Global Sensitivity Analysis of a Large Agent-Based Model of Spatial Opinion Exchange: A Heterogeneous Multi-GPU Acceleration Approach.
Sensitivity analysis is an important step in agent-based modeling of complex adaptive spatial systems to evaluate the contribution of influential variables to model response. Sensitivity analysis of agent-based models is computationally demanding, however, and this analysis tends to be intractable f...
| Publicado en: | Annals of the Association of American Geographers Vol. 104; no. 3; pp. 485 - 510 |
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| Autores principales: | , |
| Formato: | Artículo |
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Taylor & Francis Ltd
May2014
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=95807567&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 95807567 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00045608 AAG jtl: Annals of the Association of American Geographers issn: 00045608 maglogo: Y pubinfo: dt: May2014 vid: 104 iid: 3 pid: 377 pub: Taylor & Francis Ltd artinfo: ui: 95807567 10.1080/00045608.2014.892342 ppf: 485 ppct: 25 formats: tig: atl: Global Sensitivity Analysis of a Large Agent-Based Model of Spatial Opinion Exchange: A Heterogeneous Multi-GPU Acceleration Approach. aug: au: Tang, Wenwu Jia, Meijuan affil: Department of Geography and Earth Sciences and Center for Applied GIScience, University of North Carolina at Charlotte su: Sensitivity analysis Multiagent systems Graphics processing units Monte Carlo method Parallel computers sug: subj: Sensitivity analysis Multiagent systems Graphics processing units Monte Carlo method Parallel computers keyword: graphics processing units large agent-based models parallel computing sensitivity analysis spatial opinion exchange graphics processing units large agent-based models parallel computing sensitivity analysis spatial opinion exchange ab: Sensitivity analysis is an important step in agent-based modeling of complex adaptive spatial systems to evaluate the contribution of influential variables to model response. Sensitivity analysis of agent-based models is computationally demanding, however, and this analysis tends to be intractable for large agent-based modeling. This computational challenge greatly limits our ability to investigate complex spatial dynamics using large agent-based models. The objective of this study is to gain insight into this computational issue by focusing on the sensitivity analysis of large agent-based modeling of spatial opinion exchange, accelerated using multiple graphics processing units (GPUs). We present a heterogeneous parallel computing approach based on nested parallelism for the global sensitivity analysis of the model. The agent-based opinion model is parallelized using many-core GPUs for the simulation of a large number of spatially aware and interacting agents. These agents exchange opinions for developing consensus on topics through processes of spatial neighborhood search and opinion update. Global sensitivity analysis of the opinion model is conducted using a variance-based approach, requiring numerous model runs for Monte Carlo integration. Intermodel parallelization is introduced to enable Monte Carlo runs of sensitivity analysis. We conduct global sensitivity analysis on a multi-GPU cluster. 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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