A GPU-Based Programming Framework for Highly-Scalable    Multi-Agent Traffic Simulations

Yoshihito Sano; Naoki Fukuta

doi:10.20965/jaciii.2014.p0581

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JACIII Vol.18 No.4 pp. 581-589

doi: 10.20965/jaciii.2014.p0581

(2014)

Paper:

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A GPU-Based Programming Framework for Highly-Scalable Multi-Agent Traffic Simulations

Yoshihito Sano and Naoki Fukuta

Graduate School of Informatics, Shizuoka University, 3-5-1 Johoku, Hamamatsu, Shizuoka 432-8011, Japan

Received:

July 24, 2013

Accepted:

January 31, 2014

Published:

July 20, 2014

Keywords:

multiagent system, traffic simulation, GPU computing

Abstract

Highly detailed reproducibility of multi-agent simulations is strongly demanded. To realize such highly reproducible multi-agent simulations, it is important to make each agent respond to its dynamically changing environment as well as scale the simulation to cover important phenomena that could be produced. In this paper, we present a programming framework to realize highly scalable execution of them as well as detailed behaviors of agents. The framework can help simulation developers utilize many GPGPU-based parallel cores in their simulation programs by using the proposed OpenCL-based multi-platform agent code conversion engine. We show our prototype implementation of the framework and how our framework can help simulation developers to code, test, and evaluate their agent codes which select actions and path plants reactively in dynamically changing large-scale simulation environments on various hardware and software settings.

Cite this article as:

Y. Sano and N. Fukuta, “A GPU-Based Programming Framework for Highly-Scalable Multi-Agent Traffic Simulations,” J. Adv. Comput. Intell. Intell. Inform., Vol.18 No.4, pp. 581-589, 2014.

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References

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This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] M. Balmer, K. Meister, M. Rieser, K. Nagel, and K. Axhausen, “Agent-based simulation of travel demand: Structure and computational performance of MATSim-T,” 2nd TRB Conf. on Innovations in Travel Modeling, 2008.

[2] [2] T. Yamashita, T. Okada, and I. Noda, “Implementation of Simulation Environment for Control of Huge-scale Pedestrian,” Joint Agent Workshop and Symposium (JAWS), 2012. (in Japanese)

[3] [3] J. Tsai, N. Fridman, E. Bowring, M. Brown, S. Epstein, G. Kaminka, S.Marsella, A. Ogden, I. Rika, A. Sheel, M. E. Taylor, X. Wang, A. Zilka, and M. Tambe, “ESCAPES – Evacuation Simulation with Children, Authorities, Parents, Emotions, and Social comparison,” Proc. Int. Conf. on Autonomous Agents and Multiagent Systems (AAMAS 2011), pp. 457-464, 2011.

[4] [4] E. d. l. Hoz, I.Marsa-Maestre, M. A. Lopez-Carmona, and P. Perez, “Extending MATSim to allow the simulation of route coordination mechanisms,” Proc. The 1st Int. Workshop on Multi-Agent Smart Computing (MASmart 2011), pp. 1-15, 2011.

[5] [5] R. Kanamori, T. Morikawa, and T. Ito, “Evaluation of special lanes as incentive policies for promoting electric vehicles,” Proc. The 1st Int. Workshop on Multi-Agent Smart Computing (MASmart 2011), pp. 45-56, 2011.

[6] [6] Y. Nakajima, S. Yamane, and H. Hattori, “Multi-model based Simulation Platform for Urban Traffic Simulation,” Joint Agent Workshop and Symp. (JAWS), 2010. (in Japanese)

[7] [7] L. Navarro, F. Flacher, and V. Corruble, “Dynamic level of detail for large scale agent-based urban simulations,” Proc. of 10th Int. Conf. on Autonomous Agents and Multiagent Systems (AAMAS2011), pp. 701-708, 2011.

[8] [8] S. Kato, G. Yamamoto, H. Tai, and H. Mizuta, “Large-scale Traffic Simulation with Scholar SMP Supercomputing System,” Joint Agent Workshop and Symp. (JAWS), 2008. (in Japanese)

[9] [9] P. Harish and P. J. Narayanan, “Accelerating large graph algorithms on the GPU using CUDA,” Proc. of the 14th int. conf. on High performance computing, HiPC’07, Berlin, Heidelberg, 2007, Springer-Verlag, pp. 197-208.

[10] [10] P. Harish, V. Vineet, and P. J. Narayanan, “Large Graph Algorithms for Massively Multithreaded Architectures,” Technical Report IIIT/TR/2009/74, Int. Institute of Information Technology Hyderabad, 2009.

[11] [11] V. Vineet, P. Harish, S. Patidar, and P. J. Narayanan, “Fast minimum spanning tree for large graphs on the GPU,” Proc. of the Conf. on High Performance Graphics 2009, HPG ’09, New York, ACM, NY, USA, pp. 167-171, 2009.

[12] [12] Khronos OpenCL Working Group, “The OpenCL Specification Version: 1.2 Revision: 19,” 2012.

[13] [13] R. E. Korf, “Real-Time Heuristic Search,” Artif. Intell., Vol.42, Issues 2-3, pp. 189-211, 1990.

[14] [14] T. Ishida and M. Shimbo, “Path Learning by Realtime Search,” Japanese Society for Artificial Intelligence, Vol.11, No.3, pp. 411-419, 1996. (in Japanese)

[15] [15] G. Caggianese and U. Erra, “GPU Accelerated Multi-agent Path Planning based on Grid Space Decomposition,” Proc. of the Int. Conf. on Computational Science (ICCS 2012), pp. 1847-1856, 2012.