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A gpu-based implementation for range queries on spaghettis data structure

Identificadores del recurso

Lecture notes in computer science vol. 6782 (intl. Conf. On computational science and its applications, iccsa), 2011, 6782: 615-629

978-3-642-21927-6

0302-9743

http://hdl.handle.net/10578/1773

Procedència

(RUIdeRA : Repositorio Universitario Institucional de Recursos Abiertos)

Fitxa

Títol:: A gpu-based implementation for range queries on spaghettis data structure
Tema:: Ciencias de la computación y tecnología informática
Descripció:: Similarity search in a large collection of stored objects in a metric database has become a most interesting problem. The Spaghettis is an efficient metric data structure to index metric spaces. However, for real applications processing large volumes of generated data, query response times can be high enough. In these cases, it is necessary to apply mechanisms in order to significantly reduce the average query time. In this sense, the parallelization of metric structures is an interesting field of research. The recent appearance of GPUs for general purpose computing platforms offers powerful parallel processing capabilities. In this paper we propose a GPU-based implementation for Spaghettis metric structure. Firstly, we have adapted Spaghettis structure to GPU-based platform. Afterwards, we have compared both sequential and GPU-based implementation to analyse the performance, showing significant improvements in terms of time reduction, obtaining values of speed-up close to 10. Keywords: Databases ? similarity search ? metric spaces ? algorithms ? data structures ? parallel processing ? GPU ? CUDA
Idioma:
Autor/Productor:: Uribe Paredes, Roberto; Sanchez Garcia, Jose Luis; Cazorla Lopez, Diego C.; Valero Lara, Pedro; Arias Antunez, Enrique
Editor:: Springer-Verlag
Drets:: info:eu-repo/semantics/openAccess
Data:: 2012-01-13T07:10:17Z; 2011
Tipo de recurso:: info:eu-repo/semantics/article
Format:: text/plain

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        <field name="value">Similarity search in a large collection of stored objects in a metric database has become a most interesting problem. The Spaghettis is an efficient metric data structure to index metric spaces. However, for real applications processing large volumes of generated data, query response times can be high enough. In these cases, it is necessary to apply mechanisms in order to significantly reduce the average query time. In this sense, the parallelization of metric structures is an interesting field of research. The recent appearance of GPUs for general purpose computing platforms offers powerful parallel processing capabilities. In this paper we propose a GPU-based implementation for Spaghettis metric structure. Firstly, we have adapted Spaghettis structure to GPU-based platform. Afterwards, we have compared both sequential and GPU-based implementation to analyse the performance, showing significant improvements in terms of time reduction, obtaining values of speed-up close to 10. Keywords: Databases ? similarity search ? metric spaces ? algorithms ? data structures ? parallel processing ? GPU ? CUDA</field>
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