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

[YueCompl]

I'm imagining a study on programmer and maintainer's time spent on a given problem, tackled in different programming languages, maybe Python can be shown to reduce GHG on the contrary.

It goes like this: Many human programmers/administrators/managers eat beef or likes as they grow up, as cattle produces great amount of GHG, and optimization experts need more years to graduate ... So Numpy actually used less emission to gain greater yields in works demanding optimization, i.e. the ecosystem scales up to more people not being optimization experts themselves, yet get serious work done.


> On 2020-11-25, at 02:52, Benjamin Root <ben.v.root at gmail.com> wrote:
> 
> 
> 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://sustainability.aboutamazon.com/environment/the-cloud?energyType=true>
> https://azure.microsoft.com/en-au/global-infrastructure/sustainability/#energy-innovations <https://azure.microsoft.com/en-au/global-infrastructure/sustainability/#energy-innovations>
> https://www.epa.gov/ghgemissions/global-greenhouse-gas-emissions-data <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 <mailto: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 <https://www.lanl.gov/conferences/salishan/salishan2004/womble.pdf>
> 
> 
> > Cheers,
> > 
> > Jerome 
> > 
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