diff --git a/python/tvm/topi/cuda/scatter.py b/python/tvm/topi/cuda/scatter.py
index cee13d7e01a2..652a109abe7d 100644
--- a/python/tvm/topi/cuda/scatter.py
+++ b/python/tvm/topi/cuda/scatter.py
@@ -788,7 +788,7 @@ def gen_ir(data_ptr, indices_ptr, updates_ptr, out_ptr):
             fused_shape *= i
 
         # For now we avoid parallizing over dimensions indexed by `indices` as
-        # there may be repeated indices and hadling parallel accumulation can
+        # there may be repeated indices and handling parallel accumulation can
         # be hard. So we parallelize over X_M .. X_{N-1} instead. This will
         # work well when these dimensions are large enough to saturate memory
         # bandwidth, but performance will be bad when these dimensions are
@@ -801,12 +801,14 @@ def gen_ir(data_ptr, indices_ptr, updates_ptr, out_ptr):
         bdim = ceil_div(fused_updates_dimension, tdim)
         ib.scope_attr(bx, "thread_extent", bdim)
 
-        with ib.for_range(0, ceil_div(fused_shape, bdim)) as i:
+        # Copy data into the output. This loop writes to the same portions of
+        # memory as the following loop, so we do not need a memory sync.
+        with ib.for_range(0, ceil_div(fused_shape, fused_updates_dimension), name="i") as i:
             index = i * fused_updates_dimension + bx * tdim + tx
-            with ib.if_scope(index < fused_shape):
+            with ib.if_scope(bx * tdim + tx < fused_updates_dimension):
                 out[index] = data[index]
 
-        with ib.for_range(0, fused_indices_dimension) as i:
+        with ib.for_range(0, fused_indices_dimension, name="i") as i:
             j = bx * tdim + tx
             with ib.if_scope(j < fused_updates_dimension):
                 offset = fused_updates_dimension
diff --git a/python/tvm/topi/scatter.py b/python/tvm/topi/scatter.py
index 0fe29f315b43..afb0d6633a2b 100644
--- a/python/tvm/topi/scatter.py
+++ b/python/tvm/topi/scatter.py
@@ -203,7 +203,7 @@ def _verify_scatter_nd_inputs(data, indices, updates):
     mdim = int(indices.shape[0])
     assert mdim <= len(data.shape), (
         f"The first dimension of the indices ({mdim}) must be less than or equal to "
-        f"the length of the shape of the output ({len(shape)})."
+        f"the length of the shape of the output ({len(data.shape)})."
     )
     for i in range(len(indices.shape) - 1):
         if isinstance(indices.shape[i + 1], expr.Var) or isinstance(updates.shape[i], expr.Var):