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Processing Large Remote Sensing Image Data Sets on Beowulf Clusters: Open-File Report 2003-216 (in English)
United U. S. Department of the Interior
(Author)
·
Daniel R. Steinwand
(Author)
·
Bibliogov
· Paperback
Processing Large Remote Sensing Image Data Sets on Beowulf Clusters: Open-File Report 2003-216 (in English) - Steinwand, Daniel R. ; U. S. Department of the Interior, United ; Et Al
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Synopsis "Processing Large Remote Sensing Image Data Sets on Beowulf Clusters: Open-File Report 2003-216 (in English)"
High-performance computing is often concerned with the speed at which floating- point calculations can be performed. The architectures of many parallel computers and/or their network topologies are based on these investigations. Often, benchmarks resulting from these investigations are compiled with little regard to how a large dataset would move about in these systems. This part of the Beowulf study addresses that concern by looking at specific applications software and system-level modifications. Applications include an implementation of a smoothing filter for time-series data, a parallel implementation of the decision tree algorithm used in the Landcover Characterization project, a parallel Kriging algorithm used to fit point data collected in the field on invasive species to a regular grid, and modifications to the Beowulf project's resampling algorithm to handle larger, higher resolution datasets at a national scale. Systems-level investigations include a feasibility study on Flat Neighborhood Networks and modifications of that concept with Parallel File Systems.
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