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Evaluate using Profile-Guided Optimization (PGO) and Post-Link Optimization (PLO) #3512

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@zamazan4ik

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Hi!

I checked Profile-Guided Optimization (PGO) and Post-Link Optimization (PLO) improvements on multiple projects. The results are available here. According to the tests, these optimizations can help with achieving better performance in many cases for many applications: compilers and interpreters, static analysis, networking, parsers and serializers/deserializers, other simpler routines, etc. I think optimizing TensorRT (its CPU-heavy part) with PGO and PLO would be a good idea.

I can suggest the following things:

  • Perform PGO benchmarks on TensorRT. If it shows improvements - add a note to the documentation about possible improvements in TensorRT performance with PGO.
  • Providing an easier way (e.g. a build option) to build scripts with PGO can be helpful for the end-users and maintainers since they will be able to optimize TensorRT according to their workloads.
  • Optimize pre-built TensorRT binaries

As an additional optimization step after PGO, I can suggest Post-Link Optimization (PLO) with a tool like LLVM BOLT. I think it's still worth evaluating it only after the PGO integration into TensorRT.

Examples of how PGO optimization is integrated into other projects:

I have some examples of how PGO information looks in the documentation:

Regarding LLVM BOLT integration, I have the following examples:

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