Abstract: A parallel agent-based model of Von Thünen Model was proposed driven by graphics processing units (GPUs). The Von Thünen Model often involved the simulation of large numbers of geographically located individual decision-makers and a massive number of individual-level interactions. This simulation required substantial computational power. GPU-enabled computing resources provided a massively parallel processing platform based on a fine-grained shared memory paradigm. This massively parallel processing platform held considerable promise for meeting the computing requirement of agent-based models of spatial problems. A dynamic relationship table rebuilding method was proposed to enable the use of GPUs for parallel agent-based modeling of the spatial Von Thünen Model. The key algorithm played an important role in best exploiting high-performance resources in GPUs for large-scale spatial simulation. Experiments conducted to examine computing performance show that GPUs provide a computationally efficient alternative to traditional parallel computing architectures and substantially accelerate agent-based models in large-scale spatial space.

Key words: spatial index grid, multi-agent system, parallel computation, GPU