Open chrissimpkins opened 2 years ago
cc @madig cc @justvanrossum
Yes. A secret Google font project is compiled ~30% faster, but will also use ~5 GB more memory while doing it, than using CPython 3.8. Once table splitting is handled more gracefully, some of the speed gains might be reduced though.
Yes. A secret Google font project is compiled ~30% faster, but will also use ~5 GB more memory while doing it, than using CPython 3.8. Once table splitting is handled more gracefully, some of the speed gains might be reduced though.
What is the baseline cPy memory use here Nikolaus?
Around 9 GB for CPython and 14 GB for Pypy.
Around 9 GB for CPython and 14 GB for Pypy.
GH runners might be stretched thin! https://docs.github.com/en/actions/using-github-hosted-runners/about-github-hosted-runners#supported-runners-and-hardware-resources :D
Oof. You know, I was thinking before that the pipeline could do with a deep dive with https://github.com/bloomberg/memray. One day maybe!
PyPy is a Just-in-Time (JIT) Python compiler that can decrease execution times in some long running program scenarios (those that exceed the multiple second level warm up time required by the JIT compiler). It is highly compatible with Python code and has been used with the fontmake compiler Python pipeline sources.
pypy3 is the Python 3 support branch of pypy.
pypy features
Does it improve font compile times? If so, what type of font project benefits and are there downsides / workarounds required to use the approach?