On Sat, Sep 11, 2010 at 9:47 AM, Charles R Harris <[email protected]> wrote: > > > On Fri, Sep 10, 2010 at 6:41 PM, David Cournapeau <[email protected]> > wrote: >> >> On Sat, Sep 11, 2010 at 2:57 AM, Charles R Harris >> <[email protected]> wrote: >> > >> > >> > On Fri, Sep 10, 2010 at 11:36 AM, Hagen Fürstenau <[email protected]> >> > wrote: >> >> >> >> Hi, >> >> >> >> I'm multiplying two 1000x1000 arrays with numpy.dot() and seeing >> >> significant performance differences depending on the data. It seems to >> >> take much longer on matrices with many zeros than on random ones. I >> >> don't know much about optimized MM implementations, but is this normal >> >> behavior for some reason? >> >> >> > >> > Multiplication by zero used to be faster than multiplication by random >> > numbers. However, modern hardware and compilers may have changed that to >> > pretty much a wash. More likely you are seeing cache issues due to data >> > localization or even variations in the time given the thread running the >> > multiplication. >> >> That's actually most likely a denormal issue. The a and b matrix (from >> mm.py) have many very small numbers, which could cause numbers to be >> denormal. Maybe a has more denormals than b. Denormal cause >> significant performance issues on Intel hardware at least. >> >> Unfortunately, we don't have a way in numpy to check for denormal that >> I know of. >> > > The matrices could be scaled up to check that.
Indeed - and I misread the script anyway, I should not investigate this kind of things after waking up :) Anyway, seems it is indeed a denormal issue, as adding a small (1e-10) constant gives same speed for both timings. cheers, David _______________________________________________ NumPy-Discussion mailing list [email protected] http://mail.scipy.org/mailman/listinfo/numpy-discussion
