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针对MPI全局操作的低功耗优化.docx

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针对MPI全局操作的低功耗优化 Introduction MPI(MessagePassingInterface)isapopularprogrammingmodelforparallelcomputingusedinhigh-performancecomputing(HPC)applications.MPIisdesignedtoallowprocessesrunningonmultiplenodestocommunicateandcoordinatewitheachotherduringtheexecutionoftheparallelprogram.Itprovidesarangeofglobaloperations,includingreductions,scatters,gathers,andbroadcasts,thatareusedfrequentlyinparallelapplications. However,usingglobaloperationscanleadtohighenergyconsumptionandlongerrunningtimes,whicharesignificantchallengesforlarge-scaleparallelcomputations.Therefore,optimizingtheenergyefficiencyofMPI-basedparallelprogramshasbecomeacriticalresearchtopic.Inthispaper,weexploredifferenttechniquesforoptimizingtheenergyconsumptionofMPIglobaloperations. Background TheenergyconsumptionofMPIglobaloperationsisdependentonseveralfactors.Oneofthemostsignificantfactorsisthecommunicationpatternusedintheparallelprogram.Thecommunicationpatterndetermineshowmuchdataneedstobetransmittedbetweentheprocessesandhowoften.Usingacommunicationpatternthatrequiresalargeamountofdatatransmissioncanleadtosignificantenergyconsumption. TheMPIcollectiveoperationisanotherfactorthataffectstheenergyconsumptionofMPIglobaloperations.Theseoperationsareusedforcommunicationbetweenmultipleprocessesandtypicallyinvolveaconsiderableamountofdataexchange.Therefore,optimizingcollectivecommunicationcansignificantlyreduceenergyconsumption. TechniquesforOptimizingtheEnergyConsumptionofMPIGlobalOperations 1.AlgorithmicOptimization Algorithmicoptimizationistheprocessofmodifyingtheparallelprogram'scommunicationpatterntoreducetheamountofdatatransmittedandthefrequencyofcommunications.Onetechniqueforalgorithmicoptimizationisdatacompression,whichinvolvescompressingdatabeforetransmittingitbetweenprocesses.Thistechniquereducestheamountofdatatransmittedandcansignificantlyreduceenergyconsumption. Anothertechniqueisdatareordering,whichinvolvesreorderingthedatastoredinmemorytoreducethenumberofcommunicationoperationsrequired.Thistechnique