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Accelerating viability kernel computation with CUDA architecture: application to bycatch fishery management

Author

Listed:
  • Antoine Brias

    (Irstea, UR LISC Laboratoire d’Ingénierie des Systèmes Complexes)

  • Jean-Denis Mathias

    (Irstea, UR LISC Laboratoire d’Ingénierie des Systèmes Complexes)

  • Guillaume Deffuant

    (Irstea, UR LISC Laboratoire d’Ingénierie des Systèmes Complexes)

Abstract

Computing a viability kernel consumes time and memory resources which increase exponentially with the dimension of the problem. This curse of dimensionality strongly limits the applicability of this approach, otherwise promising. We report here an attempt to tackle this problem with Graphics Processing Units (GPU). We design and implement a version of the viability kernel algorithm suitable for General Purpose GPU (GPGPU) computing using Nvidia’s architecture, CUDA (Computing Unified Device Architecture). Different parts of the algorithm are parallelized on the GPU device and we test the algorithm on a dynamical system of theoretical population growth. We study computing time gains as a function of the number of dimensions and the accuracy of the grid covering the state space. The speed factor reaches up to 20 with the GPU version compared to the Central Processing Unit (CPU) version, making the approach more applicable to problems in 4 to 7 dimensions. We use the GPU version of the algorithm to compute viability kernel of bycatch fishery management problems up to 6 dimensions.

Suggested Citation

  • Antoine Brias & Jean-Denis Mathias & Guillaume Deffuant, 2016. "Accelerating viability kernel computation with CUDA architecture: application to bycatch fishery management," Computational Management Science, Springer, vol. 13(3), pages 371-391, July.
    • Handle: RePEc:spr:comgts:v:13:y:2016:i:3:d:10.1007_s10287-015-0246-x
    DOI: 10.1007/s10287-015-0246-x
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    References listed on IDEAS

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    1. Chapel, Laetitia & Deffuant, Guillaume & Martin, Sophie & Mullon, Christian, 2008. "Defining yield policies in a viability approach," Ecological Modelling, Elsevier, vol. 212(1), pages 10-15.
    2. Ehrlich, Isaac & Lui, Francis, 1997. "The problem of population and growth: A review of the literature from Malthus to contemporary models of endogenous population and endogenous growth," Journal of Economic Dynamics and Control, Elsevier, vol. 21(1), pages 205-242, January.
    3. Andrés-Domenech, Pablo & Saint-Pierre, Patrick & Smala Fanokoa, Pascaux & Zaccour, Georges, 2014. "Sustainability of the Dry Forest in Androy: A Viability Analysis," Ecological Economics, Elsevier, vol. 104(C), pages 33-49.
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    5. Doyen, L. & Thébaud, O. & Béné, C. & Martinet, V. & Gourguet, S. & Bertignac, M. & Fifas, S. & Blanchard, F., 2012. "A stochastic viability approach to ecosystem-based fisheries management," Ecological Economics, Elsevier, vol. 75(C), pages 32-42.
    6. Jacek Krawczyk & Alastair Pharo & Oana Serea & Stewart Sinclair, 2013. "Computation of viability kernels: a case study of by-catch fisheries," Computational Management Science, Springer, vol. 10(4), pages 365-396, December.
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