A GPU-enabled implicit Finite Volume solver for the ideal two-fluid plasma model on unstructured grids

TL;DR

This paper presents a GPU-accelerated implicit finite volume solver for simulating ideal two-fluid plasmas on unstructured grids, achieving significant performance improvements over CPU-based methods.

Contribution

The work introduces a novel GPU-enabled implementation of an implicit two-fluid plasma solver within the open source COOLFluiD platform, with optimized flux and source term computations.

Findings

Up to 14x speedup in computational time on GPUs.

Successful simulation of plasma wave propagation and magnetic reconnection.

Effective use of open source linear solvers PETSc and PARALUTION.

Abstract

This paper describes the main features of a pioneering unsteady solver for simulating ideal two-fluid plasmas on unstructured grids, taking profit of GPGPU (General-purpose computing on graphics processing units). The code, which has been implemented within the open source COOLFluiD platform, is implicit, second-order in time and space, relying upon a Finite Volume method for the spatial discretization and a three-point backward Euler for the time integration. In particular, the convective fluxes are computed by a multi-fluid version of the AUSM+up scheme for the plasma equations, in combination with a modified Rusanov scheme with tunable dissipation for the Maxwell equations. Source terms are integrated with a one-point rule, using the cell-centered value. Some critical aspects of the porting to GPU's are discussed, as well as the performance of two open source linear system solvers (i.e. PETSc, PARALUTION). The code design allows for computing both flux and source terms on the GPU along with their Jacobian, giving a noticeable decrease in the computational time in comparison with the original CPU-based solver. The code has been tested in a wide range of mesh sizes and in three different systems, each one with a different GPU. The increased performance (up to 14x) is demonstrated in two representative 2D benchmarks: propagation of circularly polarized waves and the more challenging Geospace Environmental Modeling (GEM) magnetic reconnection challenge.