Boosting Verification by Automatic Tuning of Decision Procedures

Hutter,; Babic,; Hoos,; Hu, Weihao

doi:10.1109/famcad.2007.9

Cited by 61 publications

(39 citation statements)

References 18 publications

(17 reference statements)

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“…With appropriate parameter settings, it was shown to be the best available solver for certain types of SAT-encoded hardware and software verification instances [8] (the same IBM and SWV instances we use here). It also won the quantifier-free bit-vector arithmetic category of the 2007 Satisfiability Modulo Theories Competition.…”

Section: Algorithms and Their Configuration Spacesmentioning

confidence: 99%

“…Specifically, it has been convincingly demonstrated that methods for automated algorithm configuration [2,3,4,5,6,7] are able to find configurations that substantially improve the state of the art for various hard combinatorial problems (e.g., SAT-based formal verification [8], mixed integer programming [1], timetabling [9], and AI planning [10]). However, much less work has been done towards the goal of explaining to algorithm designers which parameters are important and what values for these important parameters lead to good performance.…”

Section: Introductionmentioning

confidence: 99%

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Identifying Key Algorithm Parameters and Instance Features Using Forward Selection

Hutter

Hoos

Leyton‐Brown

2013

Lecture Notes in Computer Science

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Abstract. Most state-of-the-art algorithms for large scale optimization expose free parameters, giving rise to combinatorial spaces of possible configurations. Typically, these spaces are hard for humans to understand. In this work, we study a model-based approach for identifying a small set of both algorithm parameters and instance features that suffices for predicting empirical algorithm performance well. Our empirical analyses on a wide variety of hard combinatorial problem benchmarks (spanning SAT, MIP, and TSP) show that-for parameter configurations sampled uniformly at random-very good performance predictions can typically be obtained based on just two key parameters, and that similarly, few instance features and algorithm parameters suffice to predict the most salient algorithm performance characteristics in the combined configuration/feature space. We also use these models to identify settings of these key parameters that are predicted to achieve the best overall performance, both on average across instances and in an instance-specific way. This serves as a further way of evaluating model quality and also provides a tool for further understanding the parameter space. We provide software for carrying out this analysis on arbitrary problem domains and hope that it will help algorithm developers gain insights into the key parameters of their algorithms, the key features of their instances, and their interactions.

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Section: Algorithms and Their Configuration Spacesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Identifying Key Algorithm Parameters and Instance Features Using Forward Selection

Hutter

Hoos

Leyton‐Brown

2013

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

show abstract

“…Stochastic optimization of SAT solver parameters is described in [HBHH07]. It could be used for finding better strategies within our approach.…”

Section: Related Workmentioning

confidence: 99%

Instance-Based Selection of Policies for SAT Solvers

Nikolić

Marić

Janicic

2009

Lecture Notes in Computer Science

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Abstract. Execution of most of the modern DPLL-based SAT solvers is guided by a number of heuristics. Decisions made during the search process are usually driven by some fixed heuristic policies. Despite the outstanding progress in SAT solving in recent years, there is still an appealing lack of techniques for selecting policies appropriate for solving specific input formulae. In this paper we present a methodology for instancebased selection of solver's policies that uses a data-mining classification technique. The methodology also relies on analysis of relationships between formulae, their families, and their suitable solving strategies. The evaluation results are very good, demonstrate practical usability of the methodology, and encourage further efforts in this direction.

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“…Our experience with Beaver on such path feasibility queries indicates that the full power of linear constraint solving is unnecessary for practically all queries. Spear [9] is based on bit-blasting using several word-level simplification rules and a fast SAT solver with numerous optimization parameters. Boolector [3] uses bitblasting to PicoSAT with the use of under-approximation techniques that rely strongly on the connection to PicoSAT.…”

Section: Related Workmentioning

confidence: 99%

“…This theory is useful for reasoning about low-level system descriptions in languages such as C and Verilog which use finite-precision integer arithmetic and bit-wise operations on bit-vectors. Recently, there has been a resurgence of work on new QF BV SMT solvers such as BAT [10], Boolector [3], MathSAT [4], Spear [9], STP [8], UCLID [5] and Z3 [6].…”

Section: Introductionmentioning

confidence: 99%

Beaver: Engineering an Efficient SMT Solver for Bit-Vector Arithmetic

Jha

Limaye

Seshia

2009

Computer Aided Verification

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We present the key ideas in the design and implementation of Beaver, an SMT solver for quantifierfree finite-precision bit-vector logic (QF BV). Beaver uses an eager approach, encoding the original SMT problem into a Boolean satisfiability (SAT) problem using a series of word-level and bit-level transformations. In this paper, we describe the most effective transformations, such as propagating constants and equalities at the word-level, and using and-inverter graph rewriting techniques at the bit-level. We highlight the implementation details of these transformations that distinguishes Beaver from other solvers. We present an experimental evaluation and analysis of the effectiveness of Beaver's techniques on both hardware and software benchmarks with a selection of back-end SAT solvers.Beaver is an open-source tool implemented in Ocaml, usable with any back-end SAT engine, and has a well-documented extensible code base that can be used to experiment with new algorithms and techniques.

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Boosting Verification by Automatic Tuning of Decision Procedures

Cited by 61 publications

References 18 publications

Identifying Key Algorithm Parameters and Instance Features Using Forward Selection

Identifying Key Algorithm Parameters and Instance Features Using Forward Selection

Instance-Based Selection of Policies for SAT Solvers

Beaver: Engineering an Efficient SMT Solver for Bit-Vector Arithmetic

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