Massive Crowd Simulation with Parallel Computing on GPU

The ability to simulate realistic crowds is a highly sought-After capability in the fields of entertainment (video games, movies), urban planning and evacuation simulations. Traditional approaches to crowd simulation rely on heavy Central Processing Unit (CPU) computation. This approach has limitations in terms of scalability and performance, which are solvable with the use of Graphics Programming Units (GPUs) and parallel computing techniques. In fact, the development of Compute Shaders on GPU allows the execution of general-purpose operations alongside traditional rendering tasks within real-Time applications. This paper aims to contribute to the current literature on crowd simulation methods by developing a real-Time simulation model that integrates and expands several techniques from literature, adapted and optimized to exploit GPU computing capabilities. The proposed model incorporates continuous representations for crowds in order to simulate human movement and decision-making. The achieved results demonstrate a high level of scalability and efficiency. The implemented techniques and optimizations allow the model to handle a significant number of agents while maintaining real-Time performances to achieve reduced simulation time and good user experience. Stress tests showcase that the proposed model significantly outperforms other macroscopic models, maintaining a stable frame rate of 60 FPS when simulating 20,000 agents even on mid-range systems intended for personal use.