Parallel Processing for Scientific Computing
Parallel Processing for Scientific Computing
Padma Raghavan, Pennsylvania State University
Horst D. Simon, Lawrence Berkeley National Laboratory
AUD$204.55
exc GSTParallel Processing for Scientific Computing is the first in-depth discussion of parallel computing in 10 years; it reflects the mix of topics that mathematicians, scientists, and computer scientists focus on to make parallel processing effective for scientific problems. It 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 looks at applications that require parallel computing for scaling to solve larger and more realistic models that can advance science and engineering. In sum, this is 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 students interested in computational modeling and simulation.
- Up-to-date reference for researchers and application developers on the state of the art in scientific computing
- Suitable as an introduction for graduate and senior-level undergraduate students
- First book with this breadth of coverage for 10 years
Product details
March 2007Paperback
9780898716191
420 pages
253 × 174 × 19 mm
0.864kg
This item is not supplied by Cambridge University Press in your region. Please contact Soc for Industrial & Applied Mathematics for availability.
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.
