From: Gunter Winkler (guwi17_at_[hidden])
Date: 2007-09-30 15:36:48
> FYI, I also tried the following with v2 as sparse vector, but the speed were
> about as slow:
> 1) noalias(v2) = prod(v1,M)
> 2) noalias(v2) = prod(trans(M),v1)
did you try a column major matrix? My latest benchmarks show that
axpy_prod(M,v,y); // y dense
is fastest using compressed_matrix<double, column_major> and
compressed_vector or coordinate_vector. If the matrix is row major then
the products are computed as a set of inner products of sparse vectors
which is not fully optimized, yet.
Can you explain what kind of iteration do you have that you cannot use
dense vectors? IMHO using dense vectors and recompress them into an
archive is better than working with only sparse vectors.