Hi, I posted the following message to boost and have so far had no response. Maybe boost-users is the correct mailing list? Hi, Thanks to Joerg Walter's original conversion of GluCat ( http://glucat.sf.net ) to uBLAS, I have fairly painlessly been able to get GluCat going with the version of uBLAS now promoted to Boost. I'm using sparse matrices: boost::numeric::ublas::sparse_matrix< Scalar_T, boost::numeric::ublas::row_major> and comparing performance to MTL: mtl::matrix< Scalar_T, mtl::rectangle<>, mtl::compressed<>, mtl::row_major >::type The operation I'm performing is quite complicated. Basically, I am generating a large number of sparse matrices, and computing the inner product of a given matrix with each of these matrices in turn. ( If you have a copy of GluCat, see basis_element() in matrix_multi_imp.h ) Because these sparse matrices have only one non-zero per row, operations on an n x n matrix are typically O(n), and seem to be so using MTL: Size vs speed for MTL: Size Time (ms) 32 10 64 20 128 80 256 160 512 700 But with uBLAS, essentially the same operations seem to be O(n^2): Size Time (ms) Slowdown 32 30 3 64 70 3.5 128 830 10.4 256 1940 12.1 512 31770 45.4 o Is there a more appropriate data type in uBLAS? o Do you have any tips on where to tweak uBLAS to get better (ie. O(n) ) performance? o What is BOOST_UBLAS_USE_CANONICAL_ITERATOR and what effect should it have on performance? Thanks