Characterizing the Performance of the Implicit Massively Parallel Particle-in-Cell iPIC3D Code
Abstract
Optimizing iPIC3D, an implicit Particle-in-Cell (PIC) code, for large-scale 3D plasma simulations is crucial for space and astrophysical applications. This work focuses on characterizing iPIC3D's communication efficiency through strategic measures like optimal node placement, communication and computation overlap, and load balancing. Profiling and tracing tools are employed to analyze iPIC3D's communication efficiency and provide practical recommendations. Implementing optimized communication protocols addresses the Geospace Environmental Modeling (GEM) magnetic reconnection challenges in plasma physics with more precise simulations. This approach captures the complexities of 3D plasma simulations, particularly in magnetic reconnection, advancing space and astrophysical research.
Cite
@article{arxiv.2408.01983,
title = {Characterizing the Performance of the Implicit Massively Parallel Particle-in-Cell iPIC3D Code},
author = {Jeremy J. Williams and Daniel Medeiros and Ivy B. Peng and Stefano Markidis},
journal= {arXiv preprint arXiv:2408.01983},
year = {2024}
}
Comments
Accepted by SC Conference 2023 (SC23), prepared in the standardized ACM format and consists of 2 pages, which includes the main text, references, and figures. See https://sc23.supercomputing.org/proceedings/tech_poster/tech_poster_pages/rpost102.html