The Journal of Supercomputing

, Volume 72, Issue 2, pp 545–566 | Cite as

Kernel density estimation in accelerators

Implementation and performance evaluation
  • Unai Lopez-Novoa
  • Alexander Mendiburu
  • Jose Miguel-Alonso
Article

Abstract

Kernel density estimation (KDE) is a popular technique used to estimate the probability density function of a random variable. KDE is considered a fundamental data smoothing algorithm, and it is a common building block in many scientific applications. In a previous work we presented S-KDE, an efficient algorithmic approach to compute KDE that outperformed other state-of-the-art implementations, providing accurate results in much reduced execution times. Its parallel implementation targeted multi- and many-core processors. In this work we present an OpenCL implementation of S-KDE, targeting modern accelerators in a portable way. We test our implementation on three accelerators from different manufacturers, achieving speedups around \(5\times \) compared to a hand-tuned serial version of S-KDE. We also analyze the performance of the code in these accelerators, to find out to what extent our code exploits their capabilities.

Keywords

Kernel density estimation Performance analysis OpenCL  Many-core processors GPGPU 

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Unai Lopez-Novoa
    • 1
    • 2
  • Alexander Mendiburu
    • 1
  • Jose Miguel-Alonso
    • 1
  1. 1.Department of Computer Architecture and Technology, Intelligent Systems GroupUniversity of the Basque Country UPV/EHUSan SebastiánSpain
  2. 2.Deusto Institute of Technology, DeustoTechUniversity of DeustoBilbaoSpain

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