Coupling Exascale Multiphysics Applications: Methods and Lessons Learned

Date

2018-01-01

Author

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

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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, work-flow 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.

URI

https://hdl.handle.net/11511/116544

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Graduate School of Applied Mathematics, Conference / Seminar