Assessing the feasibility of OpenCL CPU implementations for agent-based simulations

Abstract

Agent-based modeling (ABM) is a bottom-up modeling approach, where each entity of the system being modeled is uniquely represented as a self-determining agent. Large scale emergent behavior in ABMs is population sensitive. As such, it is advisable that the number of agents in a simulation is able to re ect the reality of the system being modeled. This means that in domains such as social modeling, ecology, and biology, systems can contain millions or billions of individuals. Such large scale simulations are only feasible in non-distributed scenarios when the computational power of commodity processors, such as GPUs and multi-core CPUs, is fully exploited. In this paper we evaluate the feasibility of using CPUoriented OpenCL for high-performance simulations of agent-based models. We compare a CPU-oriented OpenCL implementation of a reference ABM against a parallel Java version of the same model. We show that there are considerable gains in using CPU-based OpenCL for developing and implementing ABMs, with speedups up to 10× over the parallel Java version on a 10-core hyper-threaded CPU. CCS CONCEPTS •Computing methodologies →Shared memory algorithms; Agent / discrete models; Massively parallel and high-performance simulations; Parallel programming languages; Model verication and validation; Arti cial life;