基于 GPU 粗细粒度和混合精度的 SAR 后向投影算法的并行加速研究

Synthetic Aperture Radar（SAR）backward projection imaging algorithm requires a large amount of computation，which affects the timeliness of SAR information acquisition seriously. Graphics processing unit（GPU）has strong floating point computing capability and highly parallel architecture，which has significant advantages in processing parallel tasks. This paper proposes a parallel acceleration technique for SAR backward projection imaging algorithm based on coarse grained and mixing accuracy of GPU. The scheme is based on asynchronous stream technology to reduce the time consumption of echo transmission and pulse compression and solve the problem that the pulse compression efficiency limited the size of radar echo. The efficient transpose of matrix is realized by using the GPU shared memory，which could solve the problem of insufficient GPU memory. The two-level granularity parallel optimization of the backward projection imaging algorithm is realized through managing the blocks and threads. And the data processing method based on mixed precision further improve the efficiency of the backward projection imaging algorithm and resource utility. By analyzing the measured data，the correctness and acceleration performance of the proposed GPU parallel acceleration processing scheme are verified. Compared with the CPU platform under the same conditions，the methods of double precision and mixed precision based on GPU both obtain obvious acceleration radio，which significantly improves the imaging efficiency.