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基于GPU的邻近粒子搜索优化算法 GPU-BasedNeighborParticleSearchOptimizationAlgorithm Theoptimizationofneighborparticlesearchisoneoftheimportantresearchproblemsincomputergraphicsandphysicalsimulation.Theclassicalalgorithmisbrute-forcesearch,whichhasatimecomplexityofO(N^2),makingitimpracticalforlarge-scalesimulations.Inrecentyears,someresearchershaveproposedvariousmethodstoaccelerateneighborparticlesearch,suchasspatialpartitioning,celllistsandgrid-basedsearch.Amongthesemethods,GPU-basedoptimizationhasbecomeincreasinglypopularduetoitsparallelizationcharacteristics,whichenableittoimprovethecomputationalefficiencyofneighborparticlesearch. ThefundamentalideabehindGPU-basedoptimizationistomaptheneighborparticlesearchproblemontotheGPUarchitecture,whichconsistsofalargenumberofprocessingunitscapableofexecutingthousandsofthreadssimultaneously.ThebasicworkflowoftheGPU-basedoptimizationalgorithmincludesthreesteps:particledatapreparation,neighborparticlesearch,andpost-processing. Inthefirststep,particlesareloadedintoGPUmemoryinaspecifieddatastructure,suchasabufferortexture,whichcanbefurtheraccessedbythreadsinparallelduringthesubsequentneighborparticlesearch.TheloadingtimedependsonthesizeoftheparticledatasetandthememorythroughputofGPU. Inthesecondstep,threadsarelaunchedontheGPUtosearchforneighborparticlesforeachindividualparticle.Thesearchprocessisusuallybasedonthenearestneighborcriterion,whichrequiresfindingthek-nearestneighborsofeachparticlewithinacertainneighborhoodradius.TheGPU-basedsearchalgorithmtypicallyinvolvestwostages:afirststageofcoarse-grainedsearch,whereparticlesaregroupedintoasetofcellslocatedinagrid,andasecondstageoffine-grainedsearch,whereparticleswithineachcellaresearchedindependently.Thecoarse-grainedstagecanbeimplementedusingspatialpartitioningtechniques,suchasoctreeorKD-tree,whilethefine-grainedsearchcanbefurtheroptimizedusingparallelreductionorsortingalgorithms.Aftertheneighborsearch,allparticlesstoretheresultingneighborlistinGPUmemory. Inthefinalstep,theneighborlistisproces