Hello, I'd like to just add a few data points from my Gentoo experience.
On Wed, 2025-04-30 at 07:32 +0200, Ralf Gommers via pypy-dev wrote: > On Tue, Apr 29, 2025 at 11:24 AM PIERRE AUGIER < > pierre.aug...@univ-grenoble-alpes.fr> wrote: > > > > I think getting proper compatibility of the Python scientific/data stack > > with fast Python interpreters is very important for the long-term future of > > Python for science and data. > > > > I'm not sure it is, it wouldn't rank high on my wish list. PyPy is nearing > its end-of-life, [...] I'm sorry but I don't understand what you're referring to. Sure, PyPy is not moving fast, but it definitely isn't dead. Sure, it sucks that we're still stuck in Python 3.11 era, but that doesn't make PyPy EOL. > More importantly, none of these efforts (including the "faster CPython" > project), seem critical to numerical/scientific users. We're still talking > about pure Python code that gets maybe up to 5x faster, while the gains > from doing things in compiled languages are a lot higher. So the benefits > are more important for small packages if it moves the threshold at which it > becomes necessary for them to write zero extension modules. For core > libraries like NumPy, pure Python performance isn't super critical. A user has made an interesting argument once -- while there is no gain from using, say, NumPy on PyPy compared to CPython, there are big projects that happen to 1) have major performance gains in their pure Python code, and 2) use NumPy as a dependency. I'm not saying NumPy needs to necessarily be optimized for that use case, just pointing out that it happens to be used in the wild, and for good reasons. -- Best regards, Michał Górny
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