Using a Computational Grid for Geographic Information Analysis: A Reconnaissance.
High performance computing has undergone a radical transformation during the past decade. Though monolithic supercomputers continue to be built with significantly increased computing power, geographically distributed computing resources are now routinely linked using high-speed networks to address a...
| Publicado en: | Professional Geographer Vol. 57; no. 3; pp. 365 - 376 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Taylor & Francis Ltd
Aug2005
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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=hlh&AN=17588519&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 17588519 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00330124 PGG jtl: Professional Geographer issn: 00330124 maglogo: Y pubinfo: dt: Aug2005 vid: 57 iid: 3 pid: 377 pub: Taylor & Francis Ltd artinfo: ui: 17588519 10.1111/j.0033-0124.2005.00484.x ppf: 365 ppct: 11 formats: tig: atl: Using a Computational Grid for Geographic Information Analysis: A Reconnaissance. aug: au: Armstrong, MarcP. Kathryn Cowles, Mary Wang, Shaowen affil: The University of Iowa su: Supercomputers Grids (Cartography) Cartography High performance computing Electronic data processing sug: subj: Supercomputers Grids (Cartography) Cartography High performance computing Electronic data processing keyword: Grid computing Grid portals middleware parallel computing spatial statistics ab: High performance computing has undergone a radical transformation during the past decade. Though monolithic supercomputers continue to be built with significantly increased computing power, geographically distributed computing resources are now routinely linked using high-speed networks to address a broad range of computationally complex problems. These confederated resources are referred to collectively as a computational Grid. Many geographical problems exhibit characteristics that make them candidates for this new model of computing. As an illustration, we describe a spatial statistics problem and demonstrate how it can be addressed using Grid computing strategies. A key element of this application is the development of middleware that handles domain decomposition and coordinates computational functions. We also discuss the development of Grid portals that are designed to help researchers and decision makers access and use geographic information analysis tools. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2005 holdings: @attributes: islocal: N |
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