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arXiv · 2605.16182

Tempest: A GPU-Accelerated Engine for Streaming Temporal Random Walks

Md Ashfaq Salehin·George Parisis·Luc Berthouze
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Abstract

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.

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Md Ashfaq Salehin, George Parisis, Luc Berthouze. 2026-05-15. Tempest: A GPU-Accelerated Engine for Streaming Temporal Random Walks. https://doi.org/10.1145/3832810.3832885

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