3 ms·

> very little speed-up on pypy from being in a function I believe PyPy is clever and checks for new or removed elements in module and builtin scope. If they ha

> very little speed-up on pypy from being in a function I believe PyPy is clever and checks for new or removed elements in module and builtin scope. If they haven't changed then lookup can re-use previously resolved information. > clawing back performance On my laptop, appropriately instrumented, I see Python 3.12 is faster than 2.7. (Note that they use different versions of clang): % python2.7 t.py version: 2.7.17 module scope: 1.0321419239 function scope: 0.575540065765 % python3.12 t.py version: 3.12.2 module scope: 0.9053220748901367 function scope: 0.44064807891845703 Python is far from speedy, yes. For my critical performance code I use a C extension and compiler intrinsics.

cb321 2y ago
Good data. Thanks. I got a bit of a speed-up on python-3.12.5 - 0.599 down from 0.76863, but still slower than 2.7 on the same (old) i7-6700k CPU. { EDIT: All versions on both CPUs compiled on gcc-13 -O3, FWIW. } On a different CPU i7-1370P AlderLake P-core (via Linux taskset) I got a 1.5X time ratio (py3.12.5 being 0.371 and py2.7 0.245). Anyway, I should have qualified that it's likely the kind of thing that varies across CPUs. Maybe you are on AMD. And no, I sure don't have access to the CPUs I had back in 2007 anymore. :-) So, there is no danger of this being a very scientific perf shade toss. ;-) Same pypy3 also showed very minor speed-up for being inside a function. So, I think you are probably right about PyPy's optimization there and maybe it does just vary across PyPy versions. Not sure how @mg several levels up got his big 2X boost, but down at the 10s of usec to 10s of ms scale, just fluctuations in OS scheduler or P-core/E-core kinds of things can create confusion which is part of the motivation for aforementioned https://github.com/c-blake/bu/blob/main/doc/tim.md https://github.com/c-blake/bu/blob/main/doc/tim.md. I should perhaps also have mentioned https://github.com/yglukhov/nimpy https://github.com/yglukhov/nimpy as a way to write extensions in Nim. Kind of like Cython/SWIG like stuff, but for Nim.

cb321 2y ago

Good data. Thanks. I got a bit of a speed-up on python-3.12.5 - 0.599 down from 0.76863, but still slower than 2.7 on the same (old) i7-6700k CPU. { EDIT: All versions on both CPUs compiled on gcc-13 -O3, FWIW. } On a different CPU i7-1370P AlderLake P-core (via Linux taskset) I got a 1.5X time ratio (py3.12.5 being 0.371 and py2.7 0.245). Anyway, I should have qualified that it's likely the kind of thing that varies across CPUs. Maybe you are on AMD. And no, I sure don't have access to the CPUs I had back in 2007 anymore. :-) So, there is no danger of this being a very scientific perf shade toss. ;-) Same pypy3 also showed very minor speed-up for being inside a function. So, I think you are probably right about PyPy's optimization there and maybe it does just vary across PyPy versions. Not sure how @mg several levels up got his big 2X boost, but down at the 10s of usec to 10s of ms scale, just fluctuations in OS scheduler or P-core/E-core kinds of things can create confusion which is part of the motivation for aforementioned https://github.com/c-blake/bu/blob/main/doc/tim.md https://github.com/c-blake/bu/blob/main/doc/tim.md. I should perhaps also have mentioned https://github.com/yglukhov/nimpy https://github.com/yglukhov/nimpy as a way to write extensions in Nim. Kind of like Cython/SWIG like stuff, but for Nim.