FAST统一优化时序GNN训练中的采样、内存和计算，显著提速。
FAST: A Holistic Framework for Optimizing Memory-I/O, Computation, and Sampling in Temporal GNN Training
- 通过跨批次缓存与压缩减少显存数据传输
- 设计高效图操作提升GPU缓存命中率，加速聚合与softmax
- 拓扑感知采样提升CPU缓存效率，适合大规模动态图训练
时序图神经网络（TGNN）广泛应用于推荐、社交网络分析和交通预测等动态图学习场景。然而，由于内存I/O、不规则计算和时序邻居采样三者相互耦合，大规模动态图上的TGNN训练仍面临挑战。现有系统通常孤立优化各阶段，性能提升有限。本文提出FAST，一个统一优化采样、内存I/O和计算的全流程框架。FAST引入SlimCache，利用批内压缩和批间缓存，在有限显存下减少主机与设备间数据移动。同时设计线程高效的图操作，针对稀疏时序子图优化GPU缓存局部性，降低聚合与边softmax的延迟。此外，采用拓扑感知采样策略，提升CPU缓存局部性，加速时序邻居采样。在真实世界大规模动态图上的实验表明，FAST相比当前最优系统平均提速2.1倍（最高4.7倍），且不损失模型精度。
原文摘要 · Abstract (English)
Temporal Graph Neural Networks (TGNNs) are widely used for learning from dynamic graphs in applications such as recommendation, social network analysis, and traffic forecasting. However, scaling TGNN training to large dynamic graphs remains challenging due to three intertwined bottlenecks: memory I/O, irregular computation, and temporal neighbor sampling. Existing systems often optimize these stages in isolation, leaving substantial performance headroom on the table. We present FAST, a holistic framework that accelerates end-to-end TGNN training by jointly optimizing sampling, memory I/O, and computation. FAST introduces SlimCache, which exploits within-batch compression and cross-batch caching to reduce host-device data movement under limited GPU memory budgets. It further designs thread-efficient graph operators tailored to sparse temporal subgraphs, improving GPU cache locality and reducing the latency of aggregation and edge softmax. In addition, FAST employs a topology-aware sampling strategy that improves CPU cache locality and accelerates temporal neighbor sampling. Extensive experiments on real-world large dynamic graphs show that FAST achieves an average of 2.1x (up to 4.7x) speedup over state-of-the-art systems without sacrificing model accuracy.
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