DepTGL：一种基于记忆的TGNN训练并行框架，具备自适应时序数据依赖管理能力
DepTGL: A Parallel Framework for Memory-based TGNN Training with Adaptive Temporal Data Dependency Management
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中文总结 AI 辅助
DepTGL是一种分布式训练框架，通过混合依赖管理、梯度感知缓存同步、负载感知时序剪枝策略，提升M-TGNNs训练效率，在6个真实时序图上实现平均4.99倍加速且准确率相当。
中文摘要 AI 辅助
基于记忆的时序图神经网络（M-TGNNs）通过递归更新节点状态来捕捉细粒度的时序交互。然而，现有分布式框架缺乏管理这些模型固有时序数据依赖的有效机制，导致必须执行严格的时序更新、产生大量远程同步开销，且当时序事件流倾斜时会出现严重的负载不平衡。我们提出DepTGL，一种可扩展的分布式训练框架，从数据视角重构M-TGNNs的时序依赖管理。首先，DepTGL引入混合时序依赖管理方案，通过时序事件缓存平衡通信与缓存开销，辅以选择性依赖驱动通信；其次，DepTGL纳入梯度感知的缓存同步策略，在模型优化稳定时自适应抑制边界更新，减少冗余同步；最后，DepTGL集成负载感知的时序剪枝策略，在倾斜引发的负载峰值时消除辅助重放事件，减少冗余数据处理并缓解掉队效应。在六个真实世界时序图上的实验表明，DepTGL相比最先进的基准模型实现了平均4.99倍的加速，同时保持相当的准确率。
英文摘要
Memory-based Temporal Graph Neural Networks (M-TGNNs) maintain recursively updated node states to capture fine-grained temporal interactions. However, existing distributed frameworks lack effective mechanisms for managing the temporal data dependencies inherent in these models. As a result, they must enforce strict chronological updates, incur substantial remote synchronization overhead, and experience severe load imbalance when temporal event streams are skewed. We propose DepTGL, a scalable distributed training framework that restructures temporal-dependency management for M-TGNNs from a data-centric perspective. First, DepTGL introduces a hybrid temporal-dependency management scheme that explicitly balances communication and caching overhead via temporal-event caching, supplemented by selective dependency-driven communication. Next, DepTGL incorporates a gradient-aware cache-synchronization policy that adaptively suppresses boundary updates as model optimization stabilizes, thereby reducing redundant synchronization. Finally, DepTGL integrates a load-aware temporal-pruning strategy that eliminates auxiliary replay events under skew-induced load spikes, reducing redundant data processing and mitigating straggler effects. Experiments on six real-world temporal graphs show that DepTGL achieves an average speedup of 4.99x over state-of-the-art baselines, while maintaining comparable accuracy.
