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1、CollectiveMind:cleaninguptheresearchandexperimentationmessincomputerengineeringusingcrowdsourcing,bigdataandmachinelearningGrigoriFursinINRIA,FranceGrigori.Fursin@cTuning.orgAbstractSoftwareandhardwareco-designandoptimizationofHPCsystemshasbe-comeint
2、olerablycomplex,ad-hoc,timeconsuminganderrorproneduetoenor-mousnumberofavailabledesignandoptimizationchoices,complexinteractionsbetweenallsoftwareandhardwarecomponents,andmultiplestrictrequirementsplacedonperformance,powerconsumption,size,reliability
3、andcost.Wepresentournovellong-termholisticandpracticalsolutiontothisproblembasedoncustomizable,plugin-based,schema-free,heterogeneous,open-sourceCollectiveMindrepositoryandinfrastructurewithuni?edwebinterfacesandon-lineadvisesystem.Thiscollaborativef
4、rameworkdistributesanalysisandmulti-objectiveoff-lineandon-lineauto-tuningofcomputersystemsamongmanypar-ticipantswhileutilizinganyavailablesmartphone,tablet,laptop,clusterordatacenter,andcontinuouslyobserving,classifyingandmodelingtheirrealisticbehav
5、-ior.AnyunexpectedbehaviorisanalyzedusingshareddataminingandpredictivemodelingpluginsorexposedtothecommunityatcTuning.orgforcollaborativeexplanation,top-downcomplexityreduction,incrementalproblemdecompositionanddetectionofcorrelatingprogram,architect
6、ureorrun-timeproperties(features).hal-00850880,version1-10Aug2013Graduallyincreasingoptimizationknowledgehelpstocontinuouslyimproveop-timizationheuristicsofanycompiler,predictoptimizationsfornewprogramsorsuggestef?cientrun-time(online)tuningandadapta
7、tionstrategiesdependingonend-userrequirements.Wedecidedtoshareallourpastresearchartifactsinclud-inghundredsofcodelets,numericalapplications,datasets,models,universalex-perimentalanalysisandauto-tuningpipelines,self-tuningmachinelearningbasedmetacompi
8、ler,anduni?edstatisticalanalysisandmachinelearningpluginsinapublicrepositorytoinitiatesystematic,reproducibleandcollaborativeresearch,developmentandexperimentationwithanewpublicationmodelwhereexperi-mentsandtechniquesarevalidated,rankedandimprovedbyt
