167-PFlops deep learning for electron microscopy: From learning physics to atomic manipulation

Robert M. Patton; J. Travis Johnston; Steven R. Young; Catherine D. Schuman; Don D. March; Thomas E. Potok; Derek C. Rose; Seung Hwan Lim; Thomas P. Karnowski; Maxim A. Ziatdinov; Sergei V. Kalinin

doi:10.1109/SC.2018.00053

167-PFlops deep learning for electron microscopy: From learning physics to atomic manipulation

Robert M. Patton, J. Travis Johnston, Steven R. Young, Catherine D. Schuman, Don D. March, Thomas E. Potok, Derek C. Rose, Seung Hwan Lim, Thomas P. Karnowski, Maxim A. Ziatdinov, Sergei V. Kalinin

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

30 Scopus citations

Abstract

An artificial intelligence system called MENNDL, which used 25,200 NVIDIA Volta GPUs on Oak Ridge National Laboratory's Summit machine, automatically designed an optimal deep learning network in order to extract structural information from raw atomic-resolution microscopy data. In a few hours, MENNDL creates and evaluates millions of networks using a scalable, parallel, asynchronous genetic algorithm augmented with a support vector machine to automatically find a superior deep learning network topology and hyper-parameter set than a human expert can find in months. For the application of electron microscopy, the system furthers the goal of improving our understanding of the electron-beam-matter interactions and real-time image-based feedback, which enables a huge step beyond human capacity towards nanofabricating materials automatically. MENNDL has been scaled to the 4,200 available nodes of Summit achieving a measured 152.5 PFlops, with an estimated sustained performance of 167 PFlops when the entire machine is available.

Original language	English
Title of host publication	Proceedings - International Conference for High Performance Computing, Networking, Storage, and Analysis, SC 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	638-648
Number of pages	11
ISBN (Electronic)	9781538683842
DOIs	https://doi.org/10.1109/SC.2018.00053
State	Published - Jul 2 2018
Event	2018 International Conference for High Performance Computing, Networking, Storage, and Analysis, SC 2018 - Dallas, United States Duration: Nov 11 2018 → Nov 16 2018

Publication series

Name	Proceedings - International Conference for High Performance Computing, Networking, Storage, and Analysis, SC 2018

Conference

Conference	2018 International Conference for High Performance Computing, Networking, Storage, and Analysis, SC 2018
Country/Territory	United States
City	Dallas
Period	11/11/18 → 11/16/18

Funding

Notice: This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a nonexclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan). This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, Robinson Pino, program manager, under contract number DE-AC05-00OR22725.

Keywords

Evolutionary computation
High performance computing
Machine learning

Access to Document

10.1109/SC.2018.00053

Cite this

Patton, R. M., Travis Johnston, J., Young, S. R., Schuman, C. D., March, D. D., Potok, T. E., Rose, D. C., Lim, S. H., Karnowski, T. P., Ziatdinov, M. A., & Kalinin, S. V. (2018). 167-PFlops deep learning for electron microscopy: From learning physics to atomic manipulation. In Proceedings - International Conference for High Performance Computing, Networking, Storage, and Analysis, SC 2018 (pp. 638-648). Article 8665768 (Proceedings - International Conference for High Performance Computing, Networking, Storage, and Analysis, SC 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SC.2018.00053

Patton, Robert M. ; Travis Johnston, J. ; Young, Steven R. et al. / 167-PFlops deep learning for electron microscopy : From learning physics to atomic manipulation. Proceedings - International Conference for High Performance Computing, Networking, Storage, and Analysis, SC 2018. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 638-648 (Proceedings - International Conference for High Performance Computing, Networking, Storage, and Analysis, SC 2018).

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title = "167-PFlops deep learning for electron microscopy: From learning physics to atomic manipulation",

abstract = "An artificial intelligence system called MENNDL, which used 25,200 NVIDIA Volta GPUs on Oak Ridge National Laboratory's Summit machine, automatically designed an optimal deep learning network in order to extract structural information from raw atomic-resolution microscopy data. In a few hours, MENNDL creates and evaluates millions of networks using a scalable, parallel, asynchronous genetic algorithm augmented with a support vector machine to automatically find a superior deep learning network topology and hyper-parameter set than a human expert can find in months. For the application of electron microscopy, the system furthers the goal of improving our understanding of the electron-beam-matter interactions and real-time image-based feedback, which enables a huge step beyond human capacity towards nanofabricating materials automatically. MENNDL has been scaled to the 4,200 available nodes of Summit achieving a measured 152.5 PFlops, with an estimated sustained performance of 167 PFlops when the entire machine is available.",

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author = "Patton, {Robert M.} and {Travis Johnston}, J. and Young, {Steven R.} and Schuman, {Catherine D.} and March, {Don D.} and Potok, {Thomas E.} and Rose, {Derek C.} and Lim, {Seung Hwan} and Karnowski, {Thomas P.} and Ziatdinov, {Maxim A.} and Kalinin, {Sergei V.}",

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year = "2018",

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Patton, RM, Travis Johnston, J, Young, SR, Schuman, CD, March, DD, Potok, TE , Rose, DC , Lim, SH , Karnowski, TP, Ziatdinov, MA & Kalinin, SV 2018, 167-PFlops deep learning for electron microscopy: From learning physics to atomic manipulation. in Proceedings - International Conference for High Performance Computing, Networking, Storage, and Analysis, SC 2018., 8665768, Proceedings - International Conference for High Performance Computing, Networking, Storage, and Analysis, SC 2018, Institute of Electrical and Electronics Engineers Inc., pp. 638-648, 2018 International Conference for High Performance Computing, Networking, Storage, and Analysis, SC 2018, Dallas, United States, 11/11/18. https://doi.org/10.1109/SC.2018.00053

167-PFlops deep learning for electron microscopy: From learning physics to atomic manipulation. / Patton, Robert M.; Travis Johnston, J.; Young, Steven R. et al.
Proceedings - International Conference for High Performance Computing, Networking, Storage, and Analysis, SC 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 638-648 8665768 (Proceedings - International Conference for High Performance Computing, Networking, Storage, and Analysis, SC 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Patton RM, Travis Johnston J, Young SR, Schuman CD, March DD, Potok TE et al. 167-PFlops deep learning for electron microscopy: From learning physics to atomic manipulation. In Proceedings - International Conference for High Performance Computing, Networking, Storage, and Analysis, SC 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 638-648. 8665768. (Proceedings - International Conference for High Performance Computing, Networking, Storage, and Analysis, SC 2018). doi: 10.1109/SC.2018.00053

167-PFlops deep learning for electron microscopy: From learning physics to atomic manipulation

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