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High-Performance Computing #2
Faculty
Scope
- Performance metrics and bounds
- Data dependence and scheduling
- Code profiling and tuning
- Parallel algorithms
Suggested readings
- Algorithms: Sequential, Parallel, and Distributed, by Kenneth Berman and Jerome Paul, Thomson Course Technology, 2005. Chapters 15, 16, 18, 19, and 24.
- Introduction to Parallel Computing, by Ananth Grama, Anshul Gupta, George Karypis, and Vipin Kumar, Addison Weslesy, 2003.
- The Sourcebook of Parallel Computing, by Jack Dongarra, Ian Foster, Geoffrey Fox, William Gropp, Ken Kennedy, Linda Torczon, Andy White, editors, Morgan Kaufmann, 2002. Chapters 3, 4, 9, 10, 12.
- Some aspects about the scalability of scientific applications on parallel computers. By M. Llorente, F. Tirado, and L. Vazquez, Parallel Computing, 22, pp 1169–1195, 1996.
- Models and languages for parallel computation. by D. Skillicrn and D. Talia. ACM Computing Surveys, 30(2), pp. 128-169, 1998.
- Concepts and Notations for Concurrent Programming, by Andrews, G., and Schneider, F., Computing Surveys, Vol. 15, pp. 3–43, 1983.
- Virtue: Immersive Performance Visualization of Parallel and Distributed Applications: Immersive Performance Visualization of Parallel and Distributed Applications, by Eric Shaffer, Shannon Whitmore, Benjamin Schaeffer, and Daniel A. Reed, IEEE Computer, December 1999, pp. 44-51.
- On the Impact of Communication Complexity on the Design of Parallel Numerical Algorithms, Gannon, D., and Van Rosendale, J., IEEE Trans. Comput., Vol. C-33, pp. 1180–1194, 1984.
- Twelve Ways to Fool the Masses When Giving Performance Results on Parallel Computers; by David H. Bailey; Supercomputing Review, Aug. 1991, pg. 54–55.
Suggested courses
- High Performance Parallel Computing (CS6230)
- High Performance Computer Architecture (CS 6290)
- Software Architecture and Design (CS 6310)
Related areas
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