On Mon, 2014-08-18 at 23:12 +0000, Laeeth Isharc via Digitalmars-d-learn wrote: […] > > For me, NumPy has some serious problems despite being the > > accepted norm for computational work. > > If not too offtopic, do you have a link describing, or would you > briefly summarize these problems? I am intrigued. And what > would you suggest in its place? Fortran? […]
I have no benchmark experiment data as proof yet, just anecdotal evidence forming an hypothesis, but it seems that the underlying data parallelism model of NumPy has some serious overhead problems: speed-ups are not as high as they should be, and scaling is not as good as it should be. The finance people using Python in London, and indeed the general data analysis using Python folk (cf. PyData meetings around the world) all take NumPy as a given, and that it works well enough for them. I guess those for whom NumPy is not good enough are using Cython, C++ or Fortran (or even C) for the computationally intensive stuff. Or more likely they already had the native code in place and so are not using NumPy other than for data visualization and replacement of Matlab. I think Numba is a disruptive technology here. However the danger is that the opaque type approach of NumPy (which is good) is forgotten as a good abstraction in the face of Numba speeds, with people reverting to explicit rather than implicit iteration just because things go faster. Or maybe this won't happen because all the needed computationally intensive libraries already exists. I guess the PyData meetings are the place where al this will be played out. -- Russel. ============================================================================= Dr Russel Winder t: +44 20 7585 2200 voip: sip:russel.win...@ekiga.net 41 Buckmaster Road m: +44 7770 465 077 xmpp: rus...@winder.org.uk London SW11 1EN, UK w: www.russel.org.uk skype: russel_winder