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"Rather than parallelizing sequential algorithms, the authors develop new back-substitution free parallel algorithms, using a bidirectional elimination technique for the solution of both dense and sparse linear equations. They provide full coverage of bidirectional parallel algorithms based on Gaussian elimination, LU factorization, Householder reductions and modified Gram-Schmidt orthogonalization, Givens rotations, sparse Cholesky factorization, and sparse factorization, clearly demonstrating how the bidirectional approach allows for improved speedup, numerical stability, and efficient implementation on multiprocessor systems." "Plus, the book offers a useful survey of the vast literature on direct methods, introductory material on solving systems of linear equations, and exercises. It is an invaluable resource for computer scientists, researchers in parallel linear algebra, and anyone with an interest in parallel programming."--BOOK JACKET.
Mathematics of Computing -- General.
The subject of sparse matrices has its root in such diverse fields as management science, power systems analysis, surveying, circuit theory, and structural analysis. Efficient use of sparsity is a key to solving large problems in many fields. This second edition is a complete rewrite of the first edition published 30 years ago. Much has changed since that time. Problems have grown greatly in size and complexity; nearly all examples in the first edition were of order less than 5,000 in the first edition, and are often more than a million in the second edition. Computer architectures are now much more complex, requiring new ways of adapting algorithms to parallel environments with memory hierarchies. Because the area is such an important one to all of computational science and engineering, a huge amount of research has been done in the last 30 years, some of it by the authors themselves. This new research is integrated into the text with a clear explanation of the underlying mathematics and algorithms. New research that is described includes new techniques for scaling and error control, new orderings, new combinatorial techniques for partitioning both symmetric and unsymmetric problems, and a detailed description of the multifrontal approach to solving systems that was pioneered by the research of the authors and colleagues. This includes a discussion of techniques for exploiting parallel architectures and new work for indefinite and unsymmetric systems.
Large sparse linear systems occur in many scientific and engineering applications encountered in military and civilian domains. Such systems are typically solved using either iterative or direct methods. We are developing parallel formulations of computationally intensive algorithms that underly these methods. Direct methods for solving sparse linear systems are important because of their generality and robustness. For linear systems arising in certain applications, such as linear programming and some structural engineering applications, they are the only feasible methods. Although highly parallel formulations of dense matrix factorization are well known, it has been a challenge to implement efficient sparse linear system solvers using direct methods, even on moderately parallel computers. We have recently achieved a breakthrough in developing a highly parallel sparse Cholesky factorization algorithm that substantially improves the state of the art in parallel direct solution of sparse linear systems-both in terms of scalability and overall performance. Experiments have shown that this algorithm can easily speedup Cholesky factorization by a factor of at least a few hundred up to 1024 processors, and achieve levels of performance that were unheard of and unimaginable for this problem until very recently.
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.
Parallel Numerical Computations with Applications contains selected edited papers presented at the 1998 Frontiers of Parallel Numerical Computations and Applications Workshop, along with invited papers from leading researchers around the world. These papers cover a broad spectrum of topics on parallel numerical computation with applications; such as advanced parallel numerical and computational optimization methods, novel parallel computing techniques, numerical fluid mechanics, and other applications related to material sciences, signal and image processing, semiconductor technology, and electronic circuits and systems design. This state-of-the-art volume will be an up-to-date resource for researchers in the areas of parallel and distributed computing.
Parallel Algorithms for Optimal Control of Large Scale Linear Systems is a comprehensive presentation for both linear and bilinear systems. The parallel algorithms presented in this book are applicable to a wider class of practical systems than those served by traditional methods for large scale singularly perturbed and weakly coupled systems based on the power-series expansion methods. It is intended for scientists and advance graduate students in electrical engineering and computer science who deal with parallel algorithms and control systems, especially large scale systems. The material presented is both comprehensive and unique.