Evaluate using Profile-Guided Optimization (PGO) and LLVM BOLT

[zamazan4ik]

Hi!

Recently I did many Profile-Guided Optimization (PGO) benchmarks on multiple projects (including many databases like PostgreSQL, ClickHouse, Redis, MongoDB, etc.) - the results are available here. So that's why I think it's worth trying to apply PGO to OpenMLDB to improve the database performance further.

I can suggest the following things to do:

• Evaluate PGO's results on OpenMLDB.
• If PGO helps to achieve better performance - add a note to OpenMLDB's documentation about that (probably somewhere in the README file). In this case, users and maintainers will be aware of another optimization opportunity for OpenMLDB.
• Provide PGO integration into the build scripts. It can help users and maintainers easily apply PGO for their own workloads.
• Optimize prebuilt binaries with PGO.

Here are some examples of how PGO is already integrated into other projects' build scripts:

• 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

After PGO, I can suggest evaluating LLVM BOLT as an additional optimization step after PGO.

[aceforeverd]

sounds interesting, we may investigate it later.