用即时编译技术加速大规模图神经网络推理，性能提升27倍
Distributed Graph Neural Network Inference With Just-In-Time Compilation For Industry-Scale Graphs
- 通过新编程接口与即时编译，避免子图学习的冗余计算
- 在5亿节点、224亿边的工业级图上实现最高27.4倍加速
- 适合需要高效部署超大规模图模型的工业场景
图神经网络（GNN）在多个领域取得显著成果，但图数据规模的迅速增长带来了严重的推理性能瓶颈，计算复杂度和内存消耗急剧上升，内存成为主要限制因素。尽管基于图采样的子图学习方法可缓解计算与内存压力，却存在信息丢失和子图间重复计算等问题。本文提出一种新型分布式图学习处理范式，通过新的编程接口抽象GNN，并充分运用即时编译（JIT）技术，消除子图学习的缺陷，使GNN能高效利用分布式集群的计算资源，实现更高效的推理。实验表明，在规模达5亿节点、224亿边的工业级图上，该方法性能最高提升27.4倍。
原文摘要 · Abstract (English)
Graph neural networks (GNNs) have delivered remarkable results in various fields. However, the rapid increase in the scale of graph data has introduced significant performance bottlenecks for GNN inference. Both computational complexity and memory usage have risen dramatically, with memory becoming a critical limitation. Although graph sampling-based subgraph learning methods can help mitigate computational and memory demands, they come with drawbacks such as information loss and high redundant computation among subgraphs. This paper introduces an innovative processing paradgim for distributed graph learning that abstracts GNNs with a new set of programming interfaces and leverages Just-In-Time (JIT) compilation technology to its full potential. This paradigm enables GNNs to highly exploit the computational resources of distributed clusters by eliminating the drawbacks of subgraph learning methods, leading to a more efficient inference process. Our experimental results demonstrate that on industry-scale graphs of up to \textbf{500 million nodes and 22.4 billion edges}, our method can produce a performance boost of up to \textbf{27.4 times}.
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