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

Zihao Wang, Yu Chen, Jingrong Zhang, Lun Li, Xiaohua Wan, Zhiyong Liu, Fei Sun, Fa Zhang
Institute of Computing Technology, Chinese Academy of Sciences, Beijing , China



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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.
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