Efficient parallelization for 3D-3V sparse grid Particle-In-Cell: Single GPU architectures *

In the present paper, an efficient General Purpose Graphical Processing Unit (GPGPU)-based implementation of sparse grid Particle-In-Cell (PIC) methods is proposed. The parallelization, implementing novel strategies specific to sparse-PIC methods and tailored to GPU architectures, provides speed-ups1 as large as 100 on a single Tesla V100 GPU, with respect to sequential Computing Processing unit (CPU) execution; and a four order of magnitude reduction of the computational time in comparison with a standard PIC sequential CPU simulation. In addition, the simple implementation of the parallelization with the OpenACC framework offers portability to a large class of accelerators.(c) 2023 Elsevier B.V. All rights reserved.

Automated summary

In this paper, an efficient implementation of sparse grid Particle-In-Cell (PIC) methods based on General Purpose Graphical Processing Unit (GPGPU) is proposed. The parallelization with GPU-specific strategies achieves speed-ups as large as 100 on a single Tesla V100 GPU compared to sequential CPU execution, and reduces computational time by four orders of magnitude compared to standard PIC sequential CPU simulation. Moreover, the simple implementation of parallelization with the OpenACC framework provides portability to a wide range of accelerators.