Recovering historical climate records using artificial neural networks in GPU
Resumen:
This article presents a parallel implementation of Artificial Neural Networks over Graphic Processing Units, and its application for recovering his-torical climate records from the Digi-Clima project. Several strategies are intro-duced to handle large volumes of historical pluviometer records, and the paral-lel 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 pro-ject. The parallel algorithm allows reducing the execution times, with an accel-eration factor of up to 2.15×.
| 2014 | |
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Artificial neural networks Image processing Climate records GPU |
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| Inglés | |
| Universidad de la República | |
| COLIBRI | |
| http://hdl.handle.net/20.500.12008/5169 | |
| Acceso abierto | |
| Licencia Creative Commons Atribución – No Comercial – Sin Derivadas (CC BY-NC-ND 4.0) |
| Sumario: | This article presents a parallel implementation of Artificial Neural Networks over Graphic Processing Units, and its application for recovering his-torical climate records from the Digi-Clima project. Several strategies are intro-duced to handle large volumes of historical pluviometer records, and the paral-lel 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 pro-ject. The parallel algorithm allows reducing the execution times, with an accel-eration factor of up to 2.15×. |
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