RapidGNN: Communication Efficient Large-Scale Distributed Training of Graph Neural Networks
Bibliographic record
Abstract
Graph Neural Networks (GNNs) have achieved state-of-the-art (SOTA) performance in diverse domains. However, training GNNs on large-scale graphs poses significant challenges due to high memory demands and significant communication overhead in distributed settings. Traditional sampling-based approaches mitigate computation load to some extent but often fail to address communication inefficiencies inherent in distributed environments. This paper presents RapidGNN that introduces a deterministic sampling strategy to precompute mini-batches. By leveraging the sampling strategy, RapidGNN accurately anticipates feature access patterns, enabling optimal cache construction and timely prefetching of remote features. This reduces the frequency and latency of remote data transfers without compromising the stochastic nature of training. Evaluations on Reddit and OGBN-Products datasets demonstrate that RapidGNN achieves significant reductions in training time and remote feature fetches, outperforming existing models in both communication efficiency and throughput. Our findings highlight RapidGNN's potential for scalable, high-performance GNN training across large, real-world graph datasets along with improving energy efficiency. Our model improves end-to-end training throughput by 2.10x on average over SOTA model GraphSAGE-METIS (up to 2.45x in some settings), while cutting remote feature fetches by over 4x. It also reduces energy consumption up to 23%.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".