Paper Info
Title
Accelerating Nearest Neighbor Search on Manycore Systems
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 sub linear 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.
Year
DOI
Venue
2012
10.1109/IPDPS.2012.45
international parallel and distributed processing symposium
Keywords
DocType
Volume
graphics hardware,similarity search,information retrieval,computational geometry,cluster computing,data structure,nearest neighbor search
Conference
abs/1103.2635
ISSN
Citations 
PageRank 
In Proceedings of the 2012 IEEE 26th International Parallel and Distributed Processing Symposium (IPDPS '12). IEEE Computer Society, Washington, DC, USA, 402-413
26
1.03
References 
Authors
19
1
Name
Order
Citations
PageRank
Lawrence Cayton11528.27