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A high performance algorithm using pre-processing for the sparse matrix-vector multiplication

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3 Author(s)
Agarwal, R.C. ; IBM Thomas J. Watson Res. Center, Yorktown Heights, NY, USA ; Gustavson, F.G. ; Zubair, M.

The authors propose a feature-extraction-based algorithm (FEBA) for sparse matrix-vector multiplication. The key idea of FEBA is to exploit any regular structure present in the sparse matrix by extracting it and processing it separately. The order in which these structures are extracted is determined by the relative efficiency with which they can be processed. The authors have tested FEBA on IBM 3000 VF for matrices from the Harwell Boeing and OSL collection. The results obtained were on average five times faster than the ESSL routine which is based on the ITPACK storage structure

Published in:

Supercomputing '92., Proceedings

Date of Conference:

16-20 Nov 1992