Message379528
I commented out sqlalchemy in the requirements.txt in the pyperformance source code, and it worked. I had also to skip tornado:
pyperformance run -r -b,-sqlalchemy_declarative,-sqlalchemy_imperative,-tornado_http -o ../perf_master.json
This is my result:
pyperformance compare perf_master.json perf_dict_init.json -O table | grep Significant
| 2to3 | 356 ms | 348 ms | 1.02x faster | Significant (t=7.28) |
| fannkuch | 485 ms | 468 ms | 1.04x faster | Significant (t=9.68) |
| pathlib | 22.5 ms | 22.1 ms | 1.02x faster | Significant (t=13.02) |
| pickle_dict | 29.0 us | 30.3 us | 1.05x slower | Significant (t=-92.36) |
| pickle_list | 4.55 us | 4.64 us | 1.02x slower | Significant (t=-10.87) |
| pyflate | 735 ms | 702 ms | 1.05x faster | Significant (t=6.67) |
| regex_compile | 197 ms | 193 ms | 1.02x faster | Significant (t=2.81) |
| regex_v8 | 24.5 ms | 23.9 ms | 1.02x faster | Significant (t=17.63) |
| scimark_fft | 376 ms | 386 ms | 1.03x slower | Significant (t=-15.07) |
| scimark_lu | 154 ms | 158 ms | 1.03x slower | Significant (t=-12.94) |
| sqlite_synth | 3.35 us | 3.21 us | 1.04x faster | Significant (t=17.65) |
| telco | 6.54 ms | 7.14 ms | 1.09x slower | Significant (t=-8.51) |
| unpack_sequence | 58.8 ns | 61.5 ns | 1.04x slower | Significant (t=-19.66) |
It's strange that some benchmarks are slower, since the patch only do two additional checks to dict_vectorcall. Maybe they use many little dicts?
@methane:
> Would you implement some more optimization based on your PR to demonstrate your idea?
I already done them, I'll do a PR. |
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Date |
User |
Action |
Args |
2020-10-24 12:47:25 | Marco Sulla | set | recipients:
+ Marco Sulla, methane, Mark.Shannon |
2020-10-24 12:47:25 | Marco Sulla | set | messageid: <1603543645.04.0.60323887513.issue41835@roundup.psfhosted.org> |
2020-10-24 12:47:25 | Marco Sulla | link | issue41835 messages |
2020-10-24 12:47:24 | Marco Sulla | create | |
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