Hi, On Fri, Mar 28, 2014 at 11:56 AM, Sturla Molden <sturla.mol...@gmail.com> wrote: > Matthew Brett <matthew.br...@gmail.com> wrote: > >> Does anyone know how their performance compares to MKL or the >> reference implementations? > > http://eigen.tuxfamily.org/index.php?title=Benchmark
I don't know how relevant these are to our case. If I understand correctly, the usual use of Eigen, as in these benchmarks, is to use the Eigen headers to get fast code via C++ templating. Because they know some of us need this, Eigen can also build a more standard blas / lapack library to link against, but I presume this will stop Eigen templating doing lots of clever tricks with the operations, and therefore slow it down. Happy to be corrected though. > http://gcdart.blogspot.de/2013/06/fast-matrix-multiply-and-ml.html I think this page does not use the Eigen blas libraries either [1] Also - this is on a massive linux machine ("48 core and 66GB RAM"). He's done a great job of showing what he did though. The problem for us is: We can't use MKL, ACML [2] atlas is very difficult to compile on 64 bit windows, and has some technical limitations on 64 bit [3] So I think we're down to openblas and eigen for 64-bit windows. Does anyone disagree? Cheers, Matthew [1] : https://github.com/gcdart/dense-matrix-mult/blob/master/EIGEN/compile_eigen.sh [2] : http://amd-dev.wpengine.netdna-cdn.com/wordpress/media/2013/12/ACML_June_24_2010_v2.pdf [3] : http://math-atlas.sourceforge.net/atlas_install/node57.html _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
