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

Antoine Brias (), Jean-Denis Mathias and Guillaume Deffuant
Additional contact information
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

Computational Management Science, 2016, vol. 13, issue 3, No 3, 391 pages

Abstract: 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; 90B50-management decision making; including multiple objectives; 90C39-dynamic programming (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10287-015-0246-x Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:comgts:v:13:y:2016:i:3:d:10.1007_s10287-015-0246-x

Ordering information: This journal article can be ordered from
http://www.springer. ... ch/journal/10287/PS2

DOI: 10.1007/s10287-015-0246-x

Access Statistics for this article

Computational Management Science is currently edited by Ruediger Schultz

More articles in Computational Management Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().