IOE OpenIR  > 光电技术研究所博硕士论文
基于 GPU 并行计算的太阳自适应光学图像斑点重建技术实现
宣经纬
Subtype硕士
Thesis Advisor饶长辉
2019-05
Degree Grantor中国科学院大学
Place of Conferral中国科学院光电技术研究所
Degree Name工学硕士
Degree Discipline信号与信息处理
KeywordGpu Cuda 图像重建 斑点干涉法 斑点掩模法 并行计算
Abstract

  在地基太阳观测中,光线在穿越大气层时会受到大气湍流的影响而导致图像扭曲、变形以致质量下降。为了消除或降低大气湍流的影响,包括自适应光学技术和事后图像处理技术在内的许多技术被用来获得高分辨力的太阳图像。本文采用的是基于斑点重建算法的事后图像处理技术。斑点重建算法被分为两个部分:傅里叶振幅重建和傅里叶相位重建。振幅重建部分采用的是斑点干涉法,相位重建部分则采用的是斑点掩模法。然而,由于巨大的数据量以及计算的复杂性,斑点重建算法的计算十分耗时,传统的串行计算方式难以满足实时性要求。

  GPU由于其强大的计算能力、良好的可编程性以及经济性,为提高算法的计算效率提供了新的思路。为了加速斑点重建算法的计算过程,本研究在讨论了算法原理的基础上,使用CUDA并行计算架构实现了太阳图像斑点重建算法并行化。本文作了如下的工作:

  第一,介绍了大气湍流对太阳观测的影响,并详细论述了斑点重建算法的原理和步骤。

  第二,针对斑点重建算法在串行平台上耗时长、重建效率低的问题,利用GPU平台和CUDA框架对不同的步骤提出了各自的并行优化方案。实验结果表明,在我们的运行环境下,一张TiO通道2304×1984像素大小的图像,可以在70s内完成重建,相比运行在CPU上的串行程序,加速比可达7以上。可见,并行化后的算法,重建速度得到了显著的提升。

  第三、为了进一步减少耗时,本文提出了基于双GPU的加速方法以及基于CPU-GPU协同计算的加速方法。其中基于CPU-GPU协同计算的加速方法又分为在子块内的CPU-GPU协同计算和在子块间的CPU-GPU协同计算两种。实验表明,这三种加速方法相比单GPU情况下的速度都取得了一定程度的提升。可以预见,未来在更多的GPU上,是可以实现太阳图像高分辨力实时重建的。

Other Abstract

In ground-based solar observation, when the light passes through atmosphere, it will be affected by atmospheric turbulence which will cause translation, distortion and blurring of the received image. In order to eliminate or reduce the effects of atmospheric turbulence, many techniques, including adaptive optics and post-image processing techniques, are used to obtain high-resolution solar images. The technology we used in this paper is the post-image processing which based on the speckle reconstruction algorithm. Speckle reconstruction algorithm was divided into two steps, Fourier amplitude reconstruction and Fourier phase reconstruction. Speckle interferometry is adopted to reconstruct Fourier amplitude. For phase reconstruction, speckle masking was adopted. However, due to the large data volume and complex calculation, time consumption of the two steps is pretty huge, so that the traditional serial computation is difficult to meet the real-time requirements.

GPU provides a new way to improve the computational efficiency of the algorithm, because of its powerful computing power, good programmability and economy. In order to accelerate the computation process of speckle image reconstruction, solar speckle image reconstruction algorithm has been parallelized by the parallel computing architecture-CUDA on the basis of discussing the principle of the algorithm. The following works have been done in this paper:

Firstly, we introduced the influence of atmospheric turbulence on solar observation, and discussed in detail the principle and process of speckle reconstruction algorithm.

Secondly, aiming at the problem of long time-consuming and low efficiency of speckle reconstruction algorithm on serial computing platform, different parallel optimization schemes were proposed for different steps by GPU platform and CUDA framework. Experiment result showed that a 2304 x 1984 pixel image of TiO channel can be reconstructed within 70 s under our operating environment. Compared with the program run on CPUthe speed-up radio can up to 7. It can be seen that the reconstruction speed of the parallel algorithm has been significantly improved.

Thirdly, in order to further reduce the time consuming, we proposed an acceleration method based on dual GPU and a collaborative computing method based on CPU-GPU. The acceleration method based on CPU-GPU collaborative computing can be divided into CPU-GPU collaborative computing in sub-image and CPU-GPU collaborative computing between sub-image. Experiments showed that the speed of these three acceleration methods have been improved to some extent compared with single GPU scenario. It is foreseeable that the goal of real-time reconstruction solar image can be achieved with more GPUs in the future.

MOST Discipline Catalogue工学::信息与通信工程
Language中文
Document Type学位论文
Identifierhttp://ir.ioe.ac.cn/handle/181551/9076
Collection光电技术研究所博硕士论文
Recommended Citation
GB/T 7714
宣经纬. 基于 GPU 并行计算的太阳自适应光学图像斑点重建技术实现[D]. 中国科学院光电技术研究所. 中国科学院大学,2019.
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