<div dir="ltr"><div><br></div><div>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.</div><div><br></div><div>Ben Root<br></div><div><br></div><div><a href="https://sustainability.aboutamazon.com/environment/the-cloud?energyType=true">https://sustainability.aboutamazon.com/environment/the-cloud?energyType=true</a></div><div><a href="https://azure.microsoft.com/en-au/global-infrastructure/sustainability/#energy-innovations">https://azure.microsoft.com/en-au/global-infrastructure/sustainability/#energy-innovations</a></div><div><a href="https://www.epa.gov/ghgemissions/global-greenhouse-gas-emissions-data">https://www.epa.gov/ghgemissions/global-greenhouse-gas-emissions-data</a></div></div><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">On Tue, Nov 24, 2020 at 1:25 PM Sebastian Berg <<a href="mailto:sebastian@sipsolutions.net">sebastian@sipsolutions.net</a>> wrote:<br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex">On Tue, 2020-11-24 at 18:41 +0100, Jerome Kieffer wrote:<br>
> Hi Pierre,<br>
> <br>
> I agree with your point of view: the author wants to demonstrate C++<br>
> and Fortran are better than Python... and environmentally speaking he<br>
> has some evidences.<br>
> <br>
> We develop with Python, Cython, Numpy, and OpenCL and what annoys me<br>
> most is the compilation time needed for the development of those<br>
> statically typed ahead of time extensions (C++, C, Fortran).<br>
> <br>
> Clearly the author wants to get his article viral and in a sense he<br>
> managed :). But he did not mention Julia / Numba and other JIT<br>
> compiled<br>
> languages (including matlab ?) that are probably outperforming the<br>
> C++ / Fortran when considering the development time and test-time.<br>
> Beside this the OpenMP parallelism (implicitly advertized) is far<br>
> from<br>
> scaling well on multi-socket systems and other programming paradigms<br>
> are needed to extract the best performances from spercomputers.<br>
> <br>
<br>
As an interesting aside: Algorithms may have actually improved *more*<br>
than computational speed when it comes to performance [1]. That shows<br>
the impressive scale and complexity of efficient code.<br>
<br>
So, I could possibly argue that the most important thing may well be<br>
accessibility of algorithms. And I think that is what a large chunk of<br>
Scientific Python packages are all about.<br>
<br>
Whether or not that has an impact on the environment...<br>
<br>
Cheers,<br>
<br>
Sebastian<br>
<br>
<br>
[1] This was the first resource I found, I am sure there are plenty:<br>
<a href="https://www.lanl.gov/conferences/salishan/salishan2004/womble.pdf" rel="noreferrer" target="_blank">https://www.lanl.gov/conferences/salishan/salishan2004/womble.pdf</a><br>
<br>
<br>
> Cheers,<br>
> <br>
> Jerome <br>
> <br>
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