Particle-in-Cell Code for GPU Systems

Vasileska, Ivona; Bogdanovic, L.; Kos, L.

doi:10.23919/mipro52101.2021.9596959

Cited by 5 publications

(3 citation statements)

References 3 publications

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“…This is due to the potential dependency on the order in which particles contribute their charges, possibly resulting in simultaneous additions to the same grid point. To address this concern, various strategies have been proposed [12,44,45]. The proposed strategy introduces a hardware-based solution to tackle the challenge posed by a substantial reduction in performance due to the need for synchronization in performing gridbased computations.…”

Section: Resultsmentioning

confidence: 99%

“…An optimized PIC code, known as SIMPIC, was developed based on specific hypotheses to implement the proposed PIC on contemporary GPUs. The outcomes of this endeavor underscore the code’s efficiency, showcasing a noteworthy 50% reduction in CPU execution time [ 45 ]. Another investigation explored the effective integration of hybrid CPU/GPU platforms for the implementation of large-scale 3D PIC simulations [ 46 ].…”

Section: Related Workmentioning

confidence: 99%

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Accelerating electrostatic particle-in-cell simulation: A novel FPGA-based approach for efficient plasma investigations

Almomany,

Sutcu,

Ibrahim

2024

PLoS ONE

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Particle-in-cell (PIC) simulation serves as a widely employed method for investigating plasma, a prevalent state of matter in the universe. This simulation approach is instrumental in exploring characteristics such as particle acceleration by turbulence and fluid, as well as delving into the properties of plasma at both the kinetic scale and macroscopic processes. However, the simulation itself imposes a significant computational burden. This research proposes a novel implementation approach to address the computationally intensive phase of the electrostatic PIC simulation, specifically the Particle-to-Interpolation phase. This is achieved by utilizing a high-speed Field Programmable Gate Array (FPGA) computation platform. The suggested approach incorporates various optimization techniques and diminishes memory access latency by leveraging the flexibility and performance attributes of the Intel FPGA device. The results obtained from our study highlight the effectiveness of the proposed design, showcasing the capability to execute hundreds of functional operations in each clock cycle. This stands in contrast to the limited operations performed in a general-purpose single-core computation platform (CPU). The suggested hardware approach is also scalable and can be deployed on more advanced FPGAs with higher capabilities, resulting in a significant improvement in performance.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Accelerating electrostatic particle-in-cell simulation: A novel FPGA-based approach for efficient plasma investigations

Almomany,

Sutcu,

Ibrahim

2024

PLoS ONE

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“…The traditional bottlenecks in PIC codes are the particle gathering and deposition steps. With the increasing popularity of GPUaccelerated codes, these bottlenecks could be mitigated since the gathering and deposition steps are well suited to parallelization (e.g., Joseph et al 2011;Decyk & Singh 2014;Vasileska et al 2021). The field solver, on the other hand, whether implemented on CPUs or GPUs, requires global communication of grid quantities, which hinders scaling behavior to a large number of cores, so it becomes imperative to further reduce the cost of the field-solution step through other means.…”

Section: Discussionmentioning

confidence: 99%

An Exactly Energy-conserving Electromagnetic Particle-in-cell Method in Curvilinear Coordinates

Croonen,

Pezzini,

Bacchini

et al. 2024

ApJS

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In this paper, we introduce and discuss an exactly energy-conserving particle-in-cell method for arbitrary curvilinear coordinates. The flexibility provided by curvilinear coordinates enables the study of plasmas in complex-shaped domains by aligning the grid to the given geometry or by focusing grid resolution on regions of interest without overresolving the surrounding, potentially uninteresting domain. We have achieved this through the introduction of the metric tensor, the Jacobian matrix, and contravariant operators combined with an energy-conserving fully implicit solver. We demonstrate the method’s capabilities using a Python implementation to study several one- and two-dimensional test cases: the electrostatic two-stream instability, the electromagnetic Weibel instability, and the geomagnetic environment modeling reconnection challenge. The test results confirm the capability of our new method to reproduce theoretical expectations (e.g., instability growth rates) and the corresponding results obtained with a Cartesian uniform grid when using curvilinear grids. Simultaneously, we show that the method conserves energy to machine precision in all cases.

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