On Thu, 15 Dec 2011 22:18:18 +0000 Mark Shannon <m...@hotpy.org> wrote: > > For the gcbench benchmark (from unladen swallow), > cpython with the new dict is about 9% faster and, more importantly, > reduces memory use from 99 Mbytes to 61Mbytes (a 38% reduction). > > All tests were done on my ancient 32 bit intel linux machine, > please try it out on your machines and let me know what sort of results > you get.
Benchmark results under a Core i5, 64-bit Linux: Report on Linux localhost.localdomain 2.6.38.8-desktop-8.mga #1 SMP Fri Nov 4 00:05:53 UTC 2011 x86_64 x86_64 Total CPU cores: 4 ### call_method ### Min: 0.292352 -> 0.274041: 1.07x faster Avg: 0.292978 -> 0.277124: 1.06x faster Significant (t=17.31) Stddev: 0.00053 -> 0.00351: 6.5719x larger ### call_method_slots ### Min: 0.284101 -> 0.273508: 1.04x faster Avg: 0.285029 -> 0.274534: 1.04x faster Significant (t=26.86) Stddev: 0.00068 -> 0.00135: 1.9969x larger ### call_simple ### Min: 0.225191 -> 0.222104: 1.01x faster Avg: 0.227443 -> 0.222776: 1.02x faster Significant (t=9.53) Stddev: 0.00181 -> 0.00056: 3.2266x smaller ### fastpickle ### Min: 0.482402 -> 0.493695: 1.02x slower Avg: 0.486077 -> 0.496568: 1.02x slower Significant (t=-5.35) Stddev: 0.00340 -> 0.00276: 1.2335x smaller ### fastunpickle ### Min: 0.394846 -> 0.433733: 1.10x slower Avg: 0.397362 -> 0.436318: 1.10x slower Significant (t=-23.73) Stddev: 0.00234 -> 0.00283: 1.2129x larger ### float ### Min: 0.052567 -> 0.051377: 1.02x faster Avg: 0.053812 -> 0.052669: 1.02x faster Significant (t=3.72) Stddev: 0.00110 -> 0.00107: 1.0203x smaller ### json_dump ### Min: 0.381395 -> 0.391053: 1.03x slower Avg: 0.381937 -> 0.393219: 1.03x slower Significant (t=-7.15) Stddev: 0.00043 -> 0.00350: 8.1447x larger ### json_load ### Min: 0.347112 -> 0.369763: 1.07x slower Avg: 0.347490 -> 0.370317: 1.07x slower Significant (t=-69.64) Stddev: 0.00045 -> 0.00058: 1.2717x larger ### nbody ### Min: 0.238068 -> 0.219208: 1.09x faster Avg: 0.238951 -> 0.220000: 1.09x faster Significant (t=36.09) Stddev: 0.00076 -> 0.00090: 1.1863x larger ### nqueens ### Min: 0.262282 -> 0.252576: 1.04x faster Avg: 0.263835 -> 0.254497: 1.04x faster Significant (t=7.12) Stddev: 0.00117 -> 0.00269: 2.2914x larger ### regex_effbot ### Min: 0.060298 -> 0.057791: 1.04x faster Avg: 0.060435 -> 0.058128: 1.04x faster Significant (t=17.82) Stddev: 0.00012 -> 0.00026: 2.1761x larger ### richards ### Min: 0.148266 -> 0.143755: 1.03x faster Avg: 0.150677 -> 0.145003: 1.04x faster Significant (t=5.74) Stddev: 0.00200 -> 0.00094: 2.1329x smaller ### silent_logging ### Min: 0.057191 -> 0.059082: 1.03x slower Avg: 0.057335 -> 0.059194: 1.03x slower Significant (t=-17.40) Stddev: 0.00020 -> 0.00013: 1.4948x smaller ### unpack_sequence ### Min: 0.000046 -> 0.000042: 1.10x faster Avg: 0.000048 -> 0.000044: 1.09x faster Significant (t=128.98) Stddev: 0.00000 -> 0.00000: 1.8933x smaller gcbench first showed no memory consumption difference (using "ps -u"). I then removed the "stretch tree" (which apparently reserves memory upfront) and I saw a ~30% memory saving as well as a 20% performance improvement on large sizes. Regards Antoine. _______________________________________________ Python-Dev mailing list Python-Dev@python.org http://mail.python.org/mailman/listinfo/python-dev Unsubscribe: http://mail.python.org/mailman/options/python-dev/archive%40mail-archive.com
