Am 8/16/17 um 5:38 PM schrieb Stephan Hoyer: > On Wed, Aug 16, 2017 at 2:39 AM, Paul Springer <pav...@gmx.de > <mailto:pav...@gmx.de>> wrote: > > >> What version of Numpy are you comparing to? Note that in 1.13 you >> can enable some optimization in einsum, and the coming 1.14 makes >> that the default and uses CBLAS when possible. > I was using 1.10.4; however, I am currently running the benchmark > with 1.13.1 and 'optimize=True'; this, however, seems to yield > even worse performance (see attached). > If you are interested, you can check the performance difference > yourself via: ./benchmark/python/bechmark.sh > > > This sounds like you may be using relatively small matrices, where the > overhead of calculating the optimal strategy dominates. Can you try > with a few bigger test cases? > The sizes of the tensors varies form ~5MB up to ~100MB towards the far right of the plot; this corresponds to matrices of size ~1000^2 to ~5000^2, thus the sizes should be large enough to amortize any overhead associated to calculating the optimal strategy.
_______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@python.org https://mail.python.org/mailman/listinfo/numpy-discussion
