Heterogeneous parallel computing accelerated iterative subpixel digital image correlation
Parallel computing techniques have been introduced into digital image correlation （DIC） in recent years and leads to a surge in computation speed. The graphics processing unit （GPU）-based parallel computing demonstrated a surprising effect on accel- erating the iterative subpixel DIC, compared with...
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| Veröffentlicht in: | Science China. Technological sciences Jg. 61; H. 1; S. 74 - 85 |
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| Sprache: | Englisch |
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Science China Press
2018
Springer Nature B.V |
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| ISSN: | 1674-7321, 1869-1900 |
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| Abstract | Parallel computing techniques have been introduced into digital image correlation （DIC） in recent years and leads to a surge in computation speed. The graphics processing unit （GPU）-based parallel computing demonstrated a surprising effect on accel- erating the iterative subpixel DIC, compared with CPU-based parallel computing. In this paper, the performances of the two kinds of parallel computing techniques are compared for the previously proposed path-independent DIC method, in which the initial guess for the inverse compositional Gauss-Newton （IC-GN） algorithm at each point of interest （POI） is estimated through the fast Fourier transform-based cross-correlation （FFT-CC） algorithm. Based on the performance evaluation, a heterogeneous parallel computing （HPC） model is proposed with hybrid mode of parallelisms in order to combine the computing power of GPU and multicore CPU. A scheme of trial computation test is developed to optimize the configuration of the HPC model on a specific computer. The proposed HPC model shows excellent performance on a middle-end desktop computer for real-time subpixel DIC with high resolution of more than 10000 POIs per frame. |
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| AbstractList | Parallel computing techniques have been introduced into digital image correlation (DIC) in recent years and leads to a surge in computation speed. The graphics processing unit (GPU)-based parallel computing demonstrated a surprising effect on accelerating the iterative subpixel DIC, compared with CPU-based parallel computing. In this paper, the performances of the two kinds of parallel computing techniques are compared for the previously proposed path-independent DIC method, in which the initial guess for the inverse compositional Gauss-Newton (IC-GN) algorithm at each point of interest (POI) is estimated through the fast Fourier transform-based cross-correlation (FFT-CC) algorithm. Based on the performance evaluation, a heterogeneous parallel computing (HPC) model is proposed with hybrid mode of parallelisms in order to combine the computing power of GPU and multicore CPU. A scheme of trial computation test is developed to optimize the configuration of the HPC model on a specific computer. The proposed HPC model shows excellent performance on a middle-end desktop computer for real-time subpixel DIC with high resolution of more than 10000 POIs per frame. Parallel computing techniques have been introduced into digital image correlation （DIC） in recent years and leads to a surge in computation speed. The graphics processing unit （GPU）-based parallel computing demonstrated a surprising effect on accel- erating the iterative subpixel DIC, compared with CPU-based parallel computing. In this paper, the performances of the two kinds of parallel computing techniques are compared for the previously proposed path-independent DIC method, in which the initial guess for the inverse compositional Gauss-Newton （IC-GN） algorithm at each point of interest （POI） is estimated through the fast Fourier transform-based cross-correlation （FFT-CC） algorithm. Based on the performance evaluation, a heterogeneous parallel computing （HPC） model is proposed with hybrid mode of parallelisms in order to combine the computing power of GPU and multicore CPU. A scheme of trial computation test is developed to optimize the configuration of the HPC model on a specific computer. The proposed HPC model shows excellent performance on a middle-end desktop computer for real-time subpixel DIC with high resolution of more than 10000 POIs per frame. |
| Author | HUANG JianWen;ZHANG LingQi;JIANG ZhenYu;DONG ShouBin;CHEN Wei;LIU YiPing;LIU ZeJia;ZHOU LiCheng;TANG LiQun |
| AuthorAffiliation | State Key Laboratory of Subtropical Building Science, School of Civil Engineering and Transportation, South China University of Technology, Guangzhou 510640, China;School of Computer Science, South China University of Technology, Guangzhou 510640, China;The State Key Laboratory of Nonlinear Mechanics, Institute of Mechanics, Chinese Academy of Sciences, Beijing 100190, China |
| Author_xml | – sequence: 1 givenname: JianWen surname: Huang fullname: Huang, JianWen organization: State Key Laboratory of Subtropical Building Science, School of Civil Engineering and Transportation, South China University of Technology, School of Computer Science, South China University of Technology – sequence: 2 givenname: LingQi surname: Zhang fullname: Zhang, LingQi organization: School of Computer Science, South China University of Technology – sequence: 3 givenname: ZhenYu surname: Jiang fullname: Jiang, ZhenYu email: zhenyujiang@scut.edu.cn organization: State Key Laboratory of Subtropical Building Science, School of Civil Engineering and Transportation, South China University of Technology, The State Key Laboratory of Nonlinear Mechanics, Institute of Mechanics, Chinese Academy of Sciences – sequence: 4 givenname: ShouBin surname: Dong fullname: Dong, ShouBin email: sbdong@scut.edu.cn organization: School of Computer Science, South China University of Technology – sequence: 5 givenname: Wei surname: Chen fullname: Chen, Wei organization: State Key Laboratory of Subtropical Building Science, School of Civil Engineering and Transportation, South China University of Technology – sequence: 6 givenname: YiPing surname: Liu fullname: Liu, YiPing organization: State Key Laboratory of Subtropical Building Science, School of Civil Engineering and Transportation, South China University of Technology – sequence: 7 givenname: ZeJia surname: Liu fullname: Liu, ZeJia organization: State Key Laboratory of Subtropical Building Science, School of Civil Engineering and Transportation, South China University of Technology – sequence: 8 givenname: LiCheng surname: Zhou fullname: Zhou, LiCheng organization: State Key Laboratory of Subtropical Building Science, School of Civil Engineering and Transportation, South China University of Technology – sequence: 9 givenname: LiQun surname: Tang fullname: Tang, LiQun organization: State Key Laboratory of Subtropical Building Science, School of Civil Engineering and Transportation, South China University of Technology |
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| Cites_doi | 10.1118/1.3578605 10.1023/B:VISI.0000011205.11775.fd 10.1016/j.optlaseng.2015.03.005 10.1016/j.optlaseng.2011.06.020 10.1016/j.optlaseng.2015.01.012 10.1007/s11340-013-9717-6 10.1111/str.12039 10.1088/0957-0233/17/6/045 10.1016/j.optlaseng.2011.02.023 10.1111/str.12066 10.1117/1.1314593 10.4208/cicp.110113.010813a 10.1007/s11340-017-0294-y 10.1016/j.polymertesting.2005.06.013 10.1007/s11340-014-9874-2 10.1111/str.12194 10.1016/j.taml.2016.04.003 10.1007/s11340-016-0133-6 10.1007/s11554-010-0162-9 10.1007/s11340-014-9948-1 10.1007/978-3-642-01811-4_15 10.1177/1094342013518807 10.1145/1816038.1816021 10.1007/s11340-015-0091-4 10.1117/1.OE.54.3.034106 10.1016/j.optlaseng.2014.06.011 10.1051/meca/2012025 10.2478/v10248-012-0019-x |
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| Keywords | graphics processing unit (GPU) heterogeneous parallel computing digital image correlation (DIC) real-time DIC multicore CPU inverse compositional Gauss-Newton (IC-GN) algorithm |
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| Notes | Parallel computing techniques have been introduced into digital image correlation （DIC） in recent years and leads to a surge in computation speed. The graphics processing unit （GPU）-based parallel computing demonstrated a surprising effect on accel- erating the iterative subpixel DIC, compared with CPU-based parallel computing. In this paper, the performances of the two kinds of parallel computing techniques are compared for the previously proposed path-independent DIC method, in which the initial guess for the inverse compositional Gauss-Newton （IC-GN） algorithm at each point of interest （POI） is estimated through the fast Fourier transform-based cross-correlation （FFT-CC） algorithm. Based on the performance evaluation, a heterogeneous parallel computing （HPC） model is proposed with hybrid mode of parallelisms in order to combine the computing power of GPU and multicore CPU. A scheme of trial computation test is developed to optimize the configuration of the HPC model on a specific computer. The proposed HPC model shows excellent performance on a middle-end desktop computer for real-time subpixel DIC with high resolution of more than 10000 POIs per frame. digital image correlation （DIC）, inverse compositional Gauss-Newton （IC-GN） algorithm, heterogeneous parallel com-puting, graphics processing unit （GPU）, multicore CPU, real-time DIC 11-5845/TH ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
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| SubjectTerms | Computation Cross correlation Digital imaging Engineering Fast Fourier transformations Fourier transforms Graphics processing units Iterative methods Performance evaluation Pixels |
| Title | Heterogeneous parallel computing accelerated iterative subpixel digital image correlation |
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