Hi, To run "micro"-benchmarks on "micro"-optimizations, I started to use timeit, but in my experience timeit it far from reliable.
When I say micro: I'm talking about a test which takes less than 1000 ns, sometimes even a few nanoseconds! You always have to run the same micro-benchmark when timeit *at least* 5 times to find the "real" "minimum" runtime. That's why I wrote my own tool to run microbenchmarks: https://bitbucket.org/haypo/misc/src/tip/python/benchmark.py Yury suggested me to add this tool to the Python benchmark project. I'm ok with that, but only if we rename it to "microbench.py" :-) I wrote this tool to compare micro-optimizations with a long list of very simple tests. The result is written into a file. Then you can compare two files and compare more files, and maybe even compare multiple files to a "reference". It "hides" difference smaller than 5% to ignore the noise. The main feature is benchmark.py is that it calibrates the benchmark using time to choose the number of runs and number of loops. I proposed a similar idea for perf.py: https://bugs.python.org/issue26275 What do you think? Would this tool be useful? Victor _______________________________________________ Speed mailing list Speed@python.org https://mail.python.org/mailman/listinfo/speed
