Reprint of: Parallel agent-based modeling of spatial opinion diffusion accelerated using graphics processing units

Authored by Wenwu Tang, David A. Bennett

Date Published: 2012-03-24

DOI: 10.1016/j.ecolmodel.2012.02.003

Sponsors: National Center for Supercomputing Applications (NCSA) University Research Computing at the University of North Carolina at Charlotte United States National Science Foundation (NSF)

Platforms: CUDA

Model Documentation: Other Narrative Flow charts Mathematical description

Model Code URLs: Model code not found

Abstract

In this article, we describe a parallel agent-based model of spatial opinion diffusion that is driven by graphics processing units (GPUs). Modeling opinion exchange and diffusion across landscapes often involves the simulation of large numbers of geographically located individual decision-makers and a massive number of individual-level interactions. This simulation requires substantial computational power. GPU-enabled computing resources provide a massively parallel processing platform based on a fine-grained shared memory paradigm. This massively parallel processing platform holds considerable promise for meeting the computing requirement of agent-based models of spatial problems. In this article, we focus on the parallelization of an agent-based spatial opinion model using CPU technologies. We discussed key algorithms designed for parallel agent-based opinion modeling: including domain decomposition and mutual exclusion. Experiments conducted to examine computing performance show that CPUs provide a computationally efficient alternative to traditional parallel computing architectures and substantially accelerate agent-based models of large-scale opinion exchange among individual decision makers. (C) 2012 Published by Elsevier B.V.

Tags

Agent-based models Cyberinfrastructure Graphics processing units Parallel computing Spatial opinion exchange