Email RIS file
Bookmark
Report an Issue

Ordering sparse matrices for cache-based systems [electronic resource].

Published:
Washington, D.C. : United States. Department of Energy. Office of Advanced Scientific Computing Research, 2001.
Oak Ridge, Tenn. : Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy.
Physical Description:
vp : digital, PDF file
Additional Creators:
Lawrence Berkeley National Laboratory, United States. Department of Energy. Office of Advanced Scientific Computing Research, and United States. Department of Energy. Office of Scientific and Technical Information
Access Online

Availability

I Want It
I Want It

Finding items...

Restrictions on Access:
Free-to-read Unrestricted online access
Summary:
The Conjugate Gradient (CG) algorithm is the oldest and best-known Krylov subspace method used to solve sparse linear systems. Most of the coating-point operations within each CG iteration is spent performing sparse matrix-vector multiplication (SPMV). We examine how various ordering and partitioning strategies affect the performance of CG and SPMV when different programming paradigms are used on current commercial cache-based computers. However, a multithreaded implementation on the cacheless Cray MTA demonstrates high efficiency and scalability without any special ordering or partitioning.
Report Numbers:
E 1.99:lbnl--47805
lbnl--47805
Subject(s):
Other Subject(s):
Note:
Published through SciTech Connect.
01/11/2001.
"lbnl--47805"
Tenth SIAM Conference on Parallel Processing for Scientific Computing, Portmouth, VA (US), 03/12/2001--03/14/2001.
Biswas, Rupak; Oliker, Leonid.
Funding Information:
AC03-76SF00098
618110
View MARC record | catkey: 14347243