| Description |
1 online resource |
| Contents |
Intro; Foreword; Preface; Contents; List of Contributors; Part I Theory and Algorithms; 1 Parallel Satisfiability; 1.1 Introduction; 1.2 Preliminaries; 1.2.1 Satisfiability (SAT); 1.2.2 Local Search Algorithms for SAT; 1.2.3 The DPLL Algorithm; 1.2.4 Resolution Refutation; 1.2.5 The CDCL Algorithm; 1.2.6 Parallel Computing Architectures; 1.2.7 Measuring Speedups; 1.3 Divide-and-Conquer Approaches; 1.3.1 Problem Decomposition and Load Balancing; 1.3.2 Implementations of Search-Space-Splitting Solvers; 1.3.3 Search Space Splitting in CDCL; 1.3.4 Cube and Conquer |
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1.4 Parallel Portfolios -- Diversify and Conquer!1.4.1 Virtual Best Solver; 1.4.2 Pure Portfolio Solvers and Diversification; 1.4.3 Clause-Sharing Portfolios; 1.4.4 Impact of Diversification and Clause Sharing; 1.4.5 Examples of Parallel Portfolio Solvers; 1.5 Parallel Local Search; 1.5.1 Multiple Flips; 1.5.2 Portfolios; 1.6 Future Challenges; Acknowledgements; References; 2 Cube-and-Conquer for Satisfiability; 2.1 Introduction; 2.2 Preliminaries; 2.3 Combining CDCL and Lookahead; 2.4 Creating Cubes: The Basic Method; 2.5 Creating Cubes: a General Methodology; 2.5.1 General Framework |
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2.5.2 Cutoff Heuristic2.5.3 Heuristics for Splitting; 2.6 Solving Cubes; 2.6.1 Sequential Solving; 2.6.2 Solving Cubes in Parallel; 2.7 Interleaving the Cube and Conquer Phases; 2.7.1 Ineffective Lookahead Heuristics; 2.7.2 Concurrent Cube-and-Conquer; 2.7.3 Cubes on Demand; 2.8 Proofs of Unsatisfiability; 2.9 Experimental Evaluation; 2.9.1 Application Benchmarks; 2.9.2 The Boolean Pythagorean Triples Problem; 2.10 Conclusions; Acknowledgements; References; 3 Parallel Maximum Satisfiability; 3.1 Introduction; 3.2 Maximum Satisfiability; 3.2.1 Sequential MaxSAT Algorithms |
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3.2.1.1 Linear Search Algorithms3.2.1.2 Unsatisfiability-Based Algorithms; 3.2.1.3 Other Algorithmic Solutions and Implementation Issues; 3.3 Parallel MaxSAT; 3.3.1 Portfolio Approaches; 3.3.1.1 Parallel Unsatisfiability-Based Algorithms; 3.3.1.2 Parallel Linear Search Algorithms; 3.3.1.3 Implementation Issues; 3.3.2 Search Space Splitting; 3.3.2.1 Interval Splitting; 3.3.2.2 Guiding Paths; 3.3.2.3 Other Splitting Schemes and Implementation Issues; 3.3.3 Clause Sharing; 3.3.3.1 Conditions for Safe Clause Sharing; 3.3.3.2 Clause-Sharing Heuristics |
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3.3.3.3 Comparison Between Clause-Sharing Heuristics3.4 Deterministic Parallel MaxSAT; 3.4.1 Motivation; 3.4.2 Deterministic Solver; 3.4.2.1 Standard Synchronization; 3.4.2.2 Period Synchronization; 3.4.2.3 Dynamic Synchronization; 3.4.3 Comparison Between Non-deterministic and Deterministic Solvers; 3.5 Research Directions; 3.5.1 Scalability; 3.5.2 Clause Sharing; Acknowledgments; References; 4 Parallel Solving of Quantified Boolean Formulas; 4.1 Introduction; 4.2 Background; 4.3 Sequential Search-Based QBF Solving; 4.4 Parallel QBF Solving at a Glance; 4.5 Parallel QBF-Solving Approaches |
| Summary |
This is the first book presenting a broad overview of parallelism in constraint-based reasoning formalisms. In recent years, an increasing number of contributions have been made on scaling constraint reasoning thanks to parallel architectures. The goal in this book is to overview these achievements in a concise way, assuming the reader is familiar with the classical, sequential background. It presents work demonstrating the use of multiple resources from single machine multi-core and GPU-based computations to very large scale distributed execution platforms up to 80,000 processing units. The contributions in the book cover the most important and recent contributions in parallel propositional satisfiability (SAT), maximum satisfiability (MaxSAT), quantified Boolean formulas (QBF), satisfiability modulo theory (SMT), theorem proving (TP), answer set programming (ASP), mixed integer linear programming (MILP), constraint programming (CP), stochastic local search (SLS), optimal path finding with A*, model checking for linear-time temporal logic (MC/LTL), binary decision diagrams (BDD), and model-based diagnosis (MBD). The book is suitable for researchers, graduate students, advanced undergraduates, and practitioners who wish to learn about the state of the art in parallel constraint reasoning |
| Bibliography |
Includes bibliographical references and index |
| Notes |
Online resource; title from PDF title page (SpringerLink, viewed April 11, 2018) |
| Subject |
Constraints (Artificial intelligence) -- Handbooks, manuals, etc
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Parallel programming (Computer science) -- Handbooks, manuals, etc
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COMPUTERS -- General.
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Constraints (Artificial intelligence)
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Parallel programming (Computer science)
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| Genre/Form |
handbooks.
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Handbooks and manuals
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Handbooks and manuals.
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Guides et manuels.
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| Form |
Electronic book
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| Author |
Hamadi, Youssef (Computer science researcher), editor.
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Sais, Lakhdar, editor
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| ISBN |
9783319635163 |
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3319635166 |
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