When Equations Meet Experience:
The Art of Parallel Wisdom
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In this topic, we bring together everything we have learned — the math, the code, and the engineering mindset. We learn how to use analytical models not just as equations but as decision tools that help us design better parallel algorithms. We connect formulas like T_p = T_comp + T_comm + T_overhead with real-world performance, bridging the gap between theory and practice.
Learning Objectives
Learning Objectives
Combine analytical modeling with algorithmic design for performance evaluation.
Apply quantitative reasoning to optimize real-world parallel programs.
Evaluate trade-offs between computation, communication, and scalability.
Guide Questions
Guide Questions
How do analytical models guide us in designing better algorithms?
What trade-offs exist between computation and communication?
How can profiling validate or correct what models predict?
Topic Outline
Topic Outline
From Models to Algorithms
When Formulas Build Better Code
Matching Problems to Processors
Evaluating Performance
Predicting with Models
Testing in the Real World
Balancing the Triad: Computation, Communication, and Scalability
Keeping the Equation in Harmony
Lessons from the Real World
Current Lecture Handout
When Equations Meet Experience: The Art of Parallel Wisdom, rev 2023*
Note: Links marked with an asterisk (*) lead to materials accessible only to members of the University community. Please log in with your official University account to view them.
The semester at a glance:
Readings
Readings
Amdahl, G. M. (1967). Validity of the single processor approach to achieving large scale computing capabilities. AFIPS Conference Proceedings, 30, 483–485. https://doi.org/10.1145/1465482.1465560
Blumofe, R. D., & Leiserson, C. E. (1999). Scheduling multithreaded computations by work stealing. Journal of the ACM, 46(5), 720–748. https://doi.org/10.1145/324133.324234
Culler, D. E., Singh, J. P., & Gupta, A. (1996). Parallel computer architecture: A hardware/software approach. San Francisco, CA: Morgan Kaufmann.
Grama, A., Gupta, A., Karypis, G., & Kumar, V. (2003). Introduction to parallel computing (2nd ed.). Addison-Wesley.
Hockney, R. W. (1994). The communication challenge for MPP: Intel Paragon and Meiko CS-2. Parallel Computing, 20(3), 389–398. https://doi.org/10.1016/0167-8191(94)90064-7
Shende, S. S., & Malony, A. D. (2006). The TAU parallel performance system. The International Journal of High Performance Computing Applications, 20(2), 287–311. https://doi.org/10.1177/1094342006064482
Shi, H., & Schaeffer, J. (1992). Parallel sorting by regular sampling. Journal of Parallel and Distributed Computing, 14(4), 361–372. https://doi.org/10.1016/0743-7315(92)90047-M
Williams, S., Waterman, A., & Patterson, D. (2009). Roofline: An insightful visual performance model for multicore architectures. Communications of the ACM, 52(4), 65–76. https://doi.org/10.1145/1498765.1498785
Access Note: Published research articles and books are linked to their respective sources. Some materials are freely accessible within the University network or when logged in with official University credentials. Others will be provided to enrolled students through the class learning management system (LMS).
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