Coupling Exascale Multiphysics Applications: Methods and Lessons Learned

Autor: Choong-Seock Chang, Mark Ainsworth, Dave Pugmire, Frank Jenko, Greg Eisenhauer, Stephane Ethier, Allen D. Malony, Matthew Wolf, Franck Cappello, Kenneth Moreland, Norbert Podhorszki, Seung-Hoe Ku, Manish Parashar, Mark Kim, Scott Klasky, Sheng Di, Tom Peterka, Berk Geveci, Ozan Tugluk, Ben Whitney, Jong Youl Choi, Philip E. Davis, Julien Dominski, Ian Foster, Kshitij Mehta, Todd Munson, Hanqi Guo, E. Suchyta, Kevin Huck, Bryce Allen, Jeremy Logan, Chad Wood, Gabriele Merlo, James Kress, Qing Liu, Ruonan Wang, Michael Churchill
Rok vydání: 2018
Předmět:
Zdroj: eScience
Popis: 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.
Databáze: OpenAIRE