Variable selection in a GPU cluster using delta test

A. Guillen, M. van Heeswijk, D. Sovilj, M. G. Arenas, L. J. Herrera, H. Pomares, I. Rojas
Department of Computer Architecture and Computer Technology Universidad de Granada, Spain
Advances in Computational Intelligence, Lecture Notes in Computer Science, Volume 6691/2011, 393-400, 2011


   title={Variable selection in a GPU cluster using delta test},

   author={Guill{‘e}n, A. and van Heeswijk, M. and Sovilj, D. and Arenas, M. and Herrera, L. and Pomares, H. and Rojas, I.},

   journal={Advances in Computational Intelligence},





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The work presented in this paper consists in an adaptation of a Genetic Algorithm (GA) to perform variable selection in an heterogeneous cluster where the nodes are themselves clusters of GPUs. Due to this heterogeneity, several mechanisms to perform a load balance will be discussed as well as the optimization of the fitness function to take advantage of the GPUs available. The algorithm will be compared with previous parallel implementations analysing the advantages and disadvantages of the approach, showing that for large data sets, the proposed approach is the only one that can provide a solution.
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