On Tue, Jul 19, 2011 at 11:19 AM, Charles R Harris < charlesr.har...@gmail.com> wrote:
> > > On Tue, Jul 19, 2011 at 9:49 AM, Carlos Becker <carlosbec...@gmail.com>wrote: > >> I made more tests with the same operation, restricting Matlab to use a >> single processing unit. I got: >> >> - Matlab: 0.0063 sec avg >> - Numpy: 0.026 sec avg >> - Numpy with weave.blitz: 0.0041 >> >> Note that weave.blitz is even faster than Matlab (slightly). >> I tried on an older computer, and I got similar results between matlab and >> numpy without weave.blitz, so maybe it has to do with 'new' vectorization >> opcodes. >> >> Anyhow, even though these results are not very promising, it gets worse if >> I try to do something like: >> >> result = (m - 0.5)*0.3 >> >> and I get the following timings: >> >> - Matlab: 0.0089 >> - Numpy: 0.051 >> - Numpy with blitz: 0.0043 >> >> Now blitz is considerably faster! Anyways, I am concerned about numpy >> being much slower, in this case taking 2x the time of the previous >> operation. >> I guess this is because of the way that python operands/arguments are >> passed. Should I always use weave.blitz? >> >> > Out of curiosity, what os/architecture are you running on? What version of > numpy are you using? > > By and large, you shouldn't spend time programming in blitz, it will ruin > the whole point of using numpy in the first place. If there is an > inefficiency somewhere it is better to fix the core problem, whatever it is. > > <numpy> > > Chuck > Also what version of matlab were you using? -Chris JS > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion > >
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