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 <sebast...@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 > > > > _______________________________________________ > > NumPy-Discussion mailing list > > NumPy-Discussion@python.org > > https://mail.python.org/mailman/listinfo/numpy-discussion > > > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@python.org > https://mail.python.org/mailman/listinfo/numpy-discussion >
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