A Deep Collaborative Computing Based SAR Raw Data Simulation on Multiple CPU/GPU Platform

The outstanding computing ability of a graphics processing unit (GPU) brings new vitality to the typical computing intensive issue, so does the synthetic aperture radar (SAR) raw data simulation, which is a fundamental problem in SAR system design and imaging research. However, the computing power of a CPU was underestimated, and the tunings for a CPU-based method were missing in the previous works. Meanwhile, the collaborative computing of multiple CPUs/GPUs was not exploited thoroughly. In this paper, we propose a deep multiple CPU/GPU collaborative computing framework for time-domain SAR raw data simulation, which not only introduces the advanced vector extension (AVX) method to improve the computing efficiency of a multicore single instruction multiple data CPU, but also achieves a satisfactory speedup in the CPU/GPU collaborative simulation by fine-grained task partitioning and scheduling. In addition, an irregular reduction based SAR coherent accumulation approach is proposed to eliminate the memory access conflict, which is the most difficult issue in the GPU-based raw data simulation. Experimental results show that the multicore vector extension method greatly improves the computing power of a CPU-based method through about 70× speedup, thereby outperforming the single GPU simulation. Correspondingly, compared with the baseline sequential CPU approach, the multiple CPU/GPU collaborative simulation achieves up to 250× speedup. Furthermore, the irregular reduction based atomic-free optimization boosts the performance of the single GPU method by 20% acceleration. These results prove that the deep multiple CPU/GPU collaborative method is promising, especially for the case of huge volume raw data simulation with a wide swath and high resolution.