CyberinfraV1, PascalFrancis, Curation, bibRecord, 000240

Global sensitivity analysis of a large agent-based model of spatial opinion exchange : a heterogeneous multi-GPU acceleration approach

Identifieur interne : 000240 ( PascalFrancis/Curation ); précédent : 000239; suivant : 000241

Global 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.

RBID : Francis:28424308

Descripteurs français

Pascal (Inist)
- Analyse spatiale, Modèle, Modèle agent, Echange d'opinions, Analyse de variance, Unité de traitement graphique multiple, Analyse de susceptibilité, Analyse de résolution de problèmes.

English descriptors

KwdEn :
- Analysis of variance, Model, Spatial analysis.

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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A11	`01`	`1`		`@1 TANG (W.)`
A11	`02`	`1`		`@1 JIA (M)`
A14	`01`			`@1 Dept. of Geography and Earth Sciences and Center for Applied GIScience, Univ. of North Carolina @2 Charlotte @3 USA @Z 1 aut. @Z 2 aut.`
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C01	`01`		`ENG`	@0 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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C03	`01`	`G`	`SPA`	`@0 Análisis espacial @2 NI @5 01`
C03	`02`	`G`	`FRE`	`@0 Modèle @2 NI @5 02`
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C03	`03`	`G`	`FRE`	`@0 Modèle agent @4 INC @5 03`
C03	`04`	`G`	`FRE`	`@0 Echange d'opinions @4 INC @5 04`
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C03	`08`	`G`	`FRE`	`@0 Analyse de résolution de problèmes @4 INC @5 08`
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C04	`02`	`G`		`@0 U1 @1 02!03!08,05,04`
C06				`@0 FIS @0 TAS`

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Le document en format XML

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<front><div type="abstract" xml:lang="en">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</div>
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Global sensitivity analysis of a large agent-based model of spatial opinion exchange : a heterogeneous multi-GPU acceleration approach

Global sensitivity analysis of a large agent-based model of spatial opinion exchange : a heterogeneous multi-GPU acceleration approach

Source :

Descripteurs français

English descriptors

Abstract

Links toward previous steps (curation, corpus...)

Links to Exploration step

Le document en format XML

Pour manipuler ce document sous Unix (Dilib)

Pour mettre un lien sur cette page dans le réseau Wicri