Processing Large Remote Sensing Image Data Sets on Beowulf Clusters: Open-File Report 2003-216

By Daniel R. Steinwand

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 projects 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.

• Format: Paperback
• Language: English
• ISBN: 1288745885
• ISBN13: 9781288745883
• Release Date: February 2013
• Publisher: Bibliogov
• Length: 30 Pages
• Weight: 0.16 lbs.
• Dimensions: 0.1" x 7.4" x 9.7"