相位差算法在多GPU平台上的并行化实现

Parallel Implementation of Phase Diversity Algorithm on Multi-GPU

相位差算法(PD)不仅应用于波前传感器还可以作为一种图像复原技术,在波前复原与高分辨力图像事后图像复原有很大的潜力;但是,由于相位差算法的计算量大导致了在PC平台上很难实现实时处理。以CPU与GPU之间较少的数据交互为原则,本文首先对PD计算过程进行了分析,然后在多GPU平台上,对PD算法进行任务划分及优化。实验结果表明,在相差PV=2λ条件下对于256 pixels×256 pixels大小图像,经过50次迭代单GPU和双GPU分别耗时53 ms和45 ms。

Phase diversity（PD） can not only be used as wave-front sensor but also as image restoration technique. However, its computations have been perceived as being too burdensome and it is not to satisfy the real-time requirement. On the principle of minimum data communication between CPU and GPU, we analyze the process of PD algorithm and the experiment result shows that the part of optimization is appropriate to be realized on GPU. Firstly, the process of PD is analyzed and then task partition is implemented on dual GPUs. Under the experiment condition of wavefront aberration of PV=2λ, after 50 iterations, the execution time of single GPU and dual GPUs are 53 ms and 45 ms respectively for image size of 256 pixels×256 pixels.