Abstract. Boolean Satisfiability (SAT) solving has dramatically evolved in the past decade and a half. The outcome, today, is manifested in dozens of high performance and relatively scalable SAT solvers. The significant success of SAT solving technology, specially on practical problem in-stances, is credited to the aggregation of different SAT enhancements. In this paper, we revisit the organization of modern conflict-driven clause learning (CDCL) solvers, focusing on the principal techniques that have contributed to their impressive performance. We also examine the in-teraction between input instances and SAT algorithms to better under-stand the factors that contribute to the difficulty of SAT benchmarks. At the end, the paper empirically evaluates different SAT techniques on a comprehensive suite of benchmarks taken from a range of representative applications. The diversity of our benchmarks enables us to make fair conclusions on the relation between SAT algorithms and SAT instances. 1

The Boolean Satisfiability (SAT) decision problem can be deservedly declared a success story of computer science. Although SAT was the first problem to be proved NP-complete, the last decade and a half have seen dramatic improvements in the performance of SAT solvers on many practical problem instances. These performance improvements enabled a wide range of real-world applications, several of which have key industrial significance. This article surveys the organization of modern conflict-driven clause learning (CDCL) SAT solvers, focusing on the principal techniques that have contributed to this impressive performance. The article also empirically evaluates these techniques on a comprehensive suite of problem instances taken from a range of representative applications, allowing for a better understanding of their relative contribution.