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Library | Materyal Türü | Barkod | Yer Numarası | Durum |
|---|---|---|---|---|
Searching... Pamukkale Merkez Kütüphanesi | Kitap | 0039524 | QA76.58P37 2006 | Searching... Unknown |
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Özet
Özet
Scientific computing has often been called the third approach to scientific discovery, emerging as a peer to experimentation and theory. Historically, the synergy between experimentation and theory has been well understood: experiments give insight into possible theories, theories inspire experiments, experiments reinforce or invalidate theories, and so on. As scientific computing has evolved to produce results that meet or exceed the quality of experimental and theoretical results, it has become indispensable.
Parallel processing has been an enabling technology in scientific computing for more than 20 years. This book is the first in-depth discussion of parallel computing in 10 years; it reflects the mix of topics that mathematicians, computer scientists, and computational scientists focus on to make parallel processing effective for scientific problems. Presently, the impact of parallel processing on scientific computing varies greatly across disciplines, but it plays a vital role in most problem domains and is absolutely essential in many of them.
Parallel Processing for Scientific Computing is divided into four parts: The first concerns performance modeling, analysis, and optimization; the second focuses on parallel algorithms and software for an array of problems common to many modeling and simulation applications; the third emphasizes tools and environments that can ease and enhance the process of application development; and the fourth provides a sampling of applications that require parallel computing for scaling to solve larger and realistic models that can advance science and engineering.
Author Notes
Michael A. Heroux is the Solvers Project Leader at Sandia National Laboratory; his work focuses on new algorithm development and robust parallel implementation of solver components. He leads development of the Trilinos Project, an effort to provide solution methods in a state-of-the-art software framework. He also maintains an active interest in the interaction between scientific/engineering applications and high-performance computer architectures. Padma Raghavan is a Professor in the Department of Computer Science and Engineering at Pennsylvania State University. Her research interests include parallel and distributed computing, sparse matrix graph techniques and their applications, and software environments and component architectures for large-scale computational materials science. Horst D. Simon is Associate Laboratory Director for Computing Sciences at Lawrence Berkeley National Laboratory. His recursive spectral bisection algorithm is regarded as a breakthrough in parallel algorithms for unstructured computations, and he was honored for his algorithm research efforts with the 1988 Gordon Bell Prize for parallel processing research.
Table of Contents
| List of Figures |
| List of Tables |
| Preface |
| 1 Frontiers of Scientific Computing. An Overview |
| Part I Performance Modeling, Analysis and Optimization |
| 2 Performance Analysis. From Art to Science |
| 3 Approaches to Architecture-Aware Parallel Scientific Computation |
| 4 Achieving High Performance on the BlueGene/L Supercomputer |
| 5 Performance Evaluation and Modeling of Ultra-Scale Systems |
| Part II Parallel Algorithms and Enabling Technologies |
| 6 Partitioning and Load Balancing |
| 7 Combinatorial Parallel and Scientific Computing |
| 8 Parallel Adaptive Mesh Refinement |
| 9 Parallel Sparse Solvers, Preconditioners, and Their Applications |
| 10 A Survey of Parallelization Techniques for Multigrid Solvers |
| 11 Fault Tolerance in Large-Scale Scientific Computing |
| Part III Tools and Frameworks for Parallel Applications |
| 12 Parallel Tools and Environments. A Survey |
| 13 Parallel Linear Algebra Software |
| 14 High-Performance Component Software Systems |
| 15 Integrating Component-Based Scientific Computing Software |
| Part IV Applications of Parallel Computing |
| 16 Parallel Algorithms for PDE-Constrained Optimization |
| 17 Massively Parallel Mixed-Integer Programming |
| 18 Parallel Methods and Software for Multicomponent Simulations |
| 19 Parallel Computational Biology |
| 20 Opportunities and Challenges for Parallel Computing in Science and Engineering |
| Index |