Tesis doctorales: TESEO

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Ministerio de Educación, Cultura y Deporte - Gobierno de España

Título: CONTRIBUTIONS TO THE EFFICIENT USE OF GENERAL PURPOSE COPROCESSORS: KERNEL DENSITY ESTIMATION AS CASE STUDY
Autor: LOPEZ NOVOA, UNAI
Universidad: Universidad del País Vasco/Euskal Herriko Unibertsitatea
Departamento: Arquitectura y tecnología de computadores
Fecha de Lectura: 19/06/2015
Mención Europeo / Mención Internacional: Concedido
Programa de doctorado: Ingeniería Informática
Dirección:
- MENDIBURU ALBERRO, ALEXANDER (Director)
- MIGUEL ALONSO, JOSE (Director)
Tribunal:
- RODRIGUEZ LAFUENTE, CLEMENTE (presidente)
- SAENZ AGUIRRE, JON (secretario)
- GREGORIO MONASTERIO, JOSE ANGEL (vocal)
- LOPEZ DE IPIÑA GONZALEZ DE ARTAZA, DIEGO (vocal)
- PIRES SEABARA DE SOUSA, LEONEL AUGUSTO (vocal)
Descriptores:
- INTELIGENCIA ARTIFICIAL
- ARQUITECTURA DE ORDENADORES
Localización: REPOSITORIO INSTITUCIONAL E.H.U./U.P.V.

http://hdl.handle.net/10810/15955

Marcador: https://www.educacion.es/teseo/mostrarRef.do?ref=358524Añadir a favoritos.
Resumen:
The high performance computing landscape is shifting from assemblies of homogeneous nodes towards heterogeneous systems, in which nodes consist of a combination of traditional out-of-order execution cores and accelerator devices. Accelerators provide greater theoretical performance compared to traditional multi-core CPUs, but exploiting their computing power remains as a challenging task.This dissertation discusses the issues that arise when trying to efficiently use general purpose accelerators. As a contribution to aid in this task, we present a thorough survey of performance modeling techniques and tools for general purpose coprocessors. Then we use as case study the statistical technique Kernel Density Estimation (KDE). KDE is a memory bound application that poses several challenges for its adaptation to the accelerator-based model. We present a novel algorithm for the computation of KDE that reduces considerably its computational complexity, called S-KDE. Furthermore, we have carried out two parallel implementations of S-KDE, one for multi and many-core processors, and another one for accelerators. The latter has been implemented in OpenCL in order to make it portable across a wide range of devices. We have evaluated the performance of each implementation of S-KDE in a variety of architectures, trying to highlight the bottlenecks and the limits that the code reaches in each device. Finally, we present an application of our S-KDE algorithm in the field of climatology: a novel methodology for the evaluation of environmental models.

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