Sparse matrix multiplication on vector computers

Jocelyne Erhel 1
1 CALCPAR - Calculateurs Parallèles
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, INRIA Rennes
Abstract : An important kernel of scientific software is the multiplication of a sparse matrix by a vector. The efficiency of the algorithm on a vector computer depends on the storage scheme. With a storage by rows, performances are limited in general by the small vector length. Therefore a storage by so-called generalized colums has been designed, which provides long vectors and consequently good performances. However, it is not adapted to the symmetric case. A new type of storage, by sparse diagonals, has then been defined. It still exhibits long vectors, with performances as good as previously, but it is also well-suited to symmetric matrices. Results on a Cray2, with various sparse matrices, compare the three algorithms, and show the efficiency of the storage by sparse diagonale.
Type de document :
[Research Report] RR-1101, INRIA. 1989
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Contributeur : Rapport de Recherche Inria <>
Soumis le : mercredi 24 mai 2006 - 18:15:53
Dernière modification le : mercredi 16 mai 2018 - 11:23:14
Document(s) archivé(s) le : mardi 12 avril 2011 - 23:11:56



  • HAL Id : inria-00075458, version 1


Jocelyne Erhel. Sparse matrix multiplication on vector computers. [Research Report] RR-1101, INRIA. 1989. 〈inria-00075458〉



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