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

doi:10.1089/cmb.2017.0151

	ICON-MIC: Implementing a CPU/MIC Collaboration Parallel Framework for ICON on Tianhe-2 Supercomputer
	Wang, Zihao 1,2; Chen, Yu 1,2; Zhang, Jingrong 1,2; Li, Lun 1,3; Wan, Xiaohua 1; Liu, Zhiyong 1; Sun, Fei 2,4,5; Zhang, Fa 1
	2017-11-29
发表期刊	JOURNAL OF COMPUTATIONAL BIOLOGY
ISSN	1066-5277
页码	12
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 13.3x, compared with the CPU version. Further, ICON-MIC has good scalability efficiency and overall performance on Tianhe-2 supercomputer.
关键词	electron tomography hybrid task allocation strategy ICON MIC acceleration parallel NUFFT Tianhe-2 supercomputer
DOI	10.1089/cmb.2017.0151
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2017YFA0504702] ; NSFC[U1611263] ; NSFC[U1611261] ; NSFC[61232001] ; NSFC[61472397] ; NSFC[61502455] ; NSFC[61672493] ; Special Program for Applied Research on Super Computation of the NSFC-Guangdong Joint Fund (the second phase)
WOS研究方向	Biochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Computer Science ; Mathematical & Computational Biology ; Mathematics
WOS类目	Biochemical Research Methods ; Biotechnology & Applied Microbiology ; Computer Science, Interdisciplinary Applications ; Mathematical & Computational Biology ; Statistics & Probability
WOS记录号	WOS:000417292900001
出版者	MARY ANN LIEBERT, INC
引用统计	被引频次：6[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/5559
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Liu, Zhiyong; Zhang, Fa
作者单位	1.Chinese Acad Sci, Inst Comp Technol, High Performance Comp Res Ctr, Beijing 100101, Peoples R China 2.Univ Chinese Acad Sci, Beijing, Peoples R China 3.Univ Chinese Acad Sci, Sch Math Sci, Beijing, Peoples R China 4.Chinese Acad Sci, Inst Biophys, CAS Ctr Excellence Biomacromol, Natl Key Lab Biomacromol, Beijing, Peoples R China 5.Chinese Acad Sci, Inst Biophys, Ctr Biol Imaging, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Wang, Zihao,Chen, Yu,Zhang, Jingrong,et al. ICON-MIC: Implementing a CPU/MIC Collaboration Parallel Framework for ICON on Tianhe-2 Supercomputer[J]. JOURNAL OF COMPUTATIONAL BIOLOGY,2017:12.
APA	Wang, Zihao.,Chen, Yu.,Zhang, Jingrong.,Li, Lun.,Wan, Xiaohua.,...&Zhang, Fa.(2017).ICON-MIC: Implementing a CPU/MIC Collaboration Parallel Framework for ICON on Tianhe-2 Supercomputer.JOURNAL OF COMPUTATIONAL BIOLOGY,12.
MLA	Wang, Zihao,et al."ICON-MIC: Implementing a CPU/MIC Collaboration Parallel Framework for ICON on Tianhe-2 Supercomputer".JOURNAL OF COMPUTATIONAL BIOLOGY (2017):12.