TY - THES A1 - Lindauer, T. Marius T1 - Algorithm selection, scheduling and configuration of Boolean constraint solvers N2 - Boolean constraint solving technology has made tremendous progress over the last decade, leading to industrial-strength solvers, for example, in the areas of answer set programming (ASP), the constraint satisfaction problem (CSP), propositional satisfiability (SAT) and satisfiability of quantified Boolean formulas (QBF). However, in all these areas, there exist multiple solving strategies that work well on different applications; no strategy dominates all other strategies. Therefore, no individual solver shows robust state-of-the-art performance in all kinds of applications. Additionally, the question arises how to choose a well-performing solving strategy for a given application; this is a challenging question even for solver and domain experts. One way to address this issue is the use of portfolio solvers, that is, a set of different solvers or solver configurations. We present three new automatic portfolio methods: (i) automatic construction of parallel portfolio solvers (ACPP) via algorithm configuration,(ii) solving the $NP$-hard problem of finding effective algorithm schedules with Answer Set Programming (aspeed), and (iii) a flexible algorithm selection framework (claspfolio2) allowing for fair comparison of different selection approaches. All three methods show improved performance and robustness in comparison to individual solvers on heterogeneous instance sets from many different applications. Since parallel solvers are important to effectively solve hard problems on parallel computation systems (e.g., multi-core processors), we extend all three approaches to be effectively applicable in parallel settings. We conducted extensive experimental studies different instance sets from ASP, CSP, MAXSAT, Operation Research (OR), SAT and QBF that indicate an improvement in the state-of-the-art solving heterogeneous instance sets. Last but not least, from our experimental studies, we deduce practical advice regarding the question when to apply which of our methods. N2 - Bool'sche Solver Technologie machte enormen Fortschritt im letzten Jahrzehnt, was beispielsweise zu industrie-relevanten Solvern auf der Basis von Antwortmengenprogrammierung (ASP), dem Constraint Satisfcation Problem (CSP), dem Erfüllbarkeitsproblem für aussagenlogische Formeln (SAT) und dem Erfüllbarkeitsproblem für quantifizierte boolesche Formeln (QBF) führte. Allerdings gibt es in all diesen Bereichen verschiedene Lösungsstrategien, welche bei verschiedenen Anwendungen unterschiedlich effizient sind. Dabei gibt es keine einzelne Strategie, die alle anderen Strategien dominiert. Das führt dazu, dass es keinen robusten Solver für das Lösen von allen möglichen Anwendungsprobleme gibt. Die Wahl der richtigen Strategie für eine neue Anwendung ist eine herausforderne Problemstellung selbst für Solver- und Anwendungsexperten. Eine Möglichkeit, um Solver robuster zu machen, sind Portfolio-Ansätze. Wir stellen drei automatisch einsetzbare Portfolio-Ansätze vor: (i) automatische Konstruktion von parallelen Portfolio-Solvern (ACPP) mit Algorithmen-Konfiguration, (ii) das Lösen des $NP$-harten Problems zur Algorithmen-Ablaufplanung (aspeed) mit ASP, und (iii) ein flexibles Algorithmen-Selektionsframework (claspfolio2), was viele Techniken von Algorithmen-Selektion parametrisiert implementiert und eine faire Vergleichbarkeit zwischen Ihnen erlaubt. Alle drei Methoden verbessern die Robustheit des Solvingprozesses für heterogenen Instanzmengen bestehend aus unterschiedlichsten Anwendungsproblemen. Parallele Solver sind zunehmend der Schlüssel zum effektiven Lösen auf multi-core Maschinen. Daher haben wir all unsere Ansätze auch für den Einsatz auf parallelen Architekturen erweitert. Umfangreiche Experimente auf ASP, CSP, MAXSAT, Operation Research (OR), SAT und QBF zeigen, dass der Stand der Technik durch verbesserte Performanz auf heterogenen Instanzmengen verbessert wurde. Auf Grundlage dieser Experimente leiten wir auch Ratschläge ab, in welchen Anwendungsszenarien welches unserer Verfahren angewendet werden sollte. T2 - Algorithmen-Selektion, -Ablaufplanung und -Konfiguration von Bool'schen Constraint Solvern KW - algorithm configuration KW - algorithm scheduling KW - algorithm selection KW - parallel solving KW - Boolean constraint solver KW - Algorithmenselektion KW - Algorithmenablaufplanung KW - Algorithmenkonfiguration KW - paralleles Lösen Y1 - 2014 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-71260 ER - TY - JOUR A1 - Hoos, Holger A1 - Lindauer, Marius A1 - Schaub, Torsten H. T1 - claspfolio 2 BT - advances in algorithm selection for answer set programming JF - Theory and practice of logic programming N2 - Building on the award-winning, portfolio-based ASP solver claspfolio, we present claspfolio 2, a modular and open solver architecture that integrates several different portfolio-based algorithm selection approaches and techniques. The claspfolio 2 solver framework supports various feature generators, solver selection approaches, solver portfolios, as well as solver-schedule-based pre-solving techniques. The default configuration of claspfolio 2 relies on a light-weight version of the ASP solver clasp to generate static and dynamic instance features. The flexible open design of claspfolio 2 is a distinguishing factor even beyond ASP. As such, it provides a unique framework for comparing and combining existing portfolio-based algorithm selection approaches and techniques in a single, unified framework. Taking advantage of this, we conducted an extensive experimental study to assess the impact of different feature sets, selection approaches and base solver portfolios. In addition to gaining substantial insights into the utility of the various approaches and techniques, we identified a default configuration of claspfolio 2 that achieves substantial performance gains not only over clasp's default configuration and the earlier version of claspfolio, but also over manually tuned configurations of clasp. Y1 - 2014 U6 - https://doi.org/10.1017/S1471068414000210 SN - 1471-0684 SN - 1475-3081 VL - 14 SP - 569 EP - 585 PB - Cambridge Univ. Press CY - New York ER - TY - GEN A1 - Hoos, Holger A1 - Lindauer, Marius A1 - Schaub, Torsten H. T1 - claspfolio 2 BT - advances in algorithm selection for answer set programming T2 - Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe N2 - Building on the award-winning, portfolio-based ASP solver claspfolio, we present claspfolio 2, a modular and open solver architecture that integrates several different portfolio-based algorithm selection approaches and techniques. The claspfolio 2 solver framework supports various feature generators, solver selection approaches, solver portfolios, as well as solver-schedule-based pre-solving techniques. The default configuration of claspfolio 2 relies on a light-weight version of the ASP solver clasp to generate static and dynamic instance features. The flexible open design of claspfolio 2 is a distinguishing factor even beyond ASP. As such, it provides a unique framework for comparing and combining existing portfolio-based algorithm selection approaches and techniques in a single, unified framework. Taking advantage of this, we conducted an extensive experimental study to assess the impact of different feature sets, selection approaches and base solver portfolios. In addition to gaining substantial insights into the utility of the various approaches and techniques, we identified a default configuration of claspfolio 2 that achieves substantial performance gains not only over clasp's default configuration and the earlier version of claspfolio, but also over manually tuned configurations of clasp. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 606 KW - solver KW - sat Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-416129 SN - 1866-8372 IS - 606 ER - TY - JOUR A1 - Lindauer, Marius A1 - Hoos, Holger H. A1 - Hutter, Frank A1 - Schaub, Torsten H. T1 - An automatically configured algorithm selector JF - The journal of artificial intelligence research N2 - Algorithm selection (AS) techniques - which involve choosing from a set of algorithms the one expected to solve a given problem instance most efficiently - have substantially improved the state of the art in solving many prominent AI problems, such as SAT, CSP, ASP, MAXSAT and QBF. Although several AS procedures have been introduced, not too surprisingly, none of them dominates all others across all AS scenarios. Furthermore, these procedures have parameters whose optimal values vary across AS scenarios. This holds specifically for the machine learning techniques that form the core of current AS procedures, and for their hyperparameters. Therefore, to successfully apply AS to new problems, algorithms and benchmark sets, two questions need to be answered: (i) how to select an AS approach and (ii) how to set its parameters effectively. We address both of these problems simultaneously by using automated algorithm configuration. Specifically, we demonstrate that we can automatically configure claspfolio 2, which implements a large variety of different AS approaches and their respective parameters in a single, highly-parameterized algorithm framework. Our approach, dubbed AutoFolio, allows researchers and practitioners across a broad range of applications to exploit the combined power of many different AS methods. We demonstrate AutoFolio can significantly improve the performance of claspfolio 2 on 8 out of the 13 scenarios from the Algorithm Selection Library, leads to new state-of-the-art algorithm selectors for 7 of these scenarios, and matches state-of-the-art performance (statistically) on all other scenarios. Compared to the best single algorithm for each AS scenario, AutoFolio achieves average speedup factors between 1.3 and 15.4. Y1 - 2015 SN - 1076-9757 SN - 1943-5037 VL - 53 SP - 745 EP - 778 PB - AI Access Foundation CY - Marina del Rey ER - TY - JOUR A1 - Hoos, Holger A1 - Kaminski, Roland A1 - Lindauer, Marius A1 - Schaub, Torsten H. T1 - aspeed: Solver scheduling via answer set programming JF - Theory and practice of logic programming N2 - 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 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. KW - algorithm schedules KW - answer set programming KW - portfolio-based solving Y1 - 2015 U6 - https://doi.org/10.1017/S1471068414000015 SN - 1471-0684 SN - 1475-3081 VL - 15 SP - 117 EP - 142 PB - Cambridge Univ. Press CY - New York ER - TY - GEN A1 - Hoos, Holger A1 - Kaminski, Roland A1 - Lindauer, Marius A1 - Schaub, Torsten H. T1 - aspeed BT - solver scheduling via answer set programming T2 - Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe N2 - 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 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. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 588 KW - algorithm schedules KW - answer set programming KW - portfolio-based solving Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-414743 SN - 1866-8372 IS - 588 ER - TY - JOUR A1 - Lindauer, Marius A1 - Hoos, Holger A1 - Leyton-Brown, Kevin A1 - Schaub, Torsten H. T1 - Automatic construction of parallel portfolios via algorithm configuration JF - Artificial intelligence N2 - 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. KW - Algorithm configuration KW - Parallel SAT solving KW - Algorithm portfolios KW - Programming by optimization KW - Automated parallelization Y1 - 2016 U6 - https://doi.org/10.1016/j.artint.2016.05.004 SN - 0004-3702 SN - 1872-7921 VL - 244 SP - 272 EP - 290 PB - Elsevier CY - Amsterdam ER -