Albert Strasheim wrote: >Hello all > > > >>-----Original Message----- >>From: [EMAIL PROTECTED] [mailto:numpy- >>[EMAIL PROTECTED] On Behalf Of Travis Oliphant >>Sent: 03 October 2006 02:32 >>To: Discussion of Numerical Python >>Subject: Re: [Numpy-discussion] Vectorizing code, for loops, and all that >> >>Travis Oliphant wrote: >> >> >> >>>I suspect I know why, although the difference seems rather large. >>> >>> >>> >>[snip] >> >> >> >>>I'm surprised the overhead of adjusting pointers is so high, but then >>>again you are probably getting a lot of cache misses in the first case >>>so there is more to it than that, the loops may run more slowly too. >>> >>> >>> >>> >>I'm personally bothered that this example runs so much more slowly. I >>don't think it should. Perhaps it is unavoidable because of the >>memory-layout issues. It is just hard to believe that the overhead for >>calling into the loop and adjusting the pointers is so much higher. >> >> > >Firstly, thanks to Tim... I'll try his functions tomorrow. > >Meanwhile, I can confirm that the NOBUFFER_UFUNCLOOP case in >PyUFunc_GenericFunction is getting exercised in the slower case. Here's some >info on what's happening, courtesy of Rational Quantify: > >case NOBUFFER_UFUNCLOOP: >while (loop->index < loop->size) { > for (i=0; i<self->nargs; i++) > loop->bufptr[i] = loop->iters[i]->dataptr; [1] > > loop->function((char **)loop->bufptr, &(loop->bufcnt), > loop->steps, loop->funcdata); [2] > UFUNC_CHECK_ERROR(loop); > > for (i=0; i<self->nargs; i++) { > PyArray_ITER_NEXT(loop->iters[i]); [3] > } > loop->index++; >} >break; > >[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 Thanks, -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
