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    KCI등재 SCIE SCOPUS

    GPU-ACCELERATED SPECKLE MASKING RECONSTRUCTION ALGORITHM FOR HIGH-RESOLUTION SOLAR IMAGES

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    https://www.riss.kr/link?id=A106640522

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    다국어 초록 (Multilingual Abstract) kakao i 다국어 번역

    The near real-time speckle masking reconstruction technique has been developed to accelerate the processing of solar images to achieve high resolutions for ground-based solar telescopes. However, the reconstruction of solar subimages in such a speckle reconstruction is very time-consuming. We design and implement a new parallel speckle masking reconstruction algorithm based on the Compute Unified Device Architecture (CUDA) on General Purpose Graphics Processing Units (GPGPU). Tests are performed to validate the correctness of our program on NVIDIA GPGPU. Details of several parallel reconstruction steps are presented, and the parallel implementation between various modules shows a significant speed increase compared to the previous serial implementations. In addition, we present a comparison of runtimes across serial programs, the OpenMP-based method, and the new parallel method. The new parallel method shows a clear advantage for large scale data processing, and a speedup of around 9 to 10 is achieved in reconstructing one solar subimage of 256$\times$256 pixels. The speedup performance of the new parallel method exceeds that of OpenMP-based method overall. We conclude that the new parallel method would be of value, and contribute to real-time reconstruction of an entire solar image.
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    The near real-time speckle masking reconstruction technique has been developed to accelerate the processing of solar images to achieve high resolutions for ground-based solar telescopes. However, the reconstruction of solar subimages in such a speckle...

    The near real-time speckle masking reconstruction technique has been developed to accelerate the processing of solar images to achieve high resolutions for ground-based solar telescopes. However, the reconstruction of solar subimages in such a speckle reconstruction is very time-consuming. We design and implement a new parallel speckle masking reconstruction algorithm based on the Compute Unified Device Architecture (CUDA) on General Purpose Graphics Processing Units (GPGPU). Tests are performed to validate the correctness of our program on NVIDIA GPGPU. Details of several parallel reconstruction steps are presented, and the parallel implementation between various modules shows a significant speed increase compared to the previous serial implementations. In addition, we present a comparison of runtimes across serial programs, the OpenMP-based method, and the new parallel method. The new parallel method shows a clear advantage for large scale data processing, and a speedup of around 9 to 10 is achieved in reconstructing one solar subimage of 256$\times$256 pixels. The speedup performance of the new parallel method exceeds that of OpenMP-based method overall. We conclude that the new parallel method would be of value, and contribute to real-time reconstruction of an entire solar image.

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    참고문헌 (Reference)

    1 "cuFFT Library"

    2 Pehlemann, E., "Technical Aspects of the Speckle Masking Phase Reconstruction Algorithm" 216 : 337-, 1989

    3 Lohmann, A. W., "Speckle Masking in Astronomy: Triple Correlation Theory and Applications" 4028 : 4037-, 1983

    4 von der Luhe, O., "Speckle Imaging of Solar Small Scale Structure. I - Methods" 268 : 374-, 1993

    5 Gonsalves, R. A., "Phase Retrieval and Diversity in Adaptive Optics" 21 : 829-, 1982

    6 Flores, L. A., "Parallel CT Image Reconstruction Based on GPUs" 247 : 250-, 2014

    7 Xue-Bao Li, "PARALLEL IMAGE RECONSTRUCTION FOR NEW VACUUM SOLAR TELESCOPE" 한국천문학회 47 (47): 43-47, 2014

    8 Woger, F., "KISIP: A Software Package for Speckle Interferometry of Adaptive Optics Corrected Solar Data" 7019 : 70191E-, 2008

    9 Paxman, R. G., "Joint Estimation of Object and Aberrations by Using Phase Diversity" 9 : 1072-, 1992

    10 Li, X. B., "High-Performance Parallel Image Reconstruction for the New Vacuum Solar Telescope" 67 : 47-, 2015

    1 "cuFFT Library"

    2 Pehlemann, E., "Technical Aspects of the Speckle Masking Phase Reconstruction Algorithm" 216 : 337-, 1989

    3 Lohmann, A. W., "Speckle Masking in Astronomy: Triple Correlation Theory and Applications" 4028 : 4037-, 1983

    4 von der Luhe, O., "Speckle Imaging of Solar Small Scale Structure. I - Methods" 268 : 374-, 1993

    5 Gonsalves, R. A., "Phase Retrieval and Diversity in Adaptive Optics" 21 : 829-, 1982

    6 Flores, L. A., "Parallel CT Image Reconstruction Based on GPUs" 247 : 250-, 2014

    7 Xue-Bao Li, "PARALLEL IMAGE RECONSTRUCTION FOR NEW VACUUM SOLAR TELESCOPE" 한국천문학회 47 (47): 43-47, 2014

    8 Woger, F., "KISIP: A Software Package for Speckle Interferometry of Adaptive Optics Corrected Solar Data" 7019 : 70191E-, 2008

    9 Paxman, R. G., "Joint Estimation of Object and Aberrations by Using Phase Diversity" 9 : 1072-, 1992

    10 Li, X. B., "High-Performance Parallel Image Reconstruction for the New Vacuum Solar Telescope" 67 : 47-, 2015

    11 Beard, A., "DKIST Visible Broadband Imager Data Processing Pipeline" 9152 : 91521J-, 2014

    12 "CUDA Toolkit"

    13 Labeyrie, A., "Attainment of Diffraction Limited Resolution in Large Telescopes by Fourier Analysing Speckle Patterns in Star Images" 85 : 87-, 1970

    14 Woger, F., "Accelerated Speckle Imag- ing with the ATST Visible Broadband Imager" 8451 : 84511C-1, 2012

    15 Xuebao Li, "A NOVEL PARALLEL METHOD FOR SPECKLE MASKING RECONSTRUCTION USING THE OPENMP" 한국천문학회 49 (49): 157-162, 2016

    16 Shi, Z., "A Method of Level 1 Frames-Selection Based on GPU for New Vacuum Solar Telescope" 1408 : 1413-, 2015

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    2023 평가 해외DB학술지평가 신청대상 (해외등재 학술지 평가)
    2020-01-01 등재 등재학술지 유지 (해외등재 학술지 평가) KCI등재
    2010-01-01 등재 등재학술지 유지 (등재유지) KCI등재
    2008-01-01 등재 등재 1차 FAIL (등재유지) KCI등재
    2006-01-01 등재 등재학술지 유지 (등재유지) KCI등재
    2003-01-01 등재 등재학술지 선정 (등재후보2차) KCI등재
    2002-01-01 등재 등재후보 1차 PASS (등재후보1차) KCI등재후보
    1998-07-01 등재 등재후보학술지 선정 (신규평가) KCI등재후보
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    2016 1.16 0.27 0.89
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    0.76 0.67 0.219 0.33
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