Characterizing Parallel Subgraph Matching Performance: A Systematic Study of Interactions, Scalability, and Enumeration
Summary: Systematic empirical study of parallel subgraph matching on shared-memory multicore: disentangles task splitting, scheduling, and match enumeration. Evaluates 100 technique combinations, exposing interaction effects, scalability limits, and when sequential algorithms can be parallelized effectively. (summarized by gpt-5.4-mini on May 27 2026)
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Authors
- 1. Tao Yu (Fudan University)
- 2. Zhijie Zhang (Fudan University)
- 3. Weiguo Zheng (Fudan University)
- 4. Jeffrey Xu Yu (Chinese University of Hong Kong)
- 5. Qiang Zhou (Ant Financial)
- 6. Chuntao Hong (Ant Financial)
BibTeX Citation
@article{yu_vldb26,
title = {{Characterizing Parallel Subgraph Matching Performance: A Systematic Study of Interactions, Scalability, and Enumeration}},
author = {Yu, Tao and Zhang, Zhijie and Zheng, Weiguo and Yu, Jeffrey Xu and Zhou, Qiang and Hong, Chuntao},
journal = {PVLDB},
series = {{VLDB} '26},
volume = {19},
number = {7},
pages = {1659--1673},
doi = {10.14778/3801059.3801076},
url = {https://doi.org/10.14778/3801059.3801076},
year = {2026}
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