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Dirichlet过程混合模型聚类的GPU实现和优化.docx

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Dirichlet过程混合模型聚类的GPU实现和优化 Title:GPUImplementationandOptimizationofDirichletProcessMixtureModelClustering Abstract: Dirichletprocessmixturemodels(DPMM)havegainedsignificantattentioninthefieldofmachinelearningfortheirabilitytoflexiblyclusterdatawithoutrequiringanapriorifixednumberofclusters.However,thecomputationalcomplexityofDPMMsposeschallengesforlarge-scaledataclusteringtasks.Inthispaper,weproposeaGPUimplementationandoptimizationapproachforDPMMclusteringtoleveragethecomputationalpowerofGPUsandimprovethescalabilityandefficiencyofclusteringalgorithms.Wedemonstratetheeffectivenessandefficiencyofourapproachthroughexperimentsonvariousdatasets. 1.Introduction: Clusteringisanessentialtaskinmachinelearninganddataminingapplications.TheDirichletprocessmixturemodel(DPMM)isanon-parametricclusteringtechniquethatoffersadvantagesoverfixed-sizeclusteringmethodsbyautomaticallydeterminingthenumberofclustersfromthedata.However,thecomputationalcomplexityofDPMMsmakesthemcomputationallydemandingforlargedatasets.Inthispaper,wepresentaGPUimplementationandoptimizationofDPMMclusteringtoaddressthesechallenges. 2.BackgroundandRelatedWork: WeprovideanoverviewofDPMMsandexistingapproachesfortheirimplementationonCPUs.WealsodiscussrelatedworkonGPUaccelerationofclusteringalgorithmsandhighlighttheneedtoexploreGPUoptimizationtechniquesforDPMMclustering. 3.GPUImplementationofDPMMClustering: WedescribeourGPUimplementationofDPMMclustering,focusingonthekeycomponentsofthealgorithmthatareamenabletoparallelizationonGPUs.WediscusstheconsiderationsfordatatransferbetweenCPUandGPU,GPUmemorymanagement,andparallelizationstrategiesforefficientcomputation. 4.OptimizationTechniques: TofurtherimprovetheperformanceofDPMMclusteringonGPUs,weproposeseveraloptimizationtechniques.ThisincludesexploitingGPUparallelism,optimizingmemoryaccesspatterns,andutilizingsharedmemoryforefficientinter-threadcommunication. 5.ExperimentalEvaluation: WepresentextensiveexperimentsonvariousdatasetstoevaluatetheperformanceofourGPUimplementat