---
title: "gDMC: A Generic Distributed Model Counting Framework via Work-Stealing"
authors:
  - "Zhenghang Xu"
  - "Minghao Yin"
  - "Junping Zhou"
  - "Jean-Marie Lagniez"
doi: "10.24963/ijcai.2026/268"
url: "https://ijcai.org/proceedings/2026/268"
pdf: "https://ijcai.org/proceedings/2026/0268.pdf"
venue: "Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence"
year: 2026
pages: "2409-2417"
track: "Main Track"
keywords:
  - "Constraint Satisfaction and Optimization: Constraint programming"
  - "Constraint Satisfaction and Optimization: Solvers and tools"
  - "Constraint Satisfaction and Optimization: Distributed constraints"
---

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

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

*Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence*, pages 2409-2417. DOI: [10.24963/ijcai.2026/268](https://doi.org/10.24963/ijcai.2026/268)

## 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.