On Thu, Mar 8, 2018 at 2:52 AM, Gregor Thalhammer < gregor.thalham...@gmail.com> wrote:
> > Hi, > > long time ago I wrote a wrapper to to use optimised and parallelized math > functions from Intels vector math library > geggo/uvml: Provide vectorized math function (MKL) for numpy > <https://github.com/geggo/uvml> > > I found it useful to inject (some of) the fast methods into numpy via > np.set_num_ops(), to gain more performance without changing my programs. > I think that was much of the original motivation for `set_num_ops` back in the Numeric days, where there was little commonality among platforms and getting hold of optimized libraries was very much an individual thing. The former cblas module, now merged with multiarray, was present for the same reasons. > > While this original project is outdated, I can imagine that a centralised > way to swap the implementation of math functions is useful. Therefor I > suggest to keep np.set_num_ops(), but admittedly I do not understand all > the technical implications of the proposed change. > I suppose we could set it up to detect and use an external library during compilation. The CBLAS implementations currently do that and should pick up the MKL version when available. Where are the MKL functions you used presented? That is an admittedly lower level interface, however. Chuck
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