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Local Search Algorithms on Graphics Processing Units. A Case Study: The Permutation Perceptron Problem

The Van Luong, Nouredine Melab, El-Ghazali Talbi
INRIA Dolphin Project / Opac LIFL CNRS, 40 avenue Halley, 59650 Villeneuve d’Ascq Cedex, France
Evolutionary Computation in Combinatorial Optimization, Lecture Notes in Computer Science, 2010, Volume 6022/2010, 264-275

@article{van2010local,

   title={Local search algorithms on graphics processing units. a case study: the permutation perceptron problem},

   author={Van Luong, T. and Melab, N. and Talbi, E.G.},

   journal={Evolutionary Computation in Combinatorial Optimization},

   pages={264–275},

   year={2010},

   publisher={Springer}

}

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Optimization problems are more and more complex and their resource requirements are ever increasing. Although metaheuristics allow to significantly reduce the computational complexity of the search process, the latter remains time-consuming for many problems in diverse domains of application. As a result, the use of GPU has been recently revealed as an efficient way to speed up the search. In this paper, we provide a new methodology to design and implement efficiently local search methods on GPU. The work has been experimented on the permuted perceptron problem and the experimental results show that the approach is very efficient especially for large problem instances.
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