Dynamic mapping of a class of independent tasks onto heterogeneous computing systems
Journal of Parallel and Distributed Computing - Special issue on software support for distributed computing
Cross-entropy and rare events for maximal cut and partition problems
ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special issue: Rare event simulation
Journal of Parallel and Distributed Computing
Genetic Algorithms in Search, Optimization and Machine Learning
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IPDPS '02 Proceedings of the 16th International Parallel and Distributed Processing Symposium
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IPDPS '02 Proceedings of the 16th International Parallel and Distributed Processing Symposium
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HCW '00 Proceedings of the 9th Heterogeneous Computing Workshop
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HCW '00 Proceedings of the 9th Heterogeneous Computing Workshop
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IEEE Transactions on Computers
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IEEE Transactions on Parallel and Distributed Systems
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CCGRID '10 Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing
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We develop in this paper a new heuristic for mapping a set of heterogeneous interacting tasks of a parallel application onto a heterogeneous computing platform. The problem is well known in literature to be an NP-Hard problem. However, we propose a completely new approach based on the Cross-Entropy (CE) method. This is a new and extremely robust rare event simulation (RES) technique which may be employed to solve difficult combinatorial optimization problems (COPs). We tailor the CE method to the requirements of the problem at hand, develop a mathematical framework, and present our algorithm, MaTCH. This globally iterative randomized procedure is then compared to a previously developed genetic algorithm (GA). Through some simple experiments we prove the power of MaTCH and we get remarkable improvements in the quality of mapping. The results indicate that, when compared to the GA, MaTCH improves upon the application execution time by over a factor of 38 on a 50 node system graph. We further attest our results by performing an ANOVA test on a sample data set to prove the significance of our results.