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Recovering Historical Climate Records using Artificial Neural Networks in GPU

Juan Pablo Balarini, Sergio Nesmachnow
Centro de Calculo, Facultad de Ingenieria, Universidad de la Republica, Uruguay
International Supercomputing Conference, 2015

@article{balarini2014recovering,

   title={Recovering Historical Climate Records using Artificial Neural Networks in GPU},

   author={Balarini, Juan Pablo and Nesmachnow, Sergio},

   year={2014}

}

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This article presents a parallel implementation of Artificial Neural Networks over Graphic Processing Units, and its application for recovering historical climate records from the Digi-Clima project. Several strategies are introduced to handle large volumes of historical pluviometer records, and the parallel deployment is described. The experimental evaluation demonstrates that the proposed approach is useful for recovering the climate information, achieving classification rates up to 76% for a set of real images from the Digi-Clima project. The parallel algorithm allows reducing the execution times, with an acceleration factor of up to 2.15x.
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