Overview Statistic: PDF-Downloads (blue) and Frontdoor-Views (gray)

Learned Clause Minimization in Parallel SAT Solvers

  • Learned clauses minimization (LCM) let to performance improvements of modern SAT solvers especially in solving hard SAT instances. Despite the success of LCM approaches in sequential solvers, they are not widely incorporated in parallel SAT solvers. In this paper we explore the potential of LCM for parallel SAT solvers by defining multiple LCM approaches based on clause vivification, comparing their runtime in different SAT solvers and discussing reasons for performance gains and losses. Results show that LCM only boosts performance of parallel SAT solvers on a fraction of SAT instances. More commonly applying LCM decreases performance. Only certain LCM approaches are able to improve the overall performance of parallel SAT solvers.

Export metadata

Additional Services

Share in Twitter Search Google Scholar Statistics - number of accesses to the document
Metadaten
Author:Marc Hartung, Florian SchintkeORCiD
Document Type:In Proceedings
Parent Title (English):Pragmatics of SAT 2019
First Page:1
Last Page:11
Year of first publication:2019
ArXiv Id:http://arxiv.org/abs/arXiv:1908.01624v1
URL:http://www.pragmaticsofsat.org/2019/
Accept ✔
Diese Webseite verwendet technisch erforderliche Session-Cookies. Durch die weitere Nutzung der Webseite stimmen Sie diesem zu. Unsere Datenschutzerklärung finden Sie hier.