Re: Matrix multiplication performance
Michael Lehn <[email protected]>
| Newsgroups | gmane.comp.lib.boost.ublas |
|---|---|
| Message-ID | <[email protected]> |
On 22 Jan 2016, at 00:28, nasos <[email protected]> wrote: > Michael, > please see below > > On 01/21/2016 05:23 PM, Michael Lehn wrote: >> Hi Nasos, >> >> first of all I don’t want to take wrong credits and want to point out that this is not my algorithm. It is based on >> >> http://www.cs.utexas.edu/users/flame/pubs/blis2_toms_rev3.pdf >> >> https://github.com/flame/blis >> >> For a few cores (4-8) it can easily made multithreaded. For many-cores like Intel Xeon Phi this is a bit more >> sophisticated but still not too hard. > Setting up Phis is indeed an issue, especially because they are "locked" with icpc. Openmp is working properly though. > >> The demo I posted does not use micro kernels that exploit SSE, AVX or >> FMA instructions. With that the matrix product is on par with Intel MKL. Just like BLIS. For my platforms I wrote >> my own micro-kernels but the interface of function ugemm is compatible to BLIS. >> > If you compile with -O3 I think you are getting near optimal SSE vectorization. gcc is truly impressive and intel is even more. >> Maybe you could help me to integrate your code in the benchmark example I posted above. >> > I will try to find some time to spend on the code. >> About Blaze: Do they have their own implementation of a matrix-matrix product? It seems to require a >> tuned BLAS implementation (“Otherwise you get only poor performance”) for the matrix-matrix product. > I will check the benchmarks I run. I think I was using MKL with Blaze, but Blaze is taking it a step further (I am not sure how) and they are getting better performance than the underlying GEMM. Their benchmarks indicate that they are faster than MKL (https://bitbucket.org/blaze-lib/blaze/wiki/Benchmarks#!row-major-matrixmatrix-multiplication) I started today with similar experiments on BLAZE and had closer look at their internal implementation. By default they are calling an external BLAS backend. On my machine I used the Intel MKL. But you are right, they also have an internal implementation that can be used if no external BLAS is available. I will publish the results on this page: http://www.mathematik.uni-ulm.de/~lehn/test_blaze/index.html At the moment the benchmarks for the internal BLAZE implementation for the matrix-matrix product seem to look poor. I asked Klaus Iglberger (the author of BLAZE) to check the compiler flags that I have used. So don’t take the current results as-is. Cheers, Michael