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Levelset函数快速步进并行重构的分区优化.docx

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Levelset函数快速步进并行重构的分区优化 Title:PartitionOptimizationforFastStepwiseParallelRefactoringofLevelSetFunctions Abstract: Levelsetfunctionsarewidelyemployedinvariousfieldsofcomputerscience,especiallyinimagesegmentation,shapeoptimization,andscientificsimulations.However,theircomputationcanbetime-consuming,particularlyforcomplexandlarge-scaleapplications.Inthispaper,weproposeapartitionoptimizationalgorithmdesignedtoenhancetheperformanceoflevelsetfunctioncomputations.Byimprovingparallelprocessingandstepwiseexecution,ourapproachaimstosignificantlyreducethetimerequiredforrefactoringlevelsetfunctions. Introduction: Levelsetfunctionsaremathematicalrepresentationsusedtodescribeevolvinginterfacesinvariouscomputationalmodels.Theyhavegainedconsiderableattentionduetotheirabilitytohandlecomplexanddynamicgeometries.However,thesefunctionsofteninvolveintricatecomputations,whichcanbetime-consuming.Therefore,optimizingtheperformanceoflevelsetfunctioncomputationsiscrucial,particularlyfordemandingapplicationsthatrequirereal-timeornearreal-timeprocessing. Inrecentyears,parallelcomputinghasemergedasapromisingapproachtoaccelerateawiderangeofscientificcomputations.Byleveragingthepowerofmultipleprocessorsorcomputingnodes,parallelprocessingallowsforfasterexecutionoftasks.However,simplyparallelizingthecomputationofalevelsetfunctionmaynotfullyexploittheavailableresourcesandmayresultinloadimbalance,limitingthepotentialspeedupachievable. Methods: Toaddresstheperformancechallengesassociatedwithlevelsetfunctioncomputations,weproposeapartitionoptimizationalgorithm.Ouralgorithmfocusesontwomainaspects:parallelprocessingandstepwiseexecution.Byoptimizingthepartitioningofdataandworkload,ourapproachaimstoachieveloadbalanceandefficientresourceutilization. 1.PartitionOptimizationforParallelProcessing: Thefirststepofouralgorithminvolvesdividingthecomputationintosmaller,manageabletasksthatcanbeprocessedinparallel.Toachieveloadbalance,weutilizetask-baseddecompositiontechniquesthatdynamicallyassigntaskstocomputingres