Global sensitivity analysis of a large agent-based model of spatial opinion exchange : a heterogeneous multi-GPU acceleration approach
Identifieur interne : 000007 ( PascalFrancis/Corpus ); précédent : 000006; suivant : 000008Global sensitivity analysis of a large agent-based model of spatial opinion exchange : a heterogeneous multi-GPU acceleration approach
Auteurs : W. Tang ; M. JiaSource :
- 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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Pour connaître la documentation sur le format Inist Standard.
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Format Inist (serveur)
NO : | FRANCIS 28424308 PRODIG-INTERGEO |
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ET : | Global sensitivity analysis of a large agent-based model of spatial opinion exchange : a heterogeneous multi-GPU acceleration approach |
AU : | TANG (W.); JIA (M) |
AF : | Dept. of Geography and Earth Sciences and Center for Applied GIScience, Univ. of North Carolina/Charlotte/Etats-Unis (1 aut., 2 aut.) |
DT : | Publication en série; Niveau analytique |
SO : | Annals of the Association of American Geographers; ISSN 0004-5608; Coden AAAGAK; Etats-Unis; Da. 2014; Vol. 104; No. 3; Pp. 485-509; Bibl. 3 p.; 15 fig., 2 tabl. |
LA : | Anglais |
EA : | 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 |
CC : | 53110; 531 |
FD : | Analyse spatiale; Modèle; Modèle agent; Echange d'opinions; Analyse de variance; Unité de traitement graphique multiple; Analyse de susceptibilité; Analyse de résolution de problèmes |
ED : | Spatial analysis; Model; Analysis of variance |
SD : | Análisis espacial; Modelo; Análisis de varianza |
LO : | INIST-5096 |
ID : | 28424308 |
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Francis:28424308Le document en format XML
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<AF>Dept. of Geography and Earth Sciences and Center for Applied GIScience, Univ. of North Carolina/Charlotte/Etats-Unis (1 aut., 2 aut.)</AF>
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