I found calibrated loop count is not stable so memory usage is very different in some benchmarks. Especially, RAM usage of logging benchmark is very relating to loop count:
$ PYTHONMALLOC=malloc LD_PRELOAD=$HOME/local/lib/libmimalloc.so ./python bm_logging.py simple --track-memory --fast --inherit-environ PYTHONMALLOC,LD_PRELOAD -v Run 1: calibrate the number of loops: 512 - calibrate 1: 12.7 MB (loops: 512) Calibration: 1 warmup, 512 loops Run 2: 0 warmups, 1 value, 512 loops - value 1: 12.9 MB Run 3: 0 warmups, 1 value, 512 loops - value 1: 12.9 MB ... $ PYTHONMALLOC=malloc LD_PRELOAD=$HOME/local/lib/libmimalloc.so ./python bm_logging.py simple --track-memory --fast --inherit-environ PYTHONMALLOC,LD_PRELOAD -v -l1024 Run 1: 0 warmups, 1 value, 1024 loops - value 1: 21.4 MB Run 2: 0 warmups, 1 value, 1024 loops - value 1: 21.4 MB Run 3: 0 warmups, 1 value, 1024 loops - value 1: 21.4 MB ... -- Inada Naoki <songofaca...@gmail.com> _______________________________________________ Python-Dev mailing list -- python-dev@python.org To unsubscribe send an email to python-dev-le...@python.org https://mail.python.org/mailman3/lists/python-dev.python.org/ Message archived at https://mail.python.org/archives/list/python-dev@python.org/message/QBXLRFXDD5TLLDATV2PWE2QNLLDWRVXY/
