August 2020

Conference Paper

A Co-Design Study Of Fusion Whole Device Modeling Using Code Coupling

By:
Choi, Jong Youl ; Logan, Jeremy S; Mehta, Kshitij V; Suchyta, Eric D; Godoy, William F; Thompson, Nicholas A; Wan, Lipeng ; Chen, Jieyang ; Podhorszki, Norbert ; Wolf, Matthew D; Klasky, Scott A; Dominski, Julien; Chang, Choong-Seock
Journal Name:
2019 IEEE/ACM 5th International Workshop on Data Analysis and Reduction for Big Scientific Data (DRBSD-5)
Page Number:
35-41
Book Title:
2019 IEEE/ACM 5th International Workshop on Data Analysis and Reduction for Big Scientific Data (DRBSD-5)
Publication Date:
August 2020
Conference Name:
2019 IEEE/ACM 5th International Workshop on Data Analysis and Reduction for Big Scientific Data (DRBSD-5)
Conference Location:
Denver, Colorado, United States of America
Conference Sponsor:
IEEE/ACM, Supercomputing 2019, SC19
View DOI Listing:
https://doi.org/10.1109/DRBSD-549595.2019.00011

Abstract

Complex workflows consisting of multiple simulation and analysis codes running concurrently through in-memory coupling is becoming popular due to inherent advantages in online management of large-scale data, resilience, and the code development process. However, orchestrating such a multi-application workflow to efficiently utilize resources on a heterogeneous architecture is challenging. In this paper, we present our results with running the Fusion Whole Device Modeling benchmark workflow on Summit, a pre-exascale supercomputer at Oak Ridge National Laboratory. We explore various resource distribution and process placement mechanisms, including sharing compute nodes between processes from separate applications. We show that fine-grained process placement can have a significant impact towards efficient utilization of the compute power of a node on Summit, and conclude that sophisticated tools for performing co-design studies of multi-application workflows can play an important role towards efficient orchestration of such workflows.

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