Processing Large Remote Sensing Image Data Sets on Beowulf Clusters: Open-File Report 2003-216
Processing Large Remote Sensing Image Data Sets on Beowulf Clusters: Open-File Report 2003-216 by Daniel R. Steinwand is published by BiblioGov on February 11, 2013, and consists of 30 pages in English. This report explores high-performance computing, particularly focusing on the efficiency of floating-point calculations and the architecture of parallel computers. It addresses the movement of large datasets within these systems, highlighting specific applications software and system-level modifications relevant to remote sensing data processing.
Readers will find detailed discussions on various applications, including a smoothing filter for time-series data and a parallel implementation of the decision tree algorithm used in land cover characterization. The report also examines a parallel Kriging algorithm for fitting field-collected data on invasive species to a regular grid, along with enhancements to the Beowulf project’s resampling algorithm for larger datasets. Additionally, it includes a feasibility study on Flat Neighborhood Networks and modifications involving Parallel File Systems, providing insights into engineering and computer modeling within the context of remote sensing and data management.
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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.
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