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Parallel algorithms for matrix computations

โœ Scribed by 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
Tongue
English
Leaves
208
Category
Library

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๐Ÿ“œ SIMILAR VOLUMES


Parallel Algorithms for Matrix Computati
โœ K. A. Gallivan, Michael T. Heath, Esmond Ng, James M. Ortega, Barry W. Peyton, R ๐Ÿ“‚ Library ๐Ÿ“… 1987 ๐Ÿ› Society for Industrial and Applied Mathematics ๐ŸŒ English

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 sp

Parallel Algorithms for Matrix Computati
โœ K. A. Gallivan, Michael T. Heath, Esmond Ng, James M. Ortega, Barry W. Peyton, R ๐Ÿ“‚ Library ๐Ÿ“… 1987 ๐Ÿ› Society for Industrial Mathematics ๐ŸŒ English

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 sp

Parallel algorithms for matrix computati
โœ K. A. Gallivan, Michael T. Heath, Esmond Ng, James M. Ortega, Barry W. Peyton, R ๐Ÿ“‚ Library ๐Ÿ“… 1987 ๐Ÿ› Society for Industrial and Applied Mathematics ๐ŸŒ English
Parallelism in Matrix Computations
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List of Figures -- List of Tables -- List of Algorithms -- Notations used in the book -- Part I Basics -- Parallel Programming Paradigms -- Computational Models -- Principles of parallel programming -- Fundamental kernels -- Vector operations -- Higher level BLAS -- General organization for dense ma

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This book is primarily intended as a research monograph that could also be used in graduate courses for the design of parallel algorithms in matrix computations. It assumes general but not extensive knowledge of numerical linear algebra, parallel architectures, and parallel programming paradigms.