GPGPU for Difficult Black-box Problems | Zendy

Marcin Pietroń | Zendy; Aleksander Byrski | Zendy; Marek KisielDorohinicki | Zendy

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GPGPU for Difficult Black-box Problems

Author(s) -

Marcin Pietroń,

Aleksander Byrski,

Marek KisielDorohinicki

Publication year - 2015

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2015.05.249

Subject(s) - computer science , golomb coding , memetic algorithm , general purpose computing on graphics processing units , ruler , evolutionary algorithm , parallel computing , algorithm , theoretical computer science , artificial intelligence , computer graphics (images) , image compression , graphics , physics , quantum mechanics , image (mathematics) , image processing

Difficult black-box problems arise in many scientific and industrial areas. In this paper, efficient use of a hardware accelerator to implement dedicated solvers for such problems is discussed and studied based on an example of Golomb Ruler problem. The actual solution of the problem is shown based on evolutionary and memetic algorithms accelerated on GPGPU. The presented results prove that GPGPU outperforms CPU in some memetic algorithms which can be used as a part of hybrid algorithm of finding near optimal solutions of Golomb Ruler problem. The presented research is a part of building heterogenous parallel algorithm for difficult black-box Golomb Ruler problem

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