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This paper introduces GreenGNN, an energy-efficient system for distributed graph neural network (GNN) training that optimizes communication by leveraging the temporal locality of neighbor sampling. By grouping mini-batches into windows and staging hot features in local caches, GreenGNN significantly reduces the energy costs associated with remote feature fetching, achieving a 27-43% reduction in total system energy and up to 3.9x improvement in end-to-end throughput. The method effectively balances communication overhead and cache miss handling, leading to substantial energy savings and enhanced performance on a 4-node GPU cluster.
Energy consumption in distributed GNN training can be slashed by up to 43% while boosting throughput nearly fourfold with the innovative GreenGNN approach.
Large-scale graph neural network (GNN) training often requires distributed clusters because graph structure and feature tensors no longer fit in a single node's memory. In sampling-based training, each mini-batch expands into a receptive field that spans partitions and triggers thousands of remote feature fetches per epoch. This wastes energy for two main reasons: each small RPC pays a fixed initiation and protocol cost, and GPUs continue drawing substantial baseline power while waiting for remote features. We present GreenGNN, an energy-aware distributed GNN training system that reduces communication energy by exploiting the bursty, short-lived temporal locality of neighbor sampling. GreenGNN groups training into windows of W consecutive mini-batches, stages each window's hot features in a local cache, and merges remote requests from each partition owner into a small number of bulk transfers. This amortizes RPC overhead across many features while preserving an on-demand path for cache misses. Because window size controls the trade-off between communication amortization and hot-set staleness, GreenGNN selects W offline using a discrete-event simulator that replays a deterministic one-epoch access trace with a hybrid energy model. We implement GreenGNN on DGL and evaluate it on a 4-node GPU cluster with benchmark datasets. Across datasets and batch sizes, GreenGNN reduces total system energy by 27--43% relative to baseline while improving end-to-end throughput by up to 3.9x. GPU energy drops by 36--71%, driven by fewer RPC initiations and lower GPU stall time.
