aspeed

aspeed: solver scheduling via answer set programming. Although Boolean Constraint Technology has made tremendous progress over the last decade, the efficacy of state-of-the-art solvers is known to vary considerably across different types of problem instances, and is known to depend strongly on algorithm parameters. This problem was addressed by means of a simple, yet effective approach using handmade, uniform, and unordered schedules of multiple solvers in {it ppfolio}, which showed very impressive performance in the 2011 Satisfiability Testing (SAT) Competition. Inspired by this, we take advantage of the modeling and solving capacities of Answer Set Programming (ASP) to automatically determine more refined, that is, nonuniform and ordered solver schedules from the existing benchmarking data. We begin by formulating the determination of such schedules as multi-criteria optimization problems and provide corresponding ASP encodings. The resulting encodings are easily customizable for different settings, and the computation of optimum schedules can mostly be done in the blink of an eye, even when dealing with large runtime data sets stemming from many solvers on hundreds to thousands of instances. Also, the fact that our approach can be customized easily enabled us to swiftly adapt it to generate parallel schedules for multi-processor machines.


References in zbMATH (referenced in 11 articles , 2 standard articles )

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  1. Wawrzyniak, Jakub; Drozdowski, Maciej; Sanlaville, Éric: Selecting algorithms for large berth allocation problems (2020)
  2. Lindauer, Marius; van Rijn, Jan N.; Kotthoff, Lars: The algorithm selection competitions 2015 and 2017 (2019)
  3. 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)
  4. Malone, Brandon; Kangas, Kustaa; Järvisalo, Matti; Koivisto, Mikko; Myllymäki, Petri: Empirical hardness of finding optimal Bayesian network structures: algorithm selection and runtime prediction (2018)
  5. Lindauer, Marius; Hoos, Holger; Leyton-Brown, Kevin; Schaub, Torsten: Automatic construction of parallel portfolios via algorithm configuration (2017)
  6. Bischl, Bernd; Kerschke, Pascal; Kotthoff, Lars; Lindauer, Marius; Malitsky, Yuri; Fréchette, Alexandre; Hoos, Holger; Hutter, Frank; Leyton-Brown, Kevin; Tierney, Kevin; Vanschoren, Joaquin: ASlib: a benchmark library for algorithm selection (2016)
  7. Amadini, Roberto; Gabbrielli, Maurizio; Mauro, Jacopo: Why CP portfolio solvers are (under)utilized? issues and challenges (2015)
  8. Hoos, Holger; Kaminski, Roland; Lindauer, Marius; Schaub, Torsten: aspeed: solver scheduling via answer set programming (2015)
  9. Núñez, Sergio; Borrajo, Daniel; Linares López, Carlos: Automatic construction of optimal static sequential portfolios for AI planning and beyond (2015)
  10. Amadini, Roberto; Gabbrielli, Maurizio; Mauro, Jacopo: SUNNY: a lazy portfolio approach for constraint solving (2014)
  11. Hoos, Holger; Kaminski, Roland; Schaub, Torsten; Schneider, Marius: aspeed: ASP-based solver scheduling (2012)