Hybrid Analysis of Fusion Data for Online Understanding of Complex Science on Extreme Scale Computers

Eric Suchyta, Jong Youl Choi, Seung Hoe Ku, David Pugmire, Ana Gainaru, Kevin Huck, Ralph Kube, Aaron Scheinberg, Frederic Suter, Choongseock Chang, Todd Munson, Norbert Podhorszki, Scott Klasky

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

The current practice for fusion scientists running first principle simulations on high performance computing plat-forms is to either run their simulations and output their data for post-hoc analysis, or to place in situ analytics into their code. In this paper we examine a complex workflow using XGC fusions simulation run on the Oak Ridge Leadership Computing Facility's supercomputer Summit, which also involve three anal-yses as part of the results necessary for scientific discovery. We discuss the challenges faced when implementing these algorithms and present an original hybrid staging technique to help enable the physicists to make discoveries during the execution of the simulation. By creating this infrastructure, we can examine complicated physics results, which may not have been possible without the infrastructure. For example, our work enables the online visualization of turbulent homoclinic tangle around the magnetic X-point, breaking the last confinement surface. This visualization could help fusion scientists to better understand and improve the turbulence spread of plasma exhaust heat, which is crucial toward realizing plasmas beyond the currently accessible physics regimes of present-day tokamak reactors. The physics of turbulent homoclinic tangle will be reported in a future physics publication, by utilizing the original online analysis/visualization framework presented in this paper.

Original languageEnglish
Title of host publicationProceedings - 2022 IEEE International Conference on Cluster Computing, CLUSTER 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages218-229
Number of pages12
ISBN (Electronic)9781665498562
DOIs
StatePublished - 2022
Event2022 IEEE International Conference on Cluster Computing, CLUSTER 2022 - Heidelberg, Germany
Duration: Sep 6 2022Sep 9 2022

Publication series

NameProceedings - IEEE International Conference on Cluster Computing, ICCC
Volume2022-September
ISSN (Print)1552-5244

Conference

Conference2022 IEEE International Conference on Cluster Computing, CLUSTER 2022
Country/TerritoryGermany
CityHeidelberg
Period09/6/2209/9/22

Funding

This research was supported by the Exascale Computing Project (ECP), Project Number: 17-SC-20-SC, a collaborative effort of two DOE organizations—the Office of Science and the National Nuclear Security Administration— responsible for the planning and preparation of a capable ex-ascale ecosystem—including software, applications, hardware, advanced system engineering, and early testbed platforms—to support the nation’s exascale computing imperative. This research was supported by the Exascale Computing Project (ECP), Project Number: 17-SC-20-SC, a collaborative effort of two DOE organizations-the Office of Science and the National Nuclear Security Administration-responsible for the planning and preparation of a capable exascale ecosystem-including software, applications, hardware, advanced system engineering, and early testbed platforms-to support the nation’s exascale computing imperative This research used resources of the Oak Ridge Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC05-00OR22725.

FundersFunder number
National Nuclear Security Administration-responsible
U.S. Department of Energy
Office of ScienceDE-AC05-00OR22725
National Nuclear Security Administration

    Keywords

    • Extreme Scale
    • Fusion Science
    • Online Analysis and Visualization
    • Workflows

    Fingerprint

    Dive into the research topics of 'Hybrid Analysis of Fusion Data for Online Understanding of Complex Science on Extreme Scale Computers'. Together they form a unique fingerprint.

    Cite this