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Nowadays, similarity search on metric spaces is becoming a research field of interest due to the fact of its application to different scientific areas. However, when these applications produce a huge amount of data, it is necessary to accelerate the searching process by means of parallel architectures such as clusters, multiprocessors (multicores) or clusters of multiprocessors (multicores). Currently, graphic accelerators have emerged as a technology that allows for good performance at a low cost. In order to exploit the underlined architecture formed by multicores and a graphic accelerator it is nedeed to carry out heterogeneous programming, where CPUs and GPU are working at the same time taking benefits of the complete architecture. In this paper, a suitable generic structure adapted to the multicore/GPU system is presented and the experimental results, obtained in terms of execution time and speed-up, show the advantages of using this heterogeneous system, as well as a study of power consumption.

Un sistema heterogéneo Multicore/GPU para acelerar la búsqueda por similitud en estructuras métricas