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arXiv · 2410.22697

MassiveGNN: Efficient Training via Prefetching for Massively Connected Distributed Graphs

Aishwarya Sarkar·Sayan Ghosh·Nathan R. Tallent·Ali Jannesari
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Abstract

Graph Neural Networks (GNN) are indispensable in learning from graph-structured data, yet their rising computational costs, especially on massively connected graphs, pose significant challenges in terms of execution performance. To tackle this, distributed-memory solutions such as partitioning the graph to concurrently train multiple replicas of GNNs are in practice. However, approaches requiring a partitioned graph usually suffer from communication overhead and load imbalance, even under optimal partitioning and communication strategies due to irregularities in the neighborhood minibatch sampling. This paper proposes practical trade-offs for improving the sampling and communication overheads for representation learning on distributed graphs (using popular GraphSAGE architecture) by developing a parameterized continuous prefetch and eviction scheme on top of the state-of-the-art Amazon DistDGL distributed GNN framework, demonstrating about 15-40% improvement in end-to-end training performance on the National Energy Research Scientific Computing Center's (NERSC) Perlmutter supercomputer for various OGB datasets.

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Aishwarya Sarkar, Sayan Ghosh, Nathan R. Tallent, Ali Jannesari. 2024-10-30. MassiveGNN: Efficient Training via Prefetching for Massively Connected Distributed Graphs. https://arxiv.org/abs/2410.22697

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