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• W4285132567 startingPage "183" @default.
• W4285132567 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." @default.
• W4285132567 created "2022-07-14" @default.
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• W4285132567 date "2022-01-01" @default.
• W4285132567 modified "2023-09-26" @default.
• W4285132567 title "GPU Permutation Index: Good Trade-Off Between Efficiency and Results Quality" @default.
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• W4285132567 doi "https://doi.org/10.1007/978-3-031-05903-2_13" @default.
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