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Lee, R. P. , Pierce, J. R., Miller, K. G., Almanza, M., Tableman, A., Decyk, V. K....Mori, W. B. (2025). Acceleration of the particle-in-cell code Osiris with graphics processing units        . Journal of Plasma Physics. 91 (1)

R. P. Lee et al.,  "Acceleration of the particle-in-cell code Osiris with graphics processing units        ", in Journal of Plasma Physics, vol. 91, no. 1, 2025

@article{lee2025_1765091021631,
	author = "Lee, R. P.  and Pierce, J. R. and Miller, K. G. and Almanza, M. and Tableman, A. and Decyk, V. K. and Fonseca, R. A. and Alves E. P. and Mori, W. B.",
	title = "Acceleration of the particle-in-cell code Osiris with graphics processing units        ",
	journal = "Journal of Plasma Physics",
	year = "2025",
	volume = "91",
	number = "1",
	doi = "10.1017/S0022377824001569",
	url = "https://www.cambridge.org/core/journals/journal-of-plasma-physics"
}

TY  - JOUR
TI  - Acceleration of the particle-in-cell code Osiris with graphics processing units        
T2  - Journal of Plasma Physics
VL  - 91
IS  - 1
AU  - Lee, R. P. 
AU  - Pierce, J. R.
AU  - Miller, K. G.
AU  - Almanza, M.
AU  - Tableman, A.
AU  - Decyk, V. K.
AU  - Fonseca, R. A.
AU  - Alves E. P.
AU  - Mori, W. B.
PY  - 2025
SN  - 0022-3778
DO  - 10.1017/S0022377824001569
UR  - https://www.cambridge.org/core/journals/journal-of-plasma-physics
AB  - Fully relativistic particle-in-cell (PIC) simulations are crucial for advancing our knowledge of plasma physics. Modern supercomputers based on graphics processing units (GPUs) offer the potential to perform PIC simulations of unprecedented scale, but require robust and feature-rich codes that can fully leverage their computational resources. In this work, this demand is addressed by adding GPU acceleration to the PIC code Osiris. An overview of the algorithm, which features a CUDA extension to the underlying Fortran architecture, is given. Detailed performance benchmarks for thermal plasmas are presented, which demonstrate excellent weak scaling on NERSC's Perlmutter supercomputer and high levels of absolute performance. The robustness of the code to model a variety of physical systems is demonstrated via simulations of Weibel filamentation and laser-wakefield acceleration run with dynamic load balancing. Finally, measurements and analysis of energy consumption are provided that indicate that the GPU algorithm is up to ∼14 times faster and ∼7 times more energy efficient than the optimized CPU algorithm on a node-to-node basis. The described development addresses the PIC simulation community's computational demands both by contributing a robust and performant GPU-accelerated PIC code and by providing insight into efficient use of GPU hardware.
ER  -