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dc.contributor.advisorAykanat, Cevdet
dc.contributor.authorKorkmaz, Zeynep
dc.date.accessioned2016-01-08T18:25:02Z
dc.date.available2016-01-08T18:25:02Z
dc.date.issued2012
dc.identifier.urihttp://hdl.handle.net/11693/15817
dc.descriptionAnkara : The Department of Computer Engineering and the Graduate School of Engineering and Science of Bilkent University, 2012.en_US
dc.descriptionThesis (Master's) -- Bilkent University, 2012.en_US
dc.descriptionIncludes bibliographical refences.en_US
dc.description.abstractThe similarity join is an important operation in data mining and it is used in many applications from varying domains. A similarity join operator takes one or two sets of data points and outputs pairs of points whose distances in the data space is within a certain threshold value, ". The baseline nested loop approach computes the distances between all pairs of objects. When considering large set of objects which yield too long query time for nested loop paradigm, accelerating such operator becomes more important. The computing capability of recent GPUs with the help of a general purpose parallel computing architecture (CUDA) has attracted many researches. With this motivation, we propose two similarity join algorithms for Graphics Processing Unit (GPU). To exploit the advantages of general purpose GPU computing, we rst propose an improved nested loop join algorithm (GPU-INLJ) for the speci c environment of GPU. Also we present a partitioning-based join algorithm (KMEANS-JOIN) that guarantees each partition can be joined independently without missing any join pair. Our experiments demonstrate massive performance gains and the suitability of our algorithms for large datasets.en_US
dc.description.statementofresponsibilityKorkmaz, Zeynepen_US
dc.format.extentxi, 63 leaves, illustrationsen_US
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectSimilarity joinen_US
dc.subjectK-means clusteringen_US
dc.subjectGeneral purpose graphics processing uniten_US
dc.subjectCUDAen_US
dc.subject.lccQA76.9.D343 K67 2012en_US
dc.subject.lcshData mining.en_US
dc.subject.lcshParallel programming (Computer science)en_US
dc.subject.lcshSimulation methods.en_US
dc.subject.lcshComputer simulation.en_US
dc.titleImproving the performance of similarity joins using graphics processing uniten_US
dc.typeThesisen_US
dc.departmentDepartment of Computer Engineeringen_US
dc.publisherBilkent Universityen_US
dc.description.degreeM.S.en_US
dc.identifier.itemidB134549


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