Symplectic structure-preserving particle-in-cell whole-volume simulation of tokamak plasmas to 111.3 trillion particles and 25.7 billion grids

Xiao, Jun; Chen, Junshi; Zheng, Jiangshan; An, Hong; Huang, Shenghong; Yang, Chao; Fang, Li; Zhang, Ziyu; Huang, Yeqi; Han, Wenting; Liu, Xin; Chen, Dexun; Liu, Zixi; Zhuang, G.; Chen, Jiale; Li, Guoqiang; Sun, Xuan; Chen, Qiang

doi:10.1145/3458817.3487398

Cited by 13 publications

(6 citation statements)

References 41 publications

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“…Warp-X, also developed based on the PIC method, was used in the simulations of plasma accelerators on exascale supercomputers [21]. Geometric methods were also used in explicit PIC scheme development to achieve preservative structure [22,23].…”

Section: Explicit Vs Implicitmentioning

confidence: 99%

“…More recently, Xiao et al proposed a new structure-preserving geometric PIC algorithm with discrete symplectic structure and symplectic integration, and studied the kinetic steady state in tokamak geometry and the kinetic ballooning instability in the edge [22]. Then they conducted the 6-Dimensional electromagnetic fully kinetic tokamak simulations using the explicit second-order charge-conservative symplectic electromagnetic PIC method [23].…”

Section: Application In Fusion Plasma Instability Studymentioning

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: Explicit Vs Implicitmentioning

confidence: 99%

Section: Application In Fusion Plasma Instability Studymentioning

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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“…The tokamak plasma PIC simulation performed by Xiao et al (2021) was nominated for the 2020 Gordon Bell Prize. It used the total system of the Sunway OceanLight, which has a higher theoretical peak performance than the Fugaku.…”

Section: Largest-scale Problemmentioning

confidence: 99%

“…One possible optimization is to not to sort SDs with a block as a key for every time step of the SDM. Although such an approach is adopted in the tokamak plasma PIC application (Xiao et al, 2021), it requires some consideration for application to the SDM. For collision-coalescence, all SDs in a block must be in the same MPI process to calculate the interaction between SDs in a cell; however, this is not necessary for SD movement and condensation processes.…”

Section: Reduction Of Data Movementmentioning

confidence: 99%

Optimization and sophistication of the super-droplet method for ultrahigh resolution cloud simulations

Matsushima

Nishizawa

Shima

2023

Preprint

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Abstract. A particle-based cloud model was developed for ultrahigh-resolution numerical simulation of warm clouds. Simplified cloud microphysics schemes have already made meter-scale numerical experiments feasible; however, such schemes are based on empirical assumptions, and hence, they contain huge uncertainties. The super-droplet method (SDM) is promising for cloud microphysical process modeling; it is based on a particle-based approach and does not make any assumptions for the droplet size distributions. However, meter-scale numerical experiments using the SDM are not feasible even on the existing high-end supercomputers because of its high computational cost. In the present study, we optimized and sophisticated the SDM for ultrahigh resolution simulations. The contributions of our work are as follows: (1) The uniform sampling method is not suitable when dealing with a large number of super-droplets (SDs). Hence, we developed a new initialization method for sampling SDs from a real droplet population. These SDs can be used for simulating spatial resolutions between centimeter and meter scales. (2) We improved the SDM algorithm to achieve high performance by reducing data movement and simplifying loop bodies by applying the concept of effective resolution. The improved algorithms can be applied to Fujitsu A64FX processor, and most of them are also effective on other many-core CPUs and graphics processing units (GPUs). Warm bubble experiments revealed that the particle-steps per time for the improved algorithms is 57.6 times faster than those for the original SDM. In the case of shallow cumuli, the simulation times when using the new SDM with 64–128 SDs per cell are shorter than those for a bin method with 32 bins and are comparable to those for a two-moment bulk method. (3) Using supercomputer Fugaku, we demonstrated that a numerical experiment with 2 m resolution and 128 SDs per cell covering 13,8242 × 3,072 m3 domain is possible. The number of grids and SDs are 104 and 442 times, respectively, those of the current state-of-the-art experiment. Our numerical model exhibited perfect weak scaling up to 36,864 nodes, which account for 23 % of the total system. The simulation achieves 7.97 PFLOPS, 7.04 % of peak ratio for overall performance, and the simulation time for SDM is 2.86 × 1013 particle·steps/s. Several challenges, such as optimization for mixed-phase clouds, inclusion of terrain, and long-time integrations, still remain, and our study will also contribute toward solving them. The developed model enables us to study turbulence and microphysics processes over a wide range of scales using combinations of DNS, laboratory experiments, and field studies. We believe that our approach advances the scientific understanding of clouds and contributes to reducing the uncertainties of weather simulation and climate projection.

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“…Partial differential equations (PDEs) often lead to extreme multiscale behavior which makes the resolution of all scales in one simulation impossible. While exascale computing will enable simulations of larger systems (e.g., Xiao et al, 2021;Ji et al, 2022), the extreme multiscale nature of many problems in space sciences requires new techniques. In the global magnetosphere, there are 10 7 degrees of separation in spatial and temporal scales, putting it beyond the conventional techniques even at exascale.…”

Section: Introductionmentioning

confidence: 99%

The need for adoption of neural HPC (NeuHPC) in space sciences

Karimabadi

Wilkes²,

Roberts³

2023

Front. Astron. Space Sci.

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A major challenge facing scientists using conventional approaches for solving PDEs is the simulation of extreme multi-scale problems. While exascale computing will enable simulations of larger systems, the extreme multiscale nature of many problems requires new techniques. Deep learning techniques have disrupted several domains, such as computer vision, language (e.g., ChatGPT), and computational biology, leading to breakthrough advances. Similarly, the adaptation of these techniques for scientific computing has led to a new and rapidly advancing branch of High-Performance Computing (HPC), which we call neural-HPC (NeuHPC). Proof of concept studies in domains such as computational fluid dynamics and material science have demonstrated advantages in both efficiency and accuracy compared to conventional solvers. However, NeuHPC is yet to be embraced in plasma simulations. This is partly due to general lack of awareness of NeuHPC in the space physics community as well as the fact that most plasma physicists do not have training in artificial intelligence and cannot easily adapt these new techniques to their problems. As we explain below, there is a solution to this. We consider NeuHPC a critical paradigm for knowledge discovery in space sciences and urgently advocate for its adoption by both researchers as well as funding agencies. Here, we provide an overview of NeuHPC and specific ways that it can overcome existing computational challenges and propose a roadmap for future direction.

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Symplectic structure-preserving particle-in-cell whole-volume simulation of tokamak plasmas to 111.3 trillion particles and 25.7 billion grids

Cited by 13 publications

References 41 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

Optimization and sophistication of the super-droplet method for ultrahigh resolution cloud simulations

The need for adoption of neural HPC (NeuHPC) in space sciences

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