@inproceedings{4dbc2c44f46845bea00a4468a6b28842,
title = "Accelerating electron tomography reconstruction algorithm ICON using the intel Xeon Phi coprocessor on Tianhe-2 supercomputer",
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 {\textquoteleft}missing wedge{\textquoteright} 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.",
keywords = "Electron tomography, Hybrid task allocation strategy, ICON, MIC acceleration, Parallel NUFFT, Tianhe-2 supercomputer",
author = "Zihao Wang and Yu Chen and Jingrong Zhang and Lun Li and Xiaohua Wan and Zhiyong Liu and Fei Sun and Fa Zhang",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 13th International Symposium on Bioinformatics Research and Applications, ISBRA 2017 ; Conference date: 29-05-2017 Through 02-06-2017",
year = "2017",
doi = "10.1007/978-3-319-59575-7_23",
language = "English",
isbn = "9783319595740",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "258--269",
editor = "Zhipeng Cai and Ovidiu Daescu and Min Li",
booktitle = "Bioinformatics Research and Applications - 13th International Symposium, ISBRA 2017, Proceedings",
address = "Germany",
}