Juan José Rodríguez-Vázquez

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This paper focuses on solving large size optimization problems using GPGPU. Evolutionary Algorithms for solving these optimization problems suffer from the curse of dimensionality, which implies that their performance deteriorates as quickly as the dimensionality of the search space increases. This difficulty makes very challenging the performance studies(More)
Diverse technologies have been used to accelerate the execution of Evolutionary Algorithms. Nowadays, the GPGPU cards have demonstrated a high efficiency in the improvement of the execution times in a wide range of scientific problems, including some excellent examples with diverse categories of Evolutionary Algorithms. Nevertheless, the studies in depth of(More)
GPU computing has spread its capacity over most of the scientific computing areas. Soft computing is aware of the potential of this computing architecture. In order to achieve high performance, practitioners have to deal with the particularities associated with the porting of the problem to the specifications of the GPU card; and specially with an efficient(More)
In this paper, several versions of a signal extraction algorithm, pertaining to the entry stage of the Cherenkov Telescope Array's Real Time Analysis pipeline, were implemented and optimised using SSE2, POSIX threads and CUDA. Results of this proof of concept let us gain an insight into the suitability of each platform, and the performance each one can(More)