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Accelerating Nearest Neighbor Search on Manycore Systems

2012

Conference Paper

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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.

Author(s): Cayton, L.
Book Title: Parallel Distributed Processing Symposium (IPDPS), 2012 IEEE 26th International
Pages: 402-413
Year: 2012
Month: May
Day: 0

Department(s): Empirical Inference
Research Project(s): Optimization and Large Scale Learning
Bibtex Type: Conference Paper (inproceedings)

DOI: 10.1109/IPDPS.2012.45
Event Name: IPDPS 2012
Event Place: Shanghai, China

State: Published

Links: Web

BibTex

@inproceedings{Cayton2012,
  title = {Accelerating Nearest Neighbor Search on Manycore Systems},
  author = {Cayton, L.},
  booktitle = {Parallel Distributed Processing Symposium (IPDPS), 2012 IEEE 26th International},
  pages = {402-413},
  month = may,
  year = {2012},
  month_numeric = {5}
}