Hello,

I think that some development about a new product algorithm was done in the branchhttps://github.com/uBLAS/ublas/tree/ublas_feature0004_fast_matrix_multiplication.

Thanks and Best Regards,

Joaquim Duran

2016-01-19 15:41 GMT+01:00 Michael Lehn <michael.lehn@uni-ulm.de>:

Sorry for the inconvenience.  I guess sending attachments to the mailing list is prohibited.  I put the

code on a website:

http://apfel.mathematik.uni-ulm.de/~lehn/test_ublas/

On 19 Jan 2016, at 15:14, palik imre <imre_palik@yahoo.co.uk> wrote:

Hi Michael,

I cannot see any attachments  ...

On Tuesday, 19 January 2016, 11:12, palik imre <imre_palik@yahoo.co.uk> wrote:

Is there a public git repo for ublas 2.0?

On Monday, 18 January 2016, 9:25, Oswin Krause <Oswin.Krause@ruhr-uni-bochum.de> wrote:

Hi Palik,

this is a known problem. In your case you should already get better 
performance when using axpy_prod instead of prod. There are currently 
moves towards a ublas 2.0 which should make this a non-problem in the 
future.

On 2016-01-17 21:23, palik imre wrote:
> Hi all,
> 
> It seems that the matrix multiplication in ublas ends up with the
> trivial algorithm.  On my machine, even the following function
> outperforms it for square matrices bigger than 173*173 (by a huge
> margin for matrices bigger than 190*190), while not performing
> considerably worse for smaller matrices:
> 
> matrix<double>
> matmul_byrow(const matrix<double> &lhs, const matrix<double> &rhs)
> {
>  assert(lhs.size2() == rhs.size1());
>  matrix<double> rv(lhs.size1(), rhs.size2());
>  matrix<double> r = trans(rhs);
>  for (unsigned c = 0; c < rhs.size2(); c++)
>    {
>      matrix_column<matrix<double> > out(rv, c);
>      matrix_row<matrix<double> > in(r, c);
>      out = prod(lhs, in);
>    }
>  return rv;
> }
> 
> 
> Is there anybody working on improving the matrix multiplication 
> performance?
> 
> If not, then I can try to find some spare cycles ...
> 
> Cheers,
> 
> Imre Palik

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