Processing large remote sensing image data sets on Beowulf clusters
Open-File Report
2003-216
Links
- More information: USGS Index Page (html)
- Document: Report
- Download citation as: RIS | Dublin Core
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.
| 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 |
| Google Analytic Metrics | Metrics page |