The computational approach of agent-based models (ABMs) supports the representation of interactions among spatially situated individuals as a decentralized process giving rise to space–time complexity in geographic systems. To cope with the computational complexity of these models,
this article proposes a parallel approach that leverages the power of multicore systems, as these architectures have quickly become ubiquitous in high-performance and desktop computing. An ABM of individual-level spatial interaction that simulates information exchange, spatial diffusion of
opinion development, and consensus building among decision makers is proposed to demonstrate the advantages of the parallel approach against its sequential counterpart. This study focuses on two key spatial properties of the interaction system of interest, the extent and range of interaction,
and examines their influence on the computing performance of the proposed parallel model and the performance scalability of the model as more computing resources are added. Significant influence from these two properties is found and can be attributed to three possible sources of effects,
namely the model level, the parallelization level, and the platform level. It is suggested that these effects should be taken into consideration when leveraging multicore computing resources for the development of parallel ABMs.
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