Variable Selection in a GPU Cluster Using Delta Test Guillén Perales, Alberto García Arenas, María Isabel Herrera Maldonado, Luis Javier Pomares Cintas, Héctor Emilio Rojas Ruiz, Ignacio Inteligencia artificial Artificial intelligence 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. 2022-11-11T12:55:44Z 2022-11-11T12:55:44Z 2011 info:eu-repo/semantics/conferenceObject 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] https://hdl.handle.net/10481/77931 eng http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess Atribución 4.0 Internacional Springer