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Granular Cloning: Intra-Object Parallelism in Ensemble Studies

Published: 14 May 2018 Publication History

Abstract

Many runs of a computer simulation are needed to model uncertainty and evaluate alternate design choices. Such an ensemble of runs often contains many commonalities among the different individual runs. Simulation cloning is a technique that capitalizes on this fact to reduce the amount of computation required by the ensemble. Granular cloning is proposed that allows the sharing of state and computations at the scale of simulation objects as small as individual variables, offering savings in computation and memory, increased parallelism and improved tractability of sample path patterns across multiple runs. The ensemble produces results that are identical to separately executed runs. Whenever simulation objects interact, granular cloning will resolve their association to subsets of runs though binary operations on tags. Algorithms and computational techniques required to efficiently implement granular cloning are presented. Results from an experimental study using a cellular automata-based transportation simulation model and a coupled transportation and land use model are presented providing evidence the approach can yield significant speed ups relative to brute force replicated runs.

References

[1]
Maria Hybinette and Richard Fujimoto. 2001. Cloning parallel simulations. Commun. ACM Transactions on Modeling and Computer Simulation (TOMACS), 11(4), 378--407.
[2]
Z Lu, D. Noonan, J. Crittenden, H. Jeong, and D. Wang. 2013. Use of impact fees to incentivize low-impact development and promote compact growth. Environmental science &technology, 47(19), 10744--10752.
[3]
John von Neumann. 1956. Probabilistic logics and the synthesis of reliable organism from unreliable components. Princeton University Press.
[4]
Philip Pecher, Michael Hunter, and Richard Fujimoto. 2015. Efficient Execution of Replicated Transportation Simulations with Uncertain Vehicle Trajectories. Procedia Computer Science 51 (2015): 2638--2647.
[5]
Karen Panetta Lentz, Elias S. Manolakos, Edward Czeck, and Jamie Heller. 1997. Multiple experiment environments for testing. Journal of Electronic Testing 11, no. 3 (1997): 247--262.
[6]
Pirooz Vakili. 1992. Massively parallel and distributed simulation of a class of discrete event systems: A different perspective. ACM Transactions on Modeling and Computer Simulation (TOMACS), 2(3), 214--238.
[7]
Steve Ferenci, Richard Fujimoto, Mostafa Ammar, Kalyan Perumalla, and George Riley. Updateable simulation of communication networks. 2002. Proceedings of the sixteenth workshop on Parallel and distributed simulation pp. 107--114. IEEE Computer Society.
[8]
Kevin Walsh and Emin Gün Sirer. Simulation of large scale networks I: staged simulation for improving scale and performance of wireless network simulations. 2003. Proceedings of the 35th conference on Winter simulation: driving innovation. pp. 667--675.
[9]
SLX FAQs. http://www.wolverinesoftware.com/SLXFAQs.htm. Accessed: 2018-01-04
[10]
James Henriksen. An introduction to SLX {simulation software}. 1995. Simulation Conference Proceedings, 1995. Winter. IEEE.
[11]
Richard M. Fujimoto. 1989. The virtual time machine. Proceedings of the first annual ACM symposium on Parallel algorithms and architectures. ACM.
[12]
Mirko Stoffers, Daniel Schemmel, Oscar Soria Dustmann, and Klaus Wehrle. 2016. Automated Memoization for Parameter Studies Implemented in Impure Languages. Proceedings of the 2016 annual ACM Conference on SIGSIM Principles of Advanced Discrete Simulation. pp. 221--232. ACM.
[13]
Kai Nagel and Michael Schreckenberg. A cellular automaton model for freeway traffic. 1992. Journal de physique I 2, no. 12 2221--2229.
[14]
John Conway. The game of life. 1970. Scientific American 223, no. 4
[15]
Richard Fujimoto, Conrad Bock, Wei Chen, Ernest Page, and J. Panchal. Research Challenges in Modeling and Simulation for Engineering Complex Systems. 2016. NSF Report
[16]
Dan Chen, Stephen J. Turner, Wentong Cai, Boon Ping Gan, and Malcolm Yoke Hean Low. Algorithms for HLA-based distributed simulation cloning. 2005. ACM Transactions on Modeling and Computer Simulation (TOMACS). 15, no. 4. 316--345.
[17]
Dan Chen, Stephen J. Turner, Wentong Cai, Georgios K. Theodoropoulos, Muzhou Xiong, and Michael Lees. Synchronization in federation community networks. 2010. Journal of parallel and distributed Computing 70, no. 2. 144--159.
[18]
Xiaosong Li, Wentong Cai, and Stephen J. Turner. Cloning Agent-Based Simulation. 2017. ACM Transactions on Modeling and Computer Simulation (TOMACS). 27, no. 2. 15.
[19]
Francesco Quaglia and Roberto Baldoni. Exploiting intra-object dependencies in parallel simulation. 1999. Information Processing Letters 70 (3), 119--125.
[20]
Nazzareno Marziale, Francesco Nobilia, Alessandro Pellegrini, and Francesco Quaglia. Granular time warp objects. 2016. Proceedings of the 2016 ACM SIGSIM Conference on Principles of Advanced and Discrete Simulation

Cited By

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  • (2022)Advanced Tutorial: Parallel and Distributed Methods for Scalable Discrete Simulation2022 Winter Simulation Conference (WSC)10.1109/WSC57314.2022.10015291(268-282)Online publication date: 11-Dec-2022
  • (undefined)SpecSims: A Scalable Speculative Tree-based Simulation Cloning Framework For Finite Memory MachinesACM Transactions on Modeling and Computer Simulation10.1145/3708885

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cover image ACM Conferences
SIGSIM-PADS '18: Proceedings of the 2018 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation
May 2018
224 pages
ISBN:9781450350921
DOI:10.1145/3200921
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Badge change: Article originally badged under Version 1.0 guidelines https://www.acm.org/publications/policies/artifact-review-badging

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Published: 14 May 2018

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Author Tags

  1. acceleration
  2. algorithms
  3. cloning
  4. design
  5. experimentation
  6. incremental simulation
  7. multiprocessors
  8. parallel algorithms
  9. parallel simulation
  10. performance
  11. scale
  12. scenario
  13. shared computation
  14. speedup

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SIGSIM-PADS '18 Paper Acceptance Rate 15 of 46 submissions, 33%;
Overall Acceptance Rate 398 of 779 submissions, 51%

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Cited By

View all
  • (2022)Advanced Tutorial: Parallel and Distributed Methods for Scalable Discrete Simulation2022 Winter Simulation Conference (WSC)10.1109/WSC57314.2022.10015291(268-282)Online publication date: 11-Dec-2022
  • (undefined)SpecSims: A Scalable Speculative Tree-based Simulation Cloning Framework For Finite Memory MachinesACM Transactions on Modeling and Computer Simulation10.1145/3708885

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