OMEGA通过选择性重计算与并行图计算，实现大图GNN低延迟服务。
OMEGA: A Low-Latency GNN Serving System for Large Graphs
- 选择性重计算预计算嵌入，减少精度损失。
- 多机并行构建与执行计算图，降低通信开销。
- 适合需要高吞吐、低延迟的工业级图神经网络应用。
图神经网络（GNN）因其在图数据上计算丰富节点表示的能力而被广泛应用。然而，在大规模图上部署GNN面临高通信、计算和内存开销，主要源于需构建并执行表示跨大邻域信息流的计算图。现有训练阶段的近似技术虽可缓解开销，但在推理阶段仍导致高延迟或精度下降。为此，我们提出OMEGA系统，通过两个关键设计实现大图上低延迟的GNN服务且精度损失极小：第一，采用选择性重计算预计算嵌入，可在复用预计算子图的同时，仅对少量部分进行重计算以最小化精度损失；第二，引入计算图并行化，通过在多台机器间并行化计算图的构建与执行来降低通信开销。在多个大型图数据集和GNN模型上的评估表明，OMEGA显著优于当前最优技术。
原文摘要 · Abstract (English)
Graph Neural Networks (GNNs) have been widely adopted for their ability to compute expressive node representations in graph datasets. However, serving GNNs on large graphs is challenging due to the high communication, computation, and memory overheads of constructing and executing computation graphs, which represent information flow across large neighborhoods. Existing approximation techniques in training can mitigate the overheads but, in serving, still lead to high latency and/or accuracy loss. To this end, we propose OMEGA, a system that enables low-latency GNN serving for large graphs with minimal accuracy loss through two key ideas. First, OMEGA employs selective recomputation of precomputed embeddings, which allows for reusing precomputed computation subgraphs while selectively recomputing a small fraction to minimize accuracy loss. Second, we develop computation graph parallelism, which reduces communication overhead by parallelizing the creation and execution of computation graphs across machines. Our evaluation with large graph datasets and GNN models shows that OMEGA significantly outperforms state-of-the-art techniques.
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