Accelerating Nearest Neighbor Search on Manycore Systems

Cayton, L

doi:10.1109/IPDPS.2012.45

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Accelerating Nearest Neighbor Search on Manycore Systems

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Cayton, L
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6267877
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Citation

Cayton, L. (2012). Accelerating Nearest Neighbor Search on Manycore Systems. In 26th IEEE International Parallel & Distributed Processing Symposium (IPDPS 2012) (pp. 402-413). Piscataway, NJ, USA: IEEE.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-B77C-9

Abstract

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.