Nested graphs: A model to efficiently distribute multi‐agent systems on HPC clusters. (16th January 2018)

• Record Type: Journal Article
• Authors: Rousset, Alban; Herrmann, Bénédicte; Lang, Christophe; Philippe, Laurent; Bride, Hadrien
• Other Names: Trunfio Giuseppe A. guestEditor.; Spataro William guestEditor.; Sirakoulis Georgios Ch. guestEditor.
• Is Part Of: Concurrency and computation. Volume 30:Number 7(2018)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-01-16
• Subjects: High Performance Computing -- multi‐agent simulation -- Nested Graph -- parallel; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.4407
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: 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.