压缩感知重构算法的并行化及GPU加速

Parallelization and GPU acceleration of compressive sensing reconstruction algorithm

针对压缩感知重构算法计算实时性太差的问题,提出压缩采样追踪匹配(compressive sampling matching pursuit,Co Sa M P)算法的并行化加速算法。基于多线程技术实现重构算法的粗粒度并行化,分析Co Sa M P算法的计算热点,将其中耗时较多的矩阵操作移植在图形处理器(graphics processing unit,GPU)上,实现算法的细粒度并行化。在测试图像上进行试验,结果表明:并行化加速算法取得50倍的加速效果,有效地降低重构算法的计算时间开销。

Aimed at the poor real-time performance of the compression sensing reconstruction algorithm,the parallel acceleration of the compressive sampling matching pursuit（ CoSaMP） algorithm was proposed. Coarse grained parallelization of reconstruction algorithm was realized based on multithreading technology. The hotspot of CoSaMP algorithm was analyzed,and the matrix operation which was time-consuming was transplanted to graphics processing unit（ GPU） to achieve fine grained parallelization of the algorithm.The experiments on the test image showed that 50-fold acceleration speedup was achieved and the study reduced the computing time cost of the reconstruction algorithm effectively.