Re: Matrix multiplication performance

Is there a public git repo for ublas 2.0?
 

    On Monday, 18 January 2016, 9:25, Oswin Krause <[email protected]> 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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