Automatic construction of parallel portfolios via algorithm configuration
Since 2004, increases in computational power described by Moore's law have substantially been realized in the form of additional cores rather than through faster clock speeds. To make effective use of modern hardware when solving hard computational problems, it is therefore necessary to employ...
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| Veröffentlicht in: | Artificial intelligence Jg. 244; S. 272 - 290 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Amsterdam
Elsevier B.V
01.03.2017
Elsevier Science Ltd |
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| ISSN: | 0004-3702, 1872-7921 |
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| Abstract | Since 2004, increases in computational power described by Moore's law have substantially been realized in the form of additional cores rather than through faster clock speeds. To make effective use of modern hardware when solving hard computational problems, it is therefore necessary to employ parallel solution strategies. In this work, we demonstrate how effective parallel solvers for propositional satisfiability (SAT), one of the most widely studied NP-complete problems, can be produced automatically from any existing sequential, highly parametric SAT solver. Our Automatic Construction of Parallel Portfolios (ACPP) approach uses an automatic algorithm configuration procedure to identify a set of configurations that perform well when executed in parallel. Applied to two prominent SAT solvers, Lingeling and clasp, our ACPP procedure identified 8-core solvers that significantly outperformed their sequential counterparts on a diverse set of instances from the application and hard combinatorial category of the 2012 SAT Challenge. We further extended our ACPP approach to produce parallel portfolio solvers consisting of several different solvers by combining their configuration spaces. Applied to the component solvers of the 2012 SAT Challenge gold medal winning SAT Solver pfolioUZK, our ACPP procedures produced a significantly better-performing parallel SAT solver. |
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| AbstractList | Since 2004, increases in computational power described by Moore's law have substantially been realized in the form of additional cores rather than through faster clock speeds. To make effective use of modern hardware when solving hard computational problems, it is therefore necessary to employ parallel solution strategies. In this work, we demonstrate how effective parallel solvers for propositional satisfiability (SAT), one of the most widely studied NP-complete problems, can be produced automatically from any existing sequential, highly parametric SAT solver. Our Automatic Construction of Parallel Portfolios (ACPP) approach uses an automatic algorithm configuration procedure to identify a set of configurations that perform well when executed in parallel. Applied to two prominent SAT solvers, Lingeling and clasp, our ACPP procedure identified 8-core solvers that significantly outperformed their sequential counterparts on a diverse set of instances from the application and hard combinatorial category of the 2012 SAT Challenge. We further extended our ACPP approach to produce parallel portfolio solvers consisting of several different solvers by combining their configuration spaces. Applied to the component solvers of the 2012 SAT Challenge gold medal winning SAT Solver pfolioUZK, our ACPP procedures produced a significantly better-performing parallel SAT solver. |
| Author | Leyton-Brown, Kevin Hoos, Holger Lindauer, Marius Schaub, Torsten |
| Author_xml | – sequence: 1 givenname: Marius surname: Lindauer fullname: Lindauer, Marius email: lindauer@informatik.uni-freiburg.de organization: University of Freiburg, Germany – sequence: 2 givenname: Holger surname: Hoos fullname: Hoos, Holger email: hoos@cs.ubc.ca organization: University of British Columbia, Vancouver, Canada – sequence: 3 givenname: Kevin orcidid: 0000-0002-7644-5327 surname: Leyton-Brown fullname: Leyton-Brown, Kevin email: kevinlb@cs.ubc.ca organization: University of British Columbia, Vancouver, Canada – sequence: 4 givenname: Torsten surname: Schaub fullname: Schaub, Torsten email: torsten@cs.uni-potsdam.de organization: University of Potsdam, Germany |
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| Cites_doi | 10.1613/jair.2861 10.1126/science.275.5296.51 10.1007/978-3-642-31612-8_18 10.1613/jair.2490 10.1017/S1471068412000166 10.1007/978-3-642-31612-8_44 10.1145/2076450.2076469 10.1016/S0004-3702(00)00081-3 |
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| Keywords | Algorithm portfolios Parallel SAT solving Programming by optimization Automated parallelization Algorithm configuration |
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