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A multi-device version of the HYFMGPU algorithm for hyperspectral scenes registration
Año del Documento
The Journal of Supercomputing
1. A Multi-Device Version of the HYFMGPU Algorithm for Hyperspectral Scenes Registration Jorge Fernández Fabeiro, Álvaro Ordóñez, Arturo González Escribano, Dora Blanco Heras. The Journal of Supercomputing, Springer, ISSN 0920-8542, marzo 2019, vol. 75, issue 3, pp 1551-1564 (Q2), 2018. DOI: 10.1007/s11227-018-2689-7.
Hyperspectral image registration is a relevant task for real-time applications like environmental disasters management or search and rescue scenarios. Traditional algorithms were not really devoted to real-time performance, even when ported to GPUs or other parallel devices. Thus, the HYFMGPU algorithm arose as a solution to such a lack. Nevertheless, as sensors are expected to evolve and thus generate images with finer resolutions and wider wavelength ranges, a multi-GPU implementation of this algorithm seems to be necessary in a near future. This work presents a multi-device MPI + CUDA implementation of the HYFMGPU algorithm that distributes all its stages among several GPUs. This version has been validated testing it for 5 different real hyperspectral images, with sizes from about 80 MB to nearly 2 GB, achieving speedups for the whole execution of the algorithm from 1.18 × to 1.59 × in 2 GPUs and from 1.26 × to 2.58 × in 4 GPUs. The parallelization efficiencies obtained are stable around 86% and 78% for 2 and 4 GPUs, respectively, which proves the scalability of this multi-device version.
Revisión por pares
Este trabajo forma parte del proyecto de investigación PCAS Grant TIN2017-88614-R y la Junta de Castilla y León, proyecto PROPHET, VA082P17.
Tipo de versión