Minisymposium Presentation

Graph neural networks (GNNs) have shown considerable promise in accelerated mesh-based modeling for applications like fluid dynamics, where models must be compatible with unstructured grids for practical simulation capability in complex geometries. To realize the vision of robust mesh-based modeling, however, the question of scalability to large graph sizes (O(10M) nodes and beyond) must be addressed, particularly when interfacing with unstructured data produced by high-fidelity computational fluid dynamics (CFD) codes. As such, we focus on the development of a distributed GNN that relies on novel alterations to the baseline message passing layer to facilitate scalable operations with consistency. Here, consistency refers to the fact that a GNN trained and evaluated on one rank is arithmetically equivalent to evaluations on multiple ranks. Demonstrations are performed in the context of in-situ coupling of GNNs with NekRS, an exascale CFD code, using the Polaris supercomputer at the Argonne Leadership Computing Facility. The crux of the NekRS-GNN approach is to show how the same CFD domain-decomposition strategy can be linked to the distributed GNN training and inference routines. Emphasis is placed on two modeling applications: (1) developing surrogates for unsteady fluid dynamics forecasts, and (2) mesh-based super-resolution of turbulent flows.