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

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:

• Rustc: a CI script for the multi-stage build
• GCC:
  • Official docs, section "Building with profile feedback" (even AutoFDO build is supported)
  • A part in a "wonderful" configure script
• Clang: Docs
• Python:
  • CPython: README
  • Pyston: README
• Go: Bash script
• V8: Bazel flag
• ChakraCore: Scripts
• Chromium: Script
• Firefox: Docs
  • Thunderbird has PGO support too
• PHP - Makefile command and old Centminmod scripts
• MySQL: CMake script
• YugabyteDB: GitHub commit
• FoundationDB: Script
• Zstd: Makefile
• Foot: Scripts
• Windows Terminal: GitHub PR
• Pydantic-core: GitHub PR
• file.d: GitHub PR
• OceanBase: CMake flag

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

• ClickHouse: https://clickhouse.com/docs/en/operations/optimizing-performance/profile-guided-optimization
• Databend: https://databend.rs/doc/contributing/pgo
• Vector: https://vector.dev/docs/administration/tuning/pgo/
• Nebula: https://docs.nebula-graph.io/3.5.0/8.service-tuning/enable_autofdo_for_nebulagraph/
• GCC: Official docs, section "Building with profile feedback" (even AutoFDO build is supported)
• Clang:
  • https://llvm.org/docs/HowToBuildWithPGO.html
  • https://llvm.org/docs/AdvancedBuilds.html
• tsv-utils: https://github.com/eBay/tsv-utils/blob/master/docs/BuildingWithLTO.md

Regarding LLVM BOLT integration, I have the following examples:

• Rustc:
  • Rustc itself (GitHub PR)
  • LLVM in Rustc (Reddit)
• CPython: GitHub PR
• YDB: GitHub comment
• Clang:
  • Slides
  • Results on building Clang
  • Linaro results
  • on AMD 7950X3D
  • GitHub comment from LLVM
• LDC: GitHub comment
• HHVM, Proxygen and others: Facebook paper
• NodeJS: Blog
• Chromium: Blog
• MySQL, MongoDB, memcached, Verilator: Paper

Start by reproducing the proposed PGO benchmarks for TensorRT's CPU-heavy part and reviewing the build scripts. Done requires benchmark evidence and a scoped decision on whether to document PGO, add a build option, or defer PLO; the issue names no TensorRT test or entry point.