Parallel implementation of a primal-dual interior-point optimization method for fast abundance maps estimation
Abundance maps estimation in hyperspectral imaging requires the resolution of a large constrained optimization problem. Recently, a primal-dual interior-point algorithm has been proven to solve efficiently this problem. Despite its relatively fast convergence, the computation time remains a major issue for large scale images. We propose here an optimization of the GPU (Graphics Processing Unit) implementation of this method. The discussion covers the difference between parallelizing the entire algorithm or every step separately, the way of using the best of both the GPU and the CPU, and the memory organization issues. Simulated data are used to illustrate the effect of these modifications on computing time. An extensive application involving a large set of data from the European planetary mission Mars Express is presented.