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- Parallel Computer Architectures
- 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
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
Parallel application developers ; computational scientists and engineers; and graduate students in these disciplines
- No. of pages:
- © Morgan Kaufmann 2003
- 11th November 2002
- Morgan Kaufmann
- Hardcover ISBN:
- eBook ISBN:
Sourcebook of Parallel Computing is an indispensable reference for parallel-computing consultants, scientists, and researchers, and a valuable addition to any computer science library. -Distributed Systems Online "The Sourcebook for Parallel Computing gives a thorough introduction to parallel applications, software technologies, enabling technologies, and algorithms. This is a great book that I highly recommend to anyone interested in a comprehensive and thoughtful treatment of the most important issues in parallel computing. " -Horst Simon, Director, Director, NERSC, Berkeley "The Sourcebook builds on the important work done at the Center for Research on Parallel Computation and within the academic community for over a decade. It is a definitive text on Parallel Computing and should be a key reference for students, researchers and practitioners in the field." -Francine Berman, Director, San Diego Supercomputer Center and the National Partnership for Advanced Computational Infrastructure
Jack Dongarra is a Professor of Computer Science at the University of Tennessee and Distinguished Scientist at Oak Ridge National Laboratory. He specializes in numerical algorithms in linear algebra, parallel computing, mathematical software development, use of advanced computer architectures, programming methodology, and tools for parallel computers.
University of Tennessee
Ian Foster is Senior Scientist in the Mathematics and Computer Science Division at Argonne National Laboratory, where he also leads the Distributed Systems Laboratory, and Associate Professor of Computer Science at the University of Chicago. His research concerns techniques, tools, and algorithms for high-performance distributed computing, parallel computing, and computational science. Foster led the research and development of software for the I-WAY wide-area distributed computing experiment, which connected supercomputers, databases, and other high-end resources at 17 sites across North America (a live experiment at the Supercomputing conference of 1995).
Argonne National Laboratory
Geoffrey Fox is a Distinguished Professor of Informatics, Computing and Physics and Associate Dean of Graduate studies and Research in the School of Informatics and Computing, Indiana University. He has taught and led many research groups at Caltech and Syracuse University, previously. He received his Ph.D. from Cambridge University, U.K. Fox is well known for his comprehensive work and extensive publications in parallel architecture, distributed programming, grid computing, web services, and Internet applications. His book on Grid Computing (coauthored with F. Berman and Tony Hey) is widely used by the research community. He has produced over 60 Ph.D. students in physics, computer science and engineering over the years.
Indiana University, USA
William Gropp is a senior computer scientist and associate director of the Mathematics and Computer Science Division at Argonne National Lab. He is also a senior scientist in the Computer Science department at the University of Chicago and a senior fellow in the Argonne-University of Chicago Computation Institute. His research interests are in parallel computing, software for scientific computing, and numerical methods for partial differential equations. He has played a major role in the development of the MPI message-passing standard.
Argonne National Laboratory
p> Ken Kennedy is the Ann and John Doerr Professor of Computational Engineering and Director of the Center for High Performance Software Research (HiPerSoft) at Rice University. He is a fellow of the Institute of Electrical and Electronics Engineers, the Association for Computing Machinery, and the American Association for the Advancement of Science and has been a member of the National Academy of Engineering since 1990. From 1997 to 1999, he served as cochair of the President's Information Technology Advisory Committee (PITAC). For his leadership in producing the PITAC report on funding of information technology research, he received the Computing Research Association Distinguished Service Award (1999) and the RCI Seymour Cray HPCC Industry Recognition Award (1999).
Professor Kennedy has published over 150 technical articles and supervised 34 Ph.D. dissertations on programming support software for high-performance computer systems. In recognition of his contributions to software for high-performance computation, he received the 1995 W. Wallace McDowell Award, the highest research award of the IEEE Computer Society. In 1999, he was named the third recipient of the ACM SIGPLAN Programming Languages Achievement Award.
Linda Torczon is a principal investigator on the Massively Scalar Compiler Project at Rice University, and the Grid Application Development Software Project sponsored by the next Generation Software program of the National Science Foundation. She also serves as the executive director of HiPerSoft and of the Los Alamos Computer Science Institute. Her research interests include code generation, interprocedural dataflow analysis and optimization, and programming environments.
Rice University, Houston, Texas
Andy White is the Special Projects Director for the Weapons Physics Directorate at Los Alamos National Laboratory. This newLab oratory enterprise focuses on research issues in computer and computational sciences
associated with employing the largest, most complex computational resources to address important national issues such as stockpile stewardship, energy and environment,
systems biology, nanotechnology and crisis management.
Los Alamos National Laboratory
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