Leveraging HPC Profiling Tracing Tools to Understand the Performance of Particle-in-Cell Monte Carlo Simulations
arxiv(2023)
摘要
Large-scale plasma simulations are critical for designing and developing
next-generation fusion energy devices and modeling industrial plasmas. BIT1 is
a massively parallel Particle-in-Cell code designed for specifically studying
plasma material interaction in fusion devices. Its most salient characteristic
is the inclusion of collision Monte Carlo models for different plasma species.
In this work, we characterize single node, multiple nodes, and I/O performances
of the BIT1 code in two realistic cases by using several HPC profilers, such as
perf, IPM, Extrae/Paraver, and Darshan tools. We find that the BIT1 sorting
function on-node performance is the main performance bottleneck. Strong scaling
tests show a parallel performance of 77
test cases. We demonstrate that communication, load imbalance and
self-synchronization are important factors impacting the performance of the
BIT1 on large-scale runs.
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