swaths organized in proper data structures. However, to form a comprehensible final image, a processing procedure ( focusing ) is needed. The focusing of a SAR image can be seen as an inherently space-variant two-dimensional correlation of the received echo data with the impulse response of the system. Radar echo data and the resulting Single-Look Complex (SLC) image are stored in matrices of complex numbers representing the in-phase and quadrature (i/q) components of the SAR signal. Several processors are available, based on three main algorithms: Range-Doppler, k, and Chirp Scaling [ 7 ]. Usually, this processing takes time and needs HPC algorithms in order to process data quickly. Heretofore, considering the limited computing hardware onboard, data had been transmitted to ground stations for further processing. Nevertheless, the vast amount of acquired data and the severely limited down-link transfer bandwidth imply that any SAR system also needs an efficient raw data compression tool. Because of structures with apparent higher entropy, a quasi-independence of in-phase and quadrature components showing histograms with nearly Gaussian shape and identical variance, conventional image compression techniques are ill-suited, and resulting compression rates are low. Thanks to advances in the development of avionic specialized computing accelerators (GPUs) [ 1 , 12 ], now the onboard SAR processing with real-time GPU-parallel focusing algorithms is possible. These could improve sensor data usability on both strategic and tactical points of view. For example, we can think of an onboard computer provided with a GPU directly connected to both a ground transmitter and a SAR sensor through GPUDirect [ 13 ] RDMA [ 5 ] technology. Several efforts have been made to implement GPU SAR processors for different raw SAR data using CUDA Toolkit. In [ 4 ], the focusing of an ERS2 image with samples on an NVIDIA Tesla C1060 was obtained in 4.4 s using a Range-Doppler algorithm. A simil