Vectorized Sparse Matrix Multiply for Compressed Row Storage Format

Vectorized Sparse Matrix Multiply for Compressed Row Storage Format

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...

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Bibliographische Detailangaben
Hauptverfasser: D’Azevedo, Eduardo F., Fahey, Mark R., Mills, Richard T.
Format: Buchkapitel
Sprache:eng
Schlagworte:
Algorithmics. Computability. Computer arithmetics
Applied sciences
Computer science
control theory
systems
Exact sciences and technology
Permutation Vector
Point Stencil
Sparse Matrix
Theoretical computing
Theoretical Peak Performance
Vector Processor
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Beschreibung
Zusammenfassung: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.
ISSN:0302-9743
1611-3349
DOI:10.1007/11428831_13