A Portable Multi-GPU Solver for Collisional Plasmas with Coulombic Interactions

TL;DR

This paper presents a GPU-accelerated particle-in-cell solver for collisional plasmas, analyzing algorithmic performance and the feasibility of Python-based HPC tools across different GPU architectures.

Contribution

It introduces a portable multi-GPU PIC solver optimized for collisional plasma simulations and evaluates Python-based HPC tools like PyKokkos for rapid prototyping.

Findings

MI250X GPU is slightly faster than V100 for most kernels

Performance varies with register pressure on different GPUs

Distributed scaling up to 16 MPI ranks is demonstrated

Abstract

We study parallel particle-in-cell (PIC) methods for low-temperature plasmas (LTPs), which discretize kinetic formulations that capture the time evolution of the probability density function of particles as a function of position and velocity. We use a kinetic description for electrons and a fluid approximation for heavy species. In this paper, we focus on GPU acceleration of algorithms for velocity-space interactions and in particular, collisions of electrons with neutrals, ions, and electrons. Our work has two thrusts. The first is algorithmic exploration and analysis. The second is examining the viability of rapid-prototyping implementations using Python-based HPC tools, in particular PyKokkos. We discuss several common PIC kernels and present performance results on NVIDIA Volta V100 and AMD MI250X GPUs. Overall, the MI250X is slightly faster for most kernels but shows more sensitivity to register pressure. We also report scaling results for a distributed memory implementation on up to 16 MPI ranks.