RapidGNN: Energy and Communication-Efficient Distributed Training on Large-Scale Graph Neural Networks
This addresses communication bottlenecks for researchers and practitioners training GNNs on large graphs, representing an incremental improvement over existing sampling-based methods.
The paper tackled the problem of high communication overhead in distributed training of large-scale Graph Neural Networks (GNNs) by introducing RapidGNN, a framework that improves training throughput by 2.46x to 3.00x and reduces remote feature fetches by over 9.70x to 15.39x.
Graph Neural Networks (GNNs) have become popular across a diverse set of tasks in exploring structural relationships between entities. However, due to the highly connected structure of the datasets, distributed training of GNNs on large-scale graphs poses significant challenges. Traditional sampling-based approaches mitigate the computational loads, yet the communication overhead remains a challenge. This paper presents RapidGNN, a distributed GNN training framework with deterministic sampling-based scheduling to enable efficient cache construction and prefetching of remote features. Evaluation on benchmark graph datasets demonstrates RapidGNN's effectiveness across different scales and topologies. RapidGNN improves end-to-end training throughput by 2.46x to 3.00x on average over baseline methods across the benchmark datasets, while cutting remote feature fetches by over 9.70x to 15.39x. RapidGNN further demonstrates near-linear scalability with an increasing number of computing units efficiently. Furthermore, it achieves increased energy efficiency over the baseline methods for both CPU and GPU by 44% and 32%, respectively.