GreenGNN: Energy-Aware Windowed Communication Optimization for Distributed GNN Training

2026-06-01

AI Summary

This work addresses the high communication energy consumption and GPU underutilization caused by frequent remote feature accesses in distributed graph neural network (GNN) training. To mitigate these issues, the authors propose a window-based training strategy that exploits the temporal locality of neighbor sampling to cache frequently accessed features within a sliding window and batch remote requests, thereby substantially reducing communication frequency. An offline discrete-event simulation framework is employed to automatically determine the optimal window size, and a hybrid energy model is integrated to evaluate system efficiency. Experiments on a 4-node GPU cluster demonstrate that the proposed approach reduces total system energy consumption by 27%–43%, improves end-to-end throughput by up to 3.9×, and lowers GPU energy usage by 36%–71%.

Abstract

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.

Problem

• distributed GNN training
• energy efficiency
• remote feature fetching
• communication overhead
• GPU power consumption

Innovation

• energy-aware training
• windowed communication
• temporal locality
• bulk RPC merging
• distributed GNN