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GPU-based 3D cryo-EM reconstruction with key-value streams: poster

Published: 16 February 2019 Publication History

Abstract

The 3D reconstruction of cryo-electron microscopy (cryo-EM) structural determination process is highly compute-intensive. It inherently requires accesses of a large 3D model in different and variable orientations, brings tough challenges to GPU architecture and has no effective solutions currently. To fill this gap, we propose a novel GPU-based parallel design for cryo-EM 3D reconstruction. The major idea is to reorganize the related problem space as streams of key-value pairs, so that we can achieve both the flexibility and efficiency to compute and accumulate the contribution to the final 3D model from all different 2D image inputs. In addition, we design a hybrid communication mechanism to reduce intra-node communications and enable the solving process on a larger scale.

References

[1]
2018. THUNDER. https://github.com/thuem/THUNDER
[2]
N Grigorieff. 2007. FREALIGN: high-resolution refinement of single particle structures. Journal of Structural Biology 157 (2007), 117--125.
[3]
L. Li, X. Li, G. Tan, M. Chen, andP. Zhang. 2011. Experience of parallelizing cryo-EM 3D reconstruction on a CPU-GPU heterogeneous system. Proceedings of the IEEE International Symposium on High Performance Distributed Computing (2011), 195--204.
[4]
Eva Nogales. 2016. The development of cryo-EM into a mainstream structural biology technique. Nature methods 13, 1 (2016), 24--27.
[5]
G. Tan, Z. Guo, M. Chen, and D. Meng. 2009. Single-particle 3D reconstruction from cryo-electron microscopy images on GPU. Proceedings of the International Conference on Super computing (2009), 380--389.
[6]
Y. Zheng and P. C. Doerschuk. 2008. A parallel software toolkit for statistical 3-D virus reconstructions from cryo electron microscopy images using computer clusters with multi-core shared-memory nodes. In 2008 IEEE International Symposium on Parallel and Distributed Processing. 1--11.

Cited By

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  • (2024)RELION Software Performance Optimization for the CPU+DCU Heterogeneous Platform2024 3rd International Conference on Big Data, Information and Computer Network (BDICN)10.1109/BDICN62775.2024.00048(204-210)Online publication date: 12-Jan-2024
  • (2021)Accelerating the cryo-EM structure determination in RELION on GPU clusterFrontiers of Computer Science10.1007/s11704-020-0169-816:3Online publication date: 20-Oct-2021

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Published In

cover image ACM Conferences
PPoPP '19: Proceedings of the 24th Symposium on Principles and Practice of Parallel Programming
February 2019
472 pages
ISBN:9781450362252
DOI:10.1145/3293883
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 February 2019

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Author Tags

  1. 3D reconstruction
  2. GPU computing

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PPoPP '19

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PPoPP '19 Paper Acceptance Rate 29 of 152 submissions, 19%;
Overall Acceptance Rate 230 of 1,014 submissions, 23%

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Cited By

View all
  • (2024)RELION Software Performance Optimization for the CPU+DCU Heterogeneous Platform2024 3rd International Conference on Big Data, Information and Computer Network (BDICN)10.1109/BDICN62775.2024.00048(204-210)Online publication date: 12-Jan-2024
  • (2021)Accelerating the cryo-EM structure determination in RELION on GPU clusterFrontiers of Computer Science10.1007/s11704-020-0169-816:3Online publication date: 20-Oct-2021

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