Yes, using numpy.dot I get 250ms, numpy.matrixmultiply 11.8s. while a sans-BLAS Numeric.matrixmultiply takes 12s. The first 100 results from numpy.dot and numpy.matrixmultiply are identical .... Use dot;) --George. On 02/06/06, Filip Wasilewski <filip@ftv.pl> wrote:
Hi,
It seems that in Numeric the matrixmultiply is alias for dot function, which "uses the BLAS optimized routines where possible", as the help() says.
In NumPy (0.9.6, not upgraded yet to 0.9.8), the matrixmultiply is a function of numpy.core.multiarray, while dot refers to numpy.core._dotblas.
On my system the timings and results with numpy.dot are quite similar to that with Numeric.matrixmultiply.
So the next question is what's the difference between matrixmultiply and dot in NumPy?
Filip
Hello! I've been using numeric for a while, and the recent list traffic prompted me to finally migrate all my old code. On a whim, we were benchmarking numpy vs numeric and have been lead to the conclusion that numpy is at least 50x slower; a 1000x1000 matmul takes 16 sec in numpy but 300 ms in numeric.
Now, of course, I don't believe this, but I can't figure out what we're doing wrong; I'm not the only person who has looked at this code, so can anyone tell me what we're doing wrong?
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