{ "item_title" : "Processing Large Remote Sensing Image Data Sets on Beowulf Clusters", "item_author" : [" United U. S. Department of the Interior", "Et Al", "Daniel R. Steinwand "], "item_description" : "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.", "item_img_path" : "https://covers4.booksamillion.com/covers/bam/1/28/874/588/1288745885_b.jpg", "price_data" : { "retail_price" : "15.75", "online_price" : "15.75", "our_price" : "15.75", "club_price" : "15.75", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }

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