RT Journal Article
T1 A multi-device version of the HYFMGPU algorithm for hyperspectral scenes registration
A1 Fernández Fabeiro, Jorge
A1 Ordóñez Iglesias, Álvaro
A1 González Escribano, Arturo
A1 Blanco Heras, Dora
K1 Hyperspectral imaging
K1 Image registration
K1 Fourier transforms
K1 Multi-GPU
K1 CUDA
K1 OpenMP
K1 MPI
K1 Remote sensing
AB 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
PB Springer
SN 0920-8542
YR 2018
FD 2018
LK http://hdl.handle.net/10347/17877
UL http://hdl.handle.net/10347/17877
LA eng
NO Fernández-Fabeiro, J., Ordóñez, Á., Gonzalez-Escribano, A., & Heras, D. (2018). A multi-device version of the HYFMGPU algorithm for hyperspectral scenes registration. The Journal Of Supercomputing. doi: 10.1007/s11227-018-2689-7
NO This is a post-peer-review, pre-copyedit version of an article published in The Journal of Supercomputing. The final authenticated version is available online at: https://doi.org/10.1007/s11227-018-2689-7
NO This work has been partially supported by: Universidad de Valladolid—Consejería de Educación of Junta de Castilla y León, Ministerio de Economía, Industria y Competitividad of Spain, and European Regional Development Fund (ERDF) program: Project PCAS (TIN2017-88614-R), Project PROPHET (VA082P17) and CAPAP-H6 network (TIN2016-81840-REDT). Universidade de Santiago de Compostela—Consellería de Cultura, Educación e Ordenación Universitaria of Xunta de Galicia (grant numbers GRC2014/008 and ED431G/08) and Ministerio de Economía, Industria y Competitividad of Spain (Grant Number TIN2016-76373-P), all co-funded by the European Regional Development Fund (ERDF) program. The work of Álvaro Ordóñez was supported by the Ministerio de Educación, Cultura y Deporte under an FPU Grant (Grant Number FPU16/03537)
DS Minerva
RD 28 abr 2026