gDMC framework achieves near-linear scalability for exact model counting
Adaptive work-stealing slashes overhead, scaling model counting across thousands of cores.
Propositional Model Counting (#SAT) is critical for probabilistic reasoning but struggles with scalability on single cores. Existing distributed approaches suffer from high initialization overhead due to static decomposition or are tied to rigid architectures. Researchers from multiple institutions introduce gDMC, a novel generic framework for distributed exact model counting. By leveraging C++ templates, gDMC cleanly separates parallel orchestration from the solver's internal logic, allowing state-of-the-art #SAT solvers to be parallelized with only minimal code modifications.
gDMC's core innovation is an adaptive work-stealing strategy that dynamically balances workloads across compute nodes, eliminating bottlenecks common in static partitioning. In experiments on standard competition benchmarks, gDMC demonstrates near-linear speedup as cores are added, and it consistently outperforms existing distributed exact model counters. This design makes distributed exact counting practical for larger problem instances—a key enabler for probabilistic inference, formal verification, and AI planning tasks that demand exact counts.
- Adaptive work-stealing dynamically balances loads across distributed nodes, avoiding static decomposition overhead.
- C++ template-based architecture decouples orchestration from solving, enabling easy parallelization of existing solvers.
- Achieves near-linear scalability on competition benchmarks, significantly outperforming previous distributed #SAT solvers.
Why It Matters
Enables faster exact probabilistic reasoning at scale, critical for AI planning and formal verification workloads.