自适应光学数值仿真成像在GPU上的实现

在自适应光学(AO)系统中，成像是不可或缺的一部分。AO仿真系统中的探测器和哈特曼-夏克波前传感器的成像过程一般用二维的离散卷积来计算，而通常它的数值算法用快速傅立叶变换(FFT)实现。但是随着矩阵维数的增加，卷积的运算量会急剧增大，成为制约整个AO仿真效率的一个瓶颈。利用图形处理器(GPU)的强大计算能力，可以使成像系统运行速度大幅提高。在NVIDIA Tesla C2050 GPU上，针对不同分辨率的图像，获得了相对于串行程序5-24倍的加速比。

Numerical Simulation of Adaptive Optical Imaging on GPUs

In the adaptive optics （AO） system, imaging is a very important process. The imaging process of the detectors and Hartman-Shack wavefront sensor in the AO simulation system is calculated with two-dimensional discrete convolution, whose numerical algorithm can be implemented with fast Fourier transformation （FFT）. With the increase of the matrix size, the computation of convolution increases dramatically, and becomes a bottleneck of the AO system. To solve the problem, convolution on graphic processing unit（GPU） was implemented, which is a powerful accelerator in modern high performance computers. By applying various optimizations, significant performance improvement is obtained. On the NVIDIA Tesla C2050 GPU, a speedup of 5 to 24 times compared to serial program on the CPU with different image sizes is achieved.