Automated modeling and analysis of agent-based simulations using the CASE framework

Decraene, James; Low, Malcolm Yoke Hean; Zeng, Fanchao; Zhou, Suiping; Cai, Wentong

doi:10.1109/icarcv.2010.5707764

Cited by 7 publications

(6 citation statements)

References 11 publications

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“…In our future work, we will use multiple GPUs to further speedup this evaluation process. Thirdly, we will explore the possibility of integrating our simulation cloning mechanisms with an evolutionary algorithm based optimization engine (e.g., ECJ [18] or CASE [10]) to holistically speed-up the task of parameter space exploration for agentbased simulations.…”

Section: Discussionmentioning

confidence: 99%

Cloning Agent-based Simulation on GPU

Cai

Turner

2015

Proceedings of the 3rd ACM SIGSIM Conference on Principles of Advanced Discrete Simulation

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Simulation cloning is an efficient way to analyze multiple configurations in a parameter exploration task. This paper presents a generic approach to perform incremental agentbased simulation cloning and discusses its implementation on GPU. Compared with the incremental cloning of parallel and distributed simulation (PADS), cloning agent-based simulation (ABS) has new challenges due to the unique way how ABS is executed. In this paper, to support incremental cloning, mechanisms for both actively and passively cloning agents are proposed. A scheme to maintain the correct context of each cloned ABS instance is developed. In addition, a strategy to restrain the propagation of passive cloning in order to maximize computation sharing amongst cloned ABS instances is also investigated. The implementation of our proposed approach on GPU supports concurrent execution of agents within each simulation instance as well as concurrent execution of multiple simulation instances. Performance of the proposed approach is evaluated and analyzed using a case study of an agent-based evacuation simulation on a NVIDIA Quadro 2000 GPU. Our experiment results demonstrate that cloning can significantly speed up the overall parameter exploration task. The proposed approach achieves 2.4 to 5.1 times speedup for parameter exploration tasks containing 8 to 125 simulation instances that evaluate different parameter configurations.

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Section: Discussionmentioning

confidence: 99%

Cloning Agent-based Simulation on GPU

Cai

Turner

2015

Proceedings of the 3rd ACM SIGSIM Conference on Principles of Advanced Discrete Simulation

Self Cite

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“…The parameter exploration is widely adopted in economics, logistics, military, sociology, and engineering design. For example, parameter space exploration is adopted in a simulation-based military methodology utilized to uncover weakness of tactical plans, known as the Automated Red Teaming (ART) [26]. and many SMs.…”

Section: Agent Interactions With Other Agentsmentioning

confidence: 99%

“…Running a large number of simulations requires a significant amount of execution time. For example, parameter space exploration is adopted to uncover weakness of military operation plans in [26]. A tactic plan contains a number of parameters.…”

Section: Inefficiency Of Abs Executionmentioning

confidence: 99%

“…High performance execution platform on the cloud has been used to execute numerous simulation runs [57]. Practical use of data farming techniques has also been explored in the military field [26,48].…”

Section: Parameter Space Explorationmentioning

confidence: 99%

“…Frameworks such as ECJ [62] and CASE [26] have already been integrated with EA. An inverse simulation strategy has been proposed to reduce the iterations of EA in parameter exploration process in [95,96], and its implementation in HPC clusters is discussed in [110,111].…”

Section: Parameter Space Explorationmentioning

confidence: 99%

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Supporting agent-based simulations on GPU

Li¹

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First and foremost, I would like to express my earnest and deepest gratitude to my PhD supervisor, Professor Cai Wentong, for his remarkable guidance, constant help and support, and lots of caring and patience in the past five years. He is a mentor of a lifetime to me, and I am honored to be his student. I believe that his immense knowledge, passion, and rigorous attitude in research not only help me completing this thesis, but will also influence my career and life in the future. I would like to thank my parents, grandparents, Aunt Qing, Cousin Ruixu, and other family members for their love, encouragements and confidences in me. Especially, I would like to thank my girlfriend, Jingyi. We have been in a long distance relationship for over three years, and she has always been patient and supportive, and looking forward for the reunion. Taking a PhD is not easy. They give me the strength to carry on. I would like to acknowledge the help and support provided by the wonderful professors and staffs in School of Computer Science and Engineering. I appreciate Professor Stephen John Turner for his guidance as my co-supervisor in the first three and half years of my PhD study. I appreciate Professor Tang Xueyan for offering me a research assistant position to support my last year of the study. I have been also learning a lot from him in our weekly meetings. I appreciate Ms. Ng-Goh Siew Lai Irene for her great skills of building the experiment test bed. I also want to express my thanks to Professor Sun Chengzhen, Professor He Bingsheng, Professor Xing Zhenchang, and many other professors for their help in many aspects.

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