotten this far, a different key took our spot
-      // before we could insert. We need to retry the insert on the
-      // same window
-    }
-    // if there are no empty slots in the current window,
-    // we move onto the next window
-    else {
-      current_slot = next_slot(current_slot);
-    }
-  }  // while true
-}
-
-template <typename Key,
-          typename Value,
-          cuda::thread_scope Scope,
-          class ProbeSequence,
-          typename Allocator>
-template <bool uses_vector_load, typename CG>
-__device__ std::enable_if_t<not uses_vector_load, void>
-static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_mutable_view::insert_impl(
-  CG g, value_type const& insert_pair) noexcept
-{
-  auto current_slot = initial_slot(g, insert_pair.first);
-
-  while (true) {
-    value_type slot_contents = *reinterpret_cast<value_type const*>(current_slot);
-    auto const& existing_key = slot_contents.first;
-
-    // The user provide `key_equal` can never be used to compare against `empty_key_sentinel` as the
-    // sentinel is not a valid key value. Therefore, first check for the sentinel
-    auto const slot_is_empty =
-      detail::bitwise_compare(existing_key, this->get_empty_key_sentinel());
-    auto const window_contains_empty = g.ballot(slot_is_empty);
-
-    if (window_contains_empty) {
-      // the first lane in the group with an empty slot will attempt the insert
-      insert_result status{insert_result::CONTINUE};
-      uint32_t src_lane = __ffs(window_contains_empty) - 1;
-
-      if (g.thread_rank() == src_lane) {
-#if __CUDA_ARCH__ < 700
-        status = cas_dependent_write(current_slot, insert_pair);
-#else
-        status = back_to_back_cas(current_slot, insert_pair);
-#endif
-      }
-
-      // successful insert
-      if (g.any(status == insert_result::SUCCESS)) { return; }
-      // if we've gotten this far, a different key took our spot
-      // before we could insert. We need to retry the insert on the
-      // same window
-    }
-    // if there are no empty slots in the current window,
-    // we move onto the next window
-    else {
-      current_slot = next_slot(current_slot);
-    }
-  }  // while true
-}
-
 template <typename Key,
           typename Value,
           cuda::thread_scope Scope,
@@ -774,7 +565,7 @@ __device__ void
 static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_mutable_view::insert(
   CG g, value_type const& insert_pair) noexcept
 {
-  insert_impl<uses_vector_load()>(g, insert_pair);
+  impl_.insert<uses_vector_load()>(g, insert_pair);
 }
 
 template <typename Key,
@@ -782,601 +573,40 @@ template <typename Key,
           cuda::thread_scope Scope,
           class ProbeSequence,
           typename Allocator>
-template <bool uses_vector_load, typename CG, typename KeyEqual>
-__device__ std::enable_if_t<uses_vector_load, bool>
-static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::contains_impl(
-  CG g, Key const& k, KeyEqual key_equal) noexcept
-{
-  auto current_slot = initial_slot(g, k);
-
-  while (true) {
-    value_type arr[2];
-    load_pair_array(&arr[0], current_slot);
-
-    auto const first_slot_is_empty =
-      detail::bitwise_compare(arr[0].first, this->get_empty_key_sentinel());
-    auto const second_slot_is_empty =
-      detail::bitwise_compare(arr[1].first, this->get_empty_key_sentinel());
-    auto const first_equals  = (not first_slot_is_empty and key_equal(arr[0].first, k));
-    auto const second_equals = (not second_slot_is_empty and key_equal(arr[1].first, k));
-
-    // the key we were searching for was found by one of the threads, so we return true
-    if (g.any(first_equals or second_equals)) { return true; }
-
-    // we found an empty slot, meaning that the key we're searching for isn't present
-    if (g.any(first_slot_is_empty or second_slot_is_empty)) { return false; }
-
-    // otherwise, all slots in the current window are full with other keys, so we move onto the next
-    // window
-    current_slot = next_slot(current_slot);
-  }
-}
-
-template <typename Key,
-          typename Value,
-          cuda::thread_scope Scope,
-          class ProbeSequence,
-          typename Allocator>
-template <bool uses_vector_load, typename CG, typename KeyEqual>
-__device__ std::enable_if_t<not uses_vector_load, bool>
-static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::contains_impl(
-  CG g, Key const& k, KeyEqual key_equal) noexcept
-{
-  auto current_slot = initial_slot(g, k);
-
-  while (true) {
-    value_type slot_contents = *reinterpret_cast<value_type const*>(current_slot);
-    auto const& existing_key = slot_contents.first;
-
-    // The user provide `key_equal` can never be used to compare against `empty_key_sentinel` as the
-    // sentinel is not a valid key value. Therefore, first check for the sentinel
-    auto const slot_is_empty =
-      detail::bitwise_compare(existing_key, this->get_empty_key_sentinel());
-
-    auto const equals = (not slot_is_empty and key_equal(existing_key, k));
-
-    // the key we were searching for was found by one of the threads, so we return true
-    if (g.any(equals)) { return true; }
-
-    // we found an empty slot, meaning that the key we're searching for isn't present
-    if (g.any(slot_is_empty)) { return false; }
-
-    // otherwise, all slots in the current window are full with other keys, so we move onto the next
-    // window
-    current_slot = next_slot(current_slot);
-  }
-}
-
-template <typename Key,
-          typename Value,
-          cuda::thread_scope Scope,
-          class ProbeSequence,
-          typename Allocator>
-template <bool uses_vector_load, bool is_outer, typename CG, typename KeyEqual>
-__device__ std::enable_if_t<uses_vector_load, std::size_t>
-static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::count_impl(
-  CG const& g, Key const& k, KeyEqual key_equal) noexcept
-{
-  std::size_t count = 0;
-  auto current_slot = initial_slot(g, k);
-
-  [[maybe_unused]] bool found_match = false;
-
-  while (true) {
-    value_type arr[2];
-    load_pair_array(&arr[0], current_slot);
-
-    auto const first_slot_is_empty =
-      detail::bitwise_compare(arr[0].first, this->get_empty_key_sentinel());
-    auto const second_slot_is_empty =
-      detail::bitwise_compare(arr[1].first, this->get_empty_key_sentinel());
-    auto const first_equals  = (not first_slot_is_empty and key_equal(arr[0].first, k));
-    auto const second_equals = (not second_slot_is_empty and key_equal(arr[1].first, k));
-
-    if constexpr (is_outer) {
-      if (g.any(first_equals or second_equals)) { found_match = true; }
-    }
-
-    count += (first_equals + second_equals);
-
-    if (g.any(first_slot_is_empty or second_slot_is_empty)) {
-      if constexpr (is_outer) {
-        if ((not found_match) && (g.thread_rank() == 0)) { count++; }
-      }
-      return count;
-    }
-
-    current_slot = next_slot(current_slot);
-  }
-}
-
-template <typename Key,
-          typename Value,
-          cuda::thread_scope Scope,
-          class ProbeSequence,
-          typename Allocator>
-template <bool uses_vector_load, bool is_outer, typename CG, typename KeyEqual>
-__device__ std::enable_if_t<not uses_vector_load, std::size_t>
-static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::count_impl(
-  CG const& g, Key const& k, KeyEqual key_equal) noexcept
-{
-  std::size_t count = 0;
-  auto current_slot = initial_slot(g, k);
-
-  [[maybe_unused]] bool found_match = false;
-
-  while (true) {
-    value_type slot_contents = *reinterpret_cast<value_type const*>(current_slot);
-    auto const& current_key  = slot_contents.first;
-
-    auto const slot_is_empty = detail::bitwise_compare(current_key, this->get_empty_key_sentinel());
-    auto const equals        = not slot_is_empty and key_equal(current_key, k);
-
-    if constexpr (is_outer) {
-      if (g.any(equals)) { found_match = true; }
-    }
-
-    count += equals;
-
-    if (g.any(slot_is_empty)) {
-      if constexpr (is_outer) {
-        if ((not found_match) && (g.thread_rank() == 0)) { count++; }
-      }
-      return count;
-    }
-
-    current_slot = next_slot(current_slot);
-  }
-}
-
-template <typename Key,
-          typename Value,
-          cuda::thread_scope Scope,
-          class ProbeSequence,
-          typename Allocator>
-template <bool uses_vector_load, bool is_outer, typename CG, typename PairEqual>
-__device__ std::enable_if_t<uses_vector_load, std::size_t>
-static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::pair_count_impl(
-  CG const& g, value_type const& pair, PairEqual pair_equal) noexcept
-{
-  std::size_t count = 0;
-  auto key          = pair.first;
-  auto current_slot = initial_slot(g, key);
-
-  [[maybe_unused]] bool found_match = false;
-
-  while (true) {
-    value_type arr[2];
-    load_pair_array(&arr[0], current_slot);
-
-    auto const first_slot_is_empty =
-      detail::bitwise_compare(arr[0].first, this->get_empty_key_sentinel());
-    auto const second_slot_is_empty =
-      detail::bitwise_compare(arr[1].first, this->get_empty_key_sentinel());
-
-    auto const first_slot_equals  = (not first_slot_is_empty and pair_equal(arr[0], pair));
-    auto const second_slot_equals = (not second_slot_is_empty and pair_equal(arr[1], pair));
-
-    if constexpr (is_outer) {
-      if (g.any(first_slot_equals or second_slot_equals)) { found_match = true; }
-    }
-
-    count += (first_slot_equals + second_slot_equals);
-
-    if (g.any(first_slot_is_empty or second_slot_is_empty)) {
-      if constexpr (is_outer) {
-        if ((not found_match) && (g.thread_rank() == 0)) { count++; }
-      }
-      return count;
-    }
-
-    current_slot = next_slot(current_slot);
-  }
-}
-
-template <typename Key,
-          typename Value,
-          cuda::thread_scope Scope,
-          class ProbeSequence,
-          typename Allocator>
-template <bool uses_vector_load, bool is_outer, typename CG, typename PairEqual>
-__device__ std::enable_if_t<not uses_vector_load, std::size_t>
-static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::pair_count_impl(
-  CG const& g, value_type const& pair, PairEqual pair_equal) noexcept
+template <typename CG>
+__device__ static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view
+static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::make_copy(
+  CG g, pair_atomic_type* const memory_to_use, device_view source_device_view) noexcept
 {
-  std::size_t count = 0;
-  auto key          = pair.first;
-  auto current_slot = initial_slot(g, key);
-
-  [[maybe_unused]] bool found_match = false;
-
-  while (true) {
-    auto slot_contents = *reinterpret_cast<value_type const*>(current_slot);
-
-    auto const slot_is_empty =
-      detail::bitwise_compare(slot_contents.first, this->get_empty_key_sentinel());
-
-    auto const equals = not slot_is_empty and pair_equal(slot_contents, pair);
-
-    if constexpr (is_outer) {
-      if (g.any(equals)) { found_match = true; }
-    }
+#if defined(CUCO_HAS_CUDA_BARRIER)
+  __shared__ cuda::barrier<cuda::thread_scope::thread_scope_block> barrier;
+  if (g.thread_rank() == 0) { init(&barrier, g.size()); }
+  g.sync();
 
-    count += equals;
+  cuda::memcpy_async(g,
+                     memory_to_use,
+                     source_device_view.get_slots(),
+                     sizeof(pair_atomic_type) * source_device_view.get_capacity(),
+                     barrier);
 
-    if (g.any(slot_is_empty)) {
-      if constexpr (is_outer) {
-        if ((not found_match) && (g.thread_rank() == 0)) { count++; }
-      }
-      return count;
-    }
-
-    current_slot = next_slot(current_slot);
+  barrier.arrive_and_wait();
+#else
+  pair_atomic_type const* const slots_ptr = source_device_view.get_slots();
+  for (std::size_t i = g.thread_rank(); i < source_device_view.get_capacity(); i += g.size()) {
+    new (&memory_to_use[i].first)
+      atomic_key_type{slots_ptr[i].first.load(cuda::memory_order_relaxed)};
+    new (&memory_to_use[i].second)
+      atomic_mapped_type{slots_ptr[i].second.load(cuda::memory_order_relaxed)};
   }
-}
-
-template <typename Key,
-          typename Value,
-          cuda::thread_scope Scope,
-          class ProbeSequence,
-          typename Allocator>
-template <uint32_t buffer_size,
-          bool is_outer,
-          typename warpT,
-          typename CG,
-          typename atomicT,
-          typename OutputIt,
-          typename KeyEqual>
-__device__ void
-static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::retrieve_impl(
-  warpT const& warp,
-  CG const& g,
-  Key const& k,
-  uint32_t* warp_counter,
-  value_type* output_buffer,
-  atomicT* num_matches,
-  OutputIt output_begin,
-  KeyEqual key_equal) noexcept
-{
-  const uint32_t cg_lane_id = g.thread_rank();
-
-  auto current_slot = initial_slot(g, k);
-
-  bool running                      = true;
-  [[maybe_unused]] bool found_match = false;
-
-  while (warp.any(running)) {
-    if (running) {
-      value_type arr[2];
-      load_pair_array(&arr[0], current_slot);
-
-      auto const first_slot_is_empty =
-        detail::bitwise_compare(arr[0].first, this->get_empty_key_sentinel());
-      auto const second_slot_is_empty =
-        detail::bitwise_compare(arr[1].first, this->get_empty_key_sentinel());
-      auto const first_equals  = (not first_slot_is_empty and key_equal(arr[0].first, k));
-      auto const second_equals = (not second_slot_is_empty and key_equal(arr[1].first, k));
-      auto const first_exists  = g.ballot(first_equals);
-      auto const second_exists = g.ballot(second_equals);
-
-      if (first_exists or second_exists) {
-        if constexpr (is_outer) { found_match = true; }
-
-        auto num_first_matches  = __popc(first_exists);
-        auto num_second_matches = __popc(second_exists);
-
-        uint32_t output_idx;
-        if (0 == cg_lane_id) {
-          output_idx = atomicAdd(warp_counter, (num_first_matches + num_second_matches));
-        }
-        output_idx = g.shfl(output_idx, 0);
-
-        if (first_equals) {
-          auto lane_offset = __popc(first_exists & ((1 << cg_lane_id) - 1));
-          Key key          = k;
-          output_buffer[output_idx + lane_offset] =
-            cuco::make_pair<Key, Value>(std::move(key), std::move(arr[0].second));
-        }
-        if (second_equals) {
-          auto lane_offset = __popc(second_exists & ((1 << cg_lane_id) - 1));
-          Key key          = k;
-          output_buffer[output_idx + num_first_matches + lane_offset] =
-            cuco::make_pair<Key, Value>(std::move(key), std::move(arr[1].second));
-        }
-      }
-      if (g.any(first_slot_is_empty or second_slot_is_empty)) {
-        running = false;
-        if constexpr (is_outer) {
-          if ((not found_match) && (cg_lane_id == 0)) {
-            auto output_idx           = atomicAdd(warp_counter, 1);
-            Key key                   = k;
-            output_buffer[output_idx] = cuco::make_pair<Key, Value>(
-              std::move(key), std::move(this->get_empty_value_sentinel()));
-          }
-        }
-      }
-    }  // if running
-
-    warp.sync();
-    if (*warp_counter + warp.size() * vector_width() > buffer_size) {
-      flush_output_buffer(warp, *warp_counter, output_buffer, num_matches, output_begin);
-      // First lane reset warp-level counter
-      if (warp.thread_rank() == 0) { *warp_counter = 0; }
-    }
-
-    current_slot = next_slot(current_slot);
-  }  // while running
-}
-
-template <typename Key,
-          typename Value,
-          cuda::thread_scope Scope,
-          class ProbeSequence,
-          typename Allocator>
-template <uint32_t cg_size,
-          uint32_t buffer_size,
-          bool is_outer,
-          typename CG,
-          typename atomicT,
-          typename OutputIt,
-          typename KeyEqual>
-__device__ void
-static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::retrieve_impl(
-  CG const& g,
-  Key const& k,
-  uint32_t* cg_counter,
-  value_type* output_buffer,
-  atomicT* num_matches,
-  OutputIt output_begin,
-  KeyEqual key_equal) noexcept
-{
-  const uint32_t lane_id = g.thread_rank();
-
-  auto current_slot = initial_slot(g, k);
-
-  bool running                      = true;
-  [[maybe_unused]] bool found_match = false;
-
-  while (running) {
-    // TODO: Replace reinterpret_cast with atomic ref when possible. The current implementation
-    // is unsafe!
-    static_assert(sizeof(Key) == sizeof(cuda::atomic<Key>));
-    static_assert(sizeof(Value) == sizeof(cuda::atomic<Value>));
-    value_type slot_contents = *reinterpret_cast<value_type const*>(current_slot);
-
-    auto const slot_is_empty =
-      detail::bitwise_compare(slot_contents.first, this->get_empty_key_sentinel());
-    auto const equals   = (not slot_is_empty and key_equal(slot_contents.first, k));
-    uint32_t output_idx = *cg_counter;
-    auto const exists   = g.ballot(equals);
-
-    if (exists) {
-      if constexpr (is_outer) { found_match = true; }
-      auto num_matches = __popc(exists);
-      if (equals) {
-        // Each match computes its lane-level offset
-        auto lane_offset = __popc(exists & ((1 << lane_id) - 1));
-        Key key          = k;
-        output_buffer[output_idx + lane_offset] =
-          cuco::make_pair<Key, Value>(std::move(key), std::move(slot_contents.second));
-      }
-      if (0 == lane_id) { (*cg_counter) += num_matches; }
-    }
-    if (g.any(slot_is_empty)) {
-      running = false;
-      if constexpr (is_outer) {
-        if ((not found_match) && (lane_id == 0)) {
-          output_idx                = (*cg_counter)++;
-          Key key                   = k;
-          output_buffer[output_idx] = cuco::make_pair<Key, Value>(
-            std::move(key), std::move(this->get_empty_value_sentinel()));
-        }
-      }
-    }
-
-    g.sync();
-
-    // Flush if the next iteration won't fit into buffer
-    if ((*cg_counter + cg_size) > buffer_size) {
-      flush_output_buffer(g, *cg_counter, output_buffer, num_matches, output_begin);
-      // First lane reset CG-level counter
-      if (lane_id == 0) { *cg_counter = 0; }
-    }
-    current_slot = next_slot(current_slot);
-  }  // while running
-}
-
-template <typename Key,
-          typename Value,
-          cuda::thread_scope Scope,
-          class ProbeSequence,
-          typename Allocator>
-template <uint32_t buffer_size,
-          bool is_outer,
-          typename warpT,
-          typename CG,
-          typename atomicT,
-          typename OutputIt1,
-          typename OutputIt2,
-          typename PairEqual>
-__device__ void
-static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::pair_retrieve_impl(
-  warpT const& warp,
-  CG const& g,
-  value_type const& pair,
-  uint32_t* warp_counter,
-  value_type* probe_output_buffer,
-  value_type* contained_output_buffer,
-  atomicT* num_matches,
-  OutputIt1 probe_output_begin,
-  OutputIt2 contained_output_begin,
-  PairEqual pair_equal) noexcept
-{
-  const uint32_t cg_lane_id = g.thread_rank();
-
-  auto key          = pair.first;
-  auto current_slot = initial_slot(g, key);
-
-  bool running                      = true;
-  [[maybe_unused]] bool found_match = false;
-
-  while (warp.any(running)) {
-    if (running) {
-      value_type arr[2];
-      load_pair_array(&arr[0], current_slot);
-
-      auto const first_slot_is_empty =
-        detail::bitwise_compare(arr[0].first, this->get_empty_key_sentinel());
-      auto const second_slot_is_empty =
-        detail::bitwise_compare(arr[1].first, this->get_empty_key_sentinel());
-      auto const first_equals  = (not first_slot_is_empty and pair_equal(arr[0], pair));
-      auto const second_equals = (not second_slot_is_empty and pair_equal(arr[1], pair));
-      auto const first_exists  = g.ballot(first_equals);
-      auto const second_exists = g.ballot(second_equals);
-
-      if (first_exists or second_exists) {
-        if constexpr (is_outer) { found_match = true; }
-
-        auto num_first_matches  = __popc(first_exists);
-        auto num_second_matches = __popc(second_exists);
-
-        uint32_t output_idx;
-        if (0 == cg_lane_id) {
-          output_idx = atomicAdd(warp_counter, (num_first_matches + num_second_matches));
-        }
-        output_idx = g.shfl(output_idx, 0);
-
-        if (first_equals) {
-          auto lane_offset = __popc(first_exists & ((1 << cg_lane_id) - 1));
-          probe_output_buffer[output_idx + lane_offset]     = pair;
-          contained_output_buffer[output_idx + lane_offset] = arr[0];
-        }
-        if (second_equals) {
-          auto lane_offset = __popc(second_exists & ((1 << cg_lane_id) - 1));
-          probe_output_buffer[output_idx + num_first_matches + lane_offset]     = pair;
-          contained_output_buffer[output_idx + num_first_matches + lane_offset] = arr[1];
-        }
-      }
-      if (g.any(first_slot_is_empty or second_slot_is_empty)) {
-        running = false;
-        if constexpr (is_outer) {
-          if ((not found_match) && (cg_lane_id == 0)) {
-            auto output_idx                 = atomicAdd(warp_counter, 1);
-            probe_output_buffer[output_idx] = pair;
-            contained_output_buffer[output_idx] =
-              cuco::make_pair<Key, Value>(std::move(this->get_empty_key_sentinel()),
-                                          std::move(this->get_empty_value_sentinel()));
-          }
-        }
-      }
-    }  // if running
-
-    warp.sync();
-    if (*warp_counter + warp.size() * vector_width() > buffer_size) {
-      flush_output_buffer(warp,
-                          *warp_counter,
-                          probe_output_buffer,
-                          contained_output_buffer,
-                          num_matches,
-                          probe_output_begin,
-                          contained_output_begin);
-      // First lane reset warp-level counter
-      if (warp.thread_rank() == 0) { *warp_counter = 0; }
-    }
-
-    current_slot = next_slot(current_slot);
-  }  // while running
-}
-
-template <typename Key,
-          typename Value,
-          cuda::thread_scope Scope,
-          class ProbeSequence,
-          typename Allocator>
-template <uint32_t cg_size,
-          uint32_t buffer_size,
-          bool is_outer,
-          typename CG,
-          typename atomicT,
-          typename OutputIt1,
-          typename OutputIt2,
-          typename PairEqual>
-__device__ void
-static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::pair_retrieve_impl(
-  CG const& g,
-  value_type const& pair,
-  uint32_t* cg_counter,
-  value_type* probe_output_buffer,
-  value_type* contained_output_buffer,
-  atomicT* num_matches,
-  OutputIt1 probe_output_begin,
-  OutputIt2 contained_output_begin,
-  PairEqual pair_equal) noexcept
-{
-  const uint32_t lane_id = g.thread_rank();
-
-  auto key          = pair.first;
-  auto current_slot = initial_slot(g, key);
-
-  bool running                      = true;
-  [[maybe_unused]] bool found_match = false;
-
-  while (running) {
-    // TODO: Replace reinterpret_cast with atomic ref when possible. The current implementation
-    // is unsafe!
-    static_assert(sizeof(Key) == sizeof(cuda::atomic<Key>));
-    static_assert(sizeof(Value) == sizeof(cuda::atomic<Value>));
-    value_type slot_contents = *reinterpret_cast<value_type const*>(current_slot);
-
-    auto const slot_is_empty =
-      detail::bitwise_compare(slot_contents.first, this->get_empty_key_sentinel());
-    auto const equals   = (not slot_is_empty and pair_equal(slot_contents, pair));
-    uint32_t output_idx = *cg_counter;
-    auto const exists   = g.ballot(equals);
-
-    if (exists) {
-      if constexpr (is_outer) { found_match = true; }
-      auto num_matches = __popc(exists);
-      if (equals) {
-        // Each match computes its lane-level offset
-        auto lane_offset                                  = __popc(exists & ((1 << lane_id) - 1));
-        probe_output_buffer[output_idx + lane_offset]     = pair;
-        contained_output_buffer[output_idx + lane_offset] = slot_contents;
-      }
-      if (0 == lane_id) { (*cg_counter) += num_matches; }
-    }
-    if (g.any(slot_is_empty)) {
-      running = false;
-      if constexpr (is_outer) {
-        if ((not found_match) && (lane_id == 0)) {
-          output_idx                          = (*cg_counter)++;
-          probe_output_buffer[output_idx]     = pair;
-          contained_output_buffer[output_idx] = cuco::make_pair<Key, Value>(
-            std::move(this->get_empty_key_sentinel()), std::move(this->get_empty_value_sentinel()));
-        }
-      }
-    }
+  g.sync();
+#endif
 
-    g.sync();
-
-    // Flush if the next iteration won't fit into buffer
-    if ((*cg_counter + cg_size) > buffer_size) {
-      flush_output_buffer(g,
-                          *cg_counter,
-                          probe_output_buffer,
-                          contained_output_buffer,
-                          num_matches,
-                          probe_output_begin,
-                          contained_output_begin);
-      // First lane reset CG-level counter
-      if (lane_id == 0) { *cg_counter = 0; }
-    }
-    current_slot = next_slot(current_slot);
-  }  // while running
+  return device_view(memory_to_use,
+                     source_device_view.get_capacity(),
+                     source_device_view.get_empty_key_sentinel(),
+                     source_device_view.get_empty_value_sentinel());
 }
 
-// public APIs
-
 template <typename Key,
           typename Value,
           cuda::thread_scope Scope,
@@ -1391,31 +621,7 @@ static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::flush
   atomicT* num_matches,
   OutputIt output_begin) noexcept
 {
-  std::size_t offset;
-  const auto lane_id = g.thread_rank();
-  if (0 == lane_id) {
-    offset = num_matches->fetch_add(num_outputs, cuda::std::memory_order_relaxed);
-  }
-  offset = g.shfl(offset, 0);
-
-  if constexpr (thrust::is_contiguous_iterator_v<OutputIt>) {
-#if defined(CUCO_HAS_CG_MEMCPY_ASYNC)
-#if defined(CUCO_HAS_CUDA_BARRIER)
-    cooperative_groups::memcpy_async(
-      g,
-      output_begin + offset,
-      output_buffer,
-      cuda::aligned_size_t<alignof(value_type)>(sizeof(value_type) * num_outputs));
-#else
-    cooperative_groups::memcpy_async(
-      g, output_begin + offset, output_buffer, sizeof(value_type) * num_outputs);
-#endif  // end CUCO_HAS_CUDA_BARRIER
-    return;
-#endif  // end CUCO_HAS_CG_MEMCPY_ASYNC
-  }
-  for (auto index = lane_id; index < num_outputs; index += g.size()) {
-    *(output_begin + offset + index) = output_buffer[index];
-  }
+  impl_.flush_output_buffer(g, num_outputs, output_buffer, num_matches, output_begin);
 }
 
 template <typename Key,
@@ -1434,21 +640,13 @@ static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::flush
   OutputIt1 probe_output_begin,
   OutputIt2 contained_output_begin) noexcept
 {
-  std::size_t offset;
-  const auto lane_id = g.thread_rank();
-  if (0 == lane_id) {
-    offset = num_matches->fetch_add(num_outputs, cuda::std::memory_order_relaxed);
-  }
-  offset = g.shfl(offset, 0);
-
-  for (auto index = lane_id; index < num_outputs; index += g.size()) {
-    auto& probe_pair                                           = probe_output_buffer[index];
-    auto& contained_pair                                       = contained_output_buffer[index];
-    thrust::get<0>(*(probe_output_begin + offset + index))     = probe_pair.first;
-    thrust::get<1>(*(probe_output_begin + offset + index))     = probe_pair.second;
-    thrust::get<0>(*(contained_output_begin + offset + index)) = contained_pair.first;
-    thrust::get<1>(*(contained_output_begin + offset + index)) = contained_pair.second;
-  }
+  impl_.flush_output_buffer(g,
+                            num_outputs,
+                            probe_output_buffer,
+                            contained_output_buffer,
+                            num_matches,
+                            probe_output_begin,
+                            contained_output_begin);
 }
 
 template <typename Key,
@@ -1460,7 +658,7 @@ template <typename CG, typename KeyEqual>
 __device__ bool static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::contains(
   CG g, Key const& k, KeyEqual key_equal) noexcept
 {
-  return contains_impl<uses_vector_load()>(g, k, key_equal);
+  return impl_.contains<uses_vector_load()>(g, k, key_equal);
 }
 
 template <typename Key,
@@ -1474,7 +672,7 @@ static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::count
   CG const& g, Key const& k, KeyEqual key_equal) noexcept
 {
   constexpr bool is_outer = false;
-  return count_impl<uses_vector_load(), is_outer>(g, k, key_equal);
+  return impl_.count<uses_vector_load(), is_outer>(g, k, key_equal);
 }
 
 template <typename Key,
@@ -1488,7 +686,7 @@ static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::count
   CG const& g, Key const& k, KeyEqual key_equal) noexcept
 {
   constexpr bool is_outer = true;
-  return count_impl<uses_vector_load(), is_outer>(g, k, key_equal);
+  return impl_.count<uses_vector_load(), is_outer>(g, k, key_equal);
 }
 
 template <typename Key,
@@ -1502,7 +700,7 @@ static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::pair_
   CG const& g, value_type const& pair, PairEqual pair_equal) noexcept
 {
   constexpr bool is_outer = false;
-  return pair_count_impl<uses_vector_load(), is_outer>(g, pair, pair_equal);
+  return impl_.pair_count<uses_vector_load(), is_outer>(g, pair, pair_equal);
 }
 
 template <typename Key,
@@ -1516,7 +714,7 @@ static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::pair_
   CG const& g, value_type const& pair, PairEqual pair_equal) noexcept
 {
   constexpr bool is_outer = true;
-  return pair_count_impl<uses_vector_load(), is_outer>(g, pair, pair_equal);
+  return impl_.pair_count<uses_vector_load(), is_outer>(g, pair, pair_equal);
 }
 
 template <typename Key,
@@ -1541,7 +739,7 @@ __device__ void static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::de
   KeyEqual key_equal) noexcept
 {
   constexpr bool is_outer = false;
-  retrieve_impl<buffer_size, is_outer>(
+  impl_.retrieve<buffer_size, is_outer>(
     warp, g, k, warp_counter, output_buffer, num_matches, output_begin, key_equal);
 }
 
@@ -1568,7 +766,7 @@ static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::retri
   KeyEqual key_equal) noexcept
 {
   constexpr bool is_outer = true;
-  retrieve_impl<buffer_size, is_outer>(
+  impl_.retrieve<buffer_size, is_outer>(
     warp, g, k, warp_counter, output_buffer, num_matches, output_begin, key_equal);
 }
 
@@ -1593,7 +791,7 @@ __device__ void static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::de
   KeyEqual key_equal) noexcept
 {
   constexpr bool is_outer = false;
-  retrieve_impl<cg_size, buffer_size, is_outer>(
+  impl_.retrieve<cg_size, buffer_size, is_outer>(
     g, k, cg_counter, output_buffer, num_matches, output_begin, key_equal);
 }
 
@@ -1619,7 +817,7 @@ static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::retri
   KeyEqual key_equal) noexcept
 {
   constexpr bool is_outer = true;
-  retrieve_impl<cg_size, buffer_size, is_outer>(
+  impl_.retrieve<cg_size, buffer_size, is_outer>(
     g, k, cg_counter, output_buffer, num_matches, output_begin, key_equal);
 }
 
@@ -1649,16 +847,16 @@ static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::pair_
   PairEqual pair_equal) noexcept
 {
   constexpr bool is_outer = false;
-  pair_retrieve_impl<buffer_size, is_outer>(warp,
-                                            g,
-                                            pair,
-                                            warp_counter,
-                                            probe_output_buffer,
-                                            contained_output_buffer,
-                                            num_matches,
-                                            probe_output_begin,
-                                            contained_output_begin,
-                                            pair_equal);
+  impl_.pair_retrieve<buffer_size, is_outer>(warp,
+                                             g,
+                                             pair,
+                                             warp_counter,
+                                             probe_output_buffer,
+                                             contained_output_buffer,
+                                             num_matches,
+                                             probe_output_begin,
+                                             contained_output_begin,
+                                             pair_equal);
 }
 
 template <typename Key,
@@ -1687,16 +885,16 @@ static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::pair_
   PairEqual pair_equal) noexcept
 {
   constexpr bool is_outer = true;
-  pair_retrieve_impl<buffer_size, is_outer>(warp,
-                                            g,
-                                            pair,
-                                            warp_counter,
-                                            probe_output_buffer,
-                                            contained_output_buffer,
-                                            num_matches,
-                                            probe_output_begin,
-                                            contained_output_begin,
-                                            pair_equal);
+  impl_.pair_retrieve<buffer_size, is_outer>(warp,
+                                             g,
+                                             pair,
+                                             warp_counter,
+                                             probe_output_buffer,
+                                             contained_output_buffer,
+                                             num_matches,
+                                             probe_output_begin,
+                                             contained_output_begin,
+                                             pair_equal);
 }
 
 template <typename Key,
@@ -1724,15 +922,15 @@ static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::pair_
   PairEqual pair_equal) noexcept
 {
   constexpr bool is_outer = false;
-  pair_retrieve_impl<cg_size, buffer_size, is_outer>(g,
-                                                     pair,
-                                                     cg_counter,
-                                                     probe_output_buffer,
-                                                     contained_output_buffer,
-                                                     num_matches,
-                                                     probe_output_begin,
-                                                     contained_output_begin,
-                                                     pair_equal);
+  impl_.pair_retrieve<cg_size, buffer_size, is_outer>(g,
+                                                      pair,
+                                                      cg_counter,
+                                                      probe_output_buffer,
+                                                      contained_output_buffer,
+                                                      num_matches,
+                                                      probe_output_begin,
+                                                      contained_output_begin,
+                                                      pair_equal);
 }
 
 template <typename Key,
@@ -1760,15 +958,15 @@ static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::pair_
   PairEqual pair_equal) noexcept
 {
   constexpr bool is_outer = true;
-  pair_retrieve_impl<cg_size, buffer_size, is_outer>(g,
-                                                     pair,
-                                                     cg_counter,
-                                                     probe_output_buffer,
-                                                     contained_output_buffer,
-                                                     num_matches,
-                                                     probe_output_begin,
-                                                     contained_output_begin,
-                                                     pair_equal);
+  impl_.pair_retrieve<cg_size, buffer_size, is_outer>(g,
+                                                      pair,
+                                                      cg_counter,
+                                                      probe_output_buffer,
+                                                      contained_output_buffer,
+                                                      num_matches,
+                                                      probe_output_begin,
+                                                      contained_output_begin,
+                                                      pair_equal);
 }
 
 template <typename Key,
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index 453790828..5a73910c5 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -551,155 +551,9 @@ class static_multimap {
     cudaStream_t& stream;
   };
 
-  class device_view_base {
-   protected:
-    // Import member type definitions from `static_multimap`
-    using value_type     = value_type;
-    using key_type       = Key;
-    using mapped_type    = Value;
-    using iterator       = pair_atomic_type*;
-    using const_iterator = pair_atomic_type const*;
-
-    /**
-     * @brief Indicates if vector-load is used.
-     *
-     * Users have no explicit control on whether vector-load is used.
-     *
-     * @return Boolean indicating if vector-load is used.
-     */
-    static constexpr bool uses_vector_load() noexcept { return ProbeSequence::uses_vector_load(); }
-
-    /**
-     * @brief Returns the number of pairs loaded with each vector-load
-     */
-    static constexpr uint32_t vector_width() noexcept { return ProbeSequence::vector_width(); }
-
-    /**
-     * @brief Load two key/value pairs from the given slot to the target pair array.
-     *
-     * @param arr The pair array to be loaded
-     * @param current_slot The given slot to load from
-     */
-    __device__ void load_pair_array(value_type* arr, const_iterator current_slot) noexcept;
-
-   private:
-    ProbeSequence probe_sequence_;  ///< Probe sequence used to probe the hash map
-    Key empty_key_sentinel_{};      ///< Key value that represents an empty slot
-    Value empty_value_sentinel_{};  ///< Initial Value of empty slot
-
-   public:
-    __host__ __device__ device_view_base(pair_atomic_type* slots,
-                                         std::size_t capacity,
-                                         Key empty_key_sentinel,
-                                         Value empty_value_sentinel) noexcept
-      : probe_sequence_{slots, capacity},
-        empty_key_sentinel_{empty_key_sentinel},
-        empty_value_sentinel_{empty_value_sentinel}
-    {
-    }
-
-    /**
-     * @brief Gets slots array.
-     *
-     * @return Slots array
-     */
-    __device__ pair_atomic_type* get_slots() noexcept { return probe_sequence_.get_slots(); }
-
-    /**
-     * @brief Gets slots array.
-     *
-     * @return Slots array
-     */
-    __device__ pair_atomic_type const* get_slots() const noexcept
-    {
-      return probe_sequence_.get_slots();
-    }
-
-    /**
-     * @brief Returns the initial slot for a given key `k`
-     *
-     * To be used for Cooperative Group based probing.
-     *
-     * @tparam CG Cooperative Group type
-     * @param g the Cooperative Group for which the initial slot is needed
-     * @param k The key to get the slot for
-     * @return Pointer to the initial slot for `k`
-     */
-    template <typename CG>
-    __device__ iterator initial_slot(CG const& g, Key const& k) noexcept
-    {
-      return probe_sequence_.initial_slot(g, k);
-    }
-
-    /**
-     * @brief Returns the initial slot for a given key `k`
-     *
-     * To be used for Cooperative Group based probing.
-     *
-     * @tparam CG Cooperative Group type
-     * @param g the Cooperative Group for which the initial slot is needed
-     * @param k The key to get the slot for
-     * @return Pointer to the initial slot for `k`
-     */
-    template <typename CG>
-    __device__ const_iterator initial_slot(CG g, Key const& k) const noexcept
-    {
-      return probe_sequence_.initial_slot(g, k);
-    }
-
-    /**
-     * @brief Given a slot `s`, returns the next slot.
-     *
-     * If `s` is the last slot, wraps back around to the first slot. To
-     * be used for Cooperative Group based probing.
-     *
-     * @param s The slot to advance
-     * @return The next slot after `s`
-     */
-    __device__ iterator next_slot(iterator s) noexcept { return probe_sequence_.next_slot(s); }
-
-    /**
-     * @brief Given a slot `s`, returns the next slot.
-     *
-     * If `s` is the last slot, wraps back around to the first slot. To
-     * be used for Cooperative Group based probing.
-     *
-     * @param s The slot to advance
-     * @return The next slot after `s`
-     */
-    __device__ const_iterator next_slot(const_iterator s) const noexcept
-    {
-      return probe_sequence_.next_slot(s);
-    }
-
-   public:
-    /**
-     * @brief Gets the maximum number of elements the hash map can hold.
-     *
-     * @return The maximum number of elements the hash map can hold
-     */
-    __host__ __device__ std::size_t get_capacity() const noexcept
-    {
-      return probe_sequence_.get_capacity();
-    }
-
-    /**
-     * @brief Gets the sentinel value used to represent an empty key slot.
-     *
-     * @return The sentinel value used to represent an empty key slot
-     */
-    __host__ __device__ Key get_empty_key_sentinel() const noexcept { return empty_key_sentinel_; }
-
-    /**
-     * @brief Gets the sentinel value used to represent an empty value slot.
-     *
-     * @return The sentinel value used to represent an empty value slot
-     */
-    __host__ __device__ Value get_empty_value_sentinel() const noexcept
-    {
-      return empty_value_sentinel_;
-    }
-  };
+  class device_view_impl_base;
+  class device_mutable_view_impl;
+  class device_view_impl;
 
  public:
   /**
@@ -722,13 +576,14 @@ class static_multimap {
    *                  });
    * \endcode
    */
-  class device_mutable_view : public device_view_base {
+  class device_mutable_view {
    public:
-    using value_type     = typename device_view_base::value_type;
-    using key_type       = typename device_view_base::key_type;
-    using mapped_type    = typename device_view_base::mapped_type;
-    using iterator       = typename device_view_base::iterator;
-    using const_iterator = typename device_view_base::const_iterator;
+    // Import member type definitions from `static_multimap`
+    using value_type     = value_type;
+    using key_type       = Key;
+    using mapped_type    = Value;
+    using iterator       = pair_atomic_type*;
+    using const_iterator = pair_atomic_type const*;
 
     /**
      * @brief Construct a mutable view of the first `capacity` slots of the
@@ -745,82 +600,10 @@ class static_multimap {
                                             std::size_t capacity,
                                             Key empty_key_sentinel,
                                             Value empty_value_sentinel) noexcept
-      : device_view_base{slots, capacity, empty_key_sentinel, empty_value_sentinel}
+      : impl_{slots, capacity, empty_key_sentinel, empty_value_sentinel}
     {
     }
 
-   private:
-    /**
-     * @brief Enumeration of the possible results of attempting to insert into a hash bucket.
-     */
-    enum class insert_result {
-      CONTINUE,  ///< Insert did not succeed, continue trying to insert
-      SUCCESS,   ///< New pair inserted successfully
-      DUPLICATE  ///< Insert did not succeed, key is already present
-    };
-
-    /**
-     * @brief Inserts the specified key/value pair with one single CAS operation.
-     *
-     * @param current_slot The slot to insert
-     * @param insert_pair The pair to insert
-     * @param key_equal The binary callable used to compare two keys for
-     * equality
-     * @return An insert result from the `insert_resullt` enumeration.
-     */
-    __device__ insert_result packed_cas(iterator current_slot,
-                                        value_type const& insert_pair) noexcept;
-
-    /**
-     * @brief Inserts the specified key/value pair with two back-to-back CAS operations.
-     *
-     * @param current_slot The slot to insert
-     * @param insert_pair The pair to insert
-     * @return An insert result from the `insert_resullt` enumeration.
-     */
-    __device__ insert_result back_to_back_cas(iterator current_slot,
-                                              value_type const& insert_pair) noexcept;
-
-    /**
-     * @brief Inserts the specified key/value pair with a CAS of the key and a dependent write
-     * of the value.
-     *
-     * @param current_slot The slot to insert
-     * @param insert_pair The pair to insert
-     * @return An insert result from the `insert_resullt` enumeration.
-     */
-    __device__ insert_result cas_dependent_write(iterator current_slot,
-                                                 value_type const& insert_pair) noexcept;
-
-    /**
-     * @brief Inserts the specified key/value pair into the map using vector loads.
-     *
-     * @tparam uses_vector_load Boolean flag indicating whether vector loads are used
-     * @tparam CG Cooperative Group type
-     *
-     * @param g The Cooperative Group that performs the insert
-     * @param insert_pair The pair to insert
-     * @return void.
-     */
-    template <bool uses_vector_load, typename CG>
-    __device__ std::enable_if_t<uses_vector_load, void> insert_impl(
-      CG g, value_type const& insert_pair) noexcept;
-
-    /**
-     * @brief Inserts the specified key/value pair into the map using scalar loads.
-     *
-     * @tparam uses_vector_load Boolean flag indicating whether vector loads are used
-     * @tparam CG Cooperative Group type
-     *
-     * @param g The Cooperative Group that performs the insert
-     * @param insert_pair The pair to insert
-     * @return void.
-     */
-    template <bool uses_vector_load, typename CG>
-    __device__ std::enable_if_t<not uses_vector_load, void> insert_impl(
-      CG g, value_type const& insert_pair) noexcept;
-
-   public:
     /**
      * @brief Inserts the specified key/value pair into the map.
      *
@@ -832,6 +615,9 @@ class static_multimap {
      */
     template <typename CG>
     __device__ void insert(CG g, value_type const& insert_pair) noexcept;
+
+   private:
+    device_mutable_view_impl impl_;
   };  // class device mutable view
 
   /**
@@ -842,13 +628,14 @@ class static_multimap {
    * value.
    *
    */
-  class device_view : public device_view_base {
+  class device_view {
    public:
-    using value_type     = typename device_view_base::value_type;
-    using key_type       = typename device_view_base::key_type;
-    using mapped_type    = typename device_view_base::mapped_type;
-    using iterator       = typename device_view_base::iterator;
-    using const_iterator = typename device_view_base::const_iterator;
+    // Import member type definitions from `static_multimap`
+    using value_type     = value_type;
+    using key_type       = Key;
+    using mapped_type    = Value;
+    using iterator       = pair_atomic_type*;
+    using const_iterator = pair_atomic_type const*;
 
     /**
      * @brief Construct a view of the first `capacity` slots of the
@@ -865,382 +652,69 @@ class static_multimap {
                                     std::size_t capacity,
                                     Key empty_key_sentinel,
                                     Value empty_value_sentinel) noexcept
-      : device_view_base{slots, capacity, empty_key_sentinel, empty_value_sentinel}
-    {
-    }
-
-    /**
-     * @brief Makes a copy of given `device_view` using non-owned memory.
-     *
-     * This function is intended to be used to create shared memory copies of small static maps,
-     * although global memory can be used as well.
-     *
-     * Example:
-     * @code{.cpp}
-     * template <typename MapType, int CAPACITY>
-     * __global__ void use_device_view(const typename MapType::device_view device_view,
-     *                                 map_key_t const* const keys_to_search,
-     *                                 map_value_t* const values_found,
-     *                                 const size_t number_of_elements)
-     * {
-     *     const size_t index = blockIdx.x * blockDim.x + threadIdx.x;
-     *
-     *     __shared__ typename MapType::pair_atomic_type sm_buffer[CAPACITY];
-     *
-     *     auto g = cg::this_thread_block();
-     *
-     *     const map_t::device_view sm_static_multimap = device_view.make_copy(g,
-     *                                                                    sm_buffer);
-     *
-     *     for (size_t i = g.thread_rank(); i < number_of_elements; i += g.size())
-     *     {
-     *         values_found[i] = sm_static_multimap.find(keys_to_search[i])->second;
-     *     }
-     * }
-     * @endcode
-     *
-     * @tparam CG The type of the cooperative thread group
-     * @param g The cooperative thread group used to copy the slots
-     * @param source_device_view `device_view` to copy from
-     * @param memory_to_use Array large enough to support `capacity` elements. Object does not
-     * take the ownership of the memory
-     * @return Copy of passed `device_view`
-     */
-    template <typename CG>
-    __device__ static device_view make_copy(CG g,
-                                            pair_atomic_type* const memory_to_use,
-                                            device_view source_device_view) noexcept
+      : impl_{slots, capacity, empty_key_sentinel, empty_value_sentinel}
     {
-#if defined(CUCO_HAS_CUDA_BARRIER)
-      __shared__ cuda::barrier<cuda::thread_scope::thread_scope_block> barrier;
-      if (g.thread_rank() == 0) { init(&barrier, g.size()); }
-      g.sync();
-
-      cuda::memcpy_async(g,
-                         memory_to_use,
-                         source_device_view.get_slots(),
-                         sizeof(pair_atomic_type) * source_device_view.get_capacity(),
-                         barrier);
-
-      barrier.arrive_and_wait();
-#else
-      pair_atomic_type const* const slots_ptr = source_device_view.get_slots();
-      for (std::size_t i = g.thread_rank(); i < source_device_view.get_capacity(); i += g.size()) {
-        new (&memory_to_use[i].first)
-          atomic_key_type{slots_ptr[i].first.load(cuda::memory_order_relaxed)};
-        new (&memory_to_use[i].second)
-          atomic_mapped_type{slots_ptr[i].second.load(cuda::memory_order_relaxed)};
-      }
-      g.sync();
-#endif
-
-      return device_view(memory_to_use,
-                         source_device_view.get_capacity(),
-                         source_device_view.get_empty_key_sentinel(),
-                         source_device_view.get_empty_value_sentinel());
     }
 
-   private:
-    /**
-     * @brief Indicates whether the key `k` exists in the map using vector loads.
-     *
-     * If the key `k` was inserted into the map, `contains` returns
-     * true. Otherwise, it returns false. Uses the CUDA Cooperative Groups API to
-     * to leverage multiple threads to perform a single `contains` operation. This provides a
-     * significant boost in throughput compared to the non Cooperative Group based
-     * `contains` at moderate to high load factors.
-     *
-     * @tparam uses_vector_load Boolean flag indicating whether vector loads are used
-     * @tparam CG Cooperative Group type
-     * @tparam KeyEqual Binary callable type
-     * @param g The Cooperative Group used to perform the contains operation
-     * @param k The key to search for
-     * @param key_equal The binary callable used to compare two keys
-     * for equality
-     * @return A boolean indicating whether the key/value pair
-     * containing `k` was inserted
-     */
-    template <bool uses_vector_load, typename CG, typename KeyEqual>
-    __device__ std::enable_if_t<uses_vector_load, bool> contains_impl(CG g,
-                                                                      Key const& k,
-                                                                      KeyEqual key_equal) noexcept;
-
-    /**
-     * @brief Indicates whether the key `k` exists in the map using scalar loads.
-     *
-     * If the key `k` was inserted into the map, `contains` returns
-     * true. Otherwise, it returns false. Uses the CUDA Cooperative Groups API to
-     * to leverage multiple threads to perform a single `contains` operation. This provides a
-     * significant boost in throughput compared to the non Cooperative Group
-     * `contains` at moderate to high load factors.
-     *
-     * @tparam uses_vector_load Boolean flag indicating whether vector loads are used
-     * @tparam CG Cooperative Group type
-     * @tparam KeyEqual Binary callable type
-     * @param g The Cooperative Group used to perform the contains operation
-     * @param k The key to search for
-     * @param key_equal The binary callable used to compare two keys
-     * for equality
-     * @return A boolean indicating whether the key/value pair
-     * containing `k` was inserted
-     */
-    template <bool uses_vector_load, typename CG, typename KeyEqual>
-    __device__ std::enable_if_t<not uses_vector_load, bool> contains_impl(
-      CG g, Key const& k, KeyEqual key_equal) noexcept;
-
-    /**
-     * @brief Counts the occurrence of a given key contained in multimap using vector loads.
-     *
-     * @tparam uses_vector_load Boolean flag indicating whether vector loads are used
-     * @tparam is_outer Boolean flag indicating whether outer join is peformed
-     * @tparam CG Cooperative Group type
-     * @tparam KeyEqual Binary callable type
-     * @param g The Cooperative Group used to perform the count operation
-     * @param k The key to search for
-     * @param key_equal The binary callable used to compare two keys
-     * for equality
-     * @return Number of matches found by the current thread
-     */
-    template <bool uses_vector_load, bool is_outer, typename CG, typename KeyEqual>
-    __device__ std::enable_if_t<uses_vector_load, std::size_t> count_impl(
-      CG const& g, Key const& k, KeyEqual key_equal) noexcept;
-
-    /**
-     * @brief Counts the occurrence of a given key contained in multimap using scalar loads.
-     *
-     * @tparam uses_vector_load Boolean flag indicating whether vector loads are used
-     * @tparam is_outer Boolean flag indicating whether outer join is peformed
-     * @tparam CG Cooperative Group type
-     * @tparam KeyEqual Binary callable type
-     * @param g The Cooperative Group used to perform the count operation
-     * @param k The key to search for
-     * @param key_equal The binary callable used to compare two keys
-     * for equality
-     * @return Number of matches found by the current thread
-     */
-    template <bool uses_vector_load, bool is_outer, typename CG, typename KeyEqual>
-    __device__ std::enable_if_t<not uses_vector_load, std::size_t> count_impl(
-      CG const& g, Key const& k, KeyEqual key_equal) noexcept;
-
     /**
-     * @brief Counts the occurrence of a given key/value pair contained in multimap using vector
-     * loads.
+     * @brief Gets slots array.
      *
-     * @tparam uses_vector_load Boolean flag indicating whether vector loads are used
-     * @tparam is_outer Boolean flag indicating whether outer join is peformed
-     * @tparam CG Cooperative Group type
-     * @tparam PairEqual Binary callable type
-     * @param g The Cooperative Group used to perform the pair_count operation
-     * @param pair The pair to search for
-     * @param pair_equal The binary callable used to compare two pairs
-     * for equality
-     * @return Number of matches found by the current thread
+     * @return Slots array
      */
-    template <bool uses_vector_load, bool is_outer, typename CG, typename PairEqual>
-    __device__ std::enable_if_t<uses_vector_load, std::size_t> pair_count_impl(
-      CG const& g, value_type const& pair, PairEqual pair_equal) noexcept;
+    __device__ pair_atomic_type* get_slots() noexcept { return impl_.get_slots(); }
 
     /**
-     * @brief Counts the occurrence of a given key/value pair contained in multimap using scalar
-     * loads.
+     * @brief Gets slots array.
      *
-     * @tparam uses_vector_load Boolean flag indicating whether vector loads are used
-     * @tparam is_outer Boolean flag indicating whether outer join is peformed
-     * @tparam CG Cooperative Group type
-     * @tparam PairEqual Binary callable type
-     * @param g The Cooperative Group used to perform the pair_count operation
-     * @param pair The pair to search for
-     * @param pair_equal The binary callable used to compare two pairs
-     * for equality
-     * @return Number of matches found by the current thread
+     * @return Slots array
      */
-    template <bool uses_vector_load, bool is_outer, typename CG, typename PairEqual>
-    __device__ std::enable_if_t<not uses_vector_load, std::size_t> pair_count_impl(
-      CG const& g, value_type const& pair, PairEqual pair_equal) noexcept;
+    __device__ pair_atomic_type const* get_slots() const noexcept { return impl_.get_slots(); }
 
     /**
-     * @brief Retrieves all the matches of a given key contained in multimap using vector
-     * loads with per-warp shared memory buffer.
-     *
-     * For key `k` existing in the map, copies `k` and all associated values to unspecified
-     * locations in `[output_begin, output_end)`. If `k` does not have any matches, copies `k` and
-     * `empty_value_sentinel()` into the output only if `is_outer` is true.
+     * @brief Gets the maximum number of elements the hash map can hold.
      *
-     * @tparam buffer_size Size of the output buffer
-     * @tparam is_outer Boolean flag indicating whether outer join is peformed
-     * @tparam warpT Warp (Cooperative Group) type
-     * @tparam CG Cooperative Group type
-     * @tparam atomicT Type of atomic storage
-     * @tparam OutputIt Device accessible output iterator whose `value_type` is
-     * constructible from the map's `value_type`
-     * @tparam KeyEqual Binary callable type
-     * @param warp The Cooperative Group (or warp) used to flush output buffer
-     * @param g The Cooperative Group used to retrieve
-     * @param k The key to search for
-     * @param warp_counter Pointer to the warp counter
-     * @param output_buffer Shared memory buffer of the key/value pair sequence
-     * @param num_matches Size of the output sequence
-     * @param output_begin Beginning of the output sequence of key/value pairs
-     * @param key_equal The binary callable used to compare two keys
-     * for equality
+     * @return The maximum number of elements the hash map can hold
      */
-    template <uint32_t buffer_size,
-              bool is_outer,
-              typename warpT,
-              typename CG,
-              typename atomicT,
-              typename OutputIt,
-              typename KeyEqual>
-    __device__ void retrieve_impl(warpT const& warp,
-                                  CG const& g,
-                                  Key const& k,
-                                  uint32_t* warp_counter,
-                                  value_type* output_buffer,
-                                  atomicT* num_matches,
-                                  OutputIt output_begin,
-                                  KeyEqual key_equal) noexcept;
+    __host__ __device__ std::size_t get_capacity() const noexcept { return impl_.get_capacity(); }
 
     /**
-     * @brief Retrieves all the matches of a given key contained in multimap using scalar
-     * loads with per-CG shared memory buffer.
-     *
-     * For key `k` existing in the map, copies `k` and all associated values to unspecified
-     * locations in `[output_begin, output_end)`. If `k` does not have any matches, copies `k` and
-     * `empty_value_sentinel()` into the output only if `is_outer` is true.
+     * @brief Gets the sentinel value used to represent an empty key slot.
      *
-     * @tparam cg_size The number of threads in CUDA Cooperative Groups
-     * @tparam buffer_size Size of the output buffer
-     * @tparam is_outer Boolean flag indicating whether outer join is peformed
-     * @tparam CG Cooperative Group type
-     * @tparam atomicT Type of atomic storage
-     * @tparam OutputIt Device accessible output iterator whose `value_type` is
-     * constructible from the map's `value_type`
-     * @tparam KeyEqual Binary callable type
-     * @param g The Cooperative Group used to retrieve
-     * @param k The key to search for
-     * @param cg_counter Pointer to the CG counter
-     * @param output_buffer Shared memory buffer of the key/value pair sequence
-     * @param num_matches Size of the output sequence
-     * @param output_begin Beginning of the output sequence of key/value pairs
-     * @param key_equal The binary callable used to compare two keys
-     * for equality
+     * @return The sentinel value used to represent an empty key slot
      */
-    template <uint32_t cg_size,
-              uint32_t buffer_size,
-              bool is_outer,
-              typename CG,
-              typename atomicT,
-              typename OutputIt,
-              typename KeyEqual>
-    __device__ void retrieve_impl(CG const& g,
-                                  Key const& k,
-                                  uint32_t* cg_counter,
-                                  value_type* output_buffer,
-                                  atomicT* num_matches,
-                                  OutputIt output_begin,
-                                  KeyEqual key_equal) noexcept;
+    __host__ __device__ Key get_empty_key_sentinel() const noexcept
+    {
+      return impl_.get_empty_key_sentinel();
+    }
 
     /**
-     * @brief Retrieves all the matches of a given pair contained in multimap using vector
-     * loads with per-warp shared memory buffer.
-     *
-     * For pair `p`, if pair_equal(p, slot[j]) returns true, copies `p` to unspecified locations
-     * in `[probe_output_begin, probe_output_end)` and copies slot[j] to unspecified locations in
-     * `[contained_output_begin, contained_output_end)`. If `p` does not have any matches, copies
-     * `p` and a pair of `empty_key_sentinel` and `empty_value_sentinel` into the output only if
-     * `is_outer` is true.
+     * @brief Gets the sentinel value used to represent an empty value slot.
      *
-     * @tparam buffer_size Size of the output buffer
-     * @tparam is_outer Boolean flag indicating whether outer join is peformed
-     * @tparam warpT Warp (Cooperative Group) type
-     * @tparam CG Cooperative Group type
-     * @tparam atomicT Type of atomic storage
-     * @tparam OutputIt1 Device accessible output iterator whose `value_type` is constructible from
-     * `InputIt`s `value_type`.
-     * @tparam OutputIt2 Device accessible output iterator whose `value_type` is constructible from
-     * the map's `value_type`.
-     * @tparam PairEqual Binary callable type
-     * @param warp The Cooperative Group (or warp) used to flush output buffer
-     * @param g The Cooperative Group used to retrieve
-     * @param pair The pair to search for
-     * @param warp_counter Pointer to the warp counter
-     * @param probe_output_buffer Buffer of the matched probe pair sequence
-     * @param contained_output_buffer Buffer of the matched contained pair sequence
-     * @param num_matches Size of the output sequence
-     * @param probe_output_begin Beginning of the output sequence of the matched probe pairs
-     * @param contained_output_begin Beginning of the output sequence of the matched contained
-     * pairs
-     * @param pair_equal The binary callable used to compare two pairs for equality
+     * @return The sentinel value used to represent an empty value slot
      */
-    template <uint32_t buffer_size,
-              bool is_outer,
-              typename warpT,
-              typename CG,
-              typename atomicT,
-              typename OutputIt1,
-              typename OutputIt2,
-              typename PairEqual>
-    __device__ void pair_retrieve_impl(warpT const& warp,
-                                       CG const& g,
-                                       value_type const& pair,
-                                       uint32_t* warp_counter,
-                                       value_type* probe_output_buffer,
-                                       value_type* contained_output_buffer,
-                                       atomicT* num_matches,
-                                       OutputIt1 probe_output_begin,
-                                       OutputIt2 contained_output_begin,
-                                       PairEqual pair_equal) noexcept;
+    __host__ __device__ Value get_empty_value_sentinel() const noexcept
+    {
+      return impl_.get_empty_value_sentinel();
+    }
 
     /**
-     * @brief Retrieves all the matches of a given pair contained in multimap using scalar
-     * loads with per-CG shared memory buffer.
+     * @brief Makes a copy of given `device_view` using non-owned memory.
      *
-     * For pair `p`, if pair_equal(p, slot[j]) returns true, copies `p` to unspecified locations
-     * in `[probe_output_begin, probe_output_end)` and copies slot[j] to unspecified locations in
-     * `[contained_output_begin, contained_output_end)`. If `p` does not have any matches, copies
-     * `p` and a pair of `empty_key_sentinel` and `empty_value_sentinel` into the output only if
-     * `is_outer` is true.
+     * This function is intended to be used to create shared memory copies of small static maps,
+     * although global memory can be used as well.
      *
-     * @tparam cg_size The number of threads in CUDA Cooperative Groups
-     * @tparam buffer_size Size of the output buffer
-     * @tparam is_outer Boolean flag indicating whether outer join is peformed
-     * @tparam CG Cooperative Group type
-     * @tparam atomicT Type of atomic storage
-     * @tparam OutputIt1 Device accessible output iterator whose `value_type` is constructible from
-     * `InputIt`s `value_type`.
-     * @tparam OutputIt2 Device accessible output iterator whose `value_type` is constructible from
-     * the map's `value_type`.
-     * @tparam PairEqual Binary callable type
-     * @param g The Cooperative Group used to retrieve
-     * @param pair The pair to search for
-     * @param cg_counter Pointer to the CG counter
-     * @param probe_output_buffer Buffer of the matched probe pair sequence
-     * @param contained_output_buffer Buffer of the matched contained pair sequence
-     * @param num_matches Size of the output sequence
-     * @param probe_output_begin Beginning of the output sequence of the matched probe pairs
-     * @param contained_output_begin Beginning of the output sequence of the matched contained
-     * pairs
-     * @param pair_equal The binary callable used to compare two pairs for equality
+     * @tparam CG The type of the cooperative thread group
+     * @param g The cooperative thread group used to copy the slots
+     * @param source_device_view `device_view` to copy from
+     * @param memory_to_use Array large enough to support `capacity` elements. Object does not
+     * take the ownership of the memory
+     * @return Copy of passed `device_view`
      */
-    template <uint32_t cg_size,
-              uint32_t buffer_size,
-              bool is_outer,
-              typename CG,
-              typename atomicT,
-              typename OutputIt1,
-              typename OutputIt2,
-              typename PairEqual>
-    __device__ void pair_retrieve_impl(CG const& g,
-                                       value_type const& pair,
-                                       uint32_t* cg_counter,
-                                       value_type* probe_output_buffer,
-                                       value_type* contained_output_buffer,
-                                       atomicT* num_matches,
-                                       OutputIt1 probe_output_begin,
-                                       OutputIt2 contained_output_begin,
-                                       PairEqual pair_equal) noexcept;
+    template <typename CG>
+    __device__ static device_view make_copy(CG g,
+                                            pair_atomic_type* const memory_to_use,
+                                            device_view source_device_view) noexcept;
 
-   public:
     /**
      * @brief Flushes per-CG buffer into the output sequence.
      *
@@ -1737,6 +1211,9 @@ class static_multimap {
                                         OutputIt1 probe_output_begin,
                                         OutputIt2 contained_output_begin,
                                         PairEqual pair_equal) noexcept;
+
+   private:
+    device_view_impl impl_;
   };  // class device_view
 
   /**
@@ -1834,3 +1311,4 @@ class static_multimap {
 }  // namespace cuco
 
 #include <cuco/detail/static_multimap.inl>
+#include <cuco/detail/static_multimap_impl.inl>

From 8c9efe869a8b2216670e141f542985e31aee9ed6 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Fri, 15 Oct 2021 18:02:39 -0400
Subject: [PATCH 246/267] Minor cleanup: remove unnecessary include

---
 include/cuco/detail/static_multimap_impl.inl | 2 --
 1 file changed, 2 deletions(-)

diff --git a/include/cuco/detail/static_multimap_impl.inl b/include/cuco/detail/static_multimap_impl.inl
index 601d030aa..dd694ac02 100644
--- a/include/cuco/detail/static_multimap_impl.inl
+++ b/include/cuco/detail/static_multimap_impl.inl
@@ -14,8 +14,6 @@
  * limitations under the License.
  */
 
-#include <cuco/static_multimap.cuh>
-
 #include <cuco/detail/bitwise_compare.cuh>
 
 namespace cuco {

From 68357743d31c698ed3f30633133f0f649d728475 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Mon, 25 Oct 2021 09:56:51 -0400
Subject: [PATCH 247/267] Move implementations into detail/static_multimap
 folder

---
 .../device_view_impl.inl}                                   | 0
 .../cuco/detail/{ => static_multimap}/static_multimap.inl   | 0
 .../{ => static_multimap}/static_multimap_kernels.cuh       | 0
 include/cuco/static_multimap.cuh                            | 6 +++---
 4 files changed, 3 insertions(+), 3 deletions(-)
 rename include/cuco/detail/{static_multimap_impl.inl => static_multimap/device_view_impl.inl} (100%)
 rename include/cuco/detail/{ => static_multimap}/static_multimap.inl (100%)
 rename include/cuco/detail/{ => static_multimap}/static_multimap_kernels.cuh (100%)

diff --git a/include/cuco/detail/static_multimap_impl.inl b/include/cuco/detail/static_multimap/device_view_impl.inl
similarity index 100%
rename from include/cuco/detail/static_multimap_impl.inl
rename to include/cuco/detail/static_multimap/device_view_impl.inl
diff --git a/include/cuco/detail/static_multimap.inl b/include/cuco/detail/static_multimap/static_multimap.inl
similarity index 100%
rename from include/cuco/detail/static_multimap.inl
rename to include/cuco/detail/static_multimap/static_multimap.inl
diff --git a/include/cuco/detail/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap/static_multimap_kernels.cuh
similarity index 100%
rename from include/cuco/detail/static_multimap_kernels.cuh
rename to include/cuco/detail/static_multimap/static_multimap_kernels.cuh
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index 5a73910c5..6cbfb0c08 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -42,7 +42,7 @@
 #include <cuco/detail/error.hpp>
 #include <cuco/detail/prime.hpp>
 #include <cuco/detail/probe_sequences.cuh>
-#include <cuco/detail/static_multimap_kernels.cuh>
+#include <cuco/detail/static_multimap/static_multimap_kernels.cuh>
 
 namespace cuco {
 
@@ -1310,5 +1310,5 @@ class static_multimap {
 
 }  // namespace cuco
 
-#include <cuco/detail/static_multimap.inl>
-#include <cuco/detail/static_multimap_impl.inl>
+#include <cuco/detail/static_multimap/device_view_impl.inl>
+#include <cuco/detail/static_multimap/static_multimap.inl>

From 092eca6ca295eac58483c2ef36df6b9bb2c0d5de Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Mon, 25 Oct 2021 11:49:10 -0400
Subject: [PATCH 248/267] Add static assert for ProbeSequence type check

---
 include/cuco/static_multimap.cuh | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index 6cbfb0c08..35272b7b4 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -152,6 +152,12 @@ class static_multimap {
     "Value type must have unique object representations or have been explicitly declared as safe "
     "for bitwise comparison via specialization of cuco::is_bitwise_comparable_v<Value>.");
 
+  static_assert(std::is_base_of_v<
+                  cuco::detail::probe_sequence_base<Key, Value, ProbeSequence::cg_size(), Scope>,
+                  ProbeSequence>,
+                "ProbeSequence must be a specialization of either cuco::detail::double_hashing or "
+                "cuco::detail::linear_probing");
+
  public:
   using value_type         = cuco::pair_type<Key, Value>;
   using key_type           = Key;

From a513ecbc2fdfa74346485f100f2f83e7dabe791a Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Mon, 25 Oct 2021 15:38:03 -0400
Subject: [PATCH 249/267] Cleanups: use flushing_cg instead of warp for
 retrieve

---
 .../static_multimap/device_view_impl.inl      |  55 +++--
 ...tatic_multimap_kernels.cuh => kernels.cuh} | 154 +++----------
 .../static_multimap/static_multimap.inl       | 217 +++++++-----------
 include/cuco/static_multimap.cuh              | 125 ++--------
 4 files changed, 168 insertions(+), 383 deletions(-)
 rename include/cuco/detail/static_multimap/{static_multimap_kernels.cuh => kernels.cuh} (84%)

diff --git a/include/cuco/detail/static_multimap/device_view_impl.inl b/include/cuco/detail/static_multimap/device_view_impl.inl
index dd694ac02..548dd7907 100644
--- a/include/cuco/detail/static_multimap/device_view_impl.inl
+++ b/include/cuco/detail/static_multimap/device_view_impl.inl
@@ -840,7 +840,7 @@ class static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view_
 
   /**
    * @brief Retrieves all the matches of a given key contained in multimap using vector
-   * loads with per-warp shared memory buffer.
+   * loads with per-flushing-CG shared memory buffer.
    *
    * For key `k` existing in the map, copies `k` and all associated values to unspecified
    * locations in `[output_begin, output_end)`. If `k` does not have any matches, copies `k` and
@@ -848,16 +848,16 @@ class static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view_
    *
    * @tparam buffer_size Size of the output buffer
    * @tparam is_outer Boolean flag indicating whether outer join is peformed
-   * @tparam warpT Warp (Cooperative Group) type
-   * @tparam CG Cooperative Group type
+   * @tparam FlushingCG Type of Cooperative Group used to flush output buffer
+   * @tparam ProbingCG Type of Cooperative Group used to retrieve
    * @tparam atomicT Type of atomic storage
    * @tparam OutputIt Device accessible output iterator whose `value_type` is
    * constructible from the map's `value_type`
    * @tparam KeyEqual Binary callable type
-   * @param warp The Cooperative Group (or warp) used to flush output buffer
-   * @param g The Cooperative Group used to retrieve
+   * @param flushing_cg The Cooperative Group used to flush output buffer
+   * @param probing_cg The Cooperative Group used to retrieve
    * @param k The key to search for
-   * @param warp_counter Pointer to the warp counter
+   * @param flushing_cg_counter Pointer to the flushing cg counter
    * @param output_buffer Shared memory buffer of the key/value pair sequence
    * @param num_matches Size of the output sequence
    * @param output_begin Beginning of the output sequence of key/value pairs
@@ -866,28 +866,28 @@ class static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view_
    */
   template <uint32_t buffer_size,
             bool is_outer,
-            typename warpT,
-            typename CG,
+            typename FlushingCG,
+            typename ProbingCG,
             typename atomicT,
             typename OutputIt,
             typename KeyEqual>
-  __device__ void retrieve(warpT const& warp,
-                           CG const& g,
+  __device__ void retrieve(FlushingCG const& flushing_cg,
+                           ProbingCG const& probing_cg,
                            Key const& k,
-                           uint32_t* warp_counter,
+                           uint32_t* flushing_cg_counter,
                            value_type* output_buffer,
                            atomicT* num_matches,
                            OutputIt output_begin,
                            KeyEqual key_equal) noexcept
   {
-    const uint32_t cg_lane_id = g.thread_rank();
+    const uint32_t cg_lane_id = probing_cg.thread_rank();
 
-    auto current_slot = initial_slot(g, k);
+    auto current_slot = initial_slot(probing_cg, k);
 
     bool running                      = true;
     [[maybe_unused]] bool found_match = false;
 
-    while (warp.any(running)) {
+    while (flushing_cg.any(running)) {
       if (running) {
         value_type arr[2];
         load_pair_array(&arr[0], current_slot);
@@ -898,8 +898,8 @@ class static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view_
           detail::bitwise_compare(arr[1].first, this->get_empty_key_sentinel());
         auto const first_equals  = (not first_slot_is_empty and key_equal(arr[0].first, k));
         auto const second_equals = (not second_slot_is_empty and key_equal(arr[1].first, k));
-        auto const first_exists  = g.ballot(first_equals);
-        auto const second_exists = g.ballot(second_equals);
+        auto const first_exists  = probing_cg.ballot(first_equals);
+        auto const second_exists = probing_cg.ballot(second_equals);
 
         if (first_exists or second_exists) {
           if constexpr (is_outer) { found_match = true; }
@@ -909,9 +909,9 @@ class static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view_
 
           uint32_t output_idx;
           if (0 == cg_lane_id) {
-            output_idx = atomicAdd(warp_counter, (num_first_matches + num_second_matches));
+            output_idx = atomicAdd(flushing_cg_counter, (num_first_matches + num_second_matches));
           }
-          output_idx = g.shfl(output_idx, 0);
+          output_idx = probing_cg.shfl(output_idx, 0);
 
           if (first_equals) {
             auto lane_offset = __popc(first_exists & ((1 << cg_lane_id) - 1));
@@ -926,11 +926,11 @@ class static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view_
               cuco::make_pair<Key, Value>(std::move(key), std::move(arr[1].second));
           }
         }
-        if (g.any(first_slot_is_empty or second_slot_is_empty)) {
+        if (probing_cg.any(first_slot_is_empty or second_slot_is_empty)) {
           running = false;
           if constexpr (is_outer) {
             if ((not found_match) && (cg_lane_id == 0)) {
-              auto output_idx           = atomicAdd(warp_counter, 1);
+              auto output_idx           = atomicAdd(flushing_cg_counter, 1);
               Key key                   = k;
               output_buffer[output_idx] = cuco::make_pair<Key, Value>(
                 std::move(key), std::move(this->get_empty_value_sentinel()));
@@ -939,11 +939,12 @@ class static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view_
         }
       }  // if running
 
-      warp.sync();
-      if (*warp_counter + warp.size() * vector_width() > buffer_size) {
-        flush_output_buffer(warp, *warp_counter, output_buffer, num_matches, output_begin);
+      flushing_cg.sync();
+      if (*flushing_cg_counter + flushing_cg.size() * vector_width() > buffer_size) {
+        flush_output_buffer(
+          flushing_cg, *flushing_cg_counter, output_buffer, num_matches, output_begin);
         // First lane reset warp-level counter
-        if (warp.thread_rank() == 0) { *warp_counter = 0; }
+        if (flushing_cg.thread_rank() == 0) { *flushing_cg_counter = 0; }
       }
 
       current_slot = next_slot(current_slot);
@@ -958,7 +959,6 @@ class static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view_
    * locations in `[output_begin, output_end)`. If `k` does not have any matches, copies `k` and
    * `empty_value_sentinel()` into the output only if `is_outer` is true.
    *
-   * @tparam cg_size The number of threads in CUDA Cooperative Groups
    * @tparam buffer_size Size of the output buffer
    * @tparam is_outer Boolean flag indicating whether outer join is peformed
    * @tparam CG Cooperative Group type
@@ -975,8 +975,7 @@ class static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view_
    * @param key_equal The binary callable used to compare two keys
    * for equality
    */
-  template <uint32_t cg_size,
-            uint32_t buffer_size,
+  template <uint32_t buffer_size,
             bool is_outer,
             typename CG,
             typename atomicT,
@@ -1037,7 +1036,7 @@ class static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view_
       g.sync();
 
       // Flush if the next iteration won't fit into buffer
-      if ((*cg_counter + cg_size) > buffer_size) {
+      if ((*cg_counter + g.size()) > buffer_size) {
         flush_output_buffer(g, *cg_counter, output_buffer, num_matches, output_begin);
         // First lane reset CG-level counter
         if (lane_id == 0) { *cg_counter = 0; }
diff --git a/include/cuco/detail/static_multimap/static_multimap_kernels.cuh b/include/cuco/detail/static_multimap/kernels.cuh
similarity index 84%
rename from include/cuco/detail/static_multimap/static_multimap_kernels.cuh
rename to include/cuco/detail/static_multimap/kernels.cuh
index d3b54af5b..e7d597423 100644
--- a/include/cuco/detail/static_multimap/static_multimap_kernels.cuh
+++ b/include/cuco/detail/static_multimap/kernels.cuh
@@ -322,8 +322,8 @@ __global__ void pair_count(
  * output_begin + *num_matches - 1)`. Use `count()` to determine the size of the output range.
  *
  * @tparam block_size The size of the thread block
- * @tparam warp_size The size of the warp
- * @tparam tile_size The number of threads in the Cooperative Groups
+ * @tparam flushing_cg_size The size of the CG for flushing output buffers
+ * @tparam probing_cg_size The size of the CG for parallel retrievals
  * @tparam buffer_size Size of the output buffer
  * @tparam is_outer Boolean flag indicating whether non-matches are included in the output
  * @tparam InputIt Device accessible input iterator whose `value_type` is
@@ -341,8 +341,8 @@ __global__ void pair_count(
  * @param key_equal The binary function to compare two keys for equality
  */
 template <uint32_t block_size,
-          uint32_t warp_size,
-          uint32_t tile_size,
+          uint32_t flushing_cg_size,
+          uint32_t probing_cg_size,
           uint32_t buffer_size,
           bool is_outer,
           typename InputIt,
@@ -350,144 +350,66 @@ template <uint32_t block_size,
           typename atomicT,
           typename viewT,
           typename KeyEqual>
-__global__ void vectorized_retrieve(InputIt first,
-                                    InputIt last,
-                                    OutputIt output_begin,
-                                    atomicT* num_matches,
-                                    viewT view,
-                                    KeyEqual key_equal)
+__global__ void retrieve(InputIt first,
+                         InputIt last,
+                         OutputIt output_begin,
+                         atomicT* num_matches,
+                         viewT view,
+                         KeyEqual key_equal)
 {
   using pair_type = typename viewT::value_type;
 
-  constexpr uint32_t num_warps = block_size / warp_size;
-  const uint32_t warp_id       = threadIdx.x / warp_size;
+  constexpr uint32_t num_flushing_cgs = block_size / flushing_cg_size;
+  const uint32_t flushing_cg_id       = threadIdx.x / flushing_cg_size;
 
-  auto warp    = cg::tiled_partition<warp_size>(cg::this_thread_block());
-  auto tile    = cg::tiled_partition<tile_size>(cg::this_thread_block());
-  auto tid     = block_size * blockIdx.x + threadIdx.x;
-  auto key_idx = tid / tile_size;
+  auto flushing_cg = cg::tiled_partition<flushing_cg_size>(cg::this_thread_block());
+  auto probing_cg  = cg::tiled_partition<probing_cg_size>(cg::this_thread_block());
+  auto tid         = block_size * blockIdx.x + threadIdx.x;
+  auto key_idx     = tid / probing_cg_size;
 
-  __shared__ pair_type output_buffer[num_warps][buffer_size];
+  __shared__ pair_type output_buffer[num_flushing_cgs][buffer_size];
   // TODO: replace this with shared memory cuda::atomic variables once the dynamiic initialization
-  // warning issue is solved __shared__ atomicT toto_countter[num_warps];
-  __shared__ uint32_t warp_counter[num_warps];
+  // warning issue is solved __shared__ atomicT counter[num_flushing_cgs][buffer_size];
+  __shared__ uint32_t flushing_cg_counter[num_flushing_cgs];
 
-  if (warp.thread_rank() == 0) { warp_counter[warp_id] = 0; }
+  if (flushing_cg.thread_rank() == 0) { flushing_cg_counter[flushing_cg_id] = 0; }
 
-  while (warp.any(first + key_idx < last)) {
-    bool active_flag = first + key_idx < last;
-    auto active_warp = cg::binary_partition<warp_size>(warp, active_flag);
+  while (flushing_cg.any(first + key_idx < last)) {
+    bool active_flag        = first + key_idx < last;
+    auto active_flushing_cg = cg::binary_partition<flushing_cg_size>(flushing_cg, active_flag);
 
     if (active_flag) {
       auto key = *(first + key_idx);
       if constexpr (is_outer) {
-        view.retrieve_outer<buffer_size>(active_warp,
-                                         tile,
+        view.retrieve_outer<buffer_size>(active_flushing_cg,
+                                         probing_cg,
                                          key,
-                                         &warp_counter[warp_id],
-                                         output_buffer[warp_id],
+                                         &flushing_cg_counter[flushing_cg_id],
+                                         output_buffer[flushing_cg_id],
                                          num_matches,
                                          output_begin,
                                          key_equal);
       } else {
-        view.retrieve<buffer_size>(active_warp,
-                                   tile,
+        view.retrieve<buffer_size>(active_flushing_cg,
+                                   probing_cg,
                                    key,
-                                   &warp_counter[warp_id],
-                                   output_buffer[warp_id],
+                                   &flushing_cg_counter[flushing_cg_id],
+                                   output_buffer[flushing_cg_id],
                                    num_matches,
                                    output_begin,
                                    key_equal);
       }
     }
-    key_idx += (gridDim.x * block_size) / tile_size;
-  }
-
-  // Final flush of output buffer
-  if (warp_counter[warp_id] > 0) {
-    view.flush_output_buffer(
-      warp, warp_counter[warp_id], output_buffer[warp_id], num_matches, output_begin);
-  }
-}
-
-/**
- * @brief Retrieves all the values corresponding to all keys in the range `[first, last)` using
- * scalar loads combined with per-CG shared memory buffer.
- *
- * For key `k = *(first + i)` existing in the map, copies `k` and all associated values to
- * unspecified locations in `[output_begin, output_end)`. If `k` does not have any matches, copies
- * `k` and `empty_value_sentinel()` into the output only if `is_outer` is true.
- *
- * Behavior is undefined if the total number of matching keys exceeds `std::distance(output_begin,
- * output_begin + *num_matches - 1)`. Use `count()` to determine the size of the output range.
- *
- * @tparam block_size The size of the thread block
- * @tparam warp_size The size of the warp
- * @tparam tile_size The number of threads in the Cooperative Groups
- * @tparam buffer_size Size of the output buffer
- * @tparam is_outer Boolean flag indicating whether non-matches are included in the output
- * @tparam InputIt Device accessible input iterator whose `value_type` is
- * convertible to the map's `key_type`
- * @tparam OutputIt Device accessible output iterator whose `value_type` is
- * constructible from the map's `value_type`
- * @tparam atomicT Type of atomic storage
- * @tparam viewT Type of device view allowing access of hash map storage
- * @tparam KeyEqual Binary callable type
- * @param first Beginning of the sequence of keys
- * @param last End of the sequence of keys
- * @param output_begin Beginning of the sequence of values retrieved for each key
- * @param num_matches Size of the output sequence
- * @param view Device view used to access the hash map's slot storage
- * @param key_equal The binary function to compare two keys for equality
- */
-template <uint32_t block_size,
-          uint32_t warp_size,
-          uint32_t tile_size,
-          uint32_t buffer_size,
-          bool is_outer,
-          typename InputIt,
-          typename OutputIt,
-          typename atomicT,
-          typename viewT,
-          typename KeyEqual>
-__global__ void retrieve(InputIt first,
-                         InputIt last,
-                         OutputIt output_begin,
-                         atomicT* num_matches,
-                         viewT view,
-                         KeyEqual key_equal)
-{
-  using pair_type = typename viewT::value_type;
-
-  auto tile    = cg::tiled_partition<tile_size>(cg::this_thread_block());
-  auto tid     = block_size * blockIdx.x + threadIdx.x;
-  auto key_idx = tid / tile_size;
-
-  constexpr uint32_t num_cgs = block_size / tile_size;
-  const uint32_t cg_id       = threadIdx.x / tile_size;
-  const uint32_t lane_id     = tile.thread_rank();
-
-  __shared__ pair_type output_buffer[num_cgs][buffer_size];
-  __shared__ uint32_t cg_counter[num_cgs];
-
-  if (lane_id == 0) { cg_counter[cg_id] = 0; }
-
-  while (first + key_idx < last) {
-    auto key = *(first + key_idx);
-    if constexpr (is_outer) {
-      view.retrieve_outer<tile_size, buffer_size>(
-        tile, key, &cg_counter[cg_id], output_buffer[cg_id], num_matches, output_begin, key_equal);
-    } else {
-      view.retrieve<tile_size, buffer_size>(
-        tile, key, &cg_counter[cg_id], output_buffer[cg_id], num_matches, output_begin, key_equal);
-    }
-    key_idx += (gridDim.x * block_size) / tile_size;
+    key_idx += (gridDim.x * block_size) / probing_cg_size;
   }
 
   // Final flush of output buffer
-  if (cg_counter[cg_id] > 0) {
-    view.flush_output_buffer(
-      tile, cg_counter[cg_id], output_buffer[cg_id], num_matches, output_begin);
+  if (flushing_cg_counter[flushing_cg_id] > 0) {
+    view.flush_output_buffer(flushing_cg,
+                             flushing_cg_counter[flushing_cg_id],
+                             output_buffer[flushing_cg_id],
+                             num_matches,
+                             output_begin);
   }
 }
 
diff --git a/include/cuco/detail/static_multimap/static_multimap.inl b/include/cuco/detail/static_multimap/static_multimap.inl
index 4a4386b48..7f65482b7 100644
--- a/include/cuco/detail/static_multimap/static_multimap.inl
+++ b/include/cuco/detail/static_multimap/static_multimap.inl
@@ -271,50 +271,33 @@ OutputIt static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::retrieve(
 
   // Using per-warp buffer for vector loads and per-CG buffer for scalar loads
   constexpr auto buffer_size = uses_vector_load() ? (warp_size() * 3u) : (cg_size() * 3u);
+  constexpr auto block_size  = 128;
   constexpr bool is_outer    = false;
 
-  auto constexpr block_size = 128;
-  auto const grid_size      = [&]() {
-    if constexpr (uses_vector_load()) {
-      return detail::get_grid_size(detail::vectorized_retrieve<block_size,
-                                                               warp_size(),
-                                                               cg_size(),
-                                                               buffer_size,
-                                                               is_outer,
-                                                               InputIt,
-                                                               OutputIt,
-                                                               atomic_ctr_type,
-                                                               device_view,
-                                                               KeyEqual>,
-                                   block_size);
-    }
-    if constexpr (not uses_vector_load()) {
-      return detail::get_grid_size(detail::retrieve<block_size,
-                                                    warp_size(),
-                                                    cg_size(),
-                                                    buffer_size,
-                                                    is_outer,
-                                                    InputIt,
-                                                    OutputIt,
-                                                    atomic_ctr_type,
-                                                    device_view,
-                                                    KeyEqual>,
-                                   block_size);
-    }
+  auto const flushing_cg_size = [&]() {
+    if constexpr (uses_vector_load()) { return warp_size(); }
+    return cg_size();
   }();
 
+  auto const grid_size = detail::get_grid_size(detail::retrieve<block_size,
+                                                                flushing_cg_size,
+                                                                cg_size(),
+                                                                buffer_size,
+                                                                is_outer,
+                                                                InputIt,
+                                                                OutputIt,
+                                                                atomic_ctr_type,
+                                                                device_view,
+                                                                KeyEqual>,
+                                               block_size);
+
   cudaMemsetAsync(d_counter_.get(), 0, sizeof(atomic_ctr_type), stream);
   std::size_t h_counter;
 
-  if constexpr (uses_vector_load()) {
-    detail::vectorized_retrieve<block_size, warp_size(), cg_size(), buffer_size, is_outer>
-      <<<grid_size, block_size, 0, stream>>>(
-        first, last, output_begin, d_counter_.get(), view, key_equal);
-  } else {
-    detail::retrieve<block_size, warp_size(), cg_size(), buffer_size, is_outer>
-      <<<grid_size, block_size, 0, stream>>>(
-        first, last, output_begin, d_counter_.get(), view, key_equal);
-  }
+  detail::retrieve<block_size, flushing_cg_size, cg_size(), buffer_size, is_outer>
+    <<<grid_size, block_size, 0, stream>>>(
+      first, last, output_begin, d_counter_.get(), view, key_equal);
+
   CUCO_CUDA_TRY(cudaMemcpyAsync(
     &h_counter, d_counter_.get(), sizeof(atomic_ctr_type), cudaMemcpyDeviceToHost, stream));
   CUCO_CUDA_TRY(cudaStreamSynchronize(stream));
@@ -337,50 +320,33 @@ OutputIt static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::retrieve_
 
   // Using per-warp buffer for vector loads and per-CG buffer for scalar loads
   constexpr auto buffer_size = uses_vector_load() ? (warp_size() * 3u) : (cg_size() * 3u);
+  constexpr auto block_size  = 128;
   constexpr bool is_outer    = true;
 
-  auto constexpr block_size = 128;
-  auto const grid_size      = [&]() {
-    if constexpr (uses_vector_load()) {
-      return detail::get_grid_size(detail::vectorized_retrieve<block_size,
-                                                               warp_size(),
-                                                               cg_size(),
-                                                               buffer_size,
-                                                               is_outer,
-                                                               InputIt,
-                                                               OutputIt,
-                                                               atomic_ctr_type,
-                                                               device_view,
-                                                               KeyEqual>,
-                                   block_size);
-    }
-    if constexpr (not uses_vector_load()) {
-      return detail::get_grid_size(detail::retrieve<block_size,
-                                                    warp_size(),
-                                                    cg_size(),
-                                                    buffer_size,
-                                                    is_outer,
-                                                    InputIt,
-                                                    OutputIt,
-                                                    atomic_ctr_type,
-                                                    device_view,
-                                                    KeyEqual>,
-                                   block_size);
-    }
+  auto const flushing_cg_size = [&]() {
+    if constexpr (uses_vector_load()) { return warp_size(); }
+    return cg_size();
   }();
 
+  auto const grid_size = detail::get_grid_size(detail::retrieve<block_size,
+                                                                flushing_cg_size,
+                                                                cg_size(),
+                                                                buffer_size,
+                                                                is_outer,
+                                                                InputIt,
+                                                                OutputIt,
+                                                                atomic_ctr_type,
+                                                                device_view,
+                                                                KeyEqual>,
+                                               block_size);
+
   cudaMemsetAsync(d_counter_.get(), 0, sizeof(atomic_ctr_type), stream);
   std::size_t h_counter;
 
-  if constexpr (uses_vector_load()) {
-    detail::vectorized_retrieve<block_size, warp_size(), cg_size(), buffer_size, is_outer>
-      <<<grid_size, block_size, 0, stream>>>(
-        first, last, output_begin, d_counter_.get(), view, key_equal);
-  } else {
-    detail::retrieve<block_size, warp_size(), cg_size(), buffer_size, is_outer>
-      <<<grid_size, block_size, 0, stream>>>(
-        first, last, output_begin, d_counter_.get(), view, key_equal);
-  }
+  detail::retrieve<block_size, flushing_cg_size, cg_size(), buffer_size, is_outer>
+    <<<grid_size, block_size, 0, stream>>>(
+      first, last, output_begin, d_counter_.get(), view, key_equal);
+
   CUCO_CUDA_TRY(cudaMemcpyAsync(
     &h_counter, d_counter_.get(), sizeof(atomic_ctr_type), cudaMemcpyDeviceToHost, stream));
   CUCO_CUDA_TRY(cudaStreamSynchronize(stream));
@@ -723,24 +689,36 @@ template <typename Key,
           class ProbeSequence,
           typename Allocator>
 template <uint32_t buffer_size,
-          typename warpT,
-          typename CG,
+          typename FlushingCG,
+          typename ProbingCG,
           typename atomicT,
           typename OutputIt,
           typename KeyEqual>
 __device__ void static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::retrieve(
-  warpT const& warp,
-  CG const& g,
+  FlushingCG const& flushing_cg,
+  ProbingCG const& probing_cg,
   Key const& k,
-  uint32_t* warp_counter,
+  uint32_t* flushing_cg_counter,
   value_type* output_buffer,
   atomicT* num_matches,
   OutputIt output_begin,
   KeyEqual key_equal) noexcept
 {
   constexpr bool is_outer = false;
-  impl_.retrieve<buffer_size, is_outer>(
-    warp, g, k, warp_counter, output_buffer, num_matches, output_begin, key_equal);
+  if constexpr (uses_vector_load()) {
+    impl_.retrieve<buffer_size, is_outer>(flushing_cg,
+                                          probing_cg,
+                                          k,
+                                          flushing_cg_counter,
+                                          output_buffer,
+                                          num_matches,
+                                          output_begin,
+                                          key_equal);
+  } else  // In the case of scalar load, flushing CG is the same as probing CG
+  {
+    impl_.retrieve<buffer_size, is_outer>(
+      probing_cg, k, flushing_cg_counter, output_buffer, num_matches, output_begin, key_equal);
+  }
 }
 
 template <typename Key,
@@ -749,76 +727,37 @@ template <typename Key,
           class ProbeSequence,
           typename Allocator>
 template <uint32_t buffer_size,
-          typename warpT,
-          typename CG,
+          typename FlushingCG,
+          typename ProbingCG,
           typename atomicT,
           typename OutputIt,
           typename KeyEqual>
 __device__ void
 static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::retrieve_outer(
-  warpT const& warp,
-  CG const& g,
+  FlushingCG const& flushing_cg,
+  ProbingCG const& probing_cg,
   Key const& k,
-  uint32_t* warp_counter,
+  uint32_t* flushing_cg_counter,
   value_type* output_buffer,
   atomicT* num_matches,
   OutputIt output_begin,
   KeyEqual key_equal) noexcept
 {
   constexpr bool is_outer = true;
-  impl_.retrieve<buffer_size, is_outer>(
-    warp, g, k, warp_counter, output_buffer, num_matches, output_begin, key_equal);
-}
-
-template <typename Key,
-          typename Value,
-          cuda::thread_scope Scope,
-          class ProbeSequence,
-          typename Allocator>
-template <uint32_t cg_size,
-          uint32_t buffer_size,
-          typename CG,
-          typename atomicT,
-          typename OutputIt,
-          typename KeyEqual>
-__device__ void static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::retrieve(
-  CG const& g,
-  Key const& k,
-  uint32_t* cg_counter,
-  value_type* output_buffer,
-  atomicT* num_matches,
-  OutputIt output_begin,
-  KeyEqual key_equal) noexcept
-{
-  constexpr bool is_outer = false;
-  impl_.retrieve<cg_size, buffer_size, is_outer>(
-    g, k, cg_counter, output_buffer, num_matches, output_begin, key_equal);
-}
-
-template <typename Key,
-          typename Value,
-          cuda::thread_scope Scope,
-          class ProbeSequence,
-          typename Allocator>
-template <uint32_t cg_size,
-          uint32_t buffer_size,
-          typename CG,
-          typename atomicT,
-          typename OutputIt,
-          typename KeyEqual>
-__device__ void
-static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::retrieve_outer(
-  CG const& g,
-  Key const& k,
-  uint32_t* cg_counter,
-  value_type* output_buffer,
-  atomicT* num_matches,
-  OutputIt output_begin,
-  KeyEqual key_equal) noexcept
-{
-  constexpr bool is_outer = true;
-  impl_.retrieve<cg_size, buffer_size, is_outer>(
-    g, k, cg_counter, output_buffer, num_matches, output_begin, key_equal);
+  if constexpr (uses_vector_load()) {
+    impl_.retrieve<buffer_size, is_outer>(flushing_cg,
+                                          probing_cg,
+                                          k,
+                                          flushing_cg_counter,
+                                          output_buffer,
+                                          num_matches,
+                                          output_begin,
+                                          key_equal);
+  } else  // In the case of scalar load, flushing CG is the same as probing CG
+  {
+    impl_.retrieve<buffer_size, is_outer>(
+      probing_cg, k, flushing_cg_counter, output_buffer, num_matches, output_begin, key_equal);
+  }
 }
 
 template <typename Key,
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index 35272b7b4..93c019dd8 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -42,7 +42,7 @@
 #include <cuco/detail/error.hpp>
 #include <cuco/detail/prime.hpp>
 #include <cuco/detail/probe_sequences.cuh>
-#include <cuco/detail/static_multimap/static_multimap_kernels.cuh>
+#include <cuco/detail/static_multimap/kernels.cuh>
 
 namespace cuco {
 
@@ -879,23 +879,23 @@ class static_multimap {
                                             PairEqual pair_equal) noexcept;
 
     /**
-     * @brief Retrieves all the matches of a given key contained in multimap using vector
-     * loads with per-warp shared memory buffer.
+     * @brief Retrieves all the matches of a given key contained in multimap with per-flushing-CG
+     * shared memory buffer.
      *
      * For key `k` existing in the map, copies `k` and all associated values to unspecified
      * locations in `[output_begin, output_end)`.
      *
      * @tparam buffer_size Size of the output buffer
-     * @tparam warpT Warp (Cooperative Group) type
-     * @tparam CG Cooperative Group type
+     * @tparam FlushingCG Type of Cooperative Group used to flush output buffer
+     * @tparam ProbingCG Type of Cooperative Group for parallel retrieval
      * @tparam atomicT Type of atomic storage
      * @tparam OutputIt Device accessible output iterator whose `value_type` is
      * constructible from the map's `value_type`
      * @tparam KeyEqual Binary callable type
-     * @param warp The Cooperative Group (or warp) used to flush output buffer
-     * @param g The Cooperative Group used to retrieve
+     * @param flushing_cg The Cooperative Group used to flush output buffer
+     * @param probing_cg The Cooperative Group used to retrieve
      * @param k The key to search for
-     * @param warp_counter Pointer to the warp counter
+     * @param flushing_cg_counter Pointer to flushing_cg counter
      * @param output_buffer Shared memory buffer of the key/value pair sequence
      * @param num_matches Size of the output sequence
      * @param output_begin Beginning of the output sequence of key/value pairs
@@ -903,39 +903,39 @@ class static_multimap {
      * for equality
      */
     template <uint32_t buffer_size,
-              typename warpT,
-              typename CG,
+              typename FlushingCG,
+              typename ProbingCG,
               typename atomicT,
               typename OutputIt,
               typename KeyEqual = thrust::equal_to<key_type>>
-    __device__ void retrieve(warpT const& warp,
-                             CG const& g,
+    __device__ void retrieve(FlushingCG const& flushing_cg,
+                             ProbingCG const& probing_cg,
                              Key const& k,
-                             uint32_t* warp_counter,
+                             uint32_t* flushing_cg_counter,
                              value_type* output_buffer,
                              atomicT* num_matches,
                              OutputIt output_begin,
                              KeyEqual key_equal = KeyEqual{}) noexcept;
 
     /**
-     * @brief Retrieves all the matches of a given key contained in multimap using vector
-     * loads with per-warp shared memory buffer.
+     * @brief Retrieves all the matches of a given key contained in multimap with per-flushing-CG
+     * shared memory buffer.
      *
      * For key `k` existing in the map, copies `k` and all associated values to unspecified
      * locations in `[output_begin, output_end)`. If `k` does not have any matches, copies `k` and
      * `empty_value_sentinel()` into the output.
      *
      * @tparam buffer_size Size of the output buffer
-     * @tparam warpT Warp (Cooperative Group) type
-     * @tparam CG Cooperative Group type
+     * @tparam FlushingCG Type of Cooperative Group used to flush output buffer
+     * @tparam ProbingCG Type of Cooperative Group for parallel retrieval
      * @tparam atomicT Type of atomic storage
      * @tparam OutputIt Device accessible output iterator whose `value_type` is
      * constructible from the map's `value_type`
      * @tparam KeyEqual Binary callable type
-     * @param warp The Cooperative Group (or warp) used to flush output buffer
-     * @param g The Cooperative Group used to retrieve
+     * @param flushing_cg The Cooperative Group used to flush output buffer
+     * @param probing_cg The Cooperative Group used to retrieve
      * @param k The key to search for
-     * @param warp_counter Pointer to the warp counter
+     * @param flushing_cg_counter Pointer to flushing_cg counter
      * @param output_buffer Shared memory buffer of the key/value pair sequence
      * @param num_matches Size of the output sequence
      * @param output_begin Beginning of the output sequence of key/value pairs
@@ -943,90 +943,15 @@ class static_multimap {
      * for equality
      */
     template <uint32_t buffer_size,
-              typename warpT,
-              typename CG,
-              typename atomicT,
-              typename OutputIt,
-              typename KeyEqual = thrust::equal_to<key_type>>
-    __device__ void retrieve_outer(warpT const& warp,
-                                   CG const& g,
-                                   Key const& k,
-                                   uint32_t* warp_counter,
-                                   value_type* output_buffer,
-                                   atomicT* num_matches,
-                                   OutputIt output_begin,
-                                   KeyEqual key_equal = KeyEqual{}) noexcept;
-
-    /**
-     * @brief Retrieves all the matches of a given key contained in multimap using scalar
-     * loads with per-CG shared memory buffer.
-     *
-     * For key `k` existing in the map, copies `k` and all associated values to unspecified
-     * locations in `[output_begin, output_end)`.
-     *
-     * @tparam cg_size The number of threads in CUDA Cooperative Groups
-     * @tparam buffer_size Size of the output buffer
-     * @tparam CG Cooperative Group type
-     * @tparam atomicT Type of atomic storage
-     * @tparam OutputIt Device accessible output iterator whose `value_type` is
-     * constructible from the map's `value_type`
-     * @tparam KeyEqual Binary callable type
-     * @param g The Cooperative Group used to retrieve
-     * @param k The key to search for
-     * @param cg_counter Pointer to the CG counter
-     * @param output_buffer Shared memory buffer of the key/value pair sequence
-     * @param num_matches Size of the output sequence
-     * @param output_begin Beginning of the output sequence of key/value pairs
-     * @param key_equal The binary callable used to compare two keys
-     * for equality
-     */
-    template <uint32_t cg_size,
-              uint32_t buffer_size,
-              typename CG,
-              typename atomicT,
-              typename OutputIt,
-              typename KeyEqual = thrust::equal_to<key_type>>
-    __device__ void retrieve(CG const& g,
-                             Key const& k,
-                             uint32_t* cg_counter,
-                             value_type* output_buffer,
-                             atomicT* num_matches,
-                             OutputIt output_begin,
-                             KeyEqual key_equal = KeyEqual{}) noexcept;
-
-    /**
-     * @brief Retrieves all the matches of a given key contained in multimap using scalar
-     * loads with per-CG shared memory buffer.
-     *
-     * For key `k` existing in the map, copies `k` and all associated values to unspecified
-     * locations in `[output_begin, output_end)`. If `k` does not have any matches, copies `k` and
-     * `empty_value_sentinel` into the output.
-     *
-     * @tparam cg_size The number of threads in CUDA Cooperative Groups
-     * @tparam buffer_size Size of the output buffer
-     * @tparam CG Cooperative Group type
-     * @tparam atomicT Type of atomic storage
-     * @tparam OutputIt Device accessible output iterator whose `value_type` is
-     * constructible from the map's `value_type`
-     * @tparam KeyEqual Binary callable type
-     * @param g The Cooperative Group used to retrieve
-     * @param k The key to search for
-     * @param cg_counter Pointer to the CG counter
-     * @param output_buffer Shared memory buffer of the key/value pair sequence
-     * @param num_matches Size of the output sequence
-     * @param output_begin Beginning of the output sequence of key/value pairs
-     * @param key_equal The binary callable used to compare two keys
-     * for equality
-     */
-    template <uint32_t cg_size,
-              uint32_t buffer_size,
-              typename CG,
+              typename FlushingCG,
+              typename ProbingCG,
               typename atomicT,
               typename OutputIt,
               typename KeyEqual = thrust::equal_to<key_type>>
-    __device__ void retrieve_outer(CG const& g,
+    __device__ void retrieve_outer(FlushingCG const& flushing_cg,
+                                   ProbingCG const& probing_cg,
                                    Key const& k,
-                                   uint32_t* cg_counter,
+                                   uint32_t* flushing_cg_counter,
                                    value_type* output_buffer,
                                    atomicT* num_matches,
                                    OutputIt output_begin,

From a4cfcbec765c60312d2c4611b949a166a857a3e4 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Mon, 25 Oct 2021 16:50:29 -0400
Subject: [PATCH 250/267] Cleanups: use flushing CG logic for pair_retrieve

---
 .../static_multimap/device_view_impl.inl      |  56 ++--
 .../cuco/detail/static_multimap/kernels.cuh   | 199 +++---------
 .../static_multimap/static_multimap.inl       | 298 ++++++------------
 include/cuco/static_multimap.cuh              | 137 ++------
 4 files changed, 192 insertions(+), 498 deletions(-)

diff --git a/include/cuco/detail/static_multimap/device_view_impl.inl b/include/cuco/detail/static_multimap/device_view_impl.inl
index 548dd7907..ad0df9fe9 100644
--- a/include/cuco/detail/static_multimap/device_view_impl.inl
+++ b/include/cuco/detail/static_multimap/device_view_impl.inl
@@ -1047,7 +1047,7 @@ class static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view_
 
   /**
    * @brief Retrieves all the matches of a given pair contained in multimap using vector
-   * loads with per-warp shared memory buffer.
+   * loads with per-flushing-CG shared memory buffer.
    *
    * For pair `p`, if pair_equal(p, slot[j]) returns true, copies `p` to unspecified locations
    * in `[probe_output_begin, probe_output_end)` and copies slot[j] to unspecified locations in
@@ -1057,18 +1057,18 @@ class static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view_
    *
    * @tparam buffer_size Size of the output buffer
    * @tparam is_outer Boolean flag indicating whether outer join is peformed
-   * @tparam warpT Warp (Cooperative Group) type
-   * @tparam CG Cooperative Group type
+   * @tparam FlushingCG Type of Cooperative Group used to flush output buffer
+   * @tparam ProbingCG Type of Cooperative Group used to retrieve
    * @tparam atomicT Type of atomic storage
    * @tparam OutputIt1 Device accessible output iterator whose `value_type` is constructible from
    * `InputIt`s `value_type`.
    * @tparam OutputIt2 Device accessible output iterator whose `value_type` is constructible from
    * the map's `value_type`.
    * @tparam PairEqual Binary callable type
-   * @param warp The Cooperative Group (or warp) used to flush output buffer
-   * @param g The Cooperative Group used to retrieve
+   * @param flushing_cg The Cooperative Group used to flush output buffer
+   * @param probing_cg The Cooperative Group used to retrieve
    * @param pair The pair to search for
-   * @param warp_counter Pointer to the warp counter
+   * @param flushing_cg_counter Pointer to the flushing CG counter
    * @param probe_output_buffer Buffer of the matched probe pair sequence
    * @param contained_output_buffer Buffer of the matched contained pair sequence
    * @param num_matches Size of the output sequence
@@ -1079,16 +1079,16 @@ class static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view_
    */
   template <uint32_t buffer_size,
             bool is_outer,
-            typename warpT,
-            typename CG,
+            typename FlushingCG,
+            typename ProbingCG,
             typename atomicT,
             typename OutputIt1,
             typename OutputIt2,
             typename PairEqual>
-  __device__ void pair_retrieve(warpT const& warp,
-                                CG const& g,
+  __device__ void pair_retrieve(FlushingCG const& flushing_cg,
+                                ProbingCG const& probing_cg,
                                 value_type const& pair,
-                                uint32_t* warp_counter,
+                                uint32_t* flushing_cg_counter,
                                 value_type* probe_output_buffer,
                                 value_type* contained_output_buffer,
                                 atomicT* num_matches,
@@ -1096,15 +1096,15 @@ class static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view_
                                 OutputIt2 contained_output_begin,
                                 PairEqual pair_equal) noexcept
   {
-    const uint32_t cg_lane_id = g.thread_rank();
+    const uint32_t cg_lane_id = probing_cg.thread_rank();
 
     auto key          = pair.first;
-    auto current_slot = initial_slot(g, key);
+    auto current_slot = initial_slot(probing_cg, key);
 
     bool running                      = true;
     [[maybe_unused]] bool found_match = false;
 
-    while (warp.any(running)) {
+    while (flushing_cg.any(running)) {
       if (running) {
         value_type arr[2];
         load_pair_array(&arr[0], current_slot);
@@ -1115,8 +1115,8 @@ class static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view_
           detail::bitwise_compare(arr[1].first, this->get_empty_key_sentinel());
         auto const first_equals  = (not first_slot_is_empty and pair_equal(arr[0], pair));
         auto const second_equals = (not second_slot_is_empty and pair_equal(arr[1], pair));
-        auto const first_exists  = g.ballot(first_equals);
-        auto const second_exists = g.ballot(second_equals);
+        auto const first_exists  = probing_cg.ballot(first_equals);
+        auto const second_exists = probing_cg.ballot(second_equals);
 
         if (first_exists or second_exists) {
           if constexpr (is_outer) { found_match = true; }
@@ -1126,9 +1126,9 @@ class static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view_
 
           uint32_t output_idx;
           if (0 == cg_lane_id) {
-            output_idx = atomicAdd(warp_counter, (num_first_matches + num_second_matches));
+            output_idx = atomicAdd(flushing_cg_counter, (num_first_matches + num_second_matches));
           }
-          output_idx = g.shfl(output_idx, 0);
+          output_idx = probing_cg.shfl(output_idx, 0);
 
           if (first_equals) {
             auto lane_offset = __popc(first_exists & ((1 << cg_lane_id) - 1));
@@ -1141,11 +1141,11 @@ class static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view_
             contained_output_buffer[output_idx + num_first_matches + lane_offset] = arr[1];
           }
         }
-        if (g.any(first_slot_is_empty or second_slot_is_empty)) {
+        if (probing_cg.any(first_slot_is_empty or second_slot_is_empty)) {
           running = false;
           if constexpr (is_outer) {
             if ((not found_match) && (cg_lane_id == 0)) {
-              auto output_idx                 = atomicAdd(warp_counter, 1);
+              auto output_idx                 = atomicAdd(flushing_cg_counter, 1);
               probe_output_buffer[output_idx] = pair;
               contained_output_buffer[output_idx] =
                 cuco::make_pair<Key, Value>(std::move(this->get_empty_key_sentinel()),
@@ -1155,17 +1155,17 @@ class static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view_
         }
       }  // if running
 
-      warp.sync();
-      if (*warp_counter + warp.size() * vector_width() > buffer_size) {
-        flush_output_buffer(warp,
-                            *warp_counter,
+      flushing_cg.sync();
+      if (*flushing_cg_counter + flushing_cg.size() * vector_width() > buffer_size) {
+        flush_output_buffer(flushing_cg,
+                            *flushing_cg_counter,
                             probe_output_buffer,
                             contained_output_buffer,
                             num_matches,
                             probe_output_begin,
                             contained_output_begin);
         // First lane reset warp-level counter
-        if (warp.thread_rank() == 0) { *warp_counter = 0; }
+        if (flushing_cg.thread_rank() == 0) { *flushing_cg_counter = 0; }
       }
 
       current_slot = next_slot(current_slot);
@@ -1182,7 +1182,6 @@ class static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view_
    * `p` and a pair of `empty_key_sentinel` and `empty_value_sentinel` into the output only if
    * `is_outer` is true.
    *
-   * @tparam cg_size The number of threads in CUDA Cooperative Groups
    * @tparam buffer_size Size of the output buffer
    * @tparam is_outer Boolean flag indicating whether outer join is peformed
    * @tparam CG Cooperative Group type
@@ -1203,8 +1202,7 @@ class static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view_
    * pairs
    * @param pair_equal The binary callable used to compare two pairs for equality
    */
-  template <uint32_t cg_size,
-            uint32_t buffer_size,
+  template <uint32_t buffer_size,
             bool is_outer,
             typename CG,
             typename atomicT,
@@ -1269,7 +1267,7 @@ class static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view_
       g.sync();
 
       // Flush if the next iteration won't fit into buffer
-      if ((*cg_counter + cg_size) > buffer_size) {
+      if ((*cg_counter + g.size()) > buffer_size) {
         flush_output_buffer(g,
                             *cg_counter,
                             probe_output_buffer,
diff --git a/include/cuco/detail/static_multimap/kernels.cuh b/include/cuco/detail/static_multimap/kernels.cuh
index e7d597423..83aa2c640 100644
--- a/include/cuco/detail/static_multimap/kernels.cuh
+++ b/include/cuco/detail/static_multimap/kernels.cuh
@@ -311,8 +311,7 @@ __global__ void pair_count(
 }
 
 /**
- * @brief Retrieves all the values corresponding to all keys in the range `[first, last)`
- * using vector loads combined with per-warp shared memory buffer.
+ * @brief Retrieves all the values corresponding to all keys in the range `[first, last)`.
  *
  * For key `k = *(first + i)` existing in the map, copies `k` and all associated values to
  * unspecified locations in `[output_begin, output_end)`. If `k` does not have any matches, copies
@@ -322,7 +321,7 @@ __global__ void pair_count(
  * output_begin + *num_matches - 1)`. Use `count()` to determine the size of the output range.
  *
  * @tparam block_size The size of the thread block
- * @tparam flushing_cg_size The size of the CG for flushing output buffers
+ * @tparam flushing_cg_size The size of the CG used to flush output buffers
  * @tparam probing_cg_size The size of the CG for parallel retrievals
  * @tparam buffer_size Size of the output buffer
  * @tparam is_outer Boolean flag indicating whether non-matches are included in the output
@@ -414,8 +413,7 @@ __global__ void retrieve(InputIt first,
 }
 
 /**
- * @brief Retrieves all pairs matching the input probe pair in the range `[first, last)`
- * using vector loads combined with per-warp shared memory buffer.
+ * @brief Retrieves all pairs matching the input probe pair in the range `[first, last)`.
  *
  * If pair_equal(*(first + i), slot[j]) returns true, then *(first+i) is stored to unspecified
  * locations in `probe_output_begin`, and slot[j] is stored to unspecified locations in
@@ -427,8 +425,8 @@ __global__ void retrieve(InputIt first,
  * output_begin + *num_matches - 1)`. Use `pair_count()` to determine the size of the output range.
  *
  * @tparam block_size The size of the thread block
- * @tparam warp_size The size of the warp
- * @tparam tile_size The number of threads in the Cooperative Groups
+ * @tparam flushing_cg_size The size of the CG used to flush output buffers
+ * @tparam probing_cg_size The size of the CG for parallel retrievals
  * @tparam buffer_size Size of the output buffer
  * @tparam is_outer Boolean flag indicating whether non-matches are included in the output
  * @tparam InputIt Device accessible random access input iterator where
@@ -450,8 +448,8 @@ __global__ void retrieve(InputIt first,
  * @param pair_equal The binary function to compare two pairs for equality
  */
 template <uint32_t block_size,
-          uint32_t warp_size,
-          uint32_t tile_size,
+          uint32_t flushing_cg_size,
+          uint32_t probing_cg_size,
           uint32_t buffer_size,
           bool is_outer,
           typename InputIt,
@@ -460,180 +458,71 @@ template <uint32_t block_size,
           typename atomicT,
           typename viewT,
           typename PairEqual>
-__global__ void vectorized_pair_retrieve(InputIt first,
-                                         InputIt last,
-                                         OutputIt1 probe_output_begin,
-                                         OutputIt2 contained_output_begin,
-                                         atomicT* num_matches,
-                                         viewT view,
-                                         PairEqual pair_equal)
+__global__ void pair_retrieve(InputIt first,
+                              InputIt last,
+                              OutputIt1 probe_output_begin,
+                              OutputIt2 contained_output_begin,
+                              atomicT* num_matches,
+                              viewT view,
+                              PairEqual pair_equal)
 {
   using pair_type = typename viewT::value_type;
 
-  constexpr uint32_t num_warps = block_size / warp_size;
-  const uint32_t warp_id       = threadIdx.x / warp_size;
+  constexpr uint32_t num_flushing_cgs = block_size / flushing_cg_size;
+  const uint32_t flushing_cg_id       = threadIdx.x / flushing_cg_size;
 
-  auto warp     = cg::tiled_partition<warp_size>(cg::this_thread_block());
-  auto tile     = cg::tiled_partition<tile_size>(cg::this_thread_block());
-  auto tid      = block_size * blockIdx.x + threadIdx.x;
-  auto pair_idx = tid / tile_size;
+  auto flushing_cg = cg::tiled_partition<flushing_cg_size>(cg::this_thread_block());
+  auto probing_cg  = cg::tiled_partition<probing_cg_size>(cg::this_thread_block());
+  auto tid         = block_size * blockIdx.x + threadIdx.x;
+  auto pair_idx    = tid / probing_cg_size;
 
-  __shared__ pair_type probe_output_buffer[num_warps][buffer_size];
-  __shared__ pair_type contained_output_buffer[num_warps][buffer_size];
+  __shared__ pair_type probe_output_buffer[num_flushing_cgs][buffer_size];
+  __shared__ pair_type contained_output_buffer[num_flushing_cgs][buffer_size];
   // TODO: replace this with shared memory cuda::atomic variables once the dynamiic initialization
-  // warning issue is solved __shared__ atomicT toto_countter[num_warps];
-  __shared__ uint32_t warp_counter[num_warps];
+  // warning issue is solved __shared__ atomicT counter[num_flushing_cgs][buffer_size];
+  __shared__ uint32_t flushing_cg_counter[num_flushing_cgs];
 
-  if (warp.thread_rank() == 0) { warp_counter[warp_id] = 0; }
+  if (flushing_cg.thread_rank() == 0) { flushing_cg_counter[flushing_cg_id] = 0; }
 
-  while (warp.any(first + pair_idx < last)) {
-    bool active_flag = first + pair_idx < last;
-    auto active_warp = cg::binary_partition<warp_size>(warp, active_flag);
+  while (flushing_cg.any(first + pair_idx < last)) {
+    bool active_flag        = first + pair_idx < last;
+    auto active_flushing_cg = cg::binary_partition<flushing_cg_size>(flushing_cg, active_flag);
 
     if (active_flag) {
       pair_type pair = *(first + pair_idx);
       if constexpr (is_outer) {
-        view.pair_retrieve_outer<buffer_size>(active_warp,
-                                              tile,
+        view.pair_retrieve_outer<buffer_size>(active_flushing_cg,
+                                              probing_cg,
                                               pair,
-                                              &warp_counter[warp_id],
-                                              probe_output_buffer[warp_id],
-                                              contained_output_buffer[warp_id],
+                                              &flushing_cg_counter[flushing_cg_id],
+                                              probe_output_buffer[flushing_cg_id],
+                                              contained_output_buffer[flushing_cg_id],
                                               num_matches,
                                               probe_output_begin,
                                               contained_output_begin,
                                               pair_equal);
       } else {
-        view.pair_retrieve<buffer_size>(active_warp,
-                                        tile,
+        view.pair_retrieve<buffer_size>(active_flushing_cg,
+                                        probing_cg,
                                         pair,
-                                        &warp_counter[warp_id],
-                                        probe_output_buffer[warp_id],
-                                        contained_output_buffer[warp_id],
+                                        &flushing_cg_counter[flushing_cg_id],
+                                        probe_output_buffer[flushing_cg_id],
+                                        contained_output_buffer[flushing_cg_id],
                                         num_matches,
                                         probe_output_begin,
                                         contained_output_begin,
                                         pair_equal);
       }
     }
-    pair_idx += (gridDim.x * block_size) / tile_size;
-  }
-
-  // Final flush of output buffer
-  if (warp_counter[warp_id] > 0) {
-    view.flush_output_buffer(warp,
-                             warp_counter[warp_id],
-                             probe_output_buffer[warp_id],
-                             contained_output_buffer[warp_id],
-                             num_matches,
-                             probe_output_begin,
-                             contained_output_begin);
-  }
-}
-
-/**
- * @brief Retrieves all pairs matching the input probe pair in the range `[first, last)`
- * using scalar loads combined with per-CG shared memory buffer.
- *
- * If pair_equal(*(first + i), slot[j]) returns true, then *(first+i) is stored to unspecified
- * locations in `probe_output_begin`, and slot[j] is stored to unspecified locations in
- * `contained_output_begin`. If the given pair has no matches in the map, copies *(first + i) in
- * `probe_output_begin` and a pair of `empty_key_sentinel` and `empty_value_sentinel` in
- * `contained_output_begin` only when `is_outer` is `true`.
- *
- * Behavior is undefined if the total number of matching pairs exceeds `std::distance(output_begin,
- * output_begin + *num_matches - 1)`. Use `pair_count()` to determine the size of the output range.
- *
- * @tparam block_size The size of the thread block
- * @tparam warp_size The size of the warp
- * @tparam tile_size The number of threads in the Cooperative Groups
- * @tparam buffer_size Size of the output buffer
- * @tparam is_outer Boolean flag indicating whether non-matches are included in the output
- * @tparam InputIt Device accessible random access input iterator where
- * `std::is_convertible<std::iterator_traits<InputIt>::value_type,
- * static_multimap<K, V>::value_type>` is `true`
- * @tparam OutputIt1 Device accessible output iterator whose `value_type` is constructible from
- * `InputIt`s `value_type`.
- * @tparam OutputIt2 Device accessible output iterator whose `value_type` is constructible from
- * the map's `value_type`.
- * @tparam atomicT Type of atomic storage
- * @tparam viewT Type of device view allowing access of hash map storage
- * @tparam PairEqual Binary callable type
- * @param first Beginning of the sequence of keys
- * @param last End of the sequence of keys
- * @param probe_output_begin Beginning of the sequence of the matched probe pairs
- * @param contained_output_begin Beginning of the sequence of the matched contained pairs
- * @param num_matches Size of the output sequence
- * @param view Device view used to access the hash map's slot storage
- * @param pair_equal The binary function to compare two pairs for equality
- */
-template <uint32_t block_size,
-          uint32_t warp_size,
-          uint32_t tile_size,
-          uint32_t buffer_size,
-          bool is_outer,
-          typename InputIt,
-          typename OutputIt1,
-          typename OutputIt2,
-          typename atomicT,
-          typename viewT,
-          typename PairEqual>
-__global__ void pair_retrieve(InputIt first,
-                              InputIt last,
-                              OutputIt1 probe_output_begin,
-                              OutputIt2 contained_output_begin,
-                              atomicT* num_matches,
-                              viewT view,
-                              PairEqual pair_equal)
-{
-  using pair_type = typename viewT::value_type;
-
-  auto tile     = cg::tiled_partition<tile_size>(cg::this_thread_block());
-  auto tid      = block_size * blockIdx.x + threadIdx.x;
-  auto pair_idx = tid / tile_size;
-
-  constexpr uint32_t num_cgs = block_size / tile_size;
-  const uint32_t cg_id       = threadIdx.x / tile_size;
-  const uint32_t lane_id     = tile.thread_rank();
-
-  __shared__ pair_type probe_output_buffer[num_cgs][buffer_size];
-  __shared__ pair_type contained_output_buffer[num_cgs][buffer_size];
-  __shared__ uint32_t cg_counter[num_cgs];
-
-  if (lane_id == 0) { cg_counter[cg_id] = 0; }
-
-  while (first + pair_idx < last) {
-    typename viewT::value_type pair = *(first + pair_idx);
-    if constexpr (is_outer) {
-      view.pair_retrieve_outer<tile_size, buffer_size>(tile,
-                                                       pair,
-                                                       &cg_counter[cg_id],
-                                                       probe_output_buffer[cg_id],
-                                                       contained_output_buffer[cg_id],
-                                                       num_matches,
-                                                       probe_output_begin,
-                                                       contained_output_begin,
-                                                       pair_equal);
-    } else {
-      view.pair_retrieve<tile_size, buffer_size>(tile,
-                                                 pair,
-                                                 &cg_counter[cg_id],
-                                                 probe_output_buffer[cg_id],
-                                                 contained_output_buffer[cg_id],
-                                                 num_matches,
-                                                 probe_output_begin,
-                                                 contained_output_begin,
-                                                 pair_equal);
-    }
-    pair_idx += (gridDim.x * block_size) / tile_size;
+    pair_idx += (gridDim.x * block_size) / probing_cg_size;
   }
 
   // Final flush of output buffer
-  if (cg_counter[cg_id] > 0) {
-    view.flush_output_buffer(tile,
-                             cg_counter[cg_id],
-                             probe_output_buffer[cg_id],
-                             contained_output_buffer[cg_id],
+  if (flushing_cg_counter[flushing_cg_id] > 0) {
+    view.flush_output_buffer(flushing_cg,
+                             flushing_cg_counter[flushing_cg_id],
+                             probe_output_buffer[flushing_cg_id],
+                             contained_output_buffer[flushing_cg_id],
                              num_matches,
                              probe_output_begin,
                              contained_output_begin);
diff --git a/include/cuco/detail/static_multimap/static_multimap.inl b/include/cuco/detail/static_multimap/static_multimap.inl
index 7f65482b7..00160b18c 100644
--- a/include/cuco/detail/static_multimap/static_multimap.inl
+++ b/include/cuco/detail/static_multimap/static_multimap.inl
@@ -375,62 +375,34 @@ static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::pair_retrieve(
 
   // Using per-warp buffer for vector loads and per-CG buffer for scalar loads
   constexpr auto buffer_size = uses_vector_load() ? (warp_size() * 3u) : (cg_size() * 3u);
+  constexpr auto block_size  = 128;
   constexpr bool is_outer    = false;
 
-  auto constexpr block_size = 128;
-  auto const grid_size      = [&]() {
-    if constexpr (uses_vector_load()) {
-      return detail::get_grid_size(detail::vectorized_pair_retrieve<block_size,
-                                                                    warp_size(),
-                                                                    cg_size(),
-                                                                    buffer_size,
-                                                                    is_outer,
-                                                                    InputIt,
-                                                                    OutputIt1,
-                                                                    OutputIt2,
-                                                                    atomic_ctr_type,
-                                                                    device_view,
-                                                                    PairEqual>,
-                                   block_size);
-    }
-    if constexpr (not uses_vector_load()) {
-      return detail::get_grid_size(detail::pair_retrieve<block_size,
-                                                         warp_size(),
-                                                         cg_size(),
-                                                         buffer_size,
-                                                         is_outer,
-                                                         InputIt,
-                                                         OutputIt1,
-                                                         OutputIt2,
-                                                         atomic_ctr_type,
-                                                         device_view,
-                                                         PairEqual>,
-                                   block_size);
-    }
+  auto const flushing_cg_size = [&]() {
+    if constexpr (uses_vector_load()) { return warp_size(); }
+    return cg_size();
   }();
 
+  auto const grid_size = detail::get_grid_size(detail::pair_retrieve<block_size,
+                                                                     flushing_cg_size,
+                                                                     cg_size(),
+                                                                     buffer_size,
+                                                                     is_outer,
+                                                                     InputIt,
+                                                                     OutputIt1,
+                                                                     OutputIt2,
+                                                                     atomic_ctr_type,
+                                                                     device_view,
+                                                                     PairEqual>,
+                                               block_size);
+
   cudaMemsetAsync(d_counter_.get(), 0, sizeof(atomic_ctr_type), stream);
   std::size_t h_counter;
 
-  if constexpr (uses_vector_load()) {
-    detail::vectorized_pair_retrieve<block_size, warp_size(), cg_size(), buffer_size, is_outer>
-      <<<grid_size, block_size, 0, stream>>>(first,
-                                             last,
-                                             probe_output_begin,
-                                             contained_output_begin,
-                                             d_counter_.get(),
-                                             view,
-                                             pair_equal);
-  } else {
-    detail::pair_retrieve<block_size, warp_size(), cg_size(), buffer_size, is_outer>
-      <<<grid_size, block_size, 0, stream>>>(first,
-                                             last,
-                                             probe_output_begin,
-                                             contained_output_begin,
-                                             d_counter_.get(),
-                                             view,
-                                             pair_equal);
-  }
+  detail::pair_retrieve<block_size, flushing_cg_size, cg_size(), buffer_size, is_outer>
+    <<<grid_size, block_size, 0, stream>>>(
+      first, last, probe_output_begin, contained_output_begin, d_counter_.get(), view, pair_equal);
+
   CUCO_CUDA_TRY(cudaMemcpyAsync(
     &h_counter, d_counter_.get(), sizeof(atomic_ctr_type), cudaMemcpyDeviceToHost, stream));
   CUCO_CUDA_TRY(cudaStreamSynchronize(stream));
@@ -458,62 +430,34 @@ static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::pair_retrieve_oute
 
   // Using per-warp buffer for vector loads and per-CG buffer for scalar loads
   constexpr auto buffer_size = uses_vector_load() ? (warp_size() * 3u) : (cg_size() * 3u);
+  constexpr auto block_size  = 128;
   constexpr bool is_outer    = true;
 
-  auto constexpr block_size = 128;
-  auto const grid_size      = [&]() {
-    if constexpr (uses_vector_load()) {
-      return detail::get_grid_size(detail::vectorized_pair_retrieve<block_size,
-                                                                    warp_size(),
-                                                                    cg_size(),
-                                                                    buffer_size,
-                                                                    is_outer,
-                                                                    InputIt,
-                                                                    OutputIt1,
-                                                                    OutputIt2,
-                                                                    atomic_ctr_type,
-                                                                    device_view,
-                                                                    PairEqual>,
-                                   block_size);
-    }
-    if constexpr (not uses_vector_load()) {
-      return detail::get_grid_size(detail::pair_retrieve<block_size,
-                                                         warp_size(),
-                                                         cg_size(),
-                                                         buffer_size,
-                                                         is_outer,
-                                                         InputIt,
-                                                         OutputIt1,
-                                                         OutputIt2,
-                                                         atomic_ctr_type,
-                                                         device_view,
-                                                         PairEqual>,
-                                   block_size);
-    }
+  auto const flushing_cg_size = [&]() {
+    if constexpr (uses_vector_load()) { return warp_size(); }
+    return cg_size();
   }();
 
+  auto const grid_size = detail::get_grid_size(detail::pair_retrieve<block_size,
+                                                                     flushing_cg_size,
+                                                                     cg_size(),
+                                                                     buffer_size,
+                                                                     is_outer,
+                                                                     InputIt,
+                                                                     OutputIt1,
+                                                                     OutputIt2,
+                                                                     atomic_ctr_type,
+                                                                     device_view,
+                                                                     PairEqual>,
+                                               block_size);
+
   cudaMemsetAsync(d_counter_.get(), 0, sizeof(atomic_ctr_type), stream);
   std::size_t h_counter;
 
-  if constexpr (uses_vector_load()) {
-    detail::vectorized_pair_retrieve<block_size, warp_size(), cg_size(), buffer_size, is_outer>
-      <<<grid_size, block_size, 0, stream>>>(first,
-                                             last,
-                                             probe_output_begin,
-                                             contained_output_begin,
-                                             d_counter_.get(),
-                                             view,
-                                             pair_equal);
-  } else {
-    detail::pair_retrieve<block_size, warp_size(), cg_size(), buffer_size, is_outer>
-      <<<grid_size, block_size, 0, stream>>>(first,
-                                             last,
-                                             probe_output_begin,
-                                             contained_output_begin,
-                                             d_counter_.get(),
-                                             view,
-                                             pair_equal);
-  }
+  detail::pair_retrieve<block_size, flushing_cg_size, cg_size(), buffer_size, is_outer>
+    <<<grid_size, block_size, 0, stream>>>(
+      first, last, probe_output_begin, contained_output_begin, d_counter_.get(), view, pair_equal);
+
   CUCO_CUDA_TRY(cudaMemcpyAsync(
     &h_counter, d_counter_.get(), sizeof(atomic_ctr_type), cudaMemcpyDeviceToHost, stream));
   CUCO_CUDA_TRY(cudaStreamSynchronize(stream));
@@ -766,18 +710,18 @@ template <typename Key,
           class ProbeSequence,
           typename Allocator>
 template <uint32_t buffer_size,
-          typename warpT,
-          typename CG,
+          typename FlushingCG,
+          typename ProbingCG,
           typename atomicT,
           typename OutputIt1,
           typename OutputIt2,
           typename PairEqual>
 __device__ void
 static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::pair_retrieve(
-  warpT const& warp,
-  CG const& g,
+  FlushingCG const& flushing_cg,
+  ProbingCG const& probing_cg,
   value_type const& pair,
-  uint32_t* warp_counter,
+  uint32_t* flushing_cg_counter,
   value_type* probe_output_buffer,
   value_type* contained_output_buffer,
   atomicT* num_matches,
@@ -786,16 +730,29 @@ static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::pair_
   PairEqual pair_equal) noexcept
 {
   constexpr bool is_outer = false;
-  impl_.pair_retrieve<buffer_size, is_outer>(warp,
-                                             g,
-                                             pair,
-                                             warp_counter,
-                                             probe_output_buffer,
-                                             contained_output_buffer,
-                                             num_matches,
-                                             probe_output_begin,
-                                             contained_output_begin,
-                                             pair_equal);
+  if constexpr (uses_vector_load()) {
+    impl_.pair_retrieve<buffer_size, is_outer>(flushing_cg,
+                                               probing_cg,
+                                               pair,
+                                               flushing_cg_counter,
+                                               probe_output_buffer,
+                                               contained_output_buffer,
+                                               num_matches,
+                                               probe_output_begin,
+                                               contained_output_begin,
+                                               pair_equal);
+  } else  // In the case of scalar load, flushing CG is the same as probing CG
+  {
+    impl_.pair_retrieve<buffer_size, is_outer>(probing_cg,
+                                               pair,
+                                               flushing_cg_counter,
+                                               probe_output_buffer,
+                                               contained_output_buffer,
+                                               num_matches,
+                                               probe_output_begin,
+                                               contained_output_begin,
+                                               pair_equal);
+  }
 }
 
 template <typename Key,
@@ -804,91 +761,18 @@ template <typename Key,
           class ProbeSequence,
           typename Allocator>
 template <uint32_t buffer_size,
-          typename warpT,
-          typename CG,
-          typename atomicT,
-          typename OutputIt1,
-          typename OutputIt2,
-          typename PairEqual>
-__device__ void
-static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::pair_retrieve_outer(
-  warpT const& warp,
-  CG const& g,
-  value_type const& pair,
-  uint32_t* warp_counter,
-  value_type* probe_output_buffer,
-  value_type* contained_output_buffer,
-  atomicT* num_matches,
-  OutputIt1 probe_output_begin,
-  OutputIt2 contained_output_begin,
-  PairEqual pair_equal) noexcept
-{
-  constexpr bool is_outer = true;
-  impl_.pair_retrieve<buffer_size, is_outer>(warp,
-                                             g,
-                                             pair,
-                                             warp_counter,
-                                             probe_output_buffer,
-                                             contained_output_buffer,
-                                             num_matches,
-                                             probe_output_begin,
-                                             contained_output_begin,
-                                             pair_equal);
-}
-
-template <typename Key,
-          typename Value,
-          cuda::thread_scope Scope,
-          class ProbeSequence,
-          typename Allocator>
-template <uint32_t cg_size,
-          uint32_t buffer_size,
-          typename CG,
-          typename atomicT,
-          typename OutputIt1,
-          typename OutputIt2,
-          typename PairEqual>
-__device__ void
-static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::pair_retrieve(
-  CG const& g,
-  value_type const& pair,
-  uint32_t* cg_counter,
-  value_type* probe_output_buffer,
-  value_type* contained_output_buffer,
-  atomicT* num_matches,
-  OutputIt1 probe_output_begin,
-  OutputIt2 contained_output_begin,
-  PairEqual pair_equal) noexcept
-{
-  constexpr bool is_outer = false;
-  impl_.pair_retrieve<cg_size, buffer_size, is_outer>(g,
-                                                      pair,
-                                                      cg_counter,
-                                                      probe_output_buffer,
-                                                      contained_output_buffer,
-                                                      num_matches,
-                                                      probe_output_begin,
-                                                      contained_output_begin,
-                                                      pair_equal);
-}
-
-template <typename Key,
-          typename Value,
-          cuda::thread_scope Scope,
-          class ProbeSequence,
-          typename Allocator>
-template <uint32_t cg_size,
-          uint32_t buffer_size,
-          typename CG,
+          typename FlushingCG,
+          typename ProbingCG,
           typename atomicT,
           typename OutputIt1,
           typename OutputIt2,
           typename PairEqual>
 __device__ void
 static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::pair_retrieve_outer(
-  CG const& g,
+  FlushingCG const& flushing_cg,
+  ProbingCG const& probing_cg,
   value_type const& pair,
-  uint32_t* cg_counter,
+  uint32_t* flushing_cg_counter,
   value_type* probe_output_buffer,
   value_type* contained_output_buffer,
   atomicT* num_matches,
@@ -897,15 +781,29 @@ static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::pair_
   PairEqual pair_equal) noexcept
 {
   constexpr bool is_outer = true;
-  impl_.pair_retrieve<cg_size, buffer_size, is_outer>(g,
-                                                      pair,
-                                                      cg_counter,
-                                                      probe_output_buffer,
-                                                      contained_output_buffer,
-                                                      num_matches,
-                                                      probe_output_begin,
-                                                      contained_output_begin,
-                                                      pair_equal);
+  if constexpr (uses_vector_load()) {
+    impl_.pair_retrieve<buffer_size, is_outer>(flushing_cg,
+                                               probing_cg,
+                                               pair,
+                                               flushing_cg_counter,
+                                               probe_output_buffer,
+                                               contained_output_buffer,
+                                               num_matches,
+                                               probe_output_begin,
+                                               contained_output_begin,
+                                               pair_equal);
+  } else  // In the case of scalar load, flushing CG is the same as probing CG
+  {
+    impl_.pair_retrieve<buffer_size, is_outer>(probing_cg,
+                                               pair,
+                                               flushing_cg_counter,
+                                               probe_output_buffer,
+                                               contained_output_buffer,
+                                               num_matches,
+                                               probe_output_begin,
+                                               contained_output_begin,
+                                               pair_equal);
+  }
 }
 
 template <typename Key,
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index 93c019dd8..f4e81ce80 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -958,26 +958,26 @@ class static_multimap {
                                    KeyEqual key_equal = KeyEqual{}) noexcept;
 
     /**
-     * @brief Retrieves all the matches of a given pair contained in multimap using vector
-     * loads with per-warp shared memory buffer.
+     * @brief Retrieves all the matches of a given pair contained in multimap with per-flushing-CG
+     * shared memory buffer.
      *
      * For pair `p`, if pair_equal(p, slot[j]) returns true, copies `p` to unspecified locations
      * in `[probe_output_begin, probe_output_end)` and copies slot[j] to unspecified locations in
      * `[contained_output_begin, contained_output_end)`.
      *
      * @tparam buffer_size Size of the output buffer
-     * @tparam warpT Warp (Cooperative Group) type
-     * @tparam CG Cooperative Group type
+     * @tparam FlushingCG Type of Cooperative Group used to flush output buffer
+     * @tparam ProbingCG Type of Cooperative Group for parallel retrieval
      * @tparam atomicT Type of atomic storage
      * @tparam OutputIt1 Device accessible output iterator whose `value_type` is constructible from
      * `InputIt`s `value_type`.
      * @tparam OutputIt2 Device accessible output iterator whose `value_type` is constructible from
      * the map's `value_type`.
      * @tparam PairEqual Binary callable type
-     * @param warp The Cooperative Group (or warp) used to flush output buffer
-     * @param g The Cooperative Group used to retrieve
+     * @param flushing_cg The Cooperative Group used to flush output buffer
+     * @param probing_cg The Cooperative Group used to retrieve
      * @param pair The pair to search for
-     * @param warp_counter Pointer to the warp counter
+     * @param flushing_cg_counter Pointer to the flushing CG counter
      * @param probe_output_buffer Buffer of the matched probe pair sequence
      * @param contained_output_buffer Buffer of the matched contained pair sequence
      * @param num_matches Size of the output sequence
@@ -987,14 +987,14 @@ class static_multimap {
      * @param pair_equal The binary callable used to compare two pairs for equality
      */
     template <uint32_t buffer_size,
-              typename warpT,
-              typename CG,
+              typename FlushingCG,
+              typename ProbingCG,
               typename atomicT,
               typename OutputIt1,
               typename OutputIt2,
               typename PairEqual>
-    __device__ void pair_retrieve(warpT const& warp,
-                                  CG const& g,
+    __device__ void pair_retrieve(FlushingCG const& flushing_cg,
+                                  ProbingCG const& probing_cg,
                                   value_type const& pair,
                                   uint32_t* warp_counter,
                                   value_type* probe_output_buffer,
@@ -1005,8 +1005,8 @@ class static_multimap {
                                   PairEqual pair_equal) noexcept;
 
     /**
-     * @brief Retrieves all the matches of a given pair contained in multimap using vector
-     * loads with per-warp shared memory buffer.
+     * @brief Retrieves all the matches of a given pair contained in multimap with per-flushing-CG
+     * shared memory buffer.
      *
      * For pair `p`, if pair_equal(p, slot[j]) returns true, copies `p` to unspecified locations
      * in `[probe_output_begin, probe_output_end)` and copies slot[j] to unspecified locations in
@@ -1014,18 +1014,18 @@ class static_multimap {
      * `p` and a pair of `empty_key_sentinel` and `empty_value_sentinel` into the output.
      *
      * @tparam buffer_size Size of the output buffer
-     * @tparam warpT Warp (Cooperative Group) type
-     * @tparam CG Cooperative Group type
+     * @tparam FlushingCG Type of Cooperative Group used to flush output buffer
+     * @tparam ProbingCG Type of Cooperative Group for parallel retrieval
      * @tparam atomicT Type of atomic storage
      * @tparam OutputIt1 Device accessible output iterator whose `value_type` is constructible from
      * `InputIt`s `value_type`.
      * @tparam OutputIt2 Device accessible output iterator whose `value_type` is constructible from
      * the map's `value_type`.
      * @tparam PairEqual Binary callable type
-     * @param warp The Cooperative Group (or warp) used to flush output buffer
-     * @param g The Cooperative Group used to retrieve
+     * @param flushing_cg The Cooperative Group used to flush output buffer
+     * @param probing_cg The Cooperative Group used to retrieve
      * @param pair The pair to search for
-     * @param warp_counter Pointer to the warp counter
+     * @param flushing_cg_counter Pointer to the flushing CG counter
      * @param probe_output_buffer Buffer of the matched probe pair sequence
      * @param contained_output_buffer Buffer of the matched contained pair sequence
      * @param num_matches Size of the output sequence
@@ -1035,107 +1035,16 @@ class static_multimap {
      * @param pair_equal The binary callable used to compare two pairs for equality
      */
     template <uint32_t buffer_size,
-              typename warpT,
-              typename CG,
-              typename atomicT,
-              typename OutputIt1,
-              typename OutputIt2,
-              typename PairEqual>
-    __device__ void pair_retrieve_outer(warpT const& warp,
-                                        CG const& g,
-                                        value_type const& pair,
-                                        uint32_t* warp_counter,
-                                        value_type* probe_output_buffer,
-                                        value_type* contained_output_buffer,
-                                        atomicT* num_matches,
-                                        OutputIt1 probe_output_begin,
-                                        OutputIt2 contained_output_begin,
-                                        PairEqual pair_equal) noexcept;
-
-    /**
-     * @brief Retrieves all the matches of a given pair contained in multimap using scalar
-     * loads with per-CG shared memory buffer.
-     *
-     * For pair `p`, if pair_equal(p, slot[j]) returns true, copies `p` to unspecified locations
-     * in `[probe_output_begin, probe_output_end)` and copies slot[j] to unspecified locations in
-     * `[contained_output_begin, contained_output_end)`.
-     *
-     * @tparam cg_size The number of threads in CUDA Cooperative Groups
-     * @tparam buffer_size Size of the output buffer
-     * @tparam CG Cooperative Group type
-     * @tparam atomicT Type of atomic storage
-     * @tparam OutputIt1 Device accessible output iterator whose `value_type` is constructible from
-     * `InputIt`s `value_type`.
-     * @tparam OutputIt2 Device accessible output iterator whose `value_type` is constructible from
-     * the map's `value_type`.
-     * @tparam PairEqual Binary callable type
-     * @param g The Cooperative Group used to retrieve
-     * @param pair The pair to search for
-     * @param cg_counter Pointer to the CG counter
-     * @param probe_output_buffer Buffer of the matched probe pair sequence
-     * @param contained_output_buffer Buffer of the matched contained pair sequence
-     * @param num_matches Size of the output sequence
-     * @param probe_output_begin Beginning of the output sequence of the matched probe pairs
-     * @param contained_output_begin Beginning of the output sequence of the matched contained
-     * pairs
-     * @param pair_equal The binary callable used to compare two pairs for equality
-     */
-    template <uint32_t cg_size,
-              uint32_t buffer_size,
-              typename CG,
-              typename atomicT,
-              typename OutputIt1,
-              typename OutputIt2,
-              typename PairEqual>
-    __device__ void pair_retrieve(CG const& g,
-                                  value_type const& pair,
-                                  uint32_t* cg_counter,
-                                  value_type* probe_output_buffer,
-                                  value_type* contained_output_buffer,
-                                  atomicT* num_matches,
-                                  OutputIt1 probe_output_begin,
-                                  OutputIt2 contained_output_begin,
-                                  PairEqual pair_equal) noexcept;
-
-    /**
-     * @brief Retrieves all the matches of a given pair contained in multimap using scalar
-     * loads with per-CG shared memory buffer.
-     *
-     * For pair `p`, if pair_equal(p, slot[j]) returns true, copies `p` to unspecified locations
-     * in `[probe_output_begin, probe_output_end)` and copies slot[j] to unspecified locations in
-     * `[contained_output_begin, contained_output_end)`. If `p` does not have any matches, copies
-     * `p` and a pair of `empty_key_sentinel` and `empty_value_sentinel` into the output.
-     *
-     * @tparam cg_size The number of threads in CUDA Cooperative Groups
-     * @tparam buffer_size Size of the output buffer
-     * @tparam CG Cooperative Group type
-     * @tparam atomicT Type of atomic storage
-     * @tparam OutputIt1 Device accessible output iterator whose `value_type` is constructible from
-     * `InputIt`s `value_type`.
-     * @tparam OutputIt2 Device accessible output iterator whose `value_type` is constructible from
-     * the map's `value_type`.
-     * @tparam PairEqual Binary callable type
-     * @param g The Cooperative Group used to retrieve
-     * @param pair The pair to search for
-     * @param cg_counter Pointer to the CG counter
-     * @param probe_output_buffer Buffer of the matched probe pair sequence
-     * @param contained_output_buffer Buffer of the matched contained pair sequence
-     * @param num_matches Size of the output sequence
-     * @param probe_output_begin Beginning of the output sequence of the matched probe pairs
-     * @param contained_output_begin Beginning of the output sequence of the matched contained
-     * pairs
-     * @param pair_equal The binary callable used to compare two pairs for equality
-     */
-    template <uint32_t cg_size,
-              uint32_t buffer_size,
-              typename CG,
+              typename FlushingCG,
+              typename ProbingCG,
               typename atomicT,
               typename OutputIt1,
               typename OutputIt2,
               typename PairEqual>
-    __device__ void pair_retrieve_outer(CG const& g,
+    __device__ void pair_retrieve_outer(FlushingCG const& flushing_cg,
+                                        ProbingCG const& probing_cg,
                                         value_type const& pair,
-                                        uint32_t* cg_counter,
+                                        uint32_t* flushing_cg_counter,
                                         value_type* probe_output_buffer,
                                         value_type* contained_output_buffer,
                                         atomicT* num_matches,

From 772d74c80d72f9abd3fa5a577040a88d6e3ad94e Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Tue, 26 Oct 2021 12:20:12 -0400
Subject: [PATCH 251/267] Forinline device functions

---
 include/cuco/detail/probe_sequences.cuh       |  17 +-
 .../static_multimap/device_view_impl.inl      | 163 +++++++++---------
 .../static_multimap/static_multimap.inl       |  30 ++--
 include/cuco/static_multimap.cuh              | 155 +++++++++--------
 4 files changed, 194 insertions(+), 171 deletions(-)

diff --git a/include/cuco/detail/probe_sequences.cuh b/include/cuco/detail/probe_sequences.cuh
index b377398ee..8b725f325 100644
--- a/include/cuco/detail/probe_sequences.cuh
+++ b/include/cuco/detail/probe_sequences.cuh
@@ -88,17 +88,20 @@ class probe_sequence_base {
   /**
    * @brief Returns the capacity of the hash map.
    */
-  __host__ __device__ std::size_t get_capacity() const noexcept { return capacity_; }
+  __host__ __device__ __forceinline__ std::size_t get_capacity() const noexcept
+  {
+    return capacity_;
+  }
 
   /**
    * @brief Returns slots array.
    */
-  __device__ iterator get_slots() noexcept { return slots_; }
+  __device__ __forceinline__ iterator get_slots() noexcept { return slots_; }
 
   /**
    * @brief Returns slots array.
    */
-  __device__ const_iterator get_slots() const noexcept { return slots_; }
+  __device__ __forceinline__ const_iterator get_slots() const noexcept { return slots_; }
 
  protected:
   iterator slots_;              ///< Pointer to beginning of the hash map slots
@@ -161,7 +164,7 @@ class linear_probing : public probe_sequence_base<Key, Value, CGSize, Scope> {
    * @return Pointer to the initial slot for `k`
    */
   template <typename CG>
-  __device__ iterator initial_slot(CG const& g, Key const k) noexcept
+  __device__ __forceinline__ iterator initial_slot(CG const& g, Key const k) noexcept
   {
     auto const hash_value = [&]() {
       auto const tmp = hash_(k);
@@ -190,7 +193,7 @@ class linear_probing : public probe_sequence_base<Key, Value, CGSize, Scope> {
    * @param s The slot to advance
    * @return The next slot after `s`
    */
-  __device__ iterator next_slot(iterator s) noexcept
+  __device__ __forceinline__ iterator next_slot(iterator s) noexcept
   {
     std::size_t index = s - slots_;
     std::size_t offset;
@@ -270,7 +273,7 @@ class double_hashing : public probe_sequence_base<Key, Value, CGSize, Scope> {
    * @return Pointer to the initial slot for `k`
    */
   template <typename CG>
-  __device__ iterator initial_slot(CG const& g, Key const k) noexcept
+  __device__ __forceinline__ iterator initial_slot(CG const& g, Key const k) noexcept
   {
     std::size_t index;
     auto const hash_value = hash1_(k);
@@ -296,7 +299,7 @@ class double_hashing : public probe_sequence_base<Key, Value, CGSize, Scope> {
    * @param s The slot to advance
    * @return The next slot after `s`
    */
-  __device__ iterator next_slot(iterator s) noexcept
+  __device__ __forceinline__ iterator next_slot(iterator s) noexcept
   {
     std::size_t index = s - slots_;
     return &slots_[(index + step_size_) % capacity_];
diff --git a/include/cuco/detail/static_multimap/device_view_impl.inl b/include/cuco/detail/static_multimap/device_view_impl.inl
index ad0df9fe9..26c2502e3 100644
--- a/include/cuco/detail/static_multimap/device_view_impl.inl
+++ b/include/cuco/detail/static_multimap/device_view_impl.inl
@@ -66,14 +66,17 @@ class static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view_
    *
    * @return Slots array
    */
-  __device__ pair_atomic_type* get_slots() noexcept { return probe_sequence_.get_slots(); }
+  __device__ __forceinline__ pair_atomic_type* get_slots() noexcept
+  {
+    return probe_sequence_.get_slots();
+  }
 
   /**
    * @brief Gets slots array.
    *
    * @return Slots array
    */
-  __device__ pair_atomic_type const* get_slots() const noexcept
+  __device__ __forceinline__ pair_atomic_type const* get_slots() const noexcept
   {
     return probe_sequence_.get_slots();
   }
@@ -89,7 +92,7 @@ class static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view_
    * @return Pointer to the initial slot for `k`
    */
   template <typename CG>
-  __device__ iterator initial_slot(CG const& g, Key const& k) noexcept
+  __device__ __forceinline__ iterator initial_slot(CG const& g, Key const& k) noexcept
   {
     return probe_sequence_.initial_slot(g, k);
   }
@@ -105,7 +108,7 @@ class static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view_
    * @return Pointer to the initial slot for `k`
    */
   template <typename CG>
-  __device__ const_iterator initial_slot(CG g, Key const& k) const noexcept
+  __device__ __forceinline__ const_iterator initial_slot(CG g, Key const& k) const noexcept
   {
     return probe_sequence_.initial_slot(g, k);
   }
@@ -119,7 +122,10 @@ class static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view_
    * @param s The slot to advance
    * @return The next slot after `s`
    */
-  __device__ iterator next_slot(iterator s) noexcept { return probe_sequence_.next_slot(s); }
+  __device__ __forceinline__ iterator next_slot(iterator s) noexcept
+  {
+    return probe_sequence_.next_slot(s);
+  }
 
   /**
    * @brief Given a slot `s`, returns the next slot.
@@ -130,7 +136,7 @@ class static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view_
    * @param s The slot to advance
    * @return The next slot after `s`
    */
-  __device__ const_iterator next_slot(const_iterator s) const noexcept
+  __device__ __forceinline__ const_iterator next_slot(const_iterator s) const noexcept
   {
     return probe_sequence_.next_slot(s);
   }
@@ -140,7 +146,7 @@ class static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view_
    *
    * @return The maximum number of elements the hash map can hold
    */
-  __host__ __device__ std::size_t get_capacity() const noexcept
+  __host__ __device__ __forceinline__ std::size_t get_capacity() const noexcept
   {
     return probe_sequence_.get_capacity();
   }
@@ -150,14 +156,17 @@ class static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view_
    *
    * @return The sentinel value used to represent an empty key slot
    */
-  __host__ __device__ Key get_empty_key_sentinel() const noexcept { return empty_key_sentinel_; }
+  __host__ __device__ __forceinline__ Key get_empty_key_sentinel() const noexcept
+  {
+    return empty_key_sentinel_;
+  }
 
   /**
    * @brief Gets the sentinel value used to represent an empty value slot.
    *
    * @return The sentinel value used to represent an empty value slot
    */
-  __host__ __device__ Value get_empty_value_sentinel() const noexcept
+  __host__ __device__ __forceinline__ Value get_empty_value_sentinel() const noexcept
   {
     return empty_value_sentinel_;
   }
@@ -168,7 +177,8 @@ class static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view_
    * @param arr The pair array to be loaded
    * @param current_slot The given slot to load from
    */
-  __device__ void load_pair_array(value_type* arr, const_iterator current_slot) noexcept
+  __device__ __forceinline__ void load_pair_array(value_type* arr,
+                                                  const_iterator current_slot) noexcept
   {
     if constexpr (sizeof(value_type) == 4) {
       auto const tmp = *reinterpret_cast<ushort4 const*>(current_slot);
@@ -214,7 +224,8 @@ class static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_mutab
    * equality
    * @return An insert result from the `insert_resullt` enumeration.
    */
-  __device__ insert_result packed_cas(iterator current_slot, value_type const& insert_pair) noexcept
+  __device__ __forceinline__ insert_result packed_cas(iterator current_slot,
+                                                      value_type const& insert_pair) noexcept
   {
     auto expected_key   = this->get_empty_key_sentinel();
     auto expected_value = this->get_empty_value_sentinel();
@@ -241,8 +252,8 @@ class static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_mutab
    * @param insert_pair The pair to insert
    * @return An insert result from the `insert_resullt` enumeration.
    */
-  __device__ insert_result back_to_back_cas(iterator current_slot,
-                                            value_type const& insert_pair) noexcept
+  __device__ __forceinline__ insert_result back_to_back_cas(iterator current_slot,
+                                                            value_type const& insert_pair) noexcept
   {
     using cuda::std::memory_order_relaxed;
 
@@ -281,8 +292,8 @@ class static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_mutab
    * @param insert_pair The pair to insert
    * @return An insert result from the `insert_resullt` enumeration.
    */
-  __device__ insert_result cas_dependent_write(iterator current_slot,
-                                               value_type const& insert_pair) noexcept
+  __device__ __forceinline__ insert_result
+  cas_dependent_write(iterator current_slot, value_type const& insert_pair) noexcept
   {
     using cuda::std::memory_order_relaxed;
     auto expected_key = this->get_empty_key_sentinel();
@@ -321,8 +332,8 @@ class static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_mutab
    * @return void.
    */
   template <bool uses_vector_load, typename CG>
-  __device__ std::enable_if_t<uses_vector_load, void> insert(CG g,
-                                                             value_type const& insert_pair) noexcept
+  __device__ __forceinline__ std::enable_if_t<uses_vector_load, void> insert(
+    CG g, value_type const& insert_pair) noexcept
   {
     auto current_slot = initial_slot(g, insert_pair.first);
     while (true) {
@@ -372,7 +383,7 @@ class static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_mutab
    * @return void.
    */
   template <bool uses_vector_load, typename CG>
-  __device__ std::enable_if_t<not uses_vector_load, void> insert(
+  __device__ __forceinline__ std::enable_if_t<not uses_vector_load, void> insert(
     CG g, value_type const& insert_pair) noexcept
   {
     auto current_slot = initial_slot(g, insert_pair.first);
@@ -457,11 +468,11 @@ class static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view_
    * @param output_begin Beginning of the output sequence of key/value pairs
    */
   template <typename CG, typename atomicT, typename OutputIt>
-  __inline__ __device__ void flush_output_buffer(CG const& g,
-                                                 uint32_t const num_outputs,
-                                                 value_type* output_buffer,
-                                                 atomicT* num_matches,
-                                                 OutputIt output_begin) noexcept
+  __device__ __forceinline__ void flush_output_buffer(CG const& g,
+                                                      uint32_t const num_outputs,
+                                                      value_type* output_buffer,
+                                                      atomicT* num_matches,
+                                                      OutputIt output_begin) noexcept
   {
     std::size_t offset;
     const auto lane_id = g.thread_rank();
@@ -514,13 +525,13 @@ class static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view_
    * pairs
    */
   template <typename CG, typename atomicT, typename OutputIt1, typename OutputIt2>
-  __inline__ __device__ void flush_output_buffer(CG const& g,
-                                                 uint32_t const num_outputs,
-                                                 value_type* probe_output_buffer,
-                                                 value_type* contained_output_buffer,
-                                                 atomicT* num_matches,
-                                                 OutputIt1 probe_output_begin,
-                                                 OutputIt2 contained_output_begin) noexcept
+  __device__ __forceinline__ void flush_output_buffer(CG const& g,
+                                                      uint32_t const num_outputs,
+                                                      value_type* probe_output_buffer,
+                                                      value_type* contained_output_buffer,
+                                                      atomicT* num_matches,
+                                                      OutputIt1 probe_output_begin,
+                                                      OutputIt2 contained_output_begin) noexcept
   {
     std::size_t offset;
     const auto lane_id = g.thread_rank();
@@ -559,9 +570,8 @@ class static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view_
    * containing `k` was inserted
    */
   template <bool uses_vector_load, typename CG, typename KeyEqual>
-  __device__ std::enable_if_t<uses_vector_load, bool> contains(CG g,
-                                                               Key const& k,
-                                                               KeyEqual key_equal) noexcept
+  __device__ __forceinline__ std::enable_if_t<uses_vector_load, bool> contains(
+    CG g, Key const& k, KeyEqual key_equal) noexcept
   {
     auto current_slot = initial_slot(g, k);
 
@@ -608,9 +618,8 @@ class static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view_
    * containing `k` was inserted
    */
   template <bool uses_vector_load, typename CG, typename KeyEqual>
-  __device__ std::enable_if_t<not uses_vector_load, bool> contains(CG g,
-                                                                   Key const& k,
-                                                                   KeyEqual key_equal) noexcept
+  __device__ __forceinline__ std::enable_if_t<not uses_vector_load, bool> contains(
+    CG g, Key const& k, KeyEqual key_equal) noexcept
   {
     auto current_slot = initial_slot(g, k);
 
@@ -651,9 +660,8 @@ class static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view_
    * @return Number of matches found by the current thread
    */
   template <bool uses_vector_load, bool is_outer, typename CG, typename KeyEqual>
-  __device__ std::enable_if_t<uses_vector_load, std::size_t> count(CG const& g,
-                                                                   Key const& k,
-                                                                   KeyEqual key_equal) noexcept
+  __device__ __forceinline__ std::enable_if_t<uses_vector_load, std::size_t> count(
+    CG const& g, Key const& k, KeyEqual key_equal) noexcept
   {
     std::size_t count = 0;
     auto current_slot = initial_slot(g, k);
@@ -702,9 +710,8 @@ class static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view_
    * @return Number of matches found by the current thread
    */
   template <bool uses_vector_load, bool is_outer, typename CG, typename KeyEqual>
-  __device__ std::enable_if_t<not uses_vector_load, std::size_t> count(CG const& g,
-                                                                       Key const& k,
-                                                                       KeyEqual key_equal) noexcept
+  __device__ __forceinline__ std::enable_if_t<not uses_vector_load, std::size_t> count(
+    CG const& g, Key const& k, KeyEqual key_equal) noexcept
   {
     std::size_t count = 0;
     auto current_slot = initial_slot(g, k);
@@ -751,7 +758,7 @@ class static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view_
    * @return Number of matches found by the current thread
    */
   template <bool uses_vector_load, bool is_outer, typename CG, typename PairEqual>
-  __device__ std::enable_if_t<uses_vector_load, std::size_t> pair_count(
+  __device__ __forceinline__ std::enable_if_t<uses_vector_load, std::size_t> pair_count(
     CG const& g, value_type const& pair, PairEqual pair_equal) noexcept
   {
     std::size_t count = 0;
@@ -804,7 +811,7 @@ class static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view_
    * @return Number of matches found by the current thread
    */
   template <bool uses_vector_load, bool is_outer, typename CG, typename PairEqual>
-  __device__ std::enable_if_t<not uses_vector_load, std::size_t> pair_count(
+  __device__ __forceinline__ std::enable_if_t<not uses_vector_load, std::size_t> pair_count(
     CG const& g, value_type const& pair, PairEqual pair_equal) noexcept
   {
     std::size_t count = 0;
@@ -871,14 +878,14 @@ class static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view_
             typename atomicT,
             typename OutputIt,
             typename KeyEqual>
-  __device__ void retrieve(FlushingCG const& flushing_cg,
-                           ProbingCG const& probing_cg,
-                           Key const& k,
-                           uint32_t* flushing_cg_counter,
-                           value_type* output_buffer,
-                           atomicT* num_matches,
-                           OutputIt output_begin,
-                           KeyEqual key_equal) noexcept
+  __device__ __forceinline__ void retrieve(FlushingCG const& flushing_cg,
+                                           ProbingCG const& probing_cg,
+                                           Key const& k,
+                                           uint32_t* flushing_cg_counter,
+                                           value_type* output_buffer,
+                                           atomicT* num_matches,
+                                           OutputIt output_begin,
+                                           KeyEqual key_equal) noexcept
   {
     const uint32_t cg_lane_id = probing_cg.thread_rank();
 
@@ -981,13 +988,13 @@ class static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view_
             typename atomicT,
             typename OutputIt,
             typename KeyEqual>
-  __device__ void retrieve(CG const& g,
-                           Key const& k,
-                           uint32_t* cg_counter,
-                           value_type* output_buffer,
-                           atomicT* num_matches,
-                           OutputIt output_begin,
-                           KeyEqual key_equal) noexcept
+  __device__ __forceinline__ void retrieve(CG const& g,
+                                           Key const& k,
+                                           uint32_t* cg_counter,
+                                           value_type* output_buffer,
+                                           atomicT* num_matches,
+                                           OutputIt output_begin,
+                                           KeyEqual key_equal) noexcept
   {
     const uint32_t lane_id = g.thread_rank();
 
@@ -1085,16 +1092,16 @@ class static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view_
             typename OutputIt1,
             typename OutputIt2,
             typename PairEqual>
-  __device__ void pair_retrieve(FlushingCG const& flushing_cg,
-                                ProbingCG const& probing_cg,
-                                value_type const& pair,
-                                uint32_t* flushing_cg_counter,
-                                value_type* probe_output_buffer,
-                                value_type* contained_output_buffer,
-                                atomicT* num_matches,
-                                OutputIt1 probe_output_begin,
-                                OutputIt2 contained_output_begin,
-                                PairEqual pair_equal) noexcept
+  __device__ __forceinline__ void pair_retrieve(FlushingCG const& flushing_cg,
+                                                ProbingCG const& probing_cg,
+                                                value_type const& pair,
+                                                uint32_t* flushing_cg_counter,
+                                                value_type* probe_output_buffer,
+                                                value_type* contained_output_buffer,
+                                                atomicT* num_matches,
+                                                OutputIt1 probe_output_begin,
+                                                OutputIt2 contained_output_begin,
+                                                PairEqual pair_equal) noexcept
   {
     const uint32_t cg_lane_id = probing_cg.thread_rank();
 
@@ -1209,15 +1216,15 @@ class static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view_
             typename OutputIt1,
             typename OutputIt2,
             typename PairEqual>
-  __device__ void pair_retrieve(CG const& g,
-                                value_type const& pair,
-                                uint32_t* cg_counter,
-                                value_type* probe_output_buffer,
-                                value_type* contained_output_buffer,
-                                atomicT* num_matches,
-                                OutputIt1 probe_output_begin,
-                                OutputIt2 contained_output_begin,
-                                PairEqual pair_equal) noexcept
+  __device__ __forceinline__ void pair_retrieve(CG const& g,
+                                                value_type const& pair,
+                                                uint32_t* cg_counter,
+                                                value_type* probe_output_buffer,
+                                                value_type* contained_output_buffer,
+                                                atomicT* num_matches,
+                                                OutputIt1 probe_output_begin,
+                                                OutputIt2 contained_output_begin,
+                                                PairEqual pair_equal) noexcept
   {
     const uint32_t lane_id = g.thread_rank();
 
diff --git a/include/cuco/detail/static_multimap/static_multimap.inl b/include/cuco/detail/static_multimap/static_multimap.inl
index 00160b18c..2445db8dc 100644
--- a/include/cuco/detail/static_multimap/static_multimap.inl
+++ b/include/cuco/detail/static_multimap/static_multimap.inl
@@ -27,7 +27,7 @@ namespace {
 template <typename Key>
 struct slot_is_filled {
   slot_is_filled(Key s) : empty_key_sentinel{s} {}
-  __device__ bool operator()(Key const& k) { return k != empty_key_sentinel; }
+  __device__ __forceinline__ bool operator()(Key const& k) { return k != empty_key_sentinel; }
   Key empty_key_sentinel;
 };
 }  // anonymous namespace
@@ -471,7 +471,7 @@ template <typename Key,
           class ProbeSequence,
           typename Allocator>
 template <typename CG>
-__device__ void
+__device__ __forceinline__ void
 static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_mutable_view::insert(
   CG g, value_type const& insert_pair) noexcept
 {
@@ -484,7 +484,7 @@ template <typename Key,
           class ProbeSequence,
           typename Allocator>
 template <typename CG>
-__device__ static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view
+__device__ __forceinline__ static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view
 static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::make_copy(
   CG g, pair_atomic_type* const memory_to_use, device_view source_device_view) noexcept
 {
@@ -523,7 +523,7 @@ template <typename Key,
           class ProbeSequence,
           typename Allocator>
 template <typename CG, typename atomicT, typename OutputIt>
-__inline__ __device__ void
+__device__ __forceinline__ void
 static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::flush_output_buffer(
   CG const& g,
   uint32_t const num_outputs,
@@ -540,7 +540,7 @@ template <typename Key,
           class ProbeSequence,
           typename Allocator>
 template <typename CG, typename atomicT, typename OutputIt1, typename OutputIt2>
-__inline__ __device__ void
+__device__ __forceinline__ void
 static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::flush_output_buffer(
   CG const& g,
   uint32_t const num_outputs,
@@ -565,7 +565,8 @@ template <typename Key,
           class ProbeSequence,
           typename Allocator>
 template <typename CG, typename KeyEqual>
-__device__ bool static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::contains(
+__device__ __forceinline__ bool
+static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::contains(
   CG g, Key const& k, KeyEqual key_equal) noexcept
 {
   return impl_.contains<uses_vector_load()>(g, k, key_equal);
@@ -577,7 +578,7 @@ template <typename Key,
           class ProbeSequence,
           typename Allocator>
 template <typename CG, typename KeyEqual>
-__device__ std::size_t
+__device__ __forceinline__ std::size_t
 static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::count(
   CG const& g, Key const& k, KeyEqual key_equal) noexcept
 {
@@ -591,7 +592,7 @@ template <typename Key,
           class ProbeSequence,
           typename Allocator>
 template <typename CG, typename KeyEqual>
-__device__ std::size_t
+__device__ __forceinline__ std::size_t
 static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::count_outer(
   CG const& g, Key const& k, KeyEqual key_equal) noexcept
 {
@@ -605,7 +606,7 @@ template <typename Key,
           class ProbeSequence,
           typename Allocator>
 template <typename CG, typename PairEqual>
-__device__ std::size_t
+__device__ __forceinline__ std::size_t
 static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::pair_count(
   CG const& g, value_type const& pair, PairEqual pair_equal) noexcept
 {
@@ -619,7 +620,7 @@ template <typename Key,
           class ProbeSequence,
           typename Allocator>
 template <typename CG, typename PairEqual>
-__device__ std::size_t
+__device__ __forceinline__ std::size_t
 static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::pair_count_outer(
   CG const& g, value_type const& pair, PairEqual pair_equal) noexcept
 {
@@ -638,7 +639,8 @@ template <uint32_t buffer_size,
           typename atomicT,
           typename OutputIt,
           typename KeyEqual>
-__device__ void static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::retrieve(
+__device__ __forceinline__ void
+static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::retrieve(
   FlushingCG const& flushing_cg,
   ProbingCG const& probing_cg,
   Key const& k,
@@ -676,7 +678,7 @@ template <uint32_t buffer_size,
           typename atomicT,
           typename OutputIt,
           typename KeyEqual>
-__device__ void
+__device__ __forceinline__ void
 static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::retrieve_outer(
   FlushingCG const& flushing_cg,
   ProbingCG const& probing_cg,
@@ -716,7 +718,7 @@ template <uint32_t buffer_size,
           typename OutputIt1,
           typename OutputIt2,
           typename PairEqual>
-__device__ void
+__device__ __forceinline__ void
 static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::pair_retrieve(
   FlushingCG const& flushing_cg,
   ProbingCG const& probing_cg,
@@ -767,7 +769,7 @@ template <uint32_t buffer_size,
           typename OutputIt1,
           typename OutputIt2,
           typename PairEqual>
-__device__ void
+__device__ __forceinline__ void
 static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::pair_retrieve_outer(
   FlushingCG const& flushing_cg,
   ProbingCG const& probing_cg,
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index f4e81ce80..b55abf489 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -184,7 +184,8 @@ class static_multimap {
    *
    * @return Boolean indicating if concurrent insert/find is supported.
    */
-  __host__ __device__ static constexpr bool supports_concurrent_insert_find() noexcept
+  __host__ __device__ __forceinline__ static constexpr bool
+  supports_concurrent_insert_find() noexcept
   {
     return cuco::detail::is_packable<value_type>();
   }
@@ -194,7 +195,10 @@ class static_multimap {
    *
    * @return The CG size.
    */
-  static constexpr uint32_t cg_size() noexcept { return ProbeSequence::cg_size(); }
+  __host__ __device__ __forceinline__ static constexpr uint32_t cg_size() noexcept
+  {
+    return ProbeSequence::cg_size();
+  }
 
   /**
    * @brief Construct a statically-sized map with the specified initial capacity,
@@ -620,7 +624,7 @@ class static_multimap {
      * @return void.
      */
     template <typename CG>
-    __device__ void insert(CG g, value_type const& insert_pair) noexcept;
+    __device__ __forceinline__ void insert(CG g, value_type const& insert_pair) noexcept;
 
    private:
     device_mutable_view_impl impl_;
@@ -667,28 +671,34 @@ class static_multimap {
      *
      * @return Slots array
      */
-    __device__ pair_atomic_type* get_slots() noexcept { return impl_.get_slots(); }
+    __device__ __forceinline__ pair_atomic_type* get_slots() noexcept { return impl_.get_slots(); }
 
     /**
      * @brief Gets slots array.
      *
      * @return Slots array
      */
-    __device__ pair_atomic_type const* get_slots() const noexcept { return impl_.get_slots(); }
+    __device__ __forceinline__ pair_atomic_type const* get_slots() const noexcept
+    {
+      return impl_.get_slots();
+    }
 
     /**
      * @brief Gets the maximum number of elements the hash map can hold.
      *
      * @return The maximum number of elements the hash map can hold
      */
-    __host__ __device__ std::size_t get_capacity() const noexcept { return impl_.get_capacity(); }
+    __host__ __device__ __forceinline__ std::size_t get_capacity() const noexcept
+    {
+      return impl_.get_capacity();
+    }
 
     /**
      * @brief Gets the sentinel value used to represent an empty key slot.
      *
      * @return The sentinel value used to represent an empty key slot
      */
-    __host__ __device__ Key get_empty_key_sentinel() const noexcept
+    __host__ __device__ __forceinline__ Key get_empty_key_sentinel() const noexcept
     {
       return impl_.get_empty_key_sentinel();
     }
@@ -698,7 +708,7 @@ class static_multimap {
      *
      * @return The sentinel value used to represent an empty value slot
      */
-    __host__ __device__ Value get_empty_value_sentinel() const noexcept
+    __host__ __device__ __forceinline__ Value get_empty_value_sentinel() const noexcept
     {
       return impl_.get_empty_value_sentinel();
     }
@@ -717,9 +727,8 @@ class static_multimap {
      * @return Copy of passed `device_view`
      */
     template <typename CG>
-    __device__ static device_view make_copy(CG g,
-                                            pair_atomic_type* const memory_to_use,
-                                            device_view source_device_view) noexcept;
+    __device__ __forceinline__ static device_view make_copy(
+      CG g, pair_atomic_type* const memory_to_use, device_view source_device_view) noexcept;
 
     /**
      * @brief Flushes per-CG buffer into the output sequence.
@@ -741,11 +750,11 @@ class static_multimap {
      * @param output_begin Beginning of the output sequence of key/value pairs
      */
     template <typename CG, typename atomicT, typename OutputIt>
-    __inline__ __device__ void flush_output_buffer(CG const& g,
-                                                   uint32_t const num_outputs,
-                                                   value_type* output_buffer,
-                                                   atomicT* num_matches,
-                                                   OutputIt output_begin) noexcept;
+    __device__ __forceinline__ void flush_output_buffer(CG const& g,
+                                                        uint32_t const num_outputs,
+                                                        value_type* output_buffer,
+                                                        atomicT* num_matches,
+                                                        OutputIt output_begin) noexcept;
 
     /**
      * @brief Flushes per-CG buffer into the output sequences.
@@ -771,13 +780,13 @@ class static_multimap {
      * pairs
      */
     template <typename CG, typename atomicT, typename OutputIt1, typename OutputIt2>
-    __inline__ __device__ void flush_output_buffer(CG const& g,
-                                                   uint32_t const num_outputs,
-                                                   value_type* probe_output_buffer,
-                                                   value_type* contained_output_buffer,
-                                                   atomicT* num_matches,
-                                                   OutputIt1 probe_output_begin,
-                                                   OutputIt2 contained_output_begin) noexcept;
+    __device__ __forceinline__ void flush_output_buffer(CG const& g,
+                                                        uint32_t const num_outputs,
+                                                        value_type* probe_output_buffer,
+                                                        value_type* contained_output_buffer,
+                                                        atomicT* num_matches,
+                                                        OutputIt1 probe_output_begin,
+                                                        OutputIt2 contained_output_begin) noexcept;
 
     /**
      * @brief Indicates whether the key `k` exists in the map.
@@ -798,7 +807,9 @@ class static_multimap {
      * containing `k` was inserted
      */
     template <typename CG, typename KeyEqual = thrust::equal_to<key_type>>
-    __device__ bool contains(CG g, Key const& k, KeyEqual key_equal = KeyEqual{}) noexcept;
+    __device__ __forceinline__ bool contains(CG g,
+                                             Key const& k,
+                                             KeyEqual key_equal = KeyEqual{}) noexcept;
 
     /**
      * @brief Counts the occurrence of a given key contained in multimap.
@@ -815,9 +826,9 @@ class static_multimap {
      * @return Number of matches found by the current thread
      */
     template <typename CG, typename KeyEqual = thrust::equal_to<key_type>>
-    __device__ std::size_t count(CG const& g,
-                                 Key const& k,
-                                 KeyEqual key_equal = KeyEqual{}) noexcept;
+    __device__ __forceinline__ std::size_t count(CG const& g,
+                                                 Key const& k,
+                                                 KeyEqual key_equal = KeyEqual{}) noexcept;
 
     /**
      * @brief Counts the occurrence of a given key contained in multimap. If no
@@ -835,9 +846,9 @@ class static_multimap {
      * @return Number of matches found by the current thread
      */
     template <typename CG, typename KeyEqual = thrust::equal_to<key_type>>
-    __device__ std::size_t count_outer(CG const& g,
-                                       Key const& k,
-                                       KeyEqual key_equal = KeyEqual{}) noexcept;
+    __device__ __forceinline__ std::size_t count_outer(CG const& g,
+                                                       Key const& k,
+                                                       KeyEqual key_equal = KeyEqual{}) noexcept;
 
     /**
      * @brief Counts the occurrence of a given key/value pair contained in multimap.
@@ -854,9 +865,9 @@ class static_multimap {
      * @return Number of matches found by the current thread
      */
     template <typename CG, typename PairEqual>
-    __device__ std::size_t pair_count(CG const& g,
-                                      value_type const& pair,
-                                      PairEqual pair_equal) noexcept;
+    __device__ __forceinline__ std::size_t pair_count(CG const& g,
+                                                      value_type const& pair,
+                                                      PairEqual pair_equal) noexcept;
 
     /**
      * @brief Counts the occurrence of a given key/value pair contained in multimap.
@@ -874,9 +885,9 @@ class static_multimap {
      * @return Number of matches found by the current thread
      */
     template <typename CG, typename PairEqual>
-    __device__ std::size_t pair_count_outer(CG const& g,
-                                            value_type const& pair,
-                                            PairEqual pair_equal) noexcept;
+    __device__ __forceinline__ std::size_t pair_count_outer(CG const& g,
+                                                            value_type const& pair,
+                                                            PairEqual pair_equal) noexcept;
 
     /**
      * @brief Retrieves all the matches of a given key contained in multimap with per-flushing-CG
@@ -908,14 +919,14 @@ class static_multimap {
               typename atomicT,
               typename OutputIt,
               typename KeyEqual = thrust::equal_to<key_type>>
-    __device__ void retrieve(FlushingCG const& flushing_cg,
-                             ProbingCG const& probing_cg,
-                             Key const& k,
-                             uint32_t* flushing_cg_counter,
-                             value_type* output_buffer,
-                             atomicT* num_matches,
-                             OutputIt output_begin,
-                             KeyEqual key_equal = KeyEqual{}) noexcept;
+    __device__ __forceinline__ void retrieve(FlushingCG const& flushing_cg,
+                                             ProbingCG const& probing_cg,
+                                             Key const& k,
+                                             uint32_t* flushing_cg_counter,
+                                             value_type* output_buffer,
+                                             atomicT* num_matches,
+                                             OutputIt output_begin,
+                                             KeyEqual key_equal = KeyEqual{}) noexcept;
 
     /**
      * @brief Retrieves all the matches of a given key contained in multimap with per-flushing-CG
@@ -948,14 +959,14 @@ class static_multimap {
               typename atomicT,
               typename OutputIt,
               typename KeyEqual = thrust::equal_to<key_type>>
-    __device__ void retrieve_outer(FlushingCG const& flushing_cg,
-                                   ProbingCG const& probing_cg,
-                                   Key const& k,
-                                   uint32_t* flushing_cg_counter,
-                                   value_type* output_buffer,
-                                   atomicT* num_matches,
-                                   OutputIt output_begin,
-                                   KeyEqual key_equal = KeyEqual{}) noexcept;
+    __device__ __forceinline__ void retrieve_outer(FlushingCG const& flushing_cg,
+                                                   ProbingCG const& probing_cg,
+                                                   Key const& k,
+                                                   uint32_t* flushing_cg_counter,
+                                                   value_type* output_buffer,
+                                                   atomicT* num_matches,
+                                                   OutputIt output_begin,
+                                                   KeyEqual key_equal = KeyEqual{}) noexcept;
 
     /**
      * @brief Retrieves all the matches of a given pair contained in multimap with per-flushing-CG
@@ -993,16 +1004,16 @@ class static_multimap {
               typename OutputIt1,
               typename OutputIt2,
               typename PairEqual>
-    __device__ void pair_retrieve(FlushingCG const& flushing_cg,
-                                  ProbingCG const& probing_cg,
-                                  value_type const& pair,
-                                  uint32_t* warp_counter,
-                                  value_type* probe_output_buffer,
-                                  value_type* contained_output_buffer,
-                                  atomicT* num_matches,
-                                  OutputIt1 probe_output_begin,
-                                  OutputIt2 contained_output_begin,
-                                  PairEqual pair_equal) noexcept;
+    __device__ __forceinline__ void pair_retrieve(FlushingCG const& flushing_cg,
+                                                  ProbingCG const& probing_cg,
+                                                  value_type const& pair,
+                                                  uint32_t* warp_counter,
+                                                  value_type* probe_output_buffer,
+                                                  value_type* contained_output_buffer,
+                                                  atomicT* num_matches,
+                                                  OutputIt1 probe_output_begin,
+                                                  OutputIt2 contained_output_begin,
+                                                  PairEqual pair_equal) noexcept;
 
     /**
      * @brief Retrieves all the matches of a given pair contained in multimap with per-flushing-CG
@@ -1041,16 +1052,16 @@ class static_multimap {
               typename OutputIt1,
               typename OutputIt2,
               typename PairEqual>
-    __device__ void pair_retrieve_outer(FlushingCG const& flushing_cg,
-                                        ProbingCG const& probing_cg,
-                                        value_type const& pair,
-                                        uint32_t* flushing_cg_counter,
-                                        value_type* probe_output_buffer,
-                                        value_type* contained_output_buffer,
-                                        atomicT* num_matches,
-                                        OutputIt1 probe_output_begin,
-                                        OutputIt2 contained_output_begin,
-                                        PairEqual pair_equal) noexcept;
+    __device__ __forceinline__ void pair_retrieve_outer(FlushingCG const& flushing_cg,
+                                                        ProbingCG const& probing_cg,
+                                                        value_type const& pair,
+                                                        uint32_t* flushing_cg_counter,
+                                                        value_type* probe_output_buffer,
+                                                        value_type* contained_output_buffer,
+                                                        atomicT* num_matches,
+                                                        OutputIt1 probe_output_begin,
+                                                        OutputIt2 contained_output_begin,
+                                                        PairEqual pair_equal) noexcept;
 
    private:
     device_view_impl impl_;

From 61437411cfc1a0fa7dacceaf137a1fc29c68e0c4 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Tue, 26 Oct 2021 12:51:09 -0400
Subject: [PATCH 252/267] Change template parameter order: allocator before
 probe sequence

---
 .../static_multimap/find_all_bench.cu         |   1 +
 .../static_multimap/device_view_impl.inl      |  18 +-
 .../static_multimap/static_multimap.inl       | 164 +++++++++---------
 include/cuco/static_multimap.cuh              |   4 +-
 tests/static_multimap/static_multimap_test.cu |   5 +
 5 files changed, 99 insertions(+), 93 deletions(-)

diff --git a/benchmarks/hash_table/static_multimap/find_all_bench.cu b/benchmarks/hash_table/static_multimap/find_all_bench.cu
index d98aac8d4..f498d0016 100644
--- a/benchmarks/hash_table/static_multimap/find_all_bench.cu
+++ b/benchmarks/hash_table/static_multimap/find_all_bench.cu
@@ -75,6 +75,7 @@ std::enable_if_t<(sizeof(Key) == sizeof(Value)), void> nvbench_find_all(
   cuco::static_multimap<Key,
                         Value,
                         cuda::thread_scope_device,
+                        cuco::cuda_allocator<char>,
                         cuco::detail::double_hashing<Key, Value, CGSize>>
     map{size, -1, -1};
   map.insert(d_pairs.begin(), d_pairs.end());
diff --git a/include/cuco/detail/static_multimap/device_view_impl.inl b/include/cuco/detail/static_multimap/device_view_impl.inl
index 26c2502e3..77b8465e5 100644
--- a/include/cuco/detail/static_multimap/device_view_impl.inl
+++ b/include/cuco/detail/static_multimap/device_view_impl.inl
@@ -21,9 +21,9 @@ namespace cuco {
 template <typename Key,
           typename Value,
           cuda::thread_scope Scope,
-          class ProbeSequence,
-          typename Allocator>
-class static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view_impl_base {
+          typename Allocator,
+          class ProbeSequence>
+class static_multimap<Key, Value, Scope, Allocator, ProbeSequence>::device_view_impl_base {
  private:
   ProbeSequence probe_sequence_;  ///< Probe sequence used to probe the hash map
   Key empty_key_sentinel_{};      ///< Key value that represents an empty slot
@@ -194,9 +194,9 @@ class static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view_
 template <typename Key,
           typename Value,
           cuda::thread_scope Scope,
-          class ProbeSequence,
-          typename Allocator>
-class static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_mutable_view_impl
+          typename Allocator,
+          class ProbeSequence>
+class static_multimap<Key, Value, Scope, Allocator, ProbeSequence>::device_mutable_view_impl
   : public device_view_impl_base {
  public:
   using value_type     = typename device_view_impl_base::value_type;
@@ -429,9 +429,9 @@ class static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_mutab
 template <typename Key,
           typename Value,
           cuda::thread_scope Scope,
-          class ProbeSequence,
-          typename Allocator>
-class static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view_impl
+          typename Allocator,
+          class ProbeSequence>
+class static_multimap<Key, Value, Scope, Allocator, ProbeSequence>::device_view_impl
   : public device_view_impl_base {
  public:
   using value_type     = typename device_view_impl_base::value_type;
diff --git a/include/cuco/detail/static_multimap/static_multimap.inl b/include/cuco/detail/static_multimap/static_multimap.inl
index 2445db8dc..5fe5e95f1 100644
--- a/include/cuco/detail/static_multimap/static_multimap.inl
+++ b/include/cuco/detail/static_multimap/static_multimap.inl
@@ -37,9 +37,9 @@ namespace cuco {
 template <typename Key,
           typename Value,
           cuda::thread_scope Scope,
-          class ProbeSequence,
-          typename Allocator>
-static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::static_multimap(
+          typename Allocator,
+          class ProbeSequence>
+static_multimap<Key, Value, Scope, Allocator, ProbeSequence>::static_multimap(
   std::size_t capacity,
   Key empty_key_sentinel,
   Value empty_value_sentinel,
@@ -69,10 +69,10 @@ static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::static_multimap(
 template <typename Key,
           typename Value,
           cuda::thread_scope Scope,
-          class ProbeSequence,
-          typename Allocator>
+          typename Allocator,
+          class ProbeSequence>
 template <typename InputIt>
-void static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::insert(InputIt first,
+void static_multimap<Key, Value, Scope, Allocator, ProbeSequence>::insert(InputIt first,
                                                                           InputIt last,
                                                                           cudaStream_t stream)
 {
@@ -91,10 +91,10 @@ void static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::insert(InputI
 template <typename Key,
           typename Value,
           cuda::thread_scope Scope,
-          class ProbeSequence,
-          typename Allocator>
+          typename Allocator,
+          class ProbeSequence>
 template <typename InputIt, typename StencilIt, typename Predicate>
-void static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::insert_if(
+void static_multimap<Key, Value, Scope, Allocator, ProbeSequence>::insert_if(
   InputIt first, InputIt last, StencilIt stencil, Predicate pred, cudaStream_t stream)
 {
   auto num_elements = std::distance(first, last);
@@ -113,10 +113,10 @@ void static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::insert_if(
 template <typename Key,
           typename Value,
           cuda::thread_scope Scope,
-          class ProbeSequence,
-          typename Allocator>
+          typename Allocator,
+          class ProbeSequence>
 template <typename InputIt, typename OutputIt, typename KeyEqual>
-void static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::contains(
+void static_multimap<Key, Value, Scope, Allocator, ProbeSequence>::contains(
   InputIt first, InputIt last, OutputIt output_begin, cudaStream_t stream, KeyEqual key_equal) const
 {
   auto num_keys = std::distance(first, last);
@@ -134,10 +134,10 @@ void static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::contains(
 template <typename Key,
           typename Value,
           cuda::thread_scope Scope,
-          class ProbeSequence,
-          typename Allocator>
+          typename Allocator,
+          class ProbeSequence>
 template <typename InputIt, typename KeyEqual>
-std::size_t static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::count(
+std::size_t static_multimap<Key, Value, Scope, Allocator, ProbeSequence>::count(
   InputIt first, InputIt last, cudaStream_t stream, KeyEqual key_equal) const
 {
   auto num_keys = std::distance(first, last);
@@ -165,10 +165,10 @@ std::size_t static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::count(
 template <typename Key,
           typename Value,
           cuda::thread_scope Scope,
-          class ProbeSequence,
-          typename Allocator>
+          typename Allocator,
+          class ProbeSequence>
 template <typename InputIt, typename KeyEqual>
-std::size_t static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::count_outer(
+std::size_t static_multimap<Key, Value, Scope, Allocator, ProbeSequence>::count_outer(
   InputIt first, InputIt last, cudaStream_t stream, KeyEqual key_equal) const
 {
   auto num_keys = std::distance(first, last);
@@ -196,10 +196,10 @@ std::size_t static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::count_
 template <typename Key,
           typename Value,
           cuda::thread_scope Scope,
-          class ProbeSequence,
-          typename Allocator>
+          typename Allocator,
+          class ProbeSequence>
 template <typename InputIt, typename PairEqual>
-std::size_t static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::pair_count(
+std::size_t static_multimap<Key, Value, Scope, Allocator, ProbeSequence>::pair_count(
   InputIt first, InputIt last, PairEqual pair_equal, cudaStream_t stream) const
 {
   auto num_keys = std::distance(first, last);
@@ -228,10 +228,10 @@ std::size_t static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::pair_c
 template <typename Key,
           typename Value,
           cuda::thread_scope Scope,
-          class ProbeSequence,
-          typename Allocator>
+          typename Allocator,
+          class ProbeSequence>
 template <typename InputIt, typename PairEqual>
-std::size_t static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::pair_count_outer(
+std::size_t static_multimap<Key, Value, Scope, Allocator, ProbeSequence>::pair_count_outer(
   InputIt first, InputIt last, PairEqual pair_equal, cudaStream_t stream) const
 {
   auto num_keys = std::distance(first, last);
@@ -260,10 +260,10 @@ std::size_t static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::pair_c
 template <typename Key,
           typename Value,
           cuda::thread_scope Scope,
-          class ProbeSequence,
-          typename Allocator>
+          typename Allocator,
+          class ProbeSequence>
 template <typename InputIt, typename OutputIt, typename KeyEqual>
-OutputIt static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::retrieve(
+OutputIt static_multimap<Key, Value, Scope, Allocator, ProbeSequence>::retrieve(
   InputIt first, InputIt last, OutputIt output_begin, cudaStream_t stream, KeyEqual key_equal) const
 {
   auto num_keys = std::distance(first, last);
@@ -309,10 +309,10 @@ OutputIt static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::retrieve(
 template <typename Key,
           typename Value,
           cuda::thread_scope Scope,
-          class ProbeSequence,
-          typename Allocator>
+          typename Allocator,
+          class ProbeSequence>
 template <typename InputIt, typename OutputIt, typename KeyEqual>
-OutputIt static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::retrieve_outer(
+OutputIt static_multimap<Key, Value, Scope, Allocator, ProbeSequence>::retrieve_outer(
   InputIt first, InputIt last, OutputIt output_begin, cudaStream_t stream, KeyEqual key_equal) const
 {
   auto num_keys = std::distance(first, last);
@@ -358,11 +358,11 @@ OutputIt static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::retrieve_
 template <typename Key,
           typename Value,
           cuda::thread_scope Scope,
-          class ProbeSequence,
-          typename Allocator>
+          typename Allocator,
+          class ProbeSequence>
 template <typename InputIt, typename OutputIt1, typename OutputIt2, typename PairEqual>
 std::pair<OutputIt1, OutputIt2>
-static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::pair_retrieve(
+static_multimap<Key, Value, Scope, Allocator, ProbeSequence>::pair_retrieve(
   InputIt first,
   InputIt last,
   OutputIt1 probe_output_begin,
@@ -413,11 +413,11 @@ static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::pair_retrieve(
 template <typename Key,
           typename Value,
           cuda::thread_scope Scope,
-          class ProbeSequence,
-          typename Allocator>
+          typename Allocator,
+          class ProbeSequence>
 template <typename InputIt, typename OutputIt1, typename OutputIt2, typename PairEqual>
 std::pair<OutputIt1, OutputIt2>
-static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::pair_retrieve_outer(
+static_multimap<Key, Value, Scope, Allocator, ProbeSequence>::pair_retrieve_outer(
   InputIt first,
   InputIt last,
   OutputIt1 probe_output_begin,
@@ -468,11 +468,11 @@ static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::pair_retrieve_oute
 template <typename Key,
           typename Value,
           cuda::thread_scope Scope,
-          class ProbeSequence,
-          typename Allocator>
+          typename Allocator,
+          class ProbeSequence>
 template <typename CG>
 __device__ __forceinline__ void
-static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_mutable_view::insert(
+static_multimap<Key, Value, Scope, Allocator, ProbeSequence>::device_mutable_view::insert(
   CG g, value_type const& insert_pair) noexcept
 {
   impl_.insert<uses_vector_load()>(g, insert_pair);
@@ -481,11 +481,11 @@ static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_mutable_vie
 template <typename Key,
           typename Value,
           cuda::thread_scope Scope,
-          class ProbeSequence,
-          typename Allocator>
+          typename Allocator,
+          class ProbeSequence>
 template <typename CG>
-__device__ __forceinline__ static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view
-static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::make_copy(
+__device__ __forceinline__ static_multimap<Key, Value, Scope, Allocator, ProbeSequence>::device_view
+static_multimap<Key, Value, Scope, Allocator, ProbeSequence>::device_view::make_copy(
   CG g, pair_atomic_type* const memory_to_use, device_view source_device_view) noexcept
 {
 #if defined(CUCO_HAS_CUDA_BARRIER)
@@ -520,11 +520,11 @@ static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::make_
 template <typename Key,
           typename Value,
           cuda::thread_scope Scope,
-          class ProbeSequence,
-          typename Allocator>
+          typename Allocator,
+          class ProbeSequence>
 template <typename CG, typename atomicT, typename OutputIt>
 __device__ __forceinline__ void
-static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::flush_output_buffer(
+static_multimap<Key, Value, Scope, Allocator, ProbeSequence>::device_view::flush_output_buffer(
   CG const& g,
   uint32_t const num_outputs,
   value_type* output_buffer,
@@ -537,11 +537,11 @@ static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::flush
 template <typename Key,
           typename Value,
           cuda::thread_scope Scope,
-          class ProbeSequence,
-          typename Allocator>
+          typename Allocator,
+          class ProbeSequence>
 template <typename CG, typename atomicT, typename OutputIt1, typename OutputIt2>
 __device__ __forceinline__ void
-static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::flush_output_buffer(
+static_multimap<Key, Value, Scope, Allocator, ProbeSequence>::device_view::flush_output_buffer(
   CG const& g,
   uint32_t const num_outputs,
   value_type* probe_output_buffer,
@@ -562,11 +562,11 @@ static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::flush
 template <typename Key,
           typename Value,
           cuda::thread_scope Scope,
-          class ProbeSequence,
-          typename Allocator>
+          typename Allocator,
+          class ProbeSequence>
 template <typename CG, typename KeyEqual>
 __device__ __forceinline__ bool
-static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::contains(
+static_multimap<Key, Value, Scope, Allocator, ProbeSequence>::device_view::contains(
   CG g, Key const& k, KeyEqual key_equal) noexcept
 {
   return impl_.contains<uses_vector_load()>(g, k, key_equal);
@@ -575,11 +575,11 @@ static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::conta
 template <typename Key,
           typename Value,
           cuda::thread_scope Scope,
-          class ProbeSequence,
-          typename Allocator>
+          typename Allocator,
+          class ProbeSequence>
 template <typename CG, typename KeyEqual>
 __device__ __forceinline__ std::size_t
-static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::count(
+static_multimap<Key, Value, Scope, Allocator, ProbeSequence>::device_view::count(
   CG const& g, Key const& k, KeyEqual key_equal) noexcept
 {
   constexpr bool is_outer = false;
@@ -589,11 +589,11 @@ static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::count
 template <typename Key,
           typename Value,
           cuda::thread_scope Scope,
-          class ProbeSequence,
-          typename Allocator>
+          typename Allocator,
+          class ProbeSequence>
 template <typename CG, typename KeyEqual>
 __device__ __forceinline__ std::size_t
-static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::count_outer(
+static_multimap<Key, Value, Scope, Allocator, ProbeSequence>::device_view::count_outer(
   CG const& g, Key const& k, KeyEqual key_equal) noexcept
 {
   constexpr bool is_outer = true;
@@ -603,11 +603,11 @@ static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::count
 template <typename Key,
           typename Value,
           cuda::thread_scope Scope,
-          class ProbeSequence,
-          typename Allocator>
+          typename Allocator,
+          class ProbeSequence>
 template <typename CG, typename PairEqual>
 __device__ __forceinline__ std::size_t
-static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::pair_count(
+static_multimap<Key, Value, Scope, Allocator, ProbeSequence>::device_view::pair_count(
   CG const& g, value_type const& pair, PairEqual pair_equal) noexcept
 {
   constexpr bool is_outer = false;
@@ -617,11 +617,11 @@ static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::pair_
 template <typename Key,
           typename Value,
           cuda::thread_scope Scope,
-          class ProbeSequence,
-          typename Allocator>
+          typename Allocator,
+          class ProbeSequence>
 template <typename CG, typename PairEqual>
 __device__ __forceinline__ std::size_t
-static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::pair_count_outer(
+static_multimap<Key, Value, Scope, Allocator, ProbeSequence>::device_view::pair_count_outer(
   CG const& g, value_type const& pair, PairEqual pair_equal) noexcept
 {
   constexpr bool is_outer = true;
@@ -631,8 +631,8 @@ static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::pair_
 template <typename Key,
           typename Value,
           cuda::thread_scope Scope,
-          class ProbeSequence,
-          typename Allocator>
+          typename Allocator,
+          class ProbeSequence>
 template <uint32_t buffer_size,
           typename FlushingCG,
           typename ProbingCG,
@@ -640,7 +640,7 @@ template <uint32_t buffer_size,
           typename OutputIt,
           typename KeyEqual>
 __device__ __forceinline__ void
-static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::retrieve(
+static_multimap<Key, Value, Scope, Allocator, ProbeSequence>::device_view::retrieve(
   FlushingCG const& flushing_cg,
   ProbingCG const& probing_cg,
   Key const& k,
@@ -670,8 +670,8 @@ static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::retri
 template <typename Key,
           typename Value,
           cuda::thread_scope Scope,
-          class ProbeSequence,
-          typename Allocator>
+          typename Allocator,
+          class ProbeSequence>
 template <uint32_t buffer_size,
           typename FlushingCG,
           typename ProbingCG,
@@ -679,7 +679,7 @@ template <uint32_t buffer_size,
           typename OutputIt,
           typename KeyEqual>
 __device__ __forceinline__ void
-static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::retrieve_outer(
+static_multimap<Key, Value, Scope, Allocator, ProbeSequence>::device_view::retrieve_outer(
   FlushingCG const& flushing_cg,
   ProbingCG const& probing_cg,
   Key const& k,
@@ -709,8 +709,8 @@ static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::retri
 template <typename Key,
           typename Value,
           cuda::thread_scope Scope,
-          class ProbeSequence,
-          typename Allocator>
+          typename Allocator,
+          class ProbeSequence>
 template <uint32_t buffer_size,
           typename FlushingCG,
           typename ProbingCG,
@@ -719,7 +719,7 @@ template <uint32_t buffer_size,
           typename OutputIt2,
           typename PairEqual>
 __device__ __forceinline__ void
-static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::pair_retrieve(
+static_multimap<Key, Value, Scope, Allocator, ProbeSequence>::device_view::pair_retrieve(
   FlushingCG const& flushing_cg,
   ProbingCG const& probing_cg,
   value_type const& pair,
@@ -760,8 +760,8 @@ static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::pair_
 template <typename Key,
           typename Value,
           cuda::thread_scope Scope,
-          class ProbeSequence,
-          typename Allocator>
+          typename Allocator,
+          class ProbeSequence>
 template <uint32_t buffer_size,
           typename FlushingCG,
           typename ProbingCG,
@@ -770,7 +770,7 @@ template <uint32_t buffer_size,
           typename OutputIt2,
           typename PairEqual>
 __device__ __forceinline__ void
-static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::pair_retrieve_outer(
+static_multimap<Key, Value, Scope, Allocator, ProbeSequence>::device_view::pair_retrieve_outer(
   FlushingCG const& flushing_cg,
   ProbingCG const& probing_cg,
   value_type const& pair,
@@ -811,9 +811,9 @@ static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::device_view::pair_
 template <typename Key,
           typename Value,
           cuda::thread_scope Scope,
-          class ProbeSequence,
-          typename Allocator>
-std::size_t static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::get_size(
+          typename Allocator,
+          class ProbeSequence>
+std::size_t static_multimap<Key, Value, Scope, Allocator, ProbeSequence>::get_size(
   cudaStream_t stream) const noexcept
 {
   auto begin = thrust::make_transform_iterator(
@@ -826,9 +826,9 @@ std::size_t static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::get_si
 template <typename Key,
           typename Value,
           cuda::thread_scope Scope,
-          class ProbeSequence,
-          typename Allocator>
-float static_multimap<Key, Value, Scope, ProbeSequence, Allocator>::get_load_factor(
+          typename Allocator,
+          class ProbeSequence>
+float static_multimap<Key, Value, Scope, Allocator, ProbeSequence>::get_load_factor(
   cudaStream_t stream) const noexcept
 {
   auto size = get_size(stream);
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index b55abf489..cdc699d35 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -134,13 +134,13 @@ namespace cuco {
 template <typename Key,
           typename Value,
           cuda::thread_scope Scope = cuda::thread_scope_device,
+          typename Allocator       = cuco::cuda_allocator<char>,
           class ProbeSequence      = cuco::detail::double_hashing<Key,
                                                              Value,
                                                              2,
                                                              cuco::detail::MurmurHash3_32<Key>,
                                                              cuco::detail::MurmurHash3_32<Key>,
-                                                             Scope>,
-          typename Allocator       = cuco::cuda_allocator<char>>
+                                                             Scope>>
 class static_multimap {
   static_assert(
     cuco::is_bitwise_comparable_v<Key>,
diff --git a/tests/static_multimap/static_multimap_test.cu b/tests/static_multimap/static_multimap_test.cu
index 4e92d7eae..1444c3b21 100644
--- a/tests/static_multimap/static_multimap_test.cu
+++ b/tests/static_multimap/static_multimap_test.cu
@@ -256,6 +256,7 @@ TEMPLATE_TEST_CASE_SIG("User defined key and value type",
     cuco::static_multimap<key_pair,
                           value_pair,
                           cuda::thread_scope_device,
+                          cuco::cuda_allocator<char>,
                           cuco::detail::linear_probing<key_pair, value_pair, 1, hash_key_pair>>
       map{capacity, sentinel_key, sentinel_value};
     test_custom_key_value_type<Key, Value>(map, insert_pairs, insert_keys.begin(), num_pairs);
@@ -406,6 +407,7 @@ TEMPLATE_TEST_CASE_SIG("Multiplicity equals two",
     cuco::static_multimap<Key,
                           Value,
                           cuda::thread_scope_device,
+                          cuco::cuda_allocator<char>,
                           cuco::detail::linear_probing<Key, Value, 1>>
       map{5, -1, -1};
     test_multiplicity_two<Key, Value>(
@@ -534,6 +536,7 @@ TEMPLATE_TEST_CASE_SIG("Tests of non-matches",
     cuco::static_multimap<Key,
                           Value,
                           cuda::thread_scope_device,
+                          cuco::cuda_allocator<char>,
                           cuco::detail::linear_probing<Key, Value, 1>>
       map{num_keys * 2, -1, -1};
     test_non_matches<Key, Value>(map, d_pairs.begin(), d_keys.begin(), num_keys);
@@ -588,6 +591,7 @@ TEMPLATE_TEST_CASE_SIG("Tests of insert_if",
     cuco::static_multimap<Key,
                           Value,
                           cuda::thread_scope_device,
+                          cuco::cuda_allocator<char>,
                           cuco::detail::linear_probing<Key, Value, 1>>
       map{num_keys * 2, -1, -1};
     test_insert_if<Key>(map, d_pairs.begin(), d_keys.begin(), num_keys);
@@ -687,6 +691,7 @@ TEMPLATE_TEST_CASE_SIG("Tests of pair functions",
     cuco::static_multimap<Key,
                           Value,
                           cuda::thread_scope_device,
+                          cuco::cuda_allocator<char>,
                           cuco::detail::linear_probing<Key, Value, 1>>
       map{num_pairs * 2, -1, -1};
     test_pair_functions<Key, Value>(map, d_pairs.begin(), num_pairs);

From 14621e19842ce5006675dac5505428a14cb1889e Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Tue, 26 Oct 2021 13:42:28 -0400
Subject: [PATCH 253/267] Move linear probing and double hashing to a new
 public header

---
 .../static_multimap/find_all_bench.cu         |   2 +-
 include/cuco/detail/probe_sequence_base.cuh   | 112 ++++++++++++++++++
 include/cuco/{detail => }/probe_sequences.cuh | 105 ++--------------
 include/cuco/static_multimap.cuh              |  14 +--
 tests/static_multimap/static_multimap_test.cu |  10 +-
 5 files changed, 134 insertions(+), 109 deletions(-)
 create mode 100644 include/cuco/detail/probe_sequence_base.cuh
 rename include/cuco/{detail => }/probe_sequences.cuh (68%)

diff --git a/benchmarks/hash_table/static_multimap/find_all_bench.cu b/benchmarks/hash_table/static_multimap/find_all_bench.cu
index f498d0016..eeddb39b4 100644
--- a/benchmarks/hash_table/static_multimap/find_all_bench.cu
+++ b/benchmarks/hash_table/static_multimap/find_all_bench.cu
@@ -76,7 +76,7 @@ std::enable_if_t<(sizeof(Key) == sizeof(Value)), void> nvbench_find_all(
                         Value,
                         cuda::thread_scope_device,
                         cuco::cuda_allocator<char>,
-                        cuco::detail::double_hashing<Key, Value, CGSize>>
+                        cuco::double_hashing<Key, Value, CGSize>>
     map{size, -1, -1};
   map.insert(d_pairs.begin(), d_pairs.end());
 
diff --git a/include/cuco/detail/probe_sequence_base.cuh b/include/cuco/detail/probe_sequence_base.cuh
new file mode 100644
index 000000000..0da624177
--- /dev/null
+++ b/include/cuco/detail/probe_sequence_base.cuh
@@ -0,0 +1,112 @@
+/*
+ * Copyright (c) 2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cuda/std/atomic>
+
+#include <cuco/detail/hash_functions.cuh>
+#include <cuco/detail/pair.cuh>
+
+namespace cuco {
+namespace detail {
+
+/**
+ * @brief Base class for a hash map probe sequence. This class should not be used directly.
+ *
+ * Hash map operations are generally memory-bandwidth bound. A vector-load loads two consecutive
+ * slots instead of one to fully utilize the 16B memory load supported by SASS/hardware thus
+ * improve memory throughput. This method (flagged by `uses_vector_load` logic) is implicitly
+ * applied to all hash map operations (e.g. `insert`, `count`, and `retrieve`, etc.) when pairs
+ * are packable (see `cuco::detail::is_packable` logic).
+ *
+ * @tparam Key Type used for keys
+ * @tparam Value Type of the mapped values
+ * @tparam CGSize Number of threads in CUDA Cooperative Groups
+ * @tparam Scope The scope in which multimap operations will be performed by
+ * individual threads
+ */
+template <typename Key, typename Value, uint32_t CGSize, cuda::thread_scope Scope>
+class probe_sequence_base {
+ protected:
+  using value_type         = cuco::pair_type<Key, Value>;
+  using key_type           = Key;
+  using mapped_type        = Value;
+  using atomic_key_type    = cuda::atomic<key_type, Scope>;
+  using atomic_mapped_type = cuda::atomic<mapped_type, Scope>;
+  using pair_atomic_type   = cuco::pair_type<atomic_key_type, atomic_mapped_type>;
+  using iterator           = pair_atomic_type*;
+  using const_iterator     = pair_atomic_type const*;
+
+ public:
+  /**
+   * @brief Returns the size of the CUDA cooperative thread group.
+   */
+  __host__ __device__ static constexpr uint32_t cg_size() noexcept { return CGSize; }
+
+  /**
+   * @brief Returns the number of elements loaded with each vector-load.
+   */
+  __host__ __device__ static constexpr uint32_t vector_width() noexcept { return 2u; }
+
+  /**
+   * @brief Indicates if vector-load is used.
+   *
+   * Users have no explicit control on whether vector-load is used.
+   *
+   * @return Boolean indicating if vector-load is used.
+   */
+  __host__ __device__ static constexpr bool uses_vector_load() noexcept
+  {
+    return cuco::detail::is_packable<value_type>();
+  }
+
+  /**
+   * @brief Constructs a probe sequence based on the given hash map features.
+   *
+   * @param slots Pointer to beginning of the hash map slots
+   * @param capacity Capacity of the hash map
+   */
+  __host__ __device__ explicit probe_sequence_base(iterator slots, std::size_t capacity)
+    : slots_{slots}, capacity_{capacity}
+  {
+  }
+
+  /**
+   * @brief Returns the capacity of the hash map.
+   */
+  __host__ __device__ __forceinline__ std::size_t get_capacity() const noexcept
+  {
+    return capacity_;
+  }
+
+  /**
+   * @brief Returns slots array.
+   */
+  __device__ __forceinline__ iterator get_slots() noexcept { return slots_; }
+
+  /**
+   * @brief Returns slots array.
+   */
+  __device__ __forceinline__ const_iterator get_slots() const noexcept { return slots_; }
+
+ protected:
+  iterator slots_;              ///< Pointer to beginning of the hash map slots
+  const std::size_t capacity_;  ///< Total number of slots
+};                              // class probe_sequence_base
+
+}  // namespace detail
+}  // namespace cuco
diff --git a/include/cuco/detail/probe_sequences.cuh b/include/cuco/probe_sequences.cuh
similarity index 68%
rename from include/cuco/detail/probe_sequences.cuh
rename to include/cuco/probe_sequences.cuh
index 8b725f325..4e84a47ab 100644
--- a/include/cuco/detail/probe_sequences.cuh
+++ b/include/cuco/probe_sequences.cuh
@@ -16,97 +16,9 @@
 
 #pragma once
 
-#include <cuda/std/atomic>
-
-#include <cuco/detail/hash_functions.cuh>
-#include <cuco/detail/pair.cuh>
+#include <cuco/detail/probe_sequence_base.cuh>
 
 namespace cuco {
-namespace detail {
-
-/**
- * @brief Base class for a hash map probe sequence. This class should not be used directly.
- *
- * Hash map operations are generally memory-bandwidth bound. A vector-load loads two consecutive
- * slots instead of one to fully utilize the 16B memory load supported by SASS/hardware thus
- * improve memory throughput. This method (flagged by `uses_vector_load` logic) is implicitly
- * applied to all hash map operations (e.g. `insert`, `count`, and `retrieve`, etc.) when pairs
- * are packable (see `cuco::detail::is_packable` logic).
- *
- * @tparam Key Type used for keys
- * @tparam Value Type of the mapped values
- * @tparam CGSize Number of threads in CUDA Cooperative Groups
- * @tparam Scope The scope in which multimap operations will be performed by
- * individual threads
- */
-template <typename Key, typename Value, uint32_t CGSize, cuda::thread_scope Scope>
-class probe_sequence_base {
- protected:
-  using value_type         = cuco::pair_type<Key, Value>;
-  using key_type           = Key;
-  using mapped_type        = Value;
-  using atomic_key_type    = cuda::atomic<key_type, Scope>;
-  using atomic_mapped_type = cuda::atomic<mapped_type, Scope>;
-  using pair_atomic_type   = cuco::pair_type<atomic_key_type, atomic_mapped_type>;
-  using iterator           = pair_atomic_type*;
-  using const_iterator     = pair_atomic_type const*;
-
- public:
-  /**
-   * @brief Returns the size of the CUDA cooperative thread group.
-   */
-  __host__ __device__ static constexpr uint32_t cg_size() noexcept { return CGSize; }
-
-  /**
-   * @brief Returns the number of elements loaded with each vector-load.
-   */
-  __host__ __device__ static constexpr uint32_t vector_width() noexcept { return 2u; }
-
-  /**
-   * @brief Indicates if vector-load is used.
-   *
-   * Users have no explicit control on whether vector-load is used.
-   *
-   * @return Boolean indicating if vector-load is used.
-   */
-  __host__ __device__ static constexpr bool uses_vector_load() noexcept
-  {
-    return cuco::detail::is_packable<value_type>();
-  }
-
-  /**
-   * @brief Constructs a probe sequence based on the given hash map features.
-   *
-   * @param slots Pointer to beginning of the hash map slots
-   * @param capacity Capacity of the hash map
-   */
-  __host__ __device__ explicit probe_sequence_base(iterator slots, std::size_t capacity)
-    : slots_{slots}, capacity_{capacity}
-  {
-  }
-
-  /**
-   * @brief Returns the capacity of the hash map.
-   */
-  __host__ __device__ __forceinline__ std::size_t get_capacity() const noexcept
-  {
-    return capacity_;
-  }
-
-  /**
-   * @brief Returns slots array.
-   */
-  __device__ __forceinline__ iterator get_slots() noexcept { return slots_; }
-
-  /**
-   * @brief Returns slots array.
-   */
-  __device__ __forceinline__ const_iterator get_slots() const noexcept { return slots_; }
-
- protected:
-  iterator slots_;              ///< Pointer to beginning of the hash map slots
-  const std::size_t capacity_;  ///< Total number of slots
-};                              // class probe_sequence_base
 
 /**
  * @brief Cooperative Groups based Linear probing scheme.
@@ -129,9 +41,9 @@ template <typename Key,
           uint32_t CGSize          = 8,
           typename Hash            = cuco::detail::MurmurHash3_32<Key>,
           cuda::thread_scope Scope = cuda::thread_scope_device>
-class linear_probing : public probe_sequence_base<Key, Value, CGSize, Scope> {
+class linear_probing : public detail::probe_sequence_base<Key, Value, CGSize, Scope> {
  public:
-  using probe_sequence_base_type = probe_sequence_base<Key, Value, CGSize, Scope>;
+  using probe_sequence_base_type = detail::probe_sequence_base<Key, Value, CGSize, Scope>;
   using iterator                 = typename probe_sequence_base_type::iterator;
   using probe_sequence_base_type::capacity_;
   using probe_sequence_base_type::cg_size;
@@ -149,7 +61,7 @@ class linear_probing : public probe_sequence_base<Key, Value, CGSize, Scope> {
   __host__ __device__ explicit linear_probing(iterator slots,
                                               std::size_t capacity,
                                               Hash hash = Hash{})
-    : probe_sequence_base<Key, Value, CGSize, Scope>{slots, capacity}, hash_(hash)
+    : detail::probe_sequence_base<Key, Value, CGSize, Scope>{slots, capacity}, hash_{hash}
   {
   }
 
@@ -233,9 +145,9 @@ template <typename Key,
           typename Hash1           = cuco::detail::MurmurHash3_32<Key>,
           typename Hash2           = cuco::detail::MurmurHash3_32<Key>,
           cuda::thread_scope Scope = cuda::thread_scope_device>
-class double_hashing : public probe_sequence_base<Key, Value, CGSize, Scope> {
+class double_hashing : public detail::probe_sequence_base<Key, Value, CGSize, Scope> {
  public:
-  using probe_sequence_base_type = probe_sequence_base<Key, Value, CGSize, Scope>;
+  using probe_sequence_base_type = detail::probe_sequence_base<Key, Value, CGSize, Scope>;
   using iterator                 = typename probe_sequence_base_type::iterator;
   using probe_sequence_base_type::capacity_;
   using probe_sequence_base_type::cg_size;
@@ -257,7 +169,9 @@ class double_hashing : public probe_sequence_base<Key, Value, CGSize, Scope> {
                                               std::size_t capacity,
                                               Hash1 hash1 = Hash1{},
                                               Hash2 hash2 = Hash2{1}) noexcept
-    : probe_sequence_base<Key, Value, CGSize, Scope>{slots, capacity}, hash1_{hash1}, hash2_{hash2}
+    : detail::probe_sequence_base<Key, Value, CGSize, Scope>{slots, capacity},
+      hash1_{hash1},
+      hash2_{hash2}
   {
   }
 
@@ -311,5 +225,4 @@ class double_hashing : public probe_sequence_base<Key, Value, CGSize, Scope> {
   std::size_t step_size_;  ///< The step stride when searching for the next slot
 };                         // class double_hashing
 
-}  // namespace detail
 }  // namespace cuco
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index cdc699d35..c7959b1f3 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -23,6 +23,7 @@
 #include <memory>
 
 #include <cuco/allocator.hpp>
+#include <cuco/probe_sequences.cuh>
 #include <cuco/traits.hpp>
 
 #if defined(CUDART_VERSION) && (CUDART_VERSION >= 11000) && defined(__CUDA_ARCH__) && \
@@ -41,7 +42,6 @@
 
 #include <cuco/detail/error.hpp>
 #include <cuco/detail/prime.hpp>
-#include <cuco/detail/probe_sequences.cuh>
 #include <cuco/detail/static_multimap/kernels.cuh>
 
 namespace cuco {
@@ -135,12 +135,12 @@ template <typename Key,
           typename Value,
           cuda::thread_scope Scope = cuda::thread_scope_device,
           typename Allocator       = cuco::cuda_allocator<char>,
-          class ProbeSequence      = cuco::detail::double_hashing<Key,
-                                                             Value,
-                                                             2,
-                                                             cuco::detail::MurmurHash3_32<Key>,
-                                                             cuco::detail::MurmurHash3_32<Key>,
-                                                             Scope>>
+          class ProbeSequence      = cuco::double_hashing<Key,
+                                                     Value,
+                                                     2,
+                                                     cuco::detail::MurmurHash3_32<Key>,
+                                                     cuco::detail::MurmurHash3_32<Key>,
+                                                     Scope>>
 class static_multimap {
   static_assert(
     cuco::is_bitwise_comparable_v<Key>,
diff --git a/tests/static_multimap/static_multimap_test.cu b/tests/static_multimap/static_multimap_test.cu
index 1444c3b21..619256220 100644
--- a/tests/static_multimap/static_multimap_test.cu
+++ b/tests/static_multimap/static_multimap_test.cu
@@ -257,7 +257,7 @@ TEMPLATE_TEST_CASE_SIG("User defined key and value type",
                           value_pair,
                           cuda::thread_scope_device,
                           cuco::cuda_allocator<char>,
-                          cuco::detail::linear_probing<key_pair, value_pair, 1, hash_key_pair>>
+                          cuco::linear_probing<key_pair, value_pair, 1, hash_key_pair>>
       map{capacity, sentinel_key, sentinel_value};
     test_custom_key_value_type<Key, Value>(map, insert_pairs, insert_keys.begin(), num_pairs);
   }
@@ -408,7 +408,7 @@ TEMPLATE_TEST_CASE_SIG("Multiplicity equals two",
                           Value,
                           cuda::thread_scope_device,
                           cuco::cuda_allocator<char>,
-                          cuco::detail::linear_probing<Key, Value, 1>>
+                          cuco::linear_probing<Key, Value, 1>>
       map{5, -1, -1};
     test_multiplicity_two<Key, Value>(
       map, d_pairs.begin(), d_keys.begin(), d_results.begin(), num_items);
@@ -537,7 +537,7 @@ TEMPLATE_TEST_CASE_SIG("Tests of non-matches",
                           Value,
                           cuda::thread_scope_device,
                           cuco::cuda_allocator<char>,
-                          cuco::detail::linear_probing<Key, Value, 1>>
+                          cuco::linear_probing<Key, Value, 1>>
       map{num_keys * 2, -1, -1};
     test_non_matches<Key, Value>(map, d_pairs.begin(), d_keys.begin(), num_keys);
   }
@@ -592,7 +592,7 @@ TEMPLATE_TEST_CASE_SIG("Tests of insert_if",
                           Value,
                           cuda::thread_scope_device,
                           cuco::cuda_allocator<char>,
-                          cuco::detail::linear_probing<Key, Value, 1>>
+                          cuco::linear_probing<Key, Value, 1>>
       map{num_keys * 2, -1, -1};
     test_insert_if<Key>(map, d_pairs.begin(), d_keys.begin(), num_keys);
   }
@@ -692,7 +692,7 @@ TEMPLATE_TEST_CASE_SIG("Tests of pair functions",
                           Value,
                           cuda::thread_scope_device,
                           cuco::cuda_allocator<char>,
-                          cuco::detail::linear_probing<Key, Value, 1>>
+                          cuco::linear_probing<Key, Value, 1>>
       map{num_pairs * 2, -1, -1};
     test_pair_functions<Key, Value>(map, d_pairs.begin(), num_pairs);
   }

From 7d5b8e8d9ba3ebd9270ba8e3c81e40f3b8673e7f Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Tue, 26 Oct 2021 14:44:38 -0400
Subject: [PATCH 254/267] Update comment

---
 include/cuco/probe_sequences.cuh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/include/cuco/probe_sequences.cuh b/include/cuco/probe_sequences.cuh
index 4e84a47ab..a65724ece 100644
--- a/include/cuco/probe_sequences.cuh
+++ b/include/cuco/probe_sequences.cuh
@@ -81,7 +81,7 @@ class linear_probing : public detail::probe_sequence_base<Key, Value, CGSize, Sc
     auto const hash_value = [&]() {
       auto const tmp = hash_(k);
       if constexpr (uses_vector_load()) {
-        // ensure initial hash value is always even
+        // initial hash value is always even
         return tmp + tmp % 2;
       }
       if constexpr (not uses_vector_load()) { return tmp; }

From 6fa48506970834f2f76d93f0738c4b0e4987466a Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Wed, 27 Oct 2021 15:28:55 -0400
Subject: [PATCH 255/267] Avoid public access to probe sequence key, value and
 scope types

---
 .../static_multimap/find_all_bench.cu         |   4 +-
 include/cuco/detail/probe_sequence_base.cuh   | 291 +++++++++++++++++-
 .../static_multimap/device_view_impl.inl      |  29 +-
 include/cuco/probe_sequences.cuh              | 228 --------------
 include/cuco/static_multimap.cuh              |  31 +-
 tests/static_multimap/static_multimap_test.cu |  10 +-
 6 files changed, 316 insertions(+), 277 deletions(-)
 delete mode 100644 include/cuco/probe_sequences.cuh

diff --git a/benchmarks/hash_table/static_multimap/find_all_bench.cu b/benchmarks/hash_table/static_multimap/find_all_bench.cu
index eeddb39b4..483e01bf7 100644
--- a/benchmarks/hash_table/static_multimap/find_all_bench.cu
+++ b/benchmarks/hash_table/static_multimap/find_all_bench.cu
@@ -76,7 +76,9 @@ std::enable_if_t<(sizeof(Key) == sizeof(Value)), void> nvbench_find_all(
                         Value,
                         cuda::thread_scope_device,
                         cuco::cuda_allocator<char>,
-                        cuco::double_hashing<Key, Value, CGSize>>
+                        cuco::double_hashing<CGSize,
+                                             cuco::detail::MurmurHash3_32<Key>,
+                                             cuco::detail::MurmurHash3_32<Key>>>
     map{size, -1, -1};
   map.insert(d_pairs.begin(), d_pairs.end());
 
diff --git a/include/cuco/detail/probe_sequence_base.cuh b/include/cuco/detail/probe_sequence_base.cuh
index 0da624177..3a9cafbd1 100644
--- a/include/cuco/detail/probe_sequence_base.cuh
+++ b/include/cuco/detail/probe_sequence_base.cuh
@@ -25,23 +25,23 @@ namespace cuco {
 namespace detail {
 
 /**
- * @brief Base class for a hash map probe sequence. This class should not be used directly.
+ * @brief Cooperative Groups based Linear probing scheme.
  *
- * Hash map operations are generally memory-bandwidth bound. A vector-load loads two consecutive
- * slots instead of one to fully utilize the 16B memory load supported by SASS/hardware thus
- * improve memory throughput. This method (flagged by `uses_vector_load` logic) is implicitly
- * applied to all hash map operations (e.g. `insert`, `count`, and `retrieve`, etc.) when pairs
- * are packable (see `cuco::detail::is_packable` logic).
+ * Linear probing is efficient only when few collisions are present. Performance hints:
+ * - Use linear probing only when collisions are rare. e.g. low occupancy or low multiplicity.
+ * - `CGSize` = 1 or 2 when hash map is small (10'000'000 or less), 4 or 8 otherwise.
+ *
+ * `Hash` should be callable object type.
  *
  * @tparam Key Type used for keys
  * @tparam Value Type of the mapped values
- * @tparam CGSize Number of threads in CUDA Cooperative Groups
+ * @tparam Hash Unary callable type
  * @tparam Scope The scope in which multimap operations will be performed by
  * individual threads
  */
-template <typename Key, typename Value, uint32_t CGSize, cuda::thread_scope Scope>
-class probe_sequence_base {
- protected:
+template <typename Key, typename Value, cuda::thread_scope Scope, uint32_t CGSize, typename Hash>
+class linear_probing {
+ public:
   using value_type         = cuco::pair_type<Key, Value>;
   using key_type           = Key;
   using mapped_type        = Value;
@@ -51,11 +51,154 @@ class probe_sequence_base {
   using iterator           = pair_atomic_type*;
   using const_iterator     = pair_atomic_type const*;
 
+  /**
+   * @brief Returns the size of the CUDA cooperative thread group.
+   */
+  static constexpr std::size_t cg_size = CGSize;
+
+  /**
+   * @brief Returns the number of elements loaded with each vector-load.
+   */
+  __host__ __device__ static constexpr uint32_t vector_width() noexcept { return 2u; }
+
+  /**
+   * @brief Indicates if vector-load is used.
+   *
+   * Users have no explicit control on whether vector-load is used.
+   *
+   * @return Boolean indicating if vector-load is used.
+   */
+  __host__ __device__ static constexpr bool uses_vector_load() noexcept
+  {
+    return cuco::detail::is_packable<value_type>();
+  }
+
+  /**
+   * @brief Constructs a linear probing scheme based on the given hash map features.
+   *
+   * @param slots Pointer to beginning of the hash map slots
+   * @param capacity Capacity of the hash map
+   * @param hash Unary function to hash each key
+   */
+  __host__ __device__ explicit linear_probing(iterator slots, std::size_t capacity)
+    : slots_{slots}, capacity_{capacity}, hash_{Hash{}}
+  {
+  }
+
+  /**
+   * @brief Returns the capacity of the hash map.
+   */
+  __host__ __device__ __forceinline__ std::size_t get_capacity() const noexcept
+  {
+    return capacity_;
+  }
+
+  /**
+   * @brief Returns slots array.
+   */
+  __device__ __forceinline__ iterator get_slots() noexcept { return slots_; }
+
+  /**
+   * @brief Returns slots array.
+   */
+  __device__ __forceinline__ const_iterator get_slots() const noexcept { return slots_; }
+
+  /**
+   * @brief Returns the initial slot for a given key `k`.
+   *
+   * If vector-load is enabled, the return slot is always even to avoid illegal memory access.
+   *
+   * @tparam CG CUDA Cooperative Groups type
+   * @param g the Cooperative Group for which the initial slot is needed
+   * @param k The key to get the slot for
+   * @return Pointer to the initial slot for `k`
+   */
+  template <typename CG>
+  __device__ __forceinline__ iterator initial_slot(CG const& g, Key const k) noexcept
+  {
+    auto const hash_value = [&]() {
+      auto const tmp = hash_(k);
+      if constexpr (uses_vector_load()) {
+        // initial hash value is always even
+        return tmp + tmp % 2;
+      }
+      if constexpr (not uses_vector_load()) { return tmp; }
+    }();
+
+    auto const offset = [&]() {
+      if constexpr (uses_vector_load()) { return g.thread_rank() * vector_width(); }
+      if constexpr (not uses_vector_load()) { return g.thread_rank(); }
+    }();
+
+    // Each CG accesses to a window of (`cg_size` * `vector_width`)
+    // slots if vector-load is used or `cg_size` slots otherwise
+    return &slots_[(hash_value + offset) % capacity_];
+  }
+
+  /**
+   * @brief Given a slot `s`, returns the next slot.
+   *
+   * If `s` is the last slot, wraps back around to the first slot.
+   *
+   * @param s The slot to advance
+   * @return The next slot after `s`
+   */
+  __device__ __forceinline__ iterator next_slot(iterator s) noexcept
+  {
+    std::size_t index = s - slots_;
+    std::size_t offset;
+    if constexpr (uses_vector_load()) {
+      offset = cg_size * vector_width();
+    } else {
+      offset = cg_size;
+    }
+    return &slots_[(index + offset) % capacity_];
+  }
+
+ protected:
+  iterator slots_;              ///< Pointer to beginning of the hash map slots
+  const std::size_t capacity_;  ///< Total number of slots
+  Hash hash_;
+};  // class linear_probing
+
+/**
+ * @brief Cooperative Groups based double hashing scheme.
+ *
+ * Default probe sequence for `cuco::static_multimap`. Double hashing shows superior
+ * performance when dealing with high multiplicty and/or high occupancy use cases. Performance
+ * hints:
+ * - `CGSize` = 1 or 2 when hash map is small (10'000'000 or less), 4 or 8 otherwise.
+ *
+ * `Hash1` and `Hash2` should be callable object type.
+ *
+ * @tparam Key Type used for keys
+ * @tparam Value Type of the mapped values
+ * @tparam Hash1 Unary callable type
+ * @tparam Hash2 Unary callable type
+ * @tparam Scope The scope in which multimap operations will be performed by
+ * individual threads
+ */
+template <typename Key,
+          typename Value,
+          cuda::thread_scope Scope,
+          uint32_t CGSize,
+          typename Hash1,
+          typename Hash2>
+class double_hashing {
  public:
+  using value_type         = cuco::pair_type<Key, Value>;
+  using key_type           = Key;
+  using mapped_type        = Value;
+  using atomic_key_type    = cuda::atomic<key_type, Scope>;
+  using atomic_mapped_type = cuda::atomic<mapped_type, Scope>;
+  using pair_atomic_type   = cuco::pair_type<atomic_key_type, atomic_mapped_type>;
+  using iterator           = pair_atomic_type*;
+  using const_iterator     = pair_atomic_type const*;
+
   /**
    * @brief Returns the size of the CUDA cooperative thread group.
    */
-  __host__ __device__ static constexpr uint32_t cg_size() noexcept { return CGSize; }
+  static constexpr std::size_t cg_size = CGSize;
 
   /**
    * @brief Returns the number of elements loaded with each vector-load.
@@ -75,13 +218,17 @@ class probe_sequence_base {
   }
 
   /**
-   * @brief Constructs a probe sequence based on the given hash map features.
+   * @brief Constructs a double hashing scheme based on the given hash map features.
+   *
+   * `hash2` takes a different seed to reduce the chance of secondary clustering.
    *
    * @param slots Pointer to beginning of the hash map slots
    * @param capacity Capacity of the hash map
+   * @param hash1 First hasher to hash each key
+   * @param hash2 Second hasher to determine step size
    */
-  __host__ __device__ explicit probe_sequence_base(iterator slots, std::size_t capacity)
-    : slots_{slots}, capacity_{capacity}
+  __host__ __device__ explicit double_hashing(iterator slots, std::size_t capacity)
+    : slots_{slots}, capacity_{capacity}, hash1_{Hash1{}}, hash2_{Hash2{}}, step_size_{}
   {
   }
 
@@ -102,11 +249,125 @@ class probe_sequence_base {
    * @brief Returns slots array.
    */
   __device__ __forceinline__ const_iterator get_slots() const noexcept { return slots_; }
+  /**
+   * @brief Returns the initial slot for a given key `k`.
+   *
+   * If vector-load is enabled, the return slot is always a multiple of (`cg_size` * `vector_width`)
+   * to avoid illegal memory access.
+   *
+   * @tparam CG CUDA Cooperative Groups type
+   * @param g the Cooperative Group for which the initial slot is needed
+   * @param k The key to get the slot for
+   * @return Pointer to the initial slot for `k`
+   */
+  template <typename CG>
+  __device__ __forceinline__ iterator initial_slot(CG const& g, Key const k) noexcept
+  {
+    std::size_t index;
+    auto const hash_value = hash1_(k);
+    if constexpr (uses_vector_load()) {
+      // step size in range [1, prime - 1] * cg_size * vector_width
+      step_size_ =
+        (hash2_(k) % (capacity_ / (cg_size * vector_width()) - 1) + 1) * cg_size * vector_width();
+      index = hash_value % (capacity_ / (cg_size * vector_width())) * cg_size * vector_width() +
+              g.thread_rank() * vector_width();
+    } else {
+      // step size in range [1, prime - 1] * cg_size
+      step_size_ = (hash2_(k) % (capacity_ / cg_size - 1) + 1) * cg_size;
+      index      = (hash_value + g.thread_rank()) % capacity_;
+    }
+    return slots_ + index;
+  }
+
+  /**
+   * @brief Given a slot `s`, returns the next slot.
+   *
+   * If `s` is the last slot, wraps back around to the first slot.
+   *
+   * @param s The slot to advance
+   * @return The next slot after `s`
+   */
+  __device__ __forceinline__ iterator next_slot(iterator s) noexcept
+  {
+    std::size_t index = s - slots_;
+    return &slots_[(index + step_size_) % capacity_];
+  }
 
  protected:
   iterator slots_;              ///< Pointer to beginning of the hash map slots
   const std::size_t capacity_;  ///< Total number of slots
-};                              // class probe_sequence_base
+  Hash1 hash1_;
+  Hash2 hash2_;
+  std::size_t step_size_;  ///< The step stride when searching for the next slot
+};                         // class double_hashing
+
+/**
+ * @brief Base class for a hash map probe sequence. This class should not be used directly.
+ *
+ * Hash map operations are generally memory-bandwidth bound. A vector-load loads two consecutive
+ * slots instead of one to fully utilize the 16B memory load supported by SASS/hardware thus
+ * improve memory throughput. This method (flagged by `uses_vector_load` logic) is implicitly
+ * applied to all hash map operations (e.g. `insert`, `count`, and `retrieve`, etc.) when pairs
+ * are packable (see `cuco::detail::is_packable` logic).
+ *
+ * @tparam Key Type used for keys
+ * @tparam Value Type of the mapped values
+ * @tparam Scope The scope in which multimap operations will be performed by
+ * individual threads
+ */
+template <typename ProbeImpl, typename Key, typename Value, cuda::thread_scope Scope>
+class probe_sequence_base : public ProbeImpl::template impl<Key, Value, Scope> {
+ public:
+  using value_type         = cuco::pair_type<Key, Value>;
+  using key_type           = Key;
+  using mapped_type        = Value;
+  using atomic_key_type    = cuda::atomic<key_type, Scope>;
+  using atomic_mapped_type = cuda::atomic<mapped_type, Scope>;
+  using pair_atomic_type   = cuco::pair_type<atomic_key_type, atomic_mapped_type>;
+  using iterator           = pair_atomic_type*;
+  using const_iterator     = pair_atomic_type const*;
 
+  static constexpr std::size_t cg_size = ProbeImpl::cg_size;
+
+  /**
+   * @brief Constructs a probe sequence based on the given hash map features.
+   *
+   * @param slots Pointer to beginning of the hash map slots
+   * @param capacity Capacity of the hash map
+   */
+  __host__ __device__ explicit probe_sequence_base(iterator slots, std::size_t capacity)
+    : ProbeImpl::template impl<Key, Value, Scope>{slots, capacity}
+  {
+  }
+};  // class probe_sequence_base
 }  // namespace detail
+
+template <uint32_t CGSize, typename Hash>
+class linear_probing {
+ public:
+  static constexpr std::size_t cg_size = CGSize;
+
+  /**
+   * @brief Returns the number of elements loaded with each vector-load.
+   */
+  __host__ __device__ static constexpr uint32_t vector_width() noexcept { return 2u; }
+
+  template <typename Key, typename Value, cuda::thread_scope Scope>
+  using impl = typename detail::linear_probing<Key, Value, Scope, CGSize, Hash>;
+};
+
+template <uint32_t CGSize, typename Hash1, typename Hash2>
+class double_hashing {
+ public:
+  static constexpr std::size_t cg_size = CGSize;
+
+  /**
+   * @brief Returns the number of elements loaded with each vector-load.
+   */
+  __host__ __device__ static constexpr uint32_t vector_width() noexcept { return 2u; }
+
+  template <typename Key, typename Value, cuda::thread_scope Scope>
+  using impl = typename detail::double_hashing<Key, Value, Scope, CGSize, Hash1, Hash2>;
+};
+
 }  // namespace cuco
diff --git a/include/cuco/detail/static_multimap/device_view_impl.inl b/include/cuco/detail/static_multimap/device_view_impl.inl
index 77b8465e5..c4e520446 100644
--- a/include/cuco/detail/static_multimap/device_view_impl.inl
+++ b/include/cuco/detail/static_multimap/device_view_impl.inl
@@ -24,18 +24,14 @@ template <typename Key,
           typename Allocator,
           class ProbeSequence>
 class static_multimap<Key, Value, Scope, Allocator, ProbeSequence>::device_view_impl_base {
- private:
-  ProbeSequence probe_sequence_;  ///< Probe sequence used to probe the hash map
-  Key empty_key_sentinel_{};      ///< Key value that represents an empty slot
-  Value empty_value_sentinel_{};  ///< Initial Value of empty slot
-
  protected:
   // Import member type definitions from `static_multimap`
-  using value_type     = value_type;
-  using key_type       = Key;
-  using mapped_type    = Value;
-  using iterator       = pair_atomic_type*;
-  using const_iterator = pair_atomic_type const*;
+  using value_type          = value_type;
+  using key_type            = Key;
+  using mapped_type         = Value;
+  using iterator            = pair_atomic_type*;
+  using const_iterator      = pair_atomic_type const*;
+  using probe_sequence_type = probe_sequence_type;
 
   /**
    * @brief Indicates if vector-load is used.
@@ -44,12 +40,15 @@ class static_multimap<Key, Value, Scope, Allocator, ProbeSequence>::device_view_
    *
    * @return Boolean indicating if vector-load is used.
    */
-  static constexpr bool uses_vector_load() noexcept { return ProbeSequence::uses_vector_load(); }
+  static constexpr bool uses_vector_load() noexcept
+  {
+    return probe_sequence_type::uses_vector_load();
+  }
 
   /**
    * @brief Returns the number of pairs loaded with each vector-load
    */
-  static constexpr uint32_t vector_width() noexcept { return ProbeSequence::vector_width(); }
+  static constexpr uint32_t vector_width() noexcept { return probe_sequence_type::vector_width(); }
 
   __host__ __device__ device_view_impl_base(pair_atomic_type* slots,
                                             std::size_t capacity,
@@ -189,7 +188,11 @@ class static_multimap<Key, Value, Scope, Allocator, ProbeSequence>::device_view_
     }
   }
 
-};  // class device_view_impl_base
+ private:
+  probe_sequence_type probe_sequence_;  ///< Probe sequence used to probe the hash map
+  Key empty_key_sentinel_{};            ///< Key value that represents an empty slot
+  Value empty_value_sentinel_{};        ///< Initial Value of empty slot
+};                                      // class device_view_impl_base
 
 template <typename Key,
           typename Value,
diff --git a/include/cuco/probe_sequences.cuh b/include/cuco/probe_sequences.cuh
deleted file mode 100644
index a65724ece..000000000
--- a/include/cuco/probe_sequences.cuh
+++ /dev/null
@@ -1,228 +0,0 @@
-/*
- * Copyright (c) 2021, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#pragma once
-
-#include <cuco/detail/probe_sequence_base.cuh>
-
-namespace cuco {
-
-/**
- * @brief Cooperative Groups based Linear probing scheme.
- *
- * Linear probing is efficient only when few collisions are present. Performance hints:
- * - Use linear probing only when collisions are rare. e.g. low occupancy or low multiplicity.
- * - `CGSize` = 1 or 2 when hash map is small (10'000'000 or less), 4 or 8 otherwise.
- *
- * `Hash` should be callable object type.
- *
- * @tparam Key Type used for keys
- * @tparam Value Type of the mapped values
- * @tparam CGSize Number of threads in CUDA Cooperative Groups
- * @tparam Hash Unary callable type
- * @tparam Scope The scope in which multimap operations will be performed by
- * individual threads
- */
-template <typename Key,
-          typename Value,
-          uint32_t CGSize          = 8,
-          typename Hash            = cuco::detail::MurmurHash3_32<Key>,
-          cuda::thread_scope Scope = cuda::thread_scope_device>
-class linear_probing : public detail::probe_sequence_base<Key, Value, CGSize, Scope> {
- public:
-  using probe_sequence_base_type = detail::probe_sequence_base<Key, Value, CGSize, Scope>;
-  using iterator                 = typename probe_sequence_base_type::iterator;
-  using probe_sequence_base_type::capacity_;
-  using probe_sequence_base_type::cg_size;
-  using probe_sequence_base_type::slots_;
-  using probe_sequence_base_type::uses_vector_load;
-  using probe_sequence_base_type::vector_width;
-
-  /**
-   * @brief Constructs a linear probing scheme based on the given hash map features.
-   *
-   * @param slots Pointer to beginning of the hash map slots
-   * @param capacity Capacity of the hash map
-   * @param hash Unary function to hash each key
-   */
-  __host__ __device__ explicit linear_probing(iterator slots,
-                                              std::size_t capacity,
-                                              Hash hash = Hash{})
-    : detail::probe_sequence_base<Key, Value, CGSize, Scope>{slots, capacity}, hash_{hash}
-  {
-  }
-
-  /**
-   * @brief Returns the initial slot for a given key `k`.
-   *
-   * If vector-load is enabled, the return slot is always even to avoid illegal memory access.
-   *
-   * @tparam CG CUDA Cooperative Groups type
-   * @param g the Cooperative Group for which the initial slot is needed
-   * @param k The key to get the slot for
-   * @return Pointer to the initial slot for `k`
-   */
-  template <typename CG>
-  __device__ __forceinline__ iterator initial_slot(CG const& g, Key const k) noexcept
-  {
-    auto const hash_value = [&]() {
-      auto const tmp = hash_(k);
-      if constexpr (uses_vector_load()) {
-        // initial hash value is always even
-        return tmp + tmp % 2;
-      }
-      if constexpr (not uses_vector_load()) { return tmp; }
-    }();
-
-    auto const offset = [&]() {
-      if constexpr (uses_vector_load()) { return g.thread_rank() * vector_width(); }
-      if constexpr (not uses_vector_load()) { return g.thread_rank(); }
-    }();
-
-    // Each CG accesses to a window of (`cg_size` * `vector_width`)
-    // slots if vector-load is used or `cg_size` slots otherwise
-    return &slots_[(hash_value + offset) % capacity_];
-  }
-
-  /**
-   * @brief Given a slot `s`, returns the next slot.
-   *
-   * If `s` is the last slot, wraps back around to the first slot.
-   *
-   * @param s The slot to advance
-   * @return The next slot after `s`
-   */
-  __device__ __forceinline__ iterator next_slot(iterator s) noexcept
-  {
-    std::size_t index = s - slots_;
-    std::size_t offset;
-    if constexpr (uses_vector_load()) {
-      offset = cg_size() * vector_width();
-    } else {
-      offset = cg_size();
-    }
-    return &slots_[(index + offset) % capacity_];
-  }
-
- private:
-  Hash hash_;  ///< The unary callable used to hash the key
-};             // class linear_probing
-
-/**
- * @brief Cooperative Groups based double hashing scheme.
- *
- * Default probe sequence for `cuco::static_multimap`. Double hashing shows superior
- * performance when dealing with high multiplicty and/or high occupancy use cases. Performance
- * hints:
- * - `CGSize` = 1 or 2 when hash map is small (10'000'000 or less), 4 or 8 otherwise.
- *
- * `Hash1` and `Hash2` should be callable object type.
- *
- * @tparam Key Type used for keys
- * @tparam Value Type of the mapped values
- * @tparam CGSize Number of threads in CUDA Cooperative Groups
- * @tparam Hash1 Unary callable type
- * @tparam Hash2 Unary callable type
- * @tparam Scope The scope in which multimap operations will be performed by
- * individual threads
- */
-template <typename Key,
-          typename Value,
-          uint32_t CGSize          = 8,
-          typename Hash1           = cuco::detail::MurmurHash3_32<Key>,
-          typename Hash2           = cuco::detail::MurmurHash3_32<Key>,
-          cuda::thread_scope Scope = cuda::thread_scope_device>
-class double_hashing : public detail::probe_sequence_base<Key, Value, CGSize, Scope> {
- public:
-  using probe_sequence_base_type = detail::probe_sequence_base<Key, Value, CGSize, Scope>;
-  using iterator                 = typename probe_sequence_base_type::iterator;
-  using probe_sequence_base_type::capacity_;
-  using probe_sequence_base_type::cg_size;
-  using probe_sequence_base_type::slots_;
-  using probe_sequence_base_type::uses_vector_load;
-  using probe_sequence_base_type::vector_width;
-
-  /**
-   * @brief Constructs a double hashing scheme based on the given hash map features.
-   *
-   * `hash2` takes a different seed to reduce the chance of secondary clustering.
-   *
-   * @param slots Pointer to beginning of the hash map slots
-   * @param capacity Capacity of the hash map
-   * @param hash1 First hasher to hash each key
-   * @param hash2 Second hasher to determine step size
-   */
-  __host__ __device__ explicit double_hashing(iterator slots,
-                                              std::size_t capacity,
-                                              Hash1 hash1 = Hash1{},
-                                              Hash2 hash2 = Hash2{1}) noexcept
-    : detail::probe_sequence_base<Key, Value, CGSize, Scope>{slots, capacity},
-      hash1_{hash1},
-      hash2_{hash2}
-  {
-  }
-
-  /**
-   * @brief Returns the initial slot for a given key `k`.
-   *
-   * If vector-load is enabled, the return slot is always a multiple of (`cg_size` * `vector_width`)
-   * to avoid illegal memory access.
-   *
-   * @tparam CG CUDA Cooperative Groups type
-   * @param g the Cooperative Group for which the initial slot is needed
-   * @param k The key to get the slot for
-   * @return Pointer to the initial slot for `k`
-   */
-  template <typename CG>
-  __device__ __forceinline__ iterator initial_slot(CG const& g, Key const k) noexcept
-  {
-    std::size_t index;
-    auto const hash_value = hash1_(k);
-    if constexpr (uses_vector_load()) {
-      // step size in range [1, prime - 1] * cg_size * vector_width
-      step_size_ = (hash2_(k) % (capacity_ / (cg_size() * vector_width()) - 1) + 1) * cg_size() *
-                   vector_width();
-      index = hash_value % (capacity_ / (cg_size() * vector_width())) * cg_size() * vector_width() +
-              g.thread_rank() * vector_width();
-    } else {
-      // step size in range [1, prime - 1] * cg_size
-      step_size_ = (hash2_(k) % (capacity_ / cg_size() - 1) + 1) * cg_size();
-      index      = (hash_value + g.thread_rank()) % capacity_;
-    }
-    return slots_ + index;
-  }
-
-  /**
-   * @brief Given a slot `s`, returns the next slot.
-   *
-   * If `s` is the last slot, wraps back around to the first slot.
-   *
-   * @param s The slot to advance
-   * @return The next slot after `s`
-   */
-  __device__ __forceinline__ iterator next_slot(iterator s) noexcept
-  {
-    std::size_t index = s - slots_;
-    return &slots_[(index + step_size_) % capacity_];
-  }
-
- private:
-  Hash1 hash1_;            ///< The first unary callable used to hash the key
-  Hash2 hash2_;            ///< The second unary callable used to determine step size
-  std::size_t step_size_;  ///< The step stride when searching for the next slot
-};                         // class double_hashing
-
-}  // namespace cuco
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index c7959b1f3..7d595a116 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -23,7 +23,6 @@
 #include <memory>
 
 #include <cuco/allocator.hpp>
-#include <cuco/probe_sequences.cuh>
 #include <cuco/traits.hpp>
 
 #if defined(CUDART_VERSION) && (CUDART_VERSION >= 11000) && defined(__CUDA_ARCH__) && \
@@ -42,6 +41,7 @@
 
 #include <cuco/detail/error.hpp>
 #include <cuco/detail/prime.hpp>
+#include <cuco/detail/probe_sequence_base.cuh>
 #include <cuco/detail/static_multimap/kernels.cuh>
 
 namespace cuco {
@@ -135,12 +135,8 @@ template <typename Key,
           typename Value,
           cuda::thread_scope Scope = cuda::thread_scope_device,
           typename Allocator       = cuco::cuda_allocator<char>,
-          class ProbeSequence      = cuco::double_hashing<Key,
-                                                     Value,
-                                                     2,
-                                                     cuco::detail::MurmurHash3_32<Key>,
-                                                     cuco::detail::MurmurHash3_32<Key>,
-                                                     Scope>>
+          class ProbeSequence =
+            cuco::double_hashing<2, detail::MurmurHash3_32<Key>, detail::MurmurHash3_32<Key>>>
 class static_multimap {
   static_assert(
     cuco::is_bitwise_comparable_v<Key>,
@@ -151,12 +147,13 @@ class static_multimap {
     cuco::is_bitwise_comparable_v<Value>,
     "Value type must have unique object representations or have been explicitly declared as safe "
     "for bitwise comparison via specialization of cuco::is_bitwise_comparable_v<Value>.");
-
-  static_assert(std::is_base_of_v<
-                  cuco::detail::probe_sequence_base<Key, Value, ProbeSequence::cg_size(), Scope>,
-                  ProbeSequence>,
-                "ProbeSequence must be a specialization of either cuco::detail::double_hashing or "
-                "cuco::detail::linear_probing");
+  /*
+    static_assert(std::is_base_of_v<
+                    cuco::detail::probe_sequence_base<Key, Value, ProbeSequence::cg_size(), Scope>,
+                    ProbeSequence>,
+                  "ProbeSequence must be a specialization of either cuco::detail::double_hashing or
+    " "cuco::detail::linear_probing");
+                  */
 
  public:
   using value_type         = cuco::pair_type<Key, Value>;
@@ -171,6 +168,7 @@ class static_multimap {
     typename std::allocator_traits<Allocator>::rebind_alloc<pair_atomic_type>;
   using counter_allocator_type =
     typename std::allocator_traits<Allocator>::rebind_alloc<atomic_ctr_type>;
+  using probe_sequence_type = detail::probe_sequence_base<ProbeSequence, Key, Value, Scope>;
 
   static_multimap(static_multimap const&) = delete;
   static_multimap& operator=(static_multimap const&) = delete;
@@ -197,7 +195,7 @@ class static_multimap {
    */
   __host__ __device__ __forceinline__ static constexpr uint32_t cg_size() noexcept
   {
-    return ProbeSequence::cg_size();
+    return ProbeSequence::cg_size;
   }
 
   /**
@@ -517,7 +515,10 @@ class static_multimap {
    *
    * @return Boolean indicating if vector-load is used.
    */
-  static constexpr bool uses_vector_load() noexcept { return ProbeSequence::uses_vector_load(); }
+  static constexpr bool uses_vector_load() noexcept
+  {
+    return cuco::detail::is_packable<value_type>();
+  }
 
   /**
    * @brief Returns the number of pairs loaded with each vector-load
diff --git a/tests/static_multimap/static_multimap_test.cu b/tests/static_multimap/static_multimap_test.cu
index 619256220..8191ab0d9 100644
--- a/tests/static_multimap/static_multimap_test.cu
+++ b/tests/static_multimap/static_multimap_test.cu
@@ -257,7 +257,7 @@ TEMPLATE_TEST_CASE_SIG("User defined key and value type",
                           value_pair,
                           cuda::thread_scope_device,
                           cuco::cuda_allocator<char>,
-                          cuco::linear_probing<key_pair, value_pair, 1, hash_key_pair>>
+                          cuco::linear_probing<1, hash_key_pair>>
       map{capacity, sentinel_key, sentinel_value};
     test_custom_key_value_type<Key, Value>(map, insert_pairs, insert_keys.begin(), num_pairs);
   }
@@ -408,7 +408,7 @@ TEMPLATE_TEST_CASE_SIG("Multiplicity equals two",
                           Value,
                           cuda::thread_scope_device,
                           cuco::cuda_allocator<char>,
-                          cuco::linear_probing<Key, Value, 1>>
+                          cuco::linear_probing<1, cuco::detail::MurmurHash3_32<Key>>>
       map{5, -1, -1};
     test_multiplicity_two<Key, Value>(
       map, d_pairs.begin(), d_keys.begin(), d_results.begin(), num_items);
@@ -537,7 +537,7 @@ TEMPLATE_TEST_CASE_SIG("Tests of non-matches",
                           Value,
                           cuda::thread_scope_device,
                           cuco::cuda_allocator<char>,
-                          cuco::linear_probing<Key, Value, 1>>
+                          cuco::linear_probing<1, cuco::detail::MurmurHash3_32<Key>>>
       map{num_keys * 2, -1, -1};
     test_non_matches<Key, Value>(map, d_pairs.begin(), d_keys.begin(), num_keys);
   }
@@ -592,7 +592,7 @@ TEMPLATE_TEST_CASE_SIG("Tests of insert_if",
                           Value,
                           cuda::thread_scope_device,
                           cuco::cuda_allocator<char>,
-                          cuco::linear_probing<Key, Value, 1>>
+                          cuco::linear_probing<1, cuco::detail::MurmurHash3_32<Key>>>
       map{num_keys * 2, -1, -1};
     test_insert_if<Key>(map, d_pairs.begin(), d_keys.begin(), num_keys);
   }
@@ -692,7 +692,7 @@ TEMPLATE_TEST_CASE_SIG("Tests of pair functions",
                           Value,
                           cuda::thread_scope_device,
                           cuco::cuda_allocator<char>,
-                          cuco::linear_probing<Key, Value, 1>>
+                          cuco::linear_probing<1, cuco::detail::MurmurHash3_32<Key>>>
       map{num_pairs * 2, -1, -1};
     test_pair_functions<Key, Value>(map, d_pairs.begin(), num_pairs);
   }

From 9c80eb691f45218fda0399b048939568920e91e9 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Wed, 27 Oct 2021 16:06:50 -0400
Subject: [PATCH 256/267] Cleanups: rename classes, create probe_sequence_base
 class, move double hashing and linear probing to a new public header

---
 ...uence_base.cuh => probe_sequence_impl.cuh} | 78 +++++++------------
 include/cuco/probe_sequences.cuh              | 57 ++++++++++++++
 include/cuco/static_multimap.cuh              | 15 ++--
 3 files changed, 91 insertions(+), 59 deletions(-)
 rename include/cuco/detail/{probe_sequence_base.cuh => probe_sequence_impl.cuh} (85%)
 create mode 100644 include/cuco/probe_sequences.cuh

diff --git a/include/cuco/detail/probe_sequence_base.cuh b/include/cuco/detail/probe_sequence_impl.cuh
similarity index 85%
rename from include/cuco/detail/probe_sequence_base.cuh
rename to include/cuco/detail/probe_sequence_impl.cuh
index 3a9cafbd1..a67463426 100644
--- a/include/cuco/detail/probe_sequence_base.cuh
+++ b/include/cuco/detail/probe_sequence_impl.cuh
@@ -39,8 +39,13 @@ namespace detail {
  * @tparam Scope The scope in which multimap operations will be performed by
  * individual threads
  */
-template <typename Key, typename Value, cuda::thread_scope Scope, uint32_t CGSize, typename Hash>
-class linear_probing {
+template <typename Key,
+          typename Value,
+          cuda::thread_scope Scope,
+          uint32_t VectorWidth,
+          int32_t CGSize,
+          typename Hash>
+class linear_probing_impl {
  public:
   using value_type         = cuco::pair_type<Key, Value>;
   using key_type           = Key;
@@ -52,14 +57,14 @@ class linear_probing {
   using const_iterator     = pair_atomic_type const*;
 
   /**
-   * @brief Returns the size of the CUDA cooperative thread group.
+   * @brief Returns the number of elements loaded with each vector-load.
    */
-  static constexpr std::size_t cg_size = CGSize;
+  static constexpr uint32_t vector_width = VectorWidth;
 
   /**
-   * @brief Returns the number of elements loaded with each vector-load.
+   * @brief Returns the size of the CUDA cooperative thread group.
    */
-  __host__ __device__ static constexpr uint32_t vector_width() noexcept { return 2u; }
+  static constexpr std::size_t cg_size = CGSize;
 
   /**
    * @brief Indicates if vector-load is used.
@@ -80,7 +85,7 @@ class linear_probing {
    * @param capacity Capacity of the hash map
    * @param hash Unary function to hash each key
    */
-  __host__ __device__ explicit linear_probing(iterator slots, std::size_t capacity)
+  __host__ __device__ explicit linear_probing_impl(iterator slots, std::size_t capacity)
     : slots_{slots}, capacity_{capacity}, hash_{Hash{}}
   {
   }
@@ -126,7 +131,7 @@ class linear_probing {
     }();
 
     auto const offset = [&]() {
-      if constexpr (uses_vector_load()) { return g.thread_rank() * vector_width(); }
+      if constexpr (uses_vector_load()) { return g.thread_rank() * vector_width; }
       if constexpr (not uses_vector_load()) { return g.thread_rank(); }
     }();
 
@@ -148,7 +153,7 @@ class linear_probing {
     std::size_t index = s - slots_;
     std::size_t offset;
     if constexpr (uses_vector_load()) {
-      offset = cg_size * vector_width();
+      offset = cg_size * vector_width;
     } else {
       offset = cg_size;
     }
@@ -181,10 +186,11 @@ class linear_probing {
 template <typename Key,
           typename Value,
           cuda::thread_scope Scope,
+          uint32_t VectorWidth,
           uint32_t CGSize,
           typename Hash1,
           typename Hash2>
-class double_hashing {
+class double_hashing_impl {
  public:
   using value_type         = cuco::pair_type<Key, Value>;
   using key_type           = Key;
@@ -196,14 +202,14 @@ class double_hashing {
   using const_iterator     = pair_atomic_type const*;
 
   /**
-   * @brief Returns the size of the CUDA cooperative thread group.
+   * @brief Returns the number of elements loaded with each vector-load.
    */
-  static constexpr std::size_t cg_size = CGSize;
+  static constexpr uint32_t vector_width = VectorWidth;
 
   /**
-   * @brief Returns the number of elements loaded with each vector-load.
+   * @brief Returns the size of the CUDA cooperative thread group.
    */
-  __host__ __device__ static constexpr uint32_t vector_width() noexcept { return 2u; }
+  static constexpr std::size_t cg_size = CGSize;
 
   /**
    * @brief Indicates if vector-load is used.
@@ -227,7 +233,7 @@ class double_hashing {
    * @param hash1 First hasher to hash each key
    * @param hash2 Second hasher to determine step size
    */
-  __host__ __device__ explicit double_hashing(iterator slots, std::size_t capacity)
+  __host__ __device__ explicit double_hashing_impl(iterator slots, std::size_t capacity)
     : slots_{slots}, capacity_{capacity}, hash1_{Hash1{}}, hash2_{Hash2{}}, step_size_{}
   {
   }
@@ -268,9 +274,9 @@ class double_hashing {
     if constexpr (uses_vector_load()) {
       // step size in range [1, prime - 1] * cg_size * vector_width
       step_size_ =
-        (hash2_(k) % (capacity_ / (cg_size * vector_width()) - 1) + 1) * cg_size * vector_width();
-      index = hash_value % (capacity_ / (cg_size * vector_width())) * cg_size * vector_width() +
-              g.thread_rank() * vector_width();
+        (hash2_(k) % (capacity_ / (cg_size * vector_width) - 1) + 1) * cg_size * vector_width;
+      index = hash_value % (capacity_ / (cg_size * vector_width)) * cg_size * vector_width +
+              g.thread_rank() * vector_width;
     } else {
       // step size in range [1, prime - 1] * cg_size
       step_size_ = (hash2_(k) % (capacity_ / cg_size - 1) + 1) * cg_size;
@@ -316,7 +322,7 @@ class double_hashing {
  * individual threads
  */
 template <typename ProbeImpl, typename Key, typename Value, cuda::thread_scope Scope>
-class probe_sequence_base : public ProbeImpl::template impl<Key, Value, Scope> {
+class probe_sequence : public ProbeImpl::template impl<Key, Value, Scope> {
  public:
   using value_type         = cuco::pair_type<Key, Value>;
   using key_type           = Key;
@@ -335,39 +341,11 @@ class probe_sequence_base : public ProbeImpl::template impl<Key, Value, Scope> {
    * @param slots Pointer to beginning of the hash map slots
    * @param capacity Capacity of the hash map
    */
-  __host__ __device__ explicit probe_sequence_base(iterator slots, std::size_t capacity)
+  __host__ __device__ explicit probe_sequence(iterator slots, std::size_t capacity)
     : ProbeImpl::template impl<Key, Value, Scope>{slots, capacity}
   {
   }
-};  // class probe_sequence_base
-}  // namespace detail
-
-template <uint32_t CGSize, typename Hash>
-class linear_probing {
- public:
-  static constexpr std::size_t cg_size = CGSize;
-
-  /**
-   * @brief Returns the number of elements loaded with each vector-load.
-   */
-  __host__ __device__ static constexpr uint32_t vector_width() noexcept { return 2u; }
-
-  template <typename Key, typename Value, cuda::thread_scope Scope>
-  using impl = typename detail::linear_probing<Key, Value, Scope, CGSize, Hash>;
-};
-
-template <uint32_t CGSize, typename Hash1, typename Hash2>
-class double_hashing {
- public:
-  static constexpr std::size_t cg_size = CGSize;
-
-  /**
-   * @brief Returns the number of elements loaded with each vector-load.
-   */
-  __host__ __device__ static constexpr uint32_t vector_width() noexcept { return 2u; }
-
-  template <typename Key, typename Value, cuda::thread_scope Scope>
-  using impl = typename detail::double_hashing<Key, Value, Scope, CGSize, Hash1, Hash2>;
-};
+};  // class probe_sequence
 
+}  // namespace detail
 }  // namespace cuco
diff --git a/include/cuco/probe_sequences.cuh b/include/cuco/probe_sequences.cuh
new file mode 100644
index 000000000..cb064a280
--- /dev/null
+++ b/include/cuco/probe_sequences.cuh
@@ -0,0 +1,57 @@
+/*
+ * Copyright (c) 2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cuco/detail/probe_sequence_impl.cuh>
+
+namespace cuco {
+
+template <uint32_t CGSize>
+class probe_sequence_base {
+ public:
+  /**
+   * @brief Returns the size of the CUDA cooperative thread group.
+   */
+  static constexpr std::size_t cg_size = CGSize;
+
+  /**
+   * @brief Returns the number of elements loaded with each vector load.
+   */
+  static constexpr uint32_t vector_width() noexcept { return 2u; }
+};
+
+template <uint32_t CGSize, typename Hash>
+class linear_probing : public probe_sequence_base<CGSize> {
+ public:
+  using probe_sequence_base<CGSize>::cg_size;
+  using probe_sequence_base<CGSize>::vector_width;
+
+  template <typename Key, typename Value, cuda::thread_scope Scope>
+  using impl = detail::linear_probing_impl<Key, Value, Scope, vector_width(), CGSize, Hash>;
+};
+
+template <uint32_t CGSize, typename Hash1, typename Hash2>
+class double_hashing : public probe_sequence_base<CGSize> {
+ public:
+  using probe_sequence_base<CGSize>::cg_size;
+  using probe_sequence_base<CGSize>::vector_width;
+
+  template <typename Key, typename Value, cuda::thread_scope Scope>
+  using impl = detail::double_hashing_impl<Key, Value, Scope, vector_width(), CGSize, Hash1, Hash2>;
+};
+
+}  // namespace cuco
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index 7d595a116..b5c8a9d42 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -23,6 +23,7 @@
 #include <memory>
 
 #include <cuco/allocator.hpp>
+#include <cuco/probe_sequences.cuh>
 #include <cuco/traits.hpp>
 
 #if defined(CUDART_VERSION) && (CUDART_VERSION >= 11000) && defined(__CUDA_ARCH__) && \
@@ -41,7 +42,6 @@
 
 #include <cuco/detail/error.hpp>
 #include <cuco/detail/prime.hpp>
-#include <cuco/detail/probe_sequence_base.cuh>
 #include <cuco/detail/static_multimap/kernels.cuh>
 
 namespace cuco {
@@ -147,13 +147,10 @@ class static_multimap {
     cuco::is_bitwise_comparable_v<Value>,
     "Value type must have unique object representations or have been explicitly declared as safe "
     "for bitwise comparison via specialization of cuco::is_bitwise_comparable_v<Value>.");
-  /*
-    static_assert(std::is_base_of_v<
-                    cuco::detail::probe_sequence_base<Key, Value, ProbeSequence::cg_size(), Scope>,
-                    ProbeSequence>,
-                  "ProbeSequence must be a specialization of either cuco::detail::double_hashing or
-    " "cuco::detail::linear_probing");
-                  */
+
+  static_assert(std::is_base_of_v<cuco::probe_sequence_base<ProbeSequence::cg_size>, ProbeSequence>,
+                "ProbeSequence must be a specialization of either cuco::double_hashing or "
+                "cuco::linear_probing");
 
  public:
   using value_type         = cuco::pair_type<Key, Value>;
@@ -168,7 +165,7 @@ class static_multimap {
     typename std::allocator_traits<Allocator>::rebind_alloc<pair_atomic_type>;
   using counter_allocator_type =
     typename std::allocator_traits<Allocator>::rebind_alloc<atomic_ctr_type>;
-  using probe_sequence_type = detail::probe_sequence_base<ProbeSequence, Key, Value, Scope>;
+  using probe_sequence_type = detail::probe_sequence<ProbeSequence, Key, Value, Scope>;
 
   static_multimap(static_multimap const&) = delete;
   static_multimap& operator=(static_multimap const&) = delete;

From 1b62dc1f0404a43dd471ce62022bb0bd29396956 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Wed, 27 Oct 2021 16:36:21 -0400
Subject: [PATCH 257/267] Create probe_sequence_impl_base class

---
 include/cuco/detail/probe_sequence_impl.cuh | 160 ++++++++++----------
 1 file changed, 83 insertions(+), 77 deletions(-)

diff --git a/include/cuco/detail/probe_sequence_impl.cuh b/include/cuco/detail/probe_sequence_impl.cuh
index a67463426..d3c2b427d 100644
--- a/include/cuco/detail/probe_sequence_impl.cuh
+++ b/include/cuco/detail/probe_sequence_impl.cuh
@@ -24,29 +24,13 @@
 namespace cuco {
 namespace detail {
 
-/**
- * @brief Cooperative Groups based Linear probing scheme.
- *
- * Linear probing is efficient only when few collisions are present. Performance hints:
- * - Use linear probing only when collisions are rare. e.g. low occupancy or low multiplicity.
- * - `CGSize` = 1 or 2 when hash map is small (10'000'000 or less), 4 or 8 otherwise.
- *
- * `Hash` should be callable object type.
- *
- * @tparam Key Type used for keys
- * @tparam Value Type of the mapped values
- * @tparam Hash Unary callable type
- * @tparam Scope The scope in which multimap operations will be performed by
- * individual threads
- */
 template <typename Key,
           typename Value,
           cuda::thread_scope Scope,
           uint32_t VectorWidth,
-          int32_t CGSize,
-          typename Hash>
-class linear_probing_impl {
- public:
+          uint32_t CGSize>
+class probe_sequence_impl_base {
+ protected:
   using value_type         = cuco::pair_type<Key, Value>;
   using key_type           = Key;
   using mapped_type        = Value;
@@ -79,14 +63,13 @@ class linear_probing_impl {
   }
 
   /**
-   * @brief Constructs a linear probing scheme based on the given hash map features.
+   * @brief Constructs a probe sequence based on the given hash map features.
    *
    * @param slots Pointer to beginning of the hash map slots
    * @param capacity Capacity of the hash map
-   * @param hash Unary function to hash each key
    */
-  __host__ __device__ explicit linear_probing_impl(iterator slots, std::size_t capacity)
-    : slots_{slots}, capacity_{capacity}, hash_{Hash{}}
+  __host__ __device__ explicit probe_sequence_impl_base(iterator slots, std::size_t capacity)
+    : slots_{slots}, capacity_{capacity}
   {
   }
 
@@ -108,6 +91,63 @@ class linear_probing_impl {
    */
   __device__ __forceinline__ const_iterator get_slots() const noexcept { return slots_; }
 
+  iterator slots_;              ///< Pointer to beginning of the hash map slots
+  const std::size_t capacity_;  ///< Total number of slots
+};                              // class probe_sequence_impl_base
+
+/**
+ * @brief Cooperative Groups based Linear probing scheme.
+ *
+ * Linear probing is efficient only when few collisions are present. Performance hints:
+ * - Use linear probing only when collisions are rare. e.g. low occupancy or low multiplicity.
+ * - `CGSize` = 1 or 2 when hash map is small (10'000'000 or less), 4 or 8 otherwise.
+ *
+ * `Hash` should be callable object type.
+ *
+ * @tparam Key Type used for keys
+ * @tparam Value Type of the mapped values
+ * @tparam Hash Unary callable type
+ * @tparam Scope The scope in which multimap operations will be performed by
+ * individual threads
+ */
+template <typename Key,
+          typename Value,
+          cuda::thread_scope Scope,
+          uint32_t VectorWidth,
+          int32_t CGSize,
+          typename Hash>
+class linear_probing_impl
+  : public probe_sequence_impl_base<Key, Value, Scope, VectorWidth, CGSize> {
+ public:
+  using probe_sequence_impl_base_type =
+    probe_sequence_impl_base<Key, Value, Scope, VectorWidth, CGSize>;
+  using value_type         = typename probe_sequence_impl_base_type::value_type;
+  using key_type           = typename probe_sequence_impl_base_type::key_type;
+  using mapped_type        = typename probe_sequence_impl_base_type::mapped_type;
+  using atomic_key_type    = typename probe_sequence_impl_base_type::atomic_key_type;
+  using atomic_mapped_type = typename probe_sequence_impl_base_type::atomic_mapped_type;
+  using pair_atomic_type   = typename probe_sequence_impl_base_type::pair_atomic_type;
+  using iterator           = typename probe_sequence_impl_base_type::iterator;
+  using const_iterator     = typename probe_sequence_impl_base_type::const_iterator;
+
+  using probe_sequence_impl_base_type::capacity_;
+  using probe_sequence_impl_base_type::cg_size;
+  using probe_sequence_impl_base_type::slots_;
+  using probe_sequence_impl_base_type::uses_vector_load;
+  using probe_sequence_impl_base_type::vector_width;
+
+  /**
+   * @brief Constructs a linear probing scheme based on the given hash map features.
+   *
+   * @param slots Pointer to beginning of the hash map slots
+   * @param capacity Capacity of the hash map
+   * @param hash Unary function to hash each key
+   */
+  __host__ __device__ explicit linear_probing_impl(iterator slots, std::size_t capacity)
+    : probe_sequence_impl_base_type{slots, capacity}, hash_{Hash{}}
+  {
+  }
+
   /**
    * @brief Returns the initial slot for a given key `k`.
    *
@@ -160,9 +200,7 @@ class linear_probing_impl {
     return &slots_[(index + offset) % capacity_];
   }
 
- protected:
-  iterator slots_;              ///< Pointer to beginning of the hash map slots
-  const std::size_t capacity_;  ///< Total number of slots
+ private:
   Hash hash_;
 };  // class linear_probing
 
@@ -190,38 +228,25 @@ template <typename Key,
           uint32_t CGSize,
           typename Hash1,
           typename Hash2>
-class double_hashing_impl {
+class double_hashing_impl
+  : public probe_sequence_impl_base<Key, Value, Scope, VectorWidth, CGSize> {
  public:
-  using value_type         = cuco::pair_type<Key, Value>;
-  using key_type           = Key;
-  using mapped_type        = Value;
-  using atomic_key_type    = cuda::atomic<key_type, Scope>;
-  using atomic_mapped_type = cuda::atomic<mapped_type, Scope>;
-  using pair_atomic_type   = cuco::pair_type<atomic_key_type, atomic_mapped_type>;
-  using iterator           = pair_atomic_type*;
-  using const_iterator     = pair_atomic_type const*;
-
-  /**
-   * @brief Returns the number of elements loaded with each vector-load.
-   */
-  static constexpr uint32_t vector_width = VectorWidth;
-
-  /**
-   * @brief Returns the size of the CUDA cooperative thread group.
-   */
-  static constexpr std::size_t cg_size = CGSize;
+  using probe_sequence_impl_base_type =
+    probe_sequence_impl_base<Key, Value, Scope, VectorWidth, CGSize>;
+  using value_type         = typename probe_sequence_impl_base_type::value_type;
+  using key_type           = typename probe_sequence_impl_base_type::key_type;
+  using mapped_type        = typename probe_sequence_impl_base_type::mapped_type;
+  using atomic_key_type    = typename probe_sequence_impl_base_type::atomic_key_type;
+  using atomic_mapped_type = typename probe_sequence_impl_base_type::atomic_mapped_type;
+  using pair_atomic_type   = typename probe_sequence_impl_base_type::pair_atomic_type;
+  using iterator           = typename probe_sequence_impl_base_type::iterator;
+  using const_iterator     = typename probe_sequence_impl_base_type::const_iterator;
 
-  /**
-   * @brief Indicates if vector-load is used.
-   *
-   * Users have no explicit control on whether vector-load is used.
-   *
-   * @return Boolean indicating if vector-load is used.
-   */
-  __host__ __device__ static constexpr bool uses_vector_load() noexcept
-  {
-    return cuco::detail::is_packable<value_type>();
-  }
+  using probe_sequence_impl_base_type::capacity_;
+  using probe_sequence_impl_base_type::cg_size;
+  using probe_sequence_impl_base_type::slots_;
+  using probe_sequence_impl_base_type::uses_vector_load;
+  using probe_sequence_impl_base_type::vector_width;
 
   /**
    * @brief Constructs a double hashing scheme based on the given hash map features.
@@ -234,27 +259,10 @@ class double_hashing_impl {
    * @param hash2 Second hasher to determine step size
    */
   __host__ __device__ explicit double_hashing_impl(iterator slots, std::size_t capacity)
-    : slots_{slots}, capacity_{capacity}, hash1_{Hash1{}}, hash2_{Hash2{}}, step_size_{}
-  {
-  }
-
-  /**
-   * @brief Returns the capacity of the hash map.
-   */
-  __host__ __device__ __forceinline__ std::size_t get_capacity() const noexcept
+    : probe_sequence_impl_base_type{slots, capacity}, hash1_{Hash1{}}, hash2_{Hash2{}}, step_size_{}
   {
-    return capacity_;
   }
 
-  /**
-   * @brief Returns slots array.
-   */
-  __device__ __forceinline__ iterator get_slots() noexcept { return slots_; }
-
-  /**
-   * @brief Returns slots array.
-   */
-  __device__ __forceinline__ const_iterator get_slots() const noexcept { return slots_; }
   /**
    * @brief Returns the initial slot for a given key `k`.
    *
@@ -299,9 +307,7 @@ class double_hashing_impl {
     return &slots_[(index + step_size_) % capacity_];
   }
 
- protected:
-  iterator slots_;              ///< Pointer to beginning of the hash map slots
-  const std::size_t capacity_;  ///< Total number of slots
+ private:
   Hash1 hash1_;
   Hash2 hash2_;
   std::size_t step_size_;  ///< The step stride when searching for the next slot

From f1ff196362059f545e2472cab3c04a123a686992 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Wed, 27 Oct 2021 17:17:01 -0400
Subject: [PATCH 258/267] Update comments + minor cleanups

---
 include/cuco/detail/probe_sequence_impl.cuh | 70 +++++++++++----------
 include/cuco/probe_sequences.cuh            | 32 ++++++++++
 2 files changed, 69 insertions(+), 33 deletions(-)

diff --git a/include/cuco/detail/probe_sequence_impl.cuh b/include/cuco/detail/probe_sequence_impl.cuh
index d3c2b427d..107120956 100644
--- a/include/cuco/detail/probe_sequence_impl.cuh
+++ b/include/cuco/detail/probe_sequence_impl.cuh
@@ -24,6 +24,22 @@
 namespace cuco {
 namespace detail {
 
+/*
+ * @brief Base class of probe sequence implementation.
+ *
+ * Hash map operations are generally memory-bandwidth bound. A vector-load loads two consecutive
+ * slots instead of one to fully utilize the 16B memory load supported by SASS/hardware thus
+ * improve memory throughput. This method (flagged by `uses_vector_load` logic) is implicitly
+ * applied to all hash map operations (e.g. `insert`, `count`, and `retrieve`, etc.) when pairs
+ * are packable (see `cuco::detail::is_packable` logic).
+ *
+ * @tparam Key Type used for keys
+ * @tparam Value Type of the mapped values
+ * @tparam Scope The scope in which multimap operations will be performed by
+ * individual threads
+ * @tparam VectorWidth Length of vector load
+ * @tparam CGSize Size of CUDA Cooperative Groups
+ */
 template <typename Key,
           typename Value,
           cuda::thread_scope Scope,
@@ -98,17 +114,13 @@ class probe_sequence_impl_base {
 /**
  * @brief Cooperative Groups based Linear probing scheme.
  *
- * Linear probing is efficient only when few collisions are present. Performance hints:
- * - Use linear probing only when collisions are rare. e.g. low occupancy or low multiplicity.
- * - `CGSize` = 1 or 2 when hash map is small (10'000'000 or less), 4 or 8 otherwise.
- *
- * `Hash` should be callable object type.
- *
  * @tparam Key Type used for keys
  * @tparam Value Type of the mapped values
- * @tparam Hash Unary callable type
  * @tparam Scope The scope in which multimap operations will be performed by
  * individual threads
+ * @tparam VectorWidth Length of vector load
+ * @tparam CGSize Size of CUDA Cooperative Groups
+ * @tparam Hash Unary callable type
  */
 template <typename Key,
           typename Value,
@@ -201,8 +213,8 @@ class linear_probing_impl
   }
 
  private:
-  Hash hash_;
-};  // class linear_probing
+  Hash hash_;  ///< The unary callable used to hash the key
+};             // class linear_probing
 
 /**
  * @brief Cooperative Groups based double hashing scheme.
@@ -216,10 +228,12 @@ class linear_probing_impl
  *
  * @tparam Key Type used for keys
  * @tparam Value Type of the mapped values
- * @tparam Hash1 Unary callable type
- * @tparam Hash2 Unary callable type
  * @tparam Scope The scope in which multimap operations will be performed by
  * individual threads
+ * @tparam VectorWidth Length of vector load
+ * @tparam CGSize Size of CUDA Cooperative Groups
+ * @tparam Hash1 Unary callable type
+ * @tparam Hash2 Unary callable type
  */
 template <typename Key,
           typename Value,
@@ -259,7 +273,10 @@ class double_hashing_impl
    * @param hash2 Second hasher to determine step size
    */
   __host__ __device__ explicit double_hashing_impl(iterator slots, std::size_t capacity)
-    : probe_sequence_impl_base_type{slots, capacity}, hash1_{Hash1{}}, hash2_{Hash2{}}, step_size_{}
+    : probe_sequence_impl_base_type{slots, capacity},
+      hash1_{Hash1{}},
+      hash2_{Hash2{1}},
+      step_size_{}
   {
   }
 
@@ -308,20 +325,15 @@ class double_hashing_impl
   }
 
  private:
-  Hash1 hash1_;
-  Hash2 hash2_;
+  Hash1 hash1_;            ///< The first unary callable used to hash the key
+  Hash2 hash2_;            ///< The second unary callable used to determine step size
   std::size_t step_size_;  ///< The step stride when searching for the next slot
 };                         // class double_hashing
 
 /**
- * @brief Base class for a hash map probe sequence. This class should not be used directly.
- *
- * Hash map operations are generally memory-bandwidth bound. A vector-load loads two consecutive
- * slots instead of one to fully utilize the 16B memory load supported by SASS/hardware thus
- * improve memory throughput. This method (flagged by `uses_vector_load` logic) is implicitly
- * applied to all hash map operations (e.g. `insert`, `count`, and `retrieve`, etc.) when pairs
- * are packable (see `cuco::detail::is_packable` logic).
+ * @brief Probe sequence used internally by hash map.
  *
+ * @tparam ProbeImpl Type of probe sequence implementation
  * @tparam Key Type used for keys
  * @tparam Value Type of the mapped values
  * @tparam Scope The scope in which multimap operations will be performed by
@@ -330,16 +342,7 @@ class double_hashing_impl
 template <typename ProbeImpl, typename Key, typename Value, cuda::thread_scope Scope>
 class probe_sequence : public ProbeImpl::template impl<Key, Value, Scope> {
  public:
-  using value_type         = cuco::pair_type<Key, Value>;
-  using key_type           = Key;
-  using mapped_type        = Value;
-  using atomic_key_type    = cuda::atomic<key_type, Scope>;
-  using atomic_mapped_type = cuda::atomic<mapped_type, Scope>;
-  using pair_atomic_type   = cuco::pair_type<atomic_key_type, atomic_mapped_type>;
-  using iterator           = pair_atomic_type*;
-  using const_iterator     = pair_atomic_type const*;
-
-  static constexpr std::size_t cg_size = ProbeImpl::cg_size;
+  using impl_type = typename ProbeImpl::template impl<Key, Value, Scope>;
 
   /**
    * @brief Constructs a probe sequence based on the given hash map features.
@@ -347,8 +350,9 @@ class probe_sequence : public ProbeImpl::template impl<Key, Value, Scope> {
    * @param slots Pointer to beginning of the hash map slots
    * @param capacity Capacity of the hash map
    */
-  __host__ __device__ explicit probe_sequence(iterator slots, std::size_t capacity)
-    : ProbeImpl::template impl<Key, Value, Scope>{slots, capacity}
+  __host__ __device__ explicit probe_sequence(typename impl_type::iterator slots,
+                                              std::size_t capacity)
+    : impl_type{slots, capacity}
   {
   }
 };  // class probe_sequence
diff --git a/include/cuco/probe_sequences.cuh b/include/cuco/probe_sequences.cuh
index cb064a280..05129b0b9 100644
--- a/include/cuco/probe_sequences.cuh
+++ b/include/cuco/probe_sequences.cuh
@@ -20,6 +20,11 @@
 
 namespace cuco {
 
+/**
+ * @brief Base class of public probe sequence. This class should not be used directly.
+ *
+ * @tparam CGSize Size of CUDA Cooperative Groups
+ */
 template <uint32_t CGSize>
 class probe_sequence_base {
  public:
@@ -34,6 +39,18 @@ class probe_sequence_base {
   static constexpr uint32_t vector_width() noexcept { return 2u; }
 };
 
+/**
+ * @brief Public linear probing scheme class.
+ *
+ * Linear probing is efficient when few collisions are present. Performance hints:
+ * - Use linear probing when collisions are rare. e.g. low occupancy or low multiplicity.
+ * - `CGSize` = 1 or 2 when hash map is small (10'000'000 or less), 4 or 8 otherwise.
+ *
+ * `Hash` should be callable object type.
+ *
+ * @tparam CGSize Size of CUDA Cooperative Groups
+ * @tparam Hash Unary callable type
+ */
 template <uint32_t CGSize, typename Hash>
 class linear_probing : public probe_sequence_base<CGSize> {
  public:
@@ -44,6 +61,21 @@ class linear_probing : public probe_sequence_base<CGSize> {
   using impl = detail::linear_probing_impl<Key, Value, Scope, vector_width(), CGSize, Hash>;
 };
 
+/**
+ *
+ * @brief Public double hashing scheme class.
+ *
+ * Default probe sequence for `cuco::static_multimap`. Double hashing shows superior
+ * performance when dealing with high multiplicty and/or high occupancy use cases. Performance
+ * hints:
+ * - `CGSize` = 1 or 2 when hash map is small (10'000'000 or less), 4 or 8 otherwise.
+ *
+ * `Hash1` and `Hash2` should be callable object type.
+ *
+ * @tparam CGSize Size of CUDA Cooperative Groups
+ * @tparam Hash1 Unary callable type
+ * @tparam Hash2 Unary callable type
+ */
 template <uint32_t CGSize, typename Hash1, typename Hash2>
 class double_hashing : public probe_sequence_base<CGSize> {
  public:

From 689a9c3847eb17500e9cb6a89bb1fc5dd4ee4616 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Wed, 27 Oct 2021 17:23:20 -0400
Subject: [PATCH 259/267] Minor update

---
 include/cuco/probe_sequences.cuh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/include/cuco/probe_sequences.cuh b/include/cuco/probe_sequences.cuh
index 05129b0b9..05bf0280c 100644
--- a/include/cuco/probe_sequences.cuh
+++ b/include/cuco/probe_sequences.cuh
@@ -27,7 +27,7 @@ namespace cuco {
  */
 template <uint32_t CGSize>
 class probe_sequence_base {
- public:
+ protected:
   /**
    * @brief Returns the size of the CUDA cooperative thread group.
    */

From c6c3aee1c175a7dc9b22b63e7249d474debde108 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Wed, 27 Oct 2021 18:06:10 -0400
Subject: [PATCH 260/267] Move probe_sequence_base to detail namespace

---
 include/cuco/detail/probe_sequence_impl.cuh | 19 ++++++++++++
 include/cuco/probe_sequences.cuh            | 33 +++++----------------
 include/cuco/static_multimap.cuh            |  7 +++--
 3 files changed, 31 insertions(+), 28 deletions(-)

diff --git a/include/cuco/detail/probe_sequence_impl.cuh b/include/cuco/detail/probe_sequence_impl.cuh
index 107120956..3145e8fa5 100644
--- a/include/cuco/detail/probe_sequence_impl.cuh
+++ b/include/cuco/detail/probe_sequence_impl.cuh
@@ -24,6 +24,25 @@
 namespace cuco {
 namespace detail {
 
+/**
+ * @brief Base class of public probe sequence. This class should not be used directly.
+ *
+ * @tparam CGSize Size of CUDA Cooperative Groups
+ */
+template <uint32_t CGSize>
+class probe_sequence_base {
+ protected:
+  /**
+   * @brief Returns the size of the CUDA cooperative thread group.
+   */
+  static constexpr std::size_t cg_size = CGSize;
+
+  /**
+   * @brief Returns the number of elements loaded with each vector load.
+   */
+  static constexpr uint32_t vector_width() noexcept { return 2u; }
+};
+
 /*
  * @brief Base class of probe sequence implementation.
  *
diff --git a/include/cuco/probe_sequences.cuh b/include/cuco/probe_sequences.cuh
index 05bf0280c..f923f9df6 100644
--- a/include/cuco/probe_sequences.cuh
+++ b/include/cuco/probe_sequences.cuh
@@ -20,25 +20,6 @@
 
 namespace cuco {
 
-/**
- * @brief Base class of public probe sequence. This class should not be used directly.
- *
- * @tparam CGSize Size of CUDA Cooperative Groups
- */
-template <uint32_t CGSize>
-class probe_sequence_base {
- protected:
-  /**
-   * @brief Returns the size of the CUDA cooperative thread group.
-   */
-  static constexpr std::size_t cg_size = CGSize;
-
-  /**
-   * @brief Returns the number of elements loaded with each vector load.
-   */
-  static constexpr uint32_t vector_width() noexcept { return 2u; }
-};
-
 /**
  * @brief Public linear probing scheme class.
  *
@@ -52,10 +33,11 @@ class probe_sequence_base {
  * @tparam Hash Unary callable type
  */
 template <uint32_t CGSize, typename Hash>
-class linear_probing : public probe_sequence_base<CGSize> {
+class linear_probing : public detail::probe_sequence_base<CGSize> {
  public:
-  using probe_sequence_base<CGSize>::cg_size;
-  using probe_sequence_base<CGSize>::vector_width;
+  using probe_sequence_base_type = detail::probe_sequence_base<CGSize>;
+  using probe_sequence_base_type::cg_size;
+  using probe_sequence_base_type::vector_width;
 
   template <typename Key, typename Value, cuda::thread_scope Scope>
   using impl = detail::linear_probing_impl<Key, Value, Scope, vector_width(), CGSize, Hash>;
@@ -77,10 +59,11 @@ class linear_probing : public probe_sequence_base<CGSize> {
  * @tparam Hash2 Unary callable type
  */
 template <uint32_t CGSize, typename Hash1, typename Hash2>
-class double_hashing : public probe_sequence_base<CGSize> {
+class double_hashing : public detail::probe_sequence_base<CGSize> {
  public:
-  using probe_sequence_base<CGSize>::cg_size;
-  using probe_sequence_base<CGSize>::vector_width;
+  using probe_sequence_base_type = detail::probe_sequence_base<CGSize>;
+  using probe_sequence_base_type::cg_size;
+  using probe_sequence_base_type::vector_width;
 
   template <typename Key, typename Value, cuda::thread_scope Scope>
   using impl = detail::double_hashing_impl<Key, Value, Scope, vector_width(), CGSize, Hash1, Hash2>;
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index b5c8a9d42..944018981 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -148,9 +148,10 @@ class static_multimap {
     "Value type must have unique object representations or have been explicitly declared as safe "
     "for bitwise comparison via specialization of cuco::is_bitwise_comparable_v<Value>.");
 
-  static_assert(std::is_base_of_v<cuco::probe_sequence_base<ProbeSequence::cg_size>, ProbeSequence>,
-                "ProbeSequence must be a specialization of either cuco::double_hashing or "
-                "cuco::linear_probing");
+  static_assert(
+    std::is_base_of_v<cuco::detail::probe_sequence_base<ProbeSequence::cg_size>, ProbeSequence>,
+    "ProbeSequence must be a specialization of either cuco::double_hashing or "
+    "cuco::linear_probing");
 
  public:
   using value_type         = cuco::pair_type<Key, Value>;

From 32ff8adf420df9e35806729969b6c61ff18edb60 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Fri, 29 Oct 2021 17:42:20 -0400
Subject: [PATCH 261/267] Set default CG size to 8

---
 include/cuco/static_multimap.cuh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index 944018981..840af103a 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -136,7 +136,7 @@ template <typename Key,
           cuda::thread_scope Scope = cuda::thread_scope_device,
           typename Allocator       = cuco::cuda_allocator<char>,
           class ProbeSequence =
-            cuco::double_hashing<2, detail::MurmurHash3_32<Key>, detail::MurmurHash3_32<Key>>>
+            cuco::double_hashing<8, detail::MurmurHash3_32<Key>, detail::MurmurHash3_32<Key>>>
 class static_multimap {
   static_assert(
     cuco::is_bitwise_comparable_v<Key>,

From cdba4805a2850fd235612efecefa399a1c4af43a Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Fri, 29 Oct 2021 17:58:56 -0400
Subject: [PATCH 262/267] Revert back to manual grid size calculation

---
 .../static_multimap/static_multimap.inl       | 111 ++++++------------
 1 file changed, 33 insertions(+), 78 deletions(-)

diff --git a/include/cuco/detail/static_multimap/static_multimap.inl b/include/cuco/detail/static_multimap/static_multimap.inl
index 5fe5e95f1..53fb37799 100644
--- a/include/cuco/detail/static_multimap/static_multimap.inl
+++ b/include/cuco/detail/static_multimap/static_multimap.inl
@@ -58,9 +58,8 @@ static_multimap<Key, Value, Scope, Allocator, ProbeSequence>::static_multimap(
     slots_{slot_allocator_.allocate(capacity_, stream_), delete_slots_}
 {
   auto constexpr block_size = 128;
-  auto const grid_size      = cuco::detail::get_grid_size(
-    detail::initialize<atomic_key_type, atomic_mapped_type, Key, Value, pair_atomic_type>,
-    block_size);
+  auto constexpr stride     = 4;
+  auto const grid_size      = (get_capacity() + stride * block_size - 1) / (stride * block_size);
 
   detail::initialize<atomic_key_type, atomic_mapped_type><<<grid_size, block_size, 0, stream_>>>(
     slots_.get(), empty_key_sentinel, empty_value_sentinel, get_capacity());
@@ -80,8 +79,8 @@ void static_multimap<Key, Value, Scope, Allocator, ProbeSequence>::insert(InputI
   auto view     = get_device_mutable_view();
 
   auto constexpr block_size = 128;
-  auto const grid_size      = cuco::detail::get_grid_size(
-    detail::insert<block_size, cg_size(), InputIt, device_mutable_view>, block_size);
+  auto constexpr stride     = 1;
+  auto const grid_size = (cg_size() * num_keys + stride * block_size - 1) / (stride * block_size);
 
   detail::insert<block_size, cg_size()>
     <<<grid_size, block_size, 0, stream>>>(first, first + num_keys, view);
@@ -101,9 +100,9 @@ void static_multimap<Key, Value, Scope, Allocator, ProbeSequence>::insert_if(
   auto view         = get_device_mutable_view();
 
   auto constexpr block_size = 128;
-  auto const grid_size      = cuco::detail::get_grid_size(
-    detail::insert_if_n<block_size, cg_size(), InputIt, StencilIt, device_mutable_view, Predicate>,
-    block_size);
+  auto constexpr stride     = 1;
+  auto const grid_size =
+    (cg_size() * num_elements + stride * block_size - 1) / (stride * block_size);
 
   detail::insert_if_n<block_size, cg_size()>
     <<<grid_size, block_size, 0, stream>>>(first, stencil, num_elements, view, pred);
@@ -123,8 +122,8 @@ void static_multimap<Key, Value, Scope, Allocator, ProbeSequence>::contains(
   auto view     = get_device_view();
 
   auto constexpr block_size = 128;
-  auto const grid_size      = cuco::detail::get_grid_size(
-    detail::contains<block_size, cg_size(), InputIt, OutputIt, device_view, KeyEqual>, block_size);
+  auto constexpr stride     = 1;
+  auto const grid_size = (cg_size() * num_keys + stride * block_size - 1) / (stride * block_size);
 
   detail::contains<block_size, cg_size()>
     <<<grid_size, block_size, 0, stream>>>(first, last, output_begin, view, key_equal);
@@ -143,12 +142,11 @@ std::size_t static_multimap<Key, Value, Scope, Allocator, ProbeSequence>::count(
   auto num_keys = std::distance(first, last);
   auto view     = get_device_view();
 
-  constexpr bool is_outer = false;
+  bool constexpr is_outer = false;
 
   auto constexpr block_size = 128;
-  auto const grid_size      = cuco::detail::get_grid_size(
-    detail::count<block_size, cg_size(), is_outer, InputIt, atomic_ctr_type, device_view, KeyEqual>,
-    block_size);
+  auto constexpr stride     = 1;
+  auto const grid_size = (cg_size() * num_keys + stride * block_size - 1) / (stride * block_size);
 
   cudaMemsetAsync(d_counter_.get(), 0, sizeof(atomic_ctr_type), stream);
   std::size_t h_counter;
@@ -174,12 +172,11 @@ std::size_t static_multimap<Key, Value, Scope, Allocator, ProbeSequence>::count_
   auto num_keys = std::distance(first, last);
   auto view     = get_device_view();
 
-  constexpr bool is_outer = true;
+  bool constexpr is_outer = true;
 
   auto constexpr block_size = 128;
-  auto const grid_size      = cuco::detail::get_grid_size(
-    detail::count<block_size, cg_size(), is_outer, InputIt, atomic_ctr_type, device_view, KeyEqual>,
-    block_size);
+  auto constexpr stride     = 1;
+  auto const grid_size = (cg_size() * num_keys + stride * block_size - 1) / (stride * block_size);
 
   cudaMemsetAsync(d_counter_.get(), 0, sizeof(atomic_ctr_type), stream);
   std::size_t h_counter;
@@ -205,13 +202,11 @@ std::size_t static_multimap<Key, Value, Scope, Allocator, ProbeSequence>::pair_c
   auto num_keys = std::distance(first, last);
   auto view     = get_device_view();
 
-  constexpr bool is_outer = false;
+  bool constexpr is_outer = false;
 
   auto constexpr block_size = 128;
-  auto const grid_size      = cuco::detail::get_grid_size(
-    detail::
-      pair_count<block_size, cg_size(), is_outer, InputIt, atomic_ctr_type, device_view, PairEqual>,
-    block_size);
+  auto constexpr stride     = 1;
+  auto const grid_size = (cg_size() * num_keys + stride * block_size - 1) / (stride * block_size);
 
   cudaMemsetAsync(d_counter_.get(), 0, sizeof(atomic_ctr_type), stream);
   std::size_t h_counter;
@@ -237,13 +232,11 @@ std::size_t static_multimap<Key, Value, Scope, Allocator, ProbeSequence>::pair_c
   auto num_keys = std::distance(first, last);
   auto view     = get_device_view();
 
-  constexpr bool is_outer = true;
+  bool constexpr is_outer = true;
 
   auto constexpr block_size = 128;
-  auto const grid_size      = cuco::detail::get_grid_size(
-    detail::
-      pair_count<block_size, cg_size(), is_outer, InputIt, atomic_ctr_type, device_view, PairEqual>,
-    block_size);
+  auto constexpr stride     = 1;
+  auto const grid_size = (cg_size() * num_keys + stride * block_size - 1) / (stride * block_size);
 
   cudaMemsetAsync(d_counter_.get(), 0, sizeof(atomic_ctr_type), stream);
   std::size_t h_counter;
@@ -272,24 +265,15 @@ OutputIt static_multimap<Key, Value, Scope, Allocator, ProbeSequence>::retrieve(
   // Using per-warp buffer for vector loads and per-CG buffer for scalar loads
   constexpr auto buffer_size = uses_vector_load() ? (warp_size() * 3u) : (cg_size() * 3u);
   constexpr auto block_size  = 128;
-  constexpr bool is_outer    = false;
+  constexpr auto is_outer    = false;
+  constexpr auto stride      = 1;
 
   auto const flushing_cg_size = [&]() {
     if constexpr (uses_vector_load()) { return warp_size(); }
     return cg_size();
   }();
 
-  auto const grid_size = detail::get_grid_size(detail::retrieve<block_size,
-                                                                flushing_cg_size,
-                                                                cg_size(),
-                                                                buffer_size,
-                                                                is_outer,
-                                                                InputIt,
-                                                                OutputIt,
-                                                                atomic_ctr_type,
-                                                                device_view,
-                                                                KeyEqual>,
-                                               block_size);
+  auto const grid_size = (cg_size() * num_keys + stride * block_size - 1) / (stride * block_size);
 
   cudaMemsetAsync(d_counter_.get(), 0, sizeof(atomic_ctr_type), stream);
   std::size_t h_counter;
@@ -321,24 +305,15 @@ OutputIt static_multimap<Key, Value, Scope, Allocator, ProbeSequence>::retrieve_
   // Using per-warp buffer for vector loads and per-CG buffer for scalar loads
   constexpr auto buffer_size = uses_vector_load() ? (warp_size() * 3u) : (cg_size() * 3u);
   constexpr auto block_size  = 128;
-  constexpr bool is_outer    = true;
+  constexpr auto is_outer    = true;
+  constexpr auto stride      = 1;
 
   auto const flushing_cg_size = [&]() {
     if constexpr (uses_vector_load()) { return warp_size(); }
     return cg_size();
   }();
 
-  auto const grid_size = detail::get_grid_size(detail::retrieve<block_size,
-                                                                flushing_cg_size,
-                                                                cg_size(),
-                                                                buffer_size,
-                                                                is_outer,
-                                                                InputIt,
-                                                                OutputIt,
-                                                                atomic_ctr_type,
-                                                                device_view,
-                                                                KeyEqual>,
-                                               block_size);
+  auto const grid_size = (cg_size() * num_keys + stride * block_size - 1) / (stride * block_size);
 
   cudaMemsetAsync(d_counter_.get(), 0, sizeof(atomic_ctr_type), stream);
   std::size_t h_counter;
@@ -376,25 +351,15 @@ static_multimap<Key, Value, Scope, Allocator, ProbeSequence>::pair_retrieve(
   // Using per-warp buffer for vector loads and per-CG buffer for scalar loads
   constexpr auto buffer_size = uses_vector_load() ? (warp_size() * 3u) : (cg_size() * 3u);
   constexpr auto block_size  = 128;
-  constexpr bool is_outer    = false;
+  constexpr auto is_outer    = false;
+  constexpr auto stride      = 1;
 
   auto const flushing_cg_size = [&]() {
     if constexpr (uses_vector_load()) { return warp_size(); }
     return cg_size();
   }();
 
-  auto const grid_size = detail::get_grid_size(detail::pair_retrieve<block_size,
-                                                                     flushing_cg_size,
-                                                                     cg_size(),
-                                                                     buffer_size,
-                                                                     is_outer,
-                                                                     InputIt,
-                                                                     OutputIt1,
-                                                                     OutputIt2,
-                                                                     atomic_ctr_type,
-                                                                     device_view,
-                                                                     PairEqual>,
-                                               block_size);
+  auto const grid_size = (cg_size() * num_pairs + stride * block_size - 1) / (stride * block_size);
 
   cudaMemsetAsync(d_counter_.get(), 0, sizeof(atomic_ctr_type), stream);
   std::size_t h_counter;
@@ -431,25 +396,15 @@ static_multimap<Key, Value, Scope, Allocator, ProbeSequence>::pair_retrieve_oute
   // Using per-warp buffer for vector loads and per-CG buffer for scalar loads
   constexpr auto buffer_size = uses_vector_load() ? (warp_size() * 3u) : (cg_size() * 3u);
   constexpr auto block_size  = 128;
-  constexpr bool is_outer    = true;
+  constexpr auto is_outer    = true;
+  constexpr auto stride      = 1;
 
   auto const flushing_cg_size = [&]() {
     if constexpr (uses_vector_load()) { return warp_size(); }
     return cg_size();
   }();
 
-  auto const grid_size = detail::get_grid_size(detail::pair_retrieve<block_size,
-                                                                     flushing_cg_size,
-                                                                     cg_size(),
-                                                                     buffer_size,
-                                                                     is_outer,
-                                                                     InputIt,
-                                                                     OutputIt1,
-                                                                     OutputIt2,
-                                                                     atomic_ctr_type,
-                                                                     device_view,
-                                                                     PairEqual>,
-                                               block_size);
+  auto const grid_size = (cg_size() * num_pairs + stride * block_size - 1) / (stride * block_size);
 
   cudaMemsetAsync(d_counter_.get(), 0, sizeof(atomic_ctr_type), stream);
   std::size_t h_counter;

From 9bf100b1e4be69a18e070bf9e91510f436c76d19 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Fri, 29 Oct 2021 22:08:28 -0400
Subject: [PATCH 263/267] Use get_grid_size for retrieve for better runtime
 performance

---
 .../static_multimap/static_multimap.inl       | 26 ++++++++++++++++---
 1 file changed, 22 insertions(+), 4 deletions(-)

diff --git a/include/cuco/detail/static_multimap/static_multimap.inl b/include/cuco/detail/static_multimap/static_multimap.inl
index 53fb37799..bbc92af78 100644
--- a/include/cuco/detail/static_multimap/static_multimap.inl
+++ b/include/cuco/detail/static_multimap/static_multimap.inl
@@ -266,14 +266,23 @@ OutputIt static_multimap<Key, Value, Scope, Allocator, ProbeSequence>::retrieve(
   constexpr auto buffer_size = uses_vector_load() ? (warp_size() * 3u) : (cg_size() * 3u);
   constexpr auto block_size  = 128;
   constexpr auto is_outer    = false;
-  constexpr auto stride      = 1;
 
   auto const flushing_cg_size = [&]() {
     if constexpr (uses_vector_load()) { return warp_size(); }
     return cg_size();
   }();
 
-  auto const grid_size = (cg_size() * num_keys + stride * block_size - 1) / (stride * block_size);
+  auto const grid_size = detail::get_grid_size(detail::retrieve<block_size,
+                                                                flushing_cg_size,
+                                                                cg_size(),
+                                                                buffer_size,
+                                                                is_outer,
+                                                                InputIt,
+                                                                OutputIt,
+                                                                atomic_ctr_type,
+                                                                device_view,
+                                                                KeyEqual>,
+                                               block_size);
 
   cudaMemsetAsync(d_counter_.get(), 0, sizeof(atomic_ctr_type), stream);
   std::size_t h_counter;
@@ -306,14 +315,23 @@ OutputIt static_multimap<Key, Value, Scope, Allocator, ProbeSequence>::retrieve_
   constexpr auto buffer_size = uses_vector_load() ? (warp_size() * 3u) : (cg_size() * 3u);
   constexpr auto block_size  = 128;
   constexpr auto is_outer    = true;
-  constexpr auto stride      = 1;
 
   auto const flushing_cg_size = [&]() {
     if constexpr (uses_vector_load()) { return warp_size(); }
     return cg_size();
   }();
 
-  auto const grid_size = (cg_size() * num_keys + stride * block_size - 1) / (stride * block_size);
+  auto const grid_size = detail::get_grid_size(detail::retrieve<block_size,
+                                                                flushing_cg_size,
+                                                                cg_size(),
+                                                                buffer_size,
+                                                                is_outer,
+                                                                InputIt,
+                                                                OutputIt,
+                                                                atomic_ctr_type,
+                                                                device_view,
+                                                                KeyEqual>,
+                                               block_size);
 
   cudaMemsetAsync(d_counter_.get(), 0, sizeof(atomic_ctr_type), stream);
   std::size_t h_counter;

From 7e17741712f77da1c4893124b4ff8f439f79261c Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Sun, 31 Oct 2021 14:18:31 -0400
Subject: [PATCH 264/267] Rename multimap benchmarks

---
 benchmarks/CMakeLists.txt                     |  4 +--
 .../{find_all_bench.cu => retrieve_bench.cu}  |  8 ++---
 .../static_multimap/static_multimap_bench.cu  | 36 +++++++++----------
 3 files changed, 24 insertions(+), 24 deletions(-)
 rename benchmarks/hash_table/static_multimap/{find_all_bench.cu => retrieve_bench.cu} (96%)

diff --git a/benchmarks/CMakeLists.txt b/benchmarks/CMakeLists.txt
index 7b02316d9..21e84c2fc 100644
--- a/benchmarks/CMakeLists.txt
+++ b/benchmarks/CMakeLists.txt
@@ -72,8 +72,8 @@ set(STATIC_MULTIMAP_BENCH_SRC "${CMAKE_CURRENT_SOURCE_DIR}/hash_table/static_mul
 ConfigureNVBench(STATIC_MULTIMAP_BENCH "${STATIC_MULTIMAP_BENCH_SRC}")
 
 ###################################################################################################
-set(FIND_ALL_BENCH_SRC "${CMAKE_CURRENT_SOURCE_DIR}/hash_table/static_multimap/find_all_bench.cu")
-ConfigureNVBench(FIND_ALL_BENCH "${FIND_ALL_BENCH_SRC}")
+set(RETRIEVE_BENCH_SRC "${CMAKE_CURRENT_SOURCE_DIR}/hash_table/static_multimap/retrieve_bench.cu")
+ConfigureNVBench(RETRIEVE_BENCH "${RETRIEVE_SRC}")
 
 ###################################################################################################
 set(RBK_BENCH_SRC "${CMAKE_CURRENT_SOURCE_DIR}/reduce_by_key/reduce_by_key.cu")
diff --git a/benchmarks/hash_table/static_multimap/find_all_bench.cu b/benchmarks/hash_table/static_multimap/retrieve_bench.cu
similarity index 96%
rename from benchmarks/hash_table/static_multimap/find_all_bench.cu
rename to benchmarks/hash_table/static_multimap/retrieve_bench.cu
index 483e01bf7..5856ed7c5 100644
--- a/benchmarks/hash_table/static_multimap/find_all_bench.cu
+++ b/benchmarks/hash_table/static_multimap/retrieve_bench.cu
@@ -46,7 +46,7 @@ static void generate_multikeys(OutputIt output_begin,
  *
  */
 template <typename Key, typename Value, nvbench::int32_t CGSize, nvbench::int32_t BufferSize>
-std::enable_if_t<(sizeof(Key) == sizeof(Value)), void> nvbench_find_all(
+std::enable_if_t<(sizeof(Key) == sizeof(Value)), void> nvbench_retrieve(
   nvbench::state& state,
   nvbench::type_list<Key, Value, nvbench::enum_type<CGSize>, nvbench::enum_type<BufferSize>>)
 {
@@ -91,7 +91,7 @@ std::enable_if_t<(sizeof(Key) == sizeof(Value)), void> nvbench_find_all(
 }
 
 template <typename Key, typename Value, nvbench::int32_t CGSize, nvbench::int32_t BufferSize>
-std::enable_if_t<(sizeof(Key) != sizeof(Value)), void> nvbench_find_all(
+std::enable_if_t<(sizeof(Key) != sizeof(Value)), void> nvbench_retrieve(
   nvbench::state& state,
   nvbench::type_list<Key, Value, nvbench::enum_type<CGSize>, nvbench::enum_type<BufferSize>>)
 {
@@ -103,7 +103,7 @@ using value_type  = nvbench::type_list<nvbench::int32_t, nvbench::int64_t>;
 using cg_size     = nvbench::enum_type_list<1, 2, 4, 8, 16, 32>;
 using buffer_size = nvbench::enum_type_list<1, 2, 4, 8, 16>;
 
-NVBENCH_BENCH_TYPES(nvbench_find_all,
+NVBENCH_BENCH_TYPES(nvbench_retrieve,
                     NVBENCH_TYPE_AXES(key_type, value_type, cg_size, nvbench::enum_type_list<2>))
   .set_type_axes_names({"Key", "Value", "CGSize", "BufferSize"})
   .set_timeout(100)                            // Custom timeout: 100 s. Default is 15 s.
@@ -113,7 +113,7 @@ NVBENCH_BENCH_TYPES(nvbench_find_all,
   .add_int64_power_of_two_axis("Multiplicity", nvbench::range(0, 8, 1));
 
 NVBENCH_BENCH_TYPES(
-  nvbench_find_all,
+  nvbench_retrieve,
   NVBENCH_TYPE_AXES(key_type, value_type, nvbench::enum_type_list<8>, buffer_size))
   .set_type_axes_names({"Key", "Value", "CGSize", "BufferSize"})
   .set_timeout(100)                            // Custom timeout: 100 s. Default is 15 s.
diff --git a/benchmarks/hash_table/static_multimap/static_multimap_bench.cu b/benchmarks/hash_table/static_multimap/static_multimap_bench.cu
index c6d3fd58a..494e8e1a3 100644
--- a/benchmarks/hash_table/static_multimap/static_multimap_bench.cu
+++ b/benchmarks/hash_table/static_multimap/static_multimap_bench.cu
@@ -124,12 +124,12 @@ std::enable_if_t<(sizeof(Key) != sizeof(Value)), void> nvbench_static_multimap_c
 }
 
 /**
- * @brief A benchmark evaluating multi-value `find_all` performance:
+ * @brief A benchmark evaluating multi-value `retrieve` performance:
  * - Total number of insertions: 100'000'000
  * - CG size: 8
  */
 template <typename Key, typename Value, dist_type Dist, nvbench::int32_t Multiplicity>
-std::enable_if_t<(sizeof(Key) == sizeof(Value)), void> nvbench_static_multimap_find_all(
+std::enable_if_t<(sizeof(Key) == sizeof(Value)), void> nvbench_static_multimap_retrieve(
   nvbench::state& state,
   nvbench::type_list<Key, Value, nvbench::enum_type<Dist>, nvbench::enum_type<Multiplicity>>)
 {
@@ -170,7 +170,7 @@ std::enable_if_t<(sizeof(Key) == sizeof(Value)), void> nvbench_static_multimap_f
 }
 
 template <typename Key, typename Value, dist_type Dist, nvbench::int32_t Multiplicity>
-std::enable_if_t<(sizeof(Key) != sizeof(Value)), void> nvbench_static_multimap_find_all(
+std::enable_if_t<(sizeof(Key) != sizeof(Value)), void> nvbench_static_multimap_retrieve(
   nvbench::state& state,
   nvbench::type_list<Key, Value, nvbench::enum_type<Dist>, nvbench::enum_type<Multiplicity>>)
 {
@@ -178,12 +178,12 @@ std::enable_if_t<(sizeof(Key) != sizeof(Value)), void> nvbench_static_multimap_f
 }
 
 /**
- * @brief A benchmark evaluating multi-value retrieve (`count` + `find_all`) performance:
+ * @brief A benchmark evaluating multi-value query (`count` + `retrieve`) performance:
  * - Total number of insertions: 100'000'000
  * - CG size: 8
  */
 template <typename Key, typename Value, dist_type Dist, nvbench::int32_t Multiplicity>
-std::enable_if_t<(sizeof(Key) == sizeof(Value)), void> nvbench_static_multimap_retrieve(
+std::enable_if_t<(sizeof(Key) == sizeof(Value)), void> nvbench_static_multimap_query(
   nvbench::state& state,
   nvbench::type_list<Key, Value, nvbench::enum_type<Dist>, nvbench::enum_type<Multiplicity>>)
 {
@@ -225,7 +225,7 @@ std::enable_if_t<(sizeof(Key) == sizeof(Value)), void> nvbench_static_multimap_r
 }
 
 template <typename Key, typename Value, dist_type Dist, nvbench::int32_t Multiplicity>
-std::enable_if_t<(sizeof(Key) != sizeof(Value)), void> nvbench_static_multimap_retrieve(
+std::enable_if_t<(sizeof(Key) != sizeof(Value)), void> nvbench_static_multimap_query(
   nvbench::state& state,
   nvbench::type_list<Key, Value, nvbench::enum_type<Dist>, nvbench::enum_type<Multiplicity>>)
 {
@@ -291,12 +291,12 @@ NVBENCH_BENCH_TYPES(nvbench_static_multimap_count,
   .add_float64_axis("Occupancy", {0.8})
   .add_float64_axis("MatchingRate", {0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1});
 
-NVBENCH_BENCH_TYPES(nvbench_static_multimap_find_all,
+NVBENCH_BENCH_TYPES(nvbench_static_multimap_retrieve,
                     NVBENCH_TYPE_AXES(key_type,
                                       value_type,
                                       nvbench::enum_type_list<dist_type::UNIFORM>,
                                       multiplicity))
-  .set_name("staic_multimap_find_all_uniform_multiplicity")
+  .set_name("staic_multimap_retrieve_uniform_multiplicity")
   .set_type_axes_names({"Key", "Value", "Distribution", "Multiplicity"})
   .set_timeout(100)                            // Custom timeout: 100 s. Default is 15 s.
   .set_max_noise(3)                            // Custom noise: 3%. By default: 0.5%.
@@ -304,9 +304,9 @@ NVBENCH_BENCH_TYPES(nvbench_static_multimap_find_all,
   .add_float64_axis("Occupancy", {0.8})
   .add_float64_axis("MatchingRate", {0.5});
 
-NVBENCH_BENCH_TYPES(nvbench_static_multimap_find_all,
+NVBENCH_BENCH_TYPES(nvbench_static_multimap_retrieve,
                     NVBENCH_TYPE_AXES(key_type, value_type, d_type, nvbench::enum_type_list<8>))
-  .set_name("staic_multimap_find_all_occupancy")
+  .set_name("staic_multimap_retrieve_occupancy")
   .set_type_axes_names({"Key", "Value", "Distribution", "Multiplicity"})
   .set_timeout(100)                            // Custom timeout: 100 s. Default is 15 s.
   .set_max_noise(3)                            // Custom noise: 3%. By default: 0.5%.
@@ -314,9 +314,9 @@ NVBENCH_BENCH_TYPES(nvbench_static_multimap_find_all,
   .add_float64_axis("Occupancy", nvbench::range(0.1, 0.9, 0.1))
   .add_float64_axis("MatchingRate", {0.5});
 
-NVBENCH_BENCH_TYPES(nvbench_static_multimap_find_all,
+NVBENCH_BENCH_TYPES(nvbench_static_multimap_retrieve,
                     NVBENCH_TYPE_AXES(key_type, value_type, d_type, nvbench::enum_type_list<8>))
-  .set_name("staic_multimap_find_all_matching_rate")
+  .set_name("staic_multimap_retrieve_matching_rate")
   .set_type_axes_names({"Key", "Value", "Distribution", "Multiplicity"})
   .set_timeout(100)                            // Custom timeout: 100 s. Default is 15 s.
   .set_max_noise(3)                            // Custom noise: 3%. By default: 0.5%.
@@ -324,12 +324,12 @@ NVBENCH_BENCH_TYPES(nvbench_static_multimap_find_all,
   .add_float64_axis("Occupancy", {0.8})
   .add_float64_axis("MatchingRate", {0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1});
 
-NVBENCH_BENCH_TYPES(nvbench_static_multimap_retrieve,
+NVBENCH_BENCH_TYPES(nvbench_static_multimap_query,
                     NVBENCH_TYPE_AXES(key_type,
                                       value_type,
                                       nvbench::enum_type_list<dist_type::UNIFORM>,
                                       multiplicity))
-  .set_name("staic_multimap_retrieve_uniform_multiplicity")
+  .set_name("staic_multimap_query_uniform_multiplicity")
   .set_type_axes_names({"Key", "Value", "Distribution", "Multiplicity"})
   .set_timeout(100)                            // Custom timeout: 100 s. Default is 15 s.
   .set_max_noise(3)                            // Custom noise: 3%. By default: 0.5%.
@@ -337,9 +337,9 @@ NVBENCH_BENCH_TYPES(nvbench_static_multimap_retrieve,
   .add_float64_axis("Occupancy", {0.8})
   .add_float64_axis("MatchingRate", {0.5});
 
-NVBENCH_BENCH_TYPES(nvbench_static_multimap_retrieve,
+NVBENCH_BENCH_TYPES(nvbench_static_multimap_query,
                     NVBENCH_TYPE_AXES(key_type, value_type, d_type, nvbench::enum_type_list<8>))
-  .set_name("staic_multimap_retrieve_occupancy")
+  .set_name("staic_multimap_query_occupancy")
   .set_type_axes_names({"Key", "Value", "Distribution", "Multiplicity"})
   .set_timeout(100)                            // Custom timeout: 100 s. Default is 15 s.
   .set_max_noise(3)                            // Custom noise: 3%. By default: 0.5%.
@@ -347,9 +347,9 @@ NVBENCH_BENCH_TYPES(nvbench_static_multimap_retrieve,
   .add_float64_axis("Occupancy", nvbench::range(0.1, 0.9, 0.1))
   .add_float64_axis("MatchingRate", {0.5});
 
-NVBENCH_BENCH_TYPES(nvbench_static_multimap_retrieve,
+NVBENCH_BENCH_TYPES(nvbench_static_multimap_query,
                     NVBENCH_TYPE_AXES(key_type, value_type, d_type, nvbench::enum_type_list<8>))
-  .set_name("staic_multimap_retrieve_matching_rate")
+  .set_name("staic_multimap_query_matching_rate")
   .set_type_axes_names({"Key", "Value", "Distribution", "Multiplicity"})
   .set_timeout(100)                            // Custom timeout: 100 s. Default is 15 s.
   .set_max_noise(3)                            // Custom noise: 3%. By default: 0.5%.

From ff407f79b4486c14040629c5e9e2eb278947d1b0 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Sun, 31 Oct 2021 18:30:19 -0400
Subject: [PATCH 265/267] Add pair_retrieve benchmark + function renaming

---
 .../static_multimap/static_multimap_bench.cu  | 98 ++++++++++++++++++-
 benchmarks/key_generator.hpp                  |  6 +-
 2 files changed, 98 insertions(+), 6 deletions(-)

diff --git a/benchmarks/hash_table/static_multimap/static_multimap_bench.cu b/benchmarks/hash_table/static_multimap/static_multimap_bench.cu
index 494e8e1a3..4ba873147 100644
--- a/benchmarks/hash_table/static_multimap/static_multimap_bench.cu
+++ b/benchmarks/hash_table/static_multimap/static_multimap_bench.cu
@@ -17,11 +17,24 @@
 #include <random>
 
 #include <thrust/device_vector.h>
+#include <thrust/iterator/discard_iterator.h>
 #include <nvbench/nvbench.cuh>
 
 #include <cuco/static_multimap.cuh>
 #include <key_generator.hpp>
 
+namespace {
+// Custom pair equal
+template <typename Key, typename Value>
+struct pair_equal {
+  __device__ bool operator()(const cuco::pair_type<Key, Value>& lhs,
+                             const cuco::pair_type<Key, Value>& rhs) const
+  {
+    return lhs.first == rhs.first;
+  }
+};
+}  // anonymous namespace
+
 /**
  * @brief A benchmark evaluating multi-value `insert` performance:
  * - Total number of insertions: 100'000'000
@@ -100,7 +113,7 @@ std::enable_if_t<(sizeof(Key) == sizeof(Value)), void> nvbench_static_multimap_c
     h_pairs[i].second = val;
   }
 
-  generate_prob_keys<Key>(matching_rate, h_keys.begin(), h_keys.end());
+  generate_probe_keys<Key>(matching_rate, h_keys.begin(), h_keys.end());
 
   thrust::device_vector<Key> d_keys(h_keys);
   thrust::device_vector<cuco::pair_type<Key, Value>> d_pairs(h_pairs);
@@ -151,7 +164,7 @@ std::enable_if_t<(sizeof(Key) == sizeof(Value)), void> nvbench_static_multimap_r
     h_pairs[i].second = val;
   }
 
-  generate_prob_keys<Key>(matching_rate, h_keys.begin(), h_keys.end());
+  generate_probe_keys<Key>(matching_rate, h_keys.begin(), h_keys.end());
 
   thrust::device_vector<Key> d_keys(h_keys);
   thrust::device_vector<cuco::pair_type<Key, Value>> d_pairs(h_pairs);
@@ -205,7 +218,7 @@ std::enable_if_t<(sizeof(Key) == sizeof(Value)), void> nvbench_static_multimap_q
     h_pairs[i].second = val;
   }
 
-  generate_prob_keys<Key>(matching_rate, h_keys.begin(), h_keys.end());
+  generate_probe_keys<Key>(matching_rate, h_keys.begin(), h_keys.end());
 
   thrust::device_vector<Key> d_keys(h_keys);
   thrust::device_vector<cuco::pair_type<Key, Value>> d_pairs(h_pairs);
@@ -232,6 +245,72 @@ std::enable_if_t<(sizeof(Key) != sizeof(Value)), void> nvbench_static_multimap_q
   state.skip("Key should be the same type as Value.");
 }
 
+/**
+ * @brief A benchmark evaluating `pair_retrieve` performance:
+ * - CG size: 8
+ */
+template <typename Key, typename Value, nvbench::int32_t Multiplicity>
+std::enable_if_t<(sizeof(Key) == sizeof(Value)), void> nvbench_static_multimap_pair_retrieve(
+  nvbench::state& state, nvbench::type_list<Key, Value, nvbench::enum_type<Multiplicity>>)
+{
+  auto constexpr matching_rate = 0.5;
+  auto constexpr occupancy     = 0.5;
+  auto constexpr dist          = dist_type::UNIFORM;
+
+  auto const num_input = state.get_int64("NumInputs");
+
+  std::size_t const size = num_input / occupancy;
+
+  std::vector<Key> h_keys(num_input);
+  std::vector<cuco::pair_type<Key, Value>> h_pairs(num_input);
+
+  generate_keys<dist, Multiplicity, Key>(h_keys.begin(), h_keys.end());
+
+  for (auto i = 0; i < num_input; ++i) {
+    Key key           = h_keys[i];
+    Value val         = h_keys[i];
+    h_pairs[i].first  = key;
+    h_pairs[i].second = val;
+  }
+
+  thrust::device_vector<cuco::pair_type<Key, Value>> d_pairs(h_pairs);
+  auto const pair_begin = d_pairs.begin();
+
+  cuco::static_multimap<Key, Value> map{size, -1, -1};
+  map.insert(pair_begin, pair_begin + num_input);
+
+  generate_probe_keys<Key>(matching_rate, h_keys.begin(), h_keys.end());
+  thrust::device_vector<Key> d_keys(h_keys);
+
+  thrust::transform(
+    thrust::device, d_keys.begin(), d_keys.begin() + num_input, pair_begin, [] __device__(Key i) {
+      return cuco::pair_type<Key, Value>{i, i};
+    });
+
+  state.add_element_count(num_input, "NumInputs");
+
+  auto const output_size =
+    map.pair_count(pair_begin, pair_begin + num_input, pair_equal<Key, Value>{});
+  thrust::device_vector<cuco::pair_type<Key, Value>> d_results(output_size);
+
+  auto out1_begin = thrust::make_zip_iterator(
+    thrust::make_tuple(thrust::make_discard_iterator(), thrust::make_discard_iterator()));
+  auto out2_begin = thrust::make_zip_iterator(
+    thrust::make_tuple(thrust::make_discard_iterator(), thrust::make_discard_iterator()));
+
+  state.exec(nvbench::exec_tag::sync, [&](nvbench::launch& launch) {
+    auto [out1_end, out2_end] = map.pair_retrieve(
+      pair_begin, pair_begin + num_input, out1_begin, out2_begin, pair_equal<Key, Value>{});
+  });
+}
+
+template <typename Key, typename Value, nvbench::int32_t Multiplicity>
+std::enable_if_t<(sizeof(Key) != sizeof(Value)), void> nvbench_static_multimap_pair_retrieve(
+  nvbench::state& state, nvbench::type_list<Key, Value, nvbench::enum_type<Multiplicity>>)
+{
+  state.skip("Key should be the same type as Value.");
+}
+
 using key_type   = nvbench::type_list<nvbench::int32_t, nvbench::int64_t>;
 using value_type = nvbench::type_list<nvbench::int32_t, nvbench::int64_t>;
 using d_type =
@@ -356,3 +435,16 @@ NVBENCH_BENCH_TYPES(nvbench_static_multimap_query,
   .add_int64_axis("NumInputs", {100'000'000})  // Total number of key/value pairs: 100'000'000
   .add_float64_axis("Occupancy", {0.8})
   .add_float64_axis("MatchingRate", {0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1});
+
+NVBENCH_BENCH_TYPES(nvbench_static_multimap_pair_retrieve,
+                    NVBENCH_TYPE_AXES(key_type, value_type, multiplicity))
+  .set_name("staic_multimap_pair_retrieve_uniform_multiplicity")
+  .set_type_axes_names({"Key", "Value", "Multiplicity"})
+  .set_timeout(100)  // Custom timeout: 100 s. Default is 15 s.
+  .set_max_noise(3)  // Custom noise: 3%. By default: 0.5%.
+  .add_int64_axis("NumInputs",
+                  {1'000,
+                   100'000,
+                   1'000'000,
+                   10'000'000,
+                   100'000'000});  // Total number of key/value pairs: 100'000'000
diff --git a/benchmarks/key_generator.hpp b/benchmarks/key_generator.hpp
index 9f0d8d8a6..09edb2654 100644
--- a/benchmarks/key_generator.hpp
+++ b/benchmarks/key_generator.hpp
@@ -89,9 +89,9 @@ static void generate_keys(OutputIt output_begin, OutputIt output_end)
 }
 
 template <typename Key, typename OutputIt>
-static void generate_prob_keys(double const matching_rate,
-                               OutputIt output_begin,
-                               OutputIt output_end)
+static void generate_probe_keys(double const matching_rate,
+                                OutputIt output_begin,
+                                OutputIt output_end)
 {
   auto const num_keys = std::distance(output_begin, output_end);
   auto const max      = std::numeric_limits<Key>::max();

From 0e80e146aa79ca7676b38e4a29f45396767162f7 Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Sun, 31 Oct 2021 19:04:47 -0400
Subject: [PATCH 266/267] Update performance notebook

---
 .../analysis/notebooks/StaticMultimap.ipynb   | 56 +++++++++----------
 1 file changed, 28 insertions(+), 28 deletions(-)

diff --git a/benchmarks/analysis/notebooks/StaticMultimap.ipynb b/benchmarks/analysis/notebooks/StaticMultimap.ipynb
index 9b6675a43..0269c5690 100644
--- a/benchmarks/analysis/notebooks/StaticMultimap.ipynb
+++ b/benchmarks/analysis/notebooks/StaticMultimap.ipynb
@@ -97,13 +97,13 @@
       "staic_multimap_insert_occupancy\n",
       "staic_multimap_count_uniform_multiplicity\n",
       "staic_multimap_count_occupancy\n",
-      "staic_multimap_count_matching_rate\n",
-      "staic_multimap_find_all_uniform_multiplicity\n",
-      "staic_multimap_find_all_occupancy\n",
-      "staic_multimap_find_all_matching_rate\n",
       "staic_multimap_retrieve_uniform_multiplicity\n",
       "staic_multimap_retrieve_occupancy\n",
-      "staic_multimap_retrieve_matching_rate\n"
+      "staic_multimap_retrieve_matching_rate\n",
+      "staic_multimap_query_uniform_multiplicity\n",
+      "staic_multimap_query_occupancy\n",
+      "staic_multimap_query_matching_rate\n",
+      "staic_multimap_count_matching_rate\n"
      ]
     }
    ],
@@ -134,7 +134,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 360x360 with 1 Axes>"
       ]
@@ -174,7 +174,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 1080x360 with 3 Axes>"
       ]
@@ -217,7 +217,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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wwQWbrlOVJvZUrX379qhq3OPHrp8+fTq5ubk0bNiQ5s2bb/FlAzjrrLMYOnQor7zyCt27dy+z/DBNmwY77rjluh13tFpEKhQVFTFq1Cj69evHmDFjmDFjBs899xzz5s1j6NChXHXVVUkdx9/P8N11F1x9NQwdapWbl16ys8V77qn8satcjTJRza9lSzvdLqlFi/LVNJKx++67s++++zJq1CjOCKo1d911FwsXLuSpp54q87ENGzZk/vz5W6xbvnw59evX32LdtddeS6dOnbj44uTHE9l333357rvv2LhxI9sE/2o3btzIhAkT2GeffYDN17Tmz5/PEUccwbBhwzj55JM3HWO33XajRo0afPTRR/Tv35+vvvoq6fLLq6z3ZZ997Aty5JGb133yCRTnr513rtz72rRpUzp27Mgee+wBwKmnnsrXX3/NbrvtRkFBAW3atAFg1apVtGnThoKCgrjH8fczfEccAc8/DyeeCOvWWW3yzTfhkEMqf+xqV6O87z4ILjFtUreurU+FwsJCVgcduJYsWcKXX35J27ZtARg8eDAjRozglVde2fSBLs3hhx/OsGHDWL58OQBvvfUW+++//xanVwANGjTgrLPO2qLVMpE2bdrQsWPHTadvAPfeey+dOnXa9MUv1rhxYx588EEeeOCBrY5z991389BDD20VUzrdeqtdNvnkE1i/3n5feqmtT4UDDjiAJUuWUHzr7MiRI2nXrh0nnXQSv/zyCzNnzmTmzJnUrVu31CQJ/n6mS+PG0KED/O9/1gKeiiQJVbBGmUiPHvb71lth9mxo3tySZPH6ypo6dSrXXXcdIoKqcv3119O+fXsAevXqRYsWLTj44IMBOO200+jTp0/c43To0IErr7ySQw89FBFhl112KbWV/LrrruOJJ54oV5xPP/00V111FW3atEFVOfjgg0v9cp566qnceeedjBo1aov1Xbt2LVeZYSg+S7zqKpg61WqY9923eX1l5eTk0K9fP44++mhUlc6dO3PZZZeV+zj+fqZHbq51C0o1US15+3Vmy8vLUx+41zkXT1ERbFvB6p+IjFPVvHjbqt2pt3Ouapo1C3bayVq8U63anXpnmmeffXarrhqHHHIIAwYMiCgiVxn+fkZn5EhYsQJKXJZNCT/1ds5VCeedZ7cszp8PpfTGKpOfejvnqjRVuyPnqKMqliQT8UTpnMt6P/xgtywefXQ4x/dE6ZzLettvD7fdBscdF87xvTHHOZf1mjVLza2KpfEapXMuq23YAB99ZIPbhMUTpXMuq333nZ1yh9F/spgnSudcVsvPt99HHRVeGZ4onXNZbeRIG9l+113DK8MTpXMua61dC6NGhVubBE+UzrksNnq0TUsbVv/JYt49yDmXtQ45BL75xobXC5MnSudc1srJgQMOCL8cP/V2zmWlFStswObvvw+/rFATpYh0E5FpIlIgIjfH2X6DiIwPfr4XkQ0i0iDMmJxzVcOoUfDEE1Bi7r5QhJYoRSQHGACcALQDuotIu9h9VLWvquaqai7wD+AzVf0trJicc1VHfj7UqgXpmMEizBplF6BAVWeo6jrgVeCUMvbvDqRo2nrnXFWXn29Jsk6d8MsKM1E2AebELBcG67YiInWBbsCbIcbjnKsiFi+G8ePD7xZULMxEGW/4zNKGU/8T8GVpp90i0lNExorI2OJpQ51z1df06bDbbuF3NC8WZvegQqBZzHJTYF4p+55DGafdqjoIGAQ2FUSqAnTOZacuXWBeadkkBGHWKMcAe4pIKxGpiSXDYSV3EpEdgT8AIY794ZyrSlRtyocwpn2IJ7REqapFwJXACGAq8LqqThaRXiLSK2bXPwMfqurKsGJxzlUdc+ZAy5YwYkT6ygz1zhxVfQ94r8S6gSWWnwOeCzMO51zVMXIkzJ4NjRunr0y/M8c5l1Xy86FRIxtaLV08UTrnsoaq1SiPPBK2SWP28kTpnMsaP/4Ic+emr/9kMU+UzrmskZMDPXvCscemt1wfZs05lzXatIGnnkp/uV6jdM5lhY0bYcIE+51uniidc1lh/HjIzYXXXkt/2Z4onXNZYeRI+33EEekv2xOlcy4r5Ofb3Djp7GhezBOlcy7jrVsHn3+e/m5BxTxROucy3jffwKpV0SVK7x7knMt4HTvC8OE2PW0UPFE65zLedtvBiSdGV76fejvnMtrKlXD33TBjRnQxeKJ0zmW0L76AO+6AgoLoYvBE6ZzLaPn5ULMmHHpodDF4onTOZbT8fDj4YKhbN7oYPFE65zLWb7/Bd9+lb7bF0niidM5lrClToHbt6PpPFvPuQc65jHXoobBkCWwbcabyROmcy2i1akUdgZ96O+cy1Lx5kJdn93hHzROlcy4j5efDuHGwww5RR+KJ0jmXofLzoWFD6NAh6kg8UTrnMpCqJcp0T0tbmlBDEJFuIjJNRApE5OZS9jlCRMaLyGQR+SzMeJxz2aGgAAoLo+8WVCy0Vm8RyQEGAMcChcAYERmmqlNi9qkPPAl0U9XZIrJLWPE457LH2rVw2mlwzDFRR2LC7B7UBShQ1RkAIvIqcAowJWafc4G3VHU2gKouCDEe51yW2G8/ePPNqKPYLMxT7ybAnJjlwmBdrL2AnUTkUxEZJyIXxDuQiPQUkbEiMnbhwoUhheucywQbN8LcuVFHsaUwE6XEWacllrcFOgMnAccDt4vIXls9SHWQquapal6jRo1SH6lzLmNMnAhNm2ZWjTLMU+9CoFnMclNgXpx9FqnqSmCliHwO7A/8GGJczrkMlp9vvw86KNo4YoVZoxwD7CkirUSkJnAOMKzEPu8Ah4nItiJSFzgQmBpiTM65DDdyJLRtC01KXqiLUGg1SlUtEpErgRFADvCMqk4WkV7B9oGqOlVEPgAmAhuBwar6fVgxOecy2/r1dsviBXFbK6IT6qAYqvoe8F6JdQNLLPcF+oYZh3MuO3zzDaxYEf34kyVlQJ9355wze+8Nzz6beYnSh1lzzmWMhg3hoouijmJrXqN0zmWEVavg3/+G+fOjjmRrniidcxnhyy/hiitgwoSoI9maJ0rnXEbIz7cpHw47LOpItuaJ0jmXEfLzrZP5dttFHcnWPFE65yK3ZImNZp4pw6qV5InSORe5cePsd6YmSu8e5JyL3DHHwMKFmTE/TjyeKJ1zGaFhw6gjKJ2fejvnIjV/Ppxwgt2+mKk8UTrnIjVyJHzwAeTkRB1J6TxROucilZ8PO+0EublRR1I6T5TOucjETkvrNUrnnItjxgyYPTvzRgsqyROlcy4yS5fCoYdmzrS0pfHuQc65yHTuDKNGRR1FYl6jdM5FQhVWrow6iuR4onTOReL77621e/jwqCNJzBOlcy4S+fk2mViHDlFHkpgnSudcJPLzYc89oVmzqCNJLKnGHBHJAw4DdgdWA98DH6vqbyHG5pyrooqK4LPP4Nxzo44kOWXWKEXkIhH5FvgHUAeYBiwADgU+EpHnRaR5+GE656qSMWNg+fLMHVatpEQ1yu2AQ1R1dbyNIpIL7AnMTnFczrkqrHlz6Ns38zuaFyuzRqmqA0pLksH28aqaX9p2EekmItNEpEBEbo6z/QgRWSYi44OfPuUL3zmXjZo0geuvz+yh1WIl1ZgTnGLXj1neSUSeSfCYHGAAcALQDuguIu3i7DpKVXODn7uTD905l43WrIE33rC7crJFsq3eHVR1afGCqi4BOiZ4TBegQFVnqOo64FXglApF6ZyrMr78Es48E776KupIkpdsotxGRHYqXhCRBiS+vtkEmBOzXBisK+lgEZkgIu+LyL5JxuOcy1KZPC1taZK91/th4CsReQNQ4CzgvgSPkTjrtMTyt0ALVV0hIicCb2ONQ1seSKQn0BOgeXNvZHcum+XnQ5cuUK9e1JEkL6kapaq+AJwO/AosBE5T1RcTPKwQiO1K2hSYV+K4v6vqiuDv94AaIrJznPIHqWqequY1atQomZCdcxlo2TIYOzZ7ugUVK8+dOQ2Alar6OLBQRFol2H8MsKeItBKRmsA5wLDYHURkNxGR4O8uQTyLyxGTcy6LfPklbNyYPd2CiiV7Z84dQB7QFngWqAG8BBxS2mNUtUhErgRGADnAM6o6WUR6BdsHAmcAl4tIEXbHzzmqWvL03DlXRZxwAvzwA7RKVM3KMJJMXhKR8Vgr97eq2jFYN1FV0347e15eno4dOzbdxTrnqjgRGaeqefG2JXvqvS6o6WlwwO1SFZxzrnr49Vc4/3yYNCnqSMov2UT5uog8BdQXkcuAj4H/hBeWc66qGTkSXnrJOpxnm6SuUapqPxE5Fvgdu07ZR1U/CjUy51yVMnIk1K8PnTpFHUn5JduYsx0wUlU/EpG2QFsRqaGq68MNzzlXVeTnwxFHZPa0tKVJ9tT7c6CWiDTBTrsvBp4LKyjnXNXy88/2k23dgoolmyhFVVcBpwGPq+qfsYEunHMuofnzoW3b7OtoXizZWxhFRA4GegCXlvOxzrlqrmtX6z+ZrZKtUV6DjXL+36DT+B7AJ+GF5ZyrKlRhw4aoo6icZO/1/lxVT1bVh4LlGap6dbihOeeqgilToFEja8zJVonmzBkkIu1L2badiFwiIj3CCc05VxXk58OSJdCmTdSRVFyi64xPArcHyfJ7bOSg2thQaDsAzwBDQo3QOZfV8vNhjz2gRYuoI6m4MhOlqo4HzhKR7bFBMRpjg1dMVdVp4YfnnMtmRUXw6adw9tlRR1I5yd6ZswL4NNxQnHNVzbffwu+/Z2+3oGLlGY/SOefKpWFDuOEGOPLIqCOpHO8L6ZwLTevW8M9/Rh1F5ZWrRunDqznnkrV2LXz2mf3OdsnO691VRKYAU4Pl/UXkyVAjc85lta++skEwPvww6kgqL9ka5aPA8QTz2ajqBODwsIJyzmW//HwbKegPf4g6kspL+tRbVeeUWJXlNyU558I0ciQccADssEPUkVResolyjoh0BVREaorI9QSn4c45F6ugAB57DEaPhkMPjTqa1Eg2UfYCegNNsPm6c4Nl55wDbPCLf/zDRgoaPtympR082K5VZrtkO5wvwoZYc865uEaMgLfesuHUtt8exoyBhQvhrLNs0N4aNaKOsOKSnQqiFXAV0DL2Map6cjhhOeeyzSuvwDXXQIMGtnzIIfb7/vvhiy+yu9N5sh3O3waeBt4FNoYWjXMua61fD7VrwyefwOuvwwMP2GRiderAunVRR1c5ySbKNar6r1Ajcc5ltZNPhkcegW22gblz4dFH4bvvYPJkOOywqKOrnGQbc/qLyB0icrCIdCr+SfQgEekmItNEpEBEbi5jvwNEZIOInJF05M65jHLmmXYdcvRo6NwZbroJjj0WBg6EunWjjq5ykq1RtgfOB45i86m3BstxiUgOMAA4FmspHyMiw1R1Spz9HgJGlC9051wmEYGVK6FxY2jVyq5VfvONjUWZ7ZJNlH8G9lDV8lxp6AIUqOoMABF5FTgFmFJiv6uAN4EDynFs51yGeeMNmDABXnwRzjsv6mhSK9lT7wlA/XIeuwkQezdPYbBuk2Ce8D8DA8t5bOdchjngALj5ZujePepIUi/ZGuWuwA8iMgbYNBZIgu5BEmedllh+DLhJVTeIxNs9OJBIT6AnQPPmzZMM2TmXTq1aWUt3VZRsoryjAscuBJrFLDcF5pXYJw94NUiSOwMnikiRqr4du5OqDgIGAeTl5ZVMts65CK1bB716wd//DvvtF3U04Uh2utrP4v0keNgYYE8RaSUiNYFzgGEljttKVVuqakvgDeCKkknSOZfZBg+GZ5+1LkGZYMgQaNnSuim1bGnLlVVmjVJEvlDVQ0VkOVueNgugqlrquCCqWiQiV2Kt2TnAM6o6WUR6Bdv9uqRzWW7VKrj3XusnedxxUUdjSbFnT4sLYNYsWwboUYmbsEW19DNZETlYVf+v4odPvby8PB07dmzUYTjngL594cYb4fPPM6NTecuWlhxLatECZs4s+7EiMk5V8+JtS3TqPSCp6Jxz1c7vv8ODD8Lxx2dGkoT4SRJg9uzKHTdRY07pTdHOuWpNBHr3hlNOiTqSzRo2hMWLt15f2c4yiRJlKxEZVtpGHz3IueqrXj24++6oo9hs8WKbyGybbWwszGJ168J991Xu2IkS5ULg4coV4ZyragYMgGbNbCCMTPGPf8Dq1ZYUBw600+3mzW25Mg05kLgx51tVTTj4RTp5Y45z0Zo/3+brPu00eOmlqKMxX38NBx8M110H/fpV7BiVacyZWbEinXNV1f33WyfzO++MOhJTVASXXw5NmsAdFbk1Jgllnnqr6mnhFOucy0azZsFTT8Ell0CbNlFHY558EsaPh6FD7bppGJKertY55+6+21q7b7896kjM/Plw223WRen008MrJ9l7vZ1zjkMPtZpks2aJ902H666zywBPPGEJPCyJbmEs2ZCjwCJVnRNvf+dc1XbxxVFHsFl+vk1odued4V8GSFSjjNc1qEEwyEV3VR2f+pCcc5lm0iSbNOyvf4VataKOxvpL9u5tre833RR+eYkac+JOMCkiecC/gMPDCMo5l1luuw0++wzOPz8zEuXDD8O0afD++zbzY9gq1JijqmOB7VMci3MuA40eDcOGwQ03wE47RR0N/Pwz3HMPnHEGdOuWnjIrlChFZFe2Hq3cOVcF3XYbNGoE11wTdSSgClddBTk5Nh1uuiRqzHmcrRNiA6ArkAEvm3MuTJ9+Ch9/bPN1b58B55DDhsHw4Xb3TdOm6Ss3UWNOyXsFFVgM/F1VF4QTknMuU9SubfdzX3551JHYVLhXX23TTVx9dXrLTtSY87yIdARaA5NVdWp6wnLOZYKDDoJ33ok6CnPvvTbQxahRUKNGessu8xqliNwOvAacDgwXkcvSEpVzLlIbN9rp7aJFUUdipkyxeC66yDq9p1uixpxzgFxV7Q4cQDBlrHOuanvrLWvlfu+9qCOxBpzeve0+7n/+M5oYEl2jXKOqqwBUdbGI+L3hzlVxGzZAnz6wzz6VH8cxFV5+2RqVBg601vcoJEqUrWNGOJcSyz7CuXNV0JAhMHWqjcaTkxNtLEuX2v3cXbrAX/4SXRyJEmXJ2TAqOCSmcy4bFI8z2bGjDcwbtdtvh4UL7RJAlEk7Uav3Z+kKxDkXvd9/h06dbLzJbSK+0DZunI012bu3xRSlRFNBnAI0VdUBwfJooPgqwY2q+kb4IW7Jp4JwrurbsMGmdpg92+7p3nHH8MuszFQQNwKxszDWwlq/jwASdkEVkW4iMk1ECkTk5jjbTxGRiSIyXkTGikgEDf/OObA7XqZmSE/pwYNhzBi7IygdSTKRRNcoa5YYe/ILVV0MLBaR7cp6oIjkAAOAY4FCYIyIDFPVKTG75QPDVFVFpAPwOrB3uZ+Fc65Sli+3PoqdO8MHH0Qby4IFNqPikUdC9+7RxlIsUY1yi7FCVPXKmMVEDfVdgAJVnaGq64BXKdE4pKordPO5/3b4QBvORaJ/f+tcngnzdN90E6xYYVPihjlqeXkkSpSj492NIyJ/Bb5J8NgmQGxttDBYV/JYfxaRH4DhwCUJjumcS7HffrO7Xk45xbrhRGnUKHjuObj+euvHmSkSnXr/DXhbRM4Fvg3WdcauVZ6a4LHx/hdsVWNU1f8C/xWRw4F7gGO2OpBIT4K7gpo3b56gWOdcefTta63d99wTbRzr18MVV0CLFja0WyZJ1D1oAdBVRI4C9g1WD1fVkUkcuxCInYKoKTCvjLI+F5HWIrKzqi4qsW0QMAis1TuJsp1zSdp2W5sLp337aOP417/g++9tEI66daONpaQyuwdV6sAi2wI/AkcDc4ExwLmqOjlmnzbA9KAxpxPwLtYdqdSgvHuQc6mnGu31wMJC2HtvOOooG3MyCmV1DwptulpVLRKRK4ERQA7wjKpOFpFewfaB2KhEF4jIemA1cHZZSdI5lzqFhfDDD3D00dE3mvztbzZiUf/+0cZRmlDn9VbV94D3SqwbGPP3Q8BDYcbgnIvvrrvghRdg1izYbbfo4vjgA3jjDbjvPmjVKro4yuKjATlXDf30Ezz7rE0/G2WSXL3ablFs29YGv8hUodYonXOZ6c47oWZNuOWWaON46CGYMQPy8zNjGtzSeI3SuWrm++/hlVds3pkoa5M//QQPPgjnnmuNOJnME6Vz1cysWdCmDdx4Y3QxFE87W6uWdXbPdJ4onatmTjrJWrsbNIguhjffhBEjbMKwxo2jiyNZniidq0beew+KiqIda3L5crj2WhscOBOmwU2GJ0rnqonPP7fa5ODB0cZx110wbx78+992V1A28ETpXDWgCrfeaqe5F14YXRyTJsFjj8Fll8GBB0YXR3llST53zlXGiBHwxRc2dFmdOtHEsHGjnWrvtBPcf380MVSUJ0rnqjhVG42nZctoZzJ84QX48kt45hlo2DC6OCrCT72dq+J+/RXWrIE77rBO5lH47Te44QY45JBoT/0rymuUzlVxu+0GEyZEG8Mtt8CSJTarYtSzO1ZEFobsnEvWhAmwbJnNiR3VvNijR8OgQXDNNdChQzQxVJbXKJ2rYn7/Hd591/orPvCA3YWTnx9NLBs2WANO48Z2f3m28kTpXBXy4Yd27/Qhh9i1ydmz4ZitJldJn3//G777Dl5/HerViy6OyvJTb+eqiBUrLEm+8w689hrMnWvTz77/Powbl/54fvnF+m4edxyccUb6y08lT5TOVRHvv2+zKB5yCDz6qI1g3revjTn58svpi2PIEOuK1LixXQY47rjoR1CvLE+UzlUR69bZpFyqNpTaaafBkUfaunXr0hPDkCHQs6eNUFSsTx9bn81Cm1wsLD65mHPxLVxoDTfffgvNm9udMEVFkJdnMxwee2z4MbRsuWWSLNaiBcycGX75lRHJ5GLOufRRhYED7fbEAw+ESy+FHXaA55+HP/whfQ068ZIkWKNSNvNTb+eyXPGAF336wPHHwyef2B04S5ZY/8VBg9JzjbCs0+vmzcMvP0xeo3Qui6naVK/9+1ujTfGdL+3bpy+GjRvt9sh774V99rFT7NWrN2+vW9dmWMxmXqN0Los99JAlyWuusT6L6b49cNUqOOccS5KXXgrjx8N//mPXJEXs96BB0KNHeuNKNa9ROpfFLrkEate2RJnuLjjz58Mpp8DYsTbvzd//bjH06JH9ibEkr1E6l2XWr4dHHrEuP7vsYtMqpDtJTphgjUZTpsDbb9uc3NneV7IsoSZKEekmItNEpEBEbo6zvYeITAx+vhKR/cOMx7lst3YtnHmmJaYPPogmhnfftU7tqjYY8MknRxNHOoWWKEUkBxgAnAC0A7qLSLsSu/0M/EFVOwD3AIPCise5bLdqlZ3qvvMOPP54+hOUKjz8sMWwzz7wzTeQm5veGKISZo2yC1CgqjNUdR3wKnBK7A6q+pWqLgkWvwaahhiPc1lrxQqbGOzDD21ysCuvTG/569bZHTfXXw+nnw6ffZYd08ymSpiJsgkwJ2a5MFhXmkuB90OMx7ms9fPPMHEivPiitS6n02+/QbdulqBvu80G3KhbN70xRC3MVu94l3bj3i8pIkdiifLQUrb3BHoCNM/2nqvOlcOaNdaq3b49zJgBO+6Y3vJ//BH++Ee74+aFF+D889NbfqYIs0ZZCDSLWW4KzCu5k4h0AAYDp6jq4ngHUtVBqpqnqnmNGjUKJVjnMs2CBday/PDDtpzuJPnJJ3DQQXaHz8iR1TdJQriJcgywp4i0EpGawDnAsNgdRKQ58BZwvqr+GGIszmWVefPsHu2ffoL9I+gLMniwDY/WuLFN5XDIIemPIZOEduqtqkUiciUwAsgBnlHVySLSK9g+EOgDNASeFOuEVVTa6B3OVRezZsHRR9sI5SNGwGGHpa/sDRvg5putA/lxx9nI5OmuyWYiH2bNuQyyahXsuy8sXWr9JA88MH1lr1hhd9QMGwa9e8Njj8G21ejePR9mzbksUbeujQKUmwsdO6av3Dlz4E9/gkmTrI9mursfZTpPlM5lgIkTYdEiOOoouPji9JY9Zox1Xl+1CoYPt65AbkueKJ2L2Nixdj2wUSOYPDm9p7tDh8IFF8Buu8HHH9tpv9uaD4rhXIS++soabnbc0SYHS1eSVLUxIs86Czp1spZtT5Kl80TpXEQ++cRqkrvuCp9/DnvskZ5y1661WuRtt8F550F+vo1C5ErnidK5iAwdagPbfvYZNGuWeP9UWLjQarAvvQT33GN329SunZ6ys5lfo3QuzdatszltHn/c5r3eaaf0lDt5srVsz59v/SPPPDM95VYFXqN0Lo1ef93u2547F3Jy0pckR4yArl2tZfuzzzxJlpcnSufS5IUXoHt3uyZZr176yh0wAE48EVq1sjEku3RJX9lVhSdK59Lgqafgwgutn+T779uc22EZMgRatrSpGXbYwTqPn3SSjUbug29VjF+jdC7FPvgAHn3UhkXr1MnusrnlFktWb7wRbuPJkCE2wO6qVba8fLl1OTrzTNh++/DKreq8RulcCr38siWqiy+G//3PRt3p189qk2+9FX4L8003bU6SxYqK4Pbbwy23qvMapXMpsnEj3HqrjQB+0EE2GnnPnlaj++gja+kOy8SJNjPj3Lnxt8+eHV7Z1YEnSudSZPFiG/Xn99/h8MPtmuDy5TZC+AMPpL48VWvNfuQRS8R161oj0fLlW+/r1yYrx0+9nUuRzz+3JNWtm40pOWAAXH45fP99ajuUr10Lzzxj3YxOOMGOf//9NgLQv/+99Xw2deva7Yqu4rxG6VwlqFrrMsDTT1uNrnFja9lu0cI6ef/tb3DvvZUva/FiS4RPPGGD+nboAM8/D+ecs/m0vkcP+33rrXa63by5Jcni9a6CVDWrfjp37qzORW39etUXXlDdbz/V6dNt3a+/qq5erXr99ar166s2aaK6226qTz5ZubKmTVPt1Uu1Th1VUO3WTfWjj1Q3bqz883CbAWO1lLzjNUrnymHdOus4/sAD1v2nfXsbR3KPPTYPLNG3L9x1l61v3Bhq1Ch/OaowapRNLPbuu3aM88+32qmP8pN+niidS9K6dZakCgogL8/6Sv7xj7BNnCv9detWrAFl/Xrra/nIIzZOZcOGNspP7952R4+LhidK58qwYoWN+n322XYd8PLLLVked9zma5OpsGyZzXzYv781yuy1FwwcaLXIko0zLv08UToXx7Jl1mr9yCPWiLLffpYg//731JYza5Ylx8GDrcX8D3+wck86KX5N1UXDE6VzMX7/3a4L/utf1ifyxBOtBTnV1wXHjLFy3njDls8+25Jw586pLcelhidK57C7aoprcE88AUceaQkylYlrwwZrmHn4YeuMvsMO1jhz9dXpG7jXVYwnSletzZ1r92J/+SV8/bUlr59+ggYNKn7MIUO27MfYpw+sWWONPwUF1r/y0UfhkkvCHUXIpU6oV0FEpJuITBORAhG5Oc72vUXk/0RkrYhcH2YszsWaNQuuuMK69Tz+uJ1ar1xp2yqbJHv2tOOr2u9LL7VW6wYN7D7wggK49lpPktkktBqliOQAA4BjgUJgjIgMU9UpMbv9BlwNnBpWHK76UbUBar/80qZhPfXULVuOv/rKGk1ErFZ30002qG1lFRXB9ddvPXoPWNeer79ObUu5S58wa5RdgAJVnaGq64BXgVNid1DVBao6BlgfYhyuGikqslv6une32txLL1lXm//+17r5gI3w/Y9/WIfxgQMrniQ3brRRex591OaiadAAfvkl/r4LFniSzGZhXqNsAsyJWS4EDgyxPOf4z3/sPuipU6FWLfj2WzsVPu00aN3aBpHYdlu4++7yH1vVkmt+vv188onNagiWjHv0sFbsRYu2fqyP3pPdwkyU8f5/aoUOJNIT6AnQ3D9xrgyvvw433GDdb+6+24Yf23FHG6zi5ZfL3zdx/nwYOXJzciwe17FJE0u6Rx1lP8Wt1oceuuUI4+Cj91QFYSbKQiC200NTYF5FDqSqg4BBAHl5eRVKtq5qKj79/fRT+/ntN7svevlymD7d5q7u3Rs6dkxuQq8lS+w4xclx6lRb36CBdRm6+WZLjHvtFf9U2kfvqZrCTJRjgD1FpBUwFzgHODfE8lw18uuv8Ne/2hiQS5bYutatrZGmf3945x1LlGBTMNSpA3vvvfVxVq2yPo35+ZYcv/3Wkm/dujb47iWXWGLMzU2+NtqjhyfGqia0RKmqRSJyJTACyAGeUdXJItIr2D5QRHYDxgI7ABtF5Fqgnar+HlZcLrts2GA1xs8+s5pex45wxx02H/aMGXD66XDEEZYgmza1/oonnwxt2tidNcuWWYK7/XarAa5fby3ixafS//d/tq5GDZu+oU8fOPpoa/AJc+oGl13EhmHLHnl5eTp27Niow3BpcMkl1lq9dKktt2ljk3bdckvZj3vxRbjsMhsJvFiNGtCunfVhXLnSkmanTlZbPPpou7a43XahPRWXBURknKrmxdvmd+a4UHz/Pbz5piWk008v/V7p4hpj8TXGX36B0aNtW716cMYZW9YYy7JxI/z8M1x33ZZJEqzWOHmyna4fdZQdszIdy1314onSpdyDD8Jjj9kQYWA1tuuus9bojRsteYrYHTF33LH5GmObNpbAik+F+/cvvYwlS2DSJEuysb+L766JZ8MGu4/bufLyROlS6ocfbGiyCRNsdO8NG+DYY60f44gRMG6c3Rmzzz52z3PJa4wlrV8P06ZtTobFCXFOTA/dBg1s/phLL7Xft90Wv+O39yxzFeWJ0qXE6tXWuPLQQ3D88ZYkv/vOTnOLrzGOH2+n0jk5tnzyyfYD1pl7/vwta4gTJ1r3nHXrbJ8aNSzBHn64JcTin8aNt+yqU7u292V0qeWJsgpbu9ZORXfaKTW3z61YYV1uttvOTpMXLoSzzrIGksLCzfsddJANDnHTTZYkGza0fodnnmnDiq1aZdMcxNYQJ07c8o6WJk0sCXbrZvPSdOgAbdsm1xLtfRldqnmrdxW0Zo11jH7uOaupNW0K//ynjZqdyNKlllybNLHriX/5iw07VlCw+XT2qqtsYNt166zG2Lq1Jc42bay/4nnn2Sn3mjVbHnv//a3m+dNPFhdYTW+//basIbZv7w0tLv281bua6d3b7lCZOtVGz/n4Y2tYeecd6x+4atXmrjCPPGKnyAUF9rNokY2289//Wv/DiRNt3xNP3JwMc3MtiS5ZsnmOl8JCO7UuLLSabFHR1nFNnmyn2t27b06Ke+zhUx64zOc1yhBNmmTJY489oGvX9IweU1hoXXH69bPTzVmzbNzDBg1smoMNGyzZFb+EXbvCvHmbk2CbNtapu337zQkw3u+5czdfOyxWo4bVXn/+OX5sIpZgnctEZdUoPVGGYN06O/0sHvfwu+9sYIZ334Wddy7fsTZutNrhggX2s3ChLf/1r7b90UftFr3i7UuXWg2tdu2tx0WsWxcuusgS95FHWtKbNcsSZclkWFoSbNrUBoCI97tRIyu7ZUs7bkktWsDMmeV7/s6lS7U89V6/3kaLGT7cEsR558Exx6Sn7L597TrfjBnW+KBq/QivvBJeecUaRerWtdbfKVNsgNmFCzcnuwUL4O23Yfvt4cYbbY6Vki680JLh2rU2bFj79rZ/Tg4880z8wWNXr4ann966M3aNGnZNsmlTOPBA67JTMgnuskvyp8j33eetzq5qqZKJsqjIroWtWmWNEcuWQa9ellxuv71yx1a149aoYUlw8WK7jvf77/azfLl1tn7uORg61GbWW7DA9l+/3hLg2rXW37BtW+tbWDwF6g47WELaZRer2W2zjQ0qe+mldtqqajW91avtOuKiRfazcGH8xBgv9quu2jIJNmtWviSYDG91dlVNlTz1fuMNu0Z3xRU2yMGsWVZjWrLEElPdura8666WaN58c3OiK052l10GBx9s4xpefPGW2zZuhP/9z1qRhw2DU07ZOoZeveCFF7ZOYJ07W1eZdu0sYc6da63Jy5fbKfXChZZ8S7uWt912dvreqFHpv3v1stF1SvJTX+dKV+1OvT/6yGprl1++OVHNnWu/DzvMfj/+uJ0Kz59viQWs1rbDDnaP8fHH2zF+/x12392STI0admqbk2Ojznz3nSWkU06xmt7atZtri089tbkLTKxx4+wHrBbXsOHmBLf33hZfWYmwTp3Ez3/lSj/1dS6VqmSirF/f5kKJdzpas6Z1Yv7iC7s2uHSpXZdbtcoSzPLlVss855zkyqpVyxJrcYKtV8/6C44cWfpjpk61xFe//ua7VFLJT32dS60qeeo9ZUrpo9WA1a5KJrfS/k60rbQ7RZo33/J+5GJ++utcZqp2p97t2tkp7eLFW29r3jx+15VUe+ABP/11rqqosvdE9O+/9fW8unXh/vvTU36PHjBokNUgRez3oEF++utcNqqSNUrIjOt0PneKc1VDlU2U4InKOZcaVfbU2znnUsUTpXPOJeCJ0jnnEvBE6ZxzCXiidM65BDxROudcAp4onXMuAU+UzjmXQNYNiiEiC4Hiu7V3BJaV2CXeulZAKTO5pEWqyo/33FJVdjLHLmuf0rbFK78iz6OiUvn8kxF7nPK878mWn2i/ssov7bHlXZ+sVH/vyhtPWeXHO1YLVW0Ud29VzdofYFCS61ZGHGdKyo/33FJVdjLHLmuf0rbFK78izyPs1z5VMcUepzzve7LlJ9qvrPLLeI/KtT7Vr32qX6Nkyi/vsbL91PvdJNdVFWE+t2SOXdY+5YktE9+jVMVU0eMk+7hE+1XkPSrv+qikMp5yHSvrTr0rQkRWqup21bH86vzcoy6/Oj/3qlZ+ttcok/VWNS6/Oj/3qMuvzs+9SpVfLWqUzjlXGdWlRumccxVWpROliPwoIhtFZE0EZXcRkSUislZE1ojIm+mOIYijhoisEpE4E9iGWu7bwfNeIyKzRGTHkMuL+16LyFARWRfEMTrE8ncUkRUisjoo69Ng/TfBZ2C1iMwTkRYhld9CRAqDstaKyGUx294VERWRvVJY3lavd2nPVUTqiEhB8LqsFZEPUlB+3O+XiHwqIhuCGFaLSJ+Yx5wuIstjPpdJfyardKIE/gWcF1HZa4HeqloL68/1JxH5UwRxvAEsSGeBItIZOAlorKq1sc/ZwyEXu9V7LSJ/A44CGgZxXBxi+b8DrVW1DrATkCcil2LXyXYI1s8GXgup/Hzg4+Dz1gD4ECyhAAcDG1JcXrzvVmnPtS9QI3gPdgeOEZFDK1l+Wd+v91S1TvBzN4CI1AJeAi4J4tgPiDNPa3xVOlGq6hPYGxZF2RNU9eXg7/nAYqBdOmMQkTzgMOxDnW4C7BR8QGsCBWEWVsp7/XfgQVVdHuwzJcTyVVWLa+11se+WquqDqro2WP8psGuqyxaRJkALgn8EqrpSVYtvyngLuDzVZcZ7vct4rgrUCj4LOwEbgXmVLL+836+bgPmqOjR4TIGqrk+2vCqdKDNF8N+zEfYfLZ3eBq7CPphpo6rjgHeA6cBqYJWqPpjOGAI7AycFp8RLReTCMAsLLnOsBhYBE1X1mRK7XAS8H0LRh2I1rJ+Cyyw/iEgjEbkXWFicHNLsIjY/1xuD+FYDPwGvqeqMVBUU5/t1QnDa/aOItAzWdQz2XRS8RsPLU4YnypCJyK7ACOBhVZ2bxnLvApaq6pB0lRlTdkvgSGAfYDugtog8me44sFptfaAecC0wWEQkrMJUdX1w2tkSaCsip24KRORD7B9W7xCKrom9zveral0sIQ3HnvPJIZRXpjjP9cJgeTtgX+BsEflDisoq+f3qDdQBtscuOX0Y7Lot0AzoGvw+XESuT7qgVN5ilIk/2H/bNRGVXQerXbwTQdlfAUXBzwbs9GdGmsp+GJgWs/wU8H2632tgIXBtzPJ6YO80vQYjgXeDvwcBy7FrpWGU1R4oilnuDfyGJafiz4AGv9uH9XqX9lyBScDAmOUfgUdTUH6Z36/Y+ID+QEHMto+B/yVbltcoQxLUXL4H5qjqKekuX1W7quq2qrotcB2wQFX3SFPxk4EWItIweB2OBqamqexYHwJ/BhCR47Aa5rQwChKRvWNaeXcCOgPfisitWI3qAFVdHEbZqjoJWCki3YJVZ2JJYZuYz8AGoF2wbyjKeK5zgKPFNMKup35RybLifr9EZP+Y3a4Hiq8bPwrsHnwma2Gn4mOTLjAd/12j+sFGGSquTRUBz6ax7MuDclfH/PSJ6HW4Fvg1zWV+il2XWgPMAOql+73GTvVmBDGsAv4eYvmnB2WsDsrLD9avC+Ip/gxMDqn8s4GVQRnzgZYlthcBe4X8esd9rlijzpzgdVlLOWpyZZQf9/sV836vBn4B9o95zJPBtjXA6PKU53fmOOdcAn7q7ZxzCXiidM65BDxROudcAp4onXMuAU+UzjmXgCdKl3bBSDYvxixvKyILReR/STx2RfC7pYicG7M+T0TKvKc9eMz35dj/PRGpH/xckSg2V3V5onRRWAnsJyJ1guVjgfLe3tkS2JQoVXWsql6d7IOT2V9VT1TVpdhtkJ4oqzFPlC4q72NDsQF0B14p3iAid8behysi38cMblDsQeAwERkvIn8TkSOKa6TB418UkZEi8lPs2Iwxx4zdf3sReVZEJonIRBE5PVg/U0R2DspqHZTVNzh27N0gQ0Qk7fdUu/TxROmi8ipwjojUBjoA5R1U92ZglKrmquqjcbZ3wBLxwUAfEdm9jGPdDixT1faq2gG7T7tkWdODsm4ABhMMaRYM/toVeK+c8bss4onSRUJVJ2Knz90JJ8m8o6qrVXUR8AnQpYx9jwEGxMS2pKwDq+pnQBsR2QWL/01VLUpBzC5DbRt1AK5aGwb0A44AGsasL2LLf+K1K3DskvfmlnWvriTYHs+LQA/gHOCScj7WZRmvUbooPQPcrVuPaDMT6AQgIp2wof5LWo6NM1maU0Sktog0xBLxmDL2/RC4snghGP0nUVnPYYONoKqTyzi2qwI8UbrIqGqhqvaPs+lNoIGIjMdGifkxzj4TgSIRmRDMjVPSN9jgtV8D96hqWVMP3ItNW/G9iEzABh2OjXMx8GWwvW+w7lds6Lhny3ySrkrw0YNclSMidwIrVLVfiGXUxQak7aSqy8Iqx2UGr1E6V04icgzwA/C4J8nqwWuUzjmXgNconXMuAU+UzjmXgCdK55xLwBOlc84l4InSOecS8ETpnHMJ/D8EQYMhq7o9yQAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 360x360 with 1 Axes>"
       ]
@@ -258,7 +258,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1080x360 with 3 Axes>"
       ]
@@ -299,7 +299,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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i2vZxSW+f//WVrqfY78WY2HEif474kxu23sB9Oe8jd7bcDKo3KMHzTYyAJY6qGgZEFqDYDHwaWYAisggFbozRDlwBiknAXb724sBPIrIOV33wG78CFOCSUP9uqgDNgfW+fT4Hhqqq3fYzJh365x+YOxfeecfdwAdo2BCeegr8/p9ojDEmHZo6FSpUgCxZ3M+pU1Pu2CVLlmTZsmWsXbuWX3/9lXHjxrFvn+vw1qdPH7Zs2cKGDRs4d+4c7733XpzH+f333xkxYgQfffQRW7ZsYe3atfTp0ydaErJw4UKqVavGp59+Gmv3wNiMGTOGNm3asG7dOjZt2sS4ceMAmDlzJvv27WP9+vVs2LCB2bNnU6BAgViPMX36dK699lpmzJgRrb1YsWK89tprXLhwIVGxeGX6dKhVC4KC3M/pMa/ok6lfv36MGjWKzZs389tvv1GsWLGodStXruT48eMJHiO9fv7yqzDmuzGcv3jexbh9IZ2qdaJKoSqJii8hAZ3HUVXnqeqVqlrZVxwCVZ0YWYRCnWG+9bVVdaWvfYeq1vW9giP39TvuAP9CFr62Wb5t66pqfVX9KpDnZowJnP37oWRJ2LMnentw8KVtxhjjiYULvY4gXZo6FYYMgb/+cg9p//rLLadU8pg9e3Zy5MgBQGhoKBEREVHrOnTogIggIjRu3Jg98fwP5YUXXuCRRx6hRo0aUW2dO3emefPmUcvTp0/nnnvuoVy5cixfvjxR8e3fv58yZf4tclunTp2o9pIlS5Ili7s0L1OmDAUj75z6OX36ND///DOTJ0++JHEoWrQorVq14qOPPkpULF6YPh0efdTdBD5/3v189NGUSx43bdpEWFgYbdq0AdyTyNy53di+8PBwRo0axYsvvpjgcdLr59+5XWf++P4PKrxWgT+O/MF3O77j0WaPJiq2xAho4miMMUlRpgzs3OkSxW+//bf966+hcWPv4jLGmKgvpV694PRpb2NJg+69F1q0iPt1++1uGIK/s2dde1z73Hvv5cWwe/du6tSpQ9myZXnooYcoVapUtPUXL15kypQptGvXLs5jbNy4kfr168e5/ty5cyxatIiOHTvSu3dvpicy8xk2bBi33347LVu2ZOzYsVFPQ3v06MFXX31FSEgII0eOZM2aNbHuP2fOHNq1a8eVV15JoUKFWL16dbT1Dz/8MC+//DLh4d7N+hTbZ/j2227dM89AvnyuB1GbNu5nvnwwapRb/88/l+57Of744w8KFChAt27dqFevHqNGjYr6W7z55pt07tyZkiVLJnic9Pr5jx49mqBfgjgdepr9p/YTUiIkxZ42giWOxpg05vffoXlzdyeyQAE4fBg2bHD/c5k0CR54wOsIjTGZUmio+wJq29Yt//ADJGKclIkuNPTy2pOibNmyrF+/nm3btvHRRx9x8ODBaOvvuusumjdvTrNmzRJ1vCNHjhASEsKVV17J+PHjAfj6669p2bIluXPn5uabb2b27NlRCYp/YZJIkW033HADO3bsYPDgwWzZsoV69epx+PBhypQpw9atW3n++efJkiULrVq1YtGiRZccZ/r06fTq1QuAXr16XZKwVKxYkcaNGzNt2rREnVtq27rVFbzzd8UVsC9m+cwkCgsLY+nSpYwfP54VK1awY8cOPvzwQ/bt28dnn33GiBEjLvuY6eXzD9dwylcoT9vmbcm3JR9b/tlCpys7Xfb5xseK2xtj0ox334W773YJ47x5cO4cvPWW67p69dXw009QsaLXURpjMp1z5+Daa2H1ale5a/p0CAm5dLtYupZlNq++Gv/6ChVc99SYypeHxYtTNpZSpUoRHBzM0qVL6d69OwBPPfUUhw8f5p133ol33+DgYFavXk3dunUpXLgwa9euZfz48Zz2PWWePn06P//8MxUqVABccvHDDz/QunVrChcuzLFjxyhSpAgAR48ejfodoFChQtx6663ceuutdOzYkSVLlnDzzTeTI0cO2rdvT/v27SlevDhz5syhVatWUfsdOXKE77//nt9//x0RITw8HBG5pOvlI488Qvfu3aN1q0xN8X2ONWq4G8EtW/7b9sMProo6QJEiyft3UKZMGerVq0elSpUA6Nq1K8uXL6dEiRJs27aNKlXc07ezZ89SpUoVtm3bFutx0tvnf/z8cXYe28mIeSN49NFHWdJ5CXVq1KF43uJJ/2PGwp44GmPSjKxZoXVrWL8e2reHbt1g0SLYtAneew+qpFxvC2OMSbxcuaBDB/jyS9fnLq6qqjYVR4LGjoXcMaaTy53btaeEPXv2cO7cOQCOHTvGzz//TLVq1QB47733WLBgAdOnT48aSxaXBx98kLFjx7J58+aotrO+PrYnT57kp59+4u+//2bXrl3s2rWLt956K+rpT4sWLZgyZQrgxtV98skntPRlSt9//33UcU6dOsX27dspV64cq1evjuq2GBERwfr16ylfvny0mD7//HP69evHX3/9xa5du9i9ezcVK1bkp59+irZd9erVqVmzJl9//fXl/wED7NFHXbfkH36Aixfdz9tvd+0poVGjRhw7dozDhw8D7u9ds2ZNbrzxRg4cOBD1eeXOnTvOpBHS1+e/4PsF9J/dnwvhF+hWoxvVq1enSUgTDq85nBJ/0uhUNdO+GjRooMYYb33zjeq0ae73iAj3Si5gpaaB75jkvOz7yRiPHTmi2qOH6vLlKXZI+25yPvlEtXx5VRH385NPkn1IzZMnj6qqLly4UGvXrq116tTR2rVr6zvvvBO1TVBQkFaqVEnr1q2rdevW1aeeeireY3799dfasGFDrVatml599dXaq1cv3bp1q37wwQfas2fPaNseOXJEixQpoufPn9fjx49r7969o2IYNWqUhoeHq6rqiy++qDVq1NDatWtrcHCwjh8/XlVV//e//2n9+vU1ODhYg4ODdeDAgXru3DlVVS1fvrwePnxYr7vuOv3f//4X7X1fe+01HTp0qO7cuVODg4Oj2teuXasioh988EHS/qABNG2aanCwapYs7mfkNUByRH7+qv/+G6hVq5b2799fQ0ND490+Lunh83/pvy9pyZYlNcu9WbRc1XJR7Zf7+Sf2u0nctplTw4YNdeXKlV6HYUymdO4cPPggvPnmv91QYxkWkCQiskpVG6bM0bxh30/GeOiHH+C22+DQIZg4EQalzBxo9t1kjElJN828iS+3fMnUblPpXbt3ko+T2O8m66pqjEl169dDo0YuabzvPvj++5RLGo0xJskuXoTRo6FVK8iTB5YvT7Gk0RhjUtrwRsN5r/N7yUoaL4cVxzHGpKq//3ZTahQsCAsW/Fug0BhjPPf++zBuHPznP67KS548XkdkAuyDDz7gtddei9Z2zTXX8NZbb3kUkUlN6fHzj9AIfv77Z5qVb0arSq0S3iEFWVdV625hTKoIDQXfnMxMmgRdu0LRooF5L+sOZoxJNFVXurlUKQgLcyUdW7cOyFvZd5MxJjlUlWHzhjFh5QRWDF5Bw1Ip83ViXVWNMWnGV1+5aTRWrHDLgwcHLmk0xphEO34cevWC+vXdzOORpZ2NMSaNUVUe/u5hJqycwINXP0iDkg1SPQZLHI0xAXP2LNx1F3TuDMWK2VzZxpg0ZOlSqFsXvvgC7r3X5mA0xqRpY5eO5cVlL3JXw7sY13oc4kFxCEscjTEpIizM9fiKtHYtNGwIEybAyJHw669u4l9jjPFUeDiMGQMtWkD27LBsGTz8MAQFeR2ZMcbE6tc9v/L4D4/Tr24/3ujwhidJI1jiaIxJpsWLoWlTN36xeHF44gmXRM6d63qBLVwI48f/O77RGGM8lSULbNgA/frB6tWuxLMxxqRhTco0YU7POUzuPJks4l36ZomjMSbJ1qyBHj3g/vtd8ZvZs+F//3PLjzzirs3atPE6SmNMplSokJvnJ+arcGGYORM++ADy5fM6ysynRInYP5cSJZJ12Ly+sRB//fUXDRo0ICQkhODgYCZOnBi1TZ8+fahWrRq1atVi0KBBXLx4Md5jzp8/n8aNG1O9enVCQkLo2bMnf//9NwADBgygYsWKhISEEBISwtVXXx2135w5c6hTpw7Vq1endu3azJkzJ2rdgAEDyJ07N6dOnYpqu+eeexAR/vnnHwCCgoKijhsSEsK4ceO46aabCAkJoUqVKlxxxRVR65YtW0aLFi2oVq0adevWpVGjRqxduzbq2BUqVIg67oEDB+jVqxeVK1emZs2adOjQgT/++CNpf/CkqlAh9s+/QoVkHTav31iYv//+m7Zt21KjRg1q1qzJrl27om07YsSIaNvHJS18/hWqV2D4I8O56aabeKLXE1S/srq3n7+qZtpXgwYN1BiTdP36qY4f737/8kvVwoVVK1RQveIK1SNHvIsLWKlp4DsmOS/7fjImmcD9PH5ctU8ftzx8+L/tnoRk303qRjXE/kqGPHnyqKpqaGionj9/XlVVT506peXLl9e9e/eqquo333yjERERGhERob169dK33347zuNt2LBBq1Spops2bYpq+/LLL/XHH39UVdX+/fvrZ599dsl+a9eu1cqVK+uOHTtUVXXHjh1auXJlXbduXdR+tWvX1ilTpqiqanh4uNauXVtLly6thw8fjnYusfnhhx/0xhtvjNZ23XXX6YoVK1RV9f3339fWrVtHrStfvrwePnxYIyIi9KqrrtIJEyZErVuzZo0uWbIkzvcKiLg+5xT6/FXd32PhwoWq6v4NnDlzJmrdihUrtG/fvvH+jVXTxuc/d8tczfp0Vu00rVPU8QP1+Sf2u8nmcTTGJNnWrTBgAIwYAW++CfXqwbRp0LMn/PWXu+FvjDGeWb4cbr3VTSD7zDMwerT7sjKBc++9bpB7UrRoEXt7SIibVzMRsmfPHvV7aGgoERERUcsdOnSI+r1x48bs2bMnzuO88MILPPLII9TwG5zfuXPnBN9//PjxPPLII1SsWBGAihUrMnr0aF566SWmTJkCQO/evZk5cyZ9+/Zl8eLFXHPNNfzvf/9L1PklpGnTprz00kuXtP/www9ky5aNoUOHRrWFhISkyHteIrbPsUcPVy0vrvWR/vkHuneP3rZ4caLfetOmTYSFhdHG193J/8lieHg4o0aNYtq0acyePTve43j9+S/asYhbPruF+iXrM7Xb1MSdPIH//K2rqjEmyapWhT593HXYfffBL7+4woR//eWm3zDGGE9de617lrVkCTz2mBXAySR2795NnTp1KFu2LA899BClSpWKtv7ixYtMmTKFdu3axXmMjRs3Ur9+/XjfZ9SoUVFdBvv06RO1X4MG0adJaNiwIRs3boxarlq1KocPH+bYsWNMnz6dXr16Rdv+3Llz0bqqzpw5M1HnDa57ZdeuXS9p//333y+JKyP6448/KFCgAN26daNevXqMGjWK8PBwAN588006d+5MyZIlEzyO159/m2vaIO8IZ18/y7w58xJ17hD4z9+eOBpjkuyhh+Czz9wTxyeegN9/h7vvhjvugAIFvI7OGJMpqboxU+DuaD32GFxxhbcxZSYJPRmMrxrkZTxZik/ZsmVZv349+/bto2vXrnTv3p3ixYtHrb/rrrto3rw5zZo1S9Txjhw5QqtWrTh79ixDhgzhgQceAOCll16ie4ynY6p6ScXL2Nq6devGjBkz+PXXX3nnnXeircuVK1e0cWqJ0adPH86cOUN4eDirV6++rH1TXEKfY2zrI/8+RYok699BWFgYS5cuZc2aNZQrV46ePXvy4Ycf0r59ez777DMWJ+HYqf35Z8mehUqPVmLJgCUUz1ucxEitz9+eOBpjLsupUy4x3LkTatVyN/K3boVSpVxPlG7d4PnnvY7SGJMpzZ7tnjKeOeOWX3rJksZMrFSpUgQHB7N06dKotqeeeorDhw/z3//+N959g4ODoy7ACxcuzNq1axkyZAinT59OcL+VK1dGa1u9ejU1a9aM1tarVy8ef/xx2rRpQ5Ysyb8cnzp1Kjt37uTWW29l2LBhsca1atWqZL9PWlemTBnq1atHpUqVyJo1K127dmX16tWsWbOGbdu2UaVKFSpUqMDZs2epUqVKnMfx8vPPEZTjspJGSL3P3xJHY0yirVoF9evDe++5hBGgcWNYsMBdp23f7uZsTIH/BxpjTOKdPQt33unuXJ0/D0ePun7zsVVvLFjQ62gzt+JxXAzH1X6Z9uzZw7lz5wA4duwYP//8M9WqVQPgvffeY8GCBUyfPj3BZO3BBx9k7NixbN68Oart7NmzCb7/Aw88wPPPPx9VyXPXrl0899xzjBw5Mtp25cqVY+zYsdwVOe4vBWTLlo1nn32W5cuXR4sb4Prrryc0NJRJkyZFta1YsYIff/wxxd4/UcqXj/2/y/LlU+TwjRo14tixYxw+fBiA77//npo1a3LjjTdy4MABdu3axa5du8idOzfbtm2L8ziB/Px/P/g7y08t56ZhN1G+dXk+3fgpp0JP0ebjNhw8fRDgspLGSKnx+VtXVWNMglThtdfgwQfd/9sXL4ZE9vAxxpjAWr8eeveGTZvggQdg7FjInt0ljybtOXAgoIffvHkzI0eORERQVR544AFq164NwNChQylfvjxNmzYFXHfBMWPGxHqc2rVr89prr9GvXz9OnTpF4cKFKVeuHE899VTUNqNGjeLZZ5+NWv7tt98ICQnhhRdeoFOnTly8eJFs2bLx4osvxlqI5I477oj1vSPHOEZq164d48aNS9T558qVi5EjRzJ+/HgmT54c1S4izJ49m3vvvZdx48aRM2dOKlSowKuJLDqUYmJMjZHSgoKCGD9+PK1atYqsAszgwYMv+ziB/PzfX/s+hbMWpmqtqvx0+ifOrjvL0fNHOXbwGEfOHUnTn7+4CqyZU8OGDTXm42RjzKXeeMONXezSBSZPdtOgpWUiskpVG8azvh3wGhAEvKeq42KsF9/6DsBZYICqrvat2wWcAsKBsMj3EZGXgE7ABWA7MFBVj4tIBWAzsNV3+OWq+m9ZszjY95MxidSiBWzZAh9/DG3beh1NvBL6bkoP7LvJmOSZsm4Kk1ZP4scBP3L47GGaf9Cc7ce2c99V9/Fimxc9iSmx300B7VAmIu1EZKuIbBORh2NZLyLyum/9ehGp77dul4hsEJG1IrLSr/1JEdnra18rIh381o32HWuriNwQyHMzJjMIDXU/Bw1yc2XPnp32k8aEiEgQ8BbQHqgJ9BaRmjE2aw9U9b2GABNirG+pqiExvmS/BWqpah3gD2C037rtvu1DEpM0GmMS8M8//z5R/Phj99QxjSeNxhgD0Lt2bw6cPsDcrXNpO6Utu47vokCOAjzd8mmvQ0tQwBLHAF6cAbzidxE2z/d+NYFeQDDQDnjbF4Mx5jKFhbkqqQ0auLGLefK4+RrjK4aXjjQGtqnqDlW9AMwAusTYpgvwsW+O3OVAARGJt363qi5U1TDf4nKgTEoHbowBvvsO6tRxYxoBypWDYsW8jcmkWx988EG0qS9CQkJiLS5iMiYvPv+sWbLyePPHeW7pcwCElAjhyRZPkjNrzoC+b0oI5BjHqIszABGJvDjb5LdN1MUZsFxECohISVXdn4T36wLMUNVQYKeIbPPF8EuyzsKYTGb3bjc349Kl0L+/G9+YwZQGdvst7wGaJGKb0sB+QIGFIqLAO6r6bizvMQjwn3irooisAU4Cj6nq0lj2McbE58IFePxxVym1enV45BGvIzIZwMCBAxk4cKDXYRiPePH5Xwi/QI/gHjyz5BkG1RvEm7+9ye31b0/VGJIqkIljIC/OhotIP2AlMFJVj/n2Wx7LsaIRkSG4p5uUK1cuCadlTMY1dy4MHOi6qE6ZAn37eh1RQMT23DRmehzfNteo6j4RKQZ8KyJbVHVJ1I4ijwJhwFRf036gnKoeEZEGwBwRCVbVk5cEZt9PxsRu1y645RZYuRKGDIFXXoHcub2OyhhjLktYRBi9Pu9Fzqw5eazZY/T/sj9vtn8zXTxthMCOcUyJi7P6uO6sw0Skua99AlAZCMFdkL18Ge+Hqr6rqg1VtWHRokXjPwNjMpGICHjuOVcRe/XqDJs0grupVNZvuQywL7HbqGrkz0PAbFzPBgBEpD/QEejj60mBqoaq6hHf76twhXOujC0w+34yJg65crl+87NmwTvvWNJojEl3IjSCwV8NZvaW2TQp3YRb69zKmOZj0s3TRghs4hiQizNVPaiq4aoaAUzi34u2xLyfMSaGP/5wNSayZIE5c+CXX+DKWNOaDGMFUFVEKopIdtzY6LkxtpkL9PMV8LoKOKGq+0Ukj4jkAxCRPEBb4HffcjvgIaCzqkZN9iQiRSPHW4tIJdyY7h2BPUVjMoCTJ93UGuHhbh6gDRvcPI3GGJPOqCr3zb+PD9d+yFMtnuKeq+4ha5asPNXyqXTztBECmzgG6uLMv0DFTZHtvmP1EpEcIlIRd3H2W6BOzpj05tgx+PBDmDgR/vrLtX38MdSvD/ff75ZLlIAcOTwLMVX4CtgMBxbgpsn4VFU3ishQEYmseDoPl9xtw92gipyhuTjwk4isw32/fKOq833r3gTy4bqvrhWRib725sB63z6fA0NV1SaYMyY+y5dDSIir0vXzz64tyOrdGWPSp7FLx/L6b69z31X38Xjzx70OJ8kCljgG8OLsRd80HeuBlsB9vvfbCHyKK74zHximquGBOj9j0pOvv4bKld3PX3+FevVcxdT+/d1Pv/lrMwVVnaeqV6pqZVUd62ubqKoTfb+rqg7zra+tqit97TtUta7vFRy5r29dFVUtG3PaDVWd5du2rqrWV9WvvDhnY9KsQoVcyWb/V9Om7g7XkiXQvHnCxzDpRr136iFPySWveu/US9Zx8+bNC8Bff/1FgwYNCAkJITg4mIkTJ0Zt06dPH6pVq0atWrUYNGgQFy9ejPN4Tz75JOPHj4/WVqFCBf755x/ATag+cuTIqHXjx4/nySefvGTfAQMGULFixaiKna+//joAJ06coF+/flSuXJnKlSvTr18/Tpw4AcCuXbvIlSsXISEh1KxZk379+kXFunjxYkQk2uTua9asQUQuiTctuvb9a2P9/K99/9pkHTfy8wf4+++/adu2LTVq1KBmzZrs2rUr2rYjRoyItn1sUvrz/+jOjyjycRHKbymPiKTbzz+g8zgG6OLsNt+2dVS1s38FVlUd6ztWNVX9XyDPzZj04uRJ6NcP5s+Hzz+HUaOgQAE3jnHIEPj+eyhjE0cYY7xy7Jgr36zqJo0F6NXLDby++mpvYzMprmmZpmQPyh6tLXtQdq4ukzKfdcmSJVm2bBlr167l119/Zdy4cezb50Yu9enThy1btrBhwwbOnTvHe++9l+T3yZEjB1988UVUIhGfl156ibVr17J27VruvvtuAG6//XYqVarE9u3b2b59OxUrVuQ///lP1D6VK1dm7dq1bNiwgT179vDpp59GratduzYzZ/5buHvGjBnUrVs3yeeSmhqVasS9Te5Fn9Co171N7qVx6cYJ75xI/fr1Y9SoUWzevJnffvuNYn7T9axcuZLjx48n+z0S+/mfDD3JSy+9xJ8b/+Tw9sPcc889QPr9/ANZVdUYkwbMmwfXXAONfd/JhQq513XXQf781vvLGJMGRES4gdZ33AHNmrnuEDNmeB2VSYJ759/L2gNr41wfGhZKWERYtLawiDDWHFhDiw9bxLpPSIkQXm33aqLeP3v2f5PS0NBQIiIiopY7dOgQ9Xvjxo3Zs2dPoo4Zm6xZszJkyBBeeeUVxo4dm/AOfrZt28aqVauiXfyPGTOGKlWqsH37doL8/sccFBRE48aN2bt3b1RbuXLlOHnyJAcPHqRYsWLMnz8/2rl5LbbPsUdwD+5qdBfDmwyn+pvVWb5nOTmy5iA0LJSV+1cyvo17WvbP2X/o/mn3aPsuHrA40e+9adMmwsLCaNOmDRD9SWR4eDijRo1i2rRpzJ49+/JPzE9iPv9Zm2YxZ8scKu6uSHf+Paf0/PkH9ImjMcZ7Fy+6nl+PPeZqTJQoAStWQLVqbp0xxnjm9Gn389573c/GjWHAAPelZTKkHFlzUDxPccRXDF8QSuQpcclTyOTYvXs3derUoWzZsjz00EOUKlUq2vqLFy8yZcoU2rVrl6z3GTZsGFOnTo3qYhiXUaNGRXVV3bBhA5s2bSIkJOSSBCEkJISNGzdG2/f8+fP8+uuvl8TavXt3PvvsM5YtW0b9+vXJkU4KFJTMW5LieYqz+6SbjW/3yd0Uz1OcK3JekSLH/+OPPyhQoADdunWjXr16jBo1ivBwN3LtzTffpHPnzpQsWTKBoyROfJ//1n+20ntWb4rkLsKs12ZlmM/fnjgak8Hlzu3GNi5YAJ07u+uy06fh/ffh3dimrjfGmNSwYgX06eN+z5vXdVW1hDHdS8yTwf2n9lPp9UqcDztPzqw5WXXHKkrkLZFiMZQtW5b169ezb98+unbtSvfu3SlevHjU+rvuuovmzZvTrFmzOI8hcfxb9G/Pnz8//fr14/XXXydXrlxxHuull16ie/d/nzjt2LEj1uOralT79u3bCQkJ4c8//6R79+7UqVMn2rY9evSgZ8+ebNmyhd69e7Ns2bI43z+1xfeEMHe23KwYvILgt4N55YZXaD2lNRvv2hj1+RfJXeSynjDGFBYWxtKlS1mzZg3lypWjZ8+efPjhh7Rv357PPvuMxYsTd+zkfP5/n/ibKVumENwlmJqVatLtvm4Z5vO3J47GZFAXL8Ijj7g5s4sXhzx5YOZMePJJV6ywbVvXXdUYY1JVeDg8/7wbv3j+vGt77jlLGjORkvlKMjBkIFkkCwNDBqZo0uivVKlSBAcHs3Tp0qi2p556isOHD/Pf//433n0LFy7MsWPHorWdOnWKAgUKRGu79957mTx5MmfOnEl0XMHBwaxZsyZaN9qIiAjWrVtHjRo1gH/HuG3bto3ly5czd270iQlKlChBtmzZ+Pbbb2nVqlWi3zstKJmvJP3r9qfVx63oX7d/in7+ZcqUoV69elSqVImsWbPStWtXVq9ezZo1a9i2bRtVqlShQoUKnD17lipVqsR5nKR+/ofPHGbahmkUyFGABX0XxPokPT1//pY4GpNB9evnrs1uvx22bXPzM+bP767TPv4Y3njDrtOMMR74+29Xyvnmm2HdOihY8NKqqiKu3WRYjzd/nGvLXcvj16Xs1AR79uzh3LlzABw7doyff/6ZatWqAfDee++xYMECpk+fTpYs8V8CN2/enLlz53Lq1CkAvvjiC+rWrRuteyFAoUKF6NGjR7QqlwmpUqUK9erV41m/kubPPvss9evXvySZKVmyJOPGjeP555+/5DhPP/00L7zwwiUxpQcPXvMgjUs35sFrHkzR4zZq1Ihjx45x+PBhAL7//ntq1qzJjTfeyIEDB9i1axe7du0id+7cbNu2Lc7jJPXzL5qnKO2qtGNIwyEUz1v8kuNC+v78rauqMRlMZI2Je++Fm26CHj1ce7Vqbko0Y4zxxE8/uUpdFSu6hLFyZZcgHrVpTTOjkvlK8uOAH1P8uJs3b2bkyJGICKrKAw88QO3atQEYOnQo5cuXp2nTpgB069aNMWPGxHqcOnXqMHz4cK699lpEhGLFisVZhXXkyJG8+eablxXn5MmTGTFiBFWqVEFVadq0aZzJZ9euXXnyySejPTkFuDodVx0uma8k8/vOT3jDyxQUFMT48eNp1aoVqkqDBg0YPHjwZR/ncj//N958gz0nXbGlkBIh5M0Z/3Qf6fXzF1VN9TdNKxo2bKgrV670OgxjUsTp03D33ZAvH7z2mtfReEtEVqlqQ6/jSA77fjIZxsmTMHw4TJkCs2dD165eR+SZQHw3iUg74DUgCHhPVcfFWC++9R2As8AAVV3tW3cPMBgQYJKqvprQ+9l3kzHRHTpziGYfNOP0hdNsv3s7ObPm9Dqky5bY7ybrqmpMBrBmDTRoAB9+6LqjZuL7QcaYtGTZMjeoeupU1+WhY0evI8pQRCQIeAtoD9QEeotIzRibtQeq+l5DgAm+fWvhksbGQF2go4hUTaXQjckQjp8/zg2f3MDuE7v5tPun6TJpvByWOBqTjqnCq6/CVVe5J47ffw/PPGNjF40xacB//+vmZARYutRV5spqI2RSWGNgm6ruUNULwAygS4xtugAfq7McKCAiJYEawHJVPauqYcCPwE2pGXxa8MEHH0RNlRD5GjZsmNdhmVSSnM//7MWzdJzWkY2HNjK752yuKXdNgKP1nn2DG5OO/f03PPootGsHkydDkSJeR2SMMT6VK0Pfvq4SV/78XkeTUZUGdvst7wGaJGKb0sDvwFgRKQycw3VlzXR9UAcOHMjAgQO9DsN4JDmf/xu/vsEve35hZveZ3FDlhhSOLG2yxNGYdGjDBqhVC8qXd1Oh1ahhTxmNMR5TdSWbT5xwA667dHEvE0ixffPHHKwQ6zaqullEXgC+BU4D64CwWN9EZAiumyvlypVLerTGZCAPXP0ATcs2pXn55l6Hkmqsq6ox6cjFizB6NNStC5995tpq1rSk0RjjsWPHoFcvGDAAvv7alXc2qWEPUNZvuQywL7HbqOpkVa2vqs2Bo8Cfsb2Jqr6rqg1VtWHRokVTLHhj0htV5anFT7H35F6CsgRlqqQR7ImjMenGjh3Quzf89hsMHgw33uh1RMYYAyxeDLfdBgcOuMljR41ycwKZ1LACqCoiFYG9QC/g1hjbzAWGi8gMXDfWE6q6H0BEiqnqIREpB3QDmqZe6MakXSdDT/LHkT+itakqry5/lWm/TyN/jvzc1/Q+j6LzjiWOxqRBqtGfIs6aBQMHumuxTz+FW27xLjZjjImyeze0bQsVKsAvv0DDdD0LTrqjqmEiMhxYgJuO431V3SgiQ33rJwLzcOMXt+Gm4/Af0DXLN8bxIjBMVY+l6gkYk0ZNXDmRp358ihpFakS17T+1n32n99Gxakfuvepe74LzkN0SNCYNmToVgoMhKMiNYZw+3bUHBUHt2m7ObEsajTGprlAhdzcr5qtuXfj8czcnkCWNnlDVeap6papWVtWxvraJvqQRXzXVYb71tVV1pd++zVS1pqrWVdVFXp2DMWnNwJCB5Myak9k9Z7NyyEr61+3PvtP7yJs9L5/3+BzJpGOELHE0Jo345BMYMwbefhsuXHC1Je6+2yWPXbu6avbly3sdpTEmUzp2zHWFiIiAd96BPHlg4ULX3rmzWzbGmAyiaJ6i/Kfefxj30zguhF9g8prJFMldhFfavkKOrDm8Ds8zljgak0Y89xy8/76b9uyNN2D4cDfl2dixbr0NGTLGeOrIEejWDe64A66+2nWPMMaYDOqBqx9gxsYZHDx9kGdbPkve7HnpH9Lf67A8ZWMcjUkDVGHLFqhSxRW9mT/f3cR/+203dMgYYzwXEgIHD8LLL8O999rdLGNMhvbLnl8omrsoY5eM5Y+jfzCm+RiyBWXzOixPWeJoTBogAlde6YYLnTkDb70Fd94J33/v5mg0xhjP5czpCuA0aOB1JMYYE1ALty/kls9uIbhoMNN+n0bRPEXpW6ev12F5zhJHYzwWHu6K34wZ48Y0vvUW9O0LP/wA//kPjBvndYTGmExpzx7XFaJ1a7e8dq2NZTTGZHg/7vqRrjO6Ur1Idb7r9x3TNkyj3BXlMv3TRrDE0RhPbdgAffq4nl+33uqePD73HAwZAjVruqSxZ0+vozTGZDpffQUDBrinjDt2QMGCkDfvpdsVLJjqoRljTKD8svsXOk7vSIUCFfj2tm8plKsQwxsP9zqsNCOgAxREpJ2IbBWRbSLycCzrRURe961fLyL1/dbtEpENIrJWRFb6tb8kIlt8288WkQK+9goics63/VoRmRjIczMmOVRdAZxGjeDQoX/nbOzd2yWTYWGwfr0ljcaYVBYa6sYvdu7syjj/8APkyAFHj7ovrpivo0e9jtgYY1JM1ixZqVGkBt/1+45ieYp5HU6aE7AnjiISBLwFtAH2ACtEZK6qbvLbrD1Q1fdqAkzw/YzUUlX/iXHob4HRvklvXwBGAw/51m1X1ZAUPxljUtChQzBwIMyb5wrhvP8+FLPvJmOM106fhubN3ZyM99wDL7zgkkZjjMngjpw9QuHchWlUuhG//ufXTDtPY0IC+cSxMbBNVXeo6gVgBtAlxjZdgI99k9MuBwqISMn4DqqqC1U1zLe4HCiT0oEbE0hz5sCiRe6J41dfWdJojEkj8uaFVq1g7lx49VVLGo0xmcIfR/4g+O1gXl3+KoAljfEIZOJYGtjtt7zH15bYbRRYKCKrRGRIHO8xCPif33JFEVkjIj+KSLOkh25Myjp/Hlb6OlwPHgybNrl5Gu27yRjjqVOn4PbbYd06t/zSS9Cpk7cxGWNMKtl5bCetPm5FhEZwQ+UbvA4nzQtk4hjbJbFexjbXqGp9XHfWYSLSPNqOIo8CYcBUX9N+oJyq1gPuB6aJSP5LghIZIiIrRWTl4cOHE382xiTRxo3QpIkrTHjsmEsWK1XyOqrMLUDjrwuJyLci8qfvZ0G/daN9x9oqIvZ/JpM2rFoF9evDhx/C8uVeR2OMMalqz8k9tPq4FWcunOG7ft9Ro6jNf5aQQCaOe4CyfstlgH2J3UZVI38eAmbjur4CICL9gY5AH1VV33ahqnrE9/sqYDtwZcygVPVdVW2oqg2LFi2arBM0Jj6q8Pbb0LAh7N8P06ZZAcK0wG/8dXugJtBbRGrG2Mx//PUQ3Phrfy1VNURVG/q1PQwsUtWqwCLfMr5j9wKCgXbA274YjPGGKrzyCjRt6rpDLF4Md9zhdVTGGJNqQsNCaTOlDUfOHWHhbQupU7yO1yGlC4FMHFcAVUWkoohkx104zY2xzVygn+/u/lXACVXdLyJ5RCQfgIjkAdoCv/uW2+GK4XRW1bORBxKRopEXYyJSCXfBtyOA52dMnC5cgC5dYNgwaNHCVUjt0MHrqIxPQMZf+/b5yPf7R0BXv/YZvptbO4Ft+N0IMybVffAB3H+/+1Jauxaa2cgOY0zmkiNrDh6+5mHm3TqPhqUaJryDAQJYVdVX9XQ4sAAIAt5X1Y0iMtS3fiIwD+iAu5A6Cwz07V4cmO0bnJoVmKaq833r3gRyAN/61i9X1aFAc+BpEQkDwoGhqmp1wo0nsmeH0qVdfYkRIyBLQCe+MZcptrHVTRKxTWlcl/jI8dcKvKOq7/q2Ka6q+wF8N8CK+R1reSzHuoRvPPcQgHLlyl3maRmTgDNnIE8euO02yJULevWygdbGmEzl+PnjbDy0kWvKXUP/kP5eh5PuBCxxBFDVebjk0L9tot/vCgyLZb8dQN04jlkljvZZwKzkxGtMcoSGwmOPQd++ULcuTIjZudGkFSkx/nqfLzH8VkS2qOqSZL6fa3RJ6LsADRs2jHUbYy5bWBg89RRMmeLGNRYu7CaNNcaYTORU6CnaT23PpsOb2HnPTgrlKuR1SOlOQBNHYzKiEyfc8KBvvnE37fv2hauvdj/XrYOiRV3iaNKsFBt/LSKR46+XAAdFpKTvaWNJ4NBlvJ8xyVeokKvAFVNQEISHw4ABNsWGMSZTOnvxLJ2md2LF3hV8dstnljQmkXWgM+YynD8P118P27a55PHhh90c2SEhsHevm5fxwQe9jtIkICDjr337RPZ76Q986dfeS0RyiEhF3Pjr3wJ1ciYTO3bMFb6JfH3xBRQo4JLGqVPd2Ma8eb2O0hhjUlVoWCg3zbyJJX8tYcpNU7ipxk1eh5Ru2RNHYy7DjBnupv6UKW5o0EcfwY4dbkzjF19YjYn0IIDjr8cBn4rI7cDfwC2+420UkU+BTbgphIapanjgz9RkaqrwzjtQubLrnnrrrV5HZIwxnpi0ehILty9kcufJ9K5t3fSTwxJHYy7D8uXQtaurMZE3rxsmJAILFrgE0hLH9CFA46+PAK3iWDcWGJuMkI1JnHXr3Lw/5crB9OmuGI51TzXGZGJ3NbqLWsVq0aJCC69DSfesq6oxl6F4cTdXdq1acPSoe9LYrx9s3QqlSnkdnTEm04qIcD8bN4ZRo9zvBQu6LyljjMlkIjSC0d+N5q/jf5FFsljSmEIscTQmkXbudGMYV66EatXc9djFi/Dii3D2rBv7aIwxqW73bmjd2v1+443w1lvexmOMMR5SVe78+k7G/TyO2Vtmex1OhmJdVY1JhBkz4I47XLfUMWNg5kyoWdMljLVrw7x5rnChMcakql9+gQ4d3F2s3Llh9mz38lewoDexGWNMAJ04f4KBXw4kNDw0qk1V2fzPZnYd38XD1zzMPU3u8TDCjMcSR2MSoOqKEQYHw7RpUKECPPmkq6yaKxeUKeN1hMaYTCs4GNq2heeec4VwjDEmk8idLTdrDqyhb+2+XFXmKgA+Xvcxu47vomHJhjzX6jl8xexMCrGuqsbEYc0a2LPHPWWcMQOWLHFJI7i2qlUtaTTGeGDpUujc2c0PlD+/6wJhSaMxJpPJFpSNx5o9xvK9y7nxyhu5vuL1bP5nMzmCcjCvzzxLGgPAEkdjYlCFV1+Fq66KXmMiqz2fN8Z46cIFeOQRuO462LTJ3dkyxphMrF/dfmw7uo2lfy0lV7ZcNCjZgPuuuo+ieYp6HVqGZImjMX4OHYKOHeG++6BdO3jjDa8jMsYYYMsWuPpqeP55GDTIdYmoUsXrqIwxxlPZgrLRvFxzbpp5E1sOb2HuH3MZefVIr8PKsOwZijE+a9a4GhPHjrmihHfe6bqkGmOMp1Th9tth1y744gu46SavIzLGmDRh+obpTFk/hZxZczLgywHc0eAOiuQu4nVYGZYljsb4VKoEDRq4G/q1a3sdjTEm0zt4EHLkgAIF4KOPIE8eKFnS66iMMSZNmLt1LrfNvo3m5ZvTM7gnD373IF/f+rXXYWVo1lXVZGrbt7teX+fPwxVXwNdfW9JojEkDvvrKfRndd59brlLFkkZjjPFZtGMRPT7rQf2S9Znbey53NLyDdUPX2dPGALPE0WRaU6dCvXpuyrPNm72OxhhjgDNnYOhQVzW1VCl44AGvIzLGmDSncO7CXFvuWub3nU/+HPnJIlmoVLCS12FleJY4mkzn1Cno1w/69oW6dWHdOpdAGmOMp37/HerXh3ffdSWdf/3VzdNojDEGgIOnDwIQUiKE7/p9R6FchTyOKHOxxNFkWBcuwNy5MGlS9CeKAwa4p41PPAE//ADlynkWojEmMypUyFXeivm69lrIlQsWLYIXX3TjG40xxgCw+fBmak2oxYs/v+h1KJmWJY4mQ9q8GapVg5dfhmXLoGVLV5QwIgKefdYljE8+aXMzGmM8cOyYq5SqCjt2uO6o4eFw4oQr79yypdcRGmNMmrLz2E7aTGlDkARxU3WrLO0Vu2w2GY4q9Onj5skePBgOHIDdu2HOHGjeHPr3hxo1vI7SGJOpqcKUKTB8uHvaOGiQa7c5gIwxJpq9J/fS6uNWnL14lh8H/EjVwlW9DinTsieOJsPZuhX++cc9YVywwI1j/Pln6N4dpk3zOjpjjAFuu83dxapXD9avt7tZxhgTi4vhF2k3tR2Hzx5mft/51C5upe+9ZImjyXBCQ93QoFGjoF07KFYMVq50c2aHhnodnTHGANOnw9NPw/ffQ/nyXkdjjDFpUragbDzW7DG+6v0VjUs39jqcTC+giaOItBORrSKyTUQejmW9iMjrvvXrRaS+37pdIrJBRNaKyEq/9kIi8q2I/On7WdBv3WjfsbaKyA2BPDeTdtWq5eZlnDQJ7roLfvvN3cx/+21X4d4YY1KdKoSF/bu8eDE8/jgEBXkWkjHGpFVnLpxh6V9LAehZqyctKrTwNiADBDBxFJEg4C2gPVAT6C0iNWNs1h6o6nsNASbEWN9SVUNUtaFf28PAIlWtCizyLeM7di8gGGgHvO2LwWQSqjB/vhsi9MEHkC2ba58wAZo1c3Un7rzT2xiNMZnQkSPQpQs8+qhbLljQDbiOWVW1YMH4j2OMMZnA+bDzdJ3ZlbaftGX/qf1eh2P8BPKJY2Ngm6ruUNULwAygS4xtugAfq7McKCAiJRM4bhfgI9/vHwFd/dpnqGqoqu4EtvliMJnAyZNuXsb27V0PsNat3fyMpUvDrl1w333w3Xeu0r0xxqSaJUvcQOsFC6BMGdd29Oi/VVX9X0ePehurMcZ47GL4RXp93ovvdnzHhBsnUDJfQmmBSU2BrKpaGtjtt7wHaJKIbUoD+wEFFoqIAu+o6ru+bYqr6n4AVd0vIsX8jrU8lmOZDO7XX+HWW12C+PTT0KuXay9TxlVWNcaYVBceDmPHwlNPQaVK8MsvUL9+wvsZY0wmFaERDPhyAF9u/ZI32r/BgJABXodkYkhU4igiDYFmQCngHPA78J2qxnd7NLaa4noZ21yjqvt8ieG3IrJFVZck8/0QkSG4brGUs5nf07133nHV7EuVcjf2r7nG64hMalq5ciVLly5l37595MqVi1q1atG6dWuvwzIGtm2D556D3r1df/l8+byOyKRxSbzWMibDmPH7DKZtmMZz1z/H8MbDvQ7HxCLerqoiMkBEVgOjgVzAVuAQcC0umftIROLKvvYAZf2WywD7EruNqkb+PATM5t9upwcju7P6fh66jPdDVd9V1Yaq2rBo0aJxnbpJJ6pXh5tvhrVrLWnMTD788EPq16/P888/z7lz56hWrRrFihXjp59+ok2bNgAV4vluMiZwNmxwP6tVc9NsTJliSaOJVzKvtYzJMHrX6s28W+cxutlor0MxcUjoiWMe3JO/c7GtFJEQXGGbv2NZvQKoKiIVgb24wjW3xthmLjBcRGbgurGe8HU/zQNkUdVTvt/bAk/77dMfGOf7+aVf+zQR+S/ubl1V4LcEzs+kQ/Pmueuxhx+G665zL5O5nDlzhp9//plccQxaFZGDxP3dZEzKu3DB9Y1/+WX48ktXwvnKK72OyqQPybnWMibde/3X12lfpT1VC1elfdX2Xodj4hHvE0dVfSuuLzLf+rWquiiOdWHAcGABsBn4VFU3ishQERnq22wesANXyGYScJevvTjwk4iswyV/36jqfN+6cUAbEfkTaONbRlU3Ap8Cm4D5wDBVDY/37E26EhoK994LN94IM2fanIyZ2bBhw+JMGn3OxfXdZEyK27EDrr3WJY133QXuqbcxiZKcay1I9tRn94nIRhH5XUSmi0jO5J+RMYn36vJXuWf+PUxYGXNiBZMWJaqqqq+bRAG/5YIi8n5C+6nqPFW9UlUrq+pYX9tEVZ3o+11VdZhvfW1VXelr36GqdX2v4Mh9feuOqGorVa3q+3nUb91Y37Gqqer/Ev1XMGneli1w1VXw2mtw992uzkSOHF5HZbzWv39/jh8/HrV87NgxBg0alOB+ybnQ8q0PEpE1IvK1X9tM37yza33z0K71tVcQkXN+6yYm/YxNmvPFF1CvHvzxB3z+Obz1lpVvNkmSlGut5Ex9JiKlgbuBhqpaCwjC9Q4zJlW8t/o97ltwH91rdufFNi96HY5JhMRWVa2jqscjF1T1mIjUC0xIxkR36pQbvygCc+dCp05eR2TSivXr11OgQIGo5YIFC7JmzZp49/G70GqDGxu9QkTmquomv838L7Sa4C60/KtC34PrSZE/skFVe/q9x8vACb/tt6tqyGWcmkkvLl6EmjXdPEAVKngdjUnfknKtFTX1GYBv6E8XXO+rSFFTnwHLRcR/6rOsQC4RuQjkJpbaEMYk133z7+N/26I/zzkZepL9p/fTskJLpnabStYsgZzowaSUxM7jmEVEomYmFpFCBHYqD2M45+u4ky8fTJ7sxjVa0mj8RUREcOzYsajlo0ePEhYWltBuyZpjVkTKADcC78V2cBERoAcwPQmnZNKDjRthxgz3e8+e8NNPljSalJCUa624pjVLcBtV3QuMx42d3I+rM7EwibEbE6dKBStRKFch5vSaw5xec5jdczZ5suchV9ZcfNnrS7IHZfc6RJNIiU0cXwaWicgzIvI0sAywZ8omYH75xd3EnznTLXft6qbcMMbfyJEjufrqq3n88ccZM2YMV199NQ8++GBCuyX5Qsv3+6vAg0BEHMdvBhxU1T/92ir6urb+KCLNEgrQpFGqMGkSNGoEDz0E58+79qAgb+MyGUVSrrWSPPWZL0ntAlTEFRXMIyJ9Y30TkSEislJEVh4+fDiBkIyJbnCDwfx14i/OXjxL9SLVqV6kOoVzFeaN9m+QL4dVnU5PEpU4qurHwM3AQeAw0E1VpwQyMJM5LFkCzZtDtmxQvjy88IKbM7tZM9c1tXx5ryM0aVm/fv2YNWsWxYsXp2jRonzxxRfcdtttCe2WnAutjsAhVV0Vz/F7E/1p436gnKrWA+7HVX/OH9uOdnGWhhQq5L6E/F9ZssCQIa7v/K+/Qk6rI2JSThKvtZIz9VlrYKeqHlbVi8AXwNVxxGZTmZkky5k1Jw9f8zD3zL+HTtM7MXfrXE6EnmBAyACvQzOXKbFPHAEKAWdU9Q3gsG+aDWOSbNUq6N7dFSE8dcrdyH/uOXjsMbjlFlizxhXEMSY+R48eJU+ePIwYMYKiRYuyc+fOhHZJzoXWNUBnEdmF6+J6vYh8ErmRiGQFugEzI9tUNVRVj/h+XwVsB2Kdp8EuztKQY8fcE0ZVOH0aKlf+98niggVQooS38ZmM6nKvtaKmPhOR7LjiNnNjbDMX6Ocr+nUVvqnPcF1UrxKR3L4u9q1wY7eNSXGNSzfm579/Zt2BdTy79FnGNB9DUBbrrZHeJLaq6hPAQ7jJaQGyAZ/EvYcxCXv5ZZck9urlbtwfOwZhYa4g4TvvwBVXeB2hSeueeuopXnjhBZ5//nkALl68SN++sfa08pfkCy1VHa2qZVS1gm+/71XV/w1bA1tUdU9kg4gU9RXkQUQq4Qru7EjqORsP5MkDd97pukiAe/JoTApLyrVWcqY+U9Vfgc+B1cAG3DXhuyl5TsYAbPlnC52md6JAzgJkkSycvnCaHsE9vA7LJEFi/+93E9AZOAOgqvsA65RskmXzZmjY8N9rsZ49Yft2KFcO9uyJf19jAGbPns3cuXPJkycPAKVKleLUqVPx7pPMOWYT0otLi+I0B9aLm5f2c2Co/zRCJg3r1Al+/NH9PnIkXB1rLz5jUkqSrrWSOvWZb90TqlpdVWup6m2qajMkmxS189hOWn/cmiyShSUDlxCu4Txx3RP2tDGdSmxl1AuqqiKiACKSJ4AxmUyiZEnXJfXoUdi50/X8UoWDB6Fs2YT3NyZ79uyICK6XFZw5cyZR+6nqPFxy6N820e93BYYlcIzFwOIYbQNi2W4WMCtRgZm04bvv3M9vv3V3tIxJHXatZTKc82HnKZirINO6TaNWsVpsGbaFPNntn3Z6ldgnjp+KyDu4kvSDge9wd+GNuWyqbnqNH36A/fvh7ruhYEE33cYtt8DQoW4KDmMS0qNHD+644w6OHz/OpEmTaN26NYMHD/Y6LJNeXbwIo0dD27ZuecUKSLjrszEpxa61TIZx+sJpVJUaRWuwbug6ahevDWBJYzqX2Kqq43FdrGYB1YAxvoHbxlyWiAjo0wf+8x/X6+uzz2DZMjeEqGNH6NzZVVU1JjEeeOABunfvzs0338zWrVt5+umnGTFihNdhmfRq+nQYN859QRUoAHXqXFpZtWDBBA9jTFLYtZbJKE6cP8F1H17Hw989DEAWsXHhGUWiuqr6ukt8r6rfikg1oJqIZPOVbzYm0bJkcfNkP/ccPPigK1J4881eR2XSqzNnznD99dfTpk0btm7dytatW7l40b6WzGU6cMD1le/b1/WTb9kS3rUaISZ12bWWyQjOXDjDjdNuZMPBDTzb8lmvwzEpLLG3AJYAOUSkNK7rxEDgw0AFZTKW8HB4/nn46Se3/NxzrjeYzZltkqt58+aEhoayd+9eWrduzQcffMCAAQO8DsukF2fPwuDBEBwMe/e6O1stW3odlcm87FrLpGuhYaHcNPMmftnzC9Nunkb7qu29DsmksMQmjqKqZ3Hzk72hqjcBNQMXlsko9u1zw4UeeQRmWXkQk8JUldy5c/PFF18wYsQIZs+ezaZNm7wOy6QHGza4ss6TJ8Mdd0CxYl5HZIxda5l07bbZt/Htjm+Z3Hky3Wt29zocEwCJraoqItIU6APcfpn7mkzqm29gwAB3U3/yZBg40OuITEajqvzyyy9MnTqVyZMnAxAWFuZxVCZNU4UJE+D++914xYULoXVrr6MyBuxay6Rzt9a+levKX8eAkAFeh2ICJLFfSPfgJqSd7ZvvrBLwQ+DCMund99+7Yjd167p6EzVqeB2RyYhee+01nn/+eW666SaCg4PZsWMHLVu25Pfff/c6NJOW/fST65L60Uf2pNGkJXatZdIdVWXtgbXUK1mPrtW7eh2OCTBx05VlTg0bNtSVK1cmvKFJtAsXIHt2Vz11wgS4/XbImdPrqExmIyKrVLWh13Ekh30/pbClS6FIEXcX69w5yJHDjWk0JhXZd5PJSFSVB799kFeWv8Jvg3+jfsn6Xodkkiix303x/l9TRN4VkdpxrMsjIoNEpE9SgzQZhyp8+CFceeW/NSaGDbOk0QTGkCFD2LBhQ6zrzpw5A1DYvpsM4KpzPf00tGjhBlsD5MplSaNJM+xay6RXY5eOZfwv47mjwR3UK1HP63BMKkioq+rbwOO+L7TfgcNATqAqkB94H5ga0AhNmnfyJAwd6rqktmjhpjozJpDuuusunnnmGTZs2ECtWrUoWrQo58+f588//+TkyZMAQbj50ExmtmePm2Ljxx/dBLJvv+11RMbExq61TLrz6vJXefyHx+lXtx9vdHgDsYu/TCHexFFV1wI9RCQv0BAoCZwDNqvq1sCHZ9K6336D3r3hr7/g2Wfh4Ydtmg0TeCEhIXz66aecPn2alStXsn//fnLlykWNGjWoVq0aInJIVUO9jtN4aN06aNUKzp933SH69bO7WiZNsmstk94s37Oc+xbcR7ca3ZjceTJZxHpwZBaJKo6jqqeBxYENxaRl27fDp5+6MYydO0M9X4+EN96AsDBYsgSuvtrbGE3mkzdvXlq0aOF1GCYtqlYNOnSAxx5zfeiNSePsWsukF01KN+Gjrh/RM7gnWbNY4d/MxG4RmAS99x40aQL797upNTp0gDvvdOvefBPWrrWk0RjjgUKF3FPEmK+CBd0A648/tqTRGGNSyPxt89l0eBMiQr+6/ciRNYfXIZlUZomjideBAzBqFPz6K7z+uqtgHx4O77/v2q64wl2jGWNMqjt2zFXmioiADz6APHmgcGE4ftzryIwxJkP5YecPdJ3RlZELR3odivHQZSWOIpLnMrdvJyJbRWSbiDwcy3oRkdd969eLSP0Y64NEZI2IfO3XNlNE1vpeu0Rkra+9goic81s38XJiNbH75hto3x7KlnUJZPv2ULy4m2Zj9myvozPG8VVSNZnRqVOuAM7AgdCokRvbaEw6drnXWsYE2vI9y+k0vRNVClXhk5s+8Toc46FEJY4icrWIbAI2+5briki85elEJAh4C2gP1AR6i0jNGJu1x1UNqwoMASbEWH9P5HtGUtWeqhqiqiHALOALv9XbI9ep6tDEnJuJX1CQuy679loYP951Uf3tNyhQwIrgGO8tW7aMmjVrUqNGDQDWrVvHXXfd5XFUJlWNGAEzZrgpN777DkqX9joiY5IkKddaxgTaugPraD+1PSXyluDb276lcO7CXodkPJTYEa2vADcAcwFUdZ2INE9gn8bANlXdASAiM4AuwCa/bboAH6uqAstFpICIlFTV/SJSBrgRGAvcH/Pg4ur+9gCuT+Q5mCTo2BHuuw8qV4bPP4ebb4Z9+1yvsPnzvY7OZHb33XcfCxYsoHPnzgDUrVuXJUuWeByVCThVN+AaYOxYGDQImif0vyRj0rykXGsZkyIW7VhEh2kdLmkPiwgjX/Z8LOq3iJL5SnoQmUlLEt1VVVV3x2gKT2CX0oD/Pnt8bYnd5lXgQSAijuM3Aw6q6p9+bRV9XVt/FJFmse0kIkNEZKWIrDx8+HACp5B5nTsHY8ZA9uwwcaKrqjpnDgwZAnXquGSybl2vozQGypYtG205yB6FZ2xHj8JNN0G3bm65dGlLGk2GkYRrLWNSROPSjbkixxX8+p9fOfnwSU4+fJIDIw9QOFdhpt08jfIFynsdokkDEps47haRqwEVkewi8gAxupDGIrYJszQx24hIR+CQqq6K5/i9gel+y/uBcqpaD/eEcpqI5L/k4KrvqmpDVW1YtGjR+M8gk9qyxVVRfeYZ+N//oGdP13bNNVCrluuq+uCDXkdpjEsaly1bhohw4cIFxo8fH9Vt1WRAP/8MISEwbx60a+f6zMdVVdWY9Ccp11rGpIh8OfJxf9P7efHnFzly7giD5g5iwooJXFvuWjpUvfRJpMmcEps4DgWG4Z4G7gFCfMvx2QP4PwooA+xL5DbXAJ1FZBcwA7heRKJG44pIVqAbMDOyTVVDVfWI7/dVwHbA6rBfBlU3T3aDBq6aamTSCK4gztChcPfdUKmSp2EaE2XixIm89dZb7N27lzJlyrB27Vreeustr8MyKS0iAp5/Hq67DrJlg2XLXLeHyKqqMV9Hj3odsTFJkZRrLWNSzLBGw1i4fSHNP2jOl1u+5L/L/8uY68Z4HZZJQxI1xlFV/wH6XOaxVwBVRaQisBfoBdwaY5u5wHDf+McmwAlV3Q+M9r0QkRbAA6ra12+/1sAWVd0T2SAiRYGjqhouIpVwBXd2XGbMmdoLL8Do0dCiBUydCqVKeR2RMfErUqQIU6dO9ToME2gnTsDbb0P37vDOO24eIGMymCReaxmTYsI1nBxZc7Dr+C6GNBjCgdMHCCkR4nVYJg1JVOLoS/5GABX891HVznHto6phIjIcWAAEAe+r6kYRGepbPxGYB3QAtgFngYGJjLsX0bupAjQHnhaRMNyYgKGqaredE0HV9e7q08f9/uCDVjHVpA87d+7kjTfeYNeuXYSFhXkdjklpy5a5KTYKFoQVK1zXB4lthIMx6V9SrrWMSSmnQk/Rfmp7Dp85TN7sefl046d81+87r8MyaUxiq6rOASYDXxF3sZpLqOo8XHLo3zbR73clgW4YqroYWByjbUAs283CTc9hEkkV3noLFi2CWbPcXI2jR3sdlTGJ17VrV26//XY6depEliz/9rz/6quvPIzKJFtYGDzxhOueOm6cu5tVooTXURkTaHNIwrWWMSnhyLkjHD5zmJndZ/LXib/4be9v9rTRXCKxieN5VX09oJGYVHXsGNx+O8yeDR06uMr2efN6HZUxlydnzpzcfffdXodhUtLff8Ott7pCOLffDsOHex2RManFrrVMqrsYfpGsWbJSoUAFNt61kRxZcwDgnu0YE11iE8fXROQJYCEQGtmoqqsDEpUJqF9+gV693HyML78M994LWRI9MYsxacc999zDU089Rdu2bcmRI4fX4Zjk+vZbV5Hr4kWYNg169/Y6ImNSk11rmVQVFhFGz897UjR3USZ2nBiVNAKIDQswsUhs4lgbuA24nn+7T6hv2aQjFy+6m/lBQe6GfuPGXkdkTNJt2LCBKVOm8P3330d1VbX/2aVjxYtDzZquvHOVKl5HY0xqs2stk2rCI8LpP6c/s7fM5tUbXrX/d5pESWzieBNQSVUvBDIYEziHD7spz7Jlgy+/hHLl3LIx6dns2bPZsWMH2bNnj9ae0P8ARaQd8BqucNd7qjouxnrxre+AK9w1wP+uv4gEASuBvara0df2JDAYOOzb7BHfOG9EZDRwO65w192quiAp55sh/fmnG2T98MNQpw4sXWoFcExmZddaJlWoKnd+cyfTNkzjueuf456r7vE6JJNOJLaD4jqgQADjMAH0/ffueuzJJ91ynTqWNJqMoW7duhw/fvyy9vElfW8B7YGaQG8RqRljs/a4KX2qAkOACTHW30PsE3O/oqohvldk0lgTVwk6GGgHvO2LwUybBvXrw4svur7zYEmjyczsWsukige/fZBJqyfxaLNHGd3MqiKaxEvsE8fiwBYRWUH0fvdWIjoN+eUX+O9/4Y8/oFYtN3bxq6/g2WehWjU3rtGYjOTgwYNUr16dRo0aXc4Yx8bANlXdAeCbR7YLsMlvmy7Ax77Kz8tFpICIlFTV/SJSBrgRGAvcn4j36wLMUNVQYKeIbPPF8EtiA073ChVyFblic801LoG0iWONsWstkypaVWpFUJYgnmn5jNehmHQmsYnjEwGNwiTbggXQrx889RQ88ojrjnr11a6q/cCB8MYbkCeP11Eak7KeeuqpWNsTmI6jNLDbb3kP0CQR25QG9gOvAg8C+WI59nAR6YfrxjpSVY/59lsey7Eyj2PH3Pw/4H5ecw0sX+5+X7wYsib2f0XGZGh2rWUCatPhTdQsWpN2VdrRrko7r8Mx6VCi/m+tqj8GOhCTPI8+Cu+9B506uWURVzG1QgV4/31PQzMmYK677rqk7BZbX8iYdcdj3UZEOgKHVHWViLSIsX4C8IzvWM8ALwODEvl+7k1FhuC6xlKuXLk4wk+nIhNHETcvY9680KaNJY3G+Ni1lgmkV5e/yv0L7mdRv0W0rNjS63BMOhXvGEcR+cn385SInPR7nRKRk6kToknIxYuwZg20betqTACEhMCWLbBrl5eRGRMY1157LQD58uUjf/78Ua/I5QTsAcr6LZcB9iVym2uAziKyC5gBXC8inwCo6kFVDVfVCGASrjtqYt8P3zHeVdWGqtqwaNGiCZ1H+tK3L7z9tvu9a1do3drTcIxJK+xaywTapFWTuG/BfXSr0Y1m5Zt5HY5JxxIqjjMKQFXzqWp+v1c+VU3w6sykjqxZoWBBaNoUuneHlStd++7dUDpzdYgzmcRLL70EwKlTpzh58mTUK3I5ASuAqiJSUUSy4wrXzI2xzVygnzhXASdUdb+qjlbVMqpawbff96raF0BESvrtfxPwu9+xeolIDhGpiCu481tSzz3d2bDB/ZwxA06f9jYWY9Imu9YyAfPJ+k+44+s76FC1A9NunkbWLNbLwyRdQonjW6kShUmW+fPh3DlYvx4mToSGDeHvv2HECBg+3OvojEl5w4YNS/K+qhoGDAcW4CqjfqqqG0VkqIgM9W02D9gBbMM9PbwrEYd+UUQ2iMh6oCVwn+/9NgKf4orvzAeGqWp4kk8gvVCFyZP/nSz2u+/goYe8jcmYtMmutUxAbDq8iQFzBtCyYks+v+VzsgdlT3gnY+KR0G0Hq4uexj37LDz+ONStC40auanQXn4Z/vkH7rkHknF9bUyapRrrEMHL2X8eLjn0b5vo97sC8f7Xo6qLgcV+y7fFs+1YXBXWzGPDBvjPf6BVK9cN4vpY5jAvWDD14zIm7bFrLRMQNYvWZHLnydxc82ZyZcvldTgmA0gocawoIjG7cEWxEtHeq1wZBg+G116DXLncdBx790LZslZF1WRcO3fupHNn+/pJk44dcwlhnTqwaBFcdx0E2bSVxsTDrrVMilq8azEFchYgpEQI/UP6ex2OyUASShwP4yoDmjRk4UI4cMBNv9G7t3tFypcPqlf3LjZjUkPRokUZOXJknOsTmI7DBMrHH7v+8V995RLG2J4yGmNismstk2KW7V5Gx2kdqVeyHksGLEHEHmiblJNQ4njKykOnHeHhbp7GZ5+F+vWhTx+7kW8yp3z58iV1Kg4TCGfPukHV77/vEsaqVb2OyJj0xK61TIpYvX817ae2p1S+Unx2y2eWNJoUl1BxnF2pEYRJ2IEDbsqzZ56BAQNgyRJLGk3mVaFCBa9DMJG2bIEmTeCDD9yEst99B6VKeR2VMenJLq8DMOnfxkMbaTulLQVzFmRRv0WUyFvC65BMBhTvE0dV7ZZagZi4HT8O9erBiRPw4YfQ37qrm0zuiy++8DoEE+mbb9ydrf/9D264wetojEl37FrLXI6F2xdy5OyRS9o/WPsBObLmYFG/RZS9omwsexqTfDaZSzpQoACMHu2GC9Wq5XU0xphM79w596SxXj247z647TYoVszrqIwxJsMb8b8R5M+RnyqFqkS1bTu6jWPnjvHTwJ+oWLCih9GZjC6hrqrGI4cOQYcOsHSpW777bksajTFpwB9/QNOmru/8yZOQJYsljcYYk0oeuuYhCuQswPSbp/Ny25cJiwgjV9ZcjL52tCWNJuDifeIoIvVjNCnwj6ruDlxIZskSVyn16FF3I98YE93q1aujLYsIRYoUoWxZ654TUDNnurkZs2eHKVMgf36vIzIm3UvutZaItANeA4KA91R1XIz14lvfATgLDFDV1SJSDZjpt2klYIyqvpqkEzGp4rY6t/Hskmf56o+veOjbh9h1fBcFchagX91+XodmMoGEuqrGVh66kIhkB3qr6tqUDynzioiAF1+Exx6DSpXckKE6dbyOypi0J7apOI4ePcqFCxeYPn26BxFlcGFhrtvDhAnuaePMmW6yWGNMSkjytZaIBAFvAW2APcAKEZmrqpv8NmsPVPW9mgATgCaquhUI8TvOXmB2ss/GBFS2oGzc3/R++szqw8WIi9QsUpO7Gt1FtqBsXodmMoGEiuO0jK1dRBoCrwPN49s/qXfB/NYHASuBvara0df2JDAYN+8RwCOqOs+3bjRwOxAO3K2qC+KLL6359FM3lrFXL3j3XTcnozHmUj/88EOs7StXruTuu+9O5WgygaAg1y31gQfguecgm12gGJNSknmt1RjYpqo7fPvMALoA/oljF+BjVVVguYgUEJGSqrrfb5tWwHZV/SsZp2JSwdmLZ5n5+0xOXThF/7r9+fGvH+1po0k1SSqOo6orRSRvfNsk5y6Y3/p7gM1AzP5Qr6jq+BjvVxPoBQQDpYDvRORKVQ2/3PMLpNOn4e23Yf58yJMH+vWD9u0hb17o0QNy54ZOncCm3jHm8jVs2JDTp097HUb6VKgQHDt2aXv+/K6k88cfu/GMxphUkZhrLaA04N+ldQ/Rr6Pi2qY04J849gLi7K4hIkOAIQDlypVLICQTSEfPHeXw2cPc0eAO3ln1Du91es+eNppUk6TEUUSK4/rgxydZd8FEpAxwIzAWuD8RYXUBZqhqKLBTRLb5Yvjlcs4tkM6fh1atoEwZePBBOHLE9f46dQo2b3Y9vzp39jpKY9KvgwcP2oTHSXXsGKjvaz00FEaNgjfecE8awZJGY1JZIq+1YvvCi7lPvNv4usR2BkbH9Saq+i7wLkDDhg0TiskEQHhEOCJCmfxlWH/negShZN6S9rTRpKqEiuO8waVfQIWAq3FPA+OT3LtgrwIPArF12BwuIv1w3VhHquox337LYzlWmjF9OlxxBXz+ubtGGzDATX+WLdu/12bGmISNGDHikgTx6NGjLFu2jNdee43Odgcm6XbsgJ49YeVKuPdeePVVryMyJkNL5rXWHsB/wHEZYN9lbtMeWK2qBxMbs0ldqsqwecM4c/EMH3X9iOxB2QF4osUTHkdmMpuEnjiujLGswBHgflU9lMC+Sb4LJiIdgUOqukpEWsRYPwF4xnesZ3CDygcl8v087W6xZAnccgv8+qsbx7hvH7z+OixeDBs2QHBwqoZjTLrVsGHDaMsiQuHChfnvf/9LMZsaIul++811iwgKgi++gJtussTRmMBLzrXWCqCqiFTEFbfpBdwaY5u5uBvuM3A38E/EGN/Ym3i6qRrvPbLoEd5Z9Q4PXfMQWcR6fxjvJFQc5yMRqQdUBjaq6ubLOHZy7oJ1BzqLSAcgJ5BfRD5R1b7+d8REZBLw9WW8n6fdLYoVg5074ccf3RjGn36CRo3ggw+gePHUjMSY9K1///6sWbOG7du3ExwcTI0aNbwOKX2L7KJaty706QOPPAI2jsmYVJGcay1VDROR4cACXCHC91V1o4gM9a2fCMzDFSHchitEODByfxHJjatFcUdKnY9JWS/+/CLjfh7HHQ3u4PlWz3sdjsnk4r1tISKP4+b4uRn4RkQGX8axo+6C+frP98Ld9fI3F+gnzlX47oKp6mhVLaOqFXz7fa+qfX0xlfTb/ybgd79j9RKRHL47b1WB3y4j3oD65x9o2xbee891UV2zBho0cE8cz52D667zOkJj0o9nnnmGnj17MmvWLG688UYmTZrkdUjp14IFcO217vccOWDiREsajUlFybzWQlXnqeqVqlpZVcf62ib6kkbUGeZbX1tVV/rte1ZVC6vqiRQ8JZNCJq2axEPfPUSvWr14q8NbNobfeC6hrqq9gBBVPSsihYH5QKKu0JJ7FyweL4pICK4rxy58d8l8x/4UV3wnDBiWViqqLlkCvXu74jfvvgv9+0PBgq5QYcmS8PXXVnfCmMsxY8YM1q5dS+7cuTly5Ajt2rVj8ODLutYy5865Kl1vvun6yefPH3s554IFUz82YzKXJF9rmYytSqEq9AzuycddPyYoS5DX4RiTYOJ4XlXPAqjqEZHL61jtm19xXoy2iX6/KzAsgWMsBhb7Ld8Wz7ZjcVVY04TwcHj+eXjiCahc2d3IDwmBG2+E9evddBzVq3sdpTHpT86cOcmdOzcAhQsXJiIiwuOI0pnVq6FvX1fO+Z573BdVrlxeR2VMZpWsay2T8ew/tZ+S+UrSsmJLWlaMdZpPYzyRUOJYWUQiu5dKjGVU1UoX+mzYAOvWQdWq0LgxHD3qChMuWgS33uqSxny++rDZsrluqsaYpNm+fXtU5VRVjbZs/MQ1N2PWrG7Q9cKF0KZN6sdljPFn11omytK/ltJuajsmdZrErbVj1jkyxlsJJY5dYiyPD1Qg6dX5866WxG+/QbNmsGIFlCoFM2bAmTNuTOOgQbH3ADPGJM2XX34ZbfmBBx6ItvzVV1+lZjhpl//cjH/9Bdmzu/7xIq7bQ+HC3sZnjAG71jI+q/evpuP0jpTNX5Y2leymnkl7Eqqq+mNqBZJePfec65K6Y4e7Fnv1Vdf765FH4OefbeyiMYFwnVWTujwzZsAdd7ini59/7tosaTQmTbBrLQOw9Z+ttPukHQVyFuDb276laJ6iXodkzCUSqqraRUSG+S3/KiI7fK/ugQ8v7fv4Y3jmGVc1tXVrGDXK1Zn49FOXUBpjUt6XX37JW2+9FbXcpEkTKlWqRKVKlfg8MjEyzgMPuOpctWvDeHuQYUxaY9da5mToSdpMaYOI8O1t31L2irIJ72SMBxJ6HvYg0afQyAE0AloAdwYopnTl7FnYuhXq1XPdVD/6CIYNg7AwSxyNCZQXX3wx2pjG0NBQVqxYweLFi5kwYYKHkaUhkWMbX34Zhg+HH36AChU8DckYEyu71srk8ufIz8PXPsyCvgu4svCVXodjTJwSGuOYXVV3+y3/pKpHgCMikieAcaUb1atDjx6uKM6iRe5p47vvumnRcub0OjpjMqYLFy5Qtuy/d2SvvfZaChcuTOHChTlz5oyHkaUhkXeuJk2C//zH21iMMfGxa61M6vj54+w8tpN6JetxV6O7vA7HmAQl9MQx2gReqjrcb9E6XwMvveSm1ahdG375xQ0jGjMGXnnF68iMybiOxagU+uabb0b9fvjw4dQOJ21ZvBguXIAiRaBAARg82A3A9n/Z3IzGpCV2rZUJnb14lk7TO9F6SmtOhp70OhxjEiWhxPFXEblkVm0RuQP4LTAhpT3r17vaEmvWuOXff3c9vyIioEkT2L3bVVRdtsz1BFu3DurU8TRkYzK0Jk2aMGnSpfNjv/POOzRu3NiDiNKAiAh4+mlo2dJV6YJ/q6rGfB096mmoxpho7Fork7kQfoGbP72ZZbuXMfHGieTPkd/rkIxJFNHIUu2xrRQpBswBQoHVvuYGuP73XVX1YKADDKSGDRvqypUr41x/9iz06uUSxqZN4ddf3Q38bdsgf36XKFasmHrxGmOcQ4cO0bVrV3LkyEH9+vUBWLVqFaGhocyZM4cSJUqsUtWGce0vIu2A14Ag4D1VHRdjvfjWdwDOAgNUdbXf+iBgJbBXVTv62l4COgEXgO3AQFU9LiIVgM3AVt/uy1V1aELnmND3U5xzNGbPDidOWF95Y9IgEbnkuym9XWsl+N1k4hUeEc6tX9zKpxs/ZVKnSfynvg0lMN6L7bspNglNx3EIuFpErgeCfc3fqOr3KRBjmjdmDOTK5abaUIX77oO333bzNK5c6aZDM8akvmLFirFs2TK+//57Nm7cCMCNN97I9ddfn+C+vqTvLaANsAdYISJzVXWT32btgaq+VxNggu9npHtwyaD/beJvgdGqGiYiLwCjgYd867arashln2h8Ip8m7tgBXbrApk2uaur991vSaEw6ktmvtTKbSasn8enGT3mpzUuWNJp0J6HiOAD4vrwy3RfYxx+7cYvZssHNN8MXX8CQIa7baokSXkdnjLn++usTlSzG0BjYpqo7AERkBm4Cbv/EsQvwsbouGctFpICIlFTV/SJSBrgRGAvcH7mDqi702385kDpl9E+fhuPHYf58N0/j/fcnuIsxJu3JrNdaGdHZi2fZc3JPrOsG1RtE0dxFubnmzakclTHJl6jEMbM6c8b1BgN3Lda7N7RvD++/721cxphkKQ34VzDcQ/SniXFtUxrYD7yKK5+fL573GATM9FuuKCJrgJPAY6q6NEmRx6ZOHdd/PkeOFDukMcaYpHvp55cY9/M4yub/t/r3idATHDpziHm3zrOk0QRMmf+WYe+pvZe0l85Xmj33x34z43IkVBwnU2vf3lWyB7jmGujeHd57D9q1c4UJjTHpUmz/9cYc7B3rNiLSETikqqviPLjIo0AYMNXXtB8op6r1cE8op4lIrJUQRGSIiKwUkZWXVR3WkkZjjEkzBtYbSO5suVl2+zL+GPEH9111H4fOHOKKHFfQulJrr8MzGVjnap3JHpQ9Wlv2oOx0qdYlRY5vTxzj8fzz0KIFbN4MzZu7Yjhffw3fW0cSY9KzPUBZv+UywL5EbtMd6CwiHYCcQH4R+URV+wKISH+gI9DK180VVQ3FFb1AVVeJyHbgSlxxnWhU9V3gXXAFKJJ5nsYYYzxQ7opy9KjZg5eXvUyd4nUYNm8YhXMV5vlWz5MtKJvX4ZkM7PHmj/P+muhdI4MkiMevezxFjm+JYzyqVnVTa0ye7KZGq17dLRcr5nVkxphkWAFUFZGKwF6gF3BrjG3mAsN94x+bACdUdT+u4M1oABFpATzglzS2wxXDuU5Vz0YeSESKAkdVNVxEKuEK7uxI9lkULBh71webo9EYYzw3utlogt8O5nzYeeoWr8vx0OMMCBngdVgmgzp38Rzz/pzHjI0zCIsIi2rPliUbA0MGUiJvyhRnscQxAUWKwEMPJbydMSZ98FU9HQ4swE3H8b6qbhSRob71E4F5uKk4tuGm4xiYiEO/iSuf/62bzSNq2o3mwNMiEgaEA0NVNfkTKdpcjMYYk2aVyV+GnFlzkjd7XvLnzM/dTe62p40mRV0Mv8iinYuY/vt0Zm+ezakLpyiWpxj96vZj6oapXAi/QNYsWVPsaSNY4miMyYRUdR4uOfRvm+j3uwLDEjjGYmCx33KVOLabBcxKerTGGGPSmyyShW9v+5Zr37+WPSf30LdOX69DMhlAhEbw098/MX3DdD7f/Dn/nP2HK3JcwS01b6FXrV60rNiSrFmykjNrTt5Z9U6KPm0ESxyNMcYYY4xJEZsPb+bV5a/yRoc3CCkRwkPXPEStYrXsaaNJMlVl1f5VTN8wnZkbZ7L31F5yZc1F52qd6V2rN+2qtCNH1uhF8h5v/jgLti9I0aeNYImjMcYYY4wxybbr+C7aTGlDWEQYjzR7hPIFyqf4hbvJPDYd3sSM32cw/ffpbDu6jWxZstGuSjteavMSnap1Im/2vHHuWzJfSbbfvT3FY7LE0RhjjDHGmGQ4cPoAbaa04czFM/w44EfKFyjvdUgmDYtrvsXieYpz71X3Mv336aw/uJ4skoWWFVry8DUP061GNwrm8rYAniWOxhhjjDHGJNGxc8e44ZMb2H9qP9/1+446xet4HZJJ4zpX68zkNZO5EH4hqk0QDp45yOhFo2lapimvt3udW4JvSdExismVJZAHF5F2IrJVRLaJyMOxrBcRed23fr2I1I+xPkhE1ojI135tL4nIFt/2s0WkgK+9goicE5G1vtdEjDHGGGOMCaCdx3dy8PRB5vSaw1VlrvI6HJMO3NXwLiI04pL2R5s9ys57drLs9mWMaDIiTSWNEMAnjiISBLwFtMFNpr1CROaq6ia/zdrj5jSripsrbYLvZ6R7gM1Afr+2b4HRvpL6L+DmVIucMGO7qoYE4HSMMcYYY4yJEqERZJEs1C9Zn+13bydP9jxeh2TSsENnDvHF5i/4bNNnLN61OFrimC1LNgbXH8yz1z/rYYQJC+QTx8bANlXdoaoXgBlAlxjbdAE+Vmc5UEBESgKISBngRuA9/x1UdaGqRs5suRwoE8BzMMYYY4wxJpqwiDB6fNaDF356AcCSRhOrg6cPMmHFBK7/6HpKvlySO7+5k70n9/LItY/w3W3fkSPIVUNN6fkWAyWQYxxLA7v9lvcQ/WliXNuUBvYDrwIPAvnieY9BwEy/5YoisgY4CTymqkuTFLkxxhhjjDGxiNAIhnw1hFmbZ3FtuWu9DsekMQdOH4h6srjkryVEaATVi1Tn0WaPckvNW6hVrBYiAsCgeoMCMt9ioAQycZRY2jQx24hIR+CQqq4SkRaxHlzkUSAMmOpr2g+UU9UjItIAmCMiwap6MsZ+Q4AhAOXKlUvsuRhjjDHGOPnzw6lTl7bnywcnT17abjIMVeWBhQ/wwdoPeOK6J7j3qnu9DsmkAQdOH2DWpllRyaKi1ChSg8eaPcYtwbcQXDQ4Kln0F6j5FgMlkInjHqCs33IZYF8it+kOdBaRDkBOIL+IfKKqfQFEpD/QEWilqgqgqqFAqO/3VSKyHbgSWOn/hqr6LvAuQMOGDWMmssYYY4wx8YstaYyv3WQYzy55lleWv8Ldje/mieue8Doc46H9p/Yza7NLFpf+tTQqWXy8+eP0CO5BcLHgBI8RqPkWAyWQieMKoKqIVAT2Ar2AW2NsMxcYLiIzcN1YT6jqflzBm9EAvieOD/glje1wxXCuU9WzkQcSkaLAUVUNF5FKuII7OwJ3esYYY4zJdMLDvY7AeKh43uIMChnEK+1eifUJksk44ppr8YocV1C3RN2oZLFm0ZqMuW4Mt9S8JVHJYnoWsMTRV/V0OLAACALeV9WNIjLUt34iMA/oAGwDzgIDE3HoN4EcwLe+/2CXq+pQoDnwtIiEAeHAUFU9msKnZYwxxpjMaO9emDwZ3nsv4W1NhnPk7BEK5y7MkAZDGFx/sCWNmUBscy0CnAg9wZGzR3jiuie4JfgWahat6VGEqS+QTxxR1Xm45NC/baLf7woMS+AYi4HFfstV4thuFjAr6dEaY4wxxviJiIBvv4WJE+Grr9zTxjZtYPfuhPc1GcacLXPoN7sf8/vO5+qyV1vSmAmcvnCaGkVqEBYRFq09a5asLOq3iOblm3sUmbcCmjgaY4wxxqQ7Bw/CBx/Au+/Czp1QtCiMHAmDB0OVKmCJQ6axaMcien7ek3ol6lGneB2vwzEBFB4Rznc7vuOTDZ/wxeYvOHvxLPmy5+PMxTNEaATZg7Lzn3r/ybRJI1jiaIwxxhgDqvDDD+7p4pw5cPEitGgBzz8PXbtCjhz/bpsvX9xVVU2GsXzPcrrM6MKVha9kXp955M2e1+uQTApTVdYdXMeUdVOY9vs0Dpw+QIGcBehbuy+31b2NigUqUuWNKpwPO0+QBKWb6qeBYomjMcYYYzKvf/6Bjz6Cd96BP/+EQoVg+HAYMgSqV499H5tyI8PbeWwnHaZ2oHje4izsu5BCuQp5HZJJQXtO7mHahmlMWT+F3w/9TrYs2ehQtQO31bmNG6+8kZxZc0ZtOzBkYLqaazGQLHE0xhhjTOaiCj/95J4ufv45XLgA11wDY8ZA9+6QM2fCxzAZWrkryjGkwRCGNBhCyXwlvQ7HpIBToaeYtXkWU9ZP4YedP6AoTcs05e0Ob9MjuAeFcxeOdb/0NtdiIFniaIxJs6ZOhUcfhb//hnLlYOxY6NPH66iMMelC/vyxdyfNkQMqV4ZNm+CKK+COO9zTxVq1Uj9Gk+bsO7WP8Ihwyl5RlnGtx3kdjkmmsIgwvt3+LVPWT2HOljmcCztH5YKVGXPdGPrW6UuVQrHW3Iwmvc21GEiWOBpj0qSpU9213FnfbK1//eWWwZJHY0wixJY0AoSGurGI778PPXtC7typG5dJE/ad2kf1N6tzMeJiVJuqciH8AlfkvIJ/Rv1DUJYgDyM0iRHXXItFcxfl1tq3Mv336Rw6c4hCuQoxIGQAfev0pWmZplYZN4kscTTGpDkRETBq1L9JY6SzZ90TSEscjTHxOn8+/vXLl6dOHCbNKpm3JNWLVGdE4xF0r9mdU6Gn6DCtA6v3r2Zog6GWNKYTcc21ePjsYSasnEDHKztyW53b6FC1A9mDsnsUZcaRxesAjDEmPBxWr4ZXX4WbboJixWD//ti3/fvvVA3NGJNe7N7tCtx06QKFYx+rZEwkEeHJFk/y4rIXUZRes3qx5sAaSuQtwTPXP+N1eCaR7mx4JxEaEa0tC1l4ofULHBh5gFk9ZtG1eldLGlOIPXE0xqS6sDCXKP74o3v99BOcOOHWVaoEnTvD3Llw5Mil+5Yrl7qxGmPSqLAw9+Twm2/ca8MG116+PAwYAG+/7Wl4Ju1rX6U9Ty5+kl6f9+KHXT9QrXA1Hmn2CFmz2OVxWnb8/HFmb57NjI0zWLRjEeEajiAoSrYs2RhcfzAPXvOg12FmSPbE0RiTbFOnQoUKkCWL+zl1avT1Fy7Azz+76dDatYOCBaFJE3jwQVf9vkcP+OQT9zRx+3Y39Oi11y4depQ7tyuQY4zJpA4fhilToHdv1zWhWTMYP949YXzxRdi4EXbuhLfe8jpSkw5EPnXcdnQbY5qPIVzDubX2rV6HZWJx5sIZZvw+gy4zulB8fHEGzR3EtqPbeOiah1h026KoJ4pZs2S16qcBZLdUjDHJElsRm8GDYfNmyJ7dPVH85Rc4d86tDw6Gfv3guuugeXMoEceUSJHjGK2qqjEZXFzVT/Plg+PHYc0amDfPPVX87Tc3lUbx4q5L6o03Qps2rjpqbPvHdVxjgE/Wf0K36t3Imz0vE1ZOYHzb8fa0MQ05H3ae+dvmM+P3GXz1x1ecvXiW0vlKM7zRcHrV6kXDUg2jitwMqjfI5lpMBaKqXsfgmYYNG+rKlSu9DsOYdK1CBZcsxkYE6tRxSeJ117mHA0WLBj4mEVmlqg0D/06BY99PJtOIr7phiRJw4IDbplEjlyh26AD167suDulMIL6bRKQd8BoQBLynquNirBff+g7AWWCAqq72rSsAvAfUAhQYpKq/xPd+GeW76b+//JeRC0fyyg2vULNoTR767iFWDF5hiaPHLoZf5Pud3zNj4wy+2PwFJ0NPUiR3EW6peQu9avXi2nLXkkUu/W9//6n9XPvBtfw86GdLHJMgsd9N9l+HMeayqLrupb/84l5xJY3gxigWLJh6sRljMpjrrnOJYrt2rmuqiUZEgoC3gDbAHmCFiMxV1U1+m7UHqvpeTYAJvp/gEsr5qtpdRLIDmWJuko/XfczIhSPpXrM7IxqPIChLEG0qtbEpGjwSoRH89PdPTN8wnc83f84/Z//hihxXcHONm+lVqxfXV7w+wYTe5lpMHZY4GmPideYMrFgBy5a5RHH5cvjnH7cuf37ImTP2yvfly6fdpDE5d+h964OAlcBeVe3oaysEzAQqALuAHqp6zLduNHA7EA7craoLAnl+xqRZqrBtG/z6q/sy+fXX+LefMSN14kq/GgPbVHUHgIjMALoA/oljF+BjdV3MlotIAREpCZwBmgMDAFT1AhB9ToMM6Os/vmbQl4NoVbEVn9z0SdS0G5Y0BlZc8y3myZaHAjkLsPfUXnJny03nap3pFdyLdlXakSNrDg8iNfGxxNEYE0XV1ZX45Zd/E8X16910GQDVq0OnTtC0KVx9NdSoAdOnRx/jCGm7iE0K3KEHuAfYDOT3a3sYWKSq40TkYd/yQyJSE+gFBAOlgO9E5EpVDQ/ICRqTlhw96sYlRiaKv/3m2gDy5HHdT01ylAZ2+y3vIfp3VVzblAbCgMPAByJSF1gF3KOqZ2K+iYgMAYYAlEvHpa1Dw0K585s7qVeyHrN7zrbEJBV1rtaZ91a/x8WIi9Haz4Wdo03pNrwc/DIdr+xInux5PIrQJIYljsZkElOnXlpopls3WLny326ny5bBoUNu+7x5oXFjGD3aJYpXXQWFCl163HRYxCbJd+hVdb+IlAFuBMYC98fYp4Xv94+AxcBDvvYZqhoK7BSRbb4Y4h1HZEyaEl8Bm5Mn3e8XLrg7Tf5PE//8060TcZWxbrrJlVRu0sQtBwXFP8bRJCS2P17M4hVxbZMVqA+MUNVfReQ13A2vS0pSquq7wLvgxjgmK2IP5ciag29v+5bCuQqTL4cVSUoNqsq6g+vIliUbYRFh0dZly5KNDXduoFqRah5FZy6XJY7GZAKxVT7t1w/69//3aWKVKnDDDe5JYtOmUKuWu6ZLjD590nSiGFNy7tDvB14FHgRiXnUUV9X9AL4EM3JAVmlgeSzHukRGuatvMqDYksbI9vvuc0ni6tUQGuraS5RwyeHAge5nw4Yu+YyNVT9Njj1AWb/lMsC+RG6jwB5Vjewv/Dkuccxwdh7byazNsxjZdCTVi1T3OpwMT1VZtX8Vn2/6nM83fc72Y9sJkiBK5yvN/tP7Cddwsgdl5z/1/mNJYzpjiaMxGZgqrFsHw4ZF70oKEBHhruOmTHGJYmpUO00jknyHXkQ6AodUdZWItEjB93ONGeSuvslkJk6EBg3cF03k08Ry5RL/JDHyiaVJihVAVRGpCOzFdYuPORHhXGC4r3dFE+BE5E0uEdktItVUdSvQiug9LzKEg6cP0vaTthw5e4Rba99KqXylvA4pQ1JVftv7m0sWN3/OruO7yJolK60qtuLhax+ma/WuXAy/SKXXKxEeFk6QBNl8i+mQJY7GZDDnz8MPP8BXX8HXX8Pu3XFve+oUdO6cerGlEcm5Q98d6CwiHYCcQH4R+URV+wIH/bqzlgQOXcb7GZM2HTkCn38e/zYnT0K2bKkTj4lGVcNEZDiwAFfs631V3SgiQ33rJwLzcIW+tuGKfQ30O8QIYKqvouqOGOvSvRPnT9B+anv2ndrHd7d9Z0ljCovQCJbvWR71ZHH3yd1ky5KNNpXbMKb5GLpU70KhXNHHuAwMGWjzLaZjljgakwEcOODmxv7qK/j2W/d0MXduaNsWnnwSnngC9uy5dL9M2hsyOXfoR/te+J44PuBLGiP36Q+M8/380q99moj8F1ccpyrwW0DOzJiUcPo0fPmlq3y1YAGEhcW/vSWNnlLVebjk0L9tot/vCgyLY9+1QLqe8zYu58PO02VGFzYc2sDcXnNpWrap1yFlCOER4fy8+2c+3/Q5szbPYt+pfWQPys4NlW9g7PVj6VStEwVyFohz/8ebP86C7QvsaWM6ZYmjMemQqqtB8dVX7vWbLw0pW9aNW+zUCVq2dFNlAOTIkb4qnwZSCtyhj8s44FMRuR34m/+3d+dhUlXX3se/q7sZBQGFKNpMMsgktEKIgoCAKN3KoCLgcMEWIRpN7qt5Y4yz0RiHK0n05ipGDAEVYxygFVC8IM5wGZTBGZCZKyACIlMP+/6xq+mpqrrArlNVze/zPPV01aldp1cX1rLWOfusDZeG9veJmb2AnwJWAFyvjqqSdA4cgNdf98ViXh7s2+cTyo03wuWXw+mnJzpCkcPy/vr3+WDDB0weOpnsttmJDiclRFoy46R6J/HMxc/w4qcv8vLnL/O/e/6XWum1yG6bzaUdL+XCdhdybK0I1zCXo/UWU5v5A1FHp+7du7vFixcnOgyRQ8J1Pi1uOrN/P8yfXzIFdf16v71HD18oDh4MXbpEvqwo2r6rGzNb4pxL6aPoyk8Sd4WF8Pbb8Nxz8NJLsHMnNG4Ml17qi8WePSEtzY+NpauqVEq5KVjrdq6jRcMWiQ4jZfxi5i+Y9NEkDhaWLOeZZmnUSq/FvoJ91MmowwXtLmB4h+HktM1RZ9pqJNbcFNczjlpkWyR24Tqfjhvnv9dt2+anoP7wgz9TOHAg3HknXHCBb14YixTrfCoi8eCcn6IwbRq88AJs2eLX3rnoIl8sDhgQfuqpikNJEXfPv5usE7MY1n6YisbDdHvv25n00aQy24pcEee1Po8ru1xJdptsrbN4lItb4ahFtkUOz223Vex8um8f/O1vkJnpl8+48ELo379kCqqIyCHRzgouWODPLE6bBmvWQM2a/sjT5Zf7n3XqBB+vSBX704d/4p637+G67tcxrP2wRIeTMjbt3sQzy59h8rLJZc42ZqRlkNs1lyeHPJnA6CSZxPOMoxbZFqlEURF88gnMnevPMEayfr3WyBaRSkRba7FTJz/tdMAAuP12f4axYcNAwxOJp6nLpnLTnJu4pMMlPJb9WKLDSXr7C/Yz4/MZTF42mTmr51DkiujVrBcPD3yY2+fdzoHCA9RIq8Hv+/8+0aFKEoln4ZiUi2xrgW1JJOdg1SqYN8/f3nrLT0MFyMgI37ywRQsVjSLyIz36KIwYASeckOhIRKrczC9nkjsjl/6t+vPsxc+Snpae6JCSknOORZsXMfnjyUxbOY2d+3eSeWwmvzv7d4zpOoa2x7cFYM13a7RkhoQVz8IxKRfZ1gLbErSNG0sKxXnzStZVPOkkGDTITz3t3x/efVedT0XkMO3Z46cszJ4dfdwvfxlMPCIJ8Pa6t8k6MYvpI6dTK6NWosNJOpu/3+ynon48mc+2f0btjNpc3OFicrNy6deyX4VCW0tmSCTxLBy1yLZUW9E6lG7b5rufzpvnv8999ZXffvzxfomMW2/1hWLbtmXPJBa//mjpfCoiR8A5+OILmDXLF4vvvAMHD/oGNyJHGeccZsaD5z7I3vy9atxSyv6C/eR9kcfkjyfzxuo3KHJF9GzWkycvfJIRnUbQoHaDiK/VkhkSSTwLRy2yLdVSuO6nY8fCM8/A5s1+fUXw3+P69oVrr/WXFZ12Wkln+0jU+VREKvjhBz+vffZsXzCuXeu3d+oEv/oV5ORAr15+wVaRo8TanWsZ8a8R/H3o3+n0k05HVdEYbb3F6aOmH5qK+t3+78g8NpNbet3CmKwxtDu+XQKileokboWjFtmW6up3v6vY/bR47ez+/f1Zwv79oVu38F3tRUSics5PVSguFN9+2yeZY47xR6FuuQWys/20hNLq14/cVVUkRX267VPW7lxbZtvO/Tu5+c2b2Zu/N/yLqrkhpw6psN5iuqWzr2AfPZ7qcWgq6lVdr6J/q/665lOqTFzXcXTOzcIXh6W3PVHqvgOur2Qf8/GdU4sffwsMiDD2D/gurCJVJj8fFi2qeI1iOHPnBheXiKSoSMtm1Knjpy/Mng2rQ9PE2reH66/3hWLv3tHPKmqtRamGbpt3Gx9t+YiOTToCkF+Uz4cbPuSH/B+YfcVsOv2kU4IjDN4dfe7g6Y+eLrOt0BXSulFrxnUbx4hOI2hYu2FigpNqLa6Fo0gqKiry002Lr1F85x3ffwIgKyvyQf0WWmdYRGIRadmMfftg0iR/VvGmm3yx2KpVsLGJJJmbzryJ0dNHM2PUDApdIdnPZrOvYB/ZbbIZ1GZQosMLVJEr4r317zF12VRcqf6PaZbGyE4jee6S5xIYnRwNVDjKUa94VtjcuSVLZHz7rX+uXTv4t3/zU0/POQcaN654jSOo+6mIxKCwEBYsiD5mxw6oXTuYeERSQO8WvWndqDVTlk1hRKcRFBUVUSejDo9f8HiiQwvM59s/Z+qyqTy74lnW7VpH3Rp1GdxuMHlf5JFflE+t9FpMOH9CosOUo4AKR6m2onU+3bCh5IzivHmwKXSNeWYmXHhhyRIZmZkV96vupyISsz17YM4cyMuDmTNh+/bo41U0ilRwZ587GT19NKO7jqZXs150aNKBFg2r9zSfrT9sZdqKaUxdPpUlW5aQZmkMPGUg9/W/j2Hth1GvZj1+MfMXWm9RAqXCUaqlcJ1Pr74ann7aF43FS2Q0buyXyBgwwBeKbdqUXSIjEnU/FZGINmyAV1/1t3nz/HIZjRr57qeDB8OoUYmOUCSlLNy0kJ37d/LH9/7IxKUTWTp+aaJDiou9+XuZ8fkMpi6fypzVcyh0hZx+4uk8ct4jXNb5MprWb1pmvNZblKCpcJRqKVzn04MH/TTUCy6A667zxWLnzpUvkSEiElVRESxd6gvFvDz4+GO/vU0buOEGGDLEL5eREfpfrgpHkZhNWzGNm//7Zvq37M9d8+/i591+Xq3ONhYWFTJ/7XymLp/KS5+9xJ6De2h2bDN+0/M3XNnlyqjNf7TeogRNhaNUC4WFsHgx/Pd/w5tvRu586pz/biciElWkzqf16/vupfv2+bOJeXnw2mt+Ede0NOjZEx56yJ9ZPPXU8FMYtGyGSEzmr53PVTOuok+LPsy8YiYPvPcA15xxTaLDqhIrvlnB1OVTeW7Fc2z6fhP1a9ZnRMcRXNnlSvq27Eua6ai2JB8VjpKSnINVq0oKxbfegp07/XNZWf47X7jO9Op8KiIxidT59PvvYdgwn3j27oV69WDQIF8o5uT4+e+V0bIZIpX6ZOsnDHt+GK0btWb6yOnUzqjN3efcneiwYpY5IZNN32+qsP3YWsfSqmErln2zjIy0DAa1GcQj5z3CkFOHUKdGnQREKhI7FY6SMrZt881siovF9ev99ubN4ZJLYOBAf51ikybqfCoicfTRR5Cb66eg9u0bfW1FETkiNdJr0PknnXn24mdpVKdRosM5bENOHcKkjyZxsPBgme27D+ymVkYtHst+jJGdRtLkmCYJilDk8KlwlISL1P103z54992SQrH4sqEGDXyBeMstcO654RvaqPOpiBwR52DZsuhj1q6NrYuWiBy2/QX7qZVei3bHt+Pd3HexFPysFRYV0rdFXyYumVhme4Zl8NZVb3F287MTFJnIj6PCURIqXPfT3Fy4/35YvRoOHIAaNXxfifvu82cVzzijpMdENOp8KiIxcQ4WLYKXXoIXX4Q1a6KPT8EvsiKpIL8wnyHThtCqYSsmDp6YckXjZ9s+Y8qyKTyz4hk27t5IzfSaFBQVUOSKqJFWg3FnjFPRKClNhaMk1M03V+x+mp/vl8v45S99odi7NxxzTGLiE5FqqqgIPvjAF4svveQ7amVk+GkMv/sdjBuX6AhFjirOOca9Oo4317zJ00OeTnQ4Mdu+dzvPr3yeKcumsGjzItIt/dB1i92bdqfT453YX7CfjLQMLZshKU+FowRqxw7fiPDNN/0U1M2bw48rKIBHHgk2NhGp5goK/Pz3F1+EV16BLVv89Ynnn++nNAwe7NdbBLjpJnU+FYmDwqJCHl/8OHvzyx41nrN6DnO/nsvdfe8m9/TcBEUXm4OFB5n55UymLJ/CzC9nkl+UT9aJWUw4bwKXnXYZJ9Y78dDY3KxcJi6ZSG5WbpntIqlIhaPE1YED8P77JdcpLlniZ4XVrw/9+sF33/lbec2bBx+riFRD+fn+aNWLL8L06bB9O9Sp4zugDh/uF3YNVwyq86lIXBS6Qu595156NetFm+PaALD8m+XM/XouzRs0586+dyY4wvCccyzavIgpy6YwbeU0duzbwQnHnMCvfvYrRncdTZcTuoR93R197uCN1W/obKNUCyocpUoVFcHy5SWF4rvv+iY3GRlw5plw991+JliPHn6bup+KyBGLtNZivXrw3HN+CuqMGX6tnnr1/BnFSy7xy2do/rtIQtRMr8nNPW9mwaYFPDTwIQBe++I13ln3DtMumZZ01zVu2LWBZ5Y/w5TlU/h8++fUSq/FsPbDGNN1DANbDyQjLfpX6ab1m7L6V6sDilYkvlQ4SkwidT4Ff2nQm2/629y5ftkMgI4d/WVCAwf6jvXhDuqr+6mIHLFIay3u2eOXymjY0P8cPtwnotq1Aw1PRMK7tvu1PPzBwyzcuJCfZf6M9bvX069VP3o26xloHJHWWmxarykPnPsAU5ZNYd7X83A4zm5+Nn8b/DeGdxxOw9oNA41TJFmocJRKhet8evXV8I9/+Ptffum3n3iiv1Ro4EAYMABOPjm2/av7qYhUuddf9/Pha9ZMdCQiUs4xNY/hmtOvod8/+vHnQX/mj+/9kZdGvBR4HOHWWkyzNLbt3caY6WM4pdEp3NX3Lq7sciWtj2sdeHwiyUaFo1Tq1lsrdj49eNBPR83Ohmuv9cVip07qUi+pwcwGAX8B0oGnnHMPlHveQs/nAHuBq5xzS82sNvAOUAufP190zt0Ves0/gVNDu2gI7HTOZZlZS+Az4IvQcwucc9fG8c+r3r780k8/zcuLPu7884OJR0QO2679u3jli1fYX7CfuWvm0uWELvQ4uUfgcfym5294aulTZbYVuSJGdR7Fdd2vo1ezXkk3dVYkkVQ4SgWFhfDRR37a6dy5fgppOM7BzJnBxibyY5lZOvBXYCCwEVhkZnnOuU9LDcsG2oZuPwMeD/08APR3zu0xsxrAe2Y22zm3wDk3stTveATYVWp/q51zWfH8u6qtwkJYuLCkWPz8c789KyuhYYnIkckvzOfSf13Kl99+yfgzxjNx6UQWXrMwsN+/ftd6Zn01i1lfzWLu13PJL8o/9Fy6pTP29LFMHDwxsHhEUokKR8E5fxC/uFB8662STqedOvlrE8NdStSiRbBxilSRHsAq59waADN7HhgKlC4chwJTnHMOWGBmDc2sqXNuC7AnNKZG6OZK7zx0tnIE0D++f0Y1tnevn9IwYwa89hps3eq7aZ1zDlx/vb9usXlzTXEQSTHOOa6bed2htRpHdh7J2S3OjuvZxvzCfD7Y8AEzv5rJrK9m8cm2TwBo2bAluVm5nJV5FmPzxnKg8AA102tyT7974haLSKpT4XiU2rSppFCcO9c/Bl8MXnSRv0axf39/3aI6n0o1czKwodTjjfiziZWNORnYEjpjuQRoA/zVOVf+UHlv4Bvn3FeltrUys4+A3cDtzrl3f/yfUc1s3eqLxBkzfKetfft819ScHBg61M+Lb9Cg7GsiHdXSWosiSatD4w7c0eeOQ2s1Xtnlyir/HVu+38Lrq15n1qpZzFk9h90HdlMjrQZ9WvTh6tOvJqdtDqcef+qhaajvb3hfay2KxECFYzUSrfPpjh0wf35JofhF6Gqrxo19gThggL+dckrFg/jqfCrVTLjTVC7WMc65QiDLzBoCr5hZZ+fcylLjLgOmlXq8BWjunPvWzLoB082sk3OuwkKBZjYeGA/QvDotZhpp2Yy6deGuu3yx+OGHfvpD8+YwdqwvFvv0id7cRmstiqSMvfl7qVujLr/u+esq33dhUSGLNi9i5pczmbVqFku3LAXgpPonMaLjCHLa5jDglAEcW+vYsK/XWosisYlr4agGFMEJ1/l07Fh4/nnYsgWWLvXfyY45xi+NMX68LxRPOw3S0irfvzqfSjWyEWhW6nEmsPlwxzjndprZfGAQsBLAzDKAi4FupcYdwF8biXNuiZmtBtoBi8sH5px7EngSoHv37uWL2dQVadmMvXvht7+FM87wi7wOGQJdu2oKqkgK8zP8y3pv/XsM/9dwZoyawZmZZx72PiMtm9GodiNy2ubw+qrX+Xbft6RZGj2b9eT+/veT0zaHLid0iam5jdZaFIlN3ApHNaAI1m23Vex8euCAn/nVp4//TjZgAPToATVqJCREkWSxCGhrZq2ATcAo4PJyY/KAG0LXP/4M2OWc22JmTYD8UNFYBzgXeLDU684FPnfObSzeEHrNDudcoZmdgs93a+L1xyWdAweiP79+PTRrFn2MiKSEnft3cuJ/nMiBwoqf+7o16tL2uLZHtN9wy2YAfLf/O95Y/QY5bXPIaZvDea3P47g6xx3R7xCRysXzjKMaUMRZQQG8/z68+qo/wxjJ228HF5NIsnPOFZjZDcAb+NkQTzvnPjGza0PPPwHMws+EWIWfDZEbenlT4B+hA2NpwAvOuddK7X4UZaepAvQBfm9mBUAhcK1zbkd8/roksWePX0fx5Zcrb72solGk2mhYuyHntDyHiztczPhu49m+dztnTTqLdTvXcevZt3J83eOPaL9juo5h4pKynU4zLIO8y/I4v835pFkMU6dE5EeLZ+GoBhRxsHu3/z726qv++9h33/lLgGrXhv37K45X51ORipxzs/DFYeltT5S674Drw7xuOXB6lP1eFWbbS0DwK1sHbccOn5hefhnmzPEJqXFjuPRSmDQp0dGJSEDu6nsXo14axajOoxj6/FDW71rPcXWO4ze9fnPY+9q1fxf3v3s/f17450PFYZEromZ6Ta45/Rqy22ZXdfgiEkU8D9H86AYUoWmnmUAPM+tcblykBhSnAzcBz5lZhaugzWy8mS02s8Xbtm2L7S9JsLVr4bHH4Lzz/PewkSNh9mx/OdCLL8L27fDUU77PRGnqfCoicbVpE/z1r3DuufCTn8BVV/lFYMeP9924tmzxyUlEjhpnNTuLDo07MHXZVNoc14b2jdtzb797qZkepdFVOQVFBTy+6HHaPNaGhz54iMs6X8bCsQsP7SPd0tXIRiQB4nnGMSkbUKRC84miIli0yB+8z8uDFSv89vbt4cYbfcF45pmQnl7yGnU+FZFArFrlzyq+8gosWOC3nXoq3HwzXHwxdOtWsbmNls0QOaoUn3WcMmwKY6aPYUzWmJhf+/qq1/n1nF/z6bZP6dOiDxPOm0C3k/zXvdysXC2bIZJA8Swc1YAignDLZlx0kV/vOi/PN7T55htfGPbuDY88AoMHQ9tKrilX51MROWKRlsyoVw/ee6+kWCw+ktWtG9x3ny8WO3SIvm8tmyFyVCk+63jRPy/iwXMfrHC2MVKX1FrptThQeIDWjVrz8oiXGdZ+WJmuqFo2QySx4lY4qgFFeOGWzRg92heJ+fn+u1t2ti8Us7PhODUHE5EgRFoyY88eyMryZxF794Y//ckf6dIF1CISxT3n3MMNs28Ie7YxUpdUgAnnTeD6HteHndqqZTNEEsvCrbdztOjevbtbvLjCUmpx4xxkZsLm8hN28TO2Xn658vWuRaRyZrbEOdc90XH8GEHnp6hrJz75pJ8jf8IJwcUjUg0pN3lbvt9Cy7+0LFM4pls6y69bTscmHX9siCJymGLNTepfHINnn4WWLSEtzf989tnYxxQVwYcf+jWu27cPXzSCP6h/7rkqGkUkRsce64u98rdjj419zI4dMHUqDB8e/XeNG6eiUURikjkhE7vHKtwyJ2Sy5+AeJi2dxMUvXFymaKyRVoOfd/u5ikaRJKfCsRLFU0vXrfNnDNet849LF4/hxowdCwMGwMknQ8+eMGGCn9kVaepp8+bB/D0iUk1Emlpaenu0Mf37+06oo0f7BWFFJCWZ2SAz+8LMVpnZLWGeNzN7NPT8cjM7o9Rza81shZl9bGZVMsVhyKlDKkwzzUjLoEGtBjR9pCnXvHoNu/bv4q6+d1Ervdah53Xdokjyi2dznGrhtttKrkcstnev725avPzFjTdWHHPgALz1lj+QP2wY5ORAw4YVr3EELZshIlXs0UehVq3oY7Zu9Z1Qhw6Fn/60bJtmEUkJoV4QfwUG4jvVLzKzPOfcp6WGZeMbBrbFNyJ8nLLravdzzm2vqpju6HMHf//472W2FRQV8PXOrxnZeSTjzhjHWZlnYWZs/WGruqSKpBAVjpVYvz789m3bfDPBaJyDF14ou03LZohI3P37v1c+ZuXKso+1ZIZIKuoBrHLOrQEIdakfCpQuHIcCU5xvarHAzBqaWVPn3JZ4BNS0flNys3J5csmTFLpCDKNPiz7MGDWDBrUblBmrLqkiqUVTVSsRaQpp06bw8cf+1rRp+DGRmg5ecQWsXeuvf1y7VkWjiFSx7dthU8VW91Ht3u2PdpW/aSkNkWR2MrCh1OONoW2xjnHAHDNbYmbjqyqoO/rcQUaaPzdRO6M2zw9/vkLRCCVdUnW2USQ1qHCsxB/+UDIltVjduvDww9C1q789/HD4MZp+KiIJcfzxcNJJiY5CROIvXDvk8u3yo43p5Zw7Az+d9Xoz6xP2l5iNN7PFZrZ427ZtlQbVtH5Trj79atIsTdNQRaoRFY6VuOIK34m+RQvfjLBFC/+49FnCWMaIiFSpSFNIS2+PZYyIpLKNQLNSjzOB8v3bI45xzhX/3Aq8gp/6WoFz7knnXHfnXPcmTZrEFNgdfe6gZcOWmoYqUo3oGscYXHFF5UVgLGNERKpMLFNINc1UpLpbBLQ1s1bAJmAUcHm5MXnADaHrH38G7HLObTGzY4A059z3ofvnAb+vqsCKp6GKSPWhwlFEREQkBTnnCszsBuANIB142jn3iZldG3r+CWAWkAOsAvYCuaGXnwC8Ymbgvw8+55x7PeA/QURSiApHERERkRTlnJuFLw5Lb3ui1H0HXB/mdWuArnEPUESqDV3jKCIiIiIiIlGpcBQREREREZGoVDiKiIiIiIhIVCocRUREREREJCoVjiIiIiIiIhKVCkcRERERERGJynyX5qOTmW0D1lUyrDGwPYBwqlIqxgypGbdiDsbhxtzCOdckXsEEQfkpqSjm4KRi3IcTs3JT8krFmCE141bMwajy3HRUF46xMLPFzrnuiY7jcKRizJCacSvmYKRizEFIxfdFMQcjFWOG1Iw7FWOOt1R8T1IxZkjNuBVzMOIRs6aqioiIiIiISFQqHEVERERERCQqFY6VezLRARyBVIwZUjNuxRyMVIw5CKn4vijmYKRizJCacadizPGWiu9JKsYMqRm3Yg5GlcesaxxFREREREQkKp1xFBERERERkahUOIaY2SAz+8LMVpnZLWGeNzN7NPT8cjM7IxFxloupspivCMW63Mw+MLOuiYizXExRYy417qdmVmhmw4OML5JY4jazc8zsYzP7xMzeDjrGMPFU9t9HAzN71cyWhWLOTUScpeJ52sy2mtnKCM8n3WcwCKmYm0D5KSjKTcFQfgovFfOTclMwUjE3Qerlp8Bzk3PuqL8B6cBq4BSgJrAM6FhuTA4wGzDgTGBhCsTcE2gUup+dCjGXGjcPmAUMT5H/PhoCnwLNQ49/kgIx3wo8GLrfBNgB1ExgzH2AM4CVEZ5Pqs9gEv07Jt37ovyUPDErN1VZ3MpPR/ZvmVTvi3JT8sScbLnpMOJOqvwUdG7SGUevB7DKObfGOXcQeB4YWm7MUGCK8xYADc2sadCBllJpzM65D5xz34UeLgAyA46xvFjeZ4BfAi8BW4MMLopY4r4ceNk5tx7AOZfo2GOJ2QH1zcyAevjkVxBsmKWCce6dUAyRJNtnMAipmJtA+Skoyk0BUX4KKxXzk3JTMFIxN0EK5qegc5MKR+9kYEOpxxtD2w53TJAON56x+CMOiVRpzGZ2MnAR8ESAcVUmlve6HdDIzOab2RIzGx1YdOHFEvN/Ah2AzcAK4N+dc0XBhHdEku0zGIRUzE2g/BQU5abkkYyfw3hLxfyk3BSMVMxNUD3zU5V+BjN+dDjVg4XZVr7dbCxjghRzPGbWD5/8zo5rRJWLJeY/A791zhX6gzlJIZa4M4BuwACgDvChmS1wzn0Z7+AiiCXm84GPgf5Aa+BNM3vXObc7zrEdqWT7DAYhFXMTKD8FRbkpeSTj5zDeUjE/KTcFIxVzE1TP/FSln0EVjt5GoFmpx5n4IwmHOyZIMcVjZl2Ap4Bs59y3AcUWSSwxdweeDyW+xkCOmRU456YHEmF4sf73sd059wPwg5m9A3QFEpUAY4k5F3jA+Unwq8zsa6A98D/BhHjYku0zGIRUzE2g/BQU5abkkYyfw3hLxfyk3BSMVMxNxTFVt/xUtZ/BH3OBZHW54QvoNUArSi6G7VRuzAWUvbj0f1Ig5ubAKqBnot/jWGMuN34yydEcJ5b3ugMwNzS2LrAS6JzkMT8O3B26fwKwCWic4Pe6JZEv8E6qz2AS/Tsm3fui/JQ8MSs3VWnsyk+H/2+ZVO+LclPyxJxsuekw4k66/BRkbtIZR8A5V2BmNwBv4DsqPe2c+8TMrg09/wS+S1UOPpnsxR9xSJgYY74TOB74r9BRqALnXPckjznpxBK3c+4zM3sdWA4UAU8558K2Rk6WmIF7gclmtgKfUH7rnNueqJjNbBpwDtDYzDYCdwE1IDk/g0FIxdwEyk9BUW4KjvJTRamYn5SbgpGKuSkUV8rlp6Bzk4WqUREREREREZGw1FVVREREREREolLhKCIiIiIiIlGpcBQREREREZGoVDiKiIiIiIhIVCocRUREREREJCoVjhJ3ZubMbGqpxxlmts3MXqvkdVlmlhPD/vdE2H6tmY0+/IgPvX6tma0ws+Vm9raZtahkfEszu/xIf5+IBEu5SUSSlfKTJCMVjhKEH4DOZlYn9HggfsHUymTh1545IqF1gqYc6etD+jnnugDzgdsrGdsSUPITSR3KTSKSrJSfJOmocJSgzAYuCN2/DJhW/ISZ9TCzD8zso9DPU82sJvB7YKSZfWxmI82snpn9vdSRrEtK7eMPZrbMzBaY2QmhbXeb2f8P3Z9vZg+a2f+Y2Zdm1ju0va6ZvRDa3z/NbKGZhVvo90Pg5NBrWprZu2a2NHTrGRrzANA7FO+NZpZuZg+b2aLQ/n9epe+oiFQF5SblJpFkpfyk/JRUVDhKUJ4HRplZbaALsLDUc58DfZxzpwN3Avc75w6G7v/TOZflnPsncAewyzl3WuhI1rzQ648BFjjnugLvAOMixJDhnOsB/D/grtC2XwDfhfZ3L9AtwmsHAdND97cCA51zZwAjgUdD228B3g3F+ydgbCjenwI/BcaZWauo75KIBE25SblJJFkpPyk/JZWMRAcgRwfn3HIza4k/Yjar3NMNgH+YWVvAATUi7OZcYFSpfX4XunsQKJ7zvwQ/nSOcl0uNaRm6fzbwl9D+VprZ8nKveSt0FG4rJdMtagD/aWZZQCHQLsLvOw/oYmbDQ48bAG2BryOMF5GAKTcByk0iSUn5CVB+Sio64yhBygP+g1JTLULuBd5yznUGBgO1I7ze8MmxvHznXPH2QiIfEDkQZoxVEnM/oAXwCX76B8CNwDdAV6A7UDNKvL8MHUXLcs61cs7NqeT3iUjwlJuUm0SSlfKT8lPSUOEoQXoa+L1zbkW57Q0oueD7qlLbvwfql3o8B7ih+IGZNaqCmN4DRoT21xE4rfwA59w+/BSN0WZ2XCjeLc65IuDfgPQI8b4BXGdmNUL7b2dmx1RBzCJStZSblJtEkpXyk/JT0lDhKIFxzm10zv0lzFMPAX80s/cpSSQAbwEdiy/wBu4DGpnZSjNbhj+i9WP9F9AkNM3it8ByYFeY2Lfgj/ZdH3rNGDNbgJ9q8UNo2HKgIHSh+Y3AU8CnwFIzWwlMRNPDRZKOcpNyk0iyUn5SfkomVnKWWuToY2bpQA3n3H4zaw3MBdqFLjAXEUkI5SYRSVbKT0cvVfBytKuLv4i7Bn5e/XVKfCKSBJSbRCRZKT8dpXTGUURERERERKLSNY4iIiIiIiISlQpHERERERERiUqFo4iIiIiIiESlwlFERERERESiUuEoIiIiIiIiUalwFBERERERkaj+D1y4w/JQ6jb9AAAAAElFTkSuQmCC\n",
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\n",
       "text/plain": [
        "<Figure size 1080x360 with 3 Axes>"
       ]
@@ -325,7 +325,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### `find_all` performance by varying key muliplicities\n",
+    "### `retrieve` performance by varying key muliplicities\n",
     "<ul>\n",
     "<li>100'000'000 key/value pairs inserted</li>\n",
     "<li>100'000'000 probing keys</li>\n",
@@ -342,7 +342,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 360x360 with 1 Axes>"
       ]
@@ -355,7 +355,7 @@
    ],
    "source": [
     "for bm in unique_bms:\n",
-    "    flag = \"staic_multimap_find_all_uniform_multiplicity\" == bm\n",
+    "    flag = \"staic_multimap_retrieve_uniform_multiplicity\" == bm\n",
     "    \n",
     "    if flag:\n",
     "        tmpdf = perfdf[perfdf[\"Benchmark\"] == bm]\n",
@@ -368,7 +368,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### `find_all` performance by varying occupancies\n",
+    "### `retrieve` performance by varying occupancies\n",
     "<ul>\n",
     "<li>100'000'000 key/value pairs inserted</li>\n",
     "<li>100'000'000 probing keys</li>\n",
@@ -383,7 +383,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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Bprn7AFfGO3Omadp/7JipPD12zHx2ViLo5+eHv78/ANHR0SQkJCSt69q1K0oplFK0aNGCiIiIDI/z3nvv8fLLL1OvXr2kZT179qRt27ZJn2fNmsVTTz1F5cqV2bRpk0PxnTp1iooVKyZ9btSoUdLycuXK4eVlbrMrVqxIsWLF0ux//fp1NmzYwLRp09IkAaVKlaJ9+/Z89913DsVihVmz4JVX4LPPzP/ln31mPjvrn9G+ffuIi4ujY8eOgKkhDAwMBCA+Pp4xY8bw/vvvZ3qc3Hr9O3XsRIOTDRizcgx/nfiLRWGLGNt2LD5ezunNJ0mgEMLlWraEn3+GmjXh88+hXDn4/XeTCNrKcyGEsM4//8D8+XDhgtWRtAAOaa2PaK1jgNlAL/sNtNZntdZbgPTaP/oABZRSPkAgWRj5OD1PPw3t2mX8evhhiIxMuU9kpFme0T5PP521GMLDw2nUqBGVKlXiv//9L+XLl0+xPjY2lhkzZtC5c+cMj7F3716aNm2a4fqoqChWrVpF9+7dGThwILMczGIef/xxHn74Ye666y7Gjx+fVEt5//338/PPPxMSEsJzzz3Hjh070t1/8eLFdO7cmdq1a1O8eHG2b9+eYv2LL77Ihx9+SLyFTWbSu4ZffmnWvfUWBAXBG29Ax47mPSgIxowx68+fT7tvVhw8eJCiRYvSp08fmjRpwpgxY5J+F59//jk9e/akXLlymR4nN1//EytOsDhsMSUKlODSzUtOqwUEFyeB2W3SoJSqpJRarZQKszVreMqVcQohXOu11+DFF02T0P37zVPiBx6A8eNBpTdrnxBCuNMyW9deW58eC1UAwu0+R9iWZUprfQJTO3gcOAVc0VqvSL2dM+cwjY7O2vLsqFSpEn///TeHDh3iu+++48yZMynWjxo1irZt23LHHXc4dLwLFy4QEhJC7dq1mTjRVKb+8ssv3HXXXQQGBtK3b18WLVqUlGyodP6TSlzWqVMnjhw5wqOPPsr+/ftp0qQJ586do2LFihw4cIB3330XLy8v2rdvz6pVq9IcZ9asWQwYYCprBwwYkCb5qFatGi1atODHH3906Lu524EDUKRIymVFisDJHD16SBYXF8e6deuYOHEiW7Zs4ciRI0yfPp2TJ08yb948Ro8eneVj5rbrf1uL22h0phErjqygZ52eTqsFBBeODmrXpKEjphDbopRaorXeZ7dZYpOG3ql2jwOe01pvV0oFAduUUitT7SuEyCVatjSJX9++MHEiNG5sPt91l9WRCSHyvWLFkqunatRIudz90nss5lAHJaVUMUytYTXgMjBPKTVEa/1DioNpPQWYAhAaGnrLY3/yya3PWbWqaQKaWpUqsGaNI1E7rnz58tSvX59169bRr18/AN544w3OnTvH119/fct969evz/bt22ncuDElSpRg586dTJw4kevXrwPmZnzDhg1UrVoVMInC6tWr6dChAyVKlODSpUuULGmG5L948WLSzwDFixdn0KBBDBo0iO7du7N27Vr69u2Lv78/Xbp0oUuXLpQpU4bFixfTvn37pP0uXLjA77//zp49e1BKER8fj1IqTfPGl19+mX79+qVouuhOt7qO9eqZ2j/7/8tXr4bE3KxkyZz9HVSsWJEmTZpQvXp1AHr37s2mTZsoW7Yshw4dombNmgBERkZSs2ZNDh06lO5xcvP1r96zOnOenEPFhhVpVbFV9n+Z6XBlTWC2mzRorU9prbfbfr4GhOHgkzAhhGc6fdr0AfzpJ9M0VBJAIYRHuHgx/dFBrZkeIgKoZPe5Io436ewA/Ku1Pqe1jgUWArc7Ob4Uxo9P26Q/MNAsd4aIiAiiosw8aZcuXWLDhg3UqVMHgKlTp7J8+XJmzZqV1PcqIy+88ALjx48nLCwsaVmkrR3r1atXWb9+PcePH+fo0aMcPXqUL774IqlWpl27dsyYMQMw/dB++OEH7rL9B/b7778nHefatWscPnyYypUrs3379qSmgQkJCfz9999UqVIlRUzz589n6NChHDt2jKNHjxIeHk61atVYv359iu3q1q1LcHAwv/zyS9Z/gS72yium6e/q1RAba94fftgsd4bmzZtz6dIlEmusf//9d4KDg+nWrRunT59Oul6BgYEZJoCQu6//hqgNBJYPRB/QeHs5d04tVyaB2W7SYE8pVRVoAmx2TlhCCCssXw6lSplaQCGE8BhRUSbp8wxbgFpKqWpKKT/MwC5LHNz3OHCbUipQmfZq7TEP0V1m8GCYMsXU/Cll3qdMMcudISwsjJYtW9K4cWPuvPNOnn/+eRo2bAjAyJEjOXPmDK1atSIkJIQ333wzw+M0bNiQSZMmMXToUOrWrUvr1q0JCwtj0KBBLFy4kLvvvjtpABqAXr16sWTJEqKjoxk7diyHDh2icePGNGnShJo1azJkyBAAtm3bRmhoKI0aNaJVq1Y88sgjNG/enLNnz9KjRw8aNGhAo0aN8PHx4YknnkgR06xZs7j33ntTLOvbt2+6TT9feeWVWw58Y5WBA03CP3o0BASY9/HjzXJn8Pb2ZuLEibRv356GDRuitebRRx/N8nFy6/U/H3meP479wYD/DHDJ9VeODoOa5QMrdR/QSWv9iO3zA0ALrXWaBrxKqdeB61rr1CNdFQL+AMZrrRdmcJ4RwAiAypUrNzuWXrsEIYSlEhKgTBno1Al++CHz7e0ppbZprUNdE5l7hIaG6q1bt1odhhAiPY89Bps3w44dWeqk7KqySSnVFfgEM7rnN1rr8UqpkWDmM1VKlQW2AoWBBOA6EKy1vqqUegPoj+lWswN4RGudYQ89KZuE8FzTtk/jkZ8fYfuI7TQp1yRL+zpSPrmsTyA5a9KAUsoXWADMzCgBhKy1bRdCWGPnTjNKWKdOVkcihBB2tDaDwjRv7jGjVGmtlwHLUi2bbPfzacw9VXr7vga85tIAhRBusSBsAVWLViWkbIhLju/KJDCpSQNwAtOkYZAjO9qaMUwDwrTWH7kuRCGEO/j7w4MPmiGkhRDCY+zeDRER8PrrVkcinODbb79l0qRJKZa1bt2aL774wqKIhDvltev/bKtnuXzzcrojlDqDy5qDQvabNACNgHXAbttygJdtT8cyJM0ahMh7pDmoEMJlJkyAl16CEycg1fxzmZGySQjhqaxuDpqTJg3rSX+YZCFELhMZCUePmqGkPaS1lRBCGEuXQpMmWU4AhRDClX74+wcalm5I47KuG03PpZPFCyHEqlVQvz6sXWt1JEIIkcozz8Crr1odhRBCJImMjWTEzyP4v+3/59LzuLQmUAghli8380bddpvVkQghRCp9+lgdgRBCpPDbod+IiouiTz3Xlk9SEyiEcKnly6FdOzM4jBBCeIylSyHMpdPoCSFEli0MW0iJAiVoW6WtS88jSaAQwmWOHIFDh2RqCCGEh0lIgOHD4e23rY4kdytb1nT2Tv0qWzZHhy1UqBAAx44do1mzZoSEhFC/fn0mT04aVoLBgwdTp04dGjRowEMPPURsbOwtj/nbb7/RokUL6tatS0hICP379+f48eMADBs2jGrVqhESEkJISAi333570n6LFy+mUaNG1K1bl4YNG7J48eKkdcOGDSMwMJBr164lLXvqqadQSnH+/HnATHieeNyQkBAmTJjAvffeS0hICDVr1qRIkSJJ6zZu3Ei7du2oU6cOjRs3pnnz5uzcuTPp2FWrVk067unTpxkwYAA1atQgODiYrl27cvDgwez9wrOratX0r3/Vqjk6bOL1Bzh+/Dj33HMP9erVIzg4mKNHj6bYdvTo0Sm2z0huuf5t72zLrMdmkfBVAq1atnLt9dda55lXs2bNtBDCc3z1ldag9f792T8GsFV7QPmSk5eUTUJ4mM2bTeE0c2a2DyFlkza/w4xeOVCwYEGttdbR0dH65s2bWmutr127pqtUqaJPnDihtdZ66dKlOiEhQSckJOgBAwboL7/8MsPj7d69W9esWVPv27cvadlPP/2k//jjD6211g8++KCeN29emv127typa9SooY8cOaK11vrIkSO6Ro0aeteuXUn7NWzYUM+YMUNrrXV8fLxu2LChrlChgj537lyK75Ke1atX627duqVYduedd+otW7ZorbX+5ptvdIcOHZLWValSRZ87d04nJCTo2267TX/11VdJ63bs2KHXrl2b4blcIqPr7KTrr7X5faxYsUJrbf4Gbty4kbRuy5YtesiQIbf8HWudu65/s1bNdMB/AvTSg0tzdP0dKZ+kT6AQwmUGDoQKFaB2basjEUIIO8uWgZeXNFPIzNNPg11NRJa0a5f+8pAQ+OQThw7h5+eX9HN0dDQJCQlJn7t27Zr0c4sWLYiIiMjwOO+99x4vv/wy9erVS1rWs2fPTM8/ceJEXn75ZapVqwZAtWrVeOmll/jggw+YMWMGAAMHDmTOnDkMGTKENWvW0Lp1a3799VeHvl9mWrVqxQcffJBm+erVq/H19WXkyJFJy0JCQpxyzjTSu4733w+jRmW8PtH589CvX8pla9Y4fOp9+/YRFxdHR9skw/Y1fvHx8YwZM4Yff/yRRYsW3fI4uen6F/IrxMqhK2lZvSWH4w+79PpLc1AhhMsUKQI9esjUEEIID7N0qRmtqkQJqyMRmQgPD6dRo0ZUqlSJ//73v5RPNZ1HbGwsM2bMoHPnzhkeY+/evTRt2vSW5xkzZkxSs7zBgwcn7desWbMU24WGhrJ3796kz7Vq1eLcuXNcunSJWbNmMWDAgBTbR0VFpWgOOGfOHIe+N5gmjL17906zfM+ePWniyosOHjxI0aJF6dOnD02aNGHMmDHEx8cD8Pnnn9OzZ0/KlSuX6XFyy/U3FXgQ4BOAr7evy6+/1AQKIVxi1y749Vd47DEoVszqaIQQwubyZfj7bxg3zupIPF9mNXa3esKXhRqfW6lUqRJ///03J0+epHfv3vTr148yZcokrR81ahRt27bljjvucOh4Fy5coH379kRGRjJixAief/55AD744AP6paq10lqjUn3H9Jb16dOH2bNns3nzZr7++usU6woUKJCiX5cjBg8ezI0bN4iPj2f79u1Z2tfpMruO6a1P/P2ULJmjv4O4uDjWrVvHjh07qFy5Mv3792f69Ol06dKFefPmsSYbx/bk67/m6Bq2ntzKfQPuI/ZmrMuvv9QECiFcYv58M/2W1AIKITxK0aJw7lxyczaRK5QvX5769euzbt26pGVvvPEG586d46OPPrrlvvXr10+6mS5RogQ7d+5kxIgRXL9+PdP9tm7dmmLZ9u3bCQ4OTrFswIABjB07lo4dO+LllfNb65kzZ/Lvv/8yaNAgHn/88XTj2rZtW47P4+kqVqxIkyZNqF69Oj4+PvTu3Zvt27ezY8cODh06RM2aNalatSqRkZHUrFkzw+Pkluu/IGwBUbFRfD/je7dcf0kChRAusXw5tGxp7reEEMKjFC4sTRScwa5GzqHlWRQREUFUVBQAly5dYsOGDdSpUweAqVOnsnz5cmbNmpXpjfcLL7zA+PHjCbObEiQyMjLT8z///PO8++67SSNSHj16lHfeeYfnnnsuxXaVK1dm/PjxjHLigwVfX1/efvttNm3alCJugLvvvpvo6Gj+7/+SJxPfsmULf/zxh9PO75AqVdIfHbRKFaccvnnz5ly6dIlz584B8PvvvxMcHEy3bt04ffo0R48e5ejRowQGBnLo0KEMj5Mbrn+CTmDR/kUUL1CcAr4F3HL9JQkUQjjd+fOwdauMuSCE8DCxsaZgWrbM6kjyhtOn0x8b9PRppxw+LCyMli1b0rhxY+68806ef/55GjZsCMDIkSM5c+YMrVq1IiQkhDfffDPD4zRs2JBJkyYxdOhQ6tatS+vWrQkLC2PQoEFJ29j3CQsJCSEmJoaQkBDee+89evToQd26denRowfvv/9+uoNwPPbYY9SoUSPN8tR9wl588UWHv3+BAgV47rnnmDhxYorlSikWLVrEypUrqVGjBvXr1+f1119P01/S5Y4eTf/6p5rGIbu8vb2ZOHEi7du3p2HDhmitefTRR7N8nNxw/f868Rcnr52kZMGSSctcff1VYifEvCA0NFSnrrYVQrjfnDkwYABs2mRqA3NCKbVNax3qnMisIWWTEB7ijz/MaIYLFkCfPjk6lJRNQghnGbNiDJM2T+LsmLMUDSia4+M5Uj7JwDBCCKc7dszMFRyaq2+PhBB5zrJl4OsLHTpYHYkQQiRpU7kNQf5BTkkAHSVJoBDC6V54AZ55Bry9rY5ECCHsLFsGd9xh+gSKPOfbb79l0qRJKZa1bt2aL774wqKIhDvl5uvfq24vetXt5dZzShIohHAJX1+rIxBCCDvHj8OePfDhh1ZHIlxk+PDhDB8+3OowhEVy6/XfFLGJCkEVqFSkklvPKwPDCCGcatIkuP12cGDgLSGEcJ/Ll6FjR+jWzepIhBAiycNLHuaBRQ+4/bySBAohnGrpUnOvFRhodSRCCGGnUSNYsQJsUwwIIYTV9p/fz75z++hbr6/bzy1JoBDCaSIjYe1amRpCCOFhYmLM3DVCCOFBFoYtBODeeve6/dySBAohnGbdOoiOliRQCOFhfv8dSpeGDRusjkQIIZIsCFtAywotqVi4otvPLUmgEMJpli8Hf39o29bqSIQQws6yZRAQAE2bWh1JntLk6yaoN1SaV5Ovm+TouIUKFQLg2LFjNGvWjJCQEOrXr8/kyZOTthk8eDB16tShQYMGPPTQQ8TGxmZ4vNdffz3NhNtVq1blvK12WCnFc889l7Ru4sSJvP7662n2HTZsGNWqVUua+PvTTz8F4MqVKwwdOpQaNWpQo0YNhg4dypUrVwA4evQoBQoUICQkhODgYIYOHZoU65o1a1BKMW3atKRz79ixA6VUmng9UZtv2qR7/dt80yZHx028/gDHjx/nnnvuoV69egQHB3M01UT0o0ePTrF9ejz1+i/8dSHbH9tOpcPJA8K48/pLEiiEcJrQUHj+eekPKITwIFqbzsp33w0FClgdTZ7SqmIr/Lz9Uizz8/bj9oq3O+X45cqVY+PGjezcuZPNmzczYcIETp48CZgkcP/+/ezevZuoqCimTp2a7fP4+/uzcOHCpKTgVj744AN27tzJzp07efLJJwF4+OGHqV69OocPH+bw4cNUq1aNRx55JGmfGjVqsHPnTnbv3k1ERARz585NWtewYUPmzJmT9Hn27Nk0btw429/FnZqXb87TLZ9Gv6aTXk+3fJoWFVo47RxDhw5lzJgxhIWF8ddff1G6dOmkdVu3buXy5cs5PodV1794geLUq1+P81uSz+vO6y9TRAghnGbQIKsjEEKIVA4ehCNHzBMqkSVP//Y0O0/vzHB9dFw0cQlxKZbFJcSx4/QO2k1vl+4+IWVD+KTzJw6d388vOcGMjo4mISEh6XPXrl2Tfm7RogUREREOHTM9Pj4+jBgxgo8//pjx48dnad9Dhw6xbdu2FIncuHHjqFmzJocPH8bbbsJcb29vWrRowYkTJ5KWVa5cmatXr3LmzBlKly7Nb7/9luK7WS2963h//fsZ1XwUT7R8grqf12VTxCb8ffyJjotm66mtTOxoarHOR56n39x+KfZdM2yNw+fet28fcXFxdOzYEUhZQxgfH8+YMWP48ccfWbRoUda/mB0rr3/1qtUtu/5SEyiEcIojR+DsWaujEEKIVJYtM+8edGOdV/j7+FOmYBkUCgCFomzBsmlqB3MiPDycRo0aUalSJf773/9Svnz5FOtjY2OZMWMGnTt3ztF5Hn/8cWbOnJnUjC8jY8aMSWoOuHv3bvbt20dISEiam/2QkBD27t2bYt+bN2+yefPmNLH269ePefPmsXHjRpo2bYq/v3+Ovou7lCtUjjIFyxB+NRyA8KvhlClYhiIBRZxy/IMHD1K0aFH69OlDkyZNGDNmDPHx8QB8/vnn9OzZk3LlyjnlXO6+/qeunWLMyjFcuXnFsusvNYFCCKd46SUz5kJ4OChldTRCCGHTty+UKAFVqlgdiUOUUp2BSYA3MFVrPSHV+rrAt0BT4BWt9US7dUWBqUADQAMPaa3/zG4sjtTYnbp2iuqfVudm3E0CfALY9tg2yhYqm91TplGpUiX+/vtvTp48Se/evenXrx9lypRJWj9q1Cjatm3LHXfckeExVAb/KdkvL1y4MEOHDuXTTz+lwC2aDX/wwQf065dcu3XkyJF0j6+1Tlp++PBhQkJC+Oeff+jXrx+NGjVKse39999P//792b9/PwMHDmTjxo0Znt/dblVzF+gbyJZHt1D/y/p83OljOszowN5Re5Ouf8nAklmq+UstLi6OdevWsWPHDipXrkz//v2ZPn06Xbp0Yd68eaxZ49ixPfH6f7XlK7ae2Epb1day6y81gUKIHIuPh5UroUMHSQCFEB6mcmUYOtTqKByilPIGvgC6AMHAQKVUcKrNLgJPAumNHDEJ+E1rXRdoDIS5MFwAygWVY3jIcLyUF8NDhjs1AbRXvnx56tevz7p165KWvfHGG5w7d46PPvrolvuWKFGCS5cupVh27do1ihYtmmLZ008/zbRp07hx44bDcdWvX58dO3akaKqakJDArl27qFevHpDcJ+zQoUNs2rSJJUuWpDhG2bJl8fX1ZeXKlbRv397hc3uCckHleLDxg7T/vj0PNn7Qqde/YsWKNGnShOrVq+Pj40Pv3r3Zvn07O3bs4NChQ9SsWZOqVasSGRlJzZo1MzyOJ17/BWELqFSkEkH+QZZdf0kChRA5tm0bXLokU0MIITzMX3/B99+buWtyhxbAIa31Ea11DDAb6GW/gdb6rNZ6C5BiOEylVGGgLTDNtl2M1vqyO4Ie23YsbSq3YeydY5163IiICKKiogC4dOkSGzZsoE6dOgBMnTqV5cuXM2vWLLy8bn0727ZtW5YsWcK1a9cAWLhwIY0bN07RhA+gePHi3H///SlG68xMzZo1adKkCW+//XbSsrfffpumTZumSUzKlSvHhAkTePfdd9Mc58033+S9995LE1Nu8ELrF2hRoQUvtH7Bqcdt3rw5ly5d4ty5cwD8/vvvBAcH061bN06fPs3Ro0c5evQogYGBHDp0KMPjeNr1f2v8W6w5uoY7KifXXltx/SUJFELk2PLlpgbQ1ndbCCE8w//9H4weDZkkCR6kAhBu9znCtswR1YFzwLdKqR1KqalKqYLODjA95YLK8cewP5xeCxgWFkbLli1p3Lgxd955J88//zwNGzYEYOTIkZw5c4ZWrVoREhLCm2++meFxGjVqxBNPPEGbNm0ICQlh8uTJGY4m+txzzzk0SqS9adOmcfDgQWrWrEmNGjU4ePBgholE7969iYyMTFGjCXD77bfTu3fvLJ3XU5QLKsdvQ35z+vX39vZm4sSJtG/fnoYNG6K15tFHH83ycTzt+p++dJr4o/G0rZI8n5YV119prd16QlcKDQ3VW7dutToMIfKdNm3Mg/YtW5x/bKXUNq11qPOP7D5SNglhAa2hYkW4/XaYN8/ph3dF2aSUug/opLV+xPb5AaCF1np0Otu+DlxP7BOolAoFNgGttdablVKTgKta67Gp9hsBjACoXLlys2PHjjnzKwghMrEwbCHf7fqOxf0XZ9hfMaccKZ9kYBghRI59/z1k8cGZEEK41q5dcPJkbhsVNAKoZPe5InAyC/tGaK032z7PB15MvZHWegowBcwDquyHKoTIjj71+tCnXh+rw5AkUAiRc9Wrm5cQQniMxKkhunSxNo6s2QLUUkpVA04AAwCHZmDVWp9WSoUrpeporQ8A7YF9rgvV83z77bdMmjQpxbLWrVvzxRdfWBSRcKfccP3Dr4RTNKAoQf5BVociSaAQImemToWCBWHgQKsjEUIIO2Fh0KwZlHXNaJWuoLWOU0o9ASzHTBHxjdZ6r1JqpG39ZKVUWWArUBhIUEo9DQRrra8Co4GZSik/4Agw3IrvYZXhw4czfHi++srCTm64/s+teI4tJ7dw5Mn0p5ZwJ0kChRDZpjW8/TaEhEgSKITwMDNmwPXrVkeRZVrrZcCyVMsm2/18GtNMNL19dwK5ug+1EHlVVGwUy/5ZxgONHrA8AQQZHVQIkQP//APHjsE991gdiRBCpKNQIasjEEIIAFYcXsGN2Bv0De5rdSiAJIFCiBxYvty8y/yAQgiPMmoUjBxpdRRCCJFkQdgCigUU484qd1odCiBJoBAiB1asgBo1zEsIITxCfDzMnQuRkVZHIoQQAMTGx/LzwZ/pVbcXvt6+VocDSJ9AIUQ2aW2mhejc2epIhBDCzpYtcOFCbpsaQgiRh/l4+bBu+Dp8vDwn9fKcSIQQuYpS8OefEBdndSRCCGFn6VLw8pLOykIIj6GUokHpBlaHkYI0BxVCZIu2TTHskwsfJSmlOiulDiilDiml0kymrJSqq5T6UykVrZR6Piv7CiEstmwZ3H47FC9udSRCCEF8Qjz/+eU/bDmxxepQUpAkUAiRLXfdBePGWR1F1imlvIEvgC5AMDBQKRWcarOLwJPAxGzsK4SwSkKCqQF86CGrIxFCCADWH1/P5G2T+ffyv1aHkoIkgUKILDtzBv74AwICrI4kW1oAh7TWR7TWMcBsoJf9Blrrs1rrLUBsVvcVQljIywvefRc8fMJoIUT+sTBsIQE+AXSt5Vn9lCUJFEJk2cqV5j2XTg1RAQi3+xxhW+bqfYUQrrZ7N8TEWB2FEEIAoLVm4f6FdKrRiUJ+njVvqSSBQogsW74cSpaEJk2sjiRbVDrLtLP3VUqNUEptVUptPXfunMPBCSGyKSYGWreGp5+2OhIhhABg68mtRFyNoE+9PlaHkoYkgUKILElIMDWBHTualle5UARQye5zReCks/fVWk/RWodqrUNLlSqVrUCFEFmwYQNcu5ZrmygIIfKeMzfOUKNYDXrU7mF1KGnkwnH9hBBWio6Ghx82D9xzqS1ALaVUNeAEMAAY5IZ9hRCutHQp+PpC+/ZWRyKEEAB0r92dbrW6oVR6DYmsJUmgECJLChSA8eOtjiL7tNZxSqkngOWAN/CN1nqvUmqkbf1kpVRZYCtQGEhQSj0NBGutr6a3ryVfRAiR0rJlcOedUMiz+t0IIfKnGzE3CPAJwNvL2+pQ0iVJoBAiS7ZuhQYNcu3IoABorZcBy1Itm2z382lMU0+H9hVCWOzffyEsDEaMsDoSIYQA4IONH/D1tq85/ORhAn0DrQ4nDZf26JEJmYXIW65fN3Mwv/aa1ZEIIYSdypVNn8ABA6yORAghAFgQtoBaxWt5ZAIILkwCZUJmIfKeP/6A2FgzKIwQQngMb2/zhKpsWasjEUIIDl44yJ6ze+hbr6/VoWTIlTWBMiGzEHnM8uWmT2CbNlZHIoQQNpGRZlqIffusjkQIIQAzQTzAvfXutTiSjLkyCZQJmYXIY5Yvh3btcnd/QCFEHrNmDUyaBBERVkcihBCASQKbl29O5SKVrQ4lQ64cGMZtEzIDIwAqV/bcX7QQud3Ro3DwIIwaZXUkQghhZ9kyCAyEtm2tjkQIIQB4p/07xCfEWx3GLbkyCXTbhMzAFIDQ0FBHk0whRBZVqgR//glVq1odiRBC2Ght5gds316aKAghPEaH6h2sDiFTrmwOmjSpslLKDzOp8hI37CuEcAFvb7jtNhl3QQjhQfbvN80UunWzOhIhhADgyy1fsuv0LqvDyJTLkkCtdRyQOKlyGDA3cULmxEmZlVJllVIRwLPAq0qpCKVU4Yz2dVWsQohbi4uDZ5+FnTutjkQIIez8+y+ULAldulgdiRBCcOb6GZ5Y9gSL9i+yOpRMuXSyeJmQWYi8YfNm+PhjaNUKQkKsjkYIIWy6doUzZ8DLpdMeCyGEQ3468BMa7dFTQySSUlMIkakVK8w9VgfPb+IuhMgvEhJMn0BJAIUQHiJxgvgGpRtYHUqmpOQUQmRq+XJo0QKKFbM6EiGEsFm8GGrUgCNHrI5ECCG4FHWJ3//9nT71+qBUehMdeBZJAoUQt3TxImzZAp06WR2JEELYWbrUFFAyPZQQwgPsPrubAJ+AXNEUFCQJFEJk4p9/zLgLkgQKITyG1mZ+wE6dwMelwxu4nVKqs1LqgFLqkFLqxXTW11VK/amUilZKPZ/Oem+l1A6l1C/uiVgIAdC2SlvOjTlHaPlQq0NxSN4qOYUQTteyJZw6ZXUUQghhZ8cOOH3aDAyThyilvIEvgI6YOZO3KKWWaK332W12EXgS6J3BYZ7CjKxe2IWhCiHsaK1RShHgk3vmK5WaQCFEhrROHndBxl4QQniMZbbBw/Pe1BAtgENa6yNa6xhgNtDLfgOt9Vmt9RYgNvXOSqmKQDdgqjuCFUIY8/fNp8GXDTh+5bjVoThMagKFEBnat888aP/hB7jjDqujEUIIm9tvh9deg9KlrY7E2SoA4XafI4CWWdj/E+AFIMiJMQkhMrEgbAHnIs9RIaiC045Z8aOKnLh2Is3yCkEViHg2IsfHl2f7QogMrVgBx49DlSpWRyKEEHbuvhtef93qKFwhvSEFtUM7KtUdOKu13pbJdiOUUluVUlvPnTuXnRiFEHZuxt1k6T9L6V2nN95e3k47bs86PfHz9kuxzM/bj151emWwR9ZIEiiEyNDy5VC3rgy+J4TwIHv3wp49pq163hMBVLL7XBE46eC+rYGeSqmjmGakdyulfki9kdZ6itY6VGsdWqpUqZzGK0S+t/LwSq7HXKdPvT5OPe7YtmPxUilTNW/lzdg7xzrl+JIECiHSdfMm/PEH3HOP1ZEIIYSdd96B9u3zahK4BaillKqmlPIDBgBLHNlRa/2S1rqi1rqqbb/ftdZDXBeqEAJg4f6FFA0oyl3V7nLqccsFlWN4yHCUrYGAr5cvw0OGU7ZQWaccX/oECiHStW6dSQRlagghhMeIj4fffoMePfLkaFVa6zil1BPAcsAb+EZrvVcpNdK2frJSqiywFTP6Z4JS6mkgWGt91aq4hcjPutfqTpOyTdI03XSGUc1H8dXWrwDw8fJxWi0gSBIohMhAqVLw6KNw551WRyKEEDabN5sJ4vPY1BD2tNbLgGWplk22+/k0ppnorY6xBljjgvCEEKn0DXbd5PDrj68HQKGcWgsIkgQKITIQEgJTplgdhRBC2Fm6FLy9pZ26EMIjrDqyihrFa1C1aFWXHH/2ntnUKl6LuIQ4p9YCgvQJFEKk49Il2LUrr3a5EULkWitWQOvWULSo1ZEIIfK5+IR4Bi8czJiVY1xy/BNXT7D22FqGNBrCkaeOOLUWECQJFEKkY/FiUxO4e7fVkQghhJ1Vq2CqzIMuhLDenxF/cubGGfrWc01z0Ll756LRDGgwwCXHl+agQog0VqyAsmWhYUOrIxFCCDuFC5uXEEJYbMG+Bfh5+9G1lmv6KM/eO5um5ZpSu0RtlxxfagKFECnEx8PKlabLjUpv2mIhhLDC2LHSUVkI4RG01izcv5B7atxDYX/nP5g6cukIf534iwH1XVMLCFITKIRIZft2uHBBpoYQQniQ6Gj45BMYItPeCSHcT2vNLwd/4UbsDQBOXjtJ+JVwutTswrpj67ijyh1OPd/sPbMB6N+gv1OPa0+SQCFECsuXmxrAjh2tjkQIIWzWr4fr1/P01BBCCM8Vr+MZ9tMwgksFUyGoAgA96/Rk7bG17D23l3XD1zn1fLP3zKZ1pdZULlLZqce1J0mgECKFp54yg++VKmV1JEIIYbN0Kfj7w913O//YhQvDtWtplwcFwVWZf10IYSZqf67Vc+w7t48f+vwAQIJOoMnXTXjh9heceq69Z/ey++xuPuvymVOPm5okgUKIFIKC4K67rI5CCCHsLFsG7dpBwYLOP3Z6CeCtlgsh8qUnWjxBzU9rsuTAEj7d/Cnda3fHx8uH7rW7O/U8s/fMxkt50S+4n1OPm5pDSaBSKhS4AygPRAF7gP9prS+6MDYhhJutWwdr1sDTT5tk0NNJ2SREPnDjBlSuDL17Wx1Jlkj5JETeUti/MKNbjObhJQ9zM+4mJ6+d5L0O76GcOIqe1prZe2dzV9W7nD4vYGq3HB1UKTVMKbUdeAkoABwAzgJtgJVKqe+UUq5rrCqEcKtZs+D9902rK08mZZMQ+UjBgmbempEjrY7EIVI+CZF3Xbx5kfOR5+lWqxsFfAs4vRZw+6ntHLp4iIENBjr1uOnJrCawINBaax2V3kqlVAhQCzju5LiEEBZYvtw0BfXzc83xZ86EV16B48fNg/3x42Hw4GwdSsomIfKL69ehUCGro8gKKZ+EyIN+/edXPtn0Cc3LN2fO3jksGbDEqbWAALP2zMLXy5d7693r1OOm55ZJoNb6i0zW73RqNEIIyxw6BEeOwLPPuub4M2fCiBEQGWk+HztmPkPWE0Epm4TIJyIjoUwZ88To6aetjsYhUj4JkTd9+tenNCrTiF8G/sLX2752ei1ggk5gzt45dKrZieIFijv12OlxaLJ4W9OFonafiymlvnFZVEIIt1u+3Ly7an7AV15JTgATRUaa5dklZZMQedzvv5uCokED150jo8FmctgxWsonIfKWxf0Xs2zQMkoXKs3YO8c6vRZwY/hGIq5GuKUpKDiYBAKNtNaXEz9orS8BTVwSkRDCEufOQf36ULOma45/PIOGTxktd5CUTULkZcuWmSTtDudOxJzCm2+a93/+Aa2TXzmfHkLKJyHygNl7ZnP55mX8ffypULiCy84za/csCvgUoGedni47hz1Hk0AvpVSxxA9KqeLI9BJC5Cmvvw5//+2641fOYBiEjJY7SMomIfIqrc38gB07una0qvnzISTEFU/ApHwSIpdbcXgFgxYMYsL6CS49T1xCHPP2zaN77e4U8nNPH2hHC6MPgY1KqfmABu4HxrssKiGEW2kNSoGXo4+FsuGll9IO7hcYaLr65ICUTULkNcWLw6VLyZ+PHzcFVLFicNHJsytERMCff8Lbbzv3uIaUT0LkYievnWTIwiHUL12fcXeOc+m5Vv+7mnOR59zWFBQcrAnUWn8P9AXOAOeAPlrrGa4MTAjhPmPHQuvWEB/vunP4+pr3smXN/VyVKjBlSrZHBwWkbBIiT7p0yTyZOncOPv0UTp40n+0TQ2dZuNC833ef0w8t5ZMQuVdcQhyDFgziRuwN5vabS6BvoEvPN2vPLIL8guhSq4tLz2MvK80SigM3tNbfKqVKKaWqaa3/dVVgQgj3+fVX0+3G29t155gzB6pXN6OQOrkvtZRNQuQVBw+a91Gj4MsvYfRo155v3jxo2BBq13bVGaR8EiIXen/D+/xx7A+m95pOvVL1XHqu6LhoFoYt5N569xLgE+DSc9lzdHTQ14D/YiY+BfAFfnBVUEII9zl7FrZvd92ooADnz8OqVXD//c5NAKVsEiIP2L4dnnoKatWCOnXMsj/+SDucsLOdPAkbNkC/fi45vJRPQuRew0OGM7HjRB4MedDl51p+eDlXoq+4tSkoOD4wzL1AT+AGgNb6JJCzsZOFEJZatQratTPNMgHi4lx3roULTVPT/v2dfmgpm4TIbY4fh8mT4dQp83nrVtM2vHZt+Pxzs2zvXtNp2JUWLTLNTF3QFNRGyichcpkrN68QnxBPuaByPHf7c24556w9syhRoATtq7V3y/kSOdocNEZrrZVSGkAplcGkOkKI3GDNGtMX79NPoVw5Mwr7zJlmTubUg7c4w9y55iF/48ZOP7SUTUJ4urg4U+O2bJkZ7XPvXrO8YEF44AEYMsS8Fyhglj/xhHvimjcPgoOhnsuaekn5JEQuEp8QT9+5fQn0DeSnAT85fR7A9NyIucGSA0t4oNED+Hr7uvx89hxNAucqpb4GiiqlHgUeAv7PdWEJIVzpnXdg4kTTPDMmBkJDoX176N4dHn3UuX0Dz56F1avh5Zed3hcQpGwSwjOdOQOXL5vmnefOmWYHPj7Qti089BB07Zrc9DN1jV+xYukXFsWKpV2Wk/jWrjWjYrmOlE9C5CLj141n1b+rmNZzmlsSQIBfDv5CZGyk25uCgoNJoNZ6olKqI3AVqAOM01qvdGlkQgiXiImBbdtMrV+zZuYhfKIbN8x9W4kSzjvfggWQkGASTmeTskkID5GQYJp1Jtb2bd1qnir9/LNpbrBiBbRsCYULZ34sZ08DkR7XNwWV8kmIXGT1v6t54483GNJoCMNDhrvtvLP2zKJ8UHnaVG7jtnMmcigJtDVh+F1rvVIpVQeoo5Ty1VrHujY8IYQz3LwJP/0Eixebe7SrV838yD17JreEOnAA/PygSBHnnnvuXKhbFxo0cO5xQcomIdwi9bx9iYoWTV7epYtJ9Ly8oFUrMwFo9+7J23bs6JZQHTZvnqmJrF/fZaeQ8kmI3OHM9TMMWjiIWsVr8VW3r9xWC3j55mV+PfQro0JH4e3lwuHZM+DowDBrAX+lVAXgf8BwYLqrghJC5Nzp07Brl/k5NhaGDjWDwdx3H4wbZ2r7SpUyD8N37zZ9BJ991rTYcpZTp8wgf/37u6QpKEjZJITrJc7bl5BgCpV33oE2bUyzgcQRPEeOhB9/NO2/16837b8bNbI07AydO2c6Rvfr57KCyUbKJyFygTM3zlDEvwjz7ptHIb9Cbjvv4v2LiYmPYUCDAW47pz1Hb/eU1jpSKfUw8JnW+n2l1A5XBiaEyLp//jG1fYsXw59/QosWsGkTBAWZJqD16iX392vQwEzFdeiQSQafew6eeca58SxYYO4dXdEU1EbKJiGcLTLSFCYHDsCdd5pl8+bBI4+YZgQATZua9+ho06fv3nutiTU7Fi82Ca0Lm4LaZKt8Ukp1BiYB3sBUrfWEVOvrAt8CTYFXtNYTbcsrAd8DZYEEYIrWepJTv5EQeVCjMo3YO2qv22vjZu2ZRbWi1WhRoYVbz5vI4SRQKdUKGAw8nMV9hRAuonXyg+xHH4WpU83PTZvCG29A797J26ZujnnffeZBeEyMaQbqigfic+aY8wYHO//YNlI2CZEdCQkQEWFG6CxRAvbsMU0BDhwwUzgkWrTIvNeoYToQN2sGnTtD+fKm0HDmYC3uMn8+1KzpjprKLJdPSilv4AugIxABbFFKLdFa77Pb7CLwJNA71e5xwHNa6+1KqSBgm1JqZap9hRA2a4+tZfH+xUzoMAE/bz+3nvvsjbOsOrKKF1q/4Lbmp6k5erP0FGay00Va671KqerAateFJUT+FBkJhw+b+6uMBmeJjTWD2i1ebMZc+OsvKF3a9O9r2NAkfpUrO3Y+pcDf31nRp3TihGkV9uabrjm+TbbKJgeetCvb+q5AJDBMa73dtu4Z4BFAA7uB4Vrrm877SkJkIqM+esWKpR1UJSHB9NO7fBk+/NAkeQcOmJq+qCj47DMzJUNAgDnmHXeYvnJ16ph5+xJH8GzaNLn2Lze7cMG0ix8zxtVNQSF75VML4JDW+giAUmo20AtISuS01meBs0qpbvY7aq1PAadsP19TSoUBFez3FUIY526cY+CCgRT0Lcgb7d5wexK4YN8C4nW8ZU1BwfHRQddi2rYnfj6CeQolhHACrc2UDRMmmFE7T52Cvn3NvMkBAWabQ4dM7d4vv5j7uQIFoFMnuHLFJIE9elj6FdKYN8+8u7ApaLbKJgeftHcBatleLYGvgJa2vj1PAsFa6yil1FxgANLPR7hTYh89ewkJpq33hx/CwYPJyd6DD5qCxccH3n0XqlY1iV379ub9rrvM/jVrwpYtbv8qbvfTTxAfb5pBuFg2750qAOF2nyMwZVCWKKWqAk2AzVndV4i8LkEnMHTxUC5EXmDpI0sJ8g9yewyz9swiuFQwDUs3dPu5E2XWLGEKph377nTWFQT6A9Fa65kuik+IfOHHH2H6dHMPVr26SewGDYJu3eCVV+Duu8HXF3791dT09e5tBttLPb2WJ5k710wOn1iR4Ew5LJsyfdJu+/y91loDm5RSRZVS5WzrfIACSqlYIBA46azvJXKprNTMOUprM2fLjRvmyRCYf1T//mt+HjrUDMISEmKSvMRareefN80I6tQxI3a2tOUPhQqZpgZ+2Xja7Y55+9xl/nyoVs2ltZo5LJ/Sq57U6Sy71fkLAQuAp7XWV9NZPwIYAVDZ0WYjQuQhH2z4gN8O/caXXb8kpGyI288fcTWCdcfX8Wa7Ny1rCgqZ1wR+CYxVSjUE9gDngADM0/HCwDeAJIBC5NAXX8AHH8DRo2YwlV9+MU0pExKgYkWTBFapYuY3duZE7q5y/LgZmOadd1x2ipyUTY48aU9vmwpa661KqYnAcSAKWKG1XpHD7yKczRVJ2a2kVzMHaROnqCiTuJ09a0aoPHvWbPPgg2b900+bf/iJ627ehNtuM/+YwEy78Pff5ue1a00TgISElOc6fz7jtuTZSQDBPfP2ucOlS/C//5nfs2tvvHJSPkUAlew+VyQLD5qUUr6YBHCm1nphettoracAUwBCQ0OzlGAKkdtdjLrI+HXjub/+/YwMHWlJDHP3zgWwtCkoZJIEaq13AvfbniqFAuUwNz5hWusDrg9PiLzl2jXTQmv//uTWWgUKmOaftWubqRS2bzfjFbzyiumuM8Gut1puSAAhuSmoqwbfy2HZ5MiT9nS3UUoVw9QSVgMuA/OUUkO01j+kOYk8bbeOo0lZVt24kTwtwo0b5j1xigQw86Fs2ZKcxIGpjfv1V/PzwIGmOaK9SpWSk0AwiV39+ua9VCkzIEuiFStMjV6hQuaJUXoySgAFLFliOlW7uCloDsunLUAtpVQ14ASmufkgR85r68s8zXaej7IZvhB5WvECxdn48EYqF6lsWS3crD2zaFauGbVK1LLk/Ikc7RN4HVjj2lCE8Cw7d8Lbb5t7ukqVzMNjR+4d4uNNTVhionfqFLz3nln3wAPJ94BeXqbpZ8uWcPvtsHAhzJxpKjFKl4Z168znxJZgucncuaa1Vc2arj1PNssmR560Z7RNB+BfrfU5AKXUQuB2IE0S6NFP291dU+au88XEJJ9n507z1OX6dTOnXZCtz8eHH6ZM4m7cMNXwxYvD99/D5Mkp10VGwrFjUKQIvP666bybkTlz4KuvTI1bqVJmmZdX8jC+jz1mRnAqVSo5yUvcDuCTT279/XJjYeBJ5s83o2Y1b+6W02WnfNJaxymlngCWYwau+sY2qMxI2/rJSqmywFZMrWKCUuppIBhoBDwA7FZK7bQd8mWt9TInfB0hcrUEncCqI6voWKMjDUo3yHwHFzl08RBbT27lg44fWBZDIhlKXYh0/P236XM3diy8/74ZPf2558wD/v/8x2xz5Upybd6AAabP3vjx8NZbZuqsRMWLmwFdAgJMIvngg6a7To0aySNzhoVBu3amtViXLiZRfP11mDTJ3EPmJv/+a0YsTUx8PZAjT9qXAE/Y+gu2BK5orU8ppY4DtymlAjFP9ttjbsZyxlObL7ryfIkDmZw8CYULm9qtCxdg40aTuF27lpzEDRxoqso3bzb/yOzXXbtmpjFo0cJ0rh0+3By/SZPkc+3caTqogukzB+YfbMGCpmPtuHHmGvj4mDjKlDHLE9cn/iPs0wdq1Uq5rmBBM6ImmPbP775rvo9S5rV0aXIcXbo45/eZl/roucuVK6Ym9Ykn3DEqaI7YkrZlqZZNtvv5NObBVGrrSb8VgxD53kd/fsSYlWNYMWQFHWt0tCyOOXvmAHB/fReOmucglyaBMgy7cLaICDPpeYUKZroqV/1f/u678PLL8PDD5h61Qwdzn9m2rXnYf+AAnD6dvH2LFiaxa9zY3GPUrWs+160LJUsmx9muXfrnq1fPdAWaONEkmdWqwaxZ5ny5jaubguaUI0/aMTdgXYFDmLJpuG3dZqXUfGA7Zk6uHdhq+3LEPkmKiTFPEWJizB/P0aMmYalQwazfts0kP4nbxMRA2bKmOhng//7PJEaJ66KjTUKUWI39sG26svvvN+vi4szQsiNtfSM6dDCxJCQkv4YONRNRXrpk5ohLvf7JJ+GhhyA83AxZm5CQcptE+/aZDq7Xr5taNjDf64cfYPBg2LvX1JSlFhJiksDoaHOOQoVMTVqNGubnIkXMdq1amQ62jz9uqtaDgkySZl8lfeWKaYPt65v2PIMGmVdGWrUyr4wULZrxOmfKK3303Onnn82/BzeMCiqE8Cx/hv/Ji/97kT71+tChegdLY5m1ZxZtKrehchHru4lkKQlUShXUWt9wcFsZhl04jdamJm76dHOve+CAuT9etMjc/2ZFZKQZYOXMGZPInTljRkuvWdPUYD3zjGkC+tNPZu5kMGMJtG9vKi6uXzcP9BOn0kqs1QPo3t28sqNWLfj66+zt60nmzDFJcbVq7jtnVsomcOhJuwYez2Df14DXshlq5po2NclQomrVTGL122/mc58+KSfzBjOfSGIS+OKLKZMEb29TO5Z487tmjXnfs8c0W/TxSU7IwFRHe3klv3x8kjujenmZ2ib79V5eyUmYnx80aGCeetivP3LErA8KMkleUJB5vfGGaX7ZooVZ36SJ+ccXFGSSu8QkLvH8bdvCjh0Z/+4S/0E+/jjce2/62xQunPH+2SU1c55v/nwzylbiaKlulNXySQjhPBejLjJgwQAqF6nMtJ7TLB2Nc8/ZPew9t5fPu3xuWQz2HEoClVK3A1OBQkBlpVRj4DGt9ahb7CbDsOdxFy6Y+7eNG6FcOVOREBrqmnNNn276yB05Yh62JyTAq6/CI4+YkTQTu9xERpp5gBOTu8T3YcPMdAvbtqUf4/TpJgn09zfNNkuUMPfibduaJLNePXOsuDgz9kPBgq75nrndoUNmYJtbdZtypmyWTZ7t2WdNjZu/P4weDd9+a25eE82YYTqe+vmZbfz8UiYbBw6YWi5/f/OeejShw4fNP5Z9GcwfvWFDxrEVKZKcjKanTBnTITS1H2xdJitVgil2FadvvGH6ySUKCnJOIeLupExq5jzbtWvm73bkSLe2r8+T5ZMQuYjWmuE/DefUtVNseGgDRQOKWhrP7D2z8VJe9Av2jBYJjtYEfgx0wvSTQWu9SymVWUM1twzDLiPwJYuKMvda69ebxOWhh1wzRxuYge9uv92Mt/DYY2Zu4u7d4dNPcz45eHi4abF19Wry+6RJpitQYKC5Lz571gy4smGDSQqffdZ067l6NWVrsqJFze8isbtV1aqm206ZMmZ54nvp0mZ948Ymifz1V3O/MG6ceXAcEWF+n8OHSwJ4K4n3/25sCpqdssmzPfRQ8s+jR5snGPYyayNcsqTTQ8p1JCkT9n75xTQldn9T0LxXPgmRiyilGNpoKF1rdqV5BfcMCJURrTWz9szi7mp3U6aQZwzy5XBzUK11eKoq1PhMdnHLMOxZGYEvIcHc3K9ebWp6Hngg5QN2Z/v7bzOwyB9/mCTjscfMwCCuGOb/6lXT1aZUKfP/3OHDJkH75hvT3cfZPvrItFD74gvzOT7e3Jv26WO67ly6lJzEXbliagoT//8dMcIkevbru3ZNbgpZp45JaO0VL26+m4+PGfuhZEmTwPn7m6QvcbC3UqVMk87ExC5x4JVEJUrASy9l/v26dDFJZ//+pqVcXJypdXThvHd5wty5psuUO5/HZKNsEu6uKZPmksJK8+eb/4QSm0y7kZRPQlgjJj4GP28/+gb3tToUALae3MqRS0d45Y5XrA4liaNJYLitWYNWSvlh+uuFZbKPW4Zhd1RsrOkicuKEqakKDzdjDcyY4bwB2+wdPmzGVxg3DqZONWM7PPusOf9HLpi95/PPTb+02bOT77W6djWjVrZta5KYxOmjDh82tWiJU1xFRprEtH9/s376dNNdyH59mTJmzjowyfO8eaZrzaxZJkkKDTW1coGByUmovQ4dkpPAw4dNAli4sDlukSIpW4BNmWKSt8KFzboiReDLL02ruBYtTDNUMP30/vMfE29iCx9vb+eN/j1kiBmU8Px5E0NAgHOOm1cdOAC7dmU+yr2TZads8jx5vfmi1MwJq1y/DsuWmad47h9qOW+UT0LkMpeiLnHbtNt4uc3LPBjyYOY7uMHsPbPx9fLl3roZ9Fe3gKNJ4EjMKJ4VMInbCjIYNMGORw3D/t13JlnZssXUJoG5we/f30wBld5AcfYSB+5TyiRUUVGm9isuzrzi400tlJ+fmUt43DiTdLZubY4fG2sSpoYNTWIWHp5y8L6YmOSmhr//bhIc+3UxMWb6qYAAk1TOn59yXViYGSRFKRgzBqZNM8lbdLRpElm4sKlxAzMJ+Zw5Kb9fuXLJSeCCBSaGwMDkV0xM8rZVq5p703r1zBgQgYEmAb150yRoU6ea9UWKJCdy9mMxrFp169/1kCFpl40bZ2o2778fevUy3/frr00S78r/1729ZWouR82da/7+3NziKjtlk+eRJEkI11i2zPznZM2ooHmjfBIiF9Fa89CShzhy6Qh1S9a1OhzAzFE4Z+8cOtfsTLECntMCxtHJ4s8Dg7NyYE8bhv2nn2DUKJMAbttm5mqLizNJS5Uq5mZ/6lTTxHHFCtOnKTHJS3xPHCFy/nyTQKa2ebOpqZo3zzRZhJRjIOzbZ5o6/vBDcq2avV69TBK4YYOZuzhxzIfE8R9iYkwSGBlpEk0/PzPSedGiZrDAxBqykBAz2npAgBm45fHHzXdM9OKL5qGofZJn389tyZJbT73w1lum79y4cea7lC9vfj8vvWSaAmY0KF9OJDbz/OYbM9J3hQpmoJi6nvHvW2CSwDZtkmcycIfslE1CiHxk/nzzJK9NG7efWsonIdzvs78+Y/H+xUzsOJGWFd0/GnB61h9fz4lrJzxignh7jo4OWg0YDVS130drnc6ETsk8aRh2X1/zMBBM0lO3rkkIT50yo5KXKWMSDTD9BIcPTx4Z3cfHvKpWNetDQkySltH6u+82r7JlTTLp7W0StqJFzQAq//d/Zsot+8H97Af4e/VV05cwI08+aV72Zs2CCRNMEjt4sJnq6sMPoVEjs9xeSMitf1eOjJ7bt69p/le/vjnH4cOmL2DqGkZnKlLETN8gPM++faYJcXoPN1wpu2WTECIfiIyEpUvNU19XdMbPhJRPQriO1ppGkxtx5eaVpGUx8TGcuXGGUoGleLbVsxZGl9LsPbMp4FOAHnVcMEhHDjjaHHQxMA34GUi49aaeaeBAM9BHr16mGeP8+abmYts2U7Nk36QwOPjW/Zrq1r11DVSNGmY0y7vvht69zcAsERFmYJjevU2T0FvJzhQmAwaY6bNq1DATkh8+bGowf/4568dyhFKmWel//gM7d5rmpPXqueZcwvMlNgXt6/7+14vJ5WWTEMJFfv3VJILWTRC/GCmfhHAJpRQ1i9ekTok6jGpuZl35due3fLDhA/rW62vpfID24hLimLdvHj3q9KCQXyGrw0nB0STwptb6U5dG4mL9+plROuvUMVMZhIeb0Tt/+cU1fcoaNDCJ5gsvmFq5QoVM7d9bbzn/XGBuwN9/34wov3GjqYW84w7X94MvXtwkuyL/0trUAN95p3kY4Ga5vmwSQrjI/PlmKOnMplVxHSmfhHChcW3H0e3Hbrx252sU8C3Ao00f5ZM/P+G1dk5rSJhjq46s4nzkeQY2SKcfmcUcTQInKaVew3Rqjk5cqLXe7pKoXEApM4LmqFGwZo0ZrbJnT9OnzlXatoVNm8zgLL6+7hmYrFKl5AFehHCHPXtg//60TZTdJNeXTUIIF4iKMk95Bw1KHg3O/aR8EsKFmpRrQvMKzek4oyOvt3udnw/8zPAmwylbqKzVoSWZvXc2hf0L07lmZ6tDScPRkrEh8ABwN8lNGrTtc64SHGxe7pR6rjoh8pI5c8wDDguagkIeKpuEEE60YoWZHsK6pqAg5ZMQLnX6+mmOXz7OzjM7+fWfX5nx9wz2Pb7P6rCSRMdFszBsIffWvZcAH8+bZ8zRJPBeoLrWOibTLYUQ+YbWpj/gXXdB6dKWhCBlkxAirXnzTH+Fdu2sjELKJyFc5K8Tf9FnTh8uRl0ktHwo88PmMyxkmEfVAv566FeuRl/1yKagAI42UNwFFHVhHEKIXGjnTvjnH0ubIEvZJIRIKTrazHV0772ZTwLsWlI+CeECu8/spu23bfH19uXPh/9kSvcpXIu+xgutX7A6tBRm75lNycCS3F3NMyv/Ha0JLAPsV0ptIWW7dhnmWIh8bO5cM/K6K+aGdJCUTUKIlFauhGvXrG4KClI+CeESDUo34PV2r/NI00coGVgSgNPPn8bP28/iyJLdiLnBzwd/Zmijofh6W/owKkOOJoGeM8yOEMIjJDYF7dDBDMBnESmbhBApzZtnJua1fuhqKZ+EcJLzked57JfHeL/D+9QoXoMX27yYYr0nJYAASw4sITI2koENPbMpKDiYBGqt/3B1IEKI3GXbNjhyxMwXaRUpm4QQKcTEwE8/meYJftbeFEr5JIRz7Dy9k96ze3P6+mmGNhpKjeI1rA4pU7P3zqZ8UHnaVG5jdSgZumWfQKXUetv7NaXUVbvXNaXUVfeEKITwRHPnmpHXe/d2/7mlbBJCpGvVKrhyxdKmoFI+CeE8s/fM5vZptxOv41n/0Hp61e1ldUiZuhR1iV//+ZX+9fvjpdwwP1w2ZVYTOAZAax3khliEELlEYlPQe+4xA/BZQMomIURa8+ZB4cKmnbp1pHwSwgnm7JnDwAUDaVO5DfPvm0+ZQmWsDskhi/YvIjYhlgENBlgdyi1llp5+4ZYohBC5yl9/wbFjcP/9loUgZZMQIqXYWFi8GHr1snqCXimfhHCCHnV68M7d77Bq6KpckwCCqb2sXqw6zcs3tzqUW8osCVRuiUIIkavMmWO62/SyrlWGlE1CiJRWr4ZLlzxhVFApn4TIpr1n99JzVk+uRV8j0DeQl+54yeMGfbmVszfOsurfVQyoPwClPLsoyKw5aDWl1JKMVsowx0LkPwkJpsVVp05mAD6LSNkkhEhp/nwoVMi0U7eWlE9CZMOisEU8sOgBgvyDOHr5KA3LNLQ6pCybt3ceCTrB45uCQuZJ4DngQ3cEIoTIHf78EyIiYMIES8OQskkIkSwuDhYtgh49ICDA6mikfBIiCxJ0Am+seYM3175JiwotWHj/QioUrmB1WNkye+9s6peqnysS2MySwGsyxLEQwt7cuaa7TY8eloYhZZMQItkff8D583DffVZHAlI+CZElL696mfc2vMfwkOF82e1LAnwsf5CTLeFXwll/fD1v3fWW1aE4JLMk8Kg7ghBC5A7x8aYpaNeuZgA+Cx219OxCCM8yfz4ULAidO1sdCUj5JESWPN78caoVrcaIZiM8vh/drczZOwcgVzQFhUwGhtFa93FXIEIIz7dhA5w6ZemooICUTUIIO/HxsHAhdOsGBQpYHY2UT0I4YNk/y3hg0QMk6AQqFanEY6GP5eoEEMyooKHlQ6lZvKbVoTjEc2cwFEJ4nLlzzT1W9+5WRyKEEDbr1sHZs57SFDTHlFKdlVIHlFKHlFIvprO+rlLqT6VUtFLq+azsK4TVtNa8u+5duv/Ynb1n93Ip6pLVITnFPxf+YdupbQyonztqASHz5qBCCAGYh+3z55uH7YUKWR2NEELYzJ9vnk516WJ1JDmmlPLGzDPYEYgAtiillmit99ltdhF4EuidjX2FsMyNmBsM/2k48/bNY2CDgUztOZVA30Crw3KK2XtmA3B/fYubSmXBLZNApVTTVIs0cF5rHe66kIQQnmjtWjhzxvqmoCBlkxDCJj4eFiwwHZULFrQ6GiDH5VML4JDW+ojtWLOBXkBSIqe1PgucVUp1y+q+Qlip79y+rDyykokdJ/Jsq2dzffPPRFprZu2ZxR2V76BSkUpWh+OwzGoC0xviuLhSyg8YqLXe6fyQhBCeaM4cCAw0NYEeQMomIQRs3AinT3taU9CclE8VAPtkMQJo6eB5HdpXKTUCGAFQuXJlBw8tROaWH1rOlegraZaXLliadlXbMe7OcTzb6lnuqWH5XJ5OtefsHsLOh/FF1y+sDiVLbpkEaq3vSm+5UioU+BRo64qghBCeJS7OPGzv0cMkglaTskkIAZimoAEBpibQQ+SwfEqvakQ7eGqH9tVaTwGmAISGhjp6bCEy9fCSh6lQuAJVilQBTA3ZxvCNxOk4zjx/htsr3W5xhK4xa88svJU3/YL7WR1KlmRrYBit9VZAegUJkU+sXm2m4Orf3+pIbk3KJiHykYQE83Sqc2cICrI6mkw5WD5FAPbtySoCJx08RU72FSLHXmj9AuUKlWPufXP5rvd3BPgEcPL6SaoUqUJ8QrzV4bmE1prZe2bTvnp7ShcsbXU4WZKtJFApVQbHn0wJIXK5uXPNYDCeMQVXxqRsEiIf2bQJTpzwtKagGXKwfNoC1FJKVbM1Hx0ALHHwFDnZV4gce7Tpo2w5uYU5e+Zwx7d3MHP3TEoWKMmGhzbg7eVtdXguseXkFv69/G+uGhU0UWYDw3xG2gKrOHA78JSrghJCeI7YWDMFV69eHjEFFyBlkxAC0xTUz8/j5qzJSfmktY5TSj0BLAe8gW+01nuVUiNt6ycrpcoCW4HCQIJS6mkgWGt9Nb19nfjVhLilAr4FeKrlUwxeOJjC/oWpW7Iur7Z9FV9vX6tDc5lZu2fh6+XLvfXutTqULMtsYJitqT5r4ALwrG10KiFEHrdqFVy86BmjgtqRskmI/ExrkwR26gSFC1sdTWo5Kp+01suAZamWTbb7+TSmqadD+wrhapdvXmbW7lmMDB3J6BajeW/DezzZ4klm7ZlF//oe3o8kB+IT4pmzdw5danWhaEBRq8PJsswGhvlOKdUEqAHs1VqHuScsIYSnmDvX3GN16mR1JMmkbBIin/vrLwgPh7fftjqSNKR8EvlFdFw0X275krfXvc2lqEvcVvE2mpRrwmt3vsZTvz3FzD4z82wzUID1x9dz6vopBjYYaHUo2XLLPoFKqbHAHKAvsFQp9ahbohJCeISYGFi0CHr3Bn9/q6NJJmWTEPnc/Png6ws9e1odSRpSPom8LkEn8OPuH6n7RV2eXfEsoeVD2f7YdpqUawKYvoHj2o7L07WAYEYFDfQNpEftHlaHki2ZNQcdAIRorSOVUiWA34D/c31YQghPsHIlXL7scU1BQcomIfKvxKagHTtC0aJWR5MeKZ9EnnYz7iZjVo6hdMHSrBiygo41OqZYX8C3AG/c9YZF0blHbHws8/fNp0ftHhT0K2h1ONmS2eigN7XWkQBa6wsObC+EyEPmzDH3WB07Zrqpu0nZJER+tW0bHD0K/Tx2Ti4pn0Se8/eZv3lkySPExMcQ6BvI2mFr2TZiW5oEML9Y9e8qLkRdyLVNQSHzmsAaSqnE4YVVqs9orT2vHYYQwilu3oSffoK+fc0AfB5GyiYh8qv588HHxwxZ7JmkfBJ5xvErxxm3ehzf7/qeogFFebz54zQp14QaxWtYHZrbVfyoIieunUixrPec3lQIqkDEsxEWRZV9mSWBqUvYia4KRAjhWZYvh6tXPXaCeCmbhMiPEpuCtm8PxYtbHU1GpHwSud7NuJu8tvo1Jm2eBMDztz/PS21eoliBYhZHZp2edXoybcc0YuJjkpb5efvRq47HPpC6pcxGB/3DXYEIITzL3LlQogTcfbfVkaQlZZMQ+dSuXXD4MLz4otWRZEjKJ5Gbaa1RSuHn7cfvR39nQIMBvHnXm1QuUtnq0Cw3tu1Yvt35bYpl3sqbsXeOtSiinMlsdNBeSqnH7T5vVkodsb08tjG+ECJnoqJgyRLo08cMwOdppGwSIp+aNw+8vc2QxR5KyieRGyXoBGb+PZOmU5pyIfICXsqL9cPXM733dEkAbcoFlaN9tfZJn329fBkeMpyyhcpaGFX2ZdZZ+QVgid1nf6A50A74j4tiEkJY7Ndf4fp1jxwVNJGUTULkN1qbJPCuu6BkSaujuRUpn0Su8r8j/yN0SihDFg1BoTh74ywA/j4eNDeUB9h9Zjd/HPsDhQLAx8sn19YCQuZ9Av201uF2n9fbRrq6oJTKneOhCiEyNXculCoF7dpZHUmGpGwSIr/Zswf++Qeee87qSDIj5ZPwCNeirxEVF5VmeQGfAgT5BxEdF02v2b1Yfng5VYpUYWafmQxoMAAvJQPapnbq2im6/diNwv6FubfuvczcPTNX1wJC5klgit6fWusn7D6Wcn44Qgir3bgBP/8MQ4eaAfg8lJRNQuQ38+aBlxfce6/VkWRGyifhEWp/XpsbMTcI8AlIWnYz7iZFA4py/Jnj+Pv4U7FwRT6850Meb/641Pxl4EbMDXrO7smFqAusG76OcoXKsSF8Q66uBYTMk8DNSqlHtdYpJjlVSj0G/OW6sIQQVlm2DCIjPXZU0ERSNgmR38yfD3feCaVLWx1JZqR8Eh7hwcYPcuXmFb7q/hUAl6Iu0fbbthy4cIAD5w9Qp2QdpvacanGUni1BJ/DAogfYdnIbiwcspmm5pgAcfvKwxZHlXGZJ4DPAYqXUIGC7bVkzTPv23i6MSwhhkTlzoEwZuOMOqyO5JSmbhMhP9u2DsDB44onMt7WelE/CIzzX6jlqf1abwY0G82f4n4xfN54r0VcY2GAgQf5BVoeXK/x35X9ZtH8RH3f6mJ518tYUn5lNEXEWuF0pdTdQ37Z4qdb6d5dHJoRwu+vXYelSePhhMwCfp5KySYh8Zt48UMoMWezhpHwSVoqNj2Xn6Z3ciL1Bu6rteLTZo9z13V3EJcRRIagCA+oPYHKPyVaHmStM2TaFiX9OZFToKJ5q+ZTV4TidQz1+bAWXFF5C5HE//ww3b3p8U9AkUjYJkU/Mn2+aJ5TNPYMwSPkk3OWPo3+w8shKNoRv4K8TfxEZG0mTsk3Y/th2xtw+hi+3fMnX3b/mhZUv8G6Hd60ON1dYcXgFo5aOokvNLkzqMgmllNUhOZ0M/yOESDJ3LpQvD61bWx2JaymlOiulDiilDiml0sw6rYxPbev/Vko1tVtXVCk1Xym1XykVppRq5d7ohchn9u83I4P2kyn2RP6mtebIpSPM2DWDF/+X/F/XF1u+YML6CVyLvsYjTR5hTr85/DzwZwBKFSzFEy2eYPSvo3m8+eMUK1Aso8MLmz1n93DfvPsILhXM7H6z8fHy3FHyciJvfishRJZdvWrmBxw50gzAl1cppbyBL4COQASwRSm1RGu9z26zLkAt26sl8JXtHWAS8JvWup9Syg8IdFvwQuRH8+eb91zQFFSIW3lh5QucvHYyzfLSBUvzUaePMtxv1ZFVfLX1KzaEb+D09dMAFPYvzAutX6B4geJ83Oljvun1DYX8CqW7/3OtnmNj+Eaevu1pp3yPvOzM9TN0/7E7gb6B/DLoFwr7F7Y6JJeRJFAIAcCSJRAd7dETxDtLC+CQ1voIgFJqNtALsE8CewHfa601sMlW+1cOuAG0BYYBaK1jgBg3xi5E/jN/vmmeUKGC1ZEIkSM7T++kSEARetXplbRs6T9L2XZqGwAXoy7yZ/ifbAjfwIbwDXzR9QsalG7AyWsn2XZqG+2rtad1pda0rtya+qXq4+1lOu9XKHzrfxulCpZi7fC1rvtieURkbCQ9Z/fk7I2zrB2+lspFKlsdkktJEiiEAMyooJUqwW23WR2Jy1UA7CdyjiC5lu9W21QA4oBzwLdKqcbANuAprfUN14UrRD72zz+waxd8/LHVkYg86O7v7uafi/+kWV4ysCQ7Htvh9PO9dudrDFk0hJl9ZuLr5UtUXBRv/PEGL7V+ifpf1mffOfMs0lt506RcE67cvALAkEZDeKDxA06PRyRL0AkMXTSULSe2sLD/QkLLh1odksu5NAlUSnXGNJ3yBqZqrSekWq9s67sCkcAwrfV227qiwFSgAaCBh7TWf7oyXiHyq8uXYflyGD06bzcFtUmvd7d2cBsfoCkwWmu9WSk1CXgRSDNjrFJqBDACoHLlvP00UQiXWbDAvPfta20cwi0mbZrEsSvH0iwv7F+Y19u97vTzBZcKpkrRKrzZ7s2kZZ9s+oST19M22czMoYuHuHzzcopXzeI1aVe1HTHxMTz000NmedRlqk2qRnRcNO2qtqN8UHl61+3NvLB5DGwwkNaVWtOiQgsK+hVMOnZeHJTE07y86mUWhC1gYseJ9K7b2+pw3MJlSaD0uxEi9/jpJ4iNzT2jguZQBFDJ7nNFIPX/+Blto4EIrfVm2/L5mCQwDa31FGAKQGhoaOokUwjhiHnzTPOESpUy31Y43au/v8qBCwfSLC/oW5BpPaclNUd0luWHlxObEEuXml2Slm0+sZnfDv3Ga3e+5vRk6L+t/0vjyY15r8N7RMZGcvzKcb7Z+Q3j7hzH1O1TKRVYil51TdPNJ5Y9wbErx5ISvEtRl+hUoxPTek0DoMnXTbgecz3F8R8KeYh2Vdvh6+XLlpNbCPILokbxGoSdD6N//f78dug3ZvaZSfHA4vw6+FenfjfhuKnbp/Lehvd4rNljPNvqWavDcRtX1gRKvxshcok5c6BqVWje3OpI3GILUEspVQ04AQwABqXaZgnwhK3caglc0VqfAlBKhSul6mitDwDtSVmmCSFyqnBhuHYt5TKlICjIjGAl3ObYlWPciLnB8JDhSctW/buKzSc246Wc32xkbNuxDFgwgKWDluLn7YfWmtbftGbcneMAuBFzg+sx17kec51rMdeIjY+leQXzH9fyQ8s5eOFg0vrrMdcp7F+Yt+5+C4BRS0exKWJTivX1S9dnUMNBTNw4keWHl/P3mb8BeHa5SQTaVW2XlATuP7+fSzcvUTSgKLVL1Kaof1Fuq5jcf+LbXt/i7+1P0YCiSa/iBYoDpibvwBPJyfQ9M+4h4moEtUrUol3Vdk7/PQrHrTqyiv8s/Q/31LiHz7p8lq9qXV2ZBEq/GyFygYsXYeVKePZZc5+V12mt45RSTwDLMU3Vv9Fa71VKjbStnwwswzRTP4Rpqj7c7hCjgZm2FgpHUq0TQuRU6gQws+XCZV6941XafNuG2f1mU9i/MFpr3l3/Lq/e8SqRsZFExUVRLKAY3l7enL1xluNXjnMz7maKV886PfHz9mP98fX8Gf5nmvWfdf0ML+XF11u/5qcDP3E9+jr1vqhHYf/CXLl5hQCfAO4Lvo8HFj3AzN0zU8RXKrAUZ8ecBeCrrV/x04GfANOnLsg/iLol6/IWJgks6FuQCoUrUMivEEF+QRTyK0T1YtXpXbc3jb5qxIf3fMgzy5/h0y6f0qRsE4oGFE0xncL/hv7vlr+rfsGOT2Hy2p2v0ebbNvw+9Pd8lXR4mn3n9tF3bl/qlKjD3H5z8fX2tTokt3JlEij9boTwcDNnwpNPQlwcfP89NGoEgwdbHZXraa2XYRI9+2WT7X7WwOMZ7LsTyPs9xoUQ+V6dknXoXLMzn23+jDKFyjD619HcjLtJv3nJCc+RJ49QrVg1vtnxDS+teinNMc4+f5ZSBUvx26HfGL9uPAA+Xj4E+AQQ4BPAxHsmUsC3AFeir3A+8jyVilTiwIUD1CtZj4grEYy/ezzeXt7cX/9+GpdpTCG/QkmvIgFFks4ztedUpjKVQn6F8Pf2T5NcfXDPBxl+z0ENB/H6H6/TpVYXhjYemtNfW6ZaV27Nlke30KxcM5efS6Tv7I2zdPuxGwE+ASwdtDTF31J+4cokUPrdCOHBZs6EESMgMtJ8Pn3afIb8kQgKIYTIXGJt4A/3/kCQXxDdanWjcZnGSUlcYm1Zn3p9qF+qPgE+ARTwLZBm/St3vMJLbV7C38c/3cm3X2j9Ai+0fgGAzj90pqBfQUoVLMX99c28RT3r9IQ6GcdZMrBktr/ji21eZObumYxtm6auwWXyw+iTnioqNopes3tx5voZ1gxbQ5WiVawOyRKuTAKl340QHuyVV5ITwESRkWa5JIFCCEvExlodgUglsTZwwoYJVChcgXn3zUu3CWPtErWpXaJ2hscp4FvA4XO+dudr3P7N7czqO8vpg8+kp2Lhipx5/gx+3n4uP5ewVoJOYNhPw9gcsZn598+nRYUWVodkGZcNBq+1jgMS+92EAXMT+90k9r3BNMc6gul383/AKLtDJPa7+RsIAd5xVaxC5EfH0o7CDcDx4+6NQwghANAaRo7MfLs8TinVWSl1QCl1SCmVphWUMj61rf9bKdXUbt0zSqm9Sqk9SqlZSqkAZ8T06h2vsvbYWpeM0JmeVpVa8evgX7kv+D6XnyuRJID5w9jfxzJ371ze6/Aefer1sTocS7l0nkDpdyOEZ7p2Dfz8ICadMXela60QwhJvvQXffJNx4RQU5P6Y3Cwn02sppSoATwLBWusopdRcTCus6TmNq07JOuwauYv6pern9FAO61yzs9vOJfKHb3d8yzvr3+HRpo/y/O3PWx2O5fL+tNBCiBSio+Hee81gMP7+KdcFBsL48dbEJYTIx777Dl57DR58EG7eNLWCqV/5Y3qIpOm1bNNjJU6vZS9pei2t9SYgcXotMA/3CyilfDDzK2d91vMMNCjdQEayFLnW6n9XM+KXEXSs3pEvun4hf8tIEihEvpKQAEOHwqpV8O23MG0aVKlipoaoUgWmTJH+gEIIN/vf/+CRR6BDB1MI5e+bs4ymzsp0G631CWAicBw4hRlnYYULYxUiV9h/fj995vahdonazLtvXr6bCiIjLm0OKoTwHFrDU0/B3LnwwQcmGQRJ+oQQFvr7b+jTB+rVg/nzTVPQ/C3b02sppYphagmrAZeBeUqpIVrrH1LsLFNriXzk3I1zdPuxG37efvl2KoiMSE2gEPnEO+/A55/D88+blxBCWCoiArp2hcKFYdkyKCI3Z+Rseq0OwL9a63Na61hgIXB76hNoradorUO11qGlSpVyavBCeJKbcTfpPac3J6+dZMmAJVQtWtXqkDyKJIFC5ANTp8Krr8IDD8B771kdjRAi37t6Fbp1M+9Ll0LFilZH5CmSptdSSvlhBnZZkmqbJcBQ2yiht5E8vdZx4DalVKAyHZ7aY0ZnFyLf0Vrz0E8PsTF8IzPunUHLii2tDsnjSHNQIfK4xYvhscegSxfTB9BLHv0IIawUGwt9+8K+fSYBbNzY6og8htY6TimVOL2WN/BN4vRatvWTMaOud8VMrxUJDLet26yUmg9sB+KAHcAU938LIaz32prXmLVnFu+2f5d+wf2sDscjSRIoRB62di0MGADNm8O8eeArfaGFEFbSGkaMMIPBfPst3HOP1RF5nBxOr/Ua8JpLAxTCw32/63veWvsWDzd5mP+2/q/V4XgsqRMQIo/avRt69oRq1czD9oIFrY5ICJHvvfkmTJ8Or78Ow4ZZHIwQIq/54+gfPLLkEe6udjdfdftKpoK4BakJFCIPOnoUOnWCQoVg+XIoUcLqiIQQ+Z598jdunMXBCCFyu4ofVeTEtRNplvt4+TD/vvkyFUQmpCZQiDzm3DmTAEZFmQRQRgAXQlhu5Up49FHo2FHmAhRCOEXPOj3x8047rUz/+v0pVqCYBRHlLpIECpGHXL9uBtw7fhx++QXq17c6IiFEvrdrlxkIJjjYzAUonZOFEE4wtu1YvFTKVMbP24+J90y0KKLcRZJAIfKImBhzn7V9u5kQvnVrqyMSQuR7ERHmyVThwqZzcuHCVkckhMgjygWVo0vNLkmfvZU3jzR5hLKFyloYVe4hfQKFyAMSEmD4cFixAr75Bnr0sDoiIUS+d+WKmQz+2jVYt07mAhRCOE1UbBTjVo/jpwM/JS3z8/Zj7J1jLYwqd5EkUIhcTmt47jn48Ud4912TDAohhKViYqBfPwgLg19/hUaNrI5ICJFHbIrYxPCfhrP//H4ea/YYcQlxfLvzW4aHDJdawCyQJFCIXO799+GTT+Cpp+C/Mh2OEMJq9nMBTp8OHTpYHZEQIg+4GXeT11a/xsQ/J1KxcEVWPrCSDtU7cOraKVYfXS21gFkkSaAQudi338KLL8LAgfDRRzLgnhDCA7z+Onz3HbzxBjz4oNXRCCHygL9O/MWwxcMIOx/Go00fZeI9Eynsb/oYlwsqx+EnD1scYe4jSaAQudQvvySPuD59OnjJME9CCKt9842ZEH74cBgrT+WFEDkTHRfN62te5/2N71M+qDy/Df6NTjU7WR1WniBJoBC50MaNcP/90KQJLFgAfmmnyRFCCPdavtw0A+3YEb7+WpomCCFyZOvJrQxbPIy95/bycJOH+fCeDykSUMTqsPIMSQKFyGX27oXu3c1Ae8uWQVCQ1REJIfK9nTvNQDANGshcgEKIHImOi+bNP97kvQ3vUbZQWZYNWkaXWl0y31FkiSSBQuQix49Dp04QEGCmgyhVyuqIhBD5Xni4mQuwaFGZC1AIkSPbT23nwcUPsufsHoaHDOejTh9RNKCo1WHlSZIECpFLXLhgEsDr12HtWqha1eqIhBD5XuJcgNevw/r1UKGC1REJIXKhmPgY3l77Nu+se4cyhcqwdNBSutbqanVYeZokgULkAjdumAft//5ragBlyi0hhOViYqBvX9i/H377DRo2tDoiIUQutOPUDob9NIy/z/zN0MZD+aTTJxQrUMzqsPI8SQKF8HCxsXDffbBlixkEpm1bqyMSQuR7WsMjj8CqVWY6iPbtrY5ICJHLxMTH8M66dxi/bjylAkuxZMASetTpYXVY+YYkgUJ4sIQEePhh+PVXmDIFeve2OiIhhABeew1mzDDTQQwdanU0QohcZtfpXQz7aRg7T+9kSKMhTOo8ieIFilsdVr4iSaAQHuzFF8191ltvmTkBhRDC7QoXhmvX0i739YVXX3V/PEKIXCs2PpZ317/LW2vfokSBEizuv5hedXtZHVa+JEmgEB7qww/hgw/g8cfhlVesjkYIkW+llwCCaasucwEKIRy0+8xuHlz8IDtO72BQw0F82vlTSgSWsDqsfEuSQCE8yMyZJuE7dsx8btkSJk2S+ywhhBBC5E6x8bG8t+E93vzjTYoVKMbC+xdyb717rQ4r35MkUAgPMXMmjBgBkZHJy/7+G2bPhsGDrYtLCCGEECIzFT+qyIlrJ9Is9/XyJTYhlgENBvBZl88oGVjSguhEal5WByCEMF5+OWUCCBAVJU1BhRAW0RoWL7Y6CiFELtGzTk/8vP3SLPfx8mH+ffOZ1XeWJIAeRJJAISymNSxZAsePp78+o+VCCOEyBw+aSeDvlSZbQgjHjG07FkXK/iveypu/Hv2LvsF9LYpKZESSQCEsFBYGnTtDr15moL30VK7s3piEEPnYjRumWULDhrBxI3zyCRQqlP62QUFuDU0I4bmi46JZfng5Bf0KJi3zVt481uwxGpRuYGFkIiOSBAphgcuX4ZlnoFEj2LzZ3Gf93/9BYGDK7QIDYfx4KyIUQuQrWsP8+VCvHrz7LgwYAAcOwFNPmdFBtU77unrV6qiFEBY7H3met9e+TZVPqjD8p+GUKVgGHy8z5Iiftx9j7xxrcYQiI5IECuFG8fEwdSrUrm1G/XzoIfjnH3Of9eCDZkL4KlXMaKBVqpjPMiiMEMKl9u+He+6B++6D4sVh3Tr47jsoW9bqyIQQHmr/+f2M/GUklT6uxNjVY2larikrH1jJ3lF7ebTpo3gpL4aHDKdsISlHPJWMDiqEm2zYAE8+Cdu3Q5s28Ntv0LRpym0GD5akTwjhJteuwVtvwccfQ8GC8NlnMHIk+MitgZWUUp2BSYA3MFVrPSHVemVb3xWIBIZprbfb1hUFpgINAA08pLX+033Ri7xMa83qo6v56M+PWPrPUvy9/RnaeChP3/Y0waWCk7Yb23Ysyw8vl1pADyclvRAuduIEvPAC/PgjVKgAs2ZB//4y958QwiJaw5w58NxzcPIkDB8OEyZA6dJWR5bvKaW8gS+AjkAEsEUptURrvc9usy5ALdurJfCV7R1Mcvib1rqfUsoPSNXJQIisi4mPYfae2Xz050fsOrOLUoGleP3O1/lP8/9QumDacqNcUDkOP3nYgkhFVkgSKISL3LwJH34I77xjmoG++iq8+KJ54C6EEJbYuxdGj4bVq01ThPnzoVUrq6MSyVoAh7TWRwCUUrOBXoB9EtgL+F5rrYFNSqmiSqlywA2gLTAMQGsdA8S4MXaRx1yMusjXW7/ms78+49T1UwSXCmZqj6kMbjSYAJ8Aq8MTOSRJoBBOpjX89BM8+yz8+y/06QMTJ0K1alZHJoTIt65ehTfegE8/NaN6fvkljBgB3t5WRyZSqgCE232OILmW71bbVADigHPAt0qpxsA24Cmt9Q3XhSvyooMXDjJp0ySm75pOZGwk99S4h297fcs9Ne5BSTOmPEOSQCGcaN8+M8jL//4H9eub9/btrY5KCJFvaW3aoj//PJw5A488YponlJQJmz1UenfY2sFtfICmwGit9Wal1CTgRSBFxyyl1AhgBEBlmYNI2GitWXtsLR9t+oifD/yMr7cvQxoO4enbnqZhmYZWhydcQJJAIZzg0iV4/XX44gvzkP3TT+E//5HxFYQQFtq9G554AtauhdBQ00ShRQuroxK3FgFUsvtcETjp4DYaiNBab7Ytn49JAlPQWk8BpgCEhoamTjBFPhMbH8vcvXP5aNNHbD+1nZKBJXm17auMaj5KRvbM4+QWVYgciI+HadPglVfg4kXTuuqtt+QhuxDCQpcvw2uvmadSRYuauWYeekiafuYOW4BaSqlqwAlgADAo1TZLgCds/QVbAle01qcAlFLhSqk6WusDQHtS9iUUIsmlqEtM2TaFz/76jBPXTlC3ZF2+7v41DzR6gAK+BawOT7iBJIFCZNO6dabp544d0LatmfcvJMTqqIQQ+VZCAsyYYYYjPncOHnsM3n4bSpSwOjLhIK11nFLqCWA5ZoqIb7TWe5VSI23rJwPLMNNDHMJMETHc7hCjgZm2kUGPpFon8pmKH1XkxLUTaZYX9DUj1N2IvUH7au2Z0mMKnWt2xkvJ9OH5iSSBQmRReLi5x5o9GypVMiOt33efTPkghHCTwoXNHH+peXmZRLBlS1i2DJo1c39sIse01sswiZ79ssl2P2vg8Qz23QmEujI+kXv0rNOTaTumEROfcpDYqLgoHmj0AM/c9gyNyza2KDphNUn5hbiFmTOhalVzb1W5skn26taFxYth3DjYvx/uv18SQCGEG6WXAIJJAKdNg40bJQEUIp/TWtOtVjfiE+JTLPdRPmwfsZ3pvadLApjPSU2gEBmYOdP08YuMNJ/Dw82rRQtT+1e1qqXhCSFEWg89ZHUEQgiLJOgENkVsYv6++czfN5/wq+F4KS8UCo3G18uXR5s+KsmfACQJFCJDL7+cnADaO3NGEkAhhAW0hs2bM99OCJFvJOgENoZvZN7eeSwIW8CJayfw8/bjnhr38NZdb9GiQguaTmnKzbib+Hj5MPbOsZkfVOQLkgQKkUpYGHzzDRw/nv76jJaL3EMp1RmYhBl4YarWekKq9cq2vitm4IVhWuvtduu9ga3ACa11d7cFLvKnK1dM04Svv4a//7Y6GiGExeIT4ll/fD3z9s1jYdhCTl0/hb+3P51rdmZC8AR61O5BkYAiSdsPDxnO19u+ZnjIcJn2QSRxaRIoN1oit7h2DebONd1p/vzTzO8XGJh+TaDMrZu72cqVL4COmPm2tiillmit7YdS7wLUsr1aAl/Z3hM9BYQBhd0StMh/tIatW03iN2uWKYyaNIHJk2HkSKujE0K4WVxCHGuPrWX+vvksDFvImRtnCPAJoGutrvSr14/utbsT5B+U7r5j245l+eHlUgsoUnBZEig3WsLTaW3GT5g2zSSAN25AvXowcSI88ACsXJmyTyCYxHD8eOtiFk7RAjiktT4CYJtrqxcp59PqBXxvG4Vvk1KqqFKqnNb6lFKqItANGA886+bYRV537Rr8+KNJ/nbsMIXOwIFmuofQUDMK1Zgx6Q8OE5T+DaAQIneKjY9lzdE1zN83n0X7F3Eu8hyBvoF0q9WNfsH96FqrK4X8CmV6nHJB5Tj85GE3RCxyE1fWBMqNlvBIp0/D99+bJp8HDkChQuYe66GH4Lbbkkf6HDzYvL/yimkCWrmySQATl4tcqwIQbvc5gpQPnzLapgJwCvgEeAGQO27hPNu3m8Tvxx/h+nVo1MhM9j54MBQpknLbq1etiVEI4XKx8bGs+ncV8/fNZ/H+xVyIukBB34J0r92d+4Lvo3PNzhT0K2h1mCIPcGUSKDdawmPExZlps775Bn75BeLjoXVr+O9/zbQPhTJ4kDZ4sCR9eVB6E3poR7ZRSnUHzmqttyml2t3yJEqNAEYAVJY2xCI916+bCUe//to0/SxQAPr3N7V+LVvK3DNC5EEZTeBeokAJetTpwU/7f+LSzUsE+QXRo04P7gu+j041OlHAt4AF0Yq8zJVJoNxoCcsdPGgSv+++MzWAZcrAc8+ZWr86dayOTlgkAqhk97kicNLBbfoBPZVSXYEAoLBS6get9ZDUJ9FaTwGmAISGhqYu+0R+tmuXSfx++ME06wwOhk8/hSFDoFgxq6MTQrhQRhO4X4i6wMKwhfSq04t+wf24p8Y9BPgEWBSlyA9cmQTKjZawxI0bMG+e6eu3fj14e0PXrvDww+bd19fqCIXFtgC1lFLVgBPAAGBQqm2WAE/YmrG3BK5orU8BL9le2B5QPZ9euSREGpGRZoLRr7820zz4+5tmCI89ZpolSK2fEHnajZgbbD6xmUJ+hYhLiEuxzlt5M733dO4Lvg9/H3+LIhT5jSuTQLnREm6TOH3WtGmmddX161C7NkyYAEOHQrlyVkcoPIXWOk4p9QSwHDNy8Tda671KqZG29ZOBZZhRiw9hRi4eblW8IhcoXDjjgVo2bjSJ34wZZqqHOnXgo49MwVSihPtjFUK4xalrp9gQvoENxzewPnw9O07tIF7Ho1AUK1CMyzcvk6ATkiZwH9JIbnOFe7ksCZQbLeEKM2emHKjlhRcgKso0+dy3zwykd//9prlnmzbycF2kT2u9DFP+2C+bbPezBh7P5BhrgDUuCE/kNuklgInLGzYEPz/o29fU+rVtKwWTEHlMgk4g7FwYG8I3sP74ejaEb+DIpSMAFPApQIsKLXixzYu0rtSaVpVaERUbRfVPq8sE7sJSLp0nUG60hDPNnJlyyoZjx+Bx219Py5YwZYoZU6GwTCgihPAUH3wAw4ZByZJWRyKEcJKbcTfZenIr64+vZ/3x9WwM38ilm5cAKF2wNK0rtebx5o/TulJrmpRrgp+3X4r9iwYUlQncheVcmgQK4SxHj8JTT6U/eXu5crBpk9tDEkLkZ1rDkSPwv//dervnn3dPPEKIbMlotM4KQRWIeDYCgPOR59lwfENSTd+2U9uSBnapW7Iufer1oU3lNrSu1JqaxWuiHKjtlwnchdUkCRQe6eRJWL3avH7/Hf79N+NtT592X1xCiHzs/HlTIK1caZK/o0etjkgIkUPpjdbp6+VL7RK1eWTJI6w/vp4DFw4A4OftR2j5UJ5q+RRtKrfh9kq3UzIwe7X8MoG7sJokgcIjnD8Pa9aY+6vVq2H/frO8aFFo1w6eftoM8nLqVNp9ZWYQIYRLREXBunUm4fvf/2DHDrO8SBG46y5Ty9exo8w3I0Qu9mLrF5m2Y1qKZbEJsaw+upqdp3fSunJrhoUMo03lNoSWD5VpG0SeIUmgsMSVK7B2bXLSt2uXWV6woBk34eGHzT1WSIiZ4gHMQHr2fQLBDAQzfrzbwxdC5EXx8bB9e3LSt2EDREebeWVuvx3efhs6dIBmzcDH7r/PoKCMRwcVQniUq9FX2RSxKak/36aITSlqAb2UF3dUvoMvu31J3ZJ18VJeFkYrhOtIEijc4sYNcz+VmPRt3QoJCWaqrNatzb3VXXdB8+YZz+M3eLB5tx8ddPz45OVCCJElWsPhw8lJ3++/wyUzuAONG8MTT5ik7447zBOqjFy96p54hRBZdvLayaSEb/3x9ew6s4sEnYCX8qJJ2SaMaDaC+qXqM/rX0UTHR+Pv7c/sfrNlsBaR50kSKHIk9ZQNiUlZdLQZrCUx6du0CWJjzcPzli3NPnffDbfdBgFZaFkxeLAkfUKITNxq3r7Dh1P26zt2zKyrVAnuvdckfXffDWXKuDdmIUSOJegE9p/fnyLp+/eyGVQg0DeQVhVbMbbtWNpUbkPLCi0J8k+urd9xeoeM1inyFUkCRbalN2XD8OHw7rvmPuvmTfDygqZN4ZlnzH1V69ZQqJC1cQsh8rhbzdtXurT5uUgRUyi98IJJ/GrVkvn7hMhlouOi2X5qu0n4wtez4fgGLkRdAMxUDW0qt2F0i9G0qdyGkLIh+Hpn0NQIGa1T5D+SBIosu34dwsLSn7IhNhYOHoRRo8z9Vdu2ZnAXIYRwqUuXTOHzzz+33u7tt81gLk2bpuzXJ4TwCLeasmHPqD38Gf5nUtL314m/uBl3E4DaJWrTq04v2lRuQ5vKbRyeqiGRjNYp8hv5H1Bk6MYNM0rn3r0pX5mNih4XB5984o4IhRD5yvXrJsn755/khC/x5wsXHDvGK6+4NkYhRI6kN2WDl/LiZtxNir9XHI3Gx8uHpuWaMip0lJmfr3JrShcsbWHUQuQ+kgQKoqJMzV56yZ7WZhs/PzMK+m23mZE7g4Nh9Ggzn19qMmWDECKFW/XRSz2oys2bpj25faKX+J56jpiKFU0zzr59oXZt83Pt2lCvnuu+ixDCJc5HnmfbyW0U8S9CXEJcinUJOoH6pevTvlp77qh8By0qtKCg3y0GaxJCZEqSwDwmo4FawNxbpVezd+RIcrLn62vuoZo3h2HDoH5986pZM23LqagombJBCOGAW/XR++STlIne8ePJBRKYPny1akHnzuY9MdGrUePWI3YKITzW2Rtn2XZyG9tO2V4ntxF+NTxpfRH/IlyLuUaCTsDHy4dHmjzCV92/sjBiIfIeSQLzkPQGahk2DD7+2DxsP3zYTMsAJqGrVQuaNIEhQ5KTvVq1Mp6iITWZskEIkUZcHJw5Y5oJnDhhXrfyzDOm43CtWtCmTcpEr1YtM4BLVsm8fUJ4jDPXzyQleolJX8TViKT1tYrXonXl1jQr14xm5ZrRpFwTomKjqP5pdW7G3cTXy5fX2r1m4TcQIm/Kd0ngrWrKPPF8Wpu+eefOpX2dP5/y87Zt5v7LXlycmYi9Vy8YONA046xf39xf+fnl/PvJlA1COEFWmktadT6t4cqV5MTOPsmz//nMmeSnTY44exZKlnTuyJwyb58QTnOrgVoino1IsezUtVNsO7WN7ae2JyV+9vvWLlGbOyrfYRK+8s1oUrYJRQLSPugpGlCU4SHDZcoGIVwoXyWB6dWUjRhhfnZFIpPe+R591Ixt0K5dxslc6kTv5s30j+/nB6VKmfunUqXSJoCJ4uNh/nznfz8hhJPcqrmku883d27GSV5UVNp9iheHChWgfHlo1Cj55woVkn8uVy7jWEqVcs53EiKPUEp1BiYB3sBUrfWEVOuVbX1XIBIYprXebrfeG9gKnNBad89pPOkN1OLn7UeHah34+cDPKZp0nrpu+u0qFHVK1qFd1XZJCV9I2RAK+xd2+LwyZYMQrqW0fd+LXC40NFRv3bo1w/VVqybPC2yvRAl4/30zvUFcXPqv7Kz7/feME7j0FCqUMqlL/Uq9PCgo5cPzjL5flSqZj+gphKdSSm3TWodaHUdOZFY23bIWbMoUiIkxBU3iK7PPmW2zY0fmQQcEpJ/QpX4PCMj8WLf6fnno/yCRv7iibLIlcAeBjkAEsAUYqLXeZ7dNV2A0JglsCUzSWre0W/8sEAoUziwJzLRswtTuVZtUjej46PRjRlG3ZF2alW+W1KQzpGxIionYhRDu5Uj5lK9qAo8fT3/5hQtmxMtb8fIy/eh8fEyfucSfU7/s190qAZwxI22CV6BA9r8bmKamMlCLEHlMYnOF1Ly9TYHj52feE18Zffb3N0+a/PxunQTu3m2Su2LFnNdEU/roCeGoFsAhrfURAKXUbKAXsM9um17A99o8xd+klCqqlCqntT6llKoIdAPGA886I6ByQeXoXbc3c/bOAUzSV6tELUaFjkqq4SvkV8gZpxJCuFG+SgIrV06/pqx8efjzz4yTPG9vkwRm1a1q5oYMyfrxMiMDtQiRB0VEpE3qfHyyVyglulVy16BB9o+bEemjJ4SjKgDhdp8jMLV9mW1TATjF/7d35zGT1HUex98fHAiOjKgLGGRgRILH4orHoIkag4hxMcYrJBomMY7ohmRxzSZGV5NRNxsS1mDiQXSiROYPNewfHB5B0WDAAw+OCAOeM3gsSiLoBiIeODNf/6gabWbnmadxuruqq96vpDLd1dVd3+9Tz3zy/Opq+CDwDmCme1gueulFXPnDK3lw94McvuZwrn/j9V6nJy25g/grYvlccEFzZGzS2rXNqaAnnNAMBo85ptkBvm5dc2Tu0EP//r+1VlrfPI/MbdrUnPq5Z0/zrwNAackdd9zfgmnvkbyDGQBK6rP97aHZ95zp/S6T5BXAr6vq5gOuIPmXJDcluemee+6Zqqj1R67n3GedyyE5xBu1SAMxqr8kNm1qLq/ZsKHZEb5hQ/N8XgOlRa9P0pJa6bTIeZ0uuej1SZrWXcDxE8/XA7+acpkXAK9M8jPgMuCMJJ/adwVV9fGq2lhVG49+GDdm2vKiLTzxMU/0Ri3SQIzqdFBY/Fca+BUKkla16NMlPT1T6qsbgZOTnAj8Eng9cM4+y3wOOL+9XvB5wH1VdTfwrnYiyenA26tqZhefHLvuWHb+285ZfZykjo1uEChJktRHVbUryfnANTRfEfHJqrojyXnt61uBq2nuDLqD5isiNndVr6Tl5SBQkiSpJ6rqapqB3uS8rROPC/jXVT7jOuC6OZQnaSBGdU2gJEmSJI2dg0BJkiRJGhEHgZIkSZI0Ig4CJUmSJGlEHARKkiRJ0og4CJQkSZKkEUlzp+FhSHIP8POu61jBUcC9XRcxR/a33Prc34aqOrrrIg6G2dQp+1tufe7PbJq/Pm//WRhyf0PuDfrf36r5NKhBYJ8luamqNnZdx7zY33Iben9a2dC3vf0tt6H3pwMb+vYfcn9D7g2G0Z+ng0qSJEnSiDgIlCRJkqQRcRC4OB/vuoA5s7/lNvT+tLKhb3v7W25D708HNvTtP+T+htwbDKA/rwmUJEmSpBHxSKAkSZIkjYiDwBlK8s9JfpRkR5L/2M/rm5Lc1k43JDm1izr/Xqv1N7HcaUl2Jzl7kfUdrGn6S3J6ku8luSPJ9Yuu8WBM8ft5ZJLPJ7m17W9zF3VqPsynvy5nPvWQ+TReZtNflzObemjQ2VRVTjOYgEcAO4EnAYcBtwL/uM8yzwce2z4+C/hO13XPsr+J5b4KXA2c3XXdM95+jwG+D5zQPj+m67pn3N+7gf9uHx8N/BY4rOvanRa2/c2nnk7mk/k01MlseshyZlPPpqFnk0cCZ+e5wI6qurOqHgQuA141uUBV3VBV/9c+/TawfsE1HoxV+2u9Fbgc+PUii5uBafo7B7iiqn4BUFXL1OM0/RWwLkmAI2iCbNdiy9ScmE8N86mfzKfxMpsaZlM/DTqbHATOznHA/048v6udt5JzgS/OtaLZWrW/JMcBrwG2LrCuWZlm+z0ZeGyS65LcnOQNC6vu4E3T38XA04BfAduBt1XVnsWUpzkzn8ynPjOfxstsMpv6bNDZtKbrAgYk+5m331uvJnkxTZC9cK4VzdY0/X0QeGdV7W52iCyVafpbAzwHeAnwSOBbSb5dVT+ed3EzME1/LwO+B5wBnAR8JcnXq+r+Odem+TOfzKc+M5/Gy2wym/ps0NnkIHB27gKOn3i+nmavwEMkeQZwCXBWVf1mQbXNwjT9bQQua0PsKODlSXZV1VULqfDgTNPfXcC9VfUA8ECSrwGnAssQZNP0txm4sJoT23ck+SnwVOC7iylRc2Q+mU99Zj6Nl9lkNvXZsLOp64sShzLRDKjvBE7kbxePnrLPMicAO4Dnd13vPPrbZ/ltLNfFzdNsv6cB17bLrgVuB57ede0z7O9jwPvax48Hfgkc1XXtTgvb/uZTTyfzyXwa6mQ2/b/lzaYeTUPPJo8EzkhV7UpyPnANzd2EPllVdyQ5r319K/Ae4B+Aj7Z7fHZV1cauan44puxvaU3TX1X9IMmXgNuAPcAlVXV7d1VPb8rt91/AtiTbaU6BeGdV3dtZ0ZoZ88l86jPzabzMJrOpz4aeTWlHrpIkSZKkEfDuoJIkSZI0Ig4CJUmSJGlEHARKkiRJ0og4CJQkSZKkEXEQKEmSJEkj4iBQM5VkfZLPJvlJkp1JPpTksK7rkjRuZpOkPjKb1BUHgZqZNF/gcwVwVVWdDDwZOAK4oNPCJI2a2SSpj8wmdclBoGbpDOCPVXUpQFXtBv4deFOSRyW5KMn2JLcleStAktOS3JDk1iTfTbIuyRuTXLz3Q5N8Icnp7ePfJflAkluSXJvk6Hb+W5Lc2H7O5UnWtvO3Jflwu447k5w98bnvaOu5NcmFSU5KcsvE6ycnuXnuPzVJ82Y2Seojs0mdcRCoWToFeMh//qq6H/gF8GbgROBZVfUM4NPt6Q7/A7ytqk4FzgT+sMo6HgXcUlXPBq4H3tvOv6KqTms/5wfAuRPvORZ4IfAK4EKAJGcBrwae177n/VW1E7gvyTPb920Gtj2cH4CkXjKbJPWR2aTOOAjULAWoFea/CNhaVbsAquq3wFOAu6vqxnbe/XtfP4A9NAEI8CmakAJ4epKvJ9kObKIJ1r2uqqo9VfV94PHtvDOBS6vq9xP1AFwCbE7yCOB1wGem6FtSv5lNkvrIbFJnHARqlu4ANk7OSPJo4Hj2H3Qrhd8uHvq7efgB1rn3/duA86vqn4D/3Oc9f9pnnQda9+XAWTR7v26uqt8cYN2SloPZJKmPzCZ1xkGgZulaYG2SNwC0e4U+QBM0XwbOS7Kmfe1xwA+BJyQ5rZ23rn39Z8AzkxyS5HjguRPrOATYe376OcA32sfrgLuTHEqzR2s1X6Y5537vOfCPA6iqPwLXAB8DLn24PwBJvWQ2Seojs0mdWdN1ARqOqqokrwE+mmQLTfBcDbwb2E1z16vbkvwZ+ERVXZzkdcBHkjyS5rz2M4FvAj8FtgO3A7dMrOYB4JT2wuP7aE49ANgCfAf4efu+davU+qX2HPabkjw4USfAp4HX0gSepCVnNknqI7NJXUrV/o7sSv2U5HdVdcSc1/F24Miq2jLP9UgaDrNJUh+ZTVqJRwKlCUmuBE6iuW2zJPWC2SSpj8ym5eWRQEmSJEkaEW8MI0mSJEkj4iBQkiRJkkbEQaAkSZIkjYiDQEmSJEkaEQeBkiRJkjQiDgIlSZIkaUT+AuO4sw4MHgK9AAAAAElFTkSuQmCC\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 1080x360 with 3 Axes>"
       ]
@@ -396,7 +396,7 @@
    ],
    "source": [
     "for bm in unique_bms:\n",
-    "    flag = \"staic_multimap_find_all_occupancy\" == bm\n",
+    "    flag = \"staic_multimap_retrieve_occupancy\" == bm\n",
     "    \n",
     "    if flag:\n",
     "        tmpdf = perfdf[perfdf[\"Benchmark\"] == bm]\n",
@@ -409,7 +409,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### `find_all` performance by varying matching rates\n",
+    "### `retrieve` performance by varying matching rates\n",
     "<ul>\n",
     "<li>100'000'000 key/value pairs inserted</li>\n",
     "<li>100'000'000 probing keys</li>\n",
@@ -424,7 +424,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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MMcuwiZ7zsqlOzw0wMp59b4tn+VmgbSqGqZRKSydPwhdfwJw5sGWLXbZ5MzRp4tm40qHHHoOtW+Nfv26dnfDL2dWrMGQIfPJJ3PsEBMB777kew5EjR+jcuTNBQUG89dZblCxZMtr60NBQ5syZw5QpU+I9xs6dO3nqqafiXX/t2jV+++03pk2bxvnz55k/fz5NmzZNNLaRI0dy77338uGHH9KuXTsGDx5MyZIl6d27Ny1atGDNmjW0bduWAQMGUK9evVj7f/PNN3To0IEqVapQqFAhNm/eTP36t8rV8uyzz9KxY0ceeOCBRGNxl9atYy/r3RtGjLBDOvLmhZdeilqXNy+MGQN9+8KZM3D33dH3XbXK9XP/+++/FChQgJ49e/Lff//Rrl07Jk2ahLe3Nx9++CFdu3bFz88v0eNk1O+/4l0VmTtsLjtK7aBJSBPOcS7VWgFBWwKVUm7yySe2a9DYsdCoEeTPD1Wq2PGAn30G990HEyeCxDU1i1JKucOOHVCqFDzxhO2/9v77drkmgMkSMwFMbHlylClThm3bthEUFMRnn33GyZMno60fMWIELVu25Lbb4vzdLpazZ88SEBBAlSpVmDx5MgDff/89bdq0IVeuXPTq1YslS5YQ7hirIHH8Typy2Z133smBAwcYOnQoe/bsoV69epw+fZrSpUuzd+9eXn/9dby8vGjbti2//fZbrOPMnz+fPn36ANCnTx/mx2hCq1ChAo0aNeKLL75w6b2ltb177f/bneXPD8dijr5PprCwMNasWcPkyZPZsGEDBw4cYPbs2Rw7doxFixYxevToJB8zI3z/10KvcfTSUZ7d9Cz5Kuaj1sla/Lz/Z7pW7ZpqrYCgLYFKqVQUEgLZs9vWvZw54bbbYPx4aNDA/tD+xhvw8cfg72+7C7Vp4+mIlVKZVkQErF0Ln38OxYrZX51q1ICXX7ZdEmrUsNu9+GLcv0bF0X0rq0msxa58edsFNKZy5ZLW4uOKkiVLUrNmTdasWcPdjuall156idOnTzNt2rQE961ZsyabN2+mbt26FC5cmK1btzJ58mQuX74M2JvxtWvXUr58ecAmCitXrqRdu3YULlyYkJAQihSxU/KfO3fu1nOAQoUK0a9fP/r160eXLl34/fff6dWrF9mzZ6djx4507NiR4sWL880339C2bVQnkrNnz7JixQp27NiBiBAeHo6IxOre+Pzzz3P33XdH67qYlhL6HqtXt62Azv8vX7kSInOzIkVS9u+gdOnS1KtXj4oVKwLQvXt31q1bR4kSJQgKCsLf3x+Aq1ev4u/vT1BQUJzHyUjf/4PPPEiPhT04f+0841uOp/c9venZqyflypSjaenEWyeTQlsClVIpdu4cjBtnbwg++sguGzAAvv3WJoAA9evDwoWwezd8950mgEopNwkKsomdvz+0bGkHKF1z1Nry8oLnn49KAMFewOKaHVTLQyRq4sTYXfpz5bLLU0NwcDDXHN9dSEgIa9eupWrVqgDMmDGD5cuXM3/+/Ftjr+Lz9NNPM3HiRHbv3n1r2dWrVwG4ePEif/zxB4cPH+bgwYMcPHiQjz766FarTOvWrZkzZw5gx6HNnTuXNo7/ga1YseLWcS5dusT+/fspW7YsmzdvvjWBTUREBNu2baNcuXLRYvrqq68YOHAghw4d4uDBgxw5coQKFSrwxx9/RNuuWrVq1KhRg++//z7pH6CbjR1ru/6uXAmhofbvkCF2eWpo2LAhISEhnD59GrCfd40aNejcuTMnTpy49X3lypUr3gQQMtb33/H1jpy8fJJyBcrxUpuXqFmjJnVr1yVsTxjeXqlbU0uTQKVUsp05Y++nypWzY/7uuAM6dPB0VEqpLCeyzAPYLgevvmqnI547144BfOcdz8aXSfXvD9On2/8HiNi/06fb5alh9+7dNG7cmLp169KqVSueeuopateuDcDw4cM5efIkTZs2JSAggJdffjne49SuXZspU6YwcOBAqlWrRvPmzdm9ezf9+vVj8eLF3H777bcmoAHo1q0bS5cu5caNG4wbN46goCDq1q1LvXr18Pf3Z8CAAQBs2rSJwMBA6tSpQ9OmTXnwwQdp2LAhp06d4q677qJWrVrUqVMHHx8fRo0aFS2m+fPn06NHj2jLevXqFWfXz7FjxyY48Y2n9O1rE/7RoyFHDvt34kS7PDV4e3szefJk2rZtS+3atTHGMHTo0CQfJ71//zfDbxIaHkqvXr1ocKYB3/f7njzZospeuOv7F1enQc0IAgMDzcaNGz0dhlJZRufO8OOPdpD4Cy/Y6aFTm4hsMsYEpv6R045em5RKofgKuufODXfeCd9/D3/8AQ0b2v6JPj527J8b6bVJKZVSRy4cofdXvWlSqgnvdng31Y7ryvVJWwKVUi47eRKefhqOHrWvX3/dzrOwYIF7EkCllAKiF3Q/cwZGjYLCheHKFZv8jRhhByCBbY5ycwKolFIptTxoOfWm1WPnqZ00K9Mszc+vSaBS6pYTJ2x//kKF7DwKo0fD+fNw/Dg8/jhUqABvv23LOwDUqRN9aI1SSrlNZGHnXLlg0SJo186+Dg6Gd9+1FyiVpc2aNYuAgIBoj5Ej46wCpDKhjPL9h0eE8+LKF+k4ryMl85Zk47CN3FPznjSPQ2cHVUoBttBrmza2i+f27XaQ9yuv2CQvJMS+HjDAjgGsUsXT0SqlsoSrV2HWLPv8zjvtzFI5c8Lhw5Atm51tytfXszGqdGPw4MEMHjzY02EoD8ko3/+BkAO89edb3B9wPx91+ohcvm4smJwATQKVUgB89RWULQuTJ9tJ8UqVghkzbA+rVq3srJ+VKnk6SqVUlnDmDHz4oX2cPWuXvf561Pps2TwTl1JKJVPQuSD8C/lTuXBltj28Df9C/h6NR7uDKqUAO7avXj145BEoWRK2bLGzvfXvb3+A1wRQKeV2kZPVrVhhC5A1awZr1thlPXvaEg9KKZWBGGN45693qP5RdRbsWADg8QQQtCVQKYWd8GXLFvj1V5v4DR4MRYva+7H1620SqJRSbrN+Pbz1li0o+vzzNuHbvRuqVbPrCxbUgu5KqQznwvULDP52MEv2LKFHtR509O/o6ZBu0SRQqSzu5k2oWxdOn7bzLQwdan+ADw2FMWPgxg2t/aeUcoOICFi2zCZ/v/8O+fNDo0Z2nY9PVAIIWrhdKZXhbD2xlXsW3cN/If/x9h1v83iTx5G4fszyEO1XoVQWdO4cfPCBbenLls0+373bTghz6JAdB1iqlG0h/Pln8Pb2dMRKqUxn1Ci46y747z9bzP3IEVuDRmUcJUrYFtqYjxIlUnTYPHlsoexDhw7RoEEDAgICqFmzJlOnTr21Tf/+/alatSq1atXigQceIDQ0NMFj/vTTTzRq1Ihq1aoREBDAvffey+HDhwEYNGgQFSpUuDWjZLNmUdP1f/PNN9SpU4dq1apRu3Ztvvnmm1vrBg0aRK5cubh06dKtZY8++igiwpkzZwBb8Nx5tspJkybRo0cPAgIC8Pf3J3/+/LfW/fnnn7Ru3ZqqVatSt25dGjZsyNatW28du3z58reOe+LECfr06UOlSpWoUaMGnTp14t9//03eB55c5cvH/f2XL5+iw0Z+/wCHDx/mjjvuoHr16tSoUYODkbMEO4wePTra9vFx1/fvm8OXSm9VovIHlan8QWUKtC6AiOA/yZ81h9YQ9GgQFRZW4POHP6devXrp6/s3xmSaR4MGDYxSKn4hIca8+KIx+fLZglsbNsS9XViYMeHhaRlZ/ICNJh1cX1Ly0GuTUsZegCZNMubAAfv677+NmTvXmJs3PRpWcum1yRgTVb0x9iMFcufObYwx5saNG+b69evGGGMuXbpkypUrZ44ePWqMMeaHH34wERERJiIiwvTp08f873//i/d427dvN/7+/mbXrl23ln377bdm9erVxhhj7r//frNo0aJY+23dutVUqlTJHHD8mz1w4ICpVKmS+eeff27tV7t2bTNnzhxjjDHh4eGmdu3aplSpUub06dPR3ktcVq5caTp37hxtWatWrcwGx/+cP/30U9OuXbtb68qVK2dOnz5tIiIiTJMmTczHH398a92WLVvM77//Hu+53CK+7zmVvn9j7Ofx888/G2Psv4ErV67cWrdhwwYzYMCABD9jY9z7/ZepXMaUvL+k2Xtmr9lzao+pXL2yyVskr/F72c9cD73use/fleuTdgdVKgu4ds3+0D55sq3717MnTJgAtWvHvb22/CmlkqVQIVtTJqbs2W23g0uXbL/z0aOhYUP7UOnXY4+BU0tEkrRuHffygAB47z2XDpHNaRbYGzduEBERcet1p06dbj1v1KgRwcHB8R7njTfe4Pnnn6d69eq3lnXt2jXR80+ePJnnn3+eCo4alBUqVOC5557jrbfeYs6cOQD07duXhQsXMmDAAFatWkXz5s358ccfXXp/iWnatClvvfVWrOUrV67E19eX4cOH31oWEBCQKueMJa7vsXdvGDEi/vWRzpyBu++OvmzVKpdPvWvXLsLCwmjfvj0QvYUwPDycMWPG8MUXX7BkyZIEj+PO7/+hQQ/x+vzXOXzhMEf/OcrZIme5dPwSjwY8Snaf7C6/17i4+/vX7qBKZWLGMdGeCEydCi1bwubN8PXX8SeASimVbCEhUW1BERHw4IN2fN+NG7br5+bNNgFUykVHjhyhTp06lClThmeeeYaSJUtGWx8aGsqcOXPokMDg9Z07d1K/fv0EzzNmzJhb3fL69+9/a78GDRpE2y4wMJCdO3feel25cmVOnz5NSEgI8+fPp0+fPtG2v3btWrTuoAsXLnTpfYPtwti9e/dYy3fs2BErrszo33//pUCBAvTs2ZN69eoxZswYwsPDAfjwww/p2rUrfn5+iR7Hnd9/1SpVKSElGPH1CIa+NpQb1W7gJV4Mb2gTtPT8/WtLoFKZ0NWrNumbPx/++ANy5IBt23QiPaVUGvjsMxg40P76lCsXjBwJU6bAvHmejkwlVWItdglNcpGEFp+ElClThm3btnHs2DG6d+/O3XffTfHixW+tHzFiBC1btuS2225z6Xhnz56lbdu2XL16lWHDhvHUU08B8NZbb3F3jFYrY0ysiTziWtazZ08WLFjA+vXrmTZtWrR1OXPmjDauyxX9+/fnypUrhIeHs3nz5iTtm+oS+x7jWh/5+RQpkqJ/B2FhYaxZs4YtW7ZQtmxZ7r33XmbPnk3Hjh1ZtGgRq5JxbHd8/+WalGPF6hVkO5aNhiMasu37bbdaAdPz968tgUplQNevw5df2i6ef/wR1eJ3/bqd5KVSJXjySZv0RdZZ1gRQKZXqjIF//oFXXoHAQLts0CDYu9c+nzLF5a5/yhKRDiKyV0SCROTZONZXE5G/ROSGiDwVY10BEflKRPaIyG4RaZp2kbtXyZIlqVmzJmsi60YCL730EqdPn+add95JcN+aNWveupkuXLgwW7duZdiwYVy+fDnR/TZu3Bht2ebNm6lRo0a0ZX369GHcuHG0b98er1SoZTlv3jz+++8/+vXrx8iRI+OMa9OmTSk+T3pXunRp6tWrR8WKFfHx8aF79+5s3ryZLVu2EBQUhL+/P+XLl+fq1av4+8dfd8/d33+t22vhs9qHHFVysP/8fvJkS3yimoSk1fevSaBSGcy//9qZ02fMgMOHYcgQ6NoV9u2DypVtsfeqVWH1ajuzZ4yeM0oplTKhoXDlin3+7bd2jNeLL9oxfwC7dtmLkEoyEfEGPgI6AjWAviJSI8Zm54BHgMlxHGIK8JMxphpQF9jtxnDBqUXOpeVJFBwczLVr1wAICQlh7dq1VHX825oxYwbLly9n/vz5iSZeTz/9NBMnTmT37qiP4+rVq4me/6mnnuL111+/NSPlwYMHee2113jyySejbVe2bFkmTpzIiMhxcqnA19eXV199lXXr1kWLG+D222/nxo0bfPLJJ7eWbdiwgdWrV6fa+V1Srlzcs4OWK5cqh2/YsCEhISGcPn0agBUrVlCjRg06d+7MiRMnOHjwIAcPHiRXrlwEBQXFexx3fP87Tu3g1JVTALx373tMeXMKxVoXY+xtYxFSXgYiLb5/TQKVymCGDLGtfD//bMtrff45hIfD4sXQqRP89husXGnH/ymlVKo4fx4WLIB+/aBoUfjwQ7v89tvhk0/g2DH480+7rHr1hLsJqoQ0AoKMMQeMMTeBBUA35w2MMaeMMRuAaDURRCQf0BKY6djupjHmvFujPXEi7rlBT5xIlcPv3r2bxo0bU7duXVq1asVTTz1FbceA9uHDh3Py5EmaNm1KQEAAL7/8crzHqV27NlOmTGHgwIFUq1aN5s2bs3v3bvr163drG+cxYQEBAdy8eZOAgADeeOMN7rrrLqpVq8Zdd93Fm2++GeckHA899BCVKlWKtTzmmLBnn43VuBuvnDlz8uSTTzJ5cvR8X0RYsmQJv/zyC5UqVaJmzZpMmDAh1nhJtzt4MO7vP0YZh+Ty9vZm8uTJtG3bltq1a2OMYejQoUk+Tmp////6/kvjGY1ZF7yOCBOBiDDi4RFse34bwxoMi3bu9Pz9i4nsR5YJBAYGmpjNtkplJsePQ61atpbfF1/AG2/AqVN27N+ECZAZ//mLyCZjTKCn40gJvTapDCsiwk7o8vPPEBZmE8C77rJdPuMagxXf7KAFC2a6gu/uuDaJyN1AB2PMg47X9wGNjTGj4th2AnDZGDPZ8ToAmA7swrYCbgIeNcZcibHfMGAYQNmyZRscOnQoNd+CUplWWEQYz//2PG/9+RbNyjRj0T2LKJk3fXa3cuX65NaWQBf6tYuIvO9Yv01E6jute1REdojIThF5zJ1xKpVRnDtnx/35+8NDD9n7rQUL7P2VY8IspZRKWKFCcXfhKlgQNm2yXTsjf2338oLSpW33g7Vr7S9RM2fGnQCCvUjF1TKQyRJAN4qrCdXVX+t9gPrAx8aYesAVINa9lzFmujEm0BgTWLRo0eRHqlQWcvnmZTrM7cBbf77Fw4EPs/L+lek2AXSV22YHderX3h4IBjaIyFJjzC6nzToClR2PxsDHQGMRqQUMxXaLuAn8JCI/GGP2uStepdIzY+w9WmionfmzUiXb+te6tV3Xpw/06OHpKJVSGUJkGYdIGzbArFnw8cd2chcvL9ufPCzMlneIMduhcqtgoIzT69LAsSTsG2yMWe94/RVxJIGZ2axZs5gyZUq0Zc2bN+ejjz7yUEQqLbnz+8/lm4uiuYvyaddPGVxvcIqPlx64s0TErX7tACIS2a/dOQnsBnzuqGy/zjGrlR9QHVhnjLnq2Hc10AN4043xKpXubN9ux/35+MCnn9r5F777zv5IP326nRn0++/tfAyzZnk6WqVUunb+PETWt3rsMXjiCShb1o7l++wzu3zWLOjc2Xb7VJ6wAagsIhWAo0AfoF/Cu1jGmBMickREqhpj9gJtiX7PlekNHjyYwYMzxw26Sjp3fP/zts2jednmlC9Qni96fhGrPERG5s7uoKWAI06vgx3LXNlmB9BSRAqLSC6gE9F/GVMq0zLGzuzZuTPUqWMLuxcqFPXDfZcusHu3/aH+2jV4/nk7EUzu3J6NOz1KYZf0xx3d0XeIyHwRyeFYPkFEjorIVsejU1q+J6USdeEC/PUXHD1qX69dC6VK2e6eLVrYZTNmwP799vmQIXDmjH0+aJAmgB5kjAkDRgHLsTN7fmmM2Skiw0VkOICIlBCRYOAJ4AURCXZMCgMwGpgnItuAAOC1NH8TSmUCoeGhjF42mgFLBjD5TzsxS2ZKAMG9LYGu9GuPcxtjzG4ReQP4BbgM/AOExXmS6AOckx+tUunEu+/a4TdFi9rSWyNG2CTQWYEC8PDDHgkvw0hhl/RS2CnYaxhjronIl9hf5Gc79ns3cjIGpdwmsUlWIiJs182zZ+HVV21php07o5K/Dz6AUaNsAtiuHdSsaR9dusDFi3ZfgDwpq2mlUpcxZhmwLMayqU7PT2C7ica171YgQ0+kpZSnnbh8gt6LerPm8Boeb/I4b7bPnB0R3ZkEutKvPd5tjDEzcUxzLCKvObaNxRgzHTsbFoGBgZlnqlOVZVy/bss81KwJzZvDPfdArlxw//2QM6eno8vQUtIlHez1MaeIhAK5cH1cjlKpI3LsnjGwfr1N8HbtgnfegTJlYMAAeP11yJ7dlmmoWtWWbIhM9ho2tMcpXz6qu2ekVChorZRSmc2u07toP6c9IddCmNdzHv1qu9QbO0NyZxLoSr/2pcAox81ZY+CCMeY4gIgUM8acEpGyQE+gqRtjVcptwsLA2zt22ayQEDsPw5QptszDqFE2CSxTBoYP90ysmUxc3c0bu7BNKWPMRhGZDBwGrgE/G2N+dtpulIgMBDYCTxpj4miuUSoJrlyBoCDYty/q4axjRzumL0cO+7p1azuJC9iWPOeWvcQULBh3Hb+CBZMbvVJKZQpl85elgV8DXmnzCnVL1PV0OG7ltp8CXenXju3ucAAIAj4BRjgd4msR2QV8B4zUmyyV0axda5O6HDmgcGF49lm4edOue+01m+yNHQv169sxfe+/79l4M6Fkd0kXkYLYVsIKQEkgt4gMcKz/GKiEHW9zHHg7zpOLDBORjSKy8fTp08kIX2UI8ZVbiNmHG+wg3h07YMkSePNN+4jUrJmd+emee+xA3x9/jFonAkuX2sTw8mW7bM4c6NUrapuktOxpGQeVSupNq4e8JLEe9abVS9Fx8zi6KB86dIgGDRoQEBBAzZo1mTr1Vq9Y+vfvT9WqValVqxYPPPAAoaGh8R5vwoQJsQpuly9fnjOOsbAiwpNPPnlr3eTJk5kwYUKsfQcNGkSFChVuFf5+3/E/7gsXLjBw4EAqVapEpUqVGDhwIBcuXADg4MGD5MyZk4CAAGrUqMHAgQNvxbpq1SpEhJkzZ94695YtWxCRWPGmRy0+bRHn99/i0xYpOm4epy7qhw8f5o477qB69erUqFGDgzEK0Y8ePTra9nFx5ftvdm8zLt+8TJ5seWh5tCVLpi6JtW9m+/7d2h/EGLPMGFPFGFPJGDPRsWxqZN92Y410rK9tjNnotO9txpgaxpi6xpjf3BmnUqlt1y7o3h1Gj7bdPTdvtvM0PPSQXZ8jhy3p8M8/9l6vdeu4f5hXKZKSLuntgP+MMaeNMaHAYqAZgDHmpDEm3BgTgf3xqlFcJ9daXFmEc5dNY2yit3OnXe5cWuH++20/79q1oWdPeOYZmDs3av24cbBwob1YXLxo6/E5u+02WyDU2ztt3pdSLmhauinZvLNFW5bNOxvNSjdLleP7+fnx559/snXrVtavX8+kSZM4dsxexvv378+ePXvYvn07165dY8aMGck+T/bs2Vm8ePGtpCAhb731Flu3bmXr1q088sgjAAwZMoSKFSuyf/9+9u/fT4UKFXjwwQdv7VOpUiW2bt3K9u3bCQ4O5ssvv7y1rnbt2ixcuPDW6wULFlC3bsZogWpYsiGPNX4M86K59Xis8WM0KhXn/xaTZeDAgYwZM4bdu3fz999/U6xYsVvrNm7cyPnz51N0/OCLwYiP8NfPfzF//fxEt89M3787u4MqlWW9/z48+qit37d6NbzxBvz+O2zcaFsBn3jC0xFmCcnuki4ih4EmjtmJr2GnWt8IICJ+kd3WsaVrdrj/rah05eJFO7PmgQNRy6ZMsbM6HT4cNZXviBF2ts3s2eGOO2yBz8qVox7580ftf/fdafoWlHLFYz89xtYTW+NdfyPsBmER0eftC4sIY8uJLbSe3TrOfQJKBPBeh/dcOn+2bFEJ5o0bN4iIiLj1ulOnqImZGzVqRHBwnFNHuMTHx4dhw4bx7rvvMnHixCTtGxQUxKZNm6LdyI8fPx5/f3/279+Pt9MPN97e3jRq1IijkZM3YSc1vHjxIidPnqRYsWL89NNP0d6bp8X1Pfau2ZsRDUcwqvEoqn1YjXXB68juk50bYTfYeHwjk9vbVqwzV89w95fRr22rBq1y+dy7du0iLCyM9u3bA9FbCMPDwxkzZgxffPEFS5YsSfA4xy8d54uNX/Cmiep9cfbiWSpOqUhEzgiMl6H/oP4c/Okg3OZyeEDG/v51ZLhSbvDvv7YL6B132Fa+TZvs5H01akS/b1Tuk5Iu6Y5iy18Bm4Ht2GvldMc+b4rIdscU7G2Ax9PoLankSkqXTbCzbh47BmvWwKVLdtm330KTJnba3vz5bT9u58StWDFbfuHFF2HePLvszBmbAAL07w/jx0PfvnYsn3MCGJ/IsXsxHzp2T6UT2X2yUzx3ccTRs14QSuQuEat1MCWOHDlCnTp1KFOmDM888wwlS5aMtj40NJQ5c+bQoUOHFJ1n5MiRzJs371Y3vviMGTPmVnfA7du3s2vXLgICAmLd7AcEBLAzsi6nw/Xr11m/fn2sWO+++24WLVrEn3/+Sf369ckeed1I5/zy+FE8d3GOXLRD649cPELx3MXJn8OF65sL/v33XwoUKEDPnj2pV68eY8aMITw8HIAPP/yQrl274ufnl8hRoEiuIogIn/f4nB0jdrBjxA4K5CjApRuXKJCjADl9cvK/l/6X5b5/bQlUyg1q1LD3exER8PbbtpzD9esweTJUqeLp6LIOF6ZaN8DIePZ9EXgxjuX3pXKYyt0iu2w6u3HD9ss+f97WXNm4EV5+OaqF7/p1u93q1bYop6+vnYClZ0/bolexov1b31Fasm9f+4jUv3/KkzUdo6c8zJUWu+OXjlPx/YpcD7tODp8cbHpoEyXylEi1GMqUKcO2bds4duwY3bt35+6776Z48eK31o8YMYKWLVty223xN+HEV9/NeXm+fPkYOHAg77//PjkTmJr7rbfe4m6nH4AOHDgQ5/GNMbeW79+/n4CAAPbt28fdd99NnTp1om3bu3dv7r33Xvbs2UPfvn35888/4z1/Wkuo5S6Xby42DN1Azf/V5N0736XdnHbsHLHz1vdfJFeRJLX8xRQWFsaaNWvYsmULZcuW5d5772X27Nl07NiRRYsWsWqVa8fO5pON2yvcztSNU/mmzzd2oYFCuQqx4+EdlB5fOkt+/9oSqFQqOX7cdvO8eBEeewzCw+38DoMGwZ49dgzgffdpHWal0syFC/YBcOQIDB1qSyiUKxdVf2WZ4zeCiAj47z/bTXPECPjoI/jpJ4j8n3WnTvDrr3ac39NP21bAeimb/EKpzMAvrx+DAwbjJV4MDhicqgmgs5IlS1KzZk3WrFlza9lLL73E6dOneeeddxLct3DhwoTEqLl56dIlChQoEG3ZY489xsyZM7ly5YrLcdWsWZMtW7ZE66oaERHBP//8Q/Xq1YGoMWFBQUGsW7eOpUuXRjtGiRIl8PX15ZdffqFt27Yunzs98Mvrx/1176ft5225v+79qfr9ly5dmnr16lGxYkV8fHzo3r07mzdvZsuWLQQFBeHv70/58uW5evUq/v7+8R6ncOHCVMlVhXXB6xi6dCih4aFcuHiBOX3nUCBngVvbZbXvX5NApVLo/Hmb7FWqZGszr1lj529YsQJWrbL3m7162Rne333X09EqlY4lp9vmtWv2+ZUr8MILtjWuUSMoUsS28DnNJsjSpbaFr2VL21QPtgsn2H22b4dvvrHN9yNGwJ132mMkRLtsKsW4luNoUbYF41qNS9XjBgcHc83x33hISAhr166latWqAMyYMYPly5czf/58vBKZHbdly5YsXbqUS47u3YsXL6Zu3brRuvABFCpUiN69e0ebrTEx/v7+1KtXj1dfffXWsldffZX69evHSkz8/PyYNGkSr7/+eqzjvPzyy7zxxhuxYsoInm7+NI1KNeLp5k+n6nEbNmxISEgIkTNsr1ixgho1atC5c2dOnDjBwYMHOXjwILly5SIoKCje47Rs2ZLF3yzm6pWrzNo6i6FvDCV32dx0rNIx2nZZ7fvX7qBKJVNEhK3Z/NprtrdZ377wyis2GQQ72/t333k0RKUylri6bRpjyx8YA//7n62lt39/VLfNIUPgww8hWzbb37pUKfsf4T332L+3327rs5QpAydPRj/2Sy9B2bIpi1m7bCqFX14/Vg9anerH3b17N08++SQigjGGp556itq1awMwfPhwypUrR9Omtox0z549GR/5404MderUYdSoUbRo0QIRoVixYvHOJvrkk0/y4YcfJinOmTNnMnr0aPz9/THG0LRp03gTie7duzNhwoRoLZoAzZqlzoyqnuCX14+fBvyU6sf19vZm8uTJtG3bFmMMDRo0YOjQoUk+zja2cajqIbyme4GBBXkXMPXjqXF248xK37+YmP/DzcACAwPNxo0bE99QqRQwJqqcw5132vvT11+3SZ9KfSKyyRgT6Ok4UkKvTS4Ssf8xRSZ5+/fbSVQWL7b/4ZUubZveI8fjVaoEbdpA5852/7Aw8Injt02R2MllQsuVcoFem5RK/15b8xpjV4yldfnWfHXPVyzcuZAFOxawetDqeMeKZgauXJ+0JVApFxljazy//LKdKLBcOfs6Vy5PR6ZUGipUyLbYxVSwYOKtYlu32uKYzkle3rzwyy9R2zz3nJ1ps1Il222zeXObBILtrlmgQPxFNeNKACNji2sf7bKplFKZWvMyzRnVcBTv3PkOvt6+jGg4gmENhmXqBNBVmgQq5YKVK22Psr//hurV4dQpmwRqAqiynLi6bIJNso4dgx07oid5587ZGTbB9p1etMg2n5ctaxO9GLOkcfGiTQydPfyw/ZvcpE27bCqVJcyaNYspU6ZEW9a8eXM++ugjD0Wk0lLk938z/CaXblyicK7CcX7/Pl6a/oAmgUolKDwcuna1EwiWLg0zZ8LAgfE3OCiVqV2+bP8uXmzH4x04YBO9b76xy99+2w6UBVt+IbLbZmioLbHw6qv2Ub68HcMXl5gJoFJKuWjw4MEMHjzY02EoDxk8eDDlW5fn7kV3IwgrRq+gUM54JhZTmgSqrO3aNTs3xOef28kFO3a0jRW5ckGJEuDtDbVq2bklRoyImlVeqXQvOd02w8Lg8GFbKiEyyTtwAN580zZ9R06k0KtX1LEqVYo63tCh0K2bXebnZ1v8nCVWJFO7bSqllEqmjzd8zCM/PUKVwlVY2mepJoCJ0CRQZWl9+9pWvZUr7T3z22/b3mk3b8Lvv0OTJvDGG56OUqlkSGimzb//jp7ojRhha959+62tfxfJx8e22p0+bZPAzp3h8cdh0yaoUCF2clatmn0kl3bbVEoplQyP/vgo7//9Pl2qdGFez3nky57P0yGle5oEqixr2zZ7L3vgAFy6ZHuxffCBbR1s0sTe+yqVbrjasnf9Ohw6ZJ9/9JFN9jp3trNoRs4C2Lhx1PbFikGXLjYJbNLE9nmuWNEmeaVL2+bwSJUr27/166fue1NKKaVSoHrR6jzd7Glea/sa3l4Zr9aiJ2gSqLKsnTuhWTN7j1u3LgQHQ58+tiVw717bHVSpNOFKghfZshcRASdORLXkDRxoi6B37Wrr4Dn/wx01yo7NK1vWJoGR3TG//dYmeRUqQJ48UduXKgUPPJBwrNplUymlVDqw89RODl04RKfKnRgeONzT4WQ4mgSqLGfbNvjsM/j1V7hwwd5Xv/eebeSoUweGDYOqVT0dpcpSnLtuGgNnz9okr1EjOx6vdGm7LiLCJlsXL0bff9UqmwQWK2ZrmFSoAPfdB8ePQ/HiUUlb/vz2b9euyY9Vu2wqpZTysO///Z5+X/ejeJ7itK/YHl9vX0+HlOFoEqiyhNOnYd48m/xt3WonKuzc2d57P/igrU+dP7/tPffdd3YbpTyidm3bTB3pmWegRw/73MvLvs6f33bZrFjRjsGLnJFTBMaNs8/vu0+bs5VSSmUqxhje+vMtnv31Wer51ePbPt9qAphMmgSqTOvGDfvIlw/+/NPOZxEYaMf99ekDRYrYBpVnnrGtgNeuwR132BbC4sU9Hb3Ksh56yNYmqVABune3zdX58kW15j3/vGvH0W6bSimlMpHwiHAGfzuYOdvmcG/Ne/m026fk8tWCzcmlSaDKVIyBDRtsi9+CBTBypO0d16mTrWFds2b07fPlg48/hv/9z/a089axxMrTRo+O/jpfMmc4026bSimlMhFvL2/yZc/HK21eYextY5G4fuhULtMkUGUIV67A/Pm2l1yVKtC/f+x74/feg+nTYfduOxdG9+7Qrp1d5+sbOwF0JqIJoErntGVPKaVUJvbPiX84deVUrOVHLx2lgV8DahevzQcdP9DkL5VoEqjSvaNHoXVrqFEDWra0Nf1efx2WLbMzenboYLf74w87yeL06dC7d9QcGEqle64keNqyp5RSKhN7YOkDXLpxiXIFyt1a9u/Zfzl84TCtyrVi1aBVmgCmIk0CVbo3dizccw+89prt7vnHH3DwoC1rFhYG+/aBvz988QVky+bpaJVKBk3wlFJKZXFPNHmCqZum8vOAnzEYxq8cz68HfqVs/rJ8ec+Xng4v09EkUKVrISHwzTewaxds2QI9e9oEMHduO3fGL7/YCRJBE0CllFJKqYyqT60+vPz7y/zw7w/M3DqTb/Z8Q3bv7GwYuoFiuYt5OrxMR5NAlaoiIuy4vWzZ7Ni9pLTa37xpZ+bcvNkmfFu22FJpefLA9et2xs46deCll6BVK6heHW6/3c6ar5RSSimlMi5vL2/GtxzPpLWTyOGTg4YlG9K5cmdNAN1Ek0CValautDX3vLxs0la0KMyda8fyOTPGJneRyV7FijBkiE0gu3a1LXz+/tCwoS3cvn27TfxmzYJvv7X7P/qoHfenCaBSSimlVMa349QO2lVsx8u/v8xjjR9j3MpxPNrkUU+HlWlpEqhSxbFjNimbMwfuvNMmap9+aidtWb3aljwDO7bvl19s6TOwM3IOGmSTwBw5YN0624LoPPPnpUtw111Qq5adGGb9evDxgR9/TPO3qZRSSimlUtnyoOXcs+geOvh3YHzL8QxYMoCXWr9EgRwFPB1apqVJoEoVc+bY8XqFC9u6e9u321a+o0dt4nbkiN2udGno2xfq17cTu9SqZZO/SIGBsY+dN69tZVyzxtb6u/tu7QaqlFJKKZUZTNs4jZHLRlKrWC3eufMd/PL4seHYBh5trK2A7qRJYBYTEQFTpsBHH9nWu6ZN4ZVXoFkz1/Y/eRK2bYOgIDsr5759NtFr1QoqVbJF12fPtuUZ6tWzXUEbNoza/913kxe3iE0mW7ZM3v5KKaWUUir9iDARPPPLM0z+azKdKndiQa8F5M2eF4D3Orzn2eCyAG1LyWJefBEWLrSPkydtV8xu3WDrVrs+PNzOvvnLL7ZF74kn7Di9kBC7/sMP4Y47YMQImDoVDh2yXT0bNoQlS+zxg4Pt9j/8YGe+f/xxD71ZleWJSAcR2SsiQSLybBzrRUTed6zfJiL1ndY9LiI7RWSHiMwXkRyO5YVE5BcR2ef4q9XalVJKqSQ6f/08X+3+ihGBI/i2z7e3EkCVNrQlMAu5fNkmcdu22fp669fbZTVr2uTt229h5kx46KGofXLmtJO0nD1r61YPHAht20LlylCyZNTsn2Fh8MknMHo0jBwJf/8Nb75pxwTWru2Z96uyNhHxBj4C2gPBwAYRWWqM2eW0WUegsuPRGPgYaCwipYBHgBrGmGsi8iXQB5gNPAv8ZoyZ5EgsnwWeSaO3pZRSSmVoZ66eIX/2/BTKWYiNQzdSKGchLQLvAZoEplP798Prr9txcMWLw8MPQ58+rpdcuH4dDhywx9m/H9q1s2Po8ua13TZDQ6O2zZ4dSpWyz2+/HaZPt0mev79N9JzH3lWubB8x+fjAsmW29XDSJFsiYtgwuP/+5H8GSqVQIyDIGHMAQEQWAN0A5ySwG/C5McYA60SkgIj4Odb5ADlFJBTIBRxz2qe14/lnwCo0CVRKKaUStev0Ljp/0ZkulbvwQacPKJyrsKdDyrI0CUyHDh+GFi1g+HBYvNgmcc89Z5c/43SreeGCXRcUZBO2+vVtV87bbrPj9IyJ2vaDD2wr3vnz9rh16thksHJl+PprOysn2OP4+ycv7pw5bffRJ55I7jtXKlWVAo44vQ7GtvYltk0pY8xGEZkMHAauAT8bY352bFPcGHMcwBhzXES0gJFSSimViBX/raDnwp7k8MnBwLoDPR1OlqdJYDr0zjs2YRs/3o7bO3/edq287z5bh69zZ5v4nT0btc+TT9oksFgx25pXqZJ9+Pvbv4UL21bE4cNtN9CHH7YJ4LffwsSJdvyeUplMXO3mxpVtHOP8ugEVgPPAIhEZYIyZ6/LJRYYBwwDKli3r6m5KKaVUpjN762yGfjeUqoWr8kO/HyhXoJynQ8ryNAlMoU2bYN48uHbNJmedOiWtdIExcOoUXLwY1c1y/nxbJ+9//7Nj9sDW4CtXzrb0lSwJAQGxEz2AXLngs8/iP9/rr9vumrffbhPMxo3t+Zxn8FQqkwgGyji9Lk1Ul87EtmkH/GeMOQ0gIouBZsBc4KSI+DlaAf2AU3Gd3BgzHZgOEBgYGDP5VEoppbKEk5dPMvrH0bQp34ZF9ywif478ng5JoUlgnIyxM1zmyWMnQ4nPlCm2hW74cChbFp5/3iZUc+ZETwSNsUlefse/+Q8/tGP99u2zLXqXLtlSDX/+adf7+toxdQ88ENU9s3x520W0ZEnbRTS5vL1h7Fj7iIjQWnsqU9sAVBaRCsBR7MQu/WJssxQY5Rgv2Bi44EjuDgNNRCQXtjtoW2Cj0z73A5Mcf791+ztRSimlMpiwiDB8vHwonqc4vw/6nVrFauHr7evpsJSDJoEx/PwzPPKILXFw/bothzB1qu1O6ezECZgwAf75xyaAYJPBRo1sa9v161F19IKCbAJ4+LDdbsUKO0Nn5co2sfP3jz6D5sKFcM89cO+9tn5fSIiN6c47wc+PVKMJoMrMjDFhIjIKWA54A58aY3aKyHDH+qnAMqATEARcBQY71q0Xka+AzUAYsAVHqx42+ftSRIZgxwzek3bvSimllEr/zlw9Q7cF3ehXqx8jG42knl89T4ekYshSSeDOnfDCC7BypU3qhg2Dp56yrWMAu3dD//4wd65N/q5csS1m99xjEzfn40yebMffPfmknYXz8GFbfH3QIJg1C3btsq13lSvbRK5KFdsiKGInYklols/mzW0rY//+cOOGjaNXL5uMKqVcZ4xZhk30nJdNdXpugJHx7Psi8GIcy89iWwaVUkopFcO/Z/+l07xOBF8M5tHGj3o6HBWPLJMEHjxox8E9/7wtgXDkiE3gDh+Gjz6y20ybZoug16gBX3wB//1nu3H++afthvn331C6NHz3HcyebRM5EVssvUkTOy7w/Hl7nk2bbJfOuLhS5uGee6BnTzvLZ4ECdoygUkoppZRS6dXvh36nx8IeeIkXK+9fSdMyTT0dkopHlkkCP/zQ1qx79FE7q+bhw9C+Pbz0Evz7r23FK1rUFjr//XcYMMDu5+dnSx9Uqwbh4XbZgw/C3XfbcXwffmhr8IFNGj/5BH78Mf4EMCm8vaO6miqllFJKKZVeBV8M5o45d1C+QHmW9V9GxYIVPR2SSkCWSQK3b4fHHrPPd+6EHj3sc29vm7zVrm0Lpv/wA7z7blR3zuvX7cybc+fa1kCAIkXs4+uvbYtd7dq2pW7FCnjtNTtzp1JKKaVUUolIB2AKdizzDGPMpBjrqwGzgPrAWGPM5BjrvbETWR01xnRJm6hVVrFo5yImrpkY57o327/J7O6zuaPSHRTKWSiNI1NJlWWSwMqVbX28jh1tPb0NG6B4cZuwrVwJZcrYFsKGDeHVV22r4Zo1MG6cHecXmQA6a9nSdjNdvtx2BZ02zbYmKqWUUkollSOB+whojy1hs0FElhpjdjltdg54BOgez2EeBXYDOpBEpbpKhSpxIOQAP9/3Mzl8chAaHsqEVRP49cCv+Bfy19a/DCTLzA85apQd+zd/PuTIYRPARx+1SWEZR5WwwoXhjz8gNNTW5Rs/HoYMsZPAxCdnTujeHfr21QRQKaWUUinSCAgyxhwwxtwEFgDdnDcwxpwyxmwAQmPuLCKlgc7AjLQIVmU99f3q06ZCGzYc3UC5/OV4+tenWRa0jLol6moCmMFkmZbAatXgm2/g6adh4EDIndvW4XvttejblSwJ77/vkRCVUkoplbWVAo44vQ7G1jB11XvA00De+DYQkWHAMICyOvGASoYXW71Ix3kd+d/G/3Eg5AB5fPOw4O4Fng5LJVGWSQLBll5Yu9aWXfD11Tp5SimllEpX4po/3Li0o0gX4JQxZpOItI5vO2PMdBx1TwMDA106tlLOiucuzsUbF7l04xJ9avbBx8tHWwEzoCyZBmXPrgmgUkoppdKdYKCM0+vSwDEX920OdBWRg9hupLeLyNzUDU8p8MvrR6/qvciTLQ8/7PuBsS3HejoklQyaCimllFJKpQ8bgMoiUkFEsgF9gKWu7GiMec4YU9oYU96x3wpjzAD3haqymqV7l/JfyH94iRdze86lWZlmdKvaTVsBM6gs1R1UKaWUUiq9MsaEicgoYDm2RMSnxpidIjLcsX6qiJTAloDIB0SIyGNADWPMRU/FrTK/qRunMnLZSPrV7secHnMA+KLXF/h4aSqRUek3p5RSSimVThhjlgHLYiyb6vT8BLabaELHWAWsckN4KosxxjBh1QRe/v1lulTpwrQu026ty+Wby4ORqZTSJFAppZRSSikVTVhEGCN/GMn0zdN5IOABpt01TVv+MhEdE6iUUkoppZSK5kbYDTaf2MzY28Yyo+sMTQAzGf02lVJKKaWUUgCEXAvB19uXPNnysGbwGnL45PB0SMoN3NoSKCIdRGSviASJyLNxrBcRed+xfpuI1Hda97iI7BSRHSIyX0T0X6BSSimllFJucuTCEVrMakH/xf0BNAHMxNyWBIqIN/AR0BGoAfQVkRoxNusIVHY8hgEfO/YtBTwCBBpjamFnyOrjrliVUkoppZTKynad3kWzT5sRfDGYxxo/5ulwlJu5syWwERBkjDlgjLmJLVzaLcY23YDPjbUOKCAifo51PkBOEfEBcuF6sVSllFJKKaWUi9YeXkuLT1sQFhHG74N+p02FNp4OSbmZO5PAUsARp9fBjmWJbmOMOQpMBg4Dx4ELxpif3RirUkoppZRSWU5oeCj3LbmPormL8teQv6hboq6nQ1JpwJ0Tw0gcy4wr24hIQWwrYQXgPLBIRAYYY+bGOonIMGxXUsqWLZuigJVSSimllMoqjDH4evuytO9SiucuTtHcRT0dkkojLiWBIhII3AaUBK4BO4BfjTHnEtgtGCjj9Lo0sbt0xrdNO+A/Y8xpx/kXA82AWEmgMWY6MB0gMDAwZpKplMoEknkNUkopj9HrlkrPjDG8+vurXL55mTfav0GtYrU8HZJKYwl2BxWRQSKyGXgOyAnsBU4BLYBfROQzEYmv+W0DUFlEKohINuzELktjbLMUGOiYJbQJttvncWw30CYikktEBGgL7E7me1RKZVCzZ88GqE7yrkFKKZXmUnjvpJTbhUeEM3LZSMavGs+JKyeIMBGeDkl5QGItgbmB5saYa3GtFJEA7Myeh2OuM8aEicgoYDl2ds9PjTE7RWS4Y/1UYBnQCQgCrgKDHevWi8hXwGYgDNiCo7VPKZV1XLlyBWCPMaZXXOsTugYppZSHJPveSSl3ux52nf6L+7N492Keaf4Mr7d9HdveorIaMSbz9KAMDAw0Gzdu9HQYSqlUJCKbjDGBno4jJfTapFTmo9cmlV6Fhocyc8tMroddj7bcGMOcbXPYcmIL7935Ho82edRDESp3c+X65NLsoI6uCwWcXhcUkU9TGJ9SSrlEr0FKqYxGr1vKU8Iiwnj+t+dZfWg1B88fvPX47J/P8PX2ZX6v+ZoAKpdLRNQxxpyPfGGMCQHquSUipZSKLVnXIBHpICJ7RSRIRJ6NY72IyPuO9dtEpL5jeVUR2er0uCgijznWTRCRo07rOqXau1RKZSZ676Q8IqdvTp5o+gR5s+XlvQ7vMTxwOE1KN+H01dP8r9P/6FOrj6dDVOmAqyUivESkoOMChogUSsK+SimVUkm+BomIN/AR0B47E/EGEVlqjNnltFlH7NicykBj4GOgsTFmLxDgdJyjwBKn/d41xkxOjTemlMq09N5JeczoRqPx/8CfRTsXMfyH4RhjaFq6KQ1KNvB0aCqdcPVi9Dbwp2OyFgP0Bia6LSqllIouOdegRkCQMeYAgIgswNYfdU4CuwGfGzs4ep2IFBARP8csxZHaAvuNMYdS6b0opbIGvXdSHpM/R366VulKv8X9KJOvDFdCr/Bym5c9HZZKR1xKAo0xn4vIRuB2bIH3njF+TVdKKbdJ5jWoFHDE6XUwtrUvsW1KAc5JYB9gfoz9RonIQGAj8GTkL/1KKRVJ752UJ/0U9BNf7PgCYwytyrXi7LWz2gqoonF1TCBAIeCKMeYD4LSIVHBTTEopFZekXoPimvM65nTICW7jqHHaFVjktP5joBK2u+hx7K/9sU8uMkxENorIxtOnTycSqlIqk9J7J+URaw6toXqR6oxpNobZ/8zmxVYvejoklc641BIoIi8CgUBVYBbgC8wFmrsvNKWUspJ5DQoGyji9Lg0cS+I2HYHNxpiTkQucn4vIJ8D3cZ3cGDMdR33TwMDAzFOLRynlEr13Up5w8cZF8mXPx6u3v8rztz1PhImgWpFq2gqoYnG1JbAH9tfwKwDGmGNAXncFpZRSMSTnGrQBqCwiFRwten2ApTG2WQoMdMwS2gS4EGM8YF9idAUVEb8Yce1I6ptRSmUJeu+k0tS0jdOo+mFV9p/bj4iQO1tu8mbPy/0B93s6NJUOuToxzE1jjBERAyAiud0Yk1JKxZTka5AxJkxERgHLAW/gU2PMThEZ7lg/FVgGdAKCgKvA4Mj9RSQXdmbRh2Ic+k0RCcB2Gz0Yx3qllAK9d1Jp6O0/3+apX56ic+XOlMpXytPhqAzA1STwSxGZBhQQkaHAA8An7gtLKaWiSdY1yBizDJvoOS+b6vTcACPj2fcqUDiO5fclLXSlVBal907K7YwxvLz6ZSasnsA9Ne5hbs+5ZPPO5umwVAbg6uygk0WkPXAR27d9vDHmF7dGppRSDnoNUkplNHrdUmnh0y2fMmH1BAYFDGLGXTPw9vL2dEgqg3B1YpjcwApjzC8iUhWoKiK+xphQ94anlFJ6DVJKZTx63VJpoW/tvlwJvcKoRqPwkqRM+q+yOlf/tfwOZBeRUsCv2HEzs90VlFJKxaDXIKVURqPXLeUWYRFhvPr7q1y8cZFcvrl4pPEjmgCqJHP1X4w4xsf0BD4wxvQAargvLKWUikavQUqpjEavWyrV3Qy/SZ+v+jBu5Ti+2fONp8NRGZjLSaCINAX6Az84lrk6qYxSSqWUXoOUUhmNXrdUqroWeo3uC7rz9e6veffOdxlYd6CnQ1IZmKsXo0eB54AljinWKwIr3ReWUkpFo9cgpVRGo9ctlWou3bhE1wVdWX1wNdO7TGdog6GeDkllcK7ODvo7tm975OsDwCPuCkoppZzpNUgpldHodUulppDrIRw8f5A5PebQv05/T4ejMoEEu4OKyHQRqR3Putwi8oCI6L9EpZRbDBs2DCBnXOv0GqSUSo/03kmlpvPXzxNhIiibvyy7R+7WBFClmsRaAv8HjHNczHYAp4EcQGUgH/ApMM+tESqlsqwRI0bwySef+InIbvQapJTKGPTeSaWKoxeP0m5OOzr5d+LtO98mh08OT4ekMpEEk0BjzFagt4jkAQIBP+AasNsYs9f94SmlsrKAgACAA0Br9BqklMoA9N5JpYb/Qv6j7edtOX31NN2qdfN0OCoTcnVM4GVglXtDUUqpuOk1SCmV0eh1SyXX3jN7aft5W66GXuW3gb/RqFQjT4ekMiGdqlgppZRSSqk09thPj7Fw58Joy4wxnLl6hgI5CrBq0CrqFK/joehUZqdJoFJKKaWUUmmsYcmGrD60mh/6/YAgAKz4bwXP/PoMywcsp2axmh6OUGVmrhaLB+ysVu4KRCmlEqPXIKVURqPXLRWfPrX6cDX0KnvO7GHfuX38cuAXpm6ayqR2kzQBVG7nUhIoIs1EZBew2/G6roj8z62RKaWUg16DlFIZjV63VGK8vbwZ33I8j/30GB3mduDFVS9y8vJJ+tTq4+nQVBbgakvgu8CdwFkAY8w/QEt3BaWUUjHoNUgpldHodUslqlS+Uuw4tYNiuYtRLHcxxrcaj4+XjtZS7udyd1BjzJEYi8JTORallIqXXoOUUhmNXrdUQv488id3zb+LEnlKcDP8Juevn9dWQJVmXE0Cj4hIM8CISDYReQpH9wallEoDeg1SSmU0et1SCVp9cDV+efxY/+B6CucqzEutX9JWQJVmXP2XNhyYApQCgoGfgZHuCkoppWLQa5BSKqPR65aKU1hEGD5ePjx323OMbDSSfNnzseWhLZoAqjTlarH4M0B/N8eilFJx0muQUiqj0euWisuW41u496t7+fKeLwkoEUC+7PkANAFUac6lf3EiUgEYDZR33scY09U9YSmlVBS9BimlMprkXrdEpAO2BdEbmGGMmRRjfTVgFlAfGGuMmexYXgb4HCgBRADTjTFTUuv9qJTbdnIb7ee0J3e23BTIUcDT4agsztWfHb4BZgLfYS8sSimVlr5Br0FKqYzlG5J43RIRb+AjoD22C+kGEVlqjNnltNk54BGge4zdw4AnjTGbRSQvsElEfomxr/KQnad20vbztuT0zcmKgSsoX6C8p0NSWZyrSeB1Y8z7bo1EKaXip9cgpVRGk5zrViMgyBhzAEBEFgDdgFuJnDHmFHBKRDo772iMOQ4cdzy/JCK7seMRNQn0sAMhB2j7eVt8vXxZMXAFlQpV8nRISrmcBE4RkRexg5pvRC40xmx2S1RKKRWdXoOUUhlNcq5bpQDnshLBQOOknlhEygP1gPVxrBsGDAMoW7ZsUg+tkqFk3pJ08O/Asy2epXLhyp4ORynA9SSwNnAfcDtRXRqM47VSSrlbsq5BLoytEcf6TsBVYJCjK1VVYKHTphWB8caY90SkkGNdeeAg0NsYE5Kid6eUyoySc92SOJaZpJxURPIAXwOPGWMuxjqYMdOB6QCBgYFJOrZKmgMhByiYoyAFcxZkdvfZng5HqWhcTQJ7ABWNMTfdGYxSSsUjydcgF8fWdAQqOx6NgY+BxsaYvUCA03GOAksc+zwL/GaMmSQizzpeP5OC96aUypySc+8UDJRxel0aOObqziLii00A5xljFifhvCqVHQg5QKvZrahRtAbLByz3dDhKxeJqsfh/gAJujEMppRKSnGvQrbE1jpuwyLE1zroBnxtrHVBARPxibNMW2G+MOeS0z2eO558Re3IGpZSC5F23NgCVRaSCiGQD+gBLXdnR0bNhJrDbGPNOEs+rUtGh84do81kbroZe5c12b3o6HKXi5GpLYHFgj4hsIHq/dp2eXSmVFpJzDXJlbE1c25TCMbmCQx9gvnMsjgkYMMYcF5Firr4JpVSWkuTrljEmTERGAcux3dg/NcbsFJHhjvVTRaQEsBHIB0SIyGNADaAOtvvpdhHZ6jjk88aYZan+zlS8jlw4QpvP2nDxxkV+G/gbdUvU9XRISsXJ1STwRbdGoZRSCUvONciVsTUJbuP4Jb4r8FyST66TLyiV1SXr3smRtC2LsWyq0/MT2G6iMf1B3Nc0lYaGLB3CuWvn+HXgr9T3q+/pcJSKl0tJoDFmtbsDUUqp+CTzGuTK2JrEtukIbDbGnHRadlJE/BytgH7AqXhi1skXlMrC9N4pa5rRdQYnL58ksGSgp0NRKkEJjgkUkT8cfy+JyEWnxyURiTXjlFJKpaYWLVoAyb4GuTK2ZikwUKwmwIXIrp4OfYneFTRyn/sdz+8Hvk36O1NKZVZ675T1nLx8kgmrJhBhIiibvywNSzX0dEhKJSqxlsAxAMaYvGkQi1JKRfPWW2/RrFmzZF2DXBlbg+1y1QkIwpaIGBy5v4jkws4s+lCMQ08CvhSRIcBh4J6kvzOlVCam905ZyOkrp2n7eVv+O/8fvWv2pkbRGp4OSSmXJJYEfgRoh2allEeMHDkyRfu7MLbGAHGexBhzFSgcx/Kz2BlDlVIqLnrvlEWcuXqGtp+35UDIAX7o94MmgCpDSSwJ1AHGSimPsTmaUkplKHrvlAWcu3aO9nPas+/cPr7r+x1tKrTxdEhKJUliSWAFEYm3Po2WiFBKudN///0H4B/fdUivQUqpdEjvnTKRv4/+Ta8ve8X6UfJG2A2uhF7hm3u/oV3Fdh6KTqnkSywJPA28nRaBKKVUTEWLFuXChQsn0OuQUirj0HunTKR6kepcC73G/F7zqV60OuER4RgMLT5twcxuM7nT/05Ph6hUsiSWBF7SKY6VUp6SN29egMt6HVJKZSB675SJ5M2el6eaPcWsrbOY1mUaHed1pErhKlQsWJG7qtzl6fCUSrbEksCDaRGEUkrFpXz58mzZssXTYSilVFIc9HQAKnWNbDiSilMq0u7zdmw6vokDIQeY23MuIjr8U2VcCdYJNMb0TKtAlFIqpsWLF3s6BKWUShK9d8p8cvrmpFieYvx97G+G1h+KfyF/2pTXiWBUxpZgEphSItJBRPaKSJCIPBvHehGR9x3rt4lIfcfyqiKy1elxUUQec2esSimllFJKOTPGMPz74ew6vYs8vnlY+u9SJrSeoK2AKsNLrDtosomIN7ZWTnsgGNggIkuNMbucNusIVHY8GgMfA42NMXuBAKfjHAWWuCtWpZRSSiml4lKlcBXGtxxP3ux5+WHfD9oKqDKFBJPAyJY5JwY4Y4w54sKxGwFBxpgDjmMtALoBzklgN+BzR8HmdSJSQET8jDHHnbZpC+w3xhxy4ZxKqUxk8+bNALmcrkVJuQYppVSaS+G9k0pHzl49S+FchXm6+dOAbRV8tPGj2gqoMoXEWgLjmuK4kIhkA/oaY7YmsG8pwPmCF4xt7Utsm1KAcxLYB5ifSJxKqUzoySefBChN9GuRq9cgpZTyhJTcO6l0YvbW2Ty+/HHWDF5DrWK1ABARfL19PRyZUqkjwSTQGBNne7eIBALvAy0T2D2un0lMUrZxXDC7As/FexKRYcAwgLJlyyYQjlIqo1m5ciUi8m/Ma5GL1yCllEpzKbx3UunA0r1LeXDpg7Sp0IbKhSp7Ohyl3CJZYwKNMRtFJE8imwUDZZxelwaOJXGbjsBmY8zJBGKZDkwHCAwMjJlkKqUyIRevQUoplW7odStjWH1wNb0X9aZByQYsuXcJ2X2yezoklUWVfqc0Ry8djbW8VN5SBD8RnOLjJ2t2UBEpTuxWvZg2AJVFpIKjRa8PsDTGNkuBgY5ZQpsAF2KMB+yLdgVVSsXg4jVIKaXSDb1upX97zuyh64KuVChYgR/6/UCebJqzK8/pWrUr2byzRVuWzTsb3ap2S5XjJzYxzAfEvmAVApoBjya0rzEmTERGAcsBb+BTY8xOERnuWD8VWAZ0AoKAq8Bgp3Pnws4s+lBS3pBSKvMYPXo0QBkRed9psUvXIKWU8oSU3Dspz6pYsCKD6g7iqWZPUSRXEU+Ho7K4cS3H8emWT6Mt8xZvxrUalyrHT6w76MYYrw1wFnjCGHMqsYMbY5ZhEz3nZVOdnhtgZDz7XgUKJ3YOpVTmFRgYCPYHok2ORUm6BimllAek6N5Jpb3jl47j6+1LkVxFmNJxiqfDUYqzV8+ycOdCcvnm4kb4DQB8vHwYHDCYEnlKpMo5EpsY5jMRqQdUAnYaY3anylmVUsoF999/P4MGDboKXEGvQUqpDEDvnTKWkGsh3Dn3TnL45GD9g+u1/IPymAgTwcr/VjJjywwW717MzfCb1CtRj0unLhEWEYavl2+qtQJCImMCRWQcsBDoBfwgIkNT7cxKKZWIV155BeyNlF6DlFIZgt47ZRxXQ69y1/y72HNmD6+1fU0TQOURRy8eZeLvE/F/3592c9qxPGg5wxsM55/h/7D5oc0MrT8UL/FK1VZASLw7aB8gwBhzVUQKAz8Bn6Ta2ZVSKgELFiwA2GWM6avXIKVUBqH3ThlAaHgovRf15s8jf7Lw7oW0q9jO0yGpLCQ0PJRl+5YxY8sMlu1bRoSJ4PYKtzPx9on0qN6DHD45bm07ruU4lu9fnqqtgJB4EnjdMTYPY8xZEUnWbKJKKZUcOXLkAIgAvQYppTIMvXfKAF5Y8QI/7PuBqZ2nck/Nezwdjsoigs4FMXPzTGb/M5sTl09QIk8Jnmn+DA/UewD/Qv5x7uOX14/9j+xP9VgSSwIriUhkWQeJ8RpjTNdUj0gppRz2798P4O+47ug1SCmVEei9UwbweNPHqVy4Mg/Wf9DToahM7nrYdRbvXsyMzTNYeXAlXuJF58qdebD+g3Sq3Akfr2SVbU+xxM4asxDFZHcFopRSMX377be0bt36BPC2Y5Feg5RS6Z3eO6Vj3//7PXdWupMSeUpoAqhSRXxF3YvlKsa9te5l7ra5hFwPoUKBCky8fSL3172fUvlKeSDS6BKbHXR1WgWilFIxtWrVCuCyXouUUhmFXq/Sr082fcKw74fx7p3v8liTxzwdjsokulbtyswtM7kZfvPWMkE4dfUU0zZNo1f1XjxY/0Fal2+NVzrqHZ7Y7KDdRGSk0+v1InLA8bjb/eEppbKyb7/9FqBo5Gu9Biml0ju9d0qfvt71NcN/GE5H/46MbBhniWqlkuWF216Ic/nLbV7m2BPH+KLXF9xe4fZ0lQBC4t1Bn8bOchUpO9AQyA3MAr5yU1xKKcWbb74JcN5pkV6DlFLpnd47pTMr/ltBv8X9aFK6CV/1/gpfb19Ph6QygSMXjvD5P58z+5/Z0VoBfbx8GFpvKONapu5snqktsZQ0mzHmiNPrP4wxZ40xh7EXM6WUcpubN28ChDotStI1SEQ6iMheEQkSkWfjWC8i8r5j/TYRqe+0roCIfCUie0Rkt4g0dSyfICJHRWSr49Ep5e9UKZWJ6L1TOnI97DoDFg+gSuEqfN/3e3L55vJ0SCoDuxZ6jfnb53PHnDso9145Xlj5AqXzlWZKhylk984OgK+XL+Nbj/dwpIlLrCWwoPMLY8wop5dFUUopNwoJCYn2OinXIBHxBj4C2gPBwAYRWWqM2eW0WUegsuPRGPjY8RdgCvCTMeZuEckGON85vGuM0ckelFJx0XsnD9h0bBOnr56OtTx/9vx81/c7/PL6UTBnwTj2VCphxhj+Pvo3s7fOZv6O+Vy4cYFy+csxruU47g+4n4oFKwKw58wepm2alupF3d0lsSRwvYgMNcZEK3IqIg8Bf7svLKWUgsaNG7N///4iMZe7eA1qBAQZYw449lmAnbXPOQnsBnxujDHAOkfrnx9wBWgJDAIwxtwEbqKUUonTeycPuP+b+wk34ZTLXw6wLYD7Q/YTGh7KiadOeDg6lRGduHyCOf/MYfY/s9l1ehc5fXLSq0YvBgcMjnOSF3cVdXeXxJLAx4FvRKQfsNmxrAG2f3t3N8allFK8++67fPHFF0VEZCVJvwaVApy7ZAUT1cqX0DalgDDgNDBLROoCm4BHjTFXHNuNEpGBwEbgSWNM9CZLpVRWpvdOHvBk0yeZu30uPw34ibNXz9JydktOXD7Bs81jjQRQKl43w2/y3d7vmP3PbH7c9yPhJpympZsyvct0etfsTf4c+ePd111F3d0lsRIRp4BmInI7UNOx+AdjzAq3R6aUyvKKFSsGsAd4haRfgySOZcbFbXyA+sBoY8x6EZkCPAuMw3YZfcWx3SvYGoYPxDq5yDBgGEDZsmVdCFcplRnovZNnDKgzgFfXvMryoOVMWD2BoHNB5M2Wl2dbaBKoErf1xFZmbZnFvO3zOHvtLCXzlmRMszEMChhE1SJVPR2eW7hUot5x4dKLl1LKI5J5DQoGyji9Lg0cc3EbAwQbY9Y7ln+FTQIxxpyM3FhEPgG+jyfm6cB0gMDAwJjJp1Iqk9N7p7Tl6+3L8y2eZ8CSAZy7do4mpZvQvWp3cmfTuXhU/AXd82fPT4WCFdh6YivZvLPRrWo3BgcMpn2l9vh4uZQmZVjpq2CFUkqlng1AZRGp4JjYpQ+wNMY2S4GBjllCmwAXjDHHjTEngCMiEvnzX1scYwkdYwYj9QB2uPVdKKWUckmZfGU4c/UMvar3Yv+5/YxoOMLTIal0omvVrmTzzhZr+YUbF/AWbz7s+CHHnzzOl/d8ScfKHTN9AggutgQqpVRGY4wJE5FRwHLAG/jUGLNTRIY71k8FlgGdgCDgKjDY6RCjgXmOBPKA07o3RSQA21p4EHjI/e9GKaVUYu7wv4MXW77IS7+/xJvt3tRWQHXL4IDBTN80Pdoyb/Hml/t+oU2FNh6KyrM0CVRKZVrGmGXYRM952VSn5wYYGc++W4HAOJbfl7pRKqWUSolfD/yKt3jTpkIbxrYcy4UbF7QVUBEWEcayfcuYtmkaP+77EYNBEAwGXy9fhtYfmmUTQNAkUCmllFJKZVC7Tu+i15e98C/kz4ahG/D19uXdDu96OizlQUcuHGHG5hnM3DKTo5eO4pfHj7G3jaVr1a60nN2S62HX8fHyyTClHNxFk0CllFJKKZXhnLpyis5fdCanT06W3LskVt02lXWER4SzbN8ypm+ezrJ9yzDGcKf/nXzY6UM6V+6Mr7cvYLuFZqSC7u6kSaBSSimllMpQroVeo9uCbpy8fJLVg1ZTNr+W4smKgi8GM3PzTGZsmUHwxWBK5CnBcy2e48H6D1K+QPlY22e0gu7upEmgUkoppZTKUGZtncW64HV8dc9XNCzV0NPhqDQUHhHOT0E/MW3TNH7Y9wPGGO6odAdTOkzhrip33Wr1i0tGK+juTpoEKqWUUkqlEyLSAZiCndV4hjFmUoz11YBZQH1grDFmsqv7ZiYPBz5MneJ1aFG2hadDUWkk+GIwn275lBmbZ3Dk4hGK5y7Os82f5cH6D1KhYAVPh5fhaBKolFJKKZUOiIg38BHQHggGNojIUmPMLqfNzgGPAN2TsW+Gt2T3EuqWqEvFghU1AcyE4ivqnsMnBzfDbxJhIrij0h28e+e7dK3aNcFWP5UwHUGrlFJKKZU+NAKCjDEHjDE3gQVAN+cNjDGnjDEbgNCk7pvR/X7od+796l6e++05T4ei3CS+ou6C8HSzp9n/yH6WD1hOrxq9NAFMIW0JVEoppZRKH0oBR5xeBwON02DfdG/f2X30WNiDSoUqMbXz1MR3UBlOWEQYjUs1ZtqmadGWZ/PKxp5Re3Tyn1SmSaBSSimlVPogcSwzqbmviAwDhgGULZsxbqrPXj1L5y864yVefN/3ewrmLOjpkFQq2n9uP59u+ZRZW2dx/PJxcvnm4nrYdSJMBNm8s/FgvQc1AXQDTQKVUkoppdKHYKCM0+vSwLHU3NcYMx2YDhAYGOhqgulR41eO59CFQ6wYuIJKhSp5OhyVCq6HXWfJ7iXM2DKDFf+twEu86FS5Ew/We5B6JepR9aOqXA+7jrd4azkHN9EkUCmllFJZV758cOlS7OV588LFi2kdzQagsohUAI4CfYB+abBvuvZm+ze5p+Y9NC/b3NOhqBTafnI7MzbPYM62OYRcD6FCgQq82uZVBgUMolS+Ure206Lu7qdJoFJKKaWyrrgSwISWu5ExJkxERgHLsWUePjXG7BSR4Y71U0WkBLARyAdEiMhjQA1jzMW49k3zN5GKvtz5JR39O5I3e15al2/t6XBUMl26cYmFOxfyyeZP+Pvo32TzzkaPaj14sP6D3F7hdrwk9jyVWtTd/TQJVEoppVTWFBbm6QhiMcYsA5bFWDbV6fkJbFdPl/bNqBbuWEifr/vwwm0v8Mrtr3g6HJVExhjWH13PjM0zWLBjAVdCr1CjaA3evfNdBtQZQJFcRRLcX4u6u58mgUoppZTK3EJDYf9+2LkTdu2K+rt3r6cjU3H468hf3P/N/bQo24IXWr7g6XBUEpy5eoa52+YyY/MMdp7eSS7fXPSp2YehDYbSuFRjROKav0h5giaBSimllEod7hpf5+pxb96EoKDoid7OnfDvvzYRjFShAtSoAR07wptvJj8uler+C/mPbgu6USpfKZbcu4TsPtk9HZKKIb6C7jl9chJuwrkZfpNGpRoxvct07q11L/my5/NAlCoxmgQqpZRSKnW4a3xdQsd98UWb7O3aZZO9yC6eIlCxok327rrL/q1RA6pVg9y5o46hSWC6MvS7oYRGhPJDvx8S7TKoPKNr1a7M3DKTm+E3oy2PMBEMbzCcIfWHUKd4HQ9Fp1ylSaBSKs3Mmwdjx8Lhw1C2LEycCP37ezoqpVSaKFQIvLxscublFfWI+TquZQl59VWoVMkmeN26Qc2aUclezpyJx5U3b/ytjCrNzew6k+CLwVQrUs3Toag4RJgIbit7W6yC7r5evuwZtYfyBcp7JjCVZJoEKqXSxLx5MGwYXL1qXx86ZF+DJoJKZXhBQfDeewlvM2AARESAMfZv5CPm67iW7doV/3EvX3Yt2YtP2peBUDEYY/hmzzd0q9aNcgXKUa5AOU+HpGI4c/UMs7bMYtqmaewP2U8OnxzcDL8ZraC7JoAZiyaBSim3uH4dDhyAffvs/eGECVEJYKSrV23LoCaBSmVAxsDatfDOO/DNN+CTyC3F++8n/1wJTSaRkgRQpQvv/PUOT/3yFHN7zKV/Hf0fQnphjGHtkbVM3TiVRbsWcTP8JreVvY2X27xMs9LNqP6/6lrQPQPTJFApFU1SumzeuBGV6EUme5HPjxyx94iJOXw4deNXSrlZWBh8/bVN/v7+23bzHDsWRoyAkiU9HZ1Kx77c+SVT1k+Jtuzs1bPsPbuXNuXb0Ld2Xw9FppxduH6BudvmMnXTVHac2kG+7PkYVn8YwwOHU7NYzVvbaUH3jE2TQKXULXF12Rw6FI4eherVYyd7hw9HT/QKFYLKleG22+zfypXB398+6te3x4upbNm0eW9KqRS6eBFmzIApU+x//JUrw//+B/ffD7ly2W3cNb5Ox+1lCsVzF2fX6V0suXeJHUN2Zg8jl41EED7q9FGcRcNV2tl8fDNTN07li+1fcCX0CvX96vPJXZ/Qt1ZfcmfLHWt7LeiesWkSqJQCbDL39NOxu2xeuwbPPBP1umBBe+/XooVN7pyTvUKF4j/+xInRE0yw940TJ6bu+1BKpbJDh2xXzk8+sYlYq1bwwQfQpUvsSVvcNb5Ox+1lCq3Kt6JeiXocPH+QfrX7MWDJAHL45KBX9V5UL1rd0+FlSVdDr7Jwx0KmbprK30f/JqdPTvrW6svDDR8msGRggvtqQfeMTZNApbKo8+dhwwZYtw7Wr7e9uk6fjn/7detsspdQopeQyC6lOjuoUhnE33/bLp9ffWVf33svPP44BCZ8Y6hUQl5s9SJDlg5hQJ0BfNjxQ/ot7sert7/q6bCynN2ndzNt0zQ+++czzl8/T/Ui1ZnSYQoD6w6kQI4Cng5PpQFNApXKAkJDYft2m+xFPvbsiVpfvTp07gxLl8K5c7H3L1cOGjdOeRz9+2vSp1S6Fh4O330Hb78Nf/wB+fPDE0/A6NFQpoyno1OZQMtyLcmXPR9zt81l/7n99K7RmwoFK3g6rEwnvoLuBXMUpG6Juqw6uApfL1961ejFw4EPc1vZ25CEJmBSmY4mgUplQAlN3mKMnZQlMtlbtw42b7bdOgGKFoUmTexs7Y0bQ8OG9j4v8riZqcumiHQApgDewAxjzKQY68WxvhNwFRhkjNnsWFcAmAHUAgzwgDHmLxEpBCwEygMHgd7GmJC0eD9KpYp8+eIeX5c9u030goKgfHlb8uGBB3TcnUpVE9dMZMuJLTz323PcDL/JxqEbPR1SphRfQfeQ6yEcPH+Q19u+zgP1HqBY7mIeilB5miaBSmUwcU3eMmSIbcW7ccMmfidO2HXZs9sJWR56yCZ8jRvbe7v4fuzLTF02RcQb+AhoDwQDG0RkqTHGueBYR6Cy49EY+NjxF2xy+JMx5m4RyQY4Zr7gWeA3Y8wkEXnW8dpp1KRS6VxcCSDYC0iRIvD669C9e+IlH5RKom/3fMu4leMYUGcAwReC8S/kr62AbvLCbS8wc8vMaMu8xIs5PebQp1YfnYRHaRKoVEZy9So89VTsyVtu3IAvv7Rj9tq3j0r46tSBbNmSdo5M1GWzERBkjDkAICILgG6AcxLYDfjcGGOAdSJSQET8gCtAS2AQgDHmJnDTaZ/WjuefAavQJFBlFDduJLz+r7/SJg6V5ew4tYMBSwbQsGRDpneZzs3wm2T3ye7psDKdsIgwvtr1FW//9Xa0VkBfL1+G1h9Kv9r9PBidSk80CVQqnbpyBbZuhU2boh67d0NERPz7/PtvmoWXEZQCjji9DiaqlS+hbUoBYcBpYJaI1AU2AY8aY64AxY0xxwGMMcdFJM6+NCIyDBgGUFbrYChPuXLF9gn//Xf7WLfO0xGpLOhq6FW6LehG3mx5WXLvEnL65iSnb05Ph5WpXLh+gRmbZ/D+3+9z+MJhqhSuwqS2k3hx1YvcCL+Bj5ePlnJQ0WgSqJQbuVp4/fLl2Anfnj1RCV/x4tCgAfTsCVOnxj2LZ7lybn0rGVFcnV5jlq+PbxsfoD4w2hizXkSmYLt9uvx/UGPMdGA6QGBgYMzzKuUe58/D2rVRSd/Gjba4u5cX1KsHDz8M777r6ShVFpPLNxfPt3iemsVqUipfKU+Hk6kcOn+IKeunMGPzDC7dvESrcq34sOOHdK7SGS/x4tCFQ1rQXcVJk0Cl3CSusXvDhsH161ClSuyEL7LoeokSNuG7+277t0EDKFkyahxf1aqZa/IWNwoGnKczLA0cc3EbAwQbY9Y7ln+FTQIBToqIn6MV0A84leqRKwXxT+CSN29U3bxTp2DNmqik759/7MXE1xcaNYIxY6BlS2jWzB4PNAlUaSr4YjCl85VmSP0hng4lU/n76N+8/dfbfL3rawDurXUvTzR5ggYlG0TbTgu6q/hoEqiUm4wdG3vs3tWr8OCDUa/9/GyS17t39IQvIZlp8hY32wBUFpEKwFGgDxBzMMRSYJRjvGBj4EJkV08ROSIiVY0xe4G2RI0lXArcD0xy/P3W7e9EZU3xTeBy6ZKd7en336NqveTMaRO9CRNs0te4sV0Wl7x5408ulUpFMzfPZPSPo/njgT+o71ff0+FkeOER4Szdu5R31r3DH4f/IH/2/DzR9AlGNxpNmfxxl3DRgu4qPpoEKpVKTp+2pRi2bLGPQ4fi3/a772zC5+eXvHNloslb3MYYEyYio4Dl2BIRnxpjdorIcMf6qcAybHmIIGyJiMFOhxgNzHPMDHrAad0k4EsRGQIcBu5Ji/ejVDQLF0KLFjB4sE366td3fRaoyFZEpdxo7eG1PPzDw7Sp0IY6xet4OpwM7crNK8zaOov31r3H/pD9lC9QnvfufI8H6j1A3uz6441KHrcmge6o0eXOeJVyRWQdvi1bopK+zZvhqFNN1goVbBfNmC2BYMfudemSdvFmZcaYZdhEz3nZVKfnBhgZz75bgcA4lp/FtgwqlbqMgX37YNUqWL064W3PngVv7zQJS6mkOnLhCD2/7En5AuVZ0GsBPl7a5pAcxy4d44P1HzBt0zRCrofQpHQTJrWbRPdq3fUzVSnmtn9BbqzRpVSqSmjylogIWzfZOdnbssXef4Gda6FaNWjd2s65UL8+BARAwYKZr/C6UiqVGQN790YlfatWRRX5LJHIBA6aAKp0ouv8rvx+6Pdbr40xXA69jDGGZf2WUTBnQQ9Gl/6Vfqc0Ry8djbU8p09OwiLCCDfh9KjWgyeaPkGzMs08EKHKrNz5M4K7anQplWrimrzlgQfg88/tzOr//GNn7gTb06pWLejRIyrhq1PHJnZx0bF7SqlojLF1XiKTvtWr4eRJu65kSWjTxv6i1Lq1LfrppcWcVfrXomwLAD7v8Tlg69T1WNCDK6FXYk1SomLrWrUrM7fMjFbTDyA0IpQRgSN4tMmjVCxY0UPRqczMnUmgu2p0KZUqjLET58XssnnzJvzyi51jYfDgqISvevUsXXhdKRWXhGbwPH8edu2KauVbvTqqvkupUtCunU34WrUCf/+oKYCdj6ETuKh0bkTDEbz919scvXiUSoUq4SVeHL54mPm95ns6tAzhmebP8MnmT6It8/HyYfvD26lWpJqHolJZgTuTwDSp0aUFmVVSXLwIv/4Ky5bBTz/B8eNxb2cM/PFH2samlMqAEprBs3hxOHPGvi5TBjp0iEr6KlaMnfTFpBO4qAwgT7Y8PNn0SYYsHcKJyycYUm8I1YpU066Libh88zIzNs/g7b/eJiwiDEEwGHy9fBlaf6gmgMrt3JkEuqtGVzRakFklxBjYsQN+/NEmfmvX2rrJ+fPDHXfAihVR4/ucaeF1pVSKde5sE77WraF8+cSTPqUyqLL5y7L+6HoCSwYyffN0Ft690NMhpVtnrp7hg/Uf8MHfHxByPYRW5Voxqe0khiwdwo3wG/h4+WhNP5Um3JkEuqtGl1IJunTJtvb9+KN9BAfb5XXrwlNPQadO0KSJraWsk7copZLs9GlYuhQWL054u9mz0yQcpTzp1wO/cv8391MmXxkOXzhMQIkAbQWMw6Hzh3j7r7eZsXkG18Ku0a1qN55p/gxNyzQFYO2RtUzbNI3BAYMpkSeRiaGUSgVuSwLdWKNLZVHxzeJpDOzcGZX0/fEHhIbaoTrt29vayR062CE4MenkLUoplxw5AkuW2MRvzRo7dXD58p6OSimP2nRsE90WdKNq4ap83+97Ws1uxYRWEzwdVrqy49QO3lz7Jl9s/wIRYUCdAYxpNoYaRWtE225cy3Es379cWwFVmhE7MWfmEBgYaDZu3OjpMJQbxNVily0bNG9uSzgccUwvVLu2benr2NFO7OLr65l4VeoRkU3GmFj1+jISvTZlUHv32qRv8WKI/P5q1oSePe2jbt2EZ/DMRP9/VbHptQku3rjIoz89yuttX6dEnhJEmAi8RGe1BVh7eC2T1k7i+3+/J7dvbobWH8oTTZ+gTP4yie+sVAq5cn3SSpMqQ3j++bhn8Vy1Crp3h/HjbWtf6dKeiE4plSkYYwuBRiZ+u3fb5Y0awaRJtj5MlSrR99EZPFUWdCDkACXylCBf9nzM6jbr1vKsngBGmAiW7VvGpD8msfbIWgrnLMxLrV9iZMORFM5V2NPhKRWNJoEq3Tp1yo7t+/ln21UzLsYkPixHKZXFJVTGISQE/vzTXkiWLLHFQr29oWVLGDHC/sqU0K9LOoOnysRW/rcyViHz01dO8+qaV2ldrjVf3/u1hyJLX0LDQ1m4cyFvrH2DHad2UDZ/Wd7v8D4P1HuA3Nlyezo8peKkSaBKN27csLN3/vyzfWzZYpcXKmQna4nZEgg6i6dSygUJlXEoWdL+4pQ9ux1E/OKLcNddUKRI2saoVDr04qoXuXDjArWL1QbgWug1lgUt40bYDV5o+YKHo0s7pd8pHSsZBvDL48dzLZ5j8l+TOXzhMDWL1uTz7p/Tp1YffL11PIpK37J2u73yKGNsHeX33rPj+AoWhLZt4e237Q/3EyfChg32/mz6dJsIOtNZPJVSKdamDSxcaGf8/O47GDxYE0ClHJ5p/gwRJoLPe3zOlA5T2Ht2L2ERYTwc+DD1/Op5Orw007VqV7J5Z4u2zFu8CbkewiM/PULpfKX5ru93bHt4G/fVvU8TQJUhaEugSlNnzkR18fz5Zzjq+GGtalV48EFbu69Vq9jDaXQWT6VUkoWHw8qVCW+zYEHaxKJUBtSpcicmrJ7A4t2L+eyfz9h3bh95suXhjfZveDq0NDWu5ThmbZ0VbVm4CadN2Ta82OpFWpRt4aHIlEo+TQJVqopZxuGll2yXzcikb/Nm2wJYsCC0a2eTvvbtXevW2b+/Jn1KqUQYA+vWwfz58OWXcPKkpyNSKsMSESa0msCzvz3LonsWcenGJTr6dyRPtjyeDi1NXQ+7TsUCFdl1xpas9hIv7ql+Dwvu0R+RVMalSaBKNfPmwdChcO2afX3oEAwaZJ/7+EDTpvDyyzbxa9DAzr2glFIpZgxs22YTvwUL7MUne3bo0gX69oW77/Z0hEplWJGtgV/u/JLdZ3bzfb/vPR1Smtl7Zi+v//E6c7fNxdvLG2/xJtyEk907O+91fM/T4SmVIpoEqhS7etX2uHr44agE0FnRoraWX758aR+bUioTCwqyid/8+bacg7e3/ZXp5ZftrJ6RFx0t46BUskW2BnaZ34VJbSdliVbAHad2MHHNRBbuWEh2n+yMajSKMc3GMHHNRKZtmsbggMGUyFPC02EqlSKaBKpk2bcPfvwRli2ztfpu3Ih/2zNnNAFUSqWS4GA7kcuCBVEF3Fu2hEcesS1+cU3qomUclEqRTpU78Xrb1xnZaKSnQ3GrTcc2MXHNRJbsWUKebHkY02wMTzR9guJ5igN2bODy/csZ12qchyNVKuU0CVQuuXbNJns//mgfQUF2edWqtgWwUyc7sUtc9fzKlk3TUJVSGVV89fxy54bJk22L35o1tvtngwZ22b33JlzHTymVYiLCsy2e9XQYbvPXkb94dc2rLNu3jPzZ8zO+5XgeafxIrALvfnn92P/Ifg9FqVTq0iRQxWv//qjWvpUr4fp1yJnTzqj+2GPQsSNUrBi1/WuvwbBh0ev5aRkHpZTL4qvnd+WK/bWpWjWYMAH69IEqVdI0NKVU5mKMYfWh1bzy+yus+G8FhXMWZuLtExnZcCT5c+T3dHhKuZ0mgVlQzBk8I0stXL8Ov/9uk74ff4R//7Xb+/vb5K5jR1u+IWfOuI+rZRyUUm6zdSvUqQMino5EKZWBGWNYvn85r/7+KmuPrKV47uJMbj+ZhwIfyhLjHZWKpElgFjNvXvTWukOHbG3kyZNt0nf1qp1Ur00bGDnSJn6VK7t+fC3joJRKkgsXbJH2r75KeLu6ddMmHqVUphRhIvhu73e8uuZVNh7bSOl8pfmg4wcMqTeEnL7x/LqtVCamSWAWM3Zs9O6aAKGhsGMHPPSQHdvXurXtxqmUUm5x7hwsXWoTv19+gZs3oVQpT0ellMrgSr9TmqOXjsZaXjBHQcrkL8O2k9uoWLAin9z1CQPrDiSbdzYPRKlU+uDl6QBU2jh9Gj780Lb8xSUszK7v1EkTQKWUG5w5AzNmQIcOULy47YKwfTuMGgV//hn3rFJKZUEi0kFE9opIkIjEmo1FrPcd67eJSH2ndY+LyE4R2SEi80UkR9pG71ldq3aNM7ELuR7CjbAbfN79c/aO2suD9R/UBFBledoSmIldu2Z7Wc2ZAz/9ZBM9X1/b8hdTuXJpH59SKpM7cQKWLLEtfqtXQ3i4nU3qiSdsOYfAwOhj/LSen8riRMQb+AhoDwQDG0RkqTFml9NmHYHKjkdj4GOgsYiUAh4BahhjronIl0AfYHYavgWPGtdyHJ9u+TTaMkGY2mUqQ+oNwdvL20ORKZX+aEtgJhMRYUs5DBkCJUrY2dM3b4bHH4dt22DWrNgtfTqDp8qsUviL+kER2S4iW0Vko9PyCSJy1LF8q4h0Sqv3ky7ky2cTt5iPyGKgR4/CBx/YWaRKloQRI2xtv2efhS1bbH2ZN96Ahg1jT/Jy8aIt/xDzoXX+VNbRCAgyxhwwxtwEFgDdYmzTDfjcWOuAAiLi51jnA+QUER8gF3AsrQL3NGMMG49tjDa+z8fLh+ENhjOswTBNAJWKQVsCM4ldu2yL37x5cOQI5MkDvXrBfffZMX7ejmtf7dr2r87gqTK7lPyi7rS+jTHmTByHf9cYM9k9kadz8ZVxuHQJmje3XTsBataE8eNti1/Nmjqrp1KuKQUccXodTPRrUnzblDLGbBSRycBh4BrwszHmZ3cGm15sPr6Zp35+ipUHV1KxYEWu3LxCaEQovl6+jG893tPhKZUuaRKYgZ04YWsnz51rW/u8veGOO+yP7N26xT+2T2fwVFnErV/UAUQk8hd15yTw1i/qwDoRKSAifsaY42kfbiZw5Qq88or9Bap6dU9Ho1RGFNevJcaVbUSkIPaaVgE4DywSkQHGmLnRdhYZBgwDKFu2bIoD9qQjF47wwsoXmPPPHArlLMQHHT/goQYP8ehPjzJt0zQGBwymRJ4Sng5TqXRJu4OmY/PmQfny4OVl/86bZ++x5s2zpRtKlbJDa7y84L33bC+sZcugb1+d3EUp4vm1PAnbGOBnEdnkuGlyNsrRffRTx41XLCIyTEQ2isjG06dPJ/9dZCRbt8ILL2gCqFTyBQNlnF6XJnaXzvi2aQf8Z4w5bYwJBRYDzWKewBgz3RgTaIwJLFq0aKoGn1Yu3bjECyteoMqHVVi4YyFjmo1h/yP7GdVoFL7evoxrOY7yBcozrtU4T4eqVLqlLYHpVFz1/O6/307scv267cb57LMwYIDebykVj2T/ou7429wYc0xEigG/iMgeY8zv2C6jrzi2ewV4G3gg1kGMmQ5MBwgMDIx53oxnwwaYNMnTUSiV2W0AKotIBeAodmKXfjG2WYr9IWoBtqvoBWPMcRE5DDQRkVzY7qBtgY1kImERYczcPJPxq8Zz6sop+tbqy2ttX6N8gfLRtvPL68f+R/Z7JkilMghNAtOpuOr5hYdDzpx2kr0WLWwLoFIqXin5RR1jTOTfUyKyBNu99HdjzMnIjUXkE+D71A89nTAGfvsNXn8dVqyAAgU8HZFSmZoxJkxERgHLAW/gU2PMThEZ7lg/FVgGdAKCgKvAYMe69SLyFbAZCAO24PghKqMzxvBj0I+M+WUMu07vokXZFizts5TGpWMOl1RKuUqTwHTm8mVYuDD+en5XrkDLlmkbk1IZVEp+Uc8NeBljLjme3wG8DBBjzGAPYEcavJe0FR5uSztMmgSbNoGfH7z1lu2eULq0lnFQyo2MMcuwiZ7zsqlOzw0wMp59XwRedGuAaeyfE//w1C9P8euBX/Ev5M/Xvb+mR7UeiE42pVSKaBKYDhgDf/0FM2faBPDKlfjr+WXwMdxKpZmU/KIOFAeWOG4yfIAvjDE/Oda9KSIB2O6gB4GH0uQNpYUbN+Dzz23Ct28fVK4Mn3xipxnOnt1uo+UalFJp4OjFo4xbOY7ZW2dTMGdBpnSYwvDA4VrkXalUokmgB506Ze+3Pv0Udu+G3LltXb8hQ+DAAXjooehdQrWen1JJk9xf1B0zitaN55j3pXKYnnfxIkybBu++C8ePQ4MGsGgR9OgRVV9GKaXSwOWbl3lr7VtM/msyYRFhPNn0SZ6/7XkK5oxzDi6lVDJpEpjGwsJg+XLb6vfdd/Z106YwYwb07h3Vo6pZM1tWS+v5KaXc5tQpmDIFPvoILlyAtm3tL1Nt22pdP6VUmgqPCGfW1lmMWzmOE5dP0Ltmb15v+zoVC1b0dGhKZUqaBKaR/ftti9/s2XDsGBQtCo8+alv94pvdU+v5KaXc4r//YPJke1G6cQN69oRnnoGGDT0dmVIqkyv9TmmOXjoaa7mPlw9hEWE0Ld2Uxb0X07RMUw9Ep1TWoUmgG127Bl9/bVv9Vq2ys3l26AAffABdukA27daulHKXfPninsDFx8cORPbysnVnnnoKqlZN+/iUUllS16pdmbllJjfDb0Zbnss3FzO7zqRX9V466YtSaUCLDKRQzILuc+fayfRGjLAT6t13n+3O+eqr9u8PP9gf3TUBVEq5VVwJINg+6I8/blsDP/lEE0ClVJoa13IcEqNEq6+XL9uGb+PuGndrAqhUGtGWwDjMm+faWLy4CroPHGh/ZM+RA3r1st09W7XSmn5KqTSU2Ayeb72VNnEopZSTKzev8MnmTwg34beW+Xr5MrT+UMoVKOfByJTKejQJjCGuxO6BB2yB9urV7dwJkY8FC2yXT2fGQKFCdgyg1lVWSqWa+Lp35sljux/89Rf8+af9uyPzlS5USmVc4RHhzNk2h7ErxnLs0jE6V+7MLwd+4Wb4TXy8fBjXapynQ1Qqy9EkMIaxY6OXZQC4edP2moqUNy/kzx87AYwUEqIJoFIqlcXXvfPy5agunfnzQ5MmthvChAlpFppSSsXntwO/8dQvT7H1xFYalWrEwrsX0qJsC0b8MIJpm6YxOGAwJfKU8HSYSmU5mgTGcOhQ/OvOnbM/xkeWzSpfPu7ttaC7UipNffKJrTVTvXpU33NNApVSaSS+GT+ze2fnRvgNyuUvx/xe8+ldszdeYq9R41qOY/n+5doKqJSH6Eg1h4gIePbZ+NeXKwcFC0avmzxxoi3g7kwLuiul0tyDD0LNmtEHH0cWHY0pvuVKKZVMXat2JZt37BnvDIY32r3BnlF76FOrz60EEMAvrx/7H9mvrYBKeYgmgcD169C3L7zxhq2RnDNn9PXxJXb9+8P06TZBFLF/p0/X2n5KqVQUGgpPP530/S5etIOUYz4SmzRGKaWSKK4ZP73Fm83DNvN086fJ4ZPDQ5EppeKT5ZPAM2egXTv48ks7Yd4vv9ieVa4mdv37w8GDtiXx4EFNAJVSqSg4GNq00dk8lVLp1tGLR3l33bsYzK1lvl6+PNTgIWoWq+nByJRSCclyYwKdyz/4+dnkLSTEJoH33GO36d9fkzmlVBqKb+ZPEdsV4Ysv4KGH4t5Gu3cqpdwovvF+xXIVo2Pljnyx/QvCTThdqnThp6CfdMZPpTKILNUSGFn+4dAh2yvq2DE4cQKeeSYqAVRKqTQX38yfxsDGjba/unbvVEp5QFzj/QTh1NVTLNq1iOGBwwkaHcS3fb5lSL0heImXzvipVAaQpZLAuMo/AHz2WdrHopRSLqlWzdMRKKWysLjG+xkMTzd/msOPHeb9ju9ToWCFW9uWL1BeWwGVygCyVBJ4+HDSliullFJKZWV+ef3oUqXLrdfe4s2w+sN4o90bFM5VONa2OuOnUhlDlkoC46vfp3X9lFL/b+/uQy2p6ziOvz/sAz6iliai6VpoZT5sPhGSopWlRkQk2QNZIlJWUkKgRFlmD1pB+YBZiJn9kQaFVGgZpWnZamm6rj2IZQ+WpJZY2YOtfvvjzLq36z3ec69358w5837BZc+Z85u5H2bvfOA3Z+YcSdLczj/6/CcuCV25bCVnHnHmmBNJerp6NQn0e/0kSZIWZqetd/J+P2nK9GoS6Pf6Seokv9hdUsd5v580XXr3FRF+/YOkzvETPiV13Ib7/SRNh169EyhJkiRJfeckUJIkSZJ6xEmgJEmSJPWIk0BJkiRJ6hEngZIkSZLUI04CJUmSJKlHnARKkiRJUo84CZQkSZKkHklVjTvDkknyAPC7eYZtDzzYQpylZOZ2mLk9C8m9W1XtsCnDbGp2U6dMYmaYzNzTntlu6rZJzG3mdvQh87z9NFWTwFEk+WlVHTjuHAth5naYuT2TmntTmsR9Yub2TGJuM0+HSd0nk5jbzO0w84CXg0qSJElSjzgJlCRJkqQe6eMk8AvjDrAIZm6Hmdszqbk3pUncJ2ZuzyTmNvN0mNR9Mom5zdwOM9PDewIlSZIkqc/6+E6gJEmSJPXWVE4CkxyV5FdJ7k5y+hyvJ8l5zetrk+w/jpyzMs2X+c1N1rVJbkyy3zhyzjZf7hnjDkryWJJj28w3JMu8mZMcnuS2JHcm+UHbGefIM9/fxzZJvpnk9ibzCePIOSvTJUnuT7JuyOudOw7bYD+1w25qh900PeymdkxiN4H91JZW+6mqpuoHWAb8GngOsBK4Hdhr1phjgKuBAC8GbpqAzIcA2zWPjx535lFzzxj3feAq4NiuZwa2BX4O7No8f9YEZH4/cE7zeAfgr8DKMec+DNgfWDfk9U4dhx36v+zUfpnEfrKbOpXZbpqAH7upO5lnjOtENy1gX9tPS5O7tX6axncCDwburqrfVNWjwOXAa2aNeQ1wWQ2sAbZNslPbQWeYN3NV3VhVDzVP1wC7tJxxLqPsa4BTgK8B97cZbohRMr8J+HpV/R6gqsade5TMBWydJMBWDIpsfbsxZwWqur7JMUzXjsM22E/tsJvaYTdND7upHZPYTWA/tabNfprGSeDOwB9mPL+3WbbQMW1aaJ4TGZwFGLd5cyfZGXgtcFGLuZ7KKPt6T2C7JNcluSXJ8a2lm9somS8AXgD8CbgDeE9VPd5OvEXr2nHYBvupHXZTO+ym6WE3tWMSuwnspy5ZsuNw+ZLE6ZbMsWz2R6COMqZNI+dJcgSDInvJJk00mlFyfxY4raoeG5xoGbtRMi8HDgBeBmwO/DjJmqq6a1OHG2KUzK8EbgNeCjwX+G6SG6rqb5s429PRteOwDfZTO+ymdthN08NuasckdhPYT12yZMfhNE4C7wWePeP5Lgxm+Asd06aR8iTZF7gYOLqq/tJStqcySu4DgcubItseOCbJ+qq6spWETzbq38eDVfUI8EiS64H9gHEV2SiZTwDOrsEF43cnuQd4PnBzOxEXpWvHYRvsp3bYTe2wm6aH3dSOSewmsJ+6ZOmOw8XeTNjVHwYT298Au7PxRtAXzhrzKv7/psqbJyDzrsDdwCHj3scLyT1r/KWM/8MXRtnXLwC+14zdAlgH7N3xzJ8DPtw83hH4I7B9B/5GVjH85uZOHYcd+r/s1H6ZxH6ymzqV2W6agB+7qTuZZ40fezctYF/bT0uXvZV+mrp3AqtqfZJ3A99h8MlAl1TVnUne0bx+EYNPWzqGQTH8k8GZgLEZMfMZwDOBC5uzQ+ur6sBxZW5yjZK7U0bJXFW/SPJtYC3wOHBxVc35Ub1dyQycBVya5A4GxXBaVT04rswASb4CHA5sn+Re4EPACujmcdgG+6lTmTvFbmqP3fRkdlOnMneO/dSeNvspzaxSkiRJktQD0/jpoJIkSZKkIZwESpIkSVKPOAmUJEmSpB5xEihJkiRJPeIkUJIkSZJ6xEmgFiRJJfnyjOfLkzyQ5FvzrLc6yTEjbP8fQ5a/I8nxC0/8xPq/TXJHkrVJfpBkt3nGr0rypsX+Pknts58kdZHdpC5yEqiFegTYO8nmzfMjGXy55nxWM/hek0VpvoPmssWu3ziiqvYFrgM+MM/YVYBFJk0W+0lSF9lN6hwngVqMq4FXNY/fCHxlwwtJDk5yY5KfNf8+L8lK4CPAcUluS3Jckq2SfHHGGabXzdjGx5LcnmRNkh2bZR9O8r7m8XVJzklyc5K7khzaLN8iyVeb7V2R5KYkc30p7I+BnZt1ViW5Icmtzc8hzZizgUObvKcmWZbkU0l+0mz/7Uu6RyUtFfvJfpK6yG6ymzrFSaAW43LgDUk2A/YFbprx2i+Bw6rqRcAZwMer6tHm8RVVtbqqrgA+CDxcVfs0Z5i+36y/JbCmqvYDrgdOGpJheVUdDLwX+FCz7J3AQ832zgIOGLLuUcCVzeP7gSOran/gOOC8ZvnpwA1N3s8AJzZ5DwIOAk5KsvtT7iVJ42A/2U9SF9lNdlOnLB93AE2eqlqbZBWDM1lXzXp5G+BLSfYAClgxZDMvB94wY5sPNQ8fBTZcI38Lg0sm5vL1GWNWNY9fApzbbG9dkrWz1rm2OTt2PxsvaVgBXJBkNfAYsOeQ3/cKYN8kxzbPtwH2AO4ZMl7SGNhPgP0kdY7dBNhNneI7gVqsbwCfZsblDI2zgGuram/g1cBmQ9YPg6Kb7b9VtWH5Yww/UfGfOcZknsxHALsBdzK4xALgVODPwH7AgcDKp8h7SnN2a3VV7V5V18zz+ySNh/1kP0ldZDfZTZ3hJFCLdQnwkaq6Y9bybdh4s/PbZiz/O7D1jOfXAO/e8CTJdkuQ6YfA65vt7QXsM3tAVf2LwWUQxyd5RpP3vqp6HHgLsGxI3u8AJydZ0Wx/zyRbLkFmSUvPfrKfpC6ym+ymznASqEWpqnur6tw5Xvok8IkkP2JjKQBcC+y14eZm4KPAdknWJbmdwZmmp+tCYIfmUobTgLXAw3Nkv4/BWbh3Neu8NckaBpczPNIMWwusb26yPhW4GPg5cGuSdcDn8XJqqZPsJ/tJ6iK7yW7qkmx891iabEmWASuq6t9Jngt8D9izublaksbGfpLURXZTfzkb1zTZgsENzCsYXId+siUmqSPsJ0ldZDf1lO8ESpIkSVKPeE+gJEmSJPWIk0BJkiRJ6hEngZIkSZLUI04CJUmSJKlHnARKkiRJUo84CZQkSZKkHvkfDpFII6TeO1sAAAAASUVORK5CYII=\n",
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\n",
       "text/plain": [
        "<Figure size 1080x360 with 3 Axes>"
       ]
@@ -437,7 +437,7 @@
    ],
    "source": [
     "for bm in unique_bms:\n",
-    "    flag = \"staic_multimap_find_all_matching_rate\" == bm\n",
+    "    flag = \"staic_multimap_retrieve_matching_rate\" == bm\n",
     "    \n",
     "    if flag:\n",
     "        tmpdf = perfdf[perfdf[\"Benchmark\"] == bm]\n",
@@ -450,7 +450,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### `retrieve` (`find_all`  + `count`) performance by varying key muliplicities\n",
+    "### `query` (`retrieve`  + `count`) performance by varying key muliplicities\n",
     "<ul>\n",
     "<li>100'000'000 key/value pairs inserted</li>\n",
     "<li>100'000'000 probing keys</li>\n",
@@ -467,7 +467,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 360x360 with 1 Axes>"
       ]
@@ -480,7 +480,7 @@
    ],
    "source": [
     "for bm in unique_bms:\n",
-    "    flag = \"staic_multimap_retrieve_uniform_multiplicity\" == bm\n",
+    "    flag = \"staic_multimap_query_uniform_multiplicity\" == bm\n",
     "    \n",
     "    if flag:\n",
     "        tmpdf = perfdf[perfdf[\"Benchmark\"] == bm]\n",
@@ -493,7 +493,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### `retrieve` (`find_all`  + `count`) performance by varying occupancies\n",
+    "### `query` (`retrieve`  + `count`) performance by varying occupancies\n",
     "<ul>\n",
     "<li>100'000'000 key/value pairs inserted</li>\n",
     "<li>100'000'000 probing keys</li>\n",
@@ -508,7 +508,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1080x360 with 3 Axes>"
       ]
@@ -521,7 +521,7 @@
    ],
    "source": [
     "for bm in unique_bms:\n",
-    "    flag = \"staic_multimap_retrieve_occupancy\" == bm\n",
+    "    flag = \"staic_multimap_query_occupancy\" == bm\n",
     "    \n",
     "    if flag:\n",
     "        tmpdf = perfdf[perfdf[\"Benchmark\"] == bm]\n",
@@ -534,7 +534,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### `retrevie` (`find_all`  + `count`) performance by varying matching rates\n",
+    "### `query` (`retrieve`  + `count`) performance by varying matching rates\n",
     "<ul>\n",
     "<li>100'000'000 key/value pairs inserted</li>\n",
     "<li>100'000'000 probing keys</li>\n",
@@ -549,7 +549,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1080x360 with 3 Axes>"
       ]
@@ -562,7 +562,7 @@
    ],
    "source": [
     "for bm in unique_bms:\n",
-    "    flag = \"staic_multimap_retrieve_matching_rate\" == bm\n",
+    "    flag = \"staic_multimap_query_matching_rate\" == bm\n",
     "    \n",
     "    if flag:\n",
     "        tmpdf = perfdf[perfdf[\"Benchmark\"] == bm]\n",

From f7d286b6a5f36bee1ea558c0fce70959d604663e Mon Sep 17 00:00:00 2001
From: Yunsong Wang <yunsongw@nvidia.com>
Date: Mon, 1 Nov 2021 11:19:51 -0400
Subject: [PATCH 267/267] Restrict CG type for public device view APIs

---
 .../static_multimap/static_multimap.inl       |  54 ++++---
 include/cuco/static_multimap.cuh              | 141 +++++++++---------
 2 files changed, 98 insertions(+), 97 deletions(-)

diff --git a/include/cuco/detail/static_multimap/static_multimap.inl b/include/cuco/detail/static_multimap/static_multimap.inl
index bbc92af78..6d7c36f00 100644
--- a/include/cuco/detail/static_multimap/static_multimap.inl
+++ b/include/cuco/detail/static_multimap/static_multimap.inl
@@ -142,10 +142,10 @@ std::size_t static_multimap<Key, Value, Scope, Allocator, ProbeSequence>::count(
   auto num_keys = std::distance(first, last);
   auto view     = get_device_view();
 
-  bool constexpr is_outer = false;
-
+  auto constexpr is_outer   = false;
   auto constexpr block_size = 128;
   auto constexpr stride     = 1;
+
   auto const grid_size = (cg_size() * num_keys + stride * block_size - 1) / (stride * block_size);
 
   cudaMemsetAsync(d_counter_.get(), 0, sizeof(atomic_ctr_type), stream);
@@ -172,10 +172,10 @@ std::size_t static_multimap<Key, Value, Scope, Allocator, ProbeSequence>::count_
   auto num_keys = std::distance(first, last);
   auto view     = get_device_view();
 
-  bool constexpr is_outer = true;
-
+  auto constexpr is_outer   = true;
   auto constexpr block_size = 128;
   auto constexpr stride     = 1;
+
   auto const grid_size = (cg_size() * num_keys + stride * block_size - 1) / (stride * block_size);
 
   cudaMemsetAsync(d_counter_.get(), 0, sizeof(atomic_ctr_type), stream);
@@ -443,10 +443,10 @@ template <typename Key,
           cuda::thread_scope Scope,
           typename Allocator,
           class ProbeSequence>
-template <typename CG>
 __device__ __forceinline__ void
 static_multimap<Key, Value, Scope, Allocator, ProbeSequence>::device_mutable_view::insert(
-  CG g, value_type const& insert_pair) noexcept
+  cooperative_groups::thread_block_tile<ProbeSequence::cg_size> const& g,
+  value_type const& insert_pair) noexcept
 {
   impl_.insert<uses_vector_load()>(g, insert_pair);
 }
@@ -537,10 +537,12 @@ template <typename Key,
           cuda::thread_scope Scope,
           typename Allocator,
           class ProbeSequence>
-template <typename CG, typename KeyEqual>
+template <typename KeyEqual>
 __device__ __forceinline__ bool
 static_multimap<Key, Value, Scope, Allocator, ProbeSequence>::device_view::contains(
-  CG g, Key const& k, KeyEqual key_equal) noexcept
+  cooperative_groups::thread_block_tile<ProbeSequence::cg_size> const& g,
+  Key const& k,
+  KeyEqual key_equal) noexcept
 {
   return impl_.contains<uses_vector_load()>(g, k, key_equal);
 }
@@ -550,10 +552,12 @@ template <typename Key,
           cuda::thread_scope Scope,
           typename Allocator,
           class ProbeSequence>
-template <typename CG, typename KeyEqual>
+template <typename KeyEqual>
 __device__ __forceinline__ std::size_t
 static_multimap<Key, Value, Scope, Allocator, ProbeSequence>::device_view::count(
-  CG const& g, Key const& k, KeyEqual key_equal) noexcept
+  cooperative_groups::thread_block_tile<ProbeSequence::cg_size> const& g,
+  Key const& k,
+  KeyEqual key_equal) noexcept
 {
   constexpr bool is_outer = false;
   return impl_.count<uses_vector_load(), is_outer>(g, k, key_equal);
@@ -564,10 +568,12 @@ template <typename Key,
           cuda::thread_scope Scope,
           typename Allocator,
           class ProbeSequence>
-template <typename CG, typename KeyEqual>
+template <typename KeyEqual>
 __device__ __forceinline__ std::size_t
 static_multimap<Key, Value, Scope, Allocator, ProbeSequence>::device_view::count_outer(
-  CG const& g, Key const& k, KeyEqual key_equal) noexcept
+  cooperative_groups::thread_block_tile<ProbeSequence::cg_size> const& g,
+  Key const& k,
+  KeyEqual key_equal) noexcept
 {
   constexpr bool is_outer = true;
   return impl_.count<uses_vector_load(), is_outer>(g, k, key_equal);
@@ -578,10 +584,12 @@ template <typename Key,
           cuda::thread_scope Scope,
           typename Allocator,
           class ProbeSequence>
-template <typename CG, typename PairEqual>
+template <typename PairEqual>
 __device__ __forceinline__ std::size_t
 static_multimap<Key, Value, Scope, Allocator, ProbeSequence>::device_view::pair_count(
-  CG const& g, value_type const& pair, PairEqual pair_equal) noexcept
+  cooperative_groups::thread_block_tile<ProbeSequence::cg_size> const& g,
+  value_type const& pair,
+  PairEqual pair_equal) noexcept
 {
   constexpr bool is_outer = false;
   return impl_.pair_count<uses_vector_load(), is_outer>(g, pair, pair_equal);
@@ -592,10 +600,12 @@ template <typename Key,
           cuda::thread_scope Scope,
           typename Allocator,
           class ProbeSequence>
-template <typename CG, typename PairEqual>
+template <typename PairEqual>
 __device__ __forceinline__ std::size_t
 static_multimap<Key, Value, Scope, Allocator, ProbeSequence>::device_view::pair_count_outer(
-  CG const& g, value_type const& pair, PairEqual pair_equal) noexcept
+  cooperative_groups::thread_block_tile<ProbeSequence::cg_size> const& g,
+  value_type const& pair,
+  PairEqual pair_equal) noexcept
 {
   constexpr bool is_outer = true;
   return impl_.pair_count<uses_vector_load(), is_outer>(g, pair, pair_equal);
@@ -608,14 +618,13 @@ template <typename Key,
           class ProbeSequence>
 template <uint32_t buffer_size,
           typename FlushingCG,
-          typename ProbingCG,
           typename atomicT,
           typename OutputIt,
           typename KeyEqual>
 __device__ __forceinline__ void
 static_multimap<Key, Value, Scope, Allocator, ProbeSequence>::device_view::retrieve(
   FlushingCG const& flushing_cg,
-  ProbingCG const& probing_cg,
+  cooperative_groups::thread_block_tile<ProbeSequence::cg_size> const& probing_cg,
   Key const& k,
   uint32_t* flushing_cg_counter,
   value_type* output_buffer,
@@ -647,14 +656,13 @@ template <typename Key,
           class ProbeSequence>
 template <uint32_t buffer_size,
           typename FlushingCG,
-          typename ProbingCG,
           typename atomicT,
           typename OutputIt,
           typename KeyEqual>
 __device__ __forceinline__ void
 static_multimap<Key, Value, Scope, Allocator, ProbeSequence>::device_view::retrieve_outer(
   FlushingCG const& flushing_cg,
-  ProbingCG const& probing_cg,
+  cooperative_groups::thread_block_tile<ProbeSequence::cg_size> const& probing_cg,
   Key const& k,
   uint32_t* flushing_cg_counter,
   value_type* output_buffer,
@@ -686,7 +694,6 @@ template <typename Key,
           class ProbeSequence>
 template <uint32_t buffer_size,
           typename FlushingCG,
-          typename ProbingCG,
           typename atomicT,
           typename OutputIt1,
           typename OutputIt2,
@@ -694,7 +701,7 @@ template <uint32_t buffer_size,
 __device__ __forceinline__ void
 static_multimap<Key, Value, Scope, Allocator, ProbeSequence>::device_view::pair_retrieve(
   FlushingCG const& flushing_cg,
-  ProbingCG const& probing_cg,
+  cooperative_groups::thread_block_tile<ProbeSequence::cg_size> const& probing_cg,
   value_type const& pair,
   uint32_t* flushing_cg_counter,
   value_type* probe_output_buffer,
@@ -737,7 +744,6 @@ template <typename Key,
           class ProbeSequence>
 template <uint32_t buffer_size,
           typename FlushingCG,
-          typename ProbingCG,
           typename atomicT,
           typename OutputIt1,
           typename OutputIt2,
@@ -745,7 +751,7 @@ template <uint32_t buffer_size,
 __device__ __forceinline__ void
 static_multimap<Key, Value, Scope, Allocator, ProbeSequence>::device_view::pair_retrieve_outer(
   FlushingCG const& flushing_cg,
-  ProbingCG const& probing_cg,
+  cooperative_groups::thread_block_tile<ProbeSequence::cg_size> const& probing_cg,
   value_type const& pair,
   uint32_t* flushing_cg_counter,
   value_type* probe_output_buffer,
diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh
index 840af103a..9261ef9a2 100644
--- a/include/cuco/static_multimap.cuh
+++ b/include/cuco/static_multimap.cuh
@@ -616,14 +616,13 @@ class static_multimap {
     /**
      * @brief Inserts the specified key/value pair into the map.
      *
-     * @tparam CG Cooperative Group type
-     *
      * @param g The Cooperative Group that performs the insert
      * @param insert_pair The pair to insert
      * @return void.
      */
-    template <typename CG>
-    __device__ __forceinline__ void insert(CG g, value_type const& insert_pair) noexcept;
+    __device__ __forceinline__ void insert(
+      cooperative_groups::thread_block_tile<ProbeSequence::cg_size> const& g,
+      value_type const& insert_pair) noexcept;
 
    private:
     device_mutable_view_impl impl_;
@@ -796,7 +795,6 @@ class static_multimap {
      * significant boost in throughput compared to the non Cooperative Group
      * `contains` at moderate to high load factors.
      *
-     * @tparam CG Cooperative Group type
      * @tparam KeyEqual Binary callable type
      * @param g The Cooperative Group used to perform the contains operation
      * @param k The key to search for
@@ -805,10 +803,11 @@ class static_multimap {
      * @return A boolean indicating whether the key/value pair
      * containing `k` was inserted
      */
-    template <typename CG, typename KeyEqual = thrust::equal_to<key_type>>
-    __device__ __forceinline__ bool contains(CG g,
-                                             Key const& k,
-                                             KeyEqual key_equal = KeyEqual{}) noexcept;
+    template <typename KeyEqual = thrust::equal_to<key_type>>
+    __device__ __forceinline__ bool contains(
+      cooperative_groups::thread_block_tile<ProbeSequence::cg_size> const& g,
+      Key const& k,
+      KeyEqual key_equal = KeyEqual{}) noexcept;
 
     /**
      * @brief Counts the occurrence of a given key contained in multimap.
@@ -816,7 +815,6 @@ class static_multimap {
      * For a given key, `k`, counts all matching keys, `k'`, as determined by `key_equal(k, k')` and
      * returns the sum of all matches for `k`.
      *
-     * @tparam CG Cooperative Group type
      * @tparam KeyEqual Binary callable type
      * @param g The Cooperative Group used to perform the count operation
      * @param k The key to search for
@@ -824,10 +822,11 @@ class static_multimap {
      * for equality
      * @return Number of matches found by the current thread
      */
-    template <typename CG, typename KeyEqual = thrust::equal_to<key_type>>
-    __device__ __forceinline__ std::size_t count(CG const& g,
-                                                 Key const& k,
-                                                 KeyEqual key_equal = KeyEqual{}) noexcept;
+    template <typename KeyEqual = thrust::equal_to<key_type>>
+    __device__ __forceinline__ std::size_t count(
+      cooperative_groups::thread_block_tile<ProbeSequence::cg_size> const& g,
+      Key const& k,
+      KeyEqual key_equal = KeyEqual{}) noexcept;
 
     /**
      * @brief Counts the occurrence of a given key contained in multimap. If no
@@ -836,7 +835,6 @@ class static_multimap {
      * For a given key, `k`, counts all matching keys, `k'`, as determined by `key_equal(k, k')` and
      * returns the sum of all matches for `k`. If `k` does not have any matches, returns 1.
      *
-     * @tparam CG Cooperative Group type
      * @tparam KeyEqual Binary callable type
      * @param g The Cooperative Group used to perform the count operation
      * @param k The key to search for
@@ -844,10 +842,11 @@ class static_multimap {
      * for equality
      * @return Number of matches found by the current thread
      */
-    template <typename CG, typename KeyEqual = thrust::equal_to<key_type>>
-    __device__ __forceinline__ std::size_t count_outer(CG const& g,
-                                                       Key const& k,
-                                                       KeyEqual key_equal = KeyEqual{}) noexcept;
+    template <typename KeyEqual = thrust::equal_to<key_type>>
+    __device__ __forceinline__ std::size_t count_outer(
+      cooperative_groups::thread_block_tile<ProbeSequence::cg_size> const& g,
+      Key const& k,
+      KeyEqual key_equal = KeyEqual{}) noexcept;
 
     /**
      * @brief Counts the occurrence of a given key/value pair contained in multimap.
@@ -855,7 +854,6 @@ class static_multimap {
      * For a given pair, `p`, counts all matching pairs, `p'`, as determined by `pair_equal(p, p')`
      * and returns the sum of all matches for `p`.
      *
-     * @tparam CG Cooperative Group type
      * @tparam PairEqual Binary callable type
      * @param g The Cooperative Group used to perform the pair_count operation
      * @param pair The pair to search for
@@ -863,10 +861,11 @@ class static_multimap {
      * for equality
      * @return Number of matches found by the current thread
      */
-    template <typename CG, typename PairEqual>
-    __device__ __forceinline__ std::size_t pair_count(CG const& g,
-                                                      value_type const& pair,
-                                                      PairEqual pair_equal) noexcept;
+    template <typename PairEqual>
+    __device__ __forceinline__ std::size_t pair_count(
+      cooperative_groups::thread_block_tile<ProbeSequence::cg_size> const& g,
+      value_type const& pair,
+      PairEqual pair_equal) noexcept;
 
     /**
      * @brief Counts the occurrence of a given key/value pair contained in multimap.
@@ -875,7 +874,6 @@ class static_multimap {
      * For a given pair, `p`, counts all matching pairs, `p'`, as determined by `pair_equal(p, p')`
      * and returns the sum of all matches for `p`. If `p` does not have any matches, returns 1.
      *
-     * @tparam CG Cooperative Group type
      * @tparam PairEqual Binary callable type
      * @param g The Cooperative Group used to perform the pair_count operation
      * @param pair The pair to search for
@@ -883,10 +881,11 @@ class static_multimap {
      * for equality
      * @return Number of matches found by the current thread
      */
-    template <typename CG, typename PairEqual>
-    __device__ __forceinline__ std::size_t pair_count_outer(CG const& g,
-                                                            value_type const& pair,
-                                                            PairEqual pair_equal) noexcept;
+    template <typename PairEqual>
+    __device__ __forceinline__ std::size_t pair_count_outer(
+      cooperative_groups::thread_block_tile<ProbeSequence::cg_size> const& g,
+      value_type const& pair,
+      PairEqual pair_equal) noexcept;
 
     /**
      * @brief Retrieves all the matches of a given key contained in multimap with per-flushing-CG
@@ -897,7 +896,6 @@ class static_multimap {
      *
      * @tparam buffer_size Size of the output buffer
      * @tparam FlushingCG Type of Cooperative Group used to flush output buffer
-     * @tparam ProbingCG Type of Cooperative Group for parallel retrieval
      * @tparam atomicT Type of atomic storage
      * @tparam OutputIt Device accessible output iterator whose `value_type` is
      * constructible from the map's `value_type`
@@ -914,18 +912,18 @@ class static_multimap {
      */
     template <uint32_t buffer_size,
               typename FlushingCG,
-              typename ProbingCG,
               typename atomicT,
               typename OutputIt,
               typename KeyEqual = thrust::equal_to<key_type>>
-    __device__ __forceinline__ void retrieve(FlushingCG const& flushing_cg,
-                                             ProbingCG const& probing_cg,
-                                             Key const& k,
-                                             uint32_t* flushing_cg_counter,
-                                             value_type* output_buffer,
-                                             atomicT* num_matches,
-                                             OutputIt output_begin,
-                                             KeyEqual key_equal = KeyEqual{}) noexcept;
+    __device__ __forceinline__ void retrieve(
+      FlushingCG const& flushing_cg,
+      cooperative_groups::thread_block_tile<ProbeSequence::cg_size> const& probing_cg,
+      Key const& k,
+      uint32_t* flushing_cg_counter,
+      value_type* output_buffer,
+      atomicT* num_matches,
+      OutputIt output_begin,
+      KeyEqual key_equal = KeyEqual{}) noexcept;
 
     /**
      * @brief Retrieves all the matches of a given key contained in multimap with per-flushing-CG
@@ -937,7 +935,6 @@ class static_multimap {
      *
      * @tparam buffer_size Size of the output buffer
      * @tparam FlushingCG Type of Cooperative Group used to flush output buffer
-     * @tparam ProbingCG Type of Cooperative Group for parallel retrieval
      * @tparam atomicT Type of atomic storage
      * @tparam OutputIt Device accessible output iterator whose `value_type` is
      * constructible from the map's `value_type`
@@ -954,18 +951,18 @@ class static_multimap {
      */
     template <uint32_t buffer_size,
               typename FlushingCG,
-              typename ProbingCG,
               typename atomicT,
               typename OutputIt,
               typename KeyEqual = thrust::equal_to<key_type>>
-    __device__ __forceinline__ void retrieve_outer(FlushingCG const& flushing_cg,
-                                                   ProbingCG const& probing_cg,
-                                                   Key const& k,
-                                                   uint32_t* flushing_cg_counter,
-                                                   value_type* output_buffer,
-                                                   atomicT* num_matches,
-                                                   OutputIt output_begin,
-                                                   KeyEqual key_equal = KeyEqual{}) noexcept;
+    __device__ __forceinline__ void retrieve_outer(
+      FlushingCG const& flushing_cg,
+      cooperative_groups::thread_block_tile<ProbeSequence::cg_size> const& probing_cg,
+      Key const& k,
+      uint32_t* flushing_cg_counter,
+      value_type* output_buffer,
+      atomicT* num_matches,
+      OutputIt output_begin,
+      KeyEqual key_equal = KeyEqual{}) noexcept;
 
     /**
      * @brief Retrieves all the matches of a given pair contained in multimap with per-flushing-CG
@@ -977,7 +974,6 @@ class static_multimap {
      *
      * @tparam buffer_size Size of the output buffer
      * @tparam FlushingCG Type of Cooperative Group used to flush output buffer
-     * @tparam ProbingCG Type of Cooperative Group for parallel retrieval
      * @tparam atomicT Type of atomic storage
      * @tparam OutputIt1 Device accessible output iterator whose `value_type` is constructible from
      * `InputIt`s `value_type`.
@@ -998,21 +994,21 @@ class static_multimap {
      */
     template <uint32_t buffer_size,
               typename FlushingCG,
-              typename ProbingCG,
               typename atomicT,
               typename OutputIt1,
               typename OutputIt2,
               typename PairEqual>
-    __device__ __forceinline__ void pair_retrieve(FlushingCG const& flushing_cg,
-                                                  ProbingCG const& probing_cg,
-                                                  value_type const& pair,
-                                                  uint32_t* warp_counter,
-                                                  value_type* probe_output_buffer,
-                                                  value_type* contained_output_buffer,
-                                                  atomicT* num_matches,
-                                                  OutputIt1 probe_output_begin,
-                                                  OutputIt2 contained_output_begin,
-                                                  PairEqual pair_equal) noexcept;
+    __device__ __forceinline__ void pair_retrieve(
+      FlushingCG const& flushing_cg,
+      cooperative_groups::thread_block_tile<ProbeSequence::cg_size> const& probing_cg,
+      value_type const& pair,
+      uint32_t* warp_counter,
+      value_type* probe_output_buffer,
+      value_type* contained_output_buffer,
+      atomicT* num_matches,
+      OutputIt1 probe_output_begin,
+      OutputIt2 contained_output_begin,
+      PairEqual pair_equal) noexcept;
 
     /**
      * @brief Retrieves all the matches of a given pair contained in multimap with per-flushing-CG
@@ -1025,7 +1021,6 @@ class static_multimap {
      *
      * @tparam buffer_size Size of the output buffer
      * @tparam FlushingCG Type of Cooperative Group used to flush output buffer
-     * @tparam ProbingCG Type of Cooperative Group for parallel retrieval
      * @tparam atomicT Type of atomic storage
      * @tparam OutputIt1 Device accessible output iterator whose `value_type` is constructible from
      * `InputIt`s `value_type`.
@@ -1046,21 +1041,21 @@ class static_multimap {
      */
     template <uint32_t buffer_size,
               typename FlushingCG,
-              typename ProbingCG,
               typename atomicT,
               typename OutputIt1,
               typename OutputIt2,
               typename PairEqual>
-    __device__ __forceinline__ void pair_retrieve_outer(FlushingCG const& flushing_cg,
-                                                        ProbingCG const& probing_cg,
-                                                        value_type const& pair,
-                                                        uint32_t* flushing_cg_counter,
-                                                        value_type* probe_output_buffer,
-                                                        value_type* contained_output_buffer,
-                                                        atomicT* num_matches,
-                                                        OutputIt1 probe_output_begin,
-                                                        OutputIt2 contained_output_begin,
-                                                        PairEqual pair_equal) noexcept;
+    __device__ __forceinline__ void pair_retrieve_outer(
+      FlushingCG const& flushing_cg,
+      cooperative_groups::thread_block_tile<ProbeSequence::cg_size> const& probing_cg,
+      value_type const& pair,
+      uint32_t* flushing_cg_counter,
+      value_type* probe_output_buffer,
+      value_type* contained_output_buffer,
+      atomicT* num_matches,
+      OutputIt1 probe_output_begin,
+      OutputIt2 contained_output_begin,
+      PairEqual pair_equal) noexcept;
 
    private:
     device_view_impl impl_;