Combining CPU and GPU architectures for fast similarity search

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F12%3A10124107" target="_blank" >RIV/00216208:11320/12:10124107 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/s10619-012-7092-4" target="_blank" >http://dx.doi.org/10.1007/s10619-012-7092-4</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s10619-012-7092-4" target="_blank" >10.1007/s10619-012-7092-4</a>

Result language
angličtina
Original language name
Combining CPU and GPU architectures for fast similarity search
Original language description
The Signature Quadratic Form Distance on feature signatures represents a flexible distance-based similarity model for effective content-based multimedia retrieval. Although metric indexing approaches are able to speed up query processing by two orders ofmagnitude, their applicability to large-scale multimedia databases containing billions of images is still a challenging issue. In this paper, we propose a parallel approach that balances the utilization of CPU and many-core GPUs for efficient similaritysearch with the Signature Quadratic Form Distance. In particular, we show how to process multiple distance computations and other parts of the search procedure in parallel, achieving maximal performance of the combined CPU/GPU system. The experimental evaluation demonstrates that our approach implemented on a common workstation with 2 GPU cards outperforms traditional parallel implementation on a high-end 48-core NUMA server in terms of efficiency almost by an order of magnitude. If we
Czech name
—
Czech description
—

Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
IN - Informatics
OECD FORD branch
—

Publication year
2012
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Similar results(10)