Cloning Agent-based Simulation on GPU

Abstract

Simulation cloning is an efficient way to analyze multiple configurations in a parameter exploration task. This paper presents a generic approach to perform incremental agent-based simulation cloning and discusses its implementation on GPU. Compared with the incremental cloning of parallel and distributed simulation (PADS), cloning agent-based simulation (ABS) has new challenges due to the unique way how ABS is executed. In this paper, to support incremental cloning, mechanisms for both actively and passively cloning agents are proposed. A scheme to maintain the correct context of each cloned ABS instance is developed. In addition, a strategy to restrain the propagation of passive cloning in order to maximize computation sharing amongst cloned ABS instances is also investigated. The implementation of our proposed approach on GPU supports concurrent execution of agents within each simulation instance as well as concurrent execution of multiple simulation instances. Performance of the proposed approach is evaluated and analyzed using a case study of an agent-based evacuation simulation on a NVIDIA Quadro 2000 GPU. Our experiment results demonstrate that cloning can significantly speed up the overall parameter exploration task. The proposed approach achieves 2.4 to 5.1 times speedup for parameter exploration tasks containing 8 to 125 simulation instances that evaluate different parameter configurations.