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Published Articles >> Table of Contents >> Abstract
Ninth IEEE International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunications Systems (MASCOTS'01)
p. 0173
Low-Cost Performance Prediction of Data-Dependent Data Parallel Programs
Hasyim Gautama, Delft University of Technology
Arjan J. C. Van Gemund, Delft University of Technology
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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/MASCOT.2001.948867
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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.
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Citation:
Hasyim Gautama, Arjan J. C. Van Gemund,
"Low-Cost Performance Prediction of Data-Dependent Data Parallel Programs,"
mascots,
p. 0173,
Ninth IEEE International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunications Systems (MASCOTS'01),
2001
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