high performance computing on graphics processing units: hgpu.org

The purpose of the research is to investigate agent-based approaches to virtual crowd simulation. Crowds are ubiquitous and are becoming an increasingly common phenomena in modern society, particularly in urban settings. As such, crowd simulation systems are becoming increasingly popular in training simulations, pedestrian modelling, emergency simulations, and multimedia. One of the primary challenges in crowd simulation is the ability to model realistic, large-scale crowd behaviours in real time. This is a challenging problem, as the size, visual fidelity, and complex behaviour models of the crowd all have an impact on the available computational resources. In the last few years, the graphics processing unit (GPU) has presented itself as a viable computational resource for general purpose computation. Traditionally, GPUs were used solely for their ability to efficiently compute operations related to graphics applications. However, the modern GPU is a highly parallel programmable processor, with substantially higher peak arithmetic and memory bandwidth than its central processing unit (CPU) counterpart. The GPU’s architecture makes it a suitable processing resource for computations that are parallel or distributed in nature. One attribute of multi-agent systems (MASs) is that they are inherently decentralised. As such, a MAS that leverages advancements in GPU computing may provide a solution for crowd simulation. The research investigates techniques and methods for general purpose crowd simulation, including topics in agent behavioural modes, pathplanning, collision avoidance and agent steering. The research also investigates how GPU computing has been utilised to address these computationally intensive problem domains. Based on the outcomes of the research, an agent-based model, Massively Parallel Crowds (MPCrowds), is proposed to address virtual crowd simulation, using the GPU as an additional resource for agent computation.