Efficient GPU Parallelization of the Agent-Based Models Using MASS CUDA Library

Abstract

Agent-based models (ABMs) simulate the actions and interactions of autonomous agents and their effects on the system as a whole. Many disciplines benefit from using ABMs, such as biological systems modeling or traffic simulations. However, ABMs need computational scalability for practical simulation and thus consume a lot of time. Multi-Agent Spatial Simulation (MASS) CUDA is a library, which allows using CUDA-enabled GPUs to perform multi-agent and spatial simulations efficiently while maintaining user-friendly and easily extensible API, which does not require the knowledge of CUDA on the user part. This thesis describes the optimization techniques for the spatial simulation, which allowed us to achieve up to 3.9 times speed-up compared to the sequential CPU execution of the same applications. We also propose solutions to challenges of implementing the support for dynamic agents as part of MASS CUDA library, including agent instantiation and mapping to the places, agent migration, agent replication and agent termination.