A Scheme of Full Kinetic Particle-in-cell Algorithms for GPU Acceleration Using CUDA Fortran Programming

Xiong, Qian; Huang, S. Y.; Yuan, Zhigang; Jiang, K.; Wei, Yong; Xu, S. B.; Zhang, J.; Wang, Z.; Lin, R. T.; Yu, Lei

doi:10.3847/1538-4365/ac9fd6

Cited by 4 publications

(5 citation statements)

References 19 publications

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“…Another pivotal area of exploration lies in the enhancement of PIC algorithms' computational efficiency, particularly through the utilization of Graphics Processing Units (GPUs), as presented in [45]. By harnessing the capabilities of GPUs, researchers can significantly accelerate the execution of PIC simulations, ushering in new possibilities and opportunities for in-depth scientific investigations.…”

Section: Discussionmentioning

confidence: 99%

“…Recently, Badiali et al proposed an interface to incorporate Machine-Learning based methods into PIC simulations and the results suggest that the Machine-Learning based method could achieve greater computation efficiency and obtain correct physics results at the same time [44]. Another promising avenue of research entails optimizing PIC simulations through techniques such as GPU acceleration [45] or harnessing the power of exascale supercomputers with certain modifications [21].…”

Section: Pic Methods Future Developmentmentioning

confidence: 99%

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Recent development of fully kinetic particle-in-cell method and its application to fusion plasma instability study

Ren,

Lapenta

2024

Front. Phys.

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This paper reviews the recent advancements of the algorithm and application to fusion plasma instability study of the fully kinetic Particle-in-Cell (PIC) method. The strengths and limitations of both explicit and implicit PIC methods are described and compared. Additionally, the semi-implicit PIC method and the code ECsim used in our research are introduced. Furthermore, the application of PIC methods in fusion plasma instabilities is delved into. A detailed account of the recent progress achieved in the realm of tokamak plasma simulation through fully kinetic PIC simulations is also provided. Finally the prospective future development and application of PIC methods are discussed as well.

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Section: Discussionmentioning

confidence: 99%

Section: Pic Methods Future Developmentmentioning

confidence: 99%

Recent development of fully kinetic particle-in-cell method and its application to fusion plasma instability study

Ren,

Lapenta

2024

Front. Phys.

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“…We take advantage of the 2.5D PIC simulation code of GPIC (Xiong, Huang, Yuan, Jiang, Wei, et al., 2023; Xiong, Huang, Yuan, et al., 2024). The double Harris current sheet reconnection model has been used in previous studies (e.g., Xiong et al., 2022a, 2022b, 2022c; Xiong, Huang, Yuan, Jiang, Xu, et al., 2023; Huang et al., 2014, 2024; Huang, Zhou, et al., 2015; Zhou et al., 2012, 2014).…”

Section: Data Descriptions and Methodologiesmentioning

confidence: 99%

Guide Field Dependence of Energy Conversion and Magnetic Topologies in Reconnection Turbulent Outflow

Xiong,

Huang,

Zhang

et al. 2024

Geophysical Research Letters

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Energy conversion between the fields and the particles occurs through various physical processes within space environments. Magnetic reconnection stands out as one such process capable of rapidly and massively releasing energy. The high‐speed outflow jets produced by reconnection can induce turbulence characterized by intermittent structures. In this study, we investigate the impact of guide field on the energy conversion associated with the magnetic topologies within these structures during reconnection. Utilizing both particle‐in‐cell simulations and observations from the Magnetospheric Multiscale mission, our findings suggest that a larger guide field present during reconnection leads to increased energy conversion as well as the generation of O‐type topology structures within the turbulent outflow. Our results provide significant evidence on the relationships between energy conversion and magnetic topologies within turbulent outflow of reconnection and the guide field conditions.

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“…While substantial progress has been made in understanding particle acceleration in 2D, and more recently in 3D (e.g., Guo et al 2015;Zhang et al 2021;Schoeffler et al 2023), algorithmic and computational innovation in PIC simulations of such systems has been limited. Virtually all studies employ a finite-difference time-domain Maxwell solver with a staggered Yee grid (Yee 1966; sometimes called FDTD), and only a few have explored the advantages of GPU acceleration for astrophysical PIC simulations (Bussmann et al 2013;Chien et al 2020;Xiong et al 2023). The Yee approach is secondorder in both space and time.…”

Section: Introductionmentioning

confidence: 99%

“…PIConGPU (Huebl 2019), VPIC 2.0 (Bird et al 2022), and the Plasma Simulation code (PSC; Germaschewski et al 2016) also employ similar strategies that enable performance portability and allow scaling to multiple GPU nodes. Nonrelativistic magnetic reconnection has been used as a comparison case to validate multiple GPU-accelerated PIC codes, including PSC and sputniPIC (Chien et al 2020), which can make use of a single node with multiple GPUs, and a CUDA Fortran single-GPU PIC code (Xiong et al 2023).…”

Section: Introductionmentioning

confidence: 99%

Particle-in-cell Simulations of Relativistic Magnetic Reconnection with Advanced Maxwell Solver Algorithms

Klion

Jambunathan

Rowan

et al. 2023

ApJ

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Relativistic magnetic reconnection is a nonideal plasma process that is a source of nonthermal particle acceleration in many high-energy astrophysical systems. Particle-in-cell (PIC) methods are commonly used for simulating reconnection from first principles. While much progress has been made in understanding the physics of reconnection, especially in 2D, the adoption of advanced algorithms and numerical techniques for efficiently modeling such systems has been limited. With the GPU-accelerated PIC code WarpX, we explore the accuracy and potential performance benefits of two advanced Maxwell solver algorithms: a nonstandard finite-difference scheme (CKC) and an ultrahigh-order pseudo-spectral method (PSATD). We find that, for the relativistic reconnection problem, CKC and PSATD qualitatively and quantitatively match the standard Yee-grid finite-difference method. CKC and PSATD both admit a time step that is 40% longer than that of Yee, resulting in a ∼40% faster time to solution for CKC, but no performance benefit for PSATD when using a current deposition scheme that satisfies Gauss’s law. Relaxing this constraint maintains accuracy and yields a 30% speedup. Unlike Yee and CKC, PSATD is numerically stable at any time step, allowing for a larger time step than with the finite-difference methods. We found that increasing the time step 2.4–3 times over the standard Yee step still yields accurate results, but it only translates to modest performance improvements over CKC, due to the current deposition scheme used with PSATD. Further optimization of this scheme will likely improve the effective performance of PSATD.

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A Scheme of Full Kinetic Particle-in-cell Algorithms for GPU Acceleration Using CUDA Fortran Programming

Cited by 4 publications

References 19 publications

Recent development of fully kinetic particle-in-cell method and its application to fusion plasma instability study

Recent development of fully kinetic particle-in-cell method and its application to fusion plasma instability study

Guide Field Dependence of Energy Conversion and Magnetic Topologies in Reconnection Turbulent Outflow

Particle-in-cell Simulations of Relativistic Magnetic Reconnection with Advanced Maxwell Solver Algorithms

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