> On Wed, May 4, 2011 at 6:19 AM, Christoph Groth <c...@falma.de> wrote: >> >> Dear numpy experts, >> >> I have noticed that with Numpy 1.5.1 the operation >> >> m[::2] += 1.0 >> >> takes twice as long as >> >> t = m[::2] >> t += 1.0
Mark Wiebe <mwwi...@gmail.com> writes: > You'd better time this in 1.6 too. ;) > > https://github.com/numpy/numpy/commit/f60797ba64ccf33597225d23b893b6eb11149860 This seems to be exactly what I had in mind. Thanks for finding this. > The case of boolean mask indexing can't benefit so easily from this > optimization, but I think could see a big performance benefit if > combined __index__ + __i<op>__ operators were added to > Python. Something to consider, anyway. Has something like __index_iadd__ ever been considered seriously? Not to my (limited) knowledge. Indeed, the second loop executes twice as fast than the first in the following example (again with Numpy 1.5.1). import numpy m = numpy.zeros((1000, 1000)) mask = numpy.arange(0, 1000, 2, dtype=int) for i in xrange(40): m[mask] += 1.0 for i in xrange(40): t = m[mask] t += 1.0 But wouldn't it be easy to optimize this as well, by not executing assignments where the source and the destination is indexed by the same mask object? This would be a bit weaker, as it would work only for same ("a is b"), and not for equal masks, but it should still cover the most common case. Christoph _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
