Sampling-based Distributed Training with Message Passing Neural Network

• URL: http://arxiv.org/abs/2402.15106v3
• Date: Fri, 31 May 2024 22:39:26 GMT
• Title: Sampling-based Distributed Training with Message Passing Neural Network
• Authors: Priyesh Kakka, Sheel Nidhan, Rishikesh Ranade, Jonathan F. MacArt,
• Abstract summary: We introduce a domain-decomposition-based distributed training and inference approach for message-passing neural networks (MPNN) We present a scalable graph neural network, referred to as DS-MPNN (D and S standing for distributed and sampled), capable of scaling up to $O(105)$ nodes.
• Score: 1.1088875073103417
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In this study, we introduce a domain-decomposition-based distributed training and inference approach for message-passing neural networks (MPNN). Our objective is to address the challenge of scaling edge-based graph neural networks as the number of nodes increases. Through our distributed training approach, coupled with Nystr\"om-approximation sampling techniques, we present a scalable graph neural network, referred to as DS-MPNN (D and S standing for distributed and sampled, respectively), capable of scaling up to $O(10^5)$ nodes. We validate our sampling and distributed training approach on two cases: (a) a Darcy flow dataset and (b) steady RANS simulations of 2-D airfoils, providing comparisons with both single-GPU implementation and node-based graph convolution networks (GCNs). The DS-MPNN model demonstrates comparable accuracy to single-GPU implementation, can accommodate a significantly larger number of nodes compared to the single-GPU variant (S-MPNN), and significantly outperforms the node-based GCN.

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