An Efficient GPU Parallelization Strategy for Binary Collisions in Particle-In-Cell Plasma Simulations

The Particle-In-Cell (PIC) algorithm coupled with binary collision modules is a widely applicable method to simulate plasmas over a broad range of regimes (from the collisionless kinetic regime to the collisional regime). While several popular PIC codes implement binary collision modules, their performance on GPUs can be constrained by the default parallelization strategy, which assigns one GPU thread per simulation cell. This approach can underutilize GPU resources for simulations with many macroparticles per cell, and relatively few cells per GPU. To address this limitation, we propose an alternative parallelization strategy that instead GPU distributes threads based on independent pairs of colliding particles. Our proposed strategy shows a speed improvement of up to ~ 4× for cases with relatively few cells per GPU and a similar performance otherwise.