[Numpy-discussion] Efficiency of Numpy wheels and simple way to benchmark Numpy installation?

and get a good idea of their overall performance? I explain a little bit more in the stackoverflow question... Pierre Augier ___ NumPy-Discussion mailing list NumPy-Discussion@python.org https://mail.python.org/mailman/listinfo/numpy-discussion

Re: [Numpy-discussion] Transonic Vision: unifying Python-Numpy accelerators

of these functions with pure Numpy, Numba and Pythran ? You can find out the answer in our note http://tiny.cc/transonic-vision :-) Pierre > Message: 1 > Date: Thu, 31 Oct 2019 21:16:06 +0100 (CET) > From: PIERRE AUGIER > To: numpy-discussion@python.org > Subject: [Numpy-discussion] T

[Numpy-discussion] Transonic Vision: unifying Python-Numpy accelerators

! Pierre -- Pierre Augier - CR CNRS http://www.legi.grenoble-inp.fr LEGI (UMR 5519) Laboratoire des Ecoulements Geophysiques et Industriels BP53, 38041 Grenoble Cedex, Francetel:+33.4.56.52.86.16 ___ NumPy-Discussion mailing

Re: [Numpy-discussion] Transonic Vision: unifying Python-Numpy accelerators

gt; > On Mon, Nov 4, 2019 at 4:54 PM PIERRE AUGIER < > pierre.aug...@univ-grenoble-alpes.fr> wrote: > >> Dear Python-Numpy community, >> >> Transonic is a pure Python package to easily accelerate modern >> Python-Numpy code with different accelerators (c

[Numpy-discussion] Comment published in Nature Astronomy about The ecological impact of computing with Python

Python functions with optimized compiled code). Of course, I hope I'm wrong! IMHO, it does not remove the need for a successful HPy! -- Pierre Augier - CR CNRS http://www.legi.grenoble-inp.fr LEGI (UMR 5519) Laboratoire des Ecoulements Geophysiques et Industriels BP53, 38041 G

[Numpy-discussion] Showing by examples how Python-Numpy can be efficient even for computationally intensive tasks

t; On Tue, Nov 24, 2020 at 11:12 AM Ilhan Polat < [ mailto:ilhanpo...@gmail.com | > ilhanpo...@gmail.com ] > wrote: > > Do we have to take it seriously to start with? Because, with absolutely no > offense meant, I am having significant difficulty doing so. > > On Tue, Nov 24, 20

Re: [Numpy-discussion] Comment published in Nature Astronomy about The ecological impact of computing with Python

I'd like to know if some people involved in the community are willing to be co-authors of this potential reply. Cheers, Pierre -- Pierre Augier - CR CNRS http://www.legi.grenoble-inp.fr LEGI (UMR 5519) Laboratoire des Ecoulements Geophysiques et Industriels BP53, 38041 Gren

[Numpy-discussion] Looking for a difference between Numpy 0.19.5 and 0.20 explaining a perf regression with Pythran

Hi, I'm looking for a difference between Numpy 0.19.5 and 0.20 which could explain a performance regression (~15 %) with Pythran. I observe this regression with the script https://github.com/paugier/nbabel/blob/master/py/bench.py Pythran reimplements Numpy so it is not about Numpy code for

Re: [Numpy-discussion] Looking for a difference between Numpy 0.19.5 and 0.20 explaining a perf regression with Pythran

uot;Sebastian Berg" > À: "numpy-discussion" > Envoyé: Vendredi 12 Mars 2021 22:50:24 > Objet: Re: [Numpy-discussion] Looking for a difference between Numpy 0.19.5 > and 0.20 explaining a perf regression with > Pythran > On Fri, 2021-03-12 at 21:36 +0

Re: [Numpy-discussion] Perf regression with Pythran between Numpy 0.19.5 and 0.20 (commit 4cd6e4b336fbc68d88c0e9bc45a435ce7b721f1f, ENH: implement NEP-35's `like=` argument)

s do not give to the same performance). This makes sense because it is indeed about the array creation. I haven't yet studied in details this commit (which is quite big and not simple) and I'm not sure I'm going to be able to understand it and in particular understand why it leads to such performa

[Numpy-discussion] Type annotation for Numpy arrays, accelerators and numpy.typing

Hi, When Numpy 1.20 was released, I discovered numpy.typing and its documentation https://numpy.org/doc/stable/reference/typing.html I know that it is very new but I'm a bit lost. A good API to describe Array type would be useful not only for type checkers but also for Python accelerators