A Parallel Multi-Agent Spatial Simulation Enviornment for Cluster Systems

For more than the last 20 decades, multi-agent simulations have been highlighted to model mega-scale social or biological agents and to simulate their emergent collective behavior that may be difficult only with mathematical and macroscopic approaches. A successful key for simulating mega-scale agents is to speed up the execution with parallelization. Although many parallelization attempts have been made to multi-agent simulations, most work has been done on shared-memory programming environments such as OpenMP, CUDA, and Global Array, or still has left several programming problems specific to distributed-memory systems, such as machine unawareness, ghost space management, and cross-processor agent management (including migration, propagation, and termination). To address these parallelization challenges, we have been developing MASS, a new parallel-computing library for multi-agent and spatial simulation over a cluster of computing nodes. MASS composes a user application of distributed arrays and multi-agents, each representing an individual simulation place or an active entity. All computation is enclosed in each array element or agent; all communication is scheduled as periodic data exchanges among those entities, using machine-independent identifiers; and agents migrate to a remote array element for rendezvousing with each other. This paper presents the programming model, implementation, and evaluation of the MASS library.