Provides a rapid introduction to the world of vector and parallel processing for these linear algebra applications.
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About the authors; Preface; Introduction; 1. High performance computing; 2. Overview of current high-performance computers; 3. Implementation details and overhead; 4. Performance: analysis, modeling, and measurements; 5. Building blocks in linear algebra; 6. Direct solution of sparse linear systems; 7. Krylov subspaces: projection; 8. Iterative methods for linear systems; 9. Preconditioning and parallel preconditioning; 10. Linear Eigenvalue problems Ax=lx; 11. The generalized Eigenproblem; Appendix A. Acquiring mathematical software; Appendix B. Glossary; Appendix C. Level 1, 2, and 3 BLAS quick reference; Appendix D. Operation counts for various BLAS and decompositions; Bibliography; Index.
This updated version of "Solving Linear Systems on Vector and Shared Memory Computers" (SIAM, 1990) discusses major elements of the new advanced-architecture computers and recent developments in the solution of systems of linear equations and eigenvalue algorithms for dense and sparse matrices that are designed to exploit these elements. It updates material on high-performance computers from the previous book, expands on sparse direct and iterative methods for systems of equations, and covers large sparse eigenvalue problems.
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