On Sa, 2016-01-30 at 20:27 +0100, Derek Homeier wrote: > On 27 Jan 2016, at 1:10 pm, Sebastian Berg < > sebast...@sipsolutions.net> wrote: > > > > On Mi, 2016-01-27 at 11:19 +0000, Nadav Horesh wrote: > > > Why the dot function/method is slower than @ on python 3.5.1? > > > Tested > > > from the latest 1.11 maintenance branch. > > > > > > > The explanation I think is that you do not have a blas > > optimization. In > > which case the fallback mode is probably faster in the @ case > > (since it > > has SSE2 optimization by using einsum, while np.dot does not do > > that). > > I am a bit confused now, as A @ c is just short for A.__matmul__(c) > or equivalent > to np.matmul(A,c), so why would these not use the optimised blas? > Also, I am getting almost identical results on my Mac, yet I thought > numpy would > by default build against the VecLib optimised BLAS. If I build > explicitly against > ATLAS, I am actually seeing slightly slower results. > But I also saw these kind of warnings on the first timeit runs: > > %timeit A.dot(c) > The slowest run took 6.91 times longer than the fastest. This could > mean that an intermediate result is being cached > > and when testing much larger arrays, the discrepancy between matmul > and dot rather > increases, so perhaps this is more an issue of a less memory > -efficient implementation > in np.dot?
Sorry, I missed the fact that one of the arrays was 3D. In that case I am not even sure which if the functions call into blas or what else they have to do, would have to check. Note that `np.dot` uses a different type of combinging high dimensional arrays. @/matmul broadcasts extra axes, while np.dot will do the outer combination of them, so that the result is: As = A.shape As.pop(-1) cs = c.shape cs.pop(-2) # if possible result_shape = As + cs which happens to be identical if only A.ndim > 2 and c.ndim <= 2. - Sebastian > > Cheers, > Derek > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > https://mail.scipy.org/mailman/listinfo/numpy-discussion >
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