Global sensitivity analysis of a large agent-based model of spatial opinion exchange : a heterogeneous multi-GPU acceleration approach
Identifieur interne : 000261 ( Main/Exploration ); précédent : 000260; suivant : 000262Global sensitivity analysis of a large agent-based model of spatial opinion exchange : a heterogeneous multi-GPU acceleration approach
Auteurs : W. Tang [États-Unis] ; M. Jia [États-Unis]Source :
- Annals of the Association of American Geographers [ 0004-5608 ] ; 2014.
Descripteurs français
- Pascal (Inist)
English descriptors
- KwdEn :
Abstract
The objective focuses on the sensitivity analysis of large agent-based modeling of spatial opinion exchange, accelerated using multiple graphics processing units (GPUs). It is conducted using a variance-based approach, requiring numerous model runs for Monte Carlo integration. Experimental results indicate GPU-accelerated general-purpose computation provides an efficacious and feasible solution for the sensitivity analysis of large agent-based models. The heterogeneous parallel computing approach provides valuable insight into large-scale spatiotemporal problem solving by leveraging cyberinfrastructure-enabled computational capabilities
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