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Processing large remote sensing image data sets on Beowulf clusters

Open-File Report 2003-216

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Abstract

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.

Additional Publication Details

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
Edition:
-
Year Published:
2003
Language:
ENGLISH
Contributing office(s):
Geography
Description:
27 p.
Number of Pages:
27
Online Only (Y/N):
Y