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 >
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