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基于开放运算语言加速的数字全息卷积重建算法实现

罗洪艳 周珞一 赵震 郭洪 冯晓波

罗洪艳, 周珞一, 赵震, 郭洪, 冯晓波. 基于开放运算语言加速的数字全息卷积重建算法实现[J]. 电子与信息学报, 2022, 44(9): 3258-3265. doi: 10.11999/JEIT210693
引用本文: 罗洪艳, 周珞一, 赵震, 郭洪, 冯晓波. 基于开放运算语言加速的数字全息卷积重建算法实现[J]. 电子与信息学报, 2022, 44(9): 3258-3265. doi: 10.11999/JEIT210693
LUO Hongyan, ZHOU Luoyi, ZHAO Zhen, GUO Hong, FENG Xiaobo. Implementation of Digital Holographic Convolutional Reconstruction Algorithm Based on Open Computing Language Acceleration[J]. Journal of Electronics & Information Technology, 2022, 44(9): 3258-3265. doi: 10.11999/JEIT210693
Citation: LUO Hongyan, ZHOU Luoyi, ZHAO Zhen, GUO Hong, FENG Xiaobo. Implementation of Digital Holographic Convolutional Reconstruction Algorithm Based on Open Computing Language Acceleration[J]. Journal of Electronics & Information Technology, 2022, 44(9): 3258-3265. doi: 10.11999/JEIT210693

基于开放运算语言加速的数字全息卷积重建算法实现

doi: 10.11999/JEIT210693
基金项目: 国家自然科学基金(Z20190224),重庆市研究生科研创新项目(CYS19056)
详细信息
    作者简介:

    罗洪艳:女,副教授,研究方向为医学图像处理

    周珞一:女,硕士生,研究方向为医学信号处理及应用

    赵震:男,硕士,研究方向为医学图像处理

    郭洪:女,硕士生,研究方向为生物医学信息处理

    冯晓波:男,硕士生,研究方向为生物医学传感器

    通讯作者:

    罗洪艳 h.y.luo@cqu.edu.cn

  • 中图分类号: O438.1

Implementation of Digital Holographic Convolutional Reconstruction Algorithm Based on Open Computing Language Acceleration

Funds: The National Natural Science Foundation of China (Z20190224), The Graduate Research and Innovation Foundation of Chongqing, China (CYS19056)
  • 摘要: 针对数字全息重建算法计算速度慢、实时应用能力弱以及现有GPU加速策略跨平台移植性差等问题,该文提出一种利用开放运算语言(OpenCL)架构提高数字全息重建算法执行效率的方案。该方案充分利用OpenCL架构的异构协同计算能力,对数字全息卷积重建算法进行CPU+GPU的异构运行设计,并采用数据并行模式编程实现。针对不同分辨率数字全息图、不同GPU加速平台的测试结果表明,该加速策略的平均执行时间均比CPU低1个数量级,最高总加速比达到54.2,并行运算加速比甚至高达94.7,且具有规模增长性及良好的跨平台特性,加速效率显著,更加适用于数字全息技术的工程化实现及实时性应用场合。
  • 图  1  基于CPU+GPU架构的卷积加速重建算法流程图

    图  2  卷积加速重建算法的GPU实现示意图

    图  3  20 μm聚苯乙烯微粒的同轴数字全息图

    图  4  全幅重建结果与局部区域的显微镜对比图

    图  5  不同加速平台的加速比(OpenCL版本)

    图  6  不同加速平台的内部耗时百分比(OpenCL版本)

    表  1  两种GPU加速平台参数

    平台型号频率内存流处理单元
    加速平台1CPU1:AMD Ryzen 5 36004.1 GHz16 GB
    GPU1:NVIDIA GeForce GTX 1660 SUPER1530 MHz6 GB1408
    加速平台2CPU2:AMD Ryzen 7 Mobile1.8 GHz16 GB
    GPU2:AMD Radeon(TM) Graphics1750 MHz512 MB512
    下载: 导出CSV

    表  2  不同CPU与GPU加速平台的全息重建总执行时间对比

    序号分辨率(像素)总执行时间(ms)
    CPU1加速平台1
    (OpenCL)
    加速平台1
    (CUDA)
    CPU 2加速平台2
    a512×384239121042171.3
    b1024×7688282416.71475138
    c1536×1152182239303045273.6
    d2048×1536320866.3505495445.9
    e2560×1920498998.4708758739
    f3072×23047171132.397113341147.4
    下载: 导出CSV

    表  3  不同GPU加速平台下全息重建的分项执行时间对比(OpenCL版本)(ms)

    序号分辨率(像素)串行运算用时数据传输用时并行运算用时
    CPU1CPU2CPU1-GPU1CPU2-GPU2GPU1GPU2
    a512×3841.33.30.721066
    b1024×768515.30.73.718.3119
    c1536×115212.333.321124.7229.3
    d2048×153620.356.3514.341375.3
    e2560×192032.7785.719.760641.3
    f3072×230447.3103.79.33375.71010.7
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-07-12
  • 修回日期:  2022-07-04
  • 网络出版日期:  2022-07-05
  • 刊出日期:  2022-09-19

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