PyTorch Internals 15 - Reading Operator Bottlenecks with PyTorch Profiling
The purpose of internals knowledge is to make a performance trace interpretable enough that you can actually change it
Profiling needs structure
A timeline only becomes useful when you can interpret it. The earlier topics in this series help you ask sharper questions:
- is CPU launch overhead dominant?
- are hidden copies showing up?
- is backward scheduling the issue?
- are there large idle gaps between CUDA kernels?
The next post moves into torch.compile, FX, and Inductor, which increasingly matter in modern PyTorch optimization work.