
Ublas : 
Subject: Re: [ublas] Matrix multiplication performance
From: Michael Lehn (michael.lehn_at_[hidden])
Date: 20160216 11:09:53
On 22 Jan 2016, at 00:28, nasos <nasos_i_at_[hidden]> 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 (48) it can easily made multithreaded. For manycores 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 microkernels 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 matrixmatrix product? It seems to require a
>> tuned BLAS implementation (“Otherwise you get only poor performance”) for the matrixmatrix 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/blazelib/blaze/wiki/Benchmarks#!rowmajormatrixmatrixmultiplication)
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.uniulm.de/~lehn/test_blaze/index.html
At the moment the benchmarks for the internal BLAZE implementation for the matrixmatrix 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 asis.
Cheers,
Michael