Parallel Computing for Linear Systems of Equations on Workstation Clusters

Chaojiang Fu (), Wu Zhang () and Linfeng Yang ()
Additional contact information
Chaojiang Fu: Shanghai University, School of Computer Engineering and Science
Wu Zhang: Shanghai University, School of Computer Engineering and Science
Linfeng Yang: Shanghai University, School of Computer Engineering and Science

A chapter in Current Trends in High Performance Computing and Its Applications, 2005, pp 289-293 from Springer

Abstract: Abstract In this paper the parallel algorithm of preconditioned conjugate gradient method (PCGM) is presented and implemented on DELL workstation cluster. Optimization techniques for the sparse matrix vector multiplication are adopted in programming. The storage schemes are analyzed in detail. The numerical results show that the designed parallel algorithm has good parallel performance on the high performance workstation cluster. This illustrates the power of parallel computing in solving large-scale problems much faster than on a single processor.

Keywords: parallel computing; preconditioned conjugate gradient method; network; workstation cluster (search for similar items in EconPapers)
Date: 2005
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-540-27912-9_33

Ordering information: This item can be ordered from
http://www.springer.com/9783540279129

DOI: 10.1007/3-540-27912-1_33

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().