Understanding CDCL Solvers via Scalability Studies and Proofdoors

2026-05-15

AI Summary

This study addresses the lack of systematic analysis on the scalability of CDCL SAT solvers in industrial instances and the inadequacy of traditional structural parameters in explaining performance variations. To bridge this gap, the authors construct a large-scale benchmark suite from bounded model checking (BMC) instances and introduce proofdoor—a novel structural parameter that captures the complexity of inference memory during solving. Through scalability experiments, instance perturbations, and proofdoor sampling analyses, they demonstrate that variable branching order significantly influences both proofdoor size and solver efficiency. Empirical results reveal that linearly scalable instances exhibit small, incrementally absorbable proofdoors, whereas exponentially hard instances correspond to large, intractable proofdoors. Moreover, perturbing the variable order substantially increases proofdoor size and degrades solver performance.

Abstract

Over the past several decades, CDCL SAT solvers have proven remarkably effective on large industrial formulas, despite SAT being NP-complete and widely believed to be intractable. While considerable empirical research has been done on solver performance over benchmarks like the SAT competition, as well as scaling studies on random and crafted families, surprisingly little effort has gone into systematic scaling studies over industrial instances. To address this gap, we collect a large benchmark of Bounded Model Checking (BMC) instances (76,600+ across 766 families) and perform a systematic scaling study of solver performance. We observe a spectrum: some families scale linearly, others polynomially or exponentially. Building on this foundation, we study the structural parameters that have been proposed to explain this phenomenon. We first show that previously proposed parameters -- clause-variable ratio, treewidth, and community structure -- fail to discriminate between the linear and exponential regimes. By contrast, the recently proposed \emph{proofdoor} parameter explains this phenomenon well. Informally, a proofdoor is a sequence of interpolants between chunks of a formula, where each interpolant represents the solver's memoization of reasoning effort on chunks it has already analyzed. In support of the proofdoor hypothesis, we make three key contributions. First, we empirically show that CDCL solvers do compute small proofdoors for linearly-scaling BMC instances. Second, we show that for exponentially-scaling instances, sampled proofdoors scale exponentially and are typically not incrementally absorbed. Third, we show that scrambling linearly-scaling instances yields larger proofdoor sizes relative to pre-scrambling, relating poor branching order to larger proofdoor sizes and drop in solver performance.

Problem

• CDCL solvers
• scalability
• industrial instances
• proofdoors
• Bounded Model Checking

Innovation

• proofdoor
• CDCL SAT solvers
• scalability study
• Bounded Model Checking
• structural parameters