Low-Cost Performance Prediction of Data-Dependent Data Parallel Programs

Authors:
Hasyim Gautama;Arjan J. C. Van Gemund
Affiliations:
-;-
Venue:
MASCOTS '01 Proceedings of the Ninth International Symposium in Modeling, Analysis and Simulation of Computer and Telecommunication Systems
Year:
2001

Citing 0
Cited 3

Symbolic Performance Prediction of Data-Dependent Parallel Programs

TOOLS '02 Proceedings of the 12th International Conference on Computer Performance Evaluation, Modelling Techniques and Tools
Low-Cost Static Performance Prediction of Parallel Stochastic Task Compositions

IEEE Transactions on Parallel and Distributed Systems
Parallel execution time prediction of the multitask parallel programs

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Abstract

Abstract: Current analytic solutions to the execution time distribution of an N-ary parallel composition of tasks having independent and identically distributed execution times are computationally complex, except for a limited number of distributions. In this paper we introduce an analytical solution based on approximating the execution time distributions in terms of a limited number of statistical moments. This approach allows the parallel execution time to be approximated with O(1) solution complexity for a wide range of execution time distributions, while the approximation ac-curacy outperforms comparable techniques known to date. Experiments show that the error of the predicted mean value of the parallel execution time is even less than 4% for parallel loops comprising up to 10,000 tasks whose execution times are normally distributed. Measurements on real programs (NAS-EP benchmark, PSRS sorter, and WATOR simulator) confirm these results provided the task execution distributions are independent and unimodal.