Memory-Efficient and Stabilizing Management System and Parallel Methods for RELION Using CUDA and MPI

  • Jingrong Zhang
  • Zihao Wang
  • Yu Chen
  • Zhiyong Liu
  • Fa ZhangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10847)


In cryo-electron microscopy, RELION has been proven to be a powerful tool for high-resolution reconstruction and has quickly gained its popularity. However, as the data processed in cryoEM is large and the algorithm of RELION is computation-intensive, the refinement procedure of RELION appears quite time-consuming and memory-demanding. These two problems have become major bottlenecks for its usage. Even though there have been efforts on paralleling RELION, the global memory size still may not meet its requirement. Also as by now there is no automatic memory management system on GPU (Graphics Processing Unit), the fragmentation will increase with iteration. Eventually, it would crash the program. In our work, we designed a memory-efficient and stabilizing management system to guarantee the robustness of our program and the efficiency of GPU memory usage. To reduce the memory usage, we developed a novel RELION 2.0 data structure. Also, we proposed a weight calculation parallel algorithm to speedup the calculation. Experiments show that the memory system can avoid memory fragmentation and we can achieve better speedup ratio compared with RELION 2.0.


cryoEM RELION CUDA Performance tuning 



This research is supported by the National Key Research and Development Program of China (2017YFA0504702), the NSFC projects Grant No. U1611263, U1611261, 61472397, 61502455, 61672493 and Special Program for Applied Research on Super Computation of the NSFC-Guangdong Joint Fund (the second phase).


  1. 1.
    Li, X., Grigorieff, N., Cheng, Y.: GPU-enabled FREALIGN: accelerating single particle 3D reconstruction and refinement in fourier space on graphics processors. J. Struct. Biol. 172(3), 407–412 (2010)CrossRefGoogle Scholar
  2. 2.
    Bai, X., McMullan, G., Scheres, S.H.: How cryo-EM is revolutionizing structural biology. Trends Biochem. Sci. 40(1), 49–57 (2015)CrossRefGoogle Scholar
  3. 3.
    Scheres, S.H.: A Bayesian view on cryo-EM structure determination. J. Mol. Biol. 415(2), 406–418 (2012)CrossRefGoogle Scholar
  4. 4.
    Scheres, S.H.: RELION: implementation of a Bayesian approach to cryo-EM structure determination. J. Struct. Biol. 180(3), 519–530 (2012)CrossRefGoogle Scholar
  5. 5.
    Wong, W., Bai, X., Brown, A., Fernandez, I.S., Hanssen, E., Condron, M., Tan, Y.H., Baum, J., Scheres, S.H.: Cryo-EM structure of the plasmodium falciparum 80S ribosome bound to the anti-protozoan drug emetine. Elife 3, e03080 (2014)CrossRefGoogle Scholar
  6. 6.
    Amunts, A., Brown, A., Bai, X., Llácer, J.L., Hussain, T., Emsley, P., Long, F., Murshudov, G., Scheres, S.H., Ramakrishnan, V.: Structure of the yeast mitochondrial large ribosomal subunit. Science 343(6178), 1485–1489 (2014)CrossRefGoogle Scholar
  7. 7.
    Liao, M., Cao, E., Julius, D., Cheng, Y.: Structure of the TRPV1 ion channel determined by electron cryo-microscopy. Nature 504(7478), 107–112 (2013)CrossRefGoogle Scholar
  8. 8.
    Tagare, H.D., Barthel, A., Sigworth, F.J.: An adaptive expectation-maximization algorithm with GPU implementation for electron cryomicroscopy. J. Struct. Biol. 171(3), 256–265 (2010)CrossRefGoogle Scholar
  9. 9.
    Sigworth, F.J., Doerschuk, P.C., Carazo, J., Scheres, S.H.W.: Maximum-likelihood methods in cryo-EM. Part i: theoretical basis and overview of existing approaches. Methods Enzymol. 482, 263 (2010)CrossRefGoogle Scholar
  10. 10.
    Scheres, S.H.: Single-particle processing in RELION-1.3 (2014)Google Scholar
  11. 11.
    Kimanius, D., Forsberg, B.O., Scheres, S.H., Lindahl, E.: Accelerated cryo-EM structure determination with parallelisation using GPUs in RELION-2. eLife 5, e18722 (2016)CrossRefGoogle Scholar
  12. 12.
    Su, H., Wen, W., Du, X., Lu, X., Liao, M., Li, D.: Gerelion: GPU-enhanced parallel implementation of single particle cryo-EM image processing. bioRxiv 075887 (2016)Google Scholar
  13. 13.
    Corporation N.: CUDA in C best practices guide. NVIDIA Corporation (2016)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Jingrong Zhang
    • 1
    • 2
  • Zihao Wang
    • 1
    • 2
  • Yu Chen
    • 1
    • 2
  • Zhiyong Liu
    • 1
  • Fa Zhang
    • 1
    Email author
  1. 1.High Performance Computer Research Center, Institute of Computing TechnologyChinese Academy of SciencesBeijingChina
  2. 2.University of Chinese Academy of SciencesBeijingChina

Personalised recommendations