Fernández Fabeiro, JorgeOrdóñez Iglesias, ÁlvaroGonzález Escribano, ArturoBlanco Heras, Dora2018-12-052019-11-172018Fernández-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/s11227-018-2689-70920-8542http://hdl.handle.net/10347/17877This 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/s11227-018-2689-7Hyperspectral 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 versioneng© Springer Science+Business Media, LLC, part of Springer Nature 2018Hyperspectral imagingImage registrationFourier transformsMulti-GPUCUDAOpenMPMPIRemote sensingjournal article10.1007/s11227-018-2689-71573-0484open access