An implementation of the Social Distances Model using multi-GPU systems

Abstract

We propose a new approach for using GPUs in large scale simulations of pedestrian evacuation. The Social Distances Model is designed for efficient modeling of pedestrian dynamics. This cellular automata based model, when implemented on the most modern GPUs, can simulate up to 106–108 entities. However, a valuable simulation of pedestrian evacuation must include various factors that govern pedestrian movement, for example, information provided by event organizers and navigation or allocation of other pedestrians. The most common method for introducing such information into simulations is the application of different floor fields. The floor fields provide “local knowledge” that affects pedestrians by modifying the transition functions of an applied cellular automaton. The main disadvantage of this method is its time consuming updating process. We propose a GPU based calculation of static and dynamic floor fields, whereby simulations that use several different floor fields can be efficiently calculated. A single GPU is able to cope with the Social Distance Model calculations, while other GPUs update dynamic floor fields constantly or when required. We also present the classic approach to performing cellular automata based simulations on systems with multiple processing units. The lattice is simply partitioned between the available GPUs. We compare these two approaches in terms of performance and functionality.