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Parallel Conjugate Gradient: Effects of Ordering Strategies, Programming Paradigms, and Architectural PlatformsThe Conjugate Gradient (CG) algorithm is perhaps the best-known iterative technique to solve sparse linear systems that are symmetric and positive definite. A sparse matrix-vector multiply (SPMV) usually accounts for most of the floating-point operations within a CG iteration. In this paper, we investigate the effects of various ordering and partitioning strategies on the performance of parallel CG and SPMV using different programming paradigms and architectures. Results show that for this class of applications, ordering significantly improves overall performance, that cache reuse may be more important than reducing communication, and that it is possible to achieve message passing performance using shared memory constructs through careful data ordering and distribution. However, a multi-threaded implementation of CG on the Tera MTA does not require special ordering or partitioning to obtain high efficiency and scalability.
Document ID
20000114840
Acquisition Source
Ames Research Center
Document Type
Preprint (Draft being sent to journal)
Authors
Oliker, Leonid (National Energy Research Supercomputer Center Livermore, CA United States)
Heber, Gerd (Cornell Univ. Ithaca, NY United States)
Biswas, Rupak (MRJ Technology Solutions Moffett Field, CA United States)
Date Acquired
September 7, 2013
Publication Date
January 1, 2000
Subject Category
Computer Programming And Software
Meeting Information
Meeting: Parallel and Distributed Computing Systems
Location: Las Vegas, NV
Country: United States
Start Date: August 8, 2000
End Date: August 10, 2000
Funding Number(s)
CONTRACT_GRANT: NAS2-14303
CONTRACT_GRANT: DE-AC03-76SF-00098
CONTRACT_GRANT: NSF MIPS-97-07125
CONTRACT_GRANT: NSF CISE-97-26388
PROJECT: RTOP 519-40-12
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.

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