Albert Strasheim wrote: >>> [1] 12.97% of function time >>> [2] 8.65% of functiont ime >>> [3] 62.14% of function time >>> >>> If statistics from elsewhere in the code would be helpful, let me >>> know, >>> >> and >> >>> I'll see if I can convince Quantify to cough it up. >>> >>> >> Please run the same test but using >> >> x1 = N.random.rand(39,2000) >> x2 = N.random.rand(39,64,1) >> >> z1 = x1[:,N.newaxis,:] - x2 >> > > Very similar results to what I had previously: > > [1] 10.88% > [2] 7.25% > [3] 68.25% > > Thanks,
I've got some ideas about how to speed this up by eliminating some of the unnecessary calculations going on outside of the function loop, but there will still be some speed issues depending on how the array is traversed once you get above a certain size. I'm not sure there anyway around that, ultimately, due to memory access being slow on most hardware. If anyone has any ideas, I'd love to hear them. I won't be able to get to implementing my ideas until at least Friday (also when rc2 will be released). -Travis ------------------------------------------------------------------------- Take Surveys. Earn Cash. Influence the Future of IT Join SourceForge.net's Techsay panel and you'll get the chance to share your opinions on IT & business topics through brief surveys -- and earn cash http://www.techsay.com/default.php?page=join.php&p=sourceforge&CID=DEVDEV _______________________________________________ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
