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GPU Permutation Index: Good Trade-Off Between Efficiency and Results Quality

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Computer Science – CACIC 2021 (CACIC 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1584))

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Abstract

When managing multimedia data such as text, images, videos, etc., it only makes sense to search for similar objects, because it is difficult to imagine that it would be interesting to look for if there is an element in the database exactly the same as another given as an example. Hence, a solution can be modeled through metric spaces. In this scenario, for solving efficient searches, we preprocess the database to build an index; and then, utilizing this index, minimize the number of comparisons required to answer them. However, with very large metric databases this is not enough, it is also necessary to speed up queries using high-performance computing. Then, the GPGPU appears as a profitable alternative. Moreover, there are circumstances in which it is also reasonable to accept quick answers even if they are inexact or approximate.

In this work, we evaluate the trade-off between the answer quality and performance of our GPU implementation of Permutation Index. The implementation is a pure GPU, used to solve in parallel multiple approximate similarity searches on metric databases. Our proposal has two parallelism levels: intra-queries and inter-queries, many queries are solved in parallel (inter-parallelism), and each query is figured out in parallel (intra-parallelism). The experimental results confirm that the GPU Permutation Index is a remarkable recourse to solve approximate similarity searches on large metric databases.

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Authors and Affiliations

  1. LIDIC, Universidad Nacional de San Luis, Ejército de los Andes 950, 5700, San Luis, Argentina

    Mariela Lopresti, Fabiana Piccoli & Nora Reyes

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  1. Mariela Lopresti
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  2. Fabiana Piccoli
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  3. Nora Reyes
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Correspondence to Fabiana Piccoli .

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Editors and Affiliations

  1. National University of La Plata, La Plata, Argentina

    Patricia Pesado

  2. National University of Salta, Salta, Argentina

    Gustavo Gil

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Lopresti, M., Piccoli, F., Reyes, N. (2022). GPU Permutation Index: Good Trade-Off Between Efficiency and Results Quality. In: Pesado, P., Gil, G. (eds) Computer Science – CACIC 2021. CACIC 2021. Communications in Computer and Information Science, vol 1584. Springer, Cham. https://doi.org/10.1007/978-3-031-05903-2_13

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  • DOI: https://doi.org/10.1007/978-3-031-05903-2_13

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  • Online ISBN: 978-3-031-05903-2

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