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Recently I checked Profile-Guided Optimization (PGO) improvements on multiple projects. The results are here. E.g. PGO results for LLVM-related tooling are here. According to the tests, PGO usually helps with the compiler and compiler-like workloads (like static analysis) - e.g. Clang gets +20% compilation speed with PGO, CPython achieves +10% in pybench. So I think optimizing daScript tooling with PGO would be a good idea.
I can suggest the following action points:
Perform PGO benchmarks on the daScript tooling. If it shows improvements - add a note about possible improvements in daScript's 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 the daScript tools according to their own workloads.
Optimize pre-built binaries with PGO
Testing Post-Link Optimization techniques (like LLVM BOLT) would be interesting too (CPtyhon, Clang, and Rustc already use BOLT as an addition to PGO) but I recommend starting from the usual PGO.
Here are some examples of how PGO optimization is integrated in other projects:
Hi!
Recently I checked Profile-Guided Optimization (PGO) improvements on multiple projects. The results are here. E.g. PGO results for LLVM-related tooling are here. According to the tests, PGO usually helps with the compiler and compiler-like workloads (like static analysis) - e.g. Clang gets +20% compilation speed with PGO, CPython achieves +10% in
pybench
. So I think optimizing daScript tooling with PGO would be a good idea.I can suggest the following action points:
Testing Post-Link Optimization techniques (like LLVM BOLT) would be interesting too (CPtyhon, Clang, and Rustc already use BOLT as an addition to PGO) but I recommend starting from the usual PGO.
Here are some examples of how PGO optimization is integrated in other projects:
configure
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