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Lee, R., Pierce J, Miller K, Maria Almanza, Tableman, A., Decyk, V. K....Mori, W. B. (2024). GPU acceleration of the particle-in-cell code OSIRIS. 66th Annual Meeting of the APS Division of Plasma Physics.

R. Lee et al.,  "GPU acceleration of the particle-in-cell code OSIRIS", in 66th Annu. Meeting of the APS Division of Plasma Physics, Atlanta, Georgia, 2024

@misc{lee2024_1775421362767,
	author = "Lee, R. and Pierce J and Miller K and Maria Almanza and Tableman, A. and Decyk, V. K. and Fonseca, R. A. and Alves, E. P. and Mori, W. B.",
	title = "GPU acceleration of the particle-in-cell code OSIRIS",
	year = "2024",
	howpublished = "Digital",
	url = "https://meetings.aps.org/Meeting/DPP24/Content/4590"
}

TY  - CPAPER
TI  - GPU acceleration of the particle-in-cell code OSIRIS
T2  - 66th Annual Meeting of the APS Division of Plasma Physics
AU  - Lee, R.
AU  - Pierce J
AU  - Miller K
AU  - Maria Almanza
AU  - Tableman, A.
AU  - Decyk, V. K.
AU  - Fonseca, R. A.
AU  - Alves, E. P.
AU  - Mori, W. B.
PY  - 2024
CY  - Atlanta, Georgia
UR  - https://meetings.aps.org/Meeting/DPP24/Content/4590
AB  - Fully relativistic particle-in-cell (PIC) simulations are crucial for advancing knowledge of plasma physics. Modern state-of-the-art GPU supercomputers offer the potential to perform PIC simulations of unprecedented scale, but require robust and feature-rich codes which can fully leverage the computational resources. We have addressed this demand by adding GPU acceleration to the PIC code OSIRIS. In this article, we provide an overview of our algorithm, which features a CUDA extension to the underlying Fortran architecture. We present detailed performance benchmarks for thermal plasmas, which demonstrate excellent weak scaling on NERSC's Perlmutter supercomputer and high levels of absolute performance. We also show simulations of Weibel filamentation and laser-wakefield acceleration run with dynamic load balancing, which illustrate the robustness of the code to model a variety of physical systems. Finally, we show measurements of energy consumption, which indicate that the GPU algorithm is ~14 times faster and is ~7 times more energy-efficient than the optimized CPU algorithm on a node-to-node basis. Our development provides an important contribution to the PIC simulation community's computational demands both by enabling the use of OSIRIS on modern supercomputers and by providing a path to GPU acceleration of other PIC codes.

ER  -