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Victor

---------- Forwarded message ---------- From: Victor Stinner <victor.stinner@gmail.com> Date: 2016-10-20 12:56 GMT+02:00 Subject: Benchmarking Python and micro-optimizations To: Python Dev <Python-Dev@python.org>

Hi,

Last months, I worked a lot on benchmarks. I ran benchmarks, analyzed results in depth (up to the hardware and kernel drivers!), I wrote new tools and enhanced existing tools.

• I wrote a new perf module which runs benchmarks in a reliable way and contains a LOT of features: collect metadata, JSON file format, commands to compare, render an histogram, etc.

• I rewrote the Python benchmark suite: the old benchmarks Mercurial repository moved to a new performance GitHub project which uses my perf module and contains more benchmarks.

• I also made minor enhancements to timeit in Python 3.7 -- some dev don't want major changes to not "break the backward compatibility".

For timeit, I suggest to use my perf tool which includes a reliable timeit command and has much more features like --duplicate (repeat the statements to reduce the cost of the outer loop) and --compare-to (compare two versions of Python), but also all builtin perf features (JSON output, statistics, histogram, etc.).

I added benchmarks from PyPy and Pyston benchmark suites to performance: performance 0.3.1 contains 51 benchmark scripts which run a total of 121 benchmarks. Example of tested Python modules:

• SQLAlchemy
• Dulwich (full Git implementation in Python)
• Mercurial (currently only the startup time)
• html5lib
• pyaes (AES crypto cipher in pure Python)
• sympy
• Tornado (HTTP client and server)
• Django (sadly, only the template engine right now, Pyston contains HTTP benchmarks)
• pathlib
• spambayes

More benchmarks will be added later. It would be nice to add benchmarks on numpy for example, numpy is important for a large part of our community.

All these (new or updated) tools can now be used to take smarter decisions on optimizations. Please don't push any optimization anymore without providing reliable benchmark results!

My first major action was to close the latest attempt to micro-optimize int+int in Python/ceval.c, http://bugs.python.org/issue21955 : I closed the issue as rejected, because there is no significant speedup on benchmarks other than two (tiny) microbenchmarks. To make sure that no one looses its time on trying to micro-optimize int+int, I even added a comment to Python/ceval.c :-)

https://hg.python.org/cpython/rev/61fcb12a9873 "Please don't try to micro-optimize int+int"

The perf and performance are now well tested: Travis CI runs tests on the new commits and pull requests, and the "tox" command can be used locally to test different Python versions, pep8, doc, ... in a single command.

Next steps:

• Run performance 0.3.1 on speed.python.org: the benchmark runner is currently stopped (and still uses the old benchmarks project). The website part may be updated to allow to download full JSON files which includes *all* information (all timings, metadata and more).

• I plan to run performance on CPython 2.7, CPython 3.7, PyPy and PyPy

3. Maybe also CPython 3.5 and CPython 3.6 if they don't take too much resources.

• Later, we can consider adding more implementations of Python: Jython, IronPython, MicroPython, Pyston, Pyjion, etc. All benchmarks should be run on the same hardware to be comparable.

• Later, we might also allow other projects to upload their own benchmark results, but we should find a solution to groups benchmark results per benchmark runner (ex: at least by the hostname, perf JSON contains the hostname) to not compare two results from two different hardware

• We should continue to add more benchmarks to the performance benchmark suite, especially benchmarks more representative of real applications (we have enough microbenchmarks!)

Links:

• perf: http://perf.readthedocs.io/
• performance: https://github.com/python/performance
• Python Speed mailing list: https://mail.python.org/mailman/listinfo/speed
• https://speed.python.org/ (currently outdated, and don't use performance yet)

See https://pypi.python.org/pypi/performance which contains even more links to Python benchmarks (PyPy, Pyston, Numba, Pythran, etc.)

Victor