Coupling Exascale Multiphysics Applications: Methods and Lessons Learned

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 individ...

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Published in2018 IEEE 14th International Conference on e-Science (e-Science) pp. 442 - 452
Main Authors Choi, Jong Youl, Churchill, Michael, Davis, Philip, Di, Sheng, Eisenhauer, Greg, Ethier, Stephane, Foster, Ian, Geveci, Berk, Guo, Hanqi, Huck, Kevin, Jenko, Frank, Chang, Choong-Seock, Kim, Mark, Kress, James, Ku, Seung-Hoe, Liu, Qing, Logan, Jeremy, Malony, Allen, Mehta, Kshitij, Moreland, Kenneth, Munson, Todd, Parashar, Manish, Dominski, Julien, Peterka, Tom, Podhorszki, Norbert, Pugmire, Dave, Tugluk, Ozan, Wang, Ruonan, Whitney, Ben, Wolf, Matthew, Wood, Chad, Klasky, Scott, Merlo, Gabriele, Suchyta, Eric, Ainsworth, Mark, Allen, Bryce, Cappello, Franck
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2018
Subjects
Complexity theory
Computational modeling
coupling
Couplings
in situ analysis
Mathematical model
Monitoring
Physics
staging
Tokamak devices
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Summary: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.
DOI:10.1109/eScience.2018.00133