An optimal storage format for sparse matrices
Information Processing Letters
Scientific Computations on Modern Parallel Vector Systems
Proceedings of the 2004 ACM/IEEE conference on Supercomputing
Vectorized sparse matrix multiply for compressed row storage format
ICCS'05 Proceedings of the 5th international conference on Computational Science - Volume Part I
Block-Based Approach to Solving Linear Systems
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part I: ICCS 2007
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Many applications based on finite element and finite difference methods include the solution of large sparse linear systems using preconditioned iterative methods. Matrix vector multiplication is one of the key operations that has a significant impact on the performance of any iterative solver. In this paper, recent developments in sparse storage formats on vector machines are reviewed. Then, several improvements to memory access in the sparse matrix vector product are suggested. Particularly, algorithms based on dense blocks are discussed and reasons for their superior performance are explained. Finally, the performance gain by the presented modifications is demonstrated.