What Really Determines the Speed of Your PyTorch Code?
SMRTR summary
PyTorch developers often struggle with slow training loops and need proper benchmarking techniques to identify bottlenecks. This guide explains why naive Python time measurements fail due to GPU-CPU asynchrony and demonstrates correct approaches using CUDA events, L2 cache flushing, and warmup iterations. It also covers Triton's built-in benchmarking utilities as ready-made solutions.
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