On 06/17/2013 11:03 PM, Julian Taylor wrote: > On 17.06.2013 17:11, Frédéric Bastien wrote: >> Hi, >> >> I saw that recently Julian Taylor is doing many low level optimization >> like using SSE instruction. I think it is great. >> >> Last year, Mark Florisson released the minivect[1] project that he >> worked on during is master thesis. minivect is a compiler for >> element-wise expression that do some of the same low level optimization >> that Julian is doing in NumPy right now. >> >> Mark did minivect in a way that allow it to be reused by other project. >> It is used now by Cython and Numba I think. I had plan to reuse it in >> Theano, but I didn't got the time to integrate it up to now. >> >> What about reusing it in NumPy? I think that some of Julian optimization >> aren't in minivect (I didn't check to confirm). But from I heard, >> minivect don't implement reduction and there is a pull request to >> optimize this in NumPy. > > Hi, > what I vectorized is just the really easy cases of unit stride > continuous operations, so the min/max reductions which is now in numpy > is in essence pretty trivial. > minivect goes much further in optimizing general strided access and > broadcasting via loop optimizations (it seems to have a lot of overlap > with the graphite loop optimizer available in GCC [0]) so my code is > probably not of very much use to minivect. > > The most interesting part in minivect for numpy is probably the > optimization of broadcasting loops which seem to be pretty inefficient > in numpy [0].
There's also related things like arr + arr.T which has much less than optimal performance in NumPy (unless there was recent changes). This example was one of the motivating examples for minivect. Dag Sverre _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
