Fast and Efficient Nearest Neighbor Search for Particle Simulations

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dc.contributor.authorGross, Julianen_US
dc.contributor.authorKöster, Marcelen_US
dc.contributor.authorKrüger, Antonioen_US
dc.contributor.editorVidal, Franck P. and Tam, Gary K. L. and Roberts, Jonathan C.en_US
dc.date.accessioned2019-09-11T05:09:00Z
dc.date.available2019-09-11T05:09:00Z
dc.date.issued2019
dc.description.abstractOne of the fundamental algorithms in particle simulations is the identification and iteration over nearest neighbors of every particle. Well-known examples are SPH or PBD simulations that compute forces and particle-position updates in every simulation step. In order to find nearest neighbors for all particles, hash-based, grid-based or tree-based approaches have been developed in the past. The two most prominent and fastest algorithms use virtual and explicitly allocated uniform grids to achieve high performance on Graphics Processing Units (GPUs). However, they have disadvantages with numerous particle simulation domains, either in terms of run time or memory consumption. We present a novel algorithm that can be applied to large simulation domains that significantly reduces memory consumption using a shared-memory based neighbor search. Furthermore, we achieve high-performance on our evaluation scenarios that often outperforms existing state-of-the-art methods.en_US
dc.description.sectionheadersVirtual Reality
dc.description.seriesinformationComputer Graphics and Visual Computing (CGVC)
dc.identifier.doi10.2312/cgvc.20191258
dc.identifier.isbn978-3-03868-096-3
dc.identifier.pages55-63
dc.identifier.urihttps://doi.org/10.2312/cgvc.20191258
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/cgvc20191258
dc.publisherThe Eurographics Associationen_US
dc.subjectComputing methodologies
dc.subjectShared memory algorithms
dc.subjectMassively parallel algorithms
dc.subjectGraphics processors
dc.titleFast and Efficient Nearest Neighbor Search for Particle Simulationsen_US
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