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The Montage architecture for grid-enabled science processing of large, distributed datasets

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dc.contributor.author Jacob, Joseph C.
dc.contributor.author Katz, Daniel S .
dc.contributor.author Prince, Thomas
dc.contributor.author Berriman, Bruce G.
dc.contributor.author Good, John C.
dc.contributor.author Laity, Anastasia C.
dc.contributor.author Deelman, Ewa
dc.contributor.author Singh, Gurmeet
dc.contributor.author Su, Mei-Hui
dc.date.accessioned 2006-03-13T22:14:48Z
dc.date.available 2006-03-13T22:14:48Z
dc.date.issued 2004-06-22
dc.identifier.citation NASA's Earth Science Technology Conference, Palo Alto, California, June 22-24, 2004. en
dc.identifier.clearanceno 04-1455
dc.identifier.uri http://hdl.handle.net/2014/38859
dc.description.abstract Montage is an Earth Science Technology Office (ESTO) Computational Technologies (CT) Round III Grand Challenge investigation to deploy a portable, compute-intensive, custom astronomical image mosaicking service for the National Virtual Observatory (NVO). Although Montage is developing a compute- and data-intensive service for the astronomy community, we are also helping to address a problem that spans both Earth and Space science, namely how to efficiently access and process multi-terabyte, distributed datasets. In both communities, the datasets are massive, and are stored in distributed archives that are, in most cases, remote from the available Computational resources. Therefore, state of the art computational grid technologies are a key element of the Montage portal architecture. This paper describes the aspects of the Montage design that are applicable to both the Earth and Space science communities. en
dc.description.sponsorship NASA/JPL en
dc.format.extent 1151987 bytes
dc.format.mimetype application/pdf
dc.language.iso en_US en
dc.publisher Pasadena, CA : Jet Propulsion Laboratory, National Aeronautics and Space Administration, 2004. en
dc.subject astronomical image mosaicking en
dc.subject grid portal en
dc.subject virtual observatory en
dc.title The Montage architecture for grid-enabled science processing of large, distributed datasets en
dc.type Preprint en


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