（已有1条评价）基本信息
- 原书名：Numerical Linear Algebra on High-Performance Computers
- 原出版社： Society for Industrial Mathematics
- 作者： Jack J. Dongarra Iain S. Duff Danny C. Sorensen Hank A. van der Vorst
- 丛书名： 国际著名数学图书--影印版
- 出版社：清华大学出版社
- ISBN：9787302244998
- 上架时间：2011-3-31
- 出版日期：2011 年2月
- 开本：16开
- 页码：342
- 版次：1-1
- 所属分类：数学 > 专著及论文集、工具书
内容简介
书籍
数学书籍
The purpose of this book is to unify and document in one place many of the techniques and much of the current understanding about solving systems of linear equations on vector and parallel computers. This book is not a textbook,but it is meant to provide a fast entrance to the world of vector and parallel processing for these linear algebra applications. We intend this book to be used by three groups of readers: graduate students, researchers working in computational science, and numerical analysts. As such, we hope this book can serve both as a reference and as a supplement to a teaching text on aspects of scientific computation.
The book is divided into five major parts: (1) introduction to terms and concepts, including an overview of the state of the art for high-performance computers and a discussion of performance evaluation (Chapters 1-4); (2) direct solution of dense matrix problems (Chapter 5); (3) direct solution of sparse systems of equations (Chapter 6); (4) iterative solution of sparse systems of equations (Chapters 7-9); and (5) iterative solution of sparse eigenvalue problems (Chapters 10-11). Any book that attempts to cover these topics must necessarily be somewhat out of date before it appears, because the area is in a state of flux. We have purposely avoided highly detailed descriptions of popular machines and have tried instead to focus on concepts as much as possible; nevertheless, to make the description more concrete, we do point to specific computers.
数学书籍
The purpose of this book is to unify and document in one place many of the techniques and much of the current understanding about solving systems of linear equations on vector and parallel computers. This book is not a textbook,but it is meant to provide a fast entrance to the world of vector and parallel processing for these linear algebra applications. We intend this book to be used by three groups of readers: graduate students, researchers working in computational science, and numerical analysts. As such, we hope this book can serve both as a reference and as a supplement to a teaching text on aspects of scientific computation.
The book is divided into five major parts: (1) introduction to terms and concepts, including an overview of the state of the art for high-performance computers and a discussion of performance evaluation (Chapters 1-4); (2) direct solution of dense matrix problems (Chapter 5); (3) direct solution of sparse systems of equations (Chapter 6); (4) iterative solution of sparse systems of equations (Chapters 7-9); and (5) iterative solution of sparse eigenvalue problems (Chapters 10-11). Any book that attempts to cover these topics must necessarily be somewhat out of date before it appears, because the area is in a state of flux. We have purposely avoided highly detailed descriptions of popular machines and have tried instead to focus on concepts as much as possible; nevertheless, to make the description more concrete, we do point to specific computers.
作译者
Jack J. Dongarra is a Distinguished Professor of Computer Science at the University to Tennessee and a Distinguished Scientist at Oak Ridge National Laboratory.
lain S. Duff is Group Leader of Numerical Analysis at the CCLRC Rutherford Appleton Laboratory, the Project Leader for the Parallel Algorithms Group at CERFACS in Toulouse, and a Visiting Professor of Mathematics at the University or Strathclyde.
Danny C. Sorensen is a Professor of Computational and Applied Mathematics at Rice University.
Henk A. van der Vorst is a Professor in Numerical Analysis at Utrecht University in the Netherlands.
lain S. Duff is Group Leader of Numerical Analysis at the CCLRC Rutherford Appleton Laboratory, the Project Leader for the Parallel Algorithms Group at CERFACS in Toulouse, and a Visiting Professor of Mathematics at the University or Strathclyde.
Danny C. Sorensen is a Professor of Computational and Applied Mathematics at Rice University.
Henk A. van der Vorst is a Professor in Numerical Analysis at Utrecht University in the Netherlands.
目录
《高性能计算机上的数值线性(英文影印版)》
About the Authors
Preface
Introduction
1 High-Performance Computing
1.1 Trends in Computer Design
1.2 Traditional Computers and Their Limitations
1.3 Parallelism within a Single Processor
1.3.1 Multiple Functional Units
1.3.2 Pipelining
1.3.3 Overlapping
1.3.4 RISC
1.3.5 VLIW
1.3.6 Vector Instructions
1.3.7 Chaining
1.3.8 Memory-to-Memory and Register-to-Register Organizations
1.3.9 Register Set
1.3.10 Stripmining
1.3.11 Reconfigurable Vector Registers
1.3.12 Memory Organization
About the Authors
Preface
Introduction
1 High-Performance Computing
1.1 Trends in Computer Design
1.2 Traditional Computers and Their Limitations
1.3 Parallelism within a Single Processor
1.3.1 Multiple Functional Units
1.3.2 Pipelining
1.3.3 Overlapping
1.3.4 RISC
1.3.5 VLIW
1.3.6 Vector Instructions
1.3.7 Chaining
1.3.8 Memory-to-Memory and Register-to-Register Organizations
1.3.9 Register Set
1.3.10 Stripmining
1.3.11 Reconfigurable Vector Registers
1.3.12 Memory Organization
媒体评论
"This excellent book successfully achieves the authors'purpose to unify and document in one place many of the techniques and much of the current understanding about solving systems of linear equations [and eigensystems] on vector and parallel computers. We highly recommend it as a very useful reference for both graduate students and practitioners..."
-- F. A. Smith and R. E. Funderlic, Statistical software Newsletter (part of the Computational Statistics and Data Analysis journal).
"...Numerical Linear Algebra for High-Performance Computers is a major revision to the book entitled Solving Linear Systems on Vector and Shared Memory Computers, published by SIAM in 1990. 8ut the current book updates the material focusing on vector and parallel computing for linear algebra and presents new contents on the eigenvalue problem. In short, this reviewer wants to thank the authors for writing such a good book."
-- Pan Zeng, Applied Mechanics Reviews, Vol. 52, No. 7, July 1999.
"The present revised and extended volume brings the computing aspects up to date, contains a much more detailed treatment of preconditioning for linear systems, and extends the numerical treatment to the algebraic eigenvalue problem.., many books have been published in numerical linear algebra... However, in my view, the need for the present book has increased rather than diminished..."
-- Nicholas J. Higham, University of Manchester, SIAM Review, Vol. 42, No. 3, September 2000.
"This is a volume that every numerical analyst should have, either for updating his lectures.or for his own research. I also encourage students, engineers, and practitioners to read it. It is obviously a very valuable addition to the existing literature."
-- Claude Brezinski, Numerical Algorithms 22 (1999).
-- F. A. Smith and R. E. Funderlic, Statistical software Newsletter (part of the Computational Statistics and Data Analysis journal).
"...Numerical Linear Algebra for High-Performance Computers is a major revision to the book entitled Solving Linear Systems on Vector and Shared Memory Computers, published by SIAM in 1990. 8ut the current book updates the material focusing on vector and parallel computing for linear algebra and presents new contents on the eigenvalue problem. In short, this reviewer wants to thank the authors for writing such a good book."
-- Pan Zeng, Applied Mechanics Reviews, Vol. 52, No. 7, July 1999.
"The present revised and extended volume brings the computing aspects up to date, contains a much more detailed treatment of preconditioning for linear systems, and extends the numerical treatment to the algebraic eigenvalue problem.., many books have been published in numerical linear algebra... However, in my view, the need for the present book has increased rather than diminished..."
-- Nicholas J. Higham, University of Manchester, SIAM Review, Vol. 42, No. 3, September 2000.
"This is a volume that every numerical analyst should have, either for updating his lectures.or for his own research. I also encourage students, engineers, and practitioners to read it. It is obviously a very valuable addition to the existing literature."
-- Claude Brezinski, Numerical Algorithms 22 (1999).
