J. Y. Choi et al., "A Co-Design Study Of Fusion Whole Device Modeling Using Code Coupling," 2019 IEEE/ACM 5th International Workshop on Data Analysis and Reduction for Big Scientific Data (DRBSD-5), Denver, CO, USA, 2019, pp. 35-41, doi: 10.1109/DRBSD-549595.2019.00011.
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.Read Publication Related Projects CODAR: Center for Online Data Analysis and Reduction Whole Device Modeling ADIOS: The Adaptable I/O System
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