Platform-specific performance differences when running wasm-opt.
#4650
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Wow, that orders of magnitude difference (3 to 112 seconds) on I would try using the linux One possible guess is that that pass hashes function contents. Perhaps the hash function in the STL on that machine is very slow, somehow? We do depend on system libraries there, so system differences can matter. Another thing to check is whether the slow build is a debug build - maybe Finally, I'd make sure the machine has enough memory, as maybe that pass uses more than others, and it starts to swap. Can try to build with Relevant to #4165 |
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It seems that But just setting vs Memory does not seem to an issue, though. It never seems to go even beyond 1GB used in either case. |
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An interesting thing is that we are also testing that on macOS and the same code that @MichaelMauderer is posting about is taking 30-45 seconds (!!!) on M1 macOS, which is crazily fast in comparison to Linux and Windows machines. |
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So maybe this is actually related to the version of Using a newly built version from this repo I see much more reasonable results, where more cores are faster at least.
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Interesting that the |
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Running on Windows with 16 cores and the latest binaries (107). So, it seems the huge difference is gone in the current version. |
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It seems that
wasm-opton my machine has serious differences in performance when running either on Windows 10 or Manjaro.I'm using
wasm-optthroughwasm-packon a non-trivial Rust project. On Windows, thewasm-optpass takes 133.157 seconds, on the same machine, different partition running Manjaro the same compile takes 728.146 seconds.This is the output when setting
BINARYEN_PASS_DEBUG=1for Windowsand on Manjaro
The specs of the machine used:
Processor AMD Ryzen Threadripper 2950X 16-Core Processor 3.50 GHz
Installed RAM 64.0 GB (63.9 GB usable)
Is there a known reason for this performance difference? My preferred work environment is Manjaro, but the time it takes to run
wasm-optthere is killing my workflow, and I do need to work with the fully optimized output at the moment.The text was updated successfully, but these errors were encountered: