The Community for Technology Leaders
RSS Icon
Issue No.05 - May (2012 vol.23)
pp: 873-880
Alfred J. Park , Oak Ridge National Laboratory, Oak Ridge
Richard M. Fujimoto , Georgia Institute of Technology, Atlanta
The master/worker (MW) paradigm can be used as an approach to parallel discrete event simulation (PDES) on metacomputing systems. MW PDES applications incur overheads not found in conventional PDES executions executing on tightly coupled machines. We introduce four optimization techniques in MW PDES systems on public resource and desktop grid infrastructures. Work unit caching, pipelined state updates, expedited message delivery, and adaptive work unit scheduling mechanisms in the context of MW PDES are described. These optimizations provide significant performance benefits when used in tandem. We present results showing that an optimized MW PDES system using these techniques can exhibit performance comparable to a traditional PDES system for queueing network and particle physics simulation applications while providing execution capability across metacomputing systems.
Discrete event simulation, metacomputing systems, parallel and distributed simulation, simulation support systems, master/worker.
Alfred J. Park, Richard M. Fujimoto, "Efficient Master/Worker Parallel Discrete Event Simulation on Metacomputing Systems", IEEE Transactions on Parallel & Distributed Systems, vol.23, no. 5, pp. 873-880, May 2012, doi:10.1109/TPDS.2011.207
[1] D.P. Anderson, "BOINC: A System for Public-Resource Computing and Storage," Proc. IEEE/ACM Fifth Int'l Workshop Grid Computing, pp. 4-10, 2004.
[2] D. Kondo, M. Taufer, C.L. Brooks, H. Casanova, and A.A. Chien, "Characterizing and Evaluating Desktop Grids: An Empirical Study," Proc. 18th Int'l Parallel and Distributed Processing Symp., p. 26, 2004.
[3] D.P. Anderson, J. Cobb, E. Korpela, M. Lebofsky, and D. Werthimer, "SETI@home: An Experiment in Public-Resource Computing," Comm. ACM, vol. 45, pp. 56-61, 2002.
[4] J.-P. Goux, J. Linderoth, and M. Yoder, "Metacomputing and the Master-Worker Paradigm," Math. and Computer Science Division, Argonne Nat'l Laboratory ANL/MCS-P792-0200, Feb. 2000.
[5] D.P. Anderson and G. Fedak, "The Computational and Storage Potential of Volunteer Computing," Proc. IEEE Sixth Int'l Symp. Cluster Computing and the Grid (CCGrid), pp. 73-80, 2006.
[6] R.M. Fujimoto, Parallel and Distributed Simulation Systems. John Wiley and Sons, 1999.
[7] J. Hennessy, M. Heinrich, and A. Gupta, "Cache-Coherent Distributed Shared Memory: Perspectives on Its Development and Future Challenges," Proc. IEEE, vol. 87, pp. 418-429, 1999.
[8] A. Park and R.M. Fujimoto, "Aurora: An Approach to High Throughput Parallel Simulation," Proc. 20th Workshop Principles of Advanced and Distributed Simulation, 2006.
16 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool