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Computational Management Science

, Volume 13, Issue 3, pp 371–391 | Cite as

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

  • Antoine Brias
  • Jean-Denis Mathias
  • Guillaume Deffuant
Original Paper

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.

Keywords

Viability kernel Dynamic programming CUDA GPU  Fishery management 

Mathematics Subject Classification

90B50-management decision making, including multiple objectives  90C39-dynamic programming 

Notes

Acknowledgments

This work was supported by grants from Irstea and Region Auvergne. This support is gratefully acknowledged.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.Irstea, UR LISC Laboratoire d’Ingénierie des Systèmes ComplexesAubièreFrance

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