EOD: Enabling Low Latency GNN Inference via Near-Memory Concatenate Aggregation
Taehwan Kim, Yunki Han, Seohye Ha, Jiwan Kim, Lee‐Sup Kim · 2025
As online services based on graph databases increasingly integrate with machine learning, serving low-latency Graph Neural Network (GNN) inference for individual requests has become a critical challenge.Real-time GNN inference services operate in an inductive setup, which can handle newly added, previously unseen nodes and their edges.In this setup, the system must prepare the computational graph and input node features for target nodes, followed by GNN inference using the given input data.However, the workflow of a GNN serving system presents two key challenges that hinder low-latency inference.The first challenge arises from the extensive preparation step, which involves heavy memory access in host memory and significant data I/O to devices, constituting the largest portion of end-to-end inference latency.The second challenge is the well-known neighborhood explosion problem in GNN research.As the receptive field for target nodes increases exponentially with the number of layers, this issue exacerbates overall latency.To address these challenges, we propose a Near-Memory Processing (NMP) based low-latency GNN inference serving system named EOD.To ensure low-latency real-time GNN service, we co-design the algorithm and hardware to tackle the aforementioned issues.First, to mitigate the neighborhood explosion problem, we propose a precomputation method for the training node set, reducing memory access and computational complexity from exponential to linear growth.Additionally, we introduce a concatenated ZVC compression method to minimize the overhead of storing precomputed hidden features.Finally, to alleviate heavy host-side memory access and data I/O, we design an NMP architecture that enables efficient aggregation on concatenated ZVC-compressed data.As a result, EOD achieves a geometric mean of 981.1× and 912.0× aggregation speedup over the baseline and the existing architecture for GNN aggregation.Additionally, EOD achieves a geometric mean of 17.9×, and up to 74.3× end-to-end latency speedup over the GPU baseline.