The Sourcebook of Parallel Computing

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

* Provides a solid background in parallel computing technologies
* Examines the technologies available and teaches students and practitioners how to select and apply them
* Presents case studies in a range of application areas including Chemistry, Image Processing, Data Mining, Ocean Modeling and Earthquake Simulation
* Considers the future development of parallel computing technologies and the kinds of applications they will support

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