PyTorch Internals 08 - CUDA Streams, Events, and Asynchronous Execution
Many PyTorch CUDA operations are asynchronous, so timing, synchronization, and dependency need to be reasoned about explicitly
Why async execution matters
Calling a CUDA operator in PyTorch usually does not mean the CPU waits until it is fully finished. Work is commonly enqueued asynchronously.
That affects:
- benchmarking correctness
- overlap between compute and transfer
- custom operator synchronization behavior
The next post moves into C++ extensions, where these runtime assumptions become more visible.