An implementation of a plasma physics application for distributed-memory supercomputers using a directive-based programming framework-Reference-Cited by-同舟云学术

An implementation of a plasma physics application for distributed-memory supercomputers using a directive-based programming framework

Published:2024-06-30 Issue:1 Volume:25 Page:39-47
ISSN:2539-2115
Container-title:Revista Colombiana de Computación
language:
Short-container-title:Rev. colomb. comput.

Author:

Asch Christian,Francesquini Emilio,Meneses Esteban

Abstract

To extract performance from supercomputers, programmers in the High Performance Computing (HPC) community are often required to use a combination of frameworks to take advantage of the multiple levels of parallelism. However, over the years, efforts have been made to simplify this situation by creating frameworks that can take advantage of multiple levels. This often means that the programmer has to learn a new library. On the other hand, there are frameworks that were created by extending the capabilities of established paradigms. In this paper, we explore one of this libraries, OpenMP Cluster. As its name implies, it extends the OpenMP API, which allows seasoned programmers to take advantage of their experience to use just one API to program in sharedmemory and distributed-memory parallelism. In this paper, we took an existing plasma physics code that was programmed with MPI+OpenMP and ported it over to OpenMP Cluster. We also show that under certain conditions, the performance of OpenMP Cluster is similar to that of the MPI+OpenMP code.

Publisher

Universidad Autonoma de Bucaramanga

Reference9 articles.

1. Allmann-Rahn, F., Lautenbach, S., Deisenhofer, M., & Grauer, R. (2024, March). The muphyII Code: Multiphysics Plasma Simulation on Large HPC Systems. Computer Physics Communications, 296, 109064. doi:https://doi.org/10.1016/j.cpc.2023.109064

2. Choi, J. Y., Chang, C.-S., Dominski, J., Klasky, S., Merlo, G., Suchyta, E., . . . Wood, C. (2018). Coupling Exascale Multiphysics Applications: Methods and Lessons Learned. 2018 IEEE International Conference on e-Science and Grid Computing (pp. 442-452). Amsterdam, Netherlands: IEEE. doi:10.1109/eScience.2018.00133

3. Coto-Vílchez, F., Vargas, V. I., Solano-Piedra, R., Rojas-Quesada, M. A., Araya-Solano, L. A., Ramírez, A. A., . . . Arias, S. (2020, July 8). Progress on the small modular stellarator SCR-1: new diagnostics and heating scenarios. Journal of Plasma Physics, 86(4), 815860401. doi:10.1017/S0022377820000677

4. Di Francia Rosso, P. H., & Francesquini, E. (2022). OCFTL: An MPI Implementation-Independent Fault Tolerance Library for Task-Based Applications. In I. Gitler, C. J. Barrios Hernández, & M. Esteban (Ed.), High Performance Computing. 8th Latin American Conference, CARLA 2021, Guadalajara, Mexico, October 6–8, 2021, Revised Selected Papers. 1540, pp. 131-147. Springer, Cham. doi:10.1007/978-3-031-04209-6_10

5. Jiménez, D., Campos-Duarte, L., Solano-Piedra, R., Araya-Solano, L. A., Meneses, E., & Vargas, I. (2020). BS-SOLCTRA: Towards a Parallel Magnetic Plasma Confinement Simulation Framework for Modular Stellarator Devices. In J. L. Crespo-Mariño, & E. Meneses-Rojas (Ed.), High Performance Computing. 6th Latin American Conference, CARLA 2019, Turrialba, Costa Rica, September 25–27, 2019, Revised Selected Papers. 1087, pp. 33-48. Springer, Cham. doi:10.1007/978-3-030-41005-6_3