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1、InternationalJournalofComputerInformationSystemsandIndustrialManagementApplicationsISSN2150-7988Volume3(2011)pp.355-362©MIRLabs,www.mirlabs.net/ijcisim/index.htmlGPUMLib:AnEfficientOpen-SourceGPUMachineLearningLibraryNoelLopes1andBernardeteRibeiro21UDI,PolytechnicInstituteofGuarda,P
2、ortugalCISUC,UniversityofCoimbra,Portugalnoel@ipg.pt2DepartmentofInformaticsEngineeringCISUC,UniversityofCoimbra,Portugalbribeiro@dei.uc.ptAbstract:GraphicsProcessingUnits(GPUs)placedatourdis-ciatedwithMLproblemsincreases,thetrendistohavemoreposalanunprecedentedcomputational-power,
3、largelysurpass-challengingandcomputationallydemandingproblemsthatingtheperformanceofcutting-edgeCPUs(CentralProcess-canbecomeintractablefortraditionalCPU(CentralProcess-ingUnits).Thehigh-parallelisminherenttotheGPUmakesingUnit)architectures.Therefore,thepressuretoshiftdevel-thisdev
4、iceespeciallywell-suitedtoaddressMachineLearn-opmenttowardsparallelarchitectureswithhigh-throughputing(ML)problemswithprohibitivelycomputationalintensivehasbeenaccentuated.Inthiscontext,theGraphicsProcess-tasks.Nevertheless,fewMLalgorithmshavebeenimplementedingUnit(GPU)representsac
5、ompellingsolutiontoaddressontheGPUandmostarenotopenlyshared,posingdifficul-theincreasingneedsofcomputationalperformance,inpar-tiesforresearchersandengineersaimingtodevelopGPU-basedticularintheMLfield.systems.Tomitigatethisproblem,weproposethecreationofanInthelasteightyearstheperforma
6、nceandcapabilitiesofopensourceGPUMachineLearningLibrary(GPUMLib)thattheGPUshavebeensignificantlyaugmentedandtodaysaimstoprovidethebuildingblocksforthedevelopmentofef-GPUs,includedinmainstreamcomputingsystems,arepow-ficientGPUMLsoftware.Experimentalresultsonbenchmarkerful,highlyparall
7、elandprogrammabledevicesthatcanbedatasetsshowthatthealgorithmsalreadyimplementedyieldusedforgeneral-purposecomputingapplications[2].SincesignificanttimesavingsovertheCPUcounterparts.GPUsaredesignedforhigh-performancerenderingwhereKeywords:GPUComputing,machinelearningalgorithms.repea
8、tedoperationsarecommon,the
