Antoine Pitrou <pit...@free.fr> added the comment:
Here is a micro-benchmark of GC overhead: * before: $ ./python -m timeit -s "import gc, doctest, ftplib, asyncio, email, http.client, pydoc, pdb, fractions, decimal, difflib, textwrap, statistics, shutil, shelve, lzma, concurrent.futures, telnetlib, smtpd, tkinter.tix, trace, distutils, pkgutil, tabnanny, pickletools, dis, argparse" "gc.collect()" 100 loops, best of 5: 2.41 msec per loop * after: $ ./python -m timeit -s "import gc, doctest, ftplib, asyncio, email, http.client, pydoc, pdb, fractions, decimal, difflib, textwrap, statistics, shutil, shelve, lzma, concurrent.futures, telnetlib, smtpd, tkinter.tix, trace, distutils, pkgutil, tabnanny, pickletools, dis, argparse" "gc.collect()" 100 loops, best of 5: 2.52 msec per loop So it's a 4% slowdown, but GC runs themselves are a minor fraction of usual programs' runtime, so I'm not sure that matters. Though it would be better to test on an actual GC-heavy application. ---------- _______________________________________ Python tracker <rep...@bugs.python.org> <https://bugs.python.org/issue33597> _______________________________________ _______________________________________________ Python-bugs-list mailing list Unsubscribe: https://mail.python.org/mailman/options/python-bugs-list/archive%40mail-archive.com
