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基于多核系统的视频特征提取程序并行化及性能优化方法(英文).docx

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基于多核系统的视频特征提取程序并行化及性能优化方法(英文) ParallelizationMethodsandPerformanceOptimizationforVideoFeatureExtractionProgramonMulticoreSystems Withthegrowingdemandforeffectivevideoanalysisinvariousfields,suchassecurity,entertainment,andeducation,thecomputation-intensivetaskofvideofeatureextractionhasbecomeacriticalbottleneckforprocessingefficiency.Multicoresystemsprovideapromisingsolutionfortacklingthischallengebyutilizingmultipleprocessingunitstoexecutetasksinparallel.Inthispaper,wepresentseveralparallelizationmethodsandperformanceoptimizationtechniquesthatimprovetheparallelexecutionofvideofeatureextractionprogramsonmulticoresystems,achievingsignificantspeedupandscalability. ParallelizationMethods Themostcommonparallelizationmethodforvideofeatureextractionprogramsisparallelismattheframelevel.Inthismethod,eachcoreprocessesasubsetofframesinparallel.Specifically,thevideostreamispartitionedintosmallersegmentsorchunks,andeachchunkisassignedtoaseparatecoreforprocessing.Theframeswithineachsegmentarethenprocessedbytheassignedcore,whichcanexploitdata-levelparallelisminherentinfeatureextractionalgorithmstoacceleratecomputation. Anotherapproachistaskparallelism,whichassignsdifferenttasksinthevideoprocessingpipelinetodifferentcores.Forexample,onecorecanhandlevideoinput,whileanothercoreperformsfeaturedetection,andathirdcorehandlesclassification.Thismethodismorechallengingtoimplementbutcanbemoreeffectiveinexploitingfine-grainedparallelism. PerformanceOptimizationTechniques Tofurtherimprovetheperformanceofvideofeatureextractionprogramsonmulticoresystems,severalperformanceoptimizationtechniquescanbeapplied. 1.LoadBalancing:Withparallelismattheframelevel,loadbalancingiscriticalforachievingoptimalperformance.Imbalancedworkloadscanoccurwhensomeframesrequiremorecomputationthanothers,resultinginidlecoresandwastedresources.Variousload-balancingtechniques,suchasstaticloadbalancinganddynamicloadbalancing,canbeusedtodistributeframesevenlyacrosscores. 2.WorkRedistribution:Workredistributiontechniquescani