Synthetic Aperture Radar (SAR) is a powerful tool for studying natural environments under all-weather and day-And-night conditions. SAR system design and data-processing algorithm simulation is noted for its controllable parameters. The satellite SAR echo signal simulation framework has been successfully applied to target recognition based on Radarsat-2 and TerraSAR-X images and in strip map mode. However, such SAR image simulation works only on CPU or GPU (graphics processing units) and requires huge calculations. We developed a "Load-Balancing Model (LBM)" algorithm that uses Message Passing Interface GPU (MPI-GPU) to reduce the inner loop load and improve the computational performance. The LBM algorithm uses MPI-GPU technology to build the simple GPU cluster system. The LBM algorithm is used to separate the intensive computing and controlling tasks for each node, and exploit the contemporary GPU computation capability to accelerate the computing tasks. We conducted a relevant experiment on a target radar cross section (RCS) and improved the performance by a factor of > 40 compared to a 4-core CPU accelerated program.
- Load-balancing model