Temporal random walks, which sample causality-preserving paths, are widely used to analyze time-stamped interactions in domains such as microservices, finance, and online platforms. Generating such walks at scale is challenging because real-world graphs evolve as high-volume streams, making continuous ingestion, efficient memory usage, and strict temporal ordering essential for practical deployment. We present Tempest (TEMPoral nEtwork Streaming Traversals), a GPU-accelerated engine for streaming temporal random walks. Tempest combines a GPU-native dual-index organization over a shared edge store with a hierarchical cooperative scheduler that dispatches walks at thread, warp, or block granularity based on per-step node convergence, enabling efficient start-edge selection, hop-by-hop causality enforcement, and window-based eviction without synchronization. It further provides closed-form constant-time samplers for common temporal bias functions. Our evaluation demonstrates sustained real-time processing of billion-edge streams under sliding windows, outperforming prior systems in ingestion and walk generation throughput while preserving causal correctness.

翻译：时间随机游走通过采样保持因果关系的路径，广泛应用于分析微服务、金融和在线平台等领域中带时间戳的交互数据。在大规模场景下生成此类游走极具挑战性，因为真实世界的图以高吞吐流的形式演化，这使得连续数据摄入、高效内存使用以及严格的时间序管理成为实际部署的关键。我们提出Tempest（时序网络流遍历引擎），一种面向流式时间随机游走的GPU加速引擎。Tempest结合了基于共享边存储的GPU原生双索引组织架构与分层协作调度器，后者根据每步节点收敛情况在线程、线程束或线程块粒度上调度游走，实现了高效的起始边选择、逐跳因果约束执行以及无同步机制的窗口化淘汰策略。此外，针对常见时间偏置函数，我们提供了闭合形式常数时间采样器。实验评估表明，在滑动窗口机制下，系统能够持续实时处理十亿级边流，在保持因果正确性的同时，其数据摄入与游走生成吞吐量均优于现有系统。