The Sourcebook of Parallel Computing
Series: The Morgan Kaufmann Series in Computer Architecture and Design;
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Product details:
- Publisher Elsevier Science
- Date of Publication 24 November 2002
- ISBN 9781558608719
- Binding Hardback
- No. of pages864 pages
- Size 234x187 mm
- Weight 1630 g
- Language English 0
Categories
Long description:
Parallel Computing is a compelling vision of how computation can seamlessly scale from a single processor to virtually limitless computing power. Unfortunately, the scaling of application performance has not matched peak speed, and the programming burden for these machines remains heavy. The applications must be programmed to exploit parallelism in the most efficient way possible. Today, the responsibility for achieving the vision of scalable parallelism remains in the hands of the application developer.
This book represents the collected knowledge and experience of over 60 leading parallel computing researchers. They offer students, scientists and engineers a complete sourcebook with solid coverage of parallel computing hardware, programming considerations, algorithms, software and enabling technologies, as well as several parallel application case studies. The Sourcebook of Parallel Computing offers extensive tutorials and detailed documentation of the advanced strategies produced by research over the last two decades application case studies. The Sourcebook of Parallel Computing offers extensive tutorials and detailed documentation of the advanced strategies produced by research over the last two decades
MoreTable of Contents:
I. Parallelism
1. Introduction
2. Parallel Computer Architectures
3. Parallel Programming Considerations
II. Applications
4. General Application Issues
5. Parallel Computing in CFD
6. Parallel Computing in Environment and Energy
7. Parallel Computational Chemistry
8. Application Overviews
III. Software technologies
9. Software Technologies
10. Message Passing and Threads
11. Parallel I/O
12. Languages and Compilers
13. Parallel Object-Oriented Libraries
14. Problem-Solving Environments
15. Tools for Performance Tuning and Debugging
16. The 2-D Poisson Problem
IV. Enabling Technologies and Algorithms
17. Reusable Software and Algorithms
18. Graph Partitioning for Scientific Simulations
19. Mesh Generation
20. Templates and Numerical Linear Algebra
21. Software for the Scalable Solutions of PDEs
22. Parallel Continuous Optimization
23. Path Following in Scientific Computing
24. Automatic Differentiation
V. Conclusion
25. Wrap-up and Features
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