Scalable and Consistent Graph Neural Networks for Distributed Mesh-based Data-driven Modeling
<|reference_start|>Scalable and Consistent Graph Neural Networks for Distributed Mesh-based Data-driven Modeling: This work develops a distributed graph neural network (GNN) methodology for mesh-based modeling applications using a consistent neural message passing layer. As the name implies, the focus is on enabling scalable operations that satisfy physical consistency via halo nodes at sub-graph boundaries. Here, consistency refers to the fact that a GNN trained and evaluated on one rank (one large graph) is arithmetically equivalent to evaluations on multiple ranks (a partitioned graph). This concept is demonstrated by interfacing GNNs with NekRS, a GPU-capable exascale CFD solver developed at Argonne National Laboratory. It is shown how the NekRS mesh partitioning can be linked to the distributed GNN training and inference routines, resulting in a scalable mesh-based data-driven modeling workflow. We study the impact of consistency on the scalability of mesh-based GNNs, demonstrating efficient scaling in consistent GNNs for up to O(1B) graph nodes on the Frontier exascale supercomputer.<|reference_end|>
@article{barwey2024scalable,
    title={Scalable and Consistent Graph Neural Networks for Distributed Mesh-based
  Data-driven Modeling},
    author={Shivam Barwey, Riccardo Balin, Bethany Lusch, Saumil Patel, Ramesh
  Balakrishnan, Pinaki Pal, Romit Maulik, Venkatram Vishwanath},
    journal={arXiv preprint arXiv:2410.01657},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.01657},
    primaryClass={cs.DC cs.LG physics.comp-ph}
}