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dc.contributor Dixon, Brandon
dc.contributor Borie, Richard B.
dc.contributor Zhang, Hong
dc.contributor.advisor Vrbsky, Susan V.
dc.contributor.advisor Hong, Xiaoyan
dc.contributor.author Robinson, Jeffrey A.
dc.date.accessioned 2017-07-28T14:11:52Z
dc.date.available 2017-07-28T14:11:52Z
dc.date.issued 2017
dc.identifier.other u0015_0000001_0002559
dc.identifier.other Robinson_alatus_0004D_13071
dc.identifier.uri http://ir.ua.edu/handle/123456789/3156
dc.description Electronic Thesis or Dissertation
dc.description.abstract General 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.
dc.format.extent 188 p.
dc.format.medium electronic
dc.format.mimetype application/pdf
dc.language English
dc.language.iso en_US
dc.publisher University of Alabama Libraries
dc.relation.ispartof The University of Alabama Electronic Theses and Dissertations
dc.relation.ispartof The University of Alabama Libraries Digital Collections
dc.relation.hasversion born digital
dc.rights All rights reserved by the author unless otherwise indicated.
dc.subject.other Computer science
dc.title Algorithms on the GPU
dc.type thesis
dc.type text
etdms.degree.department University of Alabama. Dept. of Computer Science
etdms.degree.discipline Computer Science
etdms.degree.grantor The University of Alabama
etdms.degree.level doctoral
etdms.degree.name Ph.D.


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