Adve, Vikram S.Vernon, Mary K.2017-08-022017-08-021998-12-03Adve, Vikram S. and Vernon, Mary K.. "A Deterministic Model for Parallel Program Performance Evaluation." (1998) https://hdl.handle.net/1911/96503.https://hdl.handle.net/1911/96503Analytical models for parallel programs have been successful at providing simple qualitative insights and bounds on scalability, but have been less successful in practice for predicting detailed, quantitative information about program performance. We develop a conceptually simple model that provides detailed performance prediction for parallel programs with arbitrary task graphs, a wide variety of task scheduling policies, shared-memory communication, and significant resource contention. Unlike many previous models, our model assumes deterministic task execution times which permits detailed analysis of synchronization, task scheduling, the order of task execution as well as mean values of communication costs. The assumption of deterministic task times is supported by a recent study of the influence of non-deterministic delays in parallel programs. We show that the deterministic task graph model is accurate and efficient for five shared-memory programs, including programs with large and/or complex task graphs, sophisticated task scheduling, highly non-uniform task times, and significant communication and resource contention. We also use three example programs to illustrate the predictive capabilities of the model. In two cases, broad insights and detailed metrics from the model are used to suggest improvements in load-balancing and the model quickly and accurately predicts the impact of these changes. In the third case, further novel metrics are used to obtain insight into the impact of program design changes that improve communication locality as well as load-balancing. Finally, we briefly present results of a comparison between our model and representative models based on stochastic task execution times.38 ppengYou are granted permission for the noncommercial reproduction, distribution, display, and performance of this technical report in any format, but this permission is only for a period of forty-five (45) days from the most recent time that you verified that this technical report is still available from the Computer Science Department of Rice University under terms that include this permission. All other rights are reserved by the author(s).Technical reportTR98-333