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De: "David Edelsohn" <dje.gcc@gmail.com> À: "PIERRE AUGIER" <pierre.augier@univ-grenoble-alpes.fr> Cc: "pypy-dev" <pypy-dev@python.org> Envoyé: Lundi 21 Décembre 2020 23:47:22 Objet: Re: [pypy-dev] Differences performance Julia / PyPy on very similar codes
You did not state on exactly what system you are conducting the experiment, but "a factor of 4" seems very close to the auto-vectorization speedup of a vector of floats.
I wrote another very simple benchmark that should not depend on auto-vectorization. The bench function is: ```python def sum_x(positions): result = 0.0 for i in range(len(positions)): result += positions[i].x return result ``` The scripts are: - https://github.com/paugier/nbabel/blob/master/py/microbench_sum_x.py - https://github.com/paugier/nbabel/blob/master/py/microbench_sum_x.jl Even on this case, Julia is again notably (~2.7 times) faster on this case: ``` $ julia microbench_sum_x.jl 1.208 μs (1 allocation: 16 bytes) In [1]: run microbench_sum_x.py sum_x(positions) 3.29 µs ± 133 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each) sum_x(positions_list) 14.5 µs ± 291 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each) ``` For `positions_list`, each `point` contains a list to store the 3 floats. How can I analyze these performance differences? How can I get more information on what happens for this code with PyPy?