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Biophysics Reports 2017, 3(1-3): 36-42 https://doi.org/10.1007/s41048-017-0041-z
Method | Open Access | Issue | Published: 04 July 2017

Accelerating electron tomography reconstruction algorithm ICON with GPU

Show Author's Information Yu Chen1,2,,  Zihao Wang1,2,,  Jingrong Zhang1,2,  Lun Li1,3,  Xiaohua Wan1,  Fei Sun2,4,5( ),  Fa Zhang1( )
Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
University of Chinese Academy of Sciences, Beijing 100049, China
School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
National Key Laboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
Center for Biological Imaging, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China

Yu Chen and Zihao Wang have contributed equally to this work.

Keywords:
GPU, Electron tomography, Acceleration, ICON, Missing wedge restoration
Cite this article:
Chen Y, Wang Z, Zhang J, et al. Accelerating electron tomography reconstruction algorithm ICON with GPU. Biophysics Reports, 2017, 3(1-3): 36-42. https://doi.org/10.1007/s41048-017-0041-z
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Abstract Full text About this article

Electron tomography (ET) plays an important role in studying in situ cell ultrastructure in three-dimensional space. Due to limited tilt angles, ET reconstruction always 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 major problem for the application of ICON. In this work, we analyzed the framework of ICON and classified the operations of major steps of ICON reconstruction into three types. Accordingly, we designed parallel strategies and implemented them on graphics processing units (GPU) to generate a parallel program ICON-GPU. With high accuracy, ICON-GPU has a great acceleration compared to its CPU version, up to 83.7×, greatly relieving ICON’s dependence on computing resource.


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About this article

Accelerating electron tomography reconstruction algorithm ICON with GPU

Show Author's information Yu Chen1,2,, Zihao Wang1,2,, Jingrong Zhang1,2, Lun Li1,3, Xiaohua Wan1, Fei Sun2,4,5( ), Fa Zhang1( )
Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
University of Chinese Academy of Sciences, Beijing 100049, China
School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
National Key Laboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
Center for Biological Imaging, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China

Yu Chen and Zihao Wang have contributed equally to this work.

Abstract

Electron tomography (ET) plays an important role in studying in situ cell ultrastructure in three-dimensional space. Due to limited tilt angles, ET reconstruction always 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 major problem for the application of ICON. In this work, we analyzed the framework of ICON and classified the operations of major steps of ICON reconstruction into three types. Accordingly, we designed parallel strategies and implemented them on graphics processing units (GPU) to generate a parallel program ICON-GPU. With high accuracy, ICON-GPU has a great acceleration compared to its CPU version, up to 83.7×, greatly relieving ICON’s dependence on computing resource.

Keywords: GPU, Electron tomography, Acceleration, ICON, Missing wedge restoration

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Publication history

Received: 09 February 2017
Accepted: 07 April 2017
Published: 04 July 2017
Issue date: June 2017

Copyright

© The Author(s) 2017

Acknowledgements

Acknowledgements

We would like to thank Prof. Wanzhong He (NIBS, Beijing) for providing the resin-embedded ET dataset. All the intensive computations were performed at TIANHE-2 supercomputer in National Supercomputer Center in Guangzhou and at the high performance computers in Center for Biological Imaging, Institute of Biophysics, Chinese Academy of Sciences (http://cbi.ibp.ac.cn). This work was supported by the National Natural Science Foundation of China (U1611263, U1611261, 61232001, 61472397, 61502455, 61672493), 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 (XDB08030202), and the “973” Program of Ministry of Science and Technology of China (2014CB910700).

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