ManySAT: a parallel SAT solver. ManySAT, a new portfolio-based parallel SAT solver, is thoroughly described. The design of ManySAT benefits from the main weaknesses of modern SAT solvers: their sensitivity to parameter tuning and their lack of robustness. ManySAT uses a portfolio of complementary sequential algorithms obtained through careful variations of the standard DPLL algorithm. Additionally, each sequential algorithm shares clauses to improve the overall performance of the whole system. This contrasts with most of the parallel SAT solvers generally designed using the divide-and-conquer paradigm. Experiments on many industrial SAT instances, and the first rank obtained by ManySAT in the parallel track of the 2008 SAT-Race clearly show the potential of our design philosophy.

References in zbMATH (referenced in 43 articles , 1 standard article )

Showing results 1 to 20 of 43.
Sorted by year (citations)

1 2 3 next

  1. Froleyks, Nils; Heule, Marijn; Iser, Markus; Järvisalo, Matti; Suda, Martin: SAT competition 2020 (2021)
  2. Prevot, Nicolas; Soos, Mate; Meel, Kuldeep S.: Leveraging GPUs for effective clause sharing in parallel SAT solving (2021)
  3. Schreiber, Dominik; Sanders, Peter: Scalable SAT solving in the cloud (2021)
  4. Heisinger, Maximilian; Fleury, Mathias; Biere, Armin: Distributed cube and conquer with Paracooba (2020)
  5. Jarvis, Padraigh; Arbelaez, Alejandro: Cooperative parallel SAT local search with path relinking (2020)
  6. Nabeshima, Hidetomo; Inoue, Katsumi: Reproducible efficient parallel SAT solving (2020)
  7. Roh, Dongyoung; Koo, Bonwook; Jung, Younghoon; Jeong, Il Woong; Lee, Dong-Geon; Kwon, Daesung; Kim, Woo-Hwan: Revised version of block cipher CHAM (2020)
  8. Vallade, Vincent; Le Frioux, Ludovic; Baarir, Souheib; Sopena, Julien; Ganesh, Vijay; Kordon, Fabrice: Community and LBD-based clause sharing policy for parallel SAT solving (2020)
  9. Zaikin, Oleg; Kochemazov, Stepan: Black-box optimization in an extended search space for SAT solving (2019)
  10. Cheng, Xi; Zhou, Min; Song, Xiaoyu; Gu, Ming; Sun, Jiaguang: Parallelizing SMT solving: lazy decomposition and conciliation (2018)
  11. Gent, Ian P.; Miguel, Ian; Nightingale, Peter; McCreesh, Ciaran; Prosser, Patrick; Moore, Neil C. A.; Unsworth, Chris: A review of literature on parallel constraint solving (2018)
  12. Liang, Jia Hui; Oh, Chanseok; Mathew, Minu; Thomas, Ciza; Li, Chunxiao; Ganesh, Vijay: Machine learning-based restart policy for CDCL SAT solvers (2018)
  13. Marques-Silva, Joao; Malik, Sharad: Propositional SAT solving (2018)
  14. Adelshin, A. V.; Kuchin, A. K.: Analysis of (L)-structure of polyhedron in the partial MAX SAT problem (2017)
  15. Audemard, Gilles; Lagniez, Jean-Marie; Szczepanski, Nicolas; Tabary, Sébastien: A distributed version of Syrup (2017)
  16. Le Frioux, Ludovic; Baarir, Souheib; Sopena, Julien; Kordon, Fabrice: Painless: a framework for parallel SAT solving (2017)
  17. Lindauer, Marius; Hoos, Holger; Leyton-Brown, Kevin; Schaub, Torsten: Automatic construction of parallel portfolios via algorithm configuration (2017)
  18. Nejati, Saeed; Newsham, Zack; Scott, Joseph; Liang, Jia Hui; Gebotys, Catherine; Poupart, Pascal; Ganesh, Vijay: A propagation rate based splitting heuristic for divide-and-conquer solvers (2017)
  19. Balyo, Tomáš; Biere, Armin; Iser, Markus; Sinz, Carsten: SAT race 2015 (2016)
  20. Hamadi, Youssef; Jabbour, Saïd; Saïs, Lakhdar: What we can learn from conflicts in propositional satisfiability (2016)

1 2 3 next