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.
> <snip>
Thanks for all code and suggestions.
Cheers,
Albert
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