Nested graphs: A model to efficiently distribute multi-agent systems on HPC clusters

Computational simulation is becoming increasingly important in numerous research fields. Depending on the modeled system, several methods such as differential equations or Monte-Carlo simulations may be used to represent the system behavior. The amount of computation and memory needed to run a simulation depends on its size and precision, and large simulations usually lead to long runs, thus requiring to adapt the model to a parallel system. Complex systems are often simulated using multi-agent systems (MASs). While linear system based models benefit from a large set of tools to take advantage of parallel resources, multi-agent systems suffer from a lack of platforms that ease the use of such resources. In this paper, we propose the use of Nested Graphs for a new modeling approach that allows the design of large, complex, and multi-scale multi-agent models, which can efficiently be distributed on parallel resources. Nested Graphs are formally defined and are illustrated on the well-known predator-prey model. We also introduce PDMAS (parallel and distributed multi-agent system): a platform that implements the Nested Graph modeling approach to ease the distribution of multi-agent models on High Performance Computing clusters. Performance results are presented to validate the efficiency of the resulting models.