Parallel orthogonal factorizations of large sparse matrices on distributed-memory multiprocessors

Conference · Fri Dec 31 00:00:00 EST 1993

OSTI ID:54441

Coleman, T F ^[1]

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1. Cornell Univ., Ithaca, NY (United States)

We describe the issues involved in the design and implementation of an efficient parallel multifrontal algorithm for computing the QR factorization of a large sparse matrix on distributed-memory multiprocessors. The proposed algorithm has the following novel features. First, a supernodal tree computed from the sparsity structure of R is used to organize the numerical factorization. Second, a new algorithm has been designed for the most crucial task in this context-the QR factorization of two upper trapezoidal matrices in parallel. Third, the overall factorization is accomplished by a sequence of Householder and Givens transformations. Experimental results on an Intel iPSC/860 are included.

• OSTI ID: 54441
• Report Number(s): DOE/ER/25151-1-Vol.1; CONF-930331-Vol.1; TRN: 94:007584-0079
• Resource Relation: Conference: 6. Society for Industrial and Applied Mathematics (SIAM) conference on parallel processing for scientific computing, Norfolk, VA (United States), 21-24 Mar 1993; Other Information: PBD: 1993; Related Information: Is Part Of Parallel processing for scientific computing: Proceedings. Volume 1; Sincovec, R.F.; Leuze, M.R. [eds.] [Oak Ridge National Lab., TN (United States)]; Keyes, D.E. [ed.] [Yale Univ., New Haven, CT (United States)]; Petzold, L.R. [ed.] [Minnesota Univ., Minneapolis, MN (United States)]; Reed, D.A. [ed.] [Illinois Univ., Chicago, IL (United States)]; PB: 522 p.
• Country of Publication: United States
• Language: English

Related Subjects

99 MATHEMATICS
COMPUTERS
INFORMATION SCIENCE
MANAGEMENT
LAW
MISCELLANEOUS
ARRAY PROCESSORS
ALGORITHMS
PARALLEL PROCESSING
MEMORY MANAGEMENT
COMPUTER ARCHITECTURE
MATRICES
NUMERICAL SOLUTION