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基于GPU的LARED-P算法加速.docx

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基于GPU的LARED-P算法加速 Introduction TheLARED-Palgorithmisapopularclusteringalgorithmusedinmachinelearningapplications.Oneofthemajorchallengeswithimplementingthisalgorithmisthecomputationaltimerequired,especiallywhenhandlinglargedatasets.Inrecentyears,theuseofgraphicsprocessingunits(GPUs)hasemergedasapowerfulsolutiontothisproblem.Inthispaper,wediscusshowGPU-basedLARED-PalgorithmaccelerationcanimprovetheefficiencyofLARED-Pclustering. Background TheLARED-Palgorithmisaclusteringmethodthatutilizesapartitioningtechniquetodividelargedatasetsintosmaller,moremanageablegroups.Thisisachievedthroughaseriesofiterationsthatassigneachdatapointtoaparticularclusterbasedonadistancemetric.Thealgorithmthenrecalculatesthecentroidofeachcluster,andrepeatstheprocessuntilconvergenceisachieved. Whilethisapproachiseffective,itrequiresasignificantamountofcomputationalresourcestohandlelargedatasets.Thisislargelyduetotherepeatedrecalculationofclustercentroids,whichcanbetime-consumingonacentralprocessingunit(CPU). Solution GPUsprovideahighlyparallelarchitecturethatcangreatlyacceleratetheLARED-Palgorithm.ByoffloadingthecomputationallyintensivepartsofthealgorithmtotheGPU,theCPUisfreeduptohandleothertasks,resultinginsignificantlyfasterperformance. TheparallelprocessingcapabilitiesofGPUsallowformultipledatapointstobeprocessedsimultaneously,greatlyreducingthetotaltimerequiredforclustering.Additionally,GPUscanperformcalculationsonfloating-pointnumbersmuchfasterthanCPUs,furtherimprovingtheoverallperformanceofthealgorithm. Implementation ToimplementtheGPU-basedLARED-Palgorithmacceleration,weusedtheCUDAprogramminglanguage.CUDAisaparallelcomputingplatformthatprovidesdeveloperswithaneasy-to-useinterfaceforprogrammingGPUs. OurimplementationinvolvedbreakingdownthealgorithmintosmalleroperationsthatcouldbeperformedinparallelontheGPU.Thisincludedthecalculationofdistancesbetweendatapointsandtherecalculationofcentroids. Toensureoptimalperformance,weutilizedsharedmemoryontheGPUtoreducememoryaccesstimes,andalsousedthread