Unit propagation with stable watches

Unit propagation is the hottest path in CDCL SAT solvers, therefore the related data-structures, algorithms and implementation details are well studied and highly optimized. State-of-the-art implementations are based on reduced occurrence tracking with two watched literals per clause and one blocking literal per watcher in order to further reduce the number of clause accesses. In this paper, we show that using runtime statistics for watched literal selection can improve the performance of state-of-the-art SAT solvers. We present a method for efficiently keeping track of spans during which literals are satisfied and using this statistic to improve watcher selection. An implementation of our method in the SAT solver CaDiCaL can solve more instances of the SAT Competition 2019 and 2020 benchmark sets and is specifically strong on satisfiable cryptographic instances

Optimal implementation of watched literals and more general techniques

Includes 2 appendixes: one with additional proofs and one with code, scripts and data.I prove that an implementation technique for scanning lists in backtracking search algorithms is optimal. The result applies to a simple general framework, which I present: applications include watched literal unit propagation in SAT and a number of examples in constraint satisfaction. Techniques like watched literals are known to be highly space efficient and effective in practice. When implemented in the 'circular' approach described here, these techniques also have optimal run time per branch in big-O terms when amortized across a search tree. This also applies when multiple list elements must be found. The constant factor overhead of the worst case is only 2. Replacing the existing non-optimal implementation of unit propagation in MiniSat speeds up propagation by 29%, though this is not enough to improve overall run time significantly.Publisher PDFPeer reviewe

Optimal implementation of watched literals and more general techniques

I prove that an implementation technique for scanning lists inbacktracking search algorithms is optimal. The result applies toa simple general framework, which I present: applications includewatched literal unit propagation in SAT and a number of examplesin constraint satisfaction. Techniques like watched literals areknown to be highly space efficient and effective in practice. Whenimplemented in the 'circular' approach described here, thesetechniques also have optimal run time per branch in big-O termswhen amortized across a search tree. This also applies when multiplelist elements must be found. The constant factor overhead of theworst case is only 2. Replacing the existing non-optimal implementationof unit propagation in MiniSat speeds up propagation by 29%, thoughthis is not enough to improve overall run time significantly

Towards Next Generation Sequential and Parallel SAT Solvers

This thesis focuses on improving the SAT solving technology. The improvements focus on two major subjects: sequential SAT solving and parallel SAT solving. To better understand sequential SAT algorithms, the abstract reduction system Generic CDCL is introduced. With Generic CDCL, the soundness of solving techniques can be modeled. Next, the conflict driven clause learning algorithm is extended with the three techniques local look-ahead, local probing and all UIP learning that allow more global reasoning during search. These techniques improve the performance of the sequential SAT solver Riss. Then, the formula simplification techniques bounded variable addition, covered literal elimination and an advanced cardinality constraint extraction are introduced. By using these techniques, the reasoning of the overall SAT solving tool chain becomes stronger than plain resolution. When using these three techniques in the formula simplification tool Coprocessor before using Riss to solve a formula, the performance can be improved further. Due to the increasing number of cores in CPUs, the scalable parallel SAT solving approach iterative partitioning has been implemented in Pcasso for the multi-core architecture. Related work on parallel SAT solving has been studied to extract main ideas that can improve Pcasso. Besides parallel formula simplification with bounded variable elimination, the major extension is the extended clause sharing level based clause tagging, which builds the basis for conflict driven node killing. The latter allows to better identify unsatisfiable search space partitions. Another improvement is to combine scattering and look-ahead as a superior search space partitioning function. In combination with Coprocessor, the introduced extensions increase the performance of the parallel solver Pcasso. The implemented system turns out to be scalable for the multi-core architecture. Hence iterative partitioning is interesting for future parallel SAT solvers. The implemented solvers participated in international SAT competitions. In 2013 and 2014 Pcasso showed a good performance. Riss in combination with Copro- cessor won several first, second and third prices, including two Kurt-Gödel-Medals. Hence, the introduced algorithms improved modern SAT solving technology