肖波, 郑华东, 刘柯健, 李飞, 高智方. 层析法计算三维物体全息图的并行加速研究[J]. 应用光学, 2019, 40(4): 620-626. DOI: 10.5768/JAO201940.0402006
引用本文: 肖波, 郑华东, 刘柯健, 李飞, 高智方. 层析法计算三维物体全息图的并行加速研究[J]. 应用光学, 2019, 40(4): 620-626. DOI: 10.5768/JAO201940.0402006
XIAO Bo, ZHENG Huadong, LIU Kejian, LI Fei, GAO Zhifang. Hologram speed-up computation of slice-based 3D objects using GPU parallel computing method[J]. Journal of Applied Optics, 2019, 40(4): 620-626. DOI: 10.5768/JAO201940.0402006
Citation: XIAO Bo, ZHENG Huadong, LIU Kejian, LI Fei, GAO Zhifang. Hologram speed-up computation of slice-based 3D objects using GPU parallel computing method[J]. Journal of Applied Optics, 2019, 40(4): 620-626. DOI: 10.5768/JAO201940.0402006

层析法计算三维物体全息图的并行加速研究

Hologram speed-up computation of slice-based 3D objects using GPU parallel computing method

  • 摘要: 随着计算空间光调制器的分辨率的尺寸逐渐变大,全息图三维动态显示的计算量也越来越大,使得对全息计算速度提出了新的要求。利用GPU并行计算处理的方式实现全息图的快速层析法计算,该方法利用GPU并行多线程和层析法中的图像二维傅里叶变换的优势对菲涅尔衍射变换算法加速计算;同时通过对GPU底层资源的调用和对CUDA中程序的流处理过程,有效减少中间的延时等待。通过对计算速度对比分析表明:与在CPU上运算相比,计算速度大幅提升,基于GPU并行计算的方法比基于CPU计算的方法速度快10倍左右。

     

    Abstract: As the resolution of the computational spatial light modulator has become larger, the computational complexity of the three-dimensional dynamic display of the hologram is also higher and higher, which makes new requirements for the holographic calculation speed. The fast calculation of hologram by slice-based method is realized by means of the graphics processing unit (GPU) parallel computation processing method, which accelerates the calculation of the Fresnel diffraction transform algorithm by taking advantage of the GPU parallel multi-threading and the image two-dimensional Fourier transform in tomography. At the same time, by calling the GPU underlying resources and the stream processing of the program in compute unified device architecture (CUDA), the intermediate delay wait is effectively reduced. The comparison of the calculation speed shows that the calculation speed is greatly improved compared with the calculation on the CPU, and the method based on GPU parallel calculation is about 10 times faster than the method based on CPU calculation.

     

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