Proceedings

gDMC: A Generic Distributed Model Counting Framework via Work-Stealing

Zhenghang Xu, Minghao Yin, Junping Zhou, Jean-Marie Lagniez

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Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence. Main Track. Pages 2409-2417.

https://doi.org/10.24963/ijcai.2026/268

Keywords

  • Constraint Satisfaction and Optimization: Constraint programming
  • Constraint Satisfaction and Optimization: Solvers and tools
  • Constraint Satisfaction and Optimization: Distributed constraints

Abstract

Propositional Model Counting (#SAT) is essential for probabilistic reasoning but faces scalability limits on single cores. Existing distributed approaches struggle with high initialization overheads (static decomposition) or rigid architecture. We propose a novel, generic framework for distributed exact model counting. Leveraging C++ templates, our architecture decouples parallel orchestration from solving logic, enabling state-of-the-art solvers to be parallelized with minimal modification. We implement an adaptive work-stealing strategy that ensures effective load balancing. Experiments on competition benchmarks show that our approach achieves near-linear scalability and significantly outperforms existing distributed solvers.

Zhenghang Xu, Minghao Yin, Junping Zhou, and Jean-Marie Lagniez. 2026. gDMC: A Generic Distributed Model Counting Framework via Work-Stealing. In Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-26. IJCAI, 2409–2417. https://doi.org/10.24963/ijcai.2026/268
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