Solving unsymmetric sparse systems of linear equations with PARDISO

Solving unsymmetric sparse systems of linear equations with PARDISO (2004)

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by Klaus Gärtner

Venue:	Journal of Future Generation Computer Systems
Citations:	62 - 5 self

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Datum	Value	Source
TITLE	Solving unsymmetric sparse systems of linear equations with PARDISO	INFERENCE
AUTHOR NAME	Klaus Gärtner	SVM HeaderParse 0.2
AUTHOR AFFIL	; Department of Computer Science, University of Basel; ¨ IBM Research Devision, T.J.Watson Research Center, P.O.Box 218, Yorktown Heights,; Weierstrass Institute for Applied Analysis and Stochastics, Mohrenstr. 39, D-10117	SVM HeaderParse 0.2
AUTHOR ADDR	; Klingelbergstrasse 50, CH-4056; Basel, Switzerland; NY, 10598; Berlin, Germany	SVM HeaderParse 0.2
ABSTRACT	Supernode partitioning for unsymmetric matrices together with complete block diagonal supernode pivoting and asynchronous computation can achieve high gigaflop rates for parallel sparse LU factorization on shared memory parallel computers. The progress in weighted graph matching algorithms helps to extend these concepts further and unsymmetric prepermutation of rows is used to place large matrix entries on the diagonal. Complete block diagonal supernode pivoting allows dynamical interchanges of columns and rows during the factorization process. The level-3 BLAS efficiency is retained and an advanced two-level left–right looking scheduling scheme results in good speedup on SMP machines. These algorithms have been integrated into the recent unsymmetric version of the PARDISO solver. Experiments demonstrate that a wide set of unsymmetric linear systems can be solved and high performance is consistently achieved for large sparse unsymmetric matrices from real world applications. Key words: Computational sciences, numerical linear algebra, direct solver, unsymmetric linear systems	SVM HeaderParse 0.2
YEAR	2004	INFERENCE
VENUE	Journal of Future Generation Computer Systems	INFERENCE
VENUE TYPE	JOURNAL	INFERENCE
PAGES	475--487	INFERENCE
VOLUME	20	INFERENCE
CITATIONS	23 found	ParsCit 1.0