Range Query Processing in a Multi-GPU Environment

Similarity search has been widely studied in the last years, as it can be applied to several fields such as searching by content in multimedia objects, text retrieval or computational biology. These applications usually work on very large databases that are often indexed off-line to enable the accel...

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Published in:2012 IEEE 10th International Symposium on Parallel and Distributed Processing with Applications pp. 419 - 426
Main Authors: Barrientos, R. J., Gomez, J. I., Tenllado, C., Matias, M. P., Marin, M.
Format: Conference Proceeding
Language:English
Published: IEEE 01.07.2012
Subjects:
GPU
Graphics processing unit
Indexes
Instruction sets
Kernel
Measurement
metric databases
metric spaces
multi-GPU
Parallel processing
range query
Similarity search
ISBN:1467316318, 9781467316316
ISSN:2158-9178
Online Access:Get full text
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Abstract Similarity search has been widely studied in the last years, as it can be applied to several fields such as searching by content in multimedia objects, text retrieval or computational biology. These applications usually work on very large databases that are often indexed off-line to enable the acceleration of on-line searches. However, to maintain an acceptable throughput, it is essential to exploit the intrinsic parallelism of the algorithms used for the on-line query solving process, even with indexed databases. Therefore, many strategies have been proposed in the literature to parallelize these algorithms, both on shared and distributed memory multiprocessor systems. Lately, GPUs have also been used to implement brute-force approaches instead of using indexing structures, due to the difficulties introduced by the index in the efficient exploitation of the GPU resources. In this work we propose a Multi-GPU metric-space technique that efficiently exploits index data structures for similarity search in large databases, and show how it outperforms previous OpenMP and GPU brute-force strategies. Furthermore, our analysis covers the effects of the database size and its nature.
AbstractList Similarity search has been widely studied in the last years, as it can be applied to several fields such as searching by content in multimedia objects, text retrieval or computational biology. These applications usually work on very large databases that are often indexed off-line to enable the acceleration of on-line searches. However, to maintain an acceptable throughput, it is essential to exploit the intrinsic parallelism of the algorithms used for the on-line query solving process, even with indexed databases. Therefore, many strategies have been proposed in the literature to parallelize these algorithms, both on shared and distributed memory multiprocessor systems. Lately, GPUs have also been used to implement brute-force approaches instead of using indexing structures, due to the difficulties introduced by the index in the efficient exploitation of the GPU resources. In this work we propose a Multi-GPU metric-space technique that efficiently exploits index data structures for similarity search in large databases, and show how it outperforms previous OpenMP and GPU brute-force strategies. Furthermore, our analysis covers the effects of the database size and its nature.
Author Matias, M. P.
Gomez, J. I.
Tenllado, C.
Marin, M.
Barrientos, R. J.
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  organization: Dept. of Comput. Archit., Complutense Univ. of Madrid, Madrid, Spain
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  surname: Marin
  fullname: Marin, M.
  email: mmarin@yahoo-inc.com
  organization: DIINF, Univ. of Santiago of Chile, Santiago, Chile
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Snippet Similarity search has been widely studied in the last years, as it can be applied to several fields such as searching by content in multimedia objects, text...
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StartPage 419
SubjectTerms GPU
Graphics processing unit
Indexes
Instruction sets
Kernel
Measurement
metric databases
metric spaces
multi-GPU
Parallel processing
range query
Similarity search
Title Range Query Processing in a Multi-GPU Environment
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