On 2/4/07, Sebastian Haase <[EMAIL PROTECTED]> wrote: > On 2/3/07, Robert Kern <[EMAIL PROTECTED]> wrote: > > Stephen Simmons wrote: > > > > > The question though is whether all of the inner loop's overhead is > > > necessary. > > > My counterexample using numpy.dot() suggests there's considerable scope > > > for improvement, at least for certain common cases. > > > > Well, yes. You most likely have an ATLAS-accelerated dot(). The ATLAS put a > > lot > > of work into making matrix products really fast. However, they did so at a > > cost: > > different architectures use different code. That's not really something we > > can > > do in the core of numpy without making numpy as difficult to build as ATLAS > > is. > > > Maybe this argument could be inverted: > maybe numpy could check if ATLAS is installed and automatically switch to the > numpy.dot(numpy.ones(a.shape[0], a.dtype), a) > variant that Stephen suggested. > > Of course -- as I see it -- the numpy.ones(...) part requires lots of > extra memory. Maybe there are other downsides ... !?
I use multiplication instead of sum in heavily used loops. I'm often able to predefine the ones outside the loop. In Octave I made my own sum functions---separate ones for axis 0 and 1---that use multiplication. Maybe it is better to make a new function rather than complicate the existing one. _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
