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Efficient GPU implementation of the Particle-in-Cell/Monte-Carlo collisions method for 1D simulation of low-pressure capacitively coupled plasmas
Open accessAbstract
In this paper, we describe an efficient, massively parallel GPU implementation strategy for speeding up one-dimensional electrostatic plasma simulations based on the Particle-in-Cell method with Monte-Carlo collisions. Relying on the Roofline performance model, we identify performance-critical points of the program and provide optimised solutions. We use four benchmark cases to verify the correctness of the CUDA and OpenCL implementations and analyse their performance properties on a number of NVIDIA and AMD cards. Plasma parameters computed with both GPU implementations differ not more than 2% from each other and respective literature reference data. Our final implementations reach over 2.6 Tflop/s sustained performance on a single card, and show speed up factors of up to 200 (when using 10 million particles). We demonstrate that GPUs can be very efficiently used for simulating collisional plasmas and argue that their further use will enable performing more accurate simulations in shorter time, increase research productivity and help in advancing the science of plasma simulation.
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“The pseudo-2D PIC scheme was shown to capture the multidimensional plasma phenomena in all 2D configurations relevant to a HT, i.e., axial-azimuthal, azimuthal-radial, and axialradial 332,333,335-337 with relatively few regions used, showing a computational speed-up with respect to the 2D simulation up to 50.”
From Plasma propulsion modeling with particle-based algorithms (2023) · cites this paper as [337]
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Cited by
Sorted by citations
eduPIC: An introductory particle based code for radio-frequency plasma simulation
Donkó, Z., Derzsi, A., Vass, M., Horváth, B., Wilczek, S., Hartmann, B., & Hartmann, P. (2021) · Plasma Sources Science and Technology · doi:10.1088/1361-6595/ac0b55 · Cited by 91
Self-citationPlasma propulsion modeling with particle-based algorithms
Taccogna, F., Cichocki, F., Eremin, D., Fubiani, G., & Garrigues, L. (2023) · Journal of Applied Physics · doi:10.1063/5.0153862 · Cited by 29
Metadata
- Authors
- Juhasz, Z., Ďurian, J., Derzsi, A., Matejčík, Š., Donkó, Z., & Hartmann, P.
- Affiliations
- Comenius University Bratislava (SK)
- HUN-REN Wigner Research Centre for Physics (HU)
- Venue
- Computer Physics Communications
- Publisher
- Elsevier BV
- Published
- 2021-06
- Volume
- 263
- Pages
- 107913
- ISSN
- 0010-4655 (ISSN-L)
- OpenAlex
- W3134899006
- Cited by
- 35
- References
- 2
- Citations by year
- 2026: 6 · 2025: 1 · 2024: 10 · 2023: 7 · 2022: 8 · 2021: 3
- FWCI
- 4.59
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Computer Physics Communications (publishedVersion, cc-by-nc-nd)
- Repository of the Academy's Library (Library of the Hungarian Academy of Sciences) (acceptedVersion)
- Funders
- Nemzeti Kutatási, Fejlesztési és Innovaciós Alap
- Univerzita Komenského v Bratislave (UK/180/2020)
- Magyar Tudományos Akadémia (2020-4.1.1, TKP2020-IKA-07)
- Agentúra na Podporu Výskumu a Vývoja (APVV-19-0386)
- Nemzeti Kutatási Fejlesztési és Innovációs Hivatal (FK-128924, K-134462)
- Topics
- Physical Sciences › Computer Science › Hardware and Architecture › Parallel Computing and Optimization Techniques
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Low-Pressure Plasma Processing and Diagnostics
- Physical Sciences › Physics and Astronomy › Nuclear and High Energy Physics › Magnetic confinement fusion research
- Keywords
- GPU computing, particle-in-cell simulation, Monte Carlo collisions, capacitively coupled plasma, low-pressure plasma, CUDA, OpenCL, massively parallel simulation, performance optimization
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- Juhasz, Z., Ďurian, J., Derzsi, A., Matejčík, Š., Donkó, Z., & Hartmann, P. (2021). Efficient GPU implementation of the Particle-in-Cell/Monte-Carlo collisions method for 1D simulation of low-pressure capacitively coupled plasmas. Computer Physics Communications. https://doi.org/10.1016/j.cpc.2021.107913
