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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)

Abstract

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.

Keywords

Multi-Agent Systems Parallel Computing GPGPU 

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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

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