X-Wim: Massive Parallelization of Weighted Matching in Bipartite Graphs
Summary: X-Wim massively parallelizes bipartite MWPM by decoupling Hungarian phases, avoiding global updates, and concurrently searching disjoint augmenting paths. Its adaptive design delivers up to 56.3× speedup on 8 cores and strong scaling to 96 cores. (summarized by gpt-5.6-luna on Aug 28 2026)
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Authors
- 1. Dayi Fan (Ohio State University)
- 2. Simon Zhang (Ohio State University)
- 3. Rubao Lee (Ohio State University)
- 4. Hanqi Guo (Ohio State University)
- 5. Xiaodong Zhang (Ohio State University)
BibTeX Citation
@article{fan_vldb26,
title = {{X-Wim: Massive Parallelization of Weighted Matching in Bipartite Graphs}},
author = {Fan, Dayi and Zhang, Simon and Lee, Rubao and Guo, Hanqi and Zhang, Xiaodong},
journal = {PVLDB},
series = {{VLDB} '26},
volume = {19},
number = {10},
pages = {2643--2657},
doi = {10.14778/3828612.3828621},
url = {https://doi.org/10.14778/3828612.3828621},
year = {2026}
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