Variable Selection in a GPU Cluster Using Delta Test
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Identificadores
URI: https://hdl.handle.net/10481/77931Exportar
Metadatos
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Springer
Materia
Inteligencia artificial Artificial intelligence
Fecha
2011Referencia 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]
Resumen
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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