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Tempest: A GPU-Accelerated Engine for Streaming Temporal ...
[Submitted on 15 May 2026 (v1), last revised 19 Jul 2026 (this v · 2026-05-16 · via cs.DC updates on arXiv.org

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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.

Submission history

From: Md Ashfaq Salehin [view email]
[v1] Fri, 15 May 2026 17:02:08 UTC (646 KB)
[v2] Sun, 19 Jul 2026 20:42:31 UTC (657 KB)