MCMAS: A toolkit for developing agent-based simulations on many-core architectures

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 e ciently 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 e cient use of many-core architectures through already de ned data structures and kernels. The toolkit provides few famous algorithms as di usion, pathnding or population dynamics that are frequently used in an agent based models. For further needs, MCMAS is based on a exible 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.