SMT Theory Arbitrage: Approximating Unbounded Constraints using Bounded Theories

SMT solvers are foundational tools for reasoning about constraints in practical problems both within and outside program analysis. Faster SMT solving improves the performance of practical tools and expands the set of tractable problems. Existing approaches to improving solver performance either focus on general algorithms applied below the level of individual theories, or focus on optimizations within a single theory. Unbounded constraints in which the number of possible variable values is infinite, such as real numbers and integers, pose a particularly difficult challenge for solvers. Bounded constraints in which the set of possible values is finite such as bitvectors and floating-point numbers, on the other hand, are decidable and have been the subject of extensive performance improvement efforts. This paper introduces a theory arbitrage: we transform unbounded constraints, which are often expensive to solve, into bounded constraints, which are typically cheaper to solve. By converting unbounded problems into bounded ones, theory arbitrage takes advantage of better performance on bounded constraints and unlocks optimization techniques that only apply to bounded theories. The transformation is achieved by harnessing a novel abstract interpretation strategy to infer bounds. The bounded transformed constraint is then an underapproximation of the semantics of the unbounded original. We realize our method for the theories of integers and real numbers with a practical tool (STAUB). Our evaluation demonstrates that theory arbitrage alone can speed up individual constraints by orders of magnitude and achieve up to a 1.4 × speedup on average across nonlinear integer benchmarks. Furthermore, it enables the use of the recent compiler optimization-based technique SLOT for unbounded SMT theories, unlocking a further speedup of up to 3 × . Finally, we incorporate STAUB into a practical termination proving tool and observe an overall 9 % improvement in performance.