Empirical Inference
Conference Paper
2012
Accelerating Nearest Neighbor Search on Manycore Systems
Web
Empirical Inference
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. |
| Links: | |
| Book Title: | Parallel Distributed Processing Symposium (IPDPS), 2012 IEEE 26th International |
| Pages: | 402-413 |
| Year: | 2012 |
| Month: | May |
| Day: | 0 |
| Bibtex Type: | Conference Paper (inproceedings) |
| DOI: | 10.1109/IPDPS.2012.45 |
| Event Name: | IPDPS 2012 |
| Event Place: | Shanghai, China |
| State: | Published |
| Electronic Archiving: | grant_archive |
BibTex
@inproceedings{Cayton2012,
title = {Accelerating Nearest Neighbor Search on Manycore Systems},
booktitle = {Parallel Distributed Processing Symposium (IPDPS), 2012 IEEE 26th International},
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. },
pages = {402-413},
month = may,
year = {2012},
slug = {cayton2012},
author = {Cayton, L.},
month_numeric = {5}
}