Open-File Report 03–216
AbstractHigh-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. |
First posted 2003 Part or all of this report is presented in Portable Document Format (PDF); the latest version of Adobe Reader or similar software is required to view it. Download the latest version of Adobe Reader, free of charge. |
Steinwand, D.R., Maddox, Brian, Beckmann, Tim, and Schmidt, Gail, 2003, Processing Large Remote Sensing Image Data Sets on Beowulf Clusters: U.S. Geological Survey Open-File Report 03–216, 27 p., available only online at http://pubs.usgs.gov/of/2003/0216/.