Parallelization techniques for the 2D Fourier Matched Filtering and Interpolation SAR algorithm

Future space application will require High Performance Computing (HPC) capabilities to be available on board of future spacecrafts. To cope with this requirement, multi and many-core processor technologies have to be integrated in the computing platforms of the spacecraft. One of the most important requirements, coming from the nature of space applications, is the efficiency in terms of performance per Watt. In order to improve the efficiency of such systems, algorithms and applications have to be optimized and scaled to the number of cores available in the computing platform. In this paper we describe the parallelization techniques applied to a Synthetic Aperture Radar (SAR) application based on the 2-Dimentional Fourier Matched Filtering and Interpolation (2DFMFI) Algorithm. Other than sequential optimizations, we applied parallelization techniques for shared memory, distributed shared memory and distributed memory environments, using parallel programming models like OpenMP and MPI. It turns out that parallelizing this type of algorithms is not an easy and straightforward task to do, but with a little bit of effort, one can improve performance and scalability, increasing the level of efficiency.