How to Accurately Time CUDA Kernels in Pytorch
Blog post from Speechmatics
The article presents a comprehensive guide to accurately timing individual operations in a computational graph, particularly for machine learning models on GPUs. It highlights the importance of host-device synchronization, CUDA events, warm-up steps, fixed clocks, cache flush, and sleep/CUDA graphs in achieving accurate and repeatable results. The guide provides examples and tips specific to PyTorch, but the principles discussed can be applied to CUDA programming in general.
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