MCMAS: A Toolkit to Benefit from Many-Core Architecure in Agent-Based Simulation

  • Guillaume Laville
  • Kamel Mazouzi
  • Christophe Lang
  • Nicolas Marilleau
  • Bénédicte Herrmann
  • Laurent Philippe
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8374)


Multi-agent models and simulations are used to describe complex systems in domains such as biological, geographical or ecological sciences. The increasing model complexity results in a growing need for computing resources and motivates the use of new architectures such as multi-cores and many-cores. Using them efficiently however remains a challenge in many models as it requires adaptations tailored to each program, using low-level code and libraries. In this paper we present MCMAS a generic toolkit allowing an efficient use of many-core architectures through already defined data structures and kernels. This toolkit promotes few famous algorithms (diffusion, path-finding, population dynamics) which are ready to be used in an Agent Based Model. For other needs, MCMAS is based on a flexible architecture and can easily be enriched by new algorithms thanks to development features. The use of the library is illustrated with two models and their performance analysis.


Multi-Agent Systems Parallel Computing GPGPU 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Sklar, E.: Netlogo, a multi-agent simulation environment. Artificial Life 13(3), 303–311 (2011)CrossRefGoogle Scholar
  2. 2.
    Taillandier, P., Vo, D.-A., Amouroux, E., Drogoul, A.: GAMA: A simulation platform that integrates geographical information data, agent-based modeling and multi-scale control. In: Desai, N., Liu, A., Winikoff, M. (eds.) PRIMA 2010. LNCS, vol. 7057, pp. 242–258. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  3. 3.
    Carillo, M., Cordasco, G., De Chiara, R., Raia, F., Scarano, V., Serrapica, F.: Enhancing the performances of D-MASON - a motivating example. In: SIMULTECH, pp. 137–143 (2012)Google Scholar
  4. 4.
    Collier, N., North, M.: Parallel agent-based simulation with REPAST for high performance computing. In: SIMULATION (2012)Google Scholar
  5. 5.
    D’souza, R.M., Lysenko, M., Rahmani, K.: Sugarscape on steroids: Simulating over a million agents at interactive rates. In: Proceedings of the Agent 2007 Conference (2007)Google Scholar
  6. 6.
    Silveira, R., Fischer, L., Ferreira, J.A.S., Prestes, E., Nedel, L.: Path-planning for RTS games based on potential fields. In: Boulic, R., Chrysanthou, Y., Komura, T. (eds.) MIG 2010. LNCS, vol. 6459, pp. 410–421. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  7. 7.
    Maitre, O., Lachiche, N., Clauss, P., Baumes, L., Corma, A., Collet, P.: Efficient parallel implementation of evolutionary algorithms on GPGPU cards. In: Sips, H., Epema, D., Lin, H.-X. (eds.) Euro-Par 2009. LNCS, vol. 5704, pp. 974–985. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  8. 8.
    Laville, G., Marilleau, N., Lang, C., Mazouzi, K., Philippe, L.: Using GPU for multi-agent soil simulation. In: PDP 2013, Belfast, Ireland, pp. 392–399. IEEE Computer Society Press (February 2013)Google Scholar
  9. 9.
    Richmond, P.: FLAME GPU Technical Report and User Guide (CS-11-03). Technical report, Department of Computer Science, University of Sheffield (2011)Google Scholar
  10. 10.
    JOCL: Java bindings for OpenCL, (June 07, 2013)
  11. 11.
    Laville, G., Mazouzi, K., Lang, C., Marilleau, N., Philippe, L.: Using GPU for multi-agent multi-scale simulations. In: Omatu, S., Paz Santana, J.F., González, S.R., Molina, J.M., Bernardos, A.M., Rodríguez, J.M.C. (eds.) Distributed Computing and Artificial Intelligence. AISC, vol. 151, pp. 197–204. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  12. 12.
    Fischer, L., Silveira, R., Nedel, L.: GPU accelerated path-planning for multi-agents in virtual environments. In: Proceedings of the 2009 VIII Brazilian Symposium on Games and Digital Entertainment, SBGAMES 2009, pp. 101–110. IEEE Computer Society, Washington, DC (2009)Google Scholar
  13. 13.
    Erra, U., Frola, B., Scarano, V., Couzin, I.: An efficient GPU implementation for large scale individual-based simulation of collective behavior. In: Proceedings of the 2009 Int. Workshop on High Performance Computational Systems Biology, HIBI 2009, pp. 51–58. IEEE Computer Society, Washington, DC (2009)CrossRefGoogle Scholar
  14. 14.
    Bousso, M., Cambier, C., Masse, D., Perrier, E.: An offer versus demand modelling approach to assess the impact of micro-organisms spatio-temporal dynamics on soil organic matter decomposition rates. In: Ecological Modelling, pp. 301–313 (2007)Google Scholar
  15. 15.
    Blanchart, E., Marilleau, N., Drogoul, A., Perrier, E., Chotte, J.L., Cambier, C.: Sworm: an agent-based model to simulate the effect of earthworms on soil structure. EJSS. European Journal of Soil Science 60, 13–21 (2009)CrossRefGoogle Scholar
  16. 16.
    Gutknecht, O., Ferber, J.: Madkit: a generic multi-agent platform. In: Proceedings of the Fourth International Conference on Autonomous Agents, AGENTS 2000, pp. 78–79. ACM, New York (2000)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Guillaume Laville
    • 1
  • Kamel Mazouzi
    • 2
  • Christophe Lang
    • 1
  • Nicolas Marilleau
    • 3
  • Bénédicte Herrmann
    • 1
  • Laurent Philippe
    • 1
  1. 1.FEMTO-ST Institute, CNRSUniversité de Franche-ComtéFrance
  2. 2.Mésocentre de calcul de Franche-ComtéUniversité de Franche-ComtéFrance
  3. 3.UMI 209 - UMMISCO, Institut de Recherche pour le Développement (IRD)UPMCFrance

Personalised recommendations