Daniel Smith <dgasmith <at> icloud.com> writes: > > Hello everyone,I originally brought an optimized einsum routine forward a few weeks back that attempts to contract numpy arrays together in an optimal way. This can greatly reduce the scaling and overall cost of the einsum expression for the cost of a few intermediate arrays. The current version (and more details) can be found here: > https://github.com/dgasmith/opt_einsum > > I think this routine is close to a finalized version, but there are two problems which I would like the community to weigh in on: > > Thank you for your time, > > > -Daniel Smith >
I wasn't aware of this work, but it's very interesting to me as a user of einsum whose principal reason for doing so is speed. Even though I use it on largish arrays I'm only concerned with the performance as I'm on x64 with plenty of memory even were it to require temporaries 5x the original size. I don't use einsum that much because I've noticed the performance can be very problem dependant so I've always profiled it to check. Hopefully this work will make the performance more consistent, allowing it to be used more generally throughout my code. Thanks, Dave * An anecdotal example from a user only. _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
