Junção de conjuntos por similaridade explorando paralelismo multinível em GPUs
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2017-08-29
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Universidade Federal de Goiás
Resumo
Similarity Join is an important operation for information retrieval, near duplicate detection, data
analysis etc. State-of-the-art algorithms for similarity join use a technique known as prefix filtering
to reduce the amount of sets to be entirely compared by previously discarding dissimilar sets.
However, prefix filtering is only effective when looking for very similar data. An alternative to
speedup the similarity join when prefix filtering is not efficient is to explore parallelism.
In this work we developed three multi-level fine-grained parallel algorithms for many-core
architectures (such as modern Graphic Processing Units) to solve the similarity join problem. The
proposed algorithms have shown speedup gains of 109x and 17x when compared with sequential
(ppjoin) and parallel (fgssjoin) state-of-the-art solutions, respectively, on standard real text
databases.
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RIBEIRO-JUNIOR, Sidney. Junção de conjuntos por similaridade explorando paralelismo multinível em GPUs. 2017. 50 f. Dissertação (Mestrado em Ciência da Computação) - Universidade Federal de Goiás, Goiânia, 2017.