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Algorithms on the GPU

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dc.contributorDixon, Brandon
dc.contributorBorie, Richard B.
dc.contributorZhang, Hong
dc.contributor.advisorVrbsky, Susan V.
dc.contributor.advisorHong, Xiaoyan
dc.contributor.authorRobinson, Jeffrey A.
dc.contributor.otherUniversity of Alabama Tuscaloosa
dc.date.accessioned2017-07-28T14:11:52Z
dc.date.available2017-07-28T14:11:52Z
dc.date.issued2017
dc.descriptionElectronic Thesis or Dissertationen_US
dc.description.abstractGeneral Purpose Programming using Graphical Processing Units (GPGPU) is a fast growing subfield in High Performance Computing (HPC). These devices provide a very high throughput with low cost to many parallel problems, with performance increasing every year while costs remain stable or in some cases even decrease. Many modern supercomputing clusters include these devices for use by scientists and engineers. In this dissertation we analyze three different algorithms on the GPGPU from the domains of large integer modular arithmetic, optimization graph problems, and ranking using machine learning, in order to study and propose new strategies to improve the performance of these algorithms. To solve the large integer modular arithmetic problem we implement a GPU-based version of the Montgomery multiplication algorithm, and in our implementation we incorporate optimizations that result in notable performance improvements compared to existing GPU implementations. In the optimization graph problem domain we present a Traveling Salesman Problem (TSP) two-opt approximation algorithm with a modification called k-swap, and with our proposed k-swap modification to the GPU implementation, we obtain a speed-up over the existing algorithm of 4.5x to 22.9x on datasets ranging from 1400 to 33810 nodes, respectively. Lastly, for ranking using machine learning, a new strategy for learning to rank is designed and studied, which combines the two machine learning approaches of clustering and ranking. Results demonstrate an improved ranking of documents for web based queries.en_US
dc.format.extent188 p.
dc.format.mediumelectronic
dc.format.mimetypeapplication/pdf
dc.identifier.otheru0015_0000001_0002559
dc.identifier.otherRobinson_alatus_0004D_13071
dc.identifier.urihttp://ir.ua.edu/handle/123456789/3156
dc.languageEnglish
dc.language.isoen_US
dc.publisherUniversity of Alabama Libraries
dc.relation.hasversionborn digital
dc.relation.ispartofThe University of Alabama Electronic Theses and Dissertations
dc.relation.ispartofThe University of Alabama Libraries Digital Collections
dc.rightsAll rights reserved by the author unless otherwise indicated.en_US
dc.subjectComputer science
dc.titleAlgorithms on the GPUen_US
dc.typethesis
dc.typetext
etdms.degree.departmentUniversity of Alabama. Department of Computer Science
etdms.degree.disciplineComputer Science
etdms.degree.grantorThe University of Alabama
etdms.degree.leveldoctoral
etdms.degree.namePh.D.

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