Arefin Niam

Arefin Niam, Tevfik Kosar, M. S. Q. Zulkar Nine

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.

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 RPCs whose fixed initiation cost and GPU-stall latency waste energy. Prior systems try to reduce this overhead with presampling and static caching, but cache policies cannot react to runtime network variation. We show that under time-varying congestion, static caching can increase energy by up to 45% because a fixed rebuild schedule is insufficient. We present GreenDyGNN, which formulates cache window management as a sequential decision problem. GreenDyGNN performs intra-epoch cache rebuilds and uses a Double-DQN agent, trained in a calibrated simulator with domain-randomized congestion, to adapt rebuild window size and per-owner cache allocation at each boundary. An asynchronous double-buffered pipeline makes adaptation effectively free. Under congestion, GreenDyGNN cuts total energy by up to 43% over Default DGL and 4-24% over the best static policy, while closely matching the optimum under clean conditions.

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 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.