Extreme SAT-based Constraint solving with R-Solve

We present the architecture of R-Solve, a high-performance constraint solver that extends the gains made in SAT performance over the past fifteen years on static decision problems to problems that require on-the-fly adaptation, solution space exploration and optimization. R-Solve brings together a modern, open SAT solver, HPC ideas in ensemble and collaborative parallel solving and a novel set of extensions supporting, for the first time, an efficient system for unrestricted incremental solving. These extensions, which we call Smart Repair, enable R-Solve to address problems in dynamic planning and constrained optimization involving complex logical and arithmetic constraints. We provide insight into the rationale and challenges of extending an aggressively optimized competitive solver to support a new class of problem, provide a performance milestone illustrating the potential of R-Solve and Smart Repair and discuss new frontiers for further HPC research in SAT-based constraint solving and constrained optimization.

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