A Supernodal Cholesky Factorization Algorithm for Shared-Memory Multiprocessors

This paper presents a parallel sparse Cholesky factorization algorithm for shared-memory MIMD multiprocessors. The algorithm is particularly well suited for vector supercomputers with multiple processors, such as the Cray Y-MP. The new algorithm is a straightforward parallelization of the left-looki...

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Bibliographic Details
Published in:SIAM journal on scientific computing Vol. 14; no. 4; pp. 761 - 769
Main Authors: Ng, Esmond, Peyton, Barry W.
Format: Journal Article
Language:English
Published: Philadelphia, PA Society for Industrial and Applied Mathematics 01.07.1993
Subjects:
Algorithms
Exact sciences and technology
Mathematics
Numerical analysis
Numerical analysis. Scientific computation
Numerical linear algebra
Sciences and techniques of general use
Sparsity
Supercomputers
Supercomputer
Parallel algorithm
Linear system
Cholesky method
Sparse matrix
MIMD computer
Factorization
Shared memory
ISSN:1064-8275, 1095-7197
Online Access:Get full text
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Summary:This paper presents a parallel sparse Cholesky factorization algorithm for shared-memory MIMD multiprocessors. The algorithm is particularly well suited for vector supercomputers with multiple processors, such as the Cray Y-MP. The new algorithm is a straightforward parallelization of the left-looking supernodal sparse Cholesky factorization algorithm. Like its sequential predecessor, it improves performance by reducing indirect addressing and memory traffic. Experimental results on a Cray Y-MP demonstrate the effectiveness of the new algorithm. On eight processors of a Cray Y-MP, the new routine performs the factorization at rates exceeding one Gflop for several test problems from the Harwell-Boeing sparse matrix collection.
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content type line 14
ISSN:1064-8275
1095-7197
DOI:10.1137/0914048