By Topic

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

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

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

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