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Large-scale plasma simulations are critical for designing and developingnext-generation fusion energy devices and modeling industrial plasmas. BIT1 isa massively parallel Particle-in-Cell code designed for specifically studyingplasma material interaction in fusion devices. Its most salient characteristicis the inclusion of collision Monte Carlo models for different plasma species.In this work, we characterize single node, multiple nodes, and I/O performancesof the BIT1 code in two realistic cases by using several HPC profilers, such asperf, IPM, Extrae/Paraver, and Darshan tools. We find that the BIT1 sortingfunction on-node performance is the main performance bottleneck. Strong scalingtests show a parallel performance of 77% and 96% on 2,560 MPI ranks for the twotest cases. We demonstrate that communication, load imbalance andself-synchronization are important factors impacting the performance of theBIT1 on large-scale runs.

Leveraging HPC Profiling & Tracing Tools to Understand the Performance of Particle-in-Cell Monte Carlo Simulations