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Accelerating Nearest Neighbor Search on Manycore Systems

Lawrence Cayton
Max Planck Institute, Tubingen, Germany
arXiv:1103.2635v1 [cs.DB] (14 Mar 2011)

@article{2011arXiv1103.2635C,

   author={Cayton}, L.},

   title={“{Accelerating Nearest Neighbor Search on Manycore Systems}”},

   journal={ArXiv e-prints},

   archivePrefix={“arXiv”},

   eprint={1103.2635},

   primaryClass={“cs.DB”},

   keywords={Computer Science – Databases, Computer Science – Computational Geometry, Computer Science – Distributed, Parallel, and Cluster Computing, Computer Science – Data Structures and Algorithms, Computer Science – Information Retrieval},

   year={2011},

   month={mar},

   adsurl={http://adsabs.harvard.edu/abs/2011arXiv1103.2635C},

   adsnote={Provided by the SAO/NASA Astrophysics Data System}

}

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We develop methods for accelerating metric similarity search that are effective on modern hardware. Our algorithms factor into easily parallelizable components, making them simple to deploy and efficient on multicore CPUs and GPUs. Despite the simple structure of our algorithms, their search performance is provably sublinear in the size of the database, with a factor dependent only on its intrinsic dimensionality. We demonstrate that our methods provide substantial speedups on a range of datasets and hardware platforms. In particular, we present results on a 48-core server machine, on graphics hardware, and on a multicore desktop.
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