On Thu, 26 Nov 2020 22:14:40 +0100 (CET) PIERRE AUGIER <pierre.aug...@univ-grenoble-alpes.fr> wrote:
> I changed the email subject because I'd like to focus less on CO2 (a very > interesting subject, but not my focus here) and more on computing... > Hi Pierre, We may turn the problem in another way around: one should more focus on the algorithm than on the programming language. I would like to share with you one example, where we published how to speed-up crystallographic computation written in Python. https://onlinelibrary.wiley.com/iucr/doi/10.1107/S1600576719008471 One referee asked us to validate vs C and Fortran equivalent code. C code was as fast as Pythran or Cython and Fortran was still faster (the std of the Fortran-compiled runtime was much smaller which allows Fortran to be faster by 3 std !) But I consider the difference to be marginal at this level ! If one considers the "Moore law", i.e. the time needed for "performance" to double in different aspects of computing, one gets 18 to 24 month for the number of transistor in a processor, 18 years for the compilers and 2 years (in average) for the development of new algorithms. In this sense one should more focus on the algorithm used. The Table 1 of the article is especially interesting: Pure Python is 10x slower than proper Numpy code, and parallel Pythran is 50x faster than Numpy (on the given computer) but using the proper algorithm, i.e. FFT in this case, is 13000x faster ! So I believe that Python, with its expressivity, helps much in understanding the algorithm and hence to design faster code. Cheers, Jerome _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@python.org https://mail.python.org/mailman/listinfo/numpy-discussion
