cfbolz changed the topic of #pypy to: #pypy PyPy, the flexible snake https://pypy.org | IRC logs: https://quodlibet.duckdns.org/irc/pypy/latest.log.html#irc-end and https://libera.irclog.whitequark.org/pypy | Matti: I made a bit of progress, the tests now only segfault towards the end
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<ltfish> hi all, I have a large Python project, and the pure Python part runs much slower in PyPy than in CPython. I profiled it using jitlog and vmprof (had to migrate vmprof-server to Python 3), and found that about 85% of code was interpreted instead of jitted.
<ltfish> what's a good way or tool for me to profile the jitting in-depth to see what's happening? any pointers will be appreciated. thanks in advance!
<mattip> you might want to read a bit about the JIT
<mattip> one key concept is that only hot code is compiled, and hot code is code that is traced over 1000 times
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<ltfish> I did read that page, and I am aware that only hot code (by default, used over 1000 times) is jitted. however, is it expected that PyPy's interpreter is 30% slower than CPython 3.11 in my use case (pure Python code, no C extensions)?
<ltfish> I will lower the limit and profile again to see if more code gets jitted (with the understanding that more code getting jitted does not mean it will run faster, because of the bootstrap time)
<ltfish> thanks again!