In this volume, designed for computational scientists and engineers working on applications requiring the memories and processing rates of large-scale parallelism, leading algorithmicists survey their own field-defining contributions, together with enough historical and bibliographical perspective to permit working one's way to the frontiers.
This book is distinguished from earlier surveys in parallel numerical algorithms by its extension of coverage beyond core linear algebraic methods into tools more directly associated with partial differential and integral equations - though still with an appealing generality - and by its focus on practical medium-granularity parallelism, approachable through traditional programming languages.
Several of the authors used their invitation to participate as a chance to stand back and create a unified overview, which nonspecialists will appreciate.

Inhalt

Preface. Parallel Numerical Algorithms: An Introduction; D.E. Keyes. Linear Systems. On Parallel Numerical Software Libraries; J.W. Demmel. Parallel Direct Methods for Sparse Linear Equations; M.T. Heath. Linear System Solvers: Sparse Iterative Methods; H.A. van der Vorst, T.F. Chan. Implicitly Restarted Arnoldi/Lanczos Methods for Large Scale Eigenvalue Calculations; D.C. Sorensen. Preconditioning. Approximate and Incomplete Factorizations; T.F. Chan, H.A. van der Vorst. Parallel Multigrid Methods; J.E. Jones, S.F. McCormick. Domain Decomposition Methods for Partial Differential Equations; B.F. Smith. Fast Application of Operators. Parallel FFTs; R.B. Pelz. Some Empirical Results on Using Multipole-Accelerated Iterative Methods to Solve 3-D Potential Integral Equations; F.T. Korsmeyer, et al. Multiresolution Representation and Numerical Algorithms: A Brief Review; A. Harten. Parallel Tools, Environments, And Benchmarking. Graph Partitioning Algorithms with Applications to Scientific Computing; A. Pothen. An Introduction to Performance Debugging for Parallel Computers; W.D. Gropp. NAS Parallel Benchmark Results; D.H. Bailey.