@inproceedings{jorgefernandezfabeiro‚2018towards, title = {Towards a Multi-device Version of the {HYFMGPU} Algorithm for Hyperspectral Scenes Registration}, booktitle = {Computational and Mathematical Methods in Science and Engineering}, year = {2018}, abstract = {Hyperspectral image registration is a relevant task for real-time applications like environmental disasters management or search and rescue scenarios. Traditional algo-rithms were not devoted to real-time performance‚ the HYFMGPU algorithm having arisen as a solution to such a lack. Sensors are expected to evolve and thus generate images with finer resolutions and wider wavelength ranges‚ so a multi-GPU implementa-tion seems to be necessary in a near future. This work presents a first approach to such a multi-device version‚ identifying some stages of the pipeline as the most suitable to run in parallel in several GPUs. An MPI+CUDA variation of the original HYFMGPU algorithm is implemented‚ achieving speedups of 1.83× in 2 GPUs and 3.08× in 4 GPUs for the stages of the pipeline distributed among several devices. Different issues related to communications-derived time overloads and to some CUDA-based libraries particularities‚ as long as some optimization possibilities out of the currently distributed stages‚ were also detected. We plan to tackle them in further development stages of this multi-GPU implementation.}, doi = {10.5281/zenodo.1475157}, url = {http://dx.doi.org/10.5281/zenodo.1475157}, author = {Jorge Fern\'{a}ndez-Fabeiro‚ \'{A}lvaro Ord\'{o}\~{n}ez‚ Arturo Gonz\'{a}lez-Escribano and Dora B. Heras} }