A supernodal Cholesky factorization algorithms for shared-memory multiprocessors

Journal Article · Thu Jul 01 04:00:00 EDT 1993 · SIAM Journal on Scientific and Statistical Computing (Society for Industrial and Applied Mathematics); (United States)

DOI:https://doi.org/10.1137/0914048· OSTI ID:7369124

Ng, Esmond; Peyton, B W ^[1]

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1. Oak Ridge National Lab., TN (United States)

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.

• DOE Contract Number: AC05-84OR21400
• OSTI ID: 7369124
• Journal Information: SIAM Journal on Scientific and Statistical Computing (Society for Industrial and Applied Mathematics); (United States), Journal Name: SIAM Journal on Scientific and Statistical Computing (Society for Industrial and Applied Mathematics); (United States) Vol. 14:4; ISSN 0196-5204; ISSN SIJCD4
• Country of Publication: United States
• Language: English

Related Subjects

99 GENERAL AND MISCELLANEOUS
990200* -- Mathematics & Computers
ALGORITHMS
ARRAY PROCESSORS
COMPUTERS
DIGITAL COMPUTERS
FACTORIZATION
IMPLEMENTATION
MATHEMATICAL LOGIC
MEMORY MANAGEMENT
PARALLEL PROCESSING
PERFORMANCE
PROGRAMMING
SUPERCOMPUTERS
VECTOR PROCESSING