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1、AnIntegratedGPUPowerandPerformanceModelSunpyoHongHyesoonKimElectricalandComputerEngineeringSchoolofComputerScienceGeorgiaInstituteofTechnologyGeorgiaInstituteofTechnologyshong9@gatech.eduhyesoon@cc.gatech.eduABSTRACT1.INTRODUCTIONGPUarchitecturesareincreasin
2、glyimportantinthemulti-coreeraTheincreasingcomputingpowerofGPUsgivesthemaconsid-duetotheirhighnumberofparallelprocessors.Performanceop-erablyhigherthroughputthanthatofCPUs.Asaresult,manypro-timizationformulti-coreprocessorshasbeenachallengeforpro-grammerstry
3、touseGPUsformorethanjustgraphicsapplications.grammers.Furthermore,optimizingforpowerconsumptionisevenHowever,optimizingGPUkernelstoachievehighperformanceismoredifficult.Unfortunately,asaresultofthehighnumberofpro-stillachallenge.Furthermore,optimizinganapplic
4、ationtoachievecessors,thepowerconsumptionofmany-coreprocessorssuchasabetterpowerefficiencyisevenmoredifficult.GPUshasincreasedsignificantly.Thenumberofcoresinsideachip,especiallyinGPUs,isin-Hence,inthispaper,weproposeanintegratedpowerandperfor-creasingdramatica
5、lly.Forexample,NVIDIA’sGTX280[2]has30mance(IPP)predictionmodelforaGPUarchitecturetopredictthestreamingmultiprocessors(SMs)with240CUDAcores,andtheoptimalnumberofactiveprocessorsforagivenapplication.ThenextgenerationGPUwillhave512CUDAcores[3].Eventhoughbasicin
6、tuitionisthatwhenanapplicationreachesthepeakmem-GPUapplicationsarehighlythroughput-oriented,notallapplica-orybandwidth,usingmorecoresdoesnotresultinperformancetionsrequireallavailablecorestoachievethebestperformance.improvement.Inthisstudy,weaimtoanswerthefo
7、llowingimportantques-WedevelopanempiricalpowermodelfortheGPU.Unlikemosttions:Doweneedallcorestoachievethehighestperformance?previousmodels,whichrequiremeasuredexecutiontimes,hard-Canwesavepowerandenergybyusingfewercores?wareperformancecounters,orarchitectura
8、lsimulations,IPPpre-Figure1showsperformance,powerconsumption,andefficiencydictsexecutiontimestocalculatedynamicpowerevents.Wethen(performanceperwatt)aswevarythenumberofactivecores.1Theusetheoutco
