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dc.contributor.authorShepard, William Erik
dc.date.accessioned2014-03-03T19:59:08Z
dc.date.available2014-03-03T19:59:08Z
dc.date.issued2000-08
dc.identifier.othershepard_william_e_200008_ms
dc.identifier.urihttp://purl.galileo.usg.edu/uga_etd/shepard_william_e_200008_ms
dc.identifier.urihttp://hdl.handle.net/10724/20103
dc.description.abstractThis research exploited a classic sequential method for nearest neighbor searching in order to develop a parallel search approach. This approach was implemented in an inverse distance weighting routine in order to demonstrate efficacy. A set of 100 sample point datasets was developed and interpolated using the sequential implementation of inverse distance weighting present in ARC/INFO and the parallel implementation developed in the course of this work. Run times were captured for each run in both sets of 100 and compared to absolutely and proportionally in order to assess improvement using the parallel methodology over the sequential methodology. Experimental results showed that, while sequential processing times increase exponentially, parallel processing times increase linearly. The mean proportion of parallel time to sequential time for the 100 runs was 17% with a 5% standard deviation (maximum of 27% and minimum of 8%), increasing exponentially as input and output sizes increased linearly.
dc.publisheruga
dc.rightspublic
dc.subjectGIS
dc.subjectParallel processing
dc.subjectNearest neighbor search
dc.subjectInverse distance weighting
dc.subjectKriging
dc.subjectProjection
dc.subjectGeostatistics
dc.subjectGeoprocessing
dc.titleA parallel approach to searching for nearest neighbors with minimal interprocess communication
dc.typeThesis
dc.description.degreeMS
dc.description.departmentGeography
dc.description.majorGeography
dc.description.advisorE. Lynn Usery
dc.description.committeeE. Lynn Usery
dc.description.committeeThomas W. Hodler
dc.description.committeeSuchendra Bhandarkar


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