Multi-GPU Approach for Training of Graph ML Models on large CFD Meshes
Sebastian Strönisch Maximilian Sander Andreas Knüpfer Marcus Meyer
Mesh-based numerical solvers are an important part in many design tool
chains. However, accurate simulations like computational fluid dynamics are
time and resource consuming which is why surrogate models are employed to
speed-up the solution process. Machine Learning based surrogate models on the
other hand are fast in predicting approximate solutions but often lack
accuracy. Thus, the development of the predictor in a predictor-corrector
approach is the focus here, where the surrogate model predicts a flow field and
the numerical solver corrects it. This paper scales a state-of-the-art
surrogate model from the domain of graph-based machine learning to
industry-relevant mesh sizes of a numerical flow simulation. The approach
partitions and distributes the flow domain to multiple GPUs and provides halo
exchange between these partitions during training. The utilized graph neural
network operates directly on the numerical mesh and is able to preserve complex
geometries as well as all other properties of the mesh. The proposed surrogate
model is evaluated with an application on a three dimensional turbomachinery
setup and compared to a traditionally trained distributed model. The results
show that the traditional approach produces superior predictions and
outperforms the proposed surrogate model. Possible explanations, improvements
and future directions are outlined.