Variable Selection in a GPU Cluster Using Delta Test

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URI: https://hdl.handle.net/10481/77931
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Author
Guillén Perales, Alberto; García Arenas, María Isabel; Herrera Maldonado, Luis Javier; Pomares Cintas, Héctor Emilio; Rojas Ruiz, Ignacio
Editorial
Springer
Materia
Inteligencia artificial
 
Artificial intelligence
 
Date
2011
Referencia bibliográfica
Published version: Guillén, A... [et al.] (2011). Variable Selection in a GPU Cluster Using Delta Test. In: Cabestany, J., Rojas, I., Joya, G. (eds) Advances in Computational Intelligence. IWANN 2011. Lecture Notes in Computer Science, vol 6691. Springer, Berlin, Heidelberg. [https://doi.org/10.1007/978-3-642-21501-8_49]
Sponsorship
Spanish CICYT Project TIN2007-60587 and TEC2008-04920; Junta Andalucia Projects P08-TIC-03674 and P08-TIC03928 and PYR-2010-17 of CEI BioTIC GENIL (CEB09-0010) of the MICINN
Abstract
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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Atribución 4.0 Internacional
Except where otherwise noted, this item's license is described as Atribución 4.0 Internacional