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

[Benjamin Root]

Given that AWS and Azure have both made commitments to have their data
centers be carbon neutral, and given that electricity and heat production
make up ~25% of GHG pollution, I find these sorts of
power-usage-analysis-for-the-sake-of-the-environment to be a bit
disingenuous. Especially since GHG pollution from power generation is
forecasted to shrink as more power is generated by alternative means. I am
fine with improving python performance, but let's not fool ourselves into
thinking that it is going to have any meaningful impact on the environment.

Ben Root

https://sustainability.aboutamazon.com/environment/the-cloud?energyType=true
https://azure.microsoft.com/en-au/global-infrastructure/sustainability/#energy-innovations
https://www.epa.gov/ghgemissions/global-greenhouse-gas-emissions-data

On Tue, Nov 24, 2020 at 1:25 PM Sebastian Berg <sebastian at sipsolutions.net>
wrote:

> On Tue, 2020-11-24 at 18:41 +0100, Jerome Kieffer wrote:
> > Hi Pierre,
> >
> > I agree with your point of view: the author wants to demonstrate C++
> > and Fortran are better than Python... and environmentally speaking he
> > has some evidences.
> >
> > We develop with Python, Cython, Numpy, and OpenCL and what annoys me
> > most is the compilation time needed for the development of those
> > statically typed ahead of time extensions (C++, C, Fortran).
> >
> > Clearly the author wants to get his article viral and in a sense he
> > managed :). But he did not mention Julia / Numba and other JIT
> > compiled
> > languages (including matlab ?) that are probably outperforming the
> > C++ / Fortran when considering the development time and test-time.
> > Beside this the OpenMP parallelism (implicitly advertized) is far
> > from
> > scaling well on multi-socket systems and other programming paradigms
> > are needed to extract the best performances from spercomputers.
> >
>
> As an interesting aside: Algorithms may have actually improved *more*
> than computational speed when it comes to performance [1].  That shows
> the impressive scale and complexity of efficient code.
>
> So, I could possibly argue that the most important thing may well be
> accessibility of algorithms. And I think that is what a large chunk of
> Scientific Python packages are all about.
>
> Whether or not that has an impact on the environment...
>
> Cheers,
>
> Sebastian
>
>
> [1] This was the first resource I found, I am sure there are plenty:
> https://www.lanl.gov/conferences/salishan/salishan2004/womble.pdf
>
>
> > Cheers,
> >
> > Jerome
> >
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> >
>
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