Processing large remote sensing image data sets on Beowulf clusters
Daniel R. Steinwand, Brian G. Maddox, Tim Beckmann, Gail L. Schmidt · Antarctica A Keystone in a Changing World · 2003
High-performance computing is often concerned with the speed at which floatingpoint 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 2 .