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Parallel Algorithms for Matrix Computations

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Describes a selection of important parallel algorithms for matrix computations. Reviews the current status and provides an overall perspective of parallel algorithms for solving problems arising in the major areas of numerical linear algebra, including (1) direct solution of dense, structured, or sparse linear systems, (2) dense or structured least squares computations, (3) dense or structured eigenvaluen and singular value computations, and (4) rapid elliptic solvers. The book emphasizes computational primitives whose efficient execution on parallel and vector computers is essential to obtain high performance algorithms.

Consists of two comprehensive survey papers on important parallel algorithms for solving problems arising in the major areas of numerical linear algebra--direct solution of linear systems, least squares computations, eigenvalue and singular value computations, and rapid elliptic solvers, plus an extensive up-to-date bibliography (2,000 items) on related research.

Author(s): K. A. Gallivan, Michael T. Heath, Esmond Ng, James M. Ortega, Barry W. Peyton, R. J. Plemmons, Charles H. Romine, A. H. Sameh, Robert G. Voigt
Publisher: Society for Industrial Mathematics
Year: 1987

Language: English
Pages: 208

Parallel Algorithms for Matrix Computations......Page 1
Contents......Page 10
PARALLEL ALGORITHMS FOR DENSE LINEAR ALGEBRA COMPUTATIONS......Page 12
PARALLEL ALGORITHMS FOR SPARSE LINEAR SYSTEMS......Page 94
A BIBLIOGRAPHY ON PARALLEL AND VECTOR NUMERICAL ALGORITHMS......Page 136