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• W76998490 abstract "An analytical performance model for parallel programs can provide qualitative insight as well as efficient quantitative evaluation and prediction of parallel program performance. While stochastic models for parallel programs can represent execution time variance due to communication and resource contention delays, a qualitative assessment of previous models shows that the stochastic assumption makes it extremely difficult to compute synchronization costs and overall execution times.This thesis first re-evaluates the need for the stochastic assumption by examining the influence of non-deterministic communication and resource contention delays on execution times in parallel programs. An analytical model of program behavior, combined with detailed program measurements, provides compelling evidence that in shared-memory programs on current systems as well as programs with similar granularity on foreseeable future systems, such delays introduce extremely low variance into the execution time of each process between synchronization points, even with high communication costs and contention.Motivated by the above results, the thesis develops a conceptually simple deterministic model for parallel program performance prediction, using deterministic values to represent mean task times including communication, and (if necessary) shared-resource contention computed from a separate, stochastic model. Experiments applying the model to several shared-memory programs demonstrate the efficiency, accuracy and ability to model programs with large and complex task graphs. A quantitative assessment of previous stochastic models shows that they have inconsistent or poor accuracy, as well as prohibitive computational cost in models applicable to complex task graphs. Furthermore, in comparison with simple, insightful speedup bounds computed using parameters such as average parallelism, the deterministic model provides additional qualitative as well as quantitative information, for comparable effort.The thesis then uses example programs to demonstrate the insight and predictive power provided by the deterministic model. The model can be used to quantify and understand nuances of program performance, and to quickly predict the impact of system changes as well as program design changes that affect load-balancing, such as changes in the partitioning and scheduling of tasks. This insight and predictive power is due to the particular task-graph-based representation of program parallelism and scheduling.In summary, the analytical and experimental results in the thesis contribute towards understanding a fundamental principle of parallel program behavior, and towards evaluating, understanding, and predicting parallel program performance." @default.
• W76998490 created "2016-06-24" @default.
• W76998490 creator A5059017058 @default.
• W76998490 date "1993-01-02" @default.
• W76998490 modified "2023-10-12" @default.
• W76998490 title "Analyzing the behavior and performance of parallel programs" @default.
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