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). Btw. thanks for all the work getting this done Chuck! - Sebastian
np.__version__ Out[39]: '1.11.0.dev0+Unknown'
%timeit A @ c 10000 loops, best of 3: 185 µs per loop
%timeit A.dot(c) 1000 loops, best of 3: 526 µs per loop
%timeit np.dot(A,c) 1000 loops, best of 3: 527 µs per loop
A.dtype, A.shape, A.flags Out[43]: (dtype('float32'), (100, 100, 3), C_CONTIGUOUS : True F_CONTIGUOUS : False OWNDATA : True WRITEABLE : True ALIGNED : True UPDATEIFCOPY : False)
c.dtype, c.shape, c.flags Out[44]: (dtype('float32'), (3, 3), C_CONTIGUOUS : True F_CONTIGUOUS : False OWNDATA : True WRITEABLE : True ALIGNED : True UPDATEIFCOPY : False)
From: NumPy-Discussion <numpy-discussion-bounces@scipy.org> on behalf of Charles R Harris <charlesr.harris@gmail.com> Sent: 26 January 2016 22:49 To: numpy-discussion; SciPy Developers List; SciPy Users List Subject: [Numpy-discussion] Numpy 1.11.0b1 is out
Hi All,
I'm pleased to announce that Numpy 1.11.0b1 is now available on sourceforge. This is a source release as the mingw32 toolchain is broken. Please test it out and report any errors that you discover. Hopefully we can do better with 1.11.0 than we did with 1.10.0 ;)
Chuck
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