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Accelerating Electron Tomography Reconstruction Algorithm ICON Using the Intel Xeon Phi Coprocessor on Tianhe-2 Supercomputer

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Bioinformatics Research and Applications (ISBRA 2017)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 10330))

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Abstract

Electron tomography (ET) is an important method for studying three-dimensional cell ultrastructure. Combining with a sub-volume averaging approach, ET provides new possibilities for investigating in situ macromolecular complexes in sub-nanometer resolution. Because of the limited sampling angles, ET reconstruction usually suffers from the ‘missing wedge’ problem. With a validation procedure, Iterative Compressed-sensing Optimized NUFFT reconstruction (ICON) demonstrates its power in the restoration of validated missing information for low SNR biological ET dataset. However, the huge computational demand has become a bottleneck for the application of ICON. In this work, we developed the strategies of parallelization for NUFFT and ICON, and then implemented them on a Xeon Phi 31SP coprocessor to generate the parallel program ICON-MIC. We also proposed a hybrid task allocation strategy and extended ICON-MIC on multiple Xeon Phi cards on Tianhe-2 supercomputer to generate program ICON-MULT-MIC. With high accuracy, ICON-MIC has a significant acceleration compared to the CPU version, up to 13.3x, and ICON-MULT-MIC has good weak and strong scalability efficiency on Tianhe-2 supercomputer.

Y. Chen—Contributes equally to this work.

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Acknowledgments

This research is supported by the NSFC projects Grant Nos. U1611263, U1611261, 61232001, 61472397, 61502455, 61672493 and Special Program for Applied Research on Super Computation of the NSFC-Guangdong Joint Fund (the second phase), the Strategic Priority Research Program of Chinese Academy of Sciences (Grant No. XDB08030202), the National Basic Research Program (973 Program) of Ministry of Science and Technology of China (2014CB910700). The authors would like to thank Prof. Wanzhong He (NIBS, Beijing) for providing the resin embedded ET dataset. All the intensive computations were performed on Tianhe-2 supercomputer at the National Supercomputer Center in Guangzhou (NSCC-GZ), China and Center for Biological Imaging, Institute of Biophysics, Chinese Academy of Sciences (http://cbi.ibp.ac.cn).

Author information

Authors and Affiliations

  1. Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China

    Zihao Wang, Yu Chen, Jingrong Zhang, Lun Li, Xiaohua Wan, Zhiyong Liu & Fa Zhang

  2. University of Chinese Academy of Sciences, Beijing, China

    Zihao Wang, Yu Chen, Jingrong Zhang & Fei Sun

  3. School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, China

    Lun Li

  4. National Key Laboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China

    Fei Sun

  5. Center for Biological Imaging, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China

    Fei Sun

Authors
  1. Zihao Wang
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  2. Yu Chen
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  3. Jingrong Zhang
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  4. Lun Li
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  5. Xiaohua Wan
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  6. Zhiyong Liu
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  7. Fei Sun
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  8. Fa Zhang
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Corresponding authors

Correspondence to Zhiyong Liu , Fei Sun or Fa Zhang .

Editor information

Editors and Affiliations

  1. Georgia State University, Atlanta, Georgia, USA

    Zhipeng Cai

  2. University of Texas at Dallas, Richardson, Texas, USA

    Ovidiu Daescu

  3. Central South University, Changsha, China

    Min Li

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Wang, Z. et al. (2017). Accelerating Electron Tomography Reconstruction Algorithm ICON Using the Intel Xeon Phi Coprocessor on Tianhe-2 Supercomputer. In: Cai, Z., Daescu, O., Li, M. (eds) Bioinformatics Research and Applications. ISBRA 2017. Lecture Notes in Computer Science(), vol 10330. Springer, Cham. https://doi.org/10.1007/978-3-319-59575-7_23

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  • DOI: https://doi.org/10.1007/978-3-319-59575-7_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59574-0

  • Online ISBN: 978-3-319-59575-7

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