A. Skrobov added the comment: I've now tried it with "perf.py -r -m", and the memory savings are as follows:
### 2to3 ### Mem max: 45976.000 -> 47440.000: 1.0318x larger ### chameleon_v2 ### Mem max: 436968.000 -> 401088.000: 1.0895x smaller ### django_v3 ### Mem max: 23808.000 -> 22584.000: 1.0542x smaller ### fastpickle ### Mem max: 10768.000 -> 9248.000: 1.1644x smaller ### fastunpickle ### Mem max: 10988.000 -> 9328.000: 1.1780x smaller ### json_dump_v2 ### Mem max: 10892.000 -> 10612.000: 1.0264x smaller ### json_load ### Mem max: 11012.000 -> 9908.000: 1.1114x smaller ### nbody ### Mem max: 8696.000 -> 7944.000: 1.0947x smaller ### regex_v8 ### Mem max: 12504.000 -> 9432.000: 1.3257x smaller ### tornado_http ### Mem max: 27636.000 -> 27608.000: 1.0010x smaller So, on these benchmarks, the saving is not threefold, of course; but still quite substantial (up to 30%). The run time difference, on these benchmarks, is between "1.04x slower" and "1.06x faster", for reasons beyond my understanding (variability of background load, possibly?) ---------- _______________________________________ Python tracker <rep...@bugs.python.org> <http://bugs.python.org/issue26415> _______________________________________ _______________________________________________ Python-bugs-list mailing list Unsubscribe: https://mail.python.org/mailman/options/python-bugs-list/archive%40mail-archive.com
