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An Efficient Computation of Dempster-Shafer Theory of Evidence Based on Native GPU Implementation

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Belief Functions: Theory and Applications (BELIEF 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12915))

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Abstract

Interest in Dempster-Shafer’s theory of evidence has often run up against problems associated with the inherently exponential complexity of the calculation of the Belief and Plausibility measures. This can be mitigated by looking at the technological possibilities offered by GPU computing. Some preliminary attempts have been oriented towards parallelization of the computation, but none of them natively use the support offered by lower-level GPUs. In this paper, we introduce a set of Python functions for operations related to the Dempster-Shafer theory and outline its implementation based natively on GPU computing, highlighting the speedup possibilities in relation to a CPU-based or CPU-derived implementation.

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Notes

  1. 1.

    https://numba.pydata.org.

  2. 2.

    https://pypi.org/project/pyds/.

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Author information

Authors and Affiliations

  1. Computer Science Department, University of Oviedo, Oviedo, Spain

    Noelia Rico & Irene Díaz

  2. Department of Innovation Systems, University of Salerno, Fisciano, Italy

    Luigi Troiano

Authors
  1. Noelia Rico
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  2. Luigi Troiano
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  3. Irene Díaz
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Corresponding author

Correspondence to Noelia Rico .

Editor information

Editors and Affiliations

  1. Université de Technologie de Compiègne, Compiègne, France

    Thierry Denœux

  2. Université d'Artois, Béthune, France

    Eric Lefèvre

  3. Northwestern Polytechnical University, Xi'an, China

    Zhunga Liu

  4. Université d'Artois, Béthune, France

    Frédéric Pichon

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Rico, N., Troiano, L., Díaz, I. (2021). An Efficient Computation of Dempster-Shafer Theory of Evidence Based on Native GPU Implementation. In: Denœux, T., Lefèvre, E., Liu, Z., Pichon, F. (eds) Belief Functions: Theory and Applications. BELIEF 2021. Lecture Notes in Computer Science(), vol 12915. Springer, Cham. https://doi.org/10.1007/978-3-030-88601-1_29

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  • DOI: https://doi.org/10.1007/978-3-030-88601-1_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-88600-4

  • Online ISBN: 978-3-030-88601-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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