Re: [Numpy-discussion] Better compatibility of the Python scientific/data stack with fast Python interpreters

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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=E2=80=AFAM PIERRE AUGIER <
> [email protected]> wrote:
>=20
>=20
> > I think getting proper compatibility of the Python scientific/data stac=
k
> > with fast Python interpreters is very important for the long-term futur=
e of
> > Python for science and data.
> >=20
>=20
> I'm not sure it is, it wouldn't rank high on my wish list. PyPy is nearin=
g
> 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 talkin=
g
> 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.


--=20
Best regards,
Micha=C5=82 G=C3=B3rny


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