On 10/11/2011 12:06 PM, Skipper Seabold wrote: > On Tue, Oct 11, 2011 at 12:41 PM, Christoph Groth<c...@falma.de> wrote: >> Skipper Seabold<jsseab...@gmail.com> writes: >> >>> So it's the dot function being called repeatedly on smallish arrays >>> that's the bottleneck? I've run into this as well. See this thread >>> [1]. >>> (...) >> Thanks for the links. "tokyo" is interesting, though I fear the >> intermediate matrix size regime where it really makes a difference will >> be rather small. My concern is in really tiny vectors, where it's not >> even worth to call BLAS. >> > IIUC, it's not so much the BLAS that's helpful but avoiding the > overhead in calling numpy.dot from cython. > >>> I'd be very interested to hear if you achieve a great speed-up with >>> cython+tokyo. >> I try to solve this problem in some way or other. I'll post here if I >> end up with something interesting. > Please do. > > Skipper > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion In the example, M is an identity 2 by 2 array. This creates a lot of overhead in creating arrays from a tuple followed by two dot operations. But the tuple code is not exactly equivalent because M is 'expanded' into a single dimension to avoid some of the unnecessary multiplications. Thus the tuple code is already a different algorithm than the numpy code so the comparison is not really correct.
All that is needed here for looping over scalar values of x, y and radius is to evaluate (x*x + y*y) < radius**2 That could probably be done with array multiplication and broadcasting. Bruce _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
