一种众核处理器拓扑重构优化近似算法-豆柴文库

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一种众核处理器拓扑重构优化近似算法
Title:AnApproximationAlgorithmforTopologyReconfigurationOptimizationofMany-CoreProcessors
Abstract:
Many-coreprocessorshavebecomeincreasinglypopularduetotheirabilitytoprovidehighcomputingpowerforparallelprocessing.However,theperformanceofmany-coreprocessorsisgreatlyinfluencedbytheirunderlyingtopology.Inthispaper,wepresentanovelapproximationalgorithmfortopologyreconfigurationoptimizationofmany-coreprocessors.Thisalgorithmaimstofindanoptimizedconfigurationthatmaximizestheoverallperformanceofthesystem.Theproposedalgorithmtakesintoaccountvariousfactorssuchaslatency,bandwidth,powerconsumption,andcommunicationoverheadtoachieveefficienttopologyreconfiguration.Weevaluatetheperformanceofthealgorithmondifferentbenchmarkapplicationsandcompareitwithexistingoptimizationtechniques.Theresultsdemonstratetheeffectivenessandefficiencyofourapproach,highlightingitspotentialforreal-worldmany-coreprocessorsystems.
1.Introduction:
Withtheriseofparallelcomputingandtheincreasingdemandforhigh-performancecomputing,many-coreprocessorshavebecomethepromisingchoiceforachievingefficientandscalableparallelprocessing.Inmany-coreprocessors,multipleprocessingcoresareintegratedontoasinglechip,allowingforconcurrentexecutionofmultiplethreads.However,theperformanceofmany-coreprocessorshighlydependsontheirunderlyingtopology,whichdefinestheconnectivityandcommunicationpatternsamongthecores.Therefore,optimizingthetopologyconfigurationbecomescrucialforachievingmaximumperformanceandenergyefficiency.
2.RelatedWork:
Severalstudieshavefocusedontopologyreconfigurationoptimizationformany-coreprocessors.Theseapproachesincludeevolutionaryalgorithms,simulatedannealing,geneticalgorithms,andgraphtheory-basedmethods.Whilethesetechniqueshaveshownpromisingresults,theyeithersufferfromlongcomputationtimes,lackscalability,orfailtoconsideralltheessentialfactorsaffectingtheperformanceofmany-coreprocessors.
3.ProblemStatement:
Theobjectiveofourresearchistodevelopanapproximationalgorithmfortopologyreconfiguration