On Thu, Jan 31, 2013 at 9:35 PM, Robert Kern <robert.k...@gmail.com> wrote: > On Thu, Jan 31, 2013 at 7:44 PM, Nathaniel Smith <n...@pobox.com> wrote: >> On Thu, Jan 31, 2013 at 8:43 AM, Oscar Villellas >> <oscar.villel...@continuum.io> wrote: >>> Hello, >>> >>> At Continuum Analytics we've been working on a submodule implementing >>> a set of lineal algebra operations as generalized ufuncs. This allows >>> specifying arrays of lineal algebra problems to be computed with a >>> single Python call, allowing broadcasting as well. As the >>> vectorization is handled in the kernel, this gives a speed edge on the >>> operations. We think this could be useful to the community and we want >>> to share the work done. >> >> It certainly does look useful. My question is -- why do we need two >> complete copies of the linear algebra routine interfaces? Can we just >> replace the existing linalg functions with these new implementations? >> Or if not, what prevents it? > > The error reporting would have to be bodged back in. >
Error reporting is one part of the equation. Right now the functions in the linalg module perform some baking of the data. For example, in linalg eigenvalue functions for real arrays check if all the eigenvalues/eigenvectors in the result are real. If that's the case it returns arrays with a real dtype. There is no way to handle that in a gufunc without a performance hit. The gufunc version always returns a matrix with complex dtype (it will involve copying otherwise). So this provides the flexibility of being able to use linalg functions as generalized ufuncs. It also provides some extra performance as there is little python wrapper code and when used as gufuncs buffer handling is quite efficient. IMO it would be great if this could evolve into a complete replacement for linalg, that's for sure, but right now it is missing functionality and is not a drop-in replacement. In the other hand it provides value, so it is a worthy addition. Cheers, Oscar _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
