Chaff

Chaff:engineering an efficient SAT solver. Boolean Satisfiability is probably the most studied of combinatorial optimization/search problems. Significant effort has been devoted to trying to provide practical solutions to this problem for problem instances encountered in a range of applications in Electronic Design Automation (EDA), as well as in Artificial Intelligence (AI). This study has culminated in the development of several SAT packages, both proprietary and in the public domain (e.g. GRASP, SATO) which find significant use in both research and industry. Most existing complete solvers are variants of the Davis-Putnam (DP) search algorithm. In this paper we describe the development of a new complete solver, Chaff, which achieves significant performance gains through careful engineering of all aspects of the search - especially a particularly efficient implementation of Boolean constraint propagation (BCP) and a novel low overhead decision strategy. Chaff has been able to obtain one to two orders of magnitude performance improvement on difficult SAT benchmarks in comparison with other solvers (DP or otherwise), including GRASP and SATO.


References in zbMATH (referenced in 564 articles )

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  1. Berkholz, Christoph; Nordström, Jakob: Supercritical space-width trade-offs for resolution (2020)
  2. Bromberger, Martin; Sturm, Thomas; Weidenbach, Christoph: A complete and terminating approach to linear integer solving (2020)
  3. Drechsler, Rolf (ed.); Soeken, Mathias (ed.): Advanced Boolean techniques. Selected papers from the 13th international workshop on Boolean problems, Bremen, Germany, September 19--21, 2018 (2020)
  4. Lagniez, Jean-Marie; Lonca, Emmanuel; Marquis, Pierre: Definability for model counting (2020)
  5. Lammich, Peter: Efficient verified (UN)SAT certificate checking (2020)
  6. Li, Chu-Min; Xiao, Fan; Luo, Mao; Manyà, Felip; Lü, Zhipeng; Li, Yu: Clause vivification by unit propagation in CDCL SAT solvers (2020)
  7. Abd El-Maksoud, Munira A.; Abdalla, Areeg: A novel SAT solver for the Van der Waerden numbers (2019)
  8. Everardo, Flavio; Janhunen, Tomi; Kaminski, Roland; Schaub, Torsten: The return of \textitxorro (2019)
  9. Wang, Wenxi; Søndergaard, Harald; Stuckey, Peter J.: Wombit: a portfolio bit-vector solver using word-level propagation (2019)
  10. Belahcène, K.; Labreuche, C.; Maudet, N.; Mousseau, V.; Ouerdane, W.: An efficient SAT formulation for learning multiple criteria non-compensatory sorting rules from examples (2018)
  11. Biere, Armin; Kröning, Daniel: SAT-based model checking (2018)
  12. Blanchette, Jasmin Christian; Fleury, Mathias; Lammich, Peter; Weidenbach, Christoph: A verified SAT solver framework with learn, forget, restart, and incrementality (2018)
  13. Cheng, Xi; Zhou, Min; Song, Xiaoyu; Gu, Ming; Sun, Jiaguang: Parallelizing SMT solving: lazy decomposition and conciliation (2018)
  14. Kreter, Stefan; Schutt, Andreas; Stuckey, Peter J.; Zimmermann, Jürgen: Mixed-integer linear programming and constraint programming formulations for solving resource availability cost problems (2018)
  15. Lauria, Massimo: Cliques enumeration and tree-like resolution proofs (2018)
  16. Lauria, Massimo: A note about (k)-DNF resolution (2018)
  17. Lyu, Yinrun; Chen, Li; Zhang, Changyou; Qu, Dacheng; Min-Allah, Nasro; Wang, Yongji: An interleaved depth-first search method for the linear optimization problem with disjunctive constraints (2018)
  18. Marescotti, Matteo; Hyvärinen, Antti E. J.; Sharygina, Natasha: SMTS: distributed, visualized constraint solving (2018)
  19. Marques-Silva, Joao; Malik, Sharad: Propositional SAT solving (2018)
  20. Nadel, Alexander: Solving MaxSAT with bit-vector optimization (2018)

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Further publications can be found at: http://www.princeton.edu/~chaff/publication.html