ICON-MIC: Implementing a CPU/MIC Collaboration Parallel Framework for ICON on Tianhe-2 Supercomputer

Journal of computational biology: A journal of computational molecular cell biology

ZhiyongLiu^1¹  ZihaoWang^1,2,3²  XiaohuaWan^1³  LunLi^1,4⁴  JingrongZhang^1,2⁵  YuChen^1,2,3⁶ 

[1] Center for Biological Imaging, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China^6;High Performance Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China^1;National Key Laboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China^5;School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, China^4;These authors contributed equally to this work^3;University of Chinese Academy of Sciences, Beijing, China^2

关键词: electron tomography;    hybrid task allocation strategy;    ICON;    MIC acceleration;    parallel NUFFT;    Tianhe-2 supercomputer.;   

DOI  :  10.1089/cmb.2017.0151

学科分类：生物科学（综合）

来源: Mary Ann Liebert, Inc. Publishers

【 摘 要 】

Electron tomography (ET) is an important technique for studying the three-dimensional structures of the biological ultrastructure. Recently, ET has reached sub-nanometer resolution for investigating the native and conformational dynamics of macromolecular complexes by combining with the sub-tomogram averaging approach. Due to the limited sampling angles, ET reconstruction typically suffers from the “missing wedge” problem. Using a validation procedure, iterative compressed-sensing optimized nonuniform fast Fourier transform (NUFFT) reconstruction (ICON) demonstrates its power in restoring validated missing information for a low-signal-to-noise ratio biological ET dataset. However, the huge computational demand has become a bottleneck for the application of ICON. In this work, we implemented a parallel acceleration technology ICON-many integrated core (MIC) on Xeon Phi cards to address the huge computational demand of ICON. During this step, we parallelize the element-wise matrix operations and use the efficient summation of a matrix to reduce the cost of matrix computation. We also developed parallel versions of NUFFT on MIC to achieve a high acceleration of ICON by using more efficient fast Fourier transform (FFT) calculation. We then proposed a hybrid task allocation strategy (two-level load balancing) to improve the overall performance of ICON-MIC by making full use of the idle resources on Tianhe-2 supercomputer. Experimental results using two different datasets show that ICON-MIC has high accuracy in biological specimens under different noise levels and a significant acceleration, up to 13.3 × , compared with the CPU version. Further, ICON-MIC has good scalability efficiency and overall performance on Tianhe-2 supercomputer.

【 授权许可】

Unknown