Affiliations: [a] FEMTO-ST Institute, CNRS/Université de Franche-Comté, France | [b] Mésocentre de calcul de Franche-Comté Université de Franche-Comté, France | [c] UMI 209 – UMMISCO, Institut de Recherche Pour le Développement, UPMC, France
Correspondence:
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Corresponding author: Laurent Philippe, FEMTO-ST Institute, CNRS/Université de Franche-Comté France. E-mail: laurent.philippe@univ-fcomte.fr
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. The toolkit provides few famous algorithms as diffusion, path-finding or population dynamics that are frequently used in an agent based models. For further needs, MCMAS is based on a flexible architecture that can easily be enriched by new algorithms thanks to development features. The use of the library is illustrated with three models and their performance analysis.
