@inproceedings{94d49cc6f4d043abbaf5755cee54811d,
title = "Coupling exascale multiphysics applications: Methods and lessons learned",
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.",
keywords = "Coupling, In situ analysis, Staging",
author = "Choi, {Jong Youl} and Chang, {Choong Seock} and Julien Dominski and Scott Klasky and Gabriele Merlo and Eric Suchyta and Mark Ainsworth and Bryce Allen and Franck Cappello and Michael Churchill and Philip Davis and Sheng Di and Greg Eisenhauer and Stephane Ethier and Ian Foster and Berk Geveci and Hanqi Guo and Kevin Huck and Frank Jenko and Mark Kim and James Kress and Ku, {Seung Hoe} and Qing Liu and Jeremy Logan and Allen Malony and Kshitij Mehta and Kenneth Moreland and Todd Munson and Manish Parashar and Tom Peterka and Norbert Podhorszki and Dave Pugmire and Ozan Tugluk and Ruonan Wang and Ben Whitney and Matthew Wolf and Chad Wood",
note = "Funding Information: 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. Funding Information: 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. Publisher Copyright: {\textcopyright} 2018 IEEE.; 14th IEEE International Conference on eScience, e-Science 2018 ; Conference date: 29-10-2018 Through 01-11-2018",
year = "2018",
month = dec,
day = "24",
doi = "10.1109/eScience.2018.00133",
language = "English (US)",
series = "Proceedings - IEEE 14th International Conference on eScience, e-Science 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "442--452",
booktitle = "Proceedings - IEEE 14th International Conference on eScience, e-Science 2018",
address = "United States",
}