Vectorized sparse matrix multiply for compressed row storage format
EF D'Azevedo, MR Fahey, RT Mills - … , Atlanta, GA, USA, May 22-25, 2005 …, 2005 - Springer
EF D'Azevedo, MR Fahey, RT Mills
Computational Science–ICCS 2005: 5th International Conference, Atlanta, GA …, 2005•SpringerThe innovation of this work is a simple vectorizable algorithm for performing sparse matrix
vector multiply in compressed sparse row (CSR) storage format. Unlike the vectorizable
jagged diagonal format (JAD), this algorithm requires no data rearrangement and can be
easily adapted to a sophisticated library framework such as PETSc. Numerical experiments
on the Cray X1 show an order of magnitude improvement over the non-vectorized algorithm.
vector multiply in compressed sparse row (CSR) storage format. Unlike the vectorizable
jagged diagonal format (JAD), this algorithm requires no data rearrangement and can be
easily adapted to a sophisticated library framework such as PETSc. Numerical experiments
on the Cray X1 show an order of magnitude improvement over the non-vectorized algorithm.
Abstract
The innovation of this work is a simple vectorizable algorithm for performing sparse matrix vector multiply in compressed sparse row (CSR) storage format. Unlike the vectorizable jagged diagonal format (JAD), this algorithm requires no data rearrangement and can be easily adapted to a sophisticated library framework such as PETSc. Numerical experiments on the Cray X1 show an order of magnitude improvement over the non-vectorized algorithm.
Springer