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Caracterización vial en base a nubes de puntos LiDAR terrestre con MPI

Authors: A. M. Esmorís; J. C. Cabaleiro; D. L. Vilariño; F. F. Rivera;

Caracterización vial en base a nubes de puntos LiDAR terrestre con MPI

Abstract

Este trabajo se centra en la implementación paralela de un algoritmo aplicable a nubes de puntos LiDAR terrestre, que corresponde a la primera etapa de un conjunto de soluciones dedicadas la detección de bordillos. Se proponen distintas alternativas para abordar el problema de una carga de trabajo desbalanceada y se analiza el impacto de la configuración de uso de la infraestructura computacional del CESGA en el rendimiento. Tras analizar los resultados se ha visto que la estrategia de balanceo dinámico de la carga propuesta ha dado mejores resultados que las dos estrategias de balanceo estático probadas. Finalmente, se ha observado que usar una configuración de ejecución basada en reducir la congestión del bus de memoria contribuye a mejorar significativamente el rendimiento.

Este trabajo fue financiado en parte por Babcock International Group PLC (Civil UAVs Initiative de la Xunta de Galicia), la Dirección General de Tráfico (Proyecto BIG-GEOMOVE, SPIP2017 02340), el Ministerio de Educación, Cultura y Deporte, [Proyecto TIN2016-76373-P], la Xunta de Galicia [Proyectos GRC R2016/045 y R2016/037], la Consellería de Cultura, Educación e Ordenación Universitaria (acreditación 2016-2019, ED431G/08, ED431C 2018/2019) y la Unión Europea (European Regional Development Fund - ERDF). Así mismo, el análisis de rendimiento de las distintas técnicas y configuraciones fueron posibles gracias al Centro de Supercomputación de Galicia (CESGA), que ofreció su infraestructura computacional para la realización de este trabajo.

Keywords

LiDAR, Computación Distribuida, HPC, MPI, Paralelismo

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This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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