I’ve tried other profilers (nvprof+nvvp) and get similar results. CUDA offers a simplified memory access using Unified Memory. This also entails keeping different pointers for the same data, one for CPU and one for GPU. I’m expecting that some of this has to do with the lack of synchronization between CPU and GPU (when I call (), some values change). a.out 2 12260 NVPROF is profiling process 12260, command. So far we had to copy date from CPU memory to GPU memory before launching the CUDA kernel and after the kernel finishes the computing transfer it back to CPU memory.
Some examples are: N5torch8autograd13CopyBackwardsE: As far as I can tell, I run everything on the GPU, but still get quite a few operations where the profiler reports much more time spent on the CPU than on the GPU. At first glance, nvprof seems to be just a GUI-less version of the graphical profiling features available in the NVIDIA Visual Profiler and NSight Eclipse edition. The idea is borrowed from the NumPy array interface. I’m profiling a large network with (use_cuda=True). CUDA 5 added a powerful new tool to the CUDA Toolkit: nvprof.nvprof is a command-line profiler available for Linux, Windows, and OS X. Memory access Global memory is accessed via 32-, 64-, or 128-byte memory transactions. CUDA Array Interface (Version 3)¶ The CUDA Array Interface (or CAI) is created for interoperability between different implementations of CUDA array-like objects in various projects. 0.19 872.49us 1 872.49us 872.49us 872.49us cudaLaunch 0.12 544.71us 2 272.36us 127.47us 417.25us cuDeviceTotalMem 0.06 258.35us 4 64.588us 3.2130us 156.
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