2016-05-18 20:54 GMT+02:00 Maciej Fijalkowski fijall@gmail.com:

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Ok. I'm not sure yet that it's feasible to get exactly the same memory addresses for "hot" objects allocated by Python between two versions of the code (...)

Well the answer is to do more statistics really in my opinion. That is, perf should report average over multiple runs in multiple processes. I started a branch for pypy benchmarks for that, but never finished it actually.

I'm not sure that I understood you correctly. As I wrote, running the same benchmark twice using two processes gives exactly the same timing.

I already modified perf.py locally to run multiple processes and focus on the average + std dev rather than min of a single process.

Example:

run 10 process x 3 loops (total: 30) Run average: 205.4 ms +/- 0.1 ms (min: 205.3 ms, max: 205.4 ms) Run average: 205.3 ms +/- 0.1 ms (min: 205.2 ms, max: 205.4 ms) Run average: 205.2 ms +/- 0.0 ms (min: 205.2 ms, max: 205.3 ms) Run average: 205.3 ms +/- 0.1 ms (min: 205.2 ms, max: 205.4 ms) Run average: 205.3 ms +/- 0.1 ms (min: 205.2 ms, max: 205.4 ms) Run average: 205.4 ms +/- 0.1 ms (min: 205.3 ms, max: 205.4 ms) Run average: 205.3 ms +/- 0.2 ms (min: 205.1 ms, max: 205.4 ms) Run average: 205.2 ms +/- 0.1 ms (min: 205.1 ms, max: 205.2 ms) Run average: 205.3 ms +/- 0.1 ms (min: 205.2 ms, max: 205.4 ms) Run average: 205.3 ms +/- 0.1 ms (min: 205.2 ms, max: 205.4 ms) Total average: 205.3 ms +/- 0.1 ms (min: 205.1 ms, max: 205.4 ms)

The "total" concatenates all lists of timings.

Note: Oh, by the way, the timing also depends on the presence of .pyc files ;-) I modified perf.py to add a first run with a single iteration just to rebuild .pyc, since the benchmark always start by removing alll .pyc files...

Victor