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Kernel density estimation in accelerators

Implementation and performance evaluation

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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.

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Notes

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  2. http://developer.amd.com/tools-and-sdks/opencl-zone/codexl/.

  3. http://developer.nvidia.com/nvidia-visual-profiler.

  4. http://software.intel.com/en-us/intel-vtune-amplifier-xe.

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Author notes

  1. Unai Lopez-Novoa

    Present address: Deusto Institute of Technology, DeustoTech, University of Deusto, Avenida de las Universidades 24, 48007, Bilbao, Spain

Authors and Affiliations

  1. Department of Computer Architecture and Technology, Intelligent Systems Group, University of the Basque Country UPV/EHU, P. Manuel Lardizabal 1, 20018, San Sebastián, Gipuzkoa, Spain

    Unai Lopez-Novoa, Alexander Mendiburu & Jose Miguel-Alonso

Authors
  1. Unai Lopez-Novoa
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  2. Alexander Mendiburu
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  3. Jose Miguel-Alonso
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Corresponding author

Correspondence to Unai Lopez-Novoa.

Additional information

This work has been partially supported by the Saiotek and Research Groups 2013-2018 (IT- 609-13) programs (Basque Government), TIN2013-41272P (Ministry of Science and Technology), COMBIOMED-RD07/0067/0003 network in computational biomedicine (Carlos III Health Institute) and by the NICaiA Project PIRSES-GA-2009-247619 (European Commission).

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Lopez-Novoa, U., Mendiburu, A. & Miguel-Alonso, J. Kernel density estimation in accelerators. J Supercomput 72, 545–566 (2016). https://doi.org/10.1007/s11227-015-1577-7

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  • DOI: https://doi.org/10.1007/s11227-015-1577-7

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