gDMC：一种通过工作窃取的通用分布式模型计数框架
gDMC: A Generic Distributed Model Counting Framework via Work-Stealing
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中文总结 AI 辅助
研究针对命题模型计数在单核上的可扩展性问题及现有分布式方法的不足，提出gDMC框架，利用C++模板解耦并行编排与逻辑求解，采用自适应工作窃取策略，实现近线性可扩展性，性能显著优于现有分布式求解器。
中文摘要 AI 辅助
命题模型计数（$\#\mathsf{SAT}$）对概率推理至关重要，但在单核上存在可扩展性限制。现有分布式方法存在初始化开销大（静态分解）或架构僵化的问题。我们提出了一种新颖的通用框架用于分布式精确模型计数。利用C++模板，架构将并行编排与逻辑求解解耦，实现自适应工作窃取策略以确保有效负载均衡。实验表明该方法实现近线性可扩展性且显著优于现有分布式求解器。
英文摘要
Propositional Model Counting ($\#\mathsf{SAT}$) 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 \emph{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.
