How to Accurately Time CUDA Kernels in Pytorch

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.