Coupling exascale multiphysics applications: Methods and lessons learned

Jong Youl Choi; Choong Seock Chang; Julien Dominski; Scott Klasky; Gabriele Merlo; Eric Suchyta; Mark Ainsworth; Bryce Allen; Franck Cappello; Michael Churchill; Philip Davis; Sheng Di; Greg Eisenhauer; Stephane Ethier; Ian Foster; Berk Geveci; Hanqi Guo; Kevin Huck; Frank Jenko; Mark Kim; James Kress; Seung Hoe Ku; Qing Liu; Jeremy Logan; Allen Malony; Kshitij Mehta; Kenneth Moreland; Todd Munson; Manish Parashar; Tom Peterka; Norbert Podhorszki; Dave Pugmire; Ozan Tugluk; Ruonan Wang; Ben Whitney; Matthew Wolf; Chad Wood

doi:10.1109/eScience.2018.00133

Coupling exascale multiphysics applications: Methods and lessons learned

Jong Youl Choi, Choong Seock Chang, Julien Dominski, Scott Klasky, Gabriele Merlo, Eric Suchyta, Mark Ainsworth, Bryce Allen, Franck Cappello, Michael Churchill, Philip Davis, Sheng Di, Greg Eisenhauer, Stephane Ethier, Ian Foster, Berk Geveci, Hanqi Guo, Kevin Huck, Frank Jenko, Mark KimJames Kress, Seung Hoe Ku, Qing Liu, Jeremy Logan, Allen Malony, Kshitij Mehta, Kenneth Moreland, Todd Munson, Manish Parashar, Tom Peterka, Norbert Podhorszki, Dave Pugmire, Ozan Tugluk, Ruonan Wang, Ben Whitney, Matthew Wolf, Chad Wood

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

32 Scopus citations

Abstract

With the growing computational complexity of science and the complexity of new and emerging hardware, it is time to re-evaluate the traditional monolithic design of computational codes. One new paradigm is constructing larger scientific computational experiments from the coupling of multiple individual scientific applications, each targeting their own physics, characteristic lengths, and/or scales. We present a framework constructed by leveraging capabilities such as in-memory communications, workflow scheduling on HPC resources, and continuous performance monitoring. This code coupling capability is demonstrated by a fusion science scenario, where differences between the plasma at the edges and at the core of a device have different physical descriptions. This infrastructure not only enables the coupling of the physics components, but it also connects in situ or online analysis, compression, and visualization that accelerate the time between a run and the analysis of the science content. Results from runs on Titan and Cori are presented as a demonstration.

Original language	English
Title of host publication	Proceedings - IEEE 14th International Conference on eScience, e-Science 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	442-452
Number of pages	11
ISBN (Electronic)	9781538691564
DOIs	https://doi.org/10.1109/eScience.2018.00133
State	Published - Dec 24 2018
Event	14th IEEE International Conference on eScience, e-Science 2018 - Amsterdam, Netherlands Duration: Oct 29 2018 → Nov 1 2018

Publication series

Name	Proceedings - IEEE 14th International Conference on eScience, e-Science 2018

Conference

Conference	14th IEEE International Conference on eScience, e-Science 2018
Country/Territory	Netherlands
City	Amsterdam
Period	10/29/18 → 11/1/18

Funding

This research was supported by the Exascale Computing Project (17-SC-20-SC), a collaborative effort of the U.S. Department of Energy Office of Science and the National Nuclear Security Administration and by the U.S. Department of Energy, Office of Science, Advanced Scientific Computing Research and Office of Fusion Energy Sciences under Contracts DE-AC02-06CH11357,DE-AC02-09CH11466,and DE-AC05-00OR22725. This work used resources of the Argonne Leadership Computing Facility at Argonne National Laboratory, the National Energy Research Scientific Computing Center, and the Oak Ridge Leadership Computing Facility, which are DOE Office of Science User Facilities supported under Contracts DE-AC02-06CH11357, DE-AC02-05CH11231, and DE-AC05-00OR22725, respectively.

Keywords

Coupling
In situ analysis
Staging

Access to Document

10.1109/eScience.2018.00133

Cite this

Choi, J. Y., Chang, C. S., Dominski, J., Klasky, S., Merlo, G., Suchyta, E., Ainsworth, M., Allen, B., Cappello, F., Churchill, M., Davis, P., Di, S., Eisenhauer, G., Ethier, S., Foster, I., Geveci, B., Guo, H., Huck, K., Jenko, F., ... Wood, C. (2018). Coupling exascale multiphysics applications: Methods and lessons learned. In Proceedings - IEEE 14th International Conference on eScience, e-Science 2018 (pp. 442-452). Article 8588752 (Proceedings - IEEE 14th International Conference on eScience, e-Science 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/eScience.2018.00133

Choi, Jong Youl ; Chang, Choong Seock ; Dominski, Julien et al. / Coupling exascale multiphysics applications : Methods and lessons learned. Proceedings - IEEE 14th International Conference on eScience, e-Science 2018. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 442-452 (Proceedings - IEEE 14th International Conference on eScience, e-Science 2018).

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abstract = "With the growing computational complexity of science and the complexity of new and emerging hardware, it is time to re-evaluate the traditional monolithic design of computational codes. One new paradigm is constructing larger scientific computational experiments from the coupling of multiple individual scientific applications, each targeting their own physics, characteristic lengths, and/or scales. We present a framework constructed by leveraging capabilities such as in-memory communications, workflow scheduling on HPC resources, and continuous performance monitoring. This code coupling capability is demonstrated by a fusion science scenario, where differences between the plasma at the edges and at the core of a device have different physical descriptions. This infrastructure not only enables the coupling of the physics components, but it also connects in situ or online analysis, compression, and visualization that accelerate the time between a run and the analysis of the science content. Results from runs on Titan and Cori are presented as a demonstration.",

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Choi, JY, Chang, CS, Dominski, J, Klasky, S, Merlo, G, Suchyta, E, Ainsworth, M, Allen, B, Cappello, F, Churchill, M, Davis, P, Di, S, Eisenhauer, G, Ethier, S, Foster, I, Geveci, B, Guo, H, Huck, K, Jenko, F, Kim, M, Kress, J, Ku, SH, Liu, Q, Logan, J, Malony, A, Mehta, K , Moreland, K, Munson, T, Parashar, M, Peterka, T, Podhorszki, N , Pugmire, D, Tugluk, O, Wang, R, Whitney, B, Wolf, M & Wood, C 2018, Coupling exascale multiphysics applications: Methods and lessons learned. in Proceedings - IEEE 14th International Conference on eScience, e-Science 2018., 8588752, Proceedings - IEEE 14th International Conference on eScience, e-Science 2018, Institute of Electrical and Electronics Engineers Inc., pp. 442-452, 14th IEEE International Conference on eScience, e-Science 2018, Amsterdam, Netherlands, 10/29/18. https://doi.org/10.1109/eScience.2018.00133

Coupling exascale multiphysics applications: Methods and lessons learned. / Choi, Jong Youl; Chang, Choong Seock; Dominski, Julien et al.
Proceedings - IEEE 14th International Conference on eScience, e-Science 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 442-452 8588752 (Proceedings - IEEE 14th International Conference on eScience, e-Science 2018).

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

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AB - With the growing computational complexity of science and the complexity of new and emerging hardware, it is time to re-evaluate the traditional monolithic design of computational codes. One new paradigm is constructing larger scientific computational experiments from the coupling of multiple individual scientific applications, each targeting their own physics, characteristic lengths, and/or scales. We present a framework constructed by leveraging capabilities such as in-memory communications, workflow scheduling on HPC resources, and continuous performance monitoring. This code coupling capability is demonstrated by a fusion science scenario, where differences between the plasma at the edges and at the core of a device have different physical descriptions. This infrastructure not only enables the coupling of the physics components, but it also connects in situ or online analysis, compression, and visualization that accelerate the time between a run and the analysis of the science content. Results from runs on Titan and Cori are presented as a demonstration.

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Choi JY, Chang CS, Dominski J, Klasky S, Merlo G, Suchyta E et al. Coupling exascale multiphysics applications: Methods and lessons learned. In Proceedings - IEEE 14th International Conference on eScience, e-Science 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 442-452. 8588752. (Proceedings - IEEE 14th International Conference on eScience, e-Science 2018). doi: 10.1109/eScience.2018.00133

Coupling exascale multiphysics applications: Methods and lessons learned

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Keywords

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