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The Journal of Supercomputing

Erschienen in:

05.01.2019

Optimization of lateral interaction in accumulative computation on GPU-based platform

verfasst von: Aurelio Bermúdez, Francisco Montero, María T. López, Antonio Fernández-Caballero, José L. Sánchez

Erschienen in: The Journal of Supercomputing | Ausgabe 3/2019

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Abstract

The lateral interaction in accumulative computation (LIAC) algorithm is a biologically inspired method that allows us to detect moving objects from image sequences acquired from fixed surveillance cameras. This method achieves excellent precision but requires a high processing time. Sequential implementation is too slow and cannot achieve real-time processing. In this paper, we present several improvements to the LIAC algorithm that increase its efficiency in terms of execution time and energy consumption. In particular, a GPU-based implementation delivers the same precision and is notably faster and more energy efficient than the sequential implementation.

Vorheriger Artikel Analytical Communication Performance Models as a metric in the partitioning of data-parallel kernels on heterogeneous platforms

Nächster Artikel On the use of many-core machines for the acceleration of a mesh truncation technique for FEM

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Titel: Optimization of lateral interaction in accumulative computation on GPU-based platform
verfasst von: Aurelio Bermúdez
Francisco Montero
María T. López
Antonio Fernández-Caballero
José L. Sánchez
Publikationsdatum: 05.01.2019
Verlag: Springer US
Erschienen in: The Journal of Supercomputing / Ausgabe 3/2019
Print ISSN: 0920-8542
Elektronische ISSN: 1573-0484
DOI: https://doi.org/10.1007/s11227-018-02736-y

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