diff --git a/monai/utils/__init__.py b/monai/utils/__init__.py
index 8f27a7faf3..d2d3e41d67 100644
--- a/monai/utils/__init__.py
+++ b/monai/utils/__init__.py
@@ -15,3 +15,4 @@
 from .enums import *
 from .misc import *
 from .module import *
+from .profiling import *
diff --git a/monai/utils/decorators.py b/monai/utils/decorators.py
index 0d14eb0dd3..35a594d077 100644
--- a/monai/utils/decorators.py
+++ b/monai/utils/decorators.py
@@ -9,28 +9,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import time
 from functools import wraps
 
 
-def timing(func):
-    """
-    This simple timing function decorator prints to stdout/logfile (it uses printFlush) how many seconds a call to the
-    original function took to execute, as well as the name before and after the call.
-    """
-
-    @wraps(func)
-    def timingwrap(*args, **kwargs):
-        print(func.__name__, flush=True)
-        start = time.perf_counter()
-        res = func(*args, **kwargs)
-        end = time.perf_counter()
-        print(func.__name__, "dT (s) =", (end - start), flush=True)
-        return res
-
-    return timingwrap
-
-
 class RestartGenerator:
     """
     Wraps a generator callable which will be called whenever this class is iterated and its result returned. This is
diff --git a/monai/utils/profiling.py b/monai/utils/profiling.py
new file mode 100644
index 0000000000..a8d6e2c630
--- /dev/null
+++ b/monai/utils/profiling.py
@@ -0,0 +1,90 @@
+# Copyright 2020 MONAI Consortium
+# 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.
+
+import time
+from functools import wraps
+
+import torch
+
+__all__ = ["torch_profiler_full", "torch_profiler_time_cpu_gpu", "torch_profiler_time_end_to_end"]
+
+
+def torch_profiler_full(func):
+    """
+    A decorator which will run the torch profiler for the decorated function,
+    printing the results in full.
+    Note: Enforces a gpu sync point which could slow down pipelines.
+    """
+
+    @wraps(func)
+    def wrapper(*args, **kwargs):
+
+        with torch.autograd.profiler.profile(use_cuda=True) as prof:
+            result = func(*args, **kwargs)
+
+        print(prof, flush=True)
+
+        return result
+
+    return wrapper
+
+
+def torch_profiler_time_cpu_gpu(func):
+    """
+    A decorator which measures the execution time of both the CPU and GPU components
+    of the decorated function, printing both results.
+    Note: Enforces a gpu sync point which could slow down pipelines.
+    """
+
+    @wraps(func)
+    def wrapper(*args, **kwargs):
+
+        with torch.autograd.profiler.profile(use_cuda=True) as prof:
+            result = func(*args, **kwargs)
+
+        cpu_time = prof.self_cpu_time_total
+        gpu_time = sum(evt.self_cuda_time_total for evt in prof.function_events)
+
+        cpu_time = torch.autograd.profiler.format_time(cpu_time)
+        gpu_time = torch.autograd.profiler.format_time(gpu_time)
+
+        print("cpu time: {}, gpu time: {}".format(cpu_time, gpu_time), flush=True)
+
+        return result
+
+    return wrapper
+
+
+def torch_profiler_time_end_to_end(func):
+    """
+    A decorator which measures the total execution time from when the decorated
+    function is called to when the last cuda operation finishes, printing the result.
+    Note: Enforces a gpu sync point which could slow down pipelines.
+    """
+
+    @wraps(func)
+    def wrapper(*args, **kwargs):
+
+        torch.cuda.synchronize()
+        start = time.perf_counter()
+
+        result = func(*args, **kwargs)
+
+        torch.cuda.synchronize()
+        end = time.perf_counter()
+
+        total_time = (end - start) * 1e6
+        total_time_str = torch.autograd.profiler.format_time(total_time)
+        print("end to end time: {}".format(total_time_str), flush=True)
+
+        return result
+
+    return wrapper