A Performance Optimization Support Framework for GPU-Based Traffic Simulations with Negotiating Agents

Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 638)

Abstract

To realize a simulation which can handle hundreds of thousands of negotiating agents keeping their detailed behaviors, massive amount of computational power is required. Also having good programmability of agents’ codes to realize complex behaviors is essential to realize it. On deploying such negotiating agents on an agent simulation, it is important to be able to handle detailed behaviors of them, as well as having a large scale simulation to cover important phenomenon that should be observed. There are strong demands to utilize GPU-based computing resources to handle large-scale but very detailed simulations. However, it is not easy task for developers to configure the sufficient parameters to be set on its compilation or execution time, analyzing their performance characteristics on various execution settings. In this paper, we present a framework to assist the coding process of negotiating agents on a traffic simulation, as well as its parameter tuning process on GPU-based programming for simulation developers to utilize GPGPU-based many parallel cores in their simulation programs efficiently. We show how our implemented prototype framework helps simulation developers optimize various parameters and coding-level optimizations to be run on various hardware and software settings.

References

  1. 1.
    AlSaber, N., Kulkarni, M.: Semcache: semantics-aware caching for efficient GPU offloading. In: Proceedings of the 27th ACM International Conference on Supercomputing (ICS ’13), pp. 421–432, New York, NY, USA. ACM (2013)Google Scholar
  2. 2.
    Balmer, M., Meister, K., Rieser, M., Nagel, K., Axhausen, K.: Agent-based simulation of travel demand: structure and computational performance of matsim-t. In: 2nd TRB Conference on Innovations in Travel Modeling (2008)Google Scholar
  3. 3.
    Caggianese, G., Erra, U.: GPU accelerated multi-agent path planning based on grid space decomposition. In: Proceedings of the International Conference on Computational Science, pp. 1847–1856 (2012)Google Scholar
  4. 4.
    de la Hoz, E., Marsa-Maestre, I., Lopez-Carmona, M.A., Perez, P.: Extending matsim to allow the simulation of route coordination mechanisms. In: Proceedings of the 1st International Workshop on Multi-Agent Smart Computing (MASmart 2011), pp. 1–15 (2011)Google Scholar
  5. 5.
    Grasso, I., Pellegrini, S., Cosenza, B., Fahringer, T.: libwater: heterogeneous distributed computing made easy. In: Proceedings of the 27th ACM International Conference on Supercomputing (ICS ’13), pp. 161–172, New York, NY, USA. ACM (2013)Google Scholar
  6. 6.
    Holewinski, J., Pouchet, L.-N., Sadayappan, P.: High-performance code generation for stencil computations on GPU architectures. In: Proceedings of the 26th ACM International Conference on Supercomputing, ICS ’12, pp. 311–320, New York, NY, USA. ACM (2012)Google Scholar
  7. 7.
    Huo, X., Krishnamoorthy, S., Agrawal, G.: Efficient scheduling of recursive control flow on GPUs. In: Proceedings of the 27th ACM International Conference on Supercomputing (ICS ’13), pp. 409–420, New York, NY, USA. ACM (2013)Google Scholar
  8. 8.
    Ishida, T., Shimbo, M.: Path learning by realtime search. Jpn. Soc. Artif. Intell. 11(3), 411–419 (1996). (In Japanese)Google Scholar
  9. 9.
    Kanamori, R., Morikawa, T., Ito, T.: Evaluation of special lanes as incentive policies for promoting electric vehicles. In: Proceedings of the 1st International Workshop on Multi-Agent Smart Computing (MASmart 2011), pp. 45–56 (2011)Google Scholar
  10. 10.
    Khronos OpenCL Working Group. The OpenCL Specification Version: 1.2 Revision: 19 (2012)Google Scholar
  11. 11.
    Kofler, K., Grasso, I., Cosenza, B., Fahringer, T.: An automatic input-sensitive approach for heterogeneous task partitioning. In: Proceedings of the 27th ACM International Conference on Supercomputing (ICS ’13), pp. 149–160, New York, NY, USA. ACM (2013)Google Scholar
  12. 12.
    Korf, R.E.: Real-time heuristic search. Artif. Intell. 42(2–3), 189–211 (1990)CrossRefMATHGoogle Scholar
  13. 13.
    Lin, R., Kraus, S., Baarslag, T., Tykhonov, D., Hindriks, K., Jonker, C.M.: Genius: an integrated environment for supporting the design of generic automated negotiators. Comput. Intell. (2012)Google Scholar
  14. 14.
    Nakajima, Y., Yamane, S., Hattori, H.: Multi-model based simulation platform for urban traffic simulation. In: 13th International Conference on Principles and Practice of Multi-Agent Systems (PRIMA 2010), pp. 228–241 (2010)Google Scholar
  15. 15.
    Navarro, L., Corruble, V., Flacher, F., Zucker, J.-D.: A flexible approach to multi-level agent-based simulation with the mesoscopic representation. In: Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2013), pp. 159–166 (2013)Google Scholar
  16. 16.
    Robbins, H.: Some aspects of the sequential design of experiments. Bull. Am. Math. Soc. 58(5), 527–535 (1952)MathSciNetCrossRefMATHGoogle Scholar
  17. 17.
    Sano, Y., Fukuta, N.: A GPU-based framework for large-scale multi-agent traffic simulations. In: Proceedings of the 2nd IIAI International Conference on Advanced Applied Informatics (IIAI AAI2013) (2013)Google Scholar
  18. 18.
    Takahashi, J., Kanamori, R., Ito, T.: Evaluation of automated negotiation system for changing route assignment to acquire efficient traffic flow. In: Proceedings of the IEEE International Conference on Service Oriented Computing and Applications (SOCA2013), pp. 351–355 (2013)Google Scholar
  19. 19.
    Tilab. Java Agent Development Framework. http://jade.tilab.com
  20. 20.
    Tran-Thanh, L., Chapman, A.C., Rogers, A., Jennings, N.R.: Knapsack based optimal policies for budget-limited multi-armed bandits. In: AAAI (2012)Google Scholar
  21. 21.
    Tsai, J., Fridman, N., Bowring, E., Brown, M., Epstein, S., Kaminka, G., Marsella, S., Ogden, A., Rika, I., Sheel, A., Taylor, M.E., Wang, X., Zilka, A., Tambe, M.: Escapes—evacuation simulation with children, authorities, parents, emotions, and social comparison. In: Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2011), pp. 457–464 (2011)Google Scholar
  22. 22.
    Tsuruhashi, Y., Fukuta, N.: An analysis framework for meta strategies in simultaneous negotiations. In: Proceedings of 6th International Workshop on Agent-based Complex Automated Negotiations (ACAN2013) (2013)Google Scholar
  23. 23.
    Tsuruhashi, Y., Fukuta, N.: A framework for analyzing simultaneous negotiations. In: 16th International Conference on Principles and Practice of Multi-Agent Systems (PRIMA 2013) (2013)Google Scholar
  24. 24.
    Vasudevan, R., Vadhiyar, S.S., Kalé, L.V.: G-charm: an adaptive runtime system for message-driven parallel applications on hybrid systems. In: Proceedings of the 27th ACM International Conference on Supercomputing (ICS ’13), pp. 349–358, New York, NY, USA. ACM (2013)Google Scholar
  25. 25.
    Vineet, V., Harish, P., Patidar, S., Narayanan, P.J.: Fast minimum spanning tree for large graphs on the GPU. In: Proceedings of the Conference on High Performance Graphics 2009, HPG ’09, pp. 167–171, New York, NY, USA. ACM (2009)Google Scholar
  26. 26.
    Yamamoto, G., Tai, H., Mizuta, H.: A platform for massive agent-based simulation and its evaluation. In: Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2007), pp. 900–902 (2007)Google Scholar
  27. 27.
    Yamashita, T., Okada, T., Noda, I.: Implementation of simulation environment for control of huge-scale pedestrian. In: Joint Agent Workshop and Symposium (JAWS) (2012) (In Japanese)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Graduate School of InformaticsShizuoka UniversityHmamamatsuJapan

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