3 papers
cs.DC2026
GreenDyGNN: Runtime-Adaptive Energy-Efficient Communication for Distributed GNN Training
Arefin Niam, Tevfik Kosar, M. S. Q. Zulkar Nine
Distributed GNN training is dominated by remote feature fetching, which can be very costly. Multi-hop neighborhood sampling crosses partition boundaries and triggers fine-grained R…
cs.LG2025
RapidGNN: Energy and Communication-Efficient Distributed Training on Large-Scale Graph Neural Networks
Arefin Niam, Tevfik Kosar, M S Q Zulkar Nine
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 structur…
cs.DC2025
RapidGNN: Communication Efficient Large-Scale Distributed Training of Graph Neural Networks
Arefin Niam, M S Q Zulkar Nine
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…