Processing large remote sensing image data sets on Beowulf clusters

Open-File Report 2003-216
By: , and 



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.
Publication type Report
Publication Subtype USGS Numbered Series
Title Processing large remote sensing image data sets on Beowulf clusters
Series title Open-File Report
Series number 2003-216
DOI 10.3133/ofr2003216
Year Published 2003
Language English
Publisher U.S. Geological Survey
Contributing office(s) Earth Resources Observation and Science (EROS) Center, Geography
Description 27 p.
Online Only (Y/N) Y
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