There has been some discussion on the Numba mailing list as well about a version of guvectorize that doesn't compile for testing and flexibility.
Having this be inside NumPy itself seems ideal. -Travis On Tue, Sep 13, 2016 at 12:59 PM, Stephan Hoyer <sho...@gmail.com> wrote: > On Tue, Sep 13, 2016 at 10:39 AM, Nathan Goldbaum <nathan12...@gmail.com> > wrote: > >> I'm curious whether you have a plan to deal with the python functional >> call overhead. Numba gets around this by JIT-compiling python functions - >> is there something analogous you can do in NumPy or will this always be >> limited by the overhead of repeatedly calling a Python implementation of >> the "core" operation? >> > > I don't think there is any way to avoid this in NumPy proper, but that's > OK (it's similar to the existing overhead of vectorize). > > Numba already has guvectorize (and it's own version of vectorize as well), > which already does exactly this. > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > https://mail.scipy.org/mailman/listinfo/numpy-discussion > > -- *Travis Oliphant, PhD* *Co-founder and CEO* @teoliphant 512-222-5440 http://www.continuum.io
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