2006 / Approx. xxiv + 396 pages / Softcover / ISBN: 978-0-898716-19-1 / List Price $100.50 / SIAM Member Price $70.35 / Order Code SE20
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.
This edited volume serves as an up-to-date reference for researchers and application developers on the state of the art in scientific computing. It also serves as an excellent overview and introduction, especially for graduate and senior-level undergraduate students interested in computational modeling and simulation and related computer science and applied mathematics aspects.
About the Editors
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.
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Chapter 1: Frontiers of Scientific Computing: An Overview
Part I: Performance Modeling, Analysis and Optimization
Chapter 2: Performance Analysis: From Art to Science
Chapter 3: Approaches to Architecture-Aware Parallel Scientific Computation
Chapter 4: Achieving High Performance on the BlueGene/L Supercomputer
Chapter 5: Performance Evaluation and Modeling of Ultra-Scale Systems
Part II: Parallel Algorithms and Enabling Technologies
Chapter 6: Partitioning and Load Balancing
Chapter 7: Combinatorial Parallel and Scientific Computing
Chapter 8: Parallel Adaptive Mesh Refinement
Chapter 9: Parallel Sparse Solvers, Preconditioners, and Their Applications
Chapter 10: A Survey of Parallelization Techniques for Multigrid Solvers
Chapter 11: Fault Tolerance in Large-Scale Scientific Computing
Part III: Tools and Frameworks for Parallel Applications
Chapter 12: Parallel Tools and Environments: A Survey
Chapter 13: Parallel Linear Algebra Software
Chapter 14: High-Performance Component Software Systems
Chapter 15: Integrating Component-Based Scientific Computing Software
Part IV: Applications of Parallel Computing
Chapter 16: Parallel Algorithms for PDE-Constrained Optimization
Chapter 17: Massively Parallel Mixed-Integer Programming
Chapter 18: Parallel Methods and Software for Multicomponent Simulations
Chapter 19: Parallel Computational Biology
Chapter 20: Opportunities and Challenges for Parallel Computing in Science and Engineering
The royalties from the sale of this book are contributed to the SIAM Student Travel Fund.
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