A multi-device version of the HYFMGPU algorithm for hyperspectral scenes registration

Abstract

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 $$\times $$ to 1.59 $$\times $$ in 2 GPUs and from 1.26 $$\times $$ to 2.58 $$\times $$ 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.