Summary#R##N#Using agent-based simulation (ABS) to analyze complex adaptive systems gains growing popularity over the past decades. One of the fundamental issues in ABS is to increase the execution speed. In this paper, we identify two common modules that widely exist in ABS applications, namely, the agent management module and the agent interaction module. Improving the efficiency of these two common modules can significantly speed up the ABS execution in general. GPU architecture, programming model, and memory hierarchy are studied. Effective strategies on GPU are proposed when we design the two modules. The first contribution of this work is to propose an AgentPool data structure to handle agent creation and deletion on GPU. The second contribution is an efficient agent interaction module, which is designed by carefully utilizing the GPU memory hierarchy. To demonstrate effectiveness and generality, the proposed strategies are applied to a range of ABS applications, including game-of-life, flocking boids, prey-and-predator, and the social force-based crowd simulation. The simulation results demonstrate that the proposed strategies achieve better performance than the commonly used CPU and GPU ABS framework, namely, Mason and FLAME, for ABS applications using continuous space. Copyright © 2016 John Wiley & Sons, Ltd.
['Xiaosong Li', 'Wentong Cai', 'Stephen John Turner']
Supporting efficient execution of continuous space agent-based simulation on GPU