A GPU-Based Programming Framework for Highly-Scalable Multi-Agent Traffic Simulations

Abstract

Highly detailed reproducibility of multi-agent simulations is strongly demanded. To realize such highly reproducible multi-agent simulations, it is important to make each agent respond to its dynamically changing environment as well as scale the simulation to cover important phenomena that could be produced. In this paper, we present a programming framework to realize highly scalable execution of them as well as detailed behaviors of agents. The framework can help simulation developers utilize many GPGPU-based parallel cores in their simulation programs by using the proposed OpenCL-based multi-platform agent code conversion engine. We show our prototype implementation of the framework and how our framework can help simulation developers to code, test, and evaluate their agent codes which select actions and path plants reactively in dynamically changing large-scale simulation environments on various hardware and software settings.