Mailman 3 October 2016 - Speed

Re: [Speed] Benchmarking Python and micro-optimizations
by Victor Stinner 22 Oct '16

22 Oct '16

Hi, I removed all old benchmarks results and I started to run manually benchmarks. The timeline view is interesting to investigate performance regression: https://speed.python.org/timeline/#/?exe=3&ben=grid&env=1&revs=50&equid=off… For example, it seems like call_method became slower between Oct 9 and Oct 20: 35.9 ms => 59.9 ms: https://speed.python.org/timeline/#/?exe=3&ben=call_method&env=1&revs=50&eq… I don't know well the hardware of the benchmark runner, so maybe it's an issue with the server running benchmarks? Victor

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Fwd: Benchmarking Python and micro-optimizations
by Victor Stinner 20 Oct '16

20 Oct '16

If you are not subscribed to the Python-Dev mailing list, here is the copy of the email I just sent. Victor ---------- Forwarded message ---------- From: Victor Stinner <victor.stinner(a)gmail.com> Date: 2016-10-20 12:56 GMT+02:00 Subject: Benchmarking Python and micro-optimizations To: Python Dev <Python-Dev(a)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

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Latest enhancements of perf 0.8.1 and performance 0.3.1
by Victor Stinner 19 Oct '16

19 Oct '16

Hi, Latest enhancements: 1) perf supports multiple benchmarks per script 2) perf calibrates the benchmark in a dedicated process 3) new --duplication option to perf timeit (1) I solved an old limitaton of my perf module: since perf 0.8, it's now possible to run multiple benchmarks in a simple script. Example of script: -- runner = perf.Runner() runner.bench_func('dict.get', dict_get, dico, keys) runner.bench_func('try/except', try_except, dico, keys) --- The runner spawns N process for the first benchmark + N process for the second benchmark. The trick is to pass an option --worker-task option to the worker process. In the worker, bench_func() does nothing (return None) if the "worker task" counter doesn't match. I rewrote the API in perf 0.8 to simpliy it and fix design issues. It should be the latest large API change before perf 1.0 which is expected before the end of the year. (2) Simple change but important one to enhance a little bit more the reliability of benchmarks, especially on Python implementations having a JIT: benchmark calibration is now done in a dedicated process. All worker processes (computing samples) should now run exactly the same workload. Before, the first worker was different, it ran a few more iterations, because of the calibration. (3) Another simple but useful change for the shortest microbenchmarks: I added a --duplicate option to timeit. Example: ------ $ python3 -m perf timeit -s 'x=1;y=2' 'x+y' ..................... Median +- std dev: 26.7 ns +- 2.0 ns $ python3 -m perf timeit -s 'x=1;y=2' 'x+y' --duplicate=1000 ..................... Median +- std dev: 19.2 ns +- 0.4 ns ------ Duplicating the statement 1000x reduces the cost of the outer loop by 28%. FYI I wrote this feature to help me to take a decision on the old and dummy "1+1" optimization for Python 3: https://bugs.python.org/issue21955 (issue open since July 2014). (Spoiler: I plan to collect benchmark results to explain that the micro optimization is useful, but I'm not 100% sure that it's useless right now :-)) Victor

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Re: [Speed] Performance 0.3 released with 10 new benchmarks
by Victor Stinner 11 Oct '16

11 Oct '16

I didn't copy/paste code from PyPy benchmaks directly. I updated 3rd party dependencies, I updated the code to use the perf API, and sometimes I even fixed bugs in the benchmarks. 2016-10-11 2:22 GMT+02:00 Victor Stinner <victor.stinner(a)gmail.com>: > * Add ``sqlalchemy_declarative`` and ``sqlalchemy_imperative`` benchmarks: > SQLAlchemy Declarative and Imperative benchmarks using SQLite. Add > ``SQLAlchemy`` dependency. For these two new benchmarks, it's unclear to me if the purpose of the benchmark is to test INSERT, SELECT or INSERT+SELECT. Currently, the benchmark test INSERT+SELECT. Compared to the PyPy benchmark, the benchmark now drops all rows of the tables before each run to get more reproductible timings. Victor

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Performance 0.3 released with 10 new benchmarks
by Victor Stinner 11 Oct '16

11 Oct '16

Hi, I just released performance 0.3, the Python benchmark suite, with 10 new benchmarks from the PyPy benchmark suite: https://github.com/python/performance Version 0.3.0 changelog. New benchmarks: * Add ``crypto_pyaes``: Benchmark a pure-Python implementation of the AES block-cipher in CTR mode using the pyaes module (version 1.6.0). Add ``pyaes`` dependency. * Add ``sympy``: Benchmark on SymPy. Add ``scipy`` dependency. * Add ``scimark`` benchmark * Add ``deltablue``: DeltaBlue benchmark * Add ``dulwich_log``: Iterate on commits of the asyncio Git repository using the Dulwich module. Add ``dulwich`` (and ``mpmath``) dependencies. * Add ``pyflate``: Pyflate benchmark, tar/bzip2 decompressor in pure Python * Add ``sqlite_synth`` benchmark: Benchmark Python aggregate for SQLite * Add ``genshi`` benchmark: Render template to XML or plain text using the Genshi module. Add ``Genshi`` dependency. * Add ``sqlalchemy_declarative`` and ``sqlalchemy_imperative`` benchmarks: SQLAlchemy Declarative and Imperative benchmarks using SQLite. Add ``SQLAlchemy`` dependency. Enhancements: * ``compare`` command now fails if the performance versions are different * ``nbody``: add ``--reference`` and ``--iterations`` command line options. * ``chaos``: add ``--width``, ``--height``, ``--thickness``, ``--filename`` and ``--rng-seed`` command line options * ``django_template``: add ``--table-size`` command line option * ``json_dumps``: add ``--cases`` command line option * ``pidigits``: add ``--digits`` command line option * ``raytrace``: add ``--width``, ``--height`` and ``--filename`` command line options * Port ``html5lib`` benchmark to Python 3 * Enable ``pickle_pure_python`` and ``unpickle_pure_python`` on Python 3 (code was already compatible with Python 3) * Creating the virtual environment doesn't inherit environment variables (especially ``PYTHONPATH``) by default anymore: ``--inherit-environ`` command line option must now be used explicitly. Bugfixes: * ``chaos`` benchmark now also reset the ``random`` module at each sample to get more reproductible benchmark results * Logging benchmarks now truncate the in-memory stream before each benchmark run Rename benchmarks: * Rename benchmarks to get a consistent name between the command line and benchmark name in the JSON file. * Rename pickle benchmarks: - ``slowpickle`` becomes ``pickle_pure_python`` - ``slowunpickle`` becomes ``unpickle_pure_python`` - ``fastpickle`` becomes ``pickle`` - ``fastunpickle`` becomes ``unpickle`` * Rename ElementTree benchmarks: replace ``etree_`` prefix with ``xml_etree_``. * Rename ``hexiom2`` to ``hexiom_level25`` and explicitly pass ``--level=25`` parameter * Rename ``json_load`` to ``json_loads`` * Rename ``json_dump_v2`` to ``json_dumps`` (and remove the deprecated ``json_dump`` benchmark) * Rename ``normal_startup`` to ``python_startup``, and ``startup_nosite`` to ``python_startup_no_site`` * Rename ``threaded_count`` to ``threading_threaded_count``, rename ``iterative_count`` to ``threading_iterative_count`` * Rename logging benchmarks: - ``silent_logging`` to ``logging_silent`` - ``simple_logging`` to ``logging_simple`` - ``formatted_logging`` to ``logging_format`` Minor changes: * Update dependencies * Remove broken ``--args`` command line option. Victor

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Speed October 2016