Automatic Partitioning of Large Scale Simulation in Grid Computing for Run Time Reduction

Nurcin Celik, Esfandyar Mazhari, John Canby, Omid Kazemi, Parag Sarfare, Majed S. Al-Otaibi and Young-Jun Son
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Nurcin Celik: The University of Arizona, USA
Esfandyar Mazhari: The University of Arizona, USA
John Canby: Schlumberger, Indonesia
Omid Kazemi: The University of Arizona, USA
Parag Sarfare: NetApp Inc., USA
Majed S. Al-Otaibi: The University of Arizona, USA
Young-Jun Son: The University of Arizona, USA

International Journal of Operations Research and Information Systems (IJORIS), 2010, vol. 1, issue 2, 64-90

Abstract: Simulating large-scale systems usually entails exhaustive computational powers and lengthy execution times. The goal of this research is to reduce execution time of large-scale simulations without sacrificing their accuracy by partitioning a monolithic model into multiple pieces automatically and executing them in a distributed computing environment. While this partitioning allows us to distribute required computational power to multiple computers, it creates a new challenge of synchronizing the partitioned models. In this article, a partitioning methodology based on a modified Prim’s algorithm is proposed to minimize the overall simulation execution time considering 1) internal computation in each of the partitioned models and 2) time synchronization between them. In addition, the authors seek to find the most advantageous number of partitioned models from the monolithic model by evaluating the tradeoff between reduced computations vs. increased time synchronization requirements. In this article, epoch- based synchronization is employed to synchronize logical times of the partitioned simulations, where an appropriate time interval is determined based on the off-line simulation analyses. A computational grid framework is employed for execution of the simulations partitioned by the proposed methodology. The experimental results reveal that the proposed approach reduces simulation execution time significantly while maintaining the accuracy as compared with the monolithic simulation execution approach.

Date: 2010
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