Accelerating Sheath Particle-in-Cell Simulations with StarPU

Particle-in-Cell (PIC) codes are widely used in plasma physics, but their computational requirements make efficient parallelization a major challenge, especially on heterogeneous systems combining CPUs and GPUs. In this paper, we investigate how StarPU, a task-based runtime system, can be integrated into a one-dimensional sheath PIC code to overcome these computational challenges. The sheath simulation models fully kinetic electrons and ions, with particles interacting with boundary walls, leading to particle loss and sheath formation over time. We decompose the PIC workflow into discrete tasks, including particle pushing, boundary treatment, and field updates, and implement them using StarPU's dynamic task scheduling. The study evaluates various StarPU scheduling strategies and optimizations to minimize communication overhead and ensure efficient resource utilization. A novel particle flagging approach replaces traditional particle compaction and simplifies GPU execution while maintaining accuracy. Performance benchmarks show significant speedups on heterogeneous systems. Profiling results provide insight into the efficiency of task execution and scheduling and highlight the effectiveness of StarPU in managing complex dependencies. This work offers a robust, scalable framework for PIC codes and allows for extending task-based parallelization to multi-dimensional simulations and other scientific computing applications.