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DCSM: Enabling Inter-Batch Parallelism for Continuous Subgraph Matching on GPU
Conference proceeding   Open access   Peer reviewed

DCSM: Enabling Inter-Batch Parallelism for Continuous Subgraph Matching on GPU

Yihua Wei and Peng Jiang
Proceedings of the 40th ACM International Conference on Supercomputing, pp.527-538
ACM Conferences
ICS '26: 2026 International Conference on Supercomputing
07/05/2026
DOI: 10.1145/3797905.3800512
Appears in  UI Libraries Support Open Access
url
https://doi.org/10.1145/3797905.3800512View
Published (Version of record) Open Access

Abstract

Continuous subgraph matching (CSM) is a fundamental building block in many real-world applications. While prior studies have explored executing CSM on heterogeneous systems with GPUs, they only exploit intra-batch parallelism and cannot process multiple batches concurrently—a capability essential for handling real-time requests. In this work, we propose a GPU-based system to accelerate CSM in practical, real-world settings. We adopt an algorithm-system co-design approach to unlock inter-batch parallelism. We introduce several key components, including a warp-specialized execution model and a multi-version graph data structure, along with version control logic for CSM tasks. Additionally, we propose optimizations such as warp-level parallel execution for data copying and incremental matching. Experimental results show that our system demonstrates optimal throughput and response time on GPU platforms across various update arrival rates.
 Continuous Subgraph Matching Streaming Graph GPU UIOWA OA Agreement

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