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Explicit Spatial Scattering for Load Balancing in Conservatively Synchronized Parallel Discrete Event Simulations

Published: 17 May 2010 Publication History

Abstract

We re-examine the problem of load balancing in conservatively synchronized parallel, discrete- event simulations executed on high-performance computing clusters, focusing on simulations where computational and messaging load tend to be spatially clustered. Such domains are frequently characterized by the presence of geographic "hot-spots'' - regions that generate significantly more simulation events than others. Examples of such domains include simulation of urban regions, transportation networks and networks where interaction between entities is often constrained by physical proximity. Noting that in conservatively synchronized parallel simulations, the speed of execution of the simulation is determined by the slowest ( i.e most heavily loaded) simulation process, we study different partitioning strategies in achieving equitable processor-load distribution in domains with spatially clustered load. In particular, we study the effectiveness of partitioning via spatial scattering to achieve optimal load balance. In this partitioning technique, nearby entities are explicitly assigned to different processors, thereby scattering the load across the cluster. This is motivated by two observations, namely, (i) since load is spatially clustered, spatial scattering should, intuitively, spread the load across the compute cluster, and (ii) in parallel simulations, equitable distribution of CPU load is a greater determinant of execution speed than message passing overhead. Through large-scale simulation experiments - both of abstracted and real simulation models - on high performance clusters, we observe that scatter partitioning - even with its greatly increased messaging overhead - often significantly outperforms more conventional spatial partitioning techniques that seek to reduce messaging overhead. Further, even if hot-spots change over the course of the simulation, if the underlying feature of spatial clustering is retained, load continues to be balanced with spatial scattering leading us to the observation that spatial scattering can often obviate the need for dynamic load balancing.

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Cited By

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  • (2021)Load-Aware Dynamic Time Synchronization in Parallel Discrete Event SimulationProceedings of the 2021 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation10.1145/3437959.3459249(95-105)Online publication date: 21-May-2021
  • (2015)Parameterized benchmarking of parallel discrete event simulation systemsProceedings of the 2015 Winter Simulation Conference10.5555/2888619.2888945(2836-2847)Online publication date: 6-Dec-2015
  • (2015)Parallelizing a discrete event simulation application using the Habanero-Java multicore libraryProceedings of the Sixth International Workshop on Programming Models and Applications for Multicores and Manycores10.1145/2712386.2712402(86-95)Online publication date: 7-Feb-2015
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Information

Published In

cover image ACM Conferences
PADS '10: Proceedings of the 2010 IEEE Workshop on Principles of Advanced and Distributed Simulation
May 2010
164 pages
ISBN:9781424472925

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IEEE Computer Society

United States

Publication History

Published: 17 May 2010

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Author Tags

  1. CPU load
  2. conservatively synchronized parallel discrete event simulations
  3. dynamic load balancing
  4. equitable processor load distribution
  5. explicit spatial scattering
  6. geographic hot-spot regions
  7. large-scale simulation
  8. message passing overhead
  9. spatial partitioning techniques
  10. spatial scattering
  11. spatially clustered load
  12. transportation networks
  13. urban region simulation

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Overall Acceptance Rate 398 of 779 submissions, 51%

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Cited By

View all
  • (2021)Load-Aware Dynamic Time Synchronization in Parallel Discrete Event SimulationProceedings of the 2021 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation10.1145/3437959.3459249(95-105)Online publication date: 21-May-2021
  • (2015)Parameterized benchmarking of parallel discrete event simulation systemsProceedings of the 2015 Winter Simulation Conference10.5555/2888619.2888945(2836-2847)Online publication date: 6-Dec-2015
  • (2015)Parallelizing a discrete event simulation application using the Habanero-Java multicore libraryProceedings of the Sixth International Workshop on Programming Models and Applications for Multicores and Manycores10.1145/2712386.2712402(86-95)Online publication date: 7-Feb-2015
  • (2014)Efficient graph-based dynamic load-balancing for parallel large-scale agent-based traffic simulationProceedings of the 2014 Winter Simulation Conference10.5555/2693848.2694282(3483-3494)Online publication date: 7-Dec-2014
  • (2014)Learning Based Distributed Orchestration of Stochastic Discrete Event SimulationsProceedings of the 2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing10.1109/UCC.2014.18(99-108)Online publication date: 8-Dec-2014
  • (2012)Partitioning on Dynamic Behavior for Parallel Discrete Event SimulationProceedings of the 2012 ACM/IEEE/SCS 26th Workshop on Principles of Advanced and Distributed Simulation10.1109/PADS.2012.32(221-230)Online publication date: 15-Jul-2012
  • (2011)A graph partitioning game for distributed simulation of networksProceedings of the 2011 International Workshop on Modeling, Analysis, and Control of Complex Networks10.5555/2043527.2043529(9-16)Online publication date: 9-Sep-2011
  • (2011)Empirical Study on Entity Interaction Graph of Large-Scale Parallel SimulationsProceedings of the 2011 IEEE Workshop on Principles of Advanced and Distributed Simulation10.1109/PADS.2011.5936762(1-6)Online publication date: 14-Jun-2011
  • (2010)CybersimProceedings of the Winter Simulation Conference10.5555/2433508.2433865(2876-2887)Online publication date: 5-Dec-2010

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