
Ublas : 
From: Sourabh (sourabh_at_[hidden])
Date: 20070303 07:27:56
On Fri, 2 Mar 2007, Gunter Winkler wrote:
> On Friday 02 March 2007 15:12, Karl Meerbergen wrote:
> > It should be possible to do this with the triangular solves from ublas
> > but I am unsure whether they efficiently handle sparse matrices. Gunter,
> > what about the new routines?
>
> I have specializations for compressed_matrix. They are hopefully as fast as
> possible ;)
>
> template < bool unit, class T, class Z, class D, size_t IB, class IA,
> class TA, class VEC >
> bool
> inplace_solve_lower(
> const compressed_matrix<T, basic_row_major<Z,D>, IB, IA, TA>& L,
> vector_expression<VEC>& ve)
> {
> ...
> }
>
The program (see below), after some modifications is still taking around
8 minutes for the arguments 1 200000 100.
Even if I remove solve, and addition methods, the statement
lembda1.assign (identlembda) is causing the program to take 1 minute.
I am not sure where I can optimize it better ?
int main (int argc, char* argv[])
{
unsigned int samples = atoi (argv[1]);
unsigned int numpaths = atoi (argv[2]);
unsigned short gatesperpath = atoi (argv[3]);
identity_matrix<double> ident (gatesperpath);
vector<double> slopes;
vector <double> delays (gatesperpath);
for (unsigned int i = 0; i < samples; ++i) {
vector <double> omegas(gatesperpath);
for (unsigned int j = 0; j < numpaths ; ++ j) {
compressed_matrix<double> lembda (gatesperpath, gatesperpath);
compressed_matrix<double> lembda1 (gatesperpath,gatesperpath);
lembda1.assign (identlembda);
slopes = solve (lembda1, omegas, lower_tag ());
noalias( delays ) = omegas;
axpy_prod( lembda1, slopes, delays, false );
}
}
}