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parallel COMPUTING 2020年 91卷

作者： Ribizel, Tobias Anzt, Hartwig Karlsruhe Inst Technol Steinbuch Ctr Comp Karlsruhe Germany Univ Tennessee Innovat Comp Lab Knoxville TN 37996 USA

We present a novel parallel selection algorithm for GPUs capable of handling single rank selection (single selection) and multiple rank selection (multiselection). The algorithm requires no assumptions on the input data distribution, and has a much lower recursion depth compared to many state-of-the-art algorithms. We implement the algorithm for different GPU generations, always leveraging the respectively-available low-level communication features, and assess the performance on server-line hardware. The computational complexity of our Sample-Select algorithm is comparable to specialized algorithms designed for - and exploiting the characteristics of - "pleasant" data distributions. At the same time, as the proposed Sample-Select algorithm does not work on the actual element values but on the element ranks of the elements only, it is robust to the input data and can complete significantly faster for adversarial data distributions. We also address the use case of approximate selection by designing a variant that radically reduces the computational cost while preserving high approximation accuracy. (C) 2019 Elsevier B.V. All rights reserved.

关键词： parallel selection algorithm GPU Multiselection kth order statistics Approximate selection

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Approximate and Exact selection on GPUs 33

Approximate and Exact Selection on GPUs

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33rd IEEE International parallel and Distributed Processing Symposium (IPDPS)

作者： Ribizel, Tobias Anzt, Hartwig Karlsruhe Inst Technol Steinbuch Ctr Comp Karlsruhe Germany Univ Tennessee Innovat Comp Lab ICL Knoxville TN USA

ISBN: (纸本)9781538655559

We present a novel algorithm for parallel selection on GPUs. The algorithm requires no assumptions on the input data distribution, and has a much lower recursion depth compared to many state-of-the-art algorithms. We implement the algorithm for different GPU generations, always using the respectively-available low-level communication features, and assess the performance on server-line hardware. The computational complexity of our SampleSelect algorithm is comparable to specialized algorithms designed for - and exploiting the characteristics of - "pleasant" data distributions. At the same time, as the SampleSelect does not work on the actual values but the ranks of the elements only, it is robust to the input data and can complete significantly faster for adversarial data distributions. Additionally to the exact SampleSelect, we address the use case of approximate selection by designing a variant that radically reduces the computational cost while preserving high approximation accuracy.

关键词： parallel selection algorithm GPU kth order statistics approximate threshold selection

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