research-article

Cloning Agent-Based Simulation

Authors:

Stephen J. TurnerAuthors Info & Claims

ACM Transactions on Modeling and Computer Simulation (TOMACS), Volume 27, Issue 2

Article No.: 15, Pages 1 - 24

https://doi.org/10.1145/3013529

Published: 27 May 2017 Publication History

Abstract

Simulation cloning is an efficient way to analyze multiple configurations in a parameter exploration task. A simulation model usually contains a set of tunable parameters for exploring different configurations of a system. To evaluate different design alternatives, multiple simulation instances need to be launched, each evaluating a different parameter configuration. It usually takes a considerable amount of time to execute these simulation instances. Simulation cloning is proposed to reuse computations among simulation instances and to shorten the overall execution time. It is a challenging task to design cloning strategies to explore the computation sharing among simulation instances while maintaining the correctness of execution. In this article, we propose two agent-based simulation (ABS) cloning strategies, the top-down cloning strategy and the bottom-up cloning strategy. The top-down cloning strategy is initially designed and can only be applied to limited scenarios. The bottom-up cloning strategy is an improved strategy to overcome the limitation of the top-down cloning strategy. In the experiments, the effectiveness of the two strategies is analyzed. To show the performance advantages and generality of the bottom-up cloning strategy, a large-scale ABS parameter exploration task is performed, and results are discussed in the article.

References

[1]

Christos Alexopoulos. 2006. A comprehensive review of methods for simulation output analysis. In Proceedings of the 38th Winter Simulation Conference. IEEE, 168--178.

[2]

Joseph Bates, A. Bryan Loyall, and W. Scott Reilly. 1992. An architecture for action, emotion, and social behavior. In Proceedings of the European Workshop on Modelling Autonomous Agents in a Multi-Agent World. Springer, 55--68.

[3]

Azer Bestavros and Biao Wang. 1993. Multi-version Speculative Concurrency Control with Delayed Commit. Technical Report. Boston University Computer Science Department.

Digital Library

[4]

Avi Bleiweiss. 2008. GPU accelerated pathfinding. In Proceedings of the 23rd ACM SIGGRAPH/EUROGRAPHICS Symposium on Graphics Hardware. Eurographics Association, Sarajevo, Bosnia, 65--74.

Digital Library

[5]

Eric Bonabeau. 2002. Agent-based modeling: Methods and techniques for simulating human systems. Proc. Natl. Acad. Sci. U.S.A. 99, suppl 3 (2002), 7280--7287.

[6]

Benoît Calvez and Guillaume Hutzler. 2005. Parameter space exploration of agent-based models. In Proceedings of the 9th International Conference on Knowledge-Based Intelligent Information and Engineering Systems. Springer, 633--639.

Digital Library

[7]

Arthur Cayley. 1889. A theorem on trees. Quart. J. Math. 23, 376-378 (1889), 69.

[8]

Dan Chen, Stephen J. Turner, Wentong Cai, Boon Ping Gan, and Malcolm Yoke Hean Low. 2005. Algorithms for HLA-based distributed simulation cloning. ACM Trans. Model. Comput. Simul. 15, 4 (2005), 316--345.

Digital Library

[9]

Dan Chen, Stephen J. Turner, Wentong Cai, Georgios K. Theodoropoulos, Muzhou Xiong, and Michael Lees. 2010. Synchronization in federation community networks. J. Parallel Distrib. Comput. 70, 2 (2010), 144--159.

Digital Library

[10]

Dan Chen, Stephen John Turner, Boon Ping Gan, Wentong Cai, Junhu Wei, and Nirupam Julka. 2003. Alternative solutions for distributed simulation cloning. Simulation 79, 5--6 (2003), 299--315.

[11]

Dan Chen, Lizhe Wang, Xiaomin Wu, Jingying Chen, Samee U. Khan, Joanna KołOdziej, Mingwei Tian, Fang Huang, and Wangyang Liu. 2013. Hybrid modelling and simulation of huge crowd over a hierarchical grid architecture. Future Gener. Comput. Syst. 29, 5 (2013), 1309--1317.

Digital Library

[12]

Dan Chen, Lizhe Wang, Albert Y Zomaya, MingGang Dou, Jingying Chen, Ze Deng, and Salim Hariri. 2015. Parallel simulation of complex evacuation scenarios with adaptive agent models. IEEE Trans. Parallel Distrib. Syst. 26, 3 (2015), 847--857.

[13]

Wayne J. Davis. 1998. On-line Simulation: Need and Evolving Research Requirements. Wiley, New York, NY, 465--516.

[14]

James Decraene, Malcolm Yoke Hean Low, Fanchao Zeng, Suiping Zhou, and Wentong Cai. 2010. Automated modeling and analysis of agent-based simulations using the case framework. In Proceedings of 2010 11th International Conference on Control Automation Robotics 8 Vision (ICARCV). IEEE, Singapore, 346--351.

[15]

Aquino L. Espindola, Daniel Girardi, Thadeu J. P. Penna, Chris T. Bauch, Alexandre S. Martinez, and Brenno C. T. Cabella. 2012. Exploration of the parameter space in an agent-based model of tuberculosis spread: Emergence of drug resistance in developing vs developed countries. Int. J. Mod. Phys. C 23, 06 (2012), 1--9.

[16]

Peter W. Glynn and Philip Heidelberger. 1991. Analysis of parallel replicated simulations under a completion time constraint. ACM Trans. Model. Comput. Simul. 1, 1 (1991), 3--23.

Digital Library

[17]

A. P. Goldberg. 1992. Virtual time synchronization of replicated processes. In Proceedings of the 6th Workshop on Parallel and Distributed Simulation. SCS Simulation Series, Newport Beach, CA, 107--116.

[18]

Dirk Helbing and Peter Molnar. 1995. Social force model for pedestrian dynamics. Phys. Rev. E 51, 5 (1995), 4282.

[19]

Gary Horne and Stephan Seichter. 2014. Data farming in support of nato operations: Methodology and proof-of-concept. In Proceedings of the 2014 Winter Simulation Conference. IEEE, 2355--2363.

[20]

Gary E. Horne and Ted E. Meyer. 2004. Data farming: Discovering surprise. In Proceedings of the 36th Winter Simulation Conference. Winter Simulation Conference, Washington, D.C., 807--813.

[21]

Maria Hybinette and Richard M Fujimoto. 2001. Cloning parallel simulations. ACM Transactions on Modeling and Computer Simulation (TOMACS) 11, 4 (2001), 378--407.

Digital Library

[22]

Dariusz Król, Michal Orzechowski, Jacek Kitowski, Christoph Niethammer, Anthony Sulisto, and Amer Wafai. 2014. A cloud-based data farming platform for molecular dynamics simulations. In Proceedings of 2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing (UCC). IEEE, 579--584.

Digital Library

[23]

Georg Kunz, Daniel Schemmel, James Gross, and Klaus Wehrle. 2012. Multi-level parallelism for time-and cost-efficient parallel discrete event simulation on gpus. In Proceedings of the 2012 ACM/IEEE/SCS 26th Workshop on Principles of Advanced and Distributed Simulation. IEEE, 23--32.

Digital Library

[24]

Guillaume Laville, Kamel Mazouzi, Christophe Lang, Nicolas Marilleau, and Laurent Philippe. 2012. Using GPU for multi-agent multi-scale simulations. In Proceedings of 9th International Conference on Distributed Computing and Artificial Intelligence. Springer, Berlin, 197--204.

[25]

Xiaosong Li, Wentong Cai, and Stephen John Turner. 2014. Efficient neighbor searching for agent-based simulation on GPU. In Proceedings of 18th IEEE International Symposium on Distributed Simulation and Real-Time Applications. IEEE/ACM, 87--96.

Digital Library

[26]

Xiaosong Li, Wentong Cai, and Stephen John Turner. 2015. Cloning agent-based simulation on GPU. In Proceedings of the 3rd ACM Conference on SIGSIM-Principles of Advanced Discrete Simulation. ACM, 173--182.

Digital Library

[27]

Xiaosong Li, Wentong Cai, and Stephen John Turner. 2016. Supporting efficient execution of continuous space agent-based simulation on GPU. Concurrency and Computation: Practice and Experience 28, 12 (2016), 3313--3332.

Digital Library

[28]

Sean Luke. 2010. The ECJ Owner’s Manual. Technical Report. ECJ Evolutionary Computation Library, George Mason University.

[29]

Sean Luke, Liviu Panait, Gabriel Balan, Sean Paus, Zbigniew Skolicki, Jeff Bassett, Robert Hubley, and A. Chircop. 2006. Ecj: A java-based evolutionary computation research system. Retrieved November 2015 from http://cs.gmu.edu/eclab/projects/ecj.

[30]

Mikola Lysenko and Roshan M. DSouza. 2008. A framework for megascale agent based model simulations on graphics processing units. J. Artif. Societies Soc. Simul. 11, 4 (2008), 10.

[31]

NVIDIA. 2013. NVIDIA CUDA Programming Guide. Technical Report. Retrieved from http://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html.

[32]

Hyungwook Park and Paul A. Fishwick. 2009. A GPU-based application framework supporting fast discrete-event simulation. Simulation 86, 10 (2009), 613--628.

Digital Library

[33]

Hyungwook Park and Paul A. Fishwick. 2011. An analysis of queuing network simulation using GPU-based hardware acceleration. ACM Trans. Model. Comput. Simul. 21, 3 (2011), 1--22.

Digital Library

[34]

Kalyan S. Perumalla and Brandon G. Aaby. 2008. Data parallel execution challenges and runtime performance of agent simulations on GPUs. In Proceedings of the Spring Simulation Multiconference. Society for Computer Simulation International, Ottawa, ON, Canada, 116--123.

[35]

Paul Richmond. 2014. Resolving conflicts between multiple competing agents in parallel simulations. In Proceedings of the European Conference on Parallel Processing. Springer, 383--394.

Digital Library

[36]

Paul Richmond, Dawn Walker, Simon Coakley, and Daniela Romano. 2010. High performance cellular level agent-based simulation with FLAME for the GPU. Brief. Bioinform. 11, 3 (2010), 334--347.

[37]

Patrick F. Riley and George F. Riley. 2003. Next generation modeling III-agents: Spades—a distributed agent simulation environment with software-in-the-loop execution. In Proceedings of the 35th Winter Simulation Conference: Driving Innovation. 817--825.

[38]

Takao Terano. 2006. Exploring the vast parameter space of multi-agent based simulation. In Proceedings of the 2006 International Conference on Multi-agent-based Simulation. Springer, 1--14.

Digital Library

[39]

Takao Terano. 2007. Exploring the vast parameter space of multi-agent based simulation. In Proceedings of International Workshop on Multi-agent-based Simulation. Springer, 1--14.

[40]

Vasily Volkov. 2010. Better performance at lower occupancy. In Proceedings of the GPU Technology Conference, Vol. 10. NVIDIA, San Jose, CA.

[41]

John Von Neumann. 1956. Probabilistic logics and the synthesis of reliable organisms from unreliable components. Automata Stud. 34 (1956), 43--98.

[42]

Michael Wagner, Wentong Cai, and Michael Harold Lees. 2013. Emergence by strategy: Flocking boids and their fitness in relation to model complexity. In Proceedings of the 2013 Winter Simulation Conference. IEEE, Washington, D.C., 1479--1490.

[43]

Sven Widmer, Dominik Wodniok, Nicolas Weber, and Michael Goesele. 2013. Fast dynamic memory allocator for massively parallel architectures. In Proceedings of the 6th Workshop on General Purpose Processor Using Graphics Processing Units. ACM, 120--126.

Digital Library

[44]

Uri Wilensky. 1999. NetLogo User Manual. Technical Report. Northwestern University.

[45]

David Bruce Wilson. 1996. Generating random spanning trees more quickly than the cover time. In Proceedings of the 28th ACM symposium on Theory of Computing. ACM, 296--303.

Digital Library

[46]

Chao Yang, Isao Ono, Setsuya Kurahashi, Bin Jiang, and Takao Terano. 2015. A grid based simulation environment for parallel exploring agent-based models with vast parameter space. In Proceedings of the 1st International Conference on Human Centered Computing. Springer, 534--548.

[47]

Fanchao Zeng, James Decraene, Malcolm Yoke Hean Low, Suiping Zhou, and Wentong Cai. 2012. Evolving optimal and diversified military operational plans for computational red teaming. Syst. J. 6, 3 (2012), 499--509.

[48]

Jinghui Zhong, Nan Hu, Wentong Cai, Michael Lees, and Linbo Luo. 2015. Density-based evolutionary framework for crowd model calibration. J. Comput. Sci. 6 (2015), 11--22.

Cited By

Zeigler B(2024)Discrete Event Systems Theory for Fast Stochastic Simulation via Tree ExpansionSystems10.3390/systems1203008012:3(80)Online publication date: 2-Mar-2024
https://doi.org/10.3390/systems12030080
Li JZhang Z(2023)A versatile dynamic noise control framework based on computer simulation and modelingNonlinear Engineering10.1515/nleng-2022-027212:1Online publication date: 7-Jun-2023
https://doi.org/10.1515/nleng-2022-0272
Andelfinger PCai W(2022)Advanced TutorialProceedings of the Winter Simulation Conference10.5555/3586210.3586232(268-282)Online publication date: 11-Dec-2022
https://dl.acm.org/doi/10.5555/3586210.3586232
Show More Cited By

Index Terms

Cloning Agent-Based Simulation
1. Computer systems organization
  1. Architectures
    1. Parallel architectures
      1. Single instruction, multiple data
2. Computing methodologies
  1. Modeling and simulation
    1. Simulation types and techniques
      1. Agent / discrete models
      2. Massively parallel and high-performance simulations

Recommendations

Cloning Agent-based Simulation on GPU
SIGSIM PADS '15: Proceedings of the 3rd ACM SIGSIM Conference on Principles of Advanced Discrete Simulation

Simulation cloning is an efficient way to analyze multiple configurations in a parameter exploration task. This paper presents a generic approach to perform incremental agent-based simulation cloning and discusses its implementation on GPU. Compared ...
Agent-directed simulation systems engineering
SCSC '07: Proceedings of the 2007 Summer Computer Simulation Conference

This article emphasizes the application of system engineering principles to the development of Modeling and Simulation (M&S) applications. Clear distinction between M&S for system engineering and system engineering (SE) for M&S is presented to clarify ...
A Systematic View of Agent-supported Simulation
SIMULTECH 2014: Proceedings of the 4th International Conference on Simulation and Modeling Methodologies, Technologies and Applications

Agent-supported simulation involves the use of intelligent agents to enhance modeling and simulation (M&S) infrastructures and consists of support by software agents: (1) to provide computer assistance for front-end and/or backend interface functions in ...

Comments

Information & Contributors

Information

Published In

cover image ACM Transactions on Modeling and Computer Simulation

ACM Transactions on Modeling and Computer Simulation Volume 27, Issue 2

Special Issue on PADS 2015

April 2017

203 pages

ISSN:1049-3301

EISSN:1558-1195

DOI:10.1145/3015562

Editor:
Adelinde M. Uhrmacher
University of Rostock, Germany

Issue’s Table of Contents

Copyright © 2017 ACM.

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 ACM 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]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 May 2017

Accepted: 01 October 2016

Revised: 01 October 2016

Received: 01 December 2015

Published in TOMACS Volume 27, Issue 2

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

15
Total Citations
View Citations
254
Total Downloads

Downloads (Last 12 months)12
Downloads (Last 6 weeks)2

Reflects downloads up to 09 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Zeigler B(2024)Discrete Event Systems Theory for Fast Stochastic Simulation via Tree ExpansionSystems10.3390/systems1203008012:3(80)Online publication date: 2-Mar-2024
https://doi.org/10.3390/systems12030080
Li JZhang Z(2023)A versatile dynamic noise control framework based on computer simulation and modelingNonlinear Engineering10.1515/nleng-2022-027212:1Online publication date: 7-Jun-2023
https://doi.org/10.1515/nleng-2022-0272
Andelfinger PCai W(2022)Advanced TutorialProceedings of the Winter Simulation Conference10.5555/3586210.3586232(268-282)Online publication date: 11-Dec-2022
https://dl.acm.org/doi/10.5555/3586210.3586232
Andelfinger PCai W(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
https://doi.org/10.1109/WSC57314.2022.10015291
Jin KLee SKim Y(2021)Adaptive and optimized agent placement scheme for parallel agent‐based simulationETRI Journal10.4218/etrij.2020-039944:2(313-326)Online publication date: 30-Nov-2021
https://doi.org/10.4218/etrij.2020-0399
Diviš RKavička A(2021)Reflective Nested Simulations Supporting Optimizations within Sequential Railway Traffic SimulatorsACM Transactions on Modeling and Computer Simulation10.1145/346796532:1(1-34)Online publication date: 27-Sep-2021
https://dl.acm.org/doi/10.1145/3467965
Yoginath SAlam MPerumalla KSon YHaas P(2019)Energy conservation through cloned execution of simulationsProceedings of the Winter Simulation Conference10.5555/3400397.3400607(2572-2582)Online publication date: 8-Dec-2019
https://dl.acm.org/doi/10.5555/3400397.3400607
Kim JKavak HManzoor UZüfle A(2019)Advancing simulation experimentation capabilities with runtime interventionsProceedings of the Annual Simulation Symposium10.5555/3338027.3338050(1-11)Online publication date: 29-Apr-2019
https://dl.acm.org/doi/10.5555/3338027.3338050
Kim JKavak HManzoor UZufle A(2019)Advancing Simulation Experimentation Capabilities with Runtime Interventions2019 Spring Simulation Conference (SpringSim)10.23919/SpringSim.2019.8732869(1-11)Online publication date: Apr-2019
https://doi.org/10.23919/SpringSim.2019.8732869
Andelfinger PIvanchev JEckhoff DCai WKnoll AJin DLiu JKale L(2019)From Effects to CausesProceedings of the 2019 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation10.1145/3316480.3322891(173-184)Online publication date: 29-May-2019
https://dl.acm.org/doi/10.1145/3316480.3322891
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Issue’s Table of Contents