Advertisement

An Agent Framework for High Performance Simulations over Multi-core Clusters

  • Franco Cicirelli
  • Libero Nigro
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 402)

Abstract

Agent based modeling and simulation is widely recognized as an effective tool for the analysis of complex systems. This paper proposes a novel approach to modeling and high-performance parallel simulation of scalable agent models based on actors and the Theatre agency. The approach aims to an exploitation of the computing power of modern clusters of multi-core machines. Key factors of the approach are (i) it allows to take advantage of the lock-free cooperative model of concurrency of actors even in a parallel/multi-threaded scenario, (ii) it avoids serialization of messages exchanged among actors residing on different theatres allocated on a same CPU. Achievable execution performance of the proposed simulation framework is demonstrated through the parallel/distributed simulation of a large-scale multi-agent system.

Keywords

multi-agent systems actors modeling parallel simulation multi-core architectures Java 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Wooldridge, M.: An introduction to multi-agent systems. John Wiley & Sons (2002)Google Scholar
  2. 2.
    Ferber, J.: Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence. Addison Wesley Longman (1999)Google Scholar
  3. 3.
    Logan, B.: Evaluating agent architectures using simulation. In: Evaluating Architectures for Intelligence: Papers from the 2007 AAAI Workshop, pp. 40–43. AAAI Press (2007); Technical Report WS–07–04Google Scholar
  4. 4.
    Pawlaszczyk, D., Strassburger, S.: Scalability in distributed simulations of agent-based models. In: Proc. of Winter Simulation Conference, pp. 1189–1200 (2009)Google Scholar
  5. 5.
    Tang, W., Wang, S.: HPABM: A hierarchical parallel simulation framework for spatially-explicit agent-based models. T. GIS 13(3), 315–333 (2009)CrossRefGoogle Scholar
  6. 6.
    Frantz, C., Nowostawski, M., Purvis, M.: Multi-agent platforms and asynchronous message passing: Frameworks overview. Information Science Discussion Papers Series 7 (2010)Google Scholar
  7. 7.
    Collier, N., North, M.: Parallel agent-based simulation with Repast for High Performance Computing. In: Simulation, Trans. of SCS (2012), http://dx.doi.org/10.1177/0037549712462620
  8. 8.
    Perumalla, K.S., Aaby, B.G.: Data parallel execution challenges and runtime performance of agent simulations on GPUs. In: Proc. of the Spring Simulation Multi Conference, pp. 116–123 (2008)Google Scholar
  9. 9.
    Richmond, P., Walker, D.C., Coakley, S., Romano, D.M.: High performance cellular level agent-based simulation with FLAME for the GPU. Briefings in Bioinformatics 11(3), 334–347 (2010)CrossRefGoogle Scholar
  10. 10.
    Fujimoto, R.M.: Parallel and distributed simulation systems. John Wiley (2000)Google Scholar
  11. 11.
    Cicirelli, F., Furfaro, A., Nigro, L.: An agent infrastructure over HLA for distributed simulation of reconfigurable systems and its application to UAV coordination. Simulation, Trans. of SCS 85(1), 17–32 (2009)CrossRefGoogle Scholar
  12. 12.
    Logan, B., Theodoropoulos, G.: The distributed simulation of multiagent systems. Proceedings of the IEEE 89(2), 174–185 (2001)CrossRefGoogle Scholar
  13. 13.
    Cicirelli, F., Giordano, A., Furfaro, A., Nigro, L.: HLA_ACTOR_REPAST: An Approach to Distributing RePast Models for High-Performance Simulations. Simulation Modelling Practice and Theory 19(1), 283–300 (2011)CrossRefGoogle Scholar
  14. 14.
    Shook, E., Wang, S., Tang, W.: A Communication Aware Framework for Parallel Spatially Explicit Agent Based Models. International Journal of Geographical Information Science (2013), http://dx.doi.org/10.1080/13658816.2013.771740
  15. 15.
    Geer, D.: Industry trends: Chip makers turn to multicore processors. Computer 38(5), 11–13 (2005)CrossRefGoogle Scholar
  16. 16.
    Bahulkar, K., Hofmann, N., Jagtap, D., Abu-Ghazaleh, N., Ponomarev, D.: Performance Evaluation of PDES on Multi-core Clusters. In: Proc. of the Intern. Symp. on Distributed Simulation and Real Time Applications, pp. 131–140 (2010)Google Scholar
  17. 17.
    Cicirelli, F., Furfaro, A., Giordano, A., Nigro, L.: Performance of a multi-agent system over a multi-core cluster managed by terracotta. In: Proc. of the Symposium on Theory of Modeling & Simulation: DEVS Integrative M&S Symposium, pp. 125–133 (2011)Google Scholar
  18. 18.
    Potuzak, T.: Distributed-parallel road traffic simulator for clusters of multi-core computers. In: Proc. of the IEEE/ACM 16th International Symposium on Distributed Simulation and Real Time Applications, pp. 195–201 (2012)Google Scholar
  19. 19.
    Suzumura, T., Kanezashi, H.: Highly scalable X10-based agent simulation platform and its application to large-scale traffic simulation. In: Proc. of the Intern. Symp. on Distributed Simulation and Real Time Applications, pp. 243–250 (2012)Google Scholar
  20. 20.
    Ricci, A., Viroli, M., Piancastelli, G.: simpA: An agent-oriented approach for programming concurrent applications on top of Java. Sci. Comput. Program. 76(1), 37–62 (2011)CrossRefzbMATHGoogle Scholar
  21. 21.
    Agha, G.: Actors: a model of concurrent computation in distributed systems. MIT Press, Cambridge (1986)Google Scholar
  22. 22.
    Cicirelli, F., Furfaro, A., Nigro, L., Pupo, F.: Agents over the grid: An experience using the globus toolkit 4. In: Proc. of the 26th European Conference on Modelling and Simulation, ECMS 2012 (2012)Google Scholar
  23. 23.
    Jim, K.C., Giles, C.L.: Talking helps: evolving communicating agents for the predator-prey pursuit problem. Artif. Life 6(3), 237–254 (2000)CrossRefGoogle Scholar
  24. 24.
    Panait, L., Luke, S.: Cooperative multi-agent learning: The state of the art. Autonomous Agents and Multi-Agent Systems 11(3), 387–434 (2005)CrossRefGoogle Scholar
  25. 25.
    Challet, D., Zhang, Y.C.: Emergence of Cooperation and Organization in an Evolutionary Game. Physica A 246(3-4), 407–418 (1997)CrossRefGoogle Scholar
  26. 26.
    Johnson, N.F., Hui, P.M., Zheng, D., Tai, C.W.: Minority game with arbitrary cutoff. Physica A 269(2-4), 493–502 (1999)CrossRefGoogle Scholar
  27. 27.
    Remondino, M., Cappellini, A.: Minority game with communication of statements and memory analysis: a multi-agent based model. International Journal of Simulation 6(5), 42–53 (2005)Google Scholar
  28. 28.
    Cicirelli, F., Giordano, A., Nigro, L.: Distributed simulation of situated multi-agent systems. In: Proc. of the IEEE/ACM 15th International Symposium on Distributed Simulation and Real Time Applications, pp. 28–35 (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Franco Cicirelli
    • 1
  • Libero Nigro
    • 1
  1. 1.Laboratorio di Ingegneria del Software, Dipartimento di Ingegneria Informatica Modellistica Elettronica e SistemisticaUniversitá della CalabriaRendeItaly

Personalised recommendations