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dc.contributor.authorLi, Shengrenen_US
dc.contributor.authorSimons, Lanceen_US
dc.contributor.authorPakaravoor, Jagadeesh Bhaskaren_US
dc.contributor.authorAbbasinejad, Fatemehen_US
dc.contributor.authorOwens, John D.en_US
dc.contributor.authorAmenta, Ninaen_US
dc.contributor.editorCarsten Dachsbacher and Jacob Munkberg and Jacopo Pantaleonien_US
dc.date.accessioned2013-10-28T10:23:56Z
dc.date.available2013-10-28T10:23:56Z
dc.date.issued2012en_US
dc.identifier.isbn978-3-905674-41-5en_US
dc.identifier.issn2079-8679en_US
dc.identifier.urihttp://dx.doi.org/10.2312/EGGH/HPG12/039-047en_US
dc.description.abstractWe describe the implementation of a simple method for finding k approximate nearest neighbors (ANNs) on the GPU. While the performance of most ANN algorithms depends heavily on the distributions of the data and query points, our approach has a very regular data access pattern. It performs as well as state of the art methods on easy distributions with small values of k, and much more quickly on more difficult problem instances. Irrespective of the distribution and also roughly of the size of the set of input data points, we can find 50 ANNs for 1M queries at a rate of about 1200 queries/ms.en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectCategories and Subject Descriptors (according to ACM CCS): I.3.1 [Computer Graphics]: Hardware Architecture- Parallel processing I.3.1 [Computer Graphics]: Hardware Architecture-Graphics processors F.2.2 [Analysis of Algorithms and Problem Complexity]: Nonnumerical Algorithms and Problems-Sorting and searchingen_US
dc.titlekANN on the GPU with Shifted Sortingen_US
dc.description.seriesinformationEurographics/ ACM SIGGRAPH Symposium on High Performance Graphicsen_US


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