The article is really interesting. Anyway, I think pyperf was developed mainly for macro benchmarks. Its goal is to make different benchmarks on different machines or in different times comparable. What I'm doing on the contrary is a micro benchmark: I would confront if an algorithm is faster in a particular case (immutable dict).
I runned the benchmarks many times, and even if the absolute values are not stable, the delta between dict and frozendict is constant. Anyway I think that adding to the output also the sigma it's a good idea. If the sigma is too high maybe the pc is under load and the bench is not reliable. Furthermore I noticed that frozendict is a little more slow at checking if a key is present in the dictionary, if the key is not present, the dictionary has only string keys and the dictionary is small. I suppose that small dicts with only strings as keys are the majority. I'll take a look. On Wed, 22 Jul 2020 at 10:03, Inada Naoki <songofaca...@gmail.com> wrote: > On Wed, Jul 22, 2020 at 4:29 PM Marco Sulla > <marco.sulla.pyt...@gmail.com> wrote: > > > > Furthermore, it seems that pyperf has not disabled ASLR. After `sudo > python -m pyperf system tune`, ASRL continues to be in "Full randomization" > mode. > > > > You are right. pyperf doesn't disable ASLR, because code performance > is changed by code layout. > pyperf runs benchmark multiple times in isolated processes and > measures stats instead. > > Victor Stinner, the author of pyperf wrote a lot of information about > measuring performance. > It's very nice to read before benchmarking. > https://vstinner.readthedocs.io/benchmark.html > > Regards, > -- > Inada Naoki <songofaca...@gmail.com> >
_______________________________________________ Python-ideas mailing list -- python-ideas@python.org To unsubscribe send an email to python-ideas-le...@python.org https://mail.python.org/mailman3/lists/python-ideas.python.org/ Message archived at https://mail.python.org/archives/list/python-ideas@python.org/message/DXRFGK7R6E4JLYMQ4X32XFV2LXOSSXOG/ Code of Conduct: http://python.org/psf/codeofconduct/
