Lazy Decomposition for Distributed Decision Procedures

  title={Lazy Decomposition for Distributed Decision Procedures},
  author={Youssef Hamadi and Joao Marques-Silva and Christoph M. Wintersteiger},
The increasing popularity of automated tools for software and hardware verification puts ever increasing demands on the underlying decision procedures. This paper presents a framework for distributed decision procedures (for first-order problems) based on Craig interpolation. Formulas are distributed in a lazy fashion, i.e., without the use of costly decomposition algorithms. Potential models which are shown to be incorrect are reconciled through the use of Craig interpolants. Experimental… 

Figures and Tables from this paper

Distributed bounded model checking

This paper presents an algorithm that dynamically unfolds the call graph of the program and frequently splits it to create sub-tasks that can be solved in parallel, and is adaptive, controlling the splitting rate according to available resources.

Towards better heuristics for solving bounded model checking problems

This paper presents a new way to improve the performance of the SAT-based bounded model checking problem on sequential and parallel procedures by exploiting relevant information identified through

Parallel Tree Search for Satisfiability

ManySAT is described, a portfolio-based parallel SAT solver which runs a set of complementary sequential algorithms obtained through careful variations of the standard CDCL algorithm which shares clauses to improve the overall performance of the whole system.

Parallel Satisfiability Modulo Theories

This chapter provides an overview of the recent advances in SMT, specifically algorithm portfolio, search-space partitioning, and problem decomposition techniques, and how they relate to each other in theory and practice.

Chapter 3 Parallel Satisfiability Modulo Theories

This chapter provides an overview of the recent advances in SMT, specifically algorithm portfolio, search-space partitioning, and problem decomposition techniques, and how they relate to each other in theory and practice.

Dissolve: A Distributed SAT Solver Based on Stalmarck's Method

A novel approach for solving SAT problems in parallel based on the combination of traditional CDCL with St̊almarck’s Dilemma Rule to partition the search space between solvers and merge their results.

Seven Challenges in Parallel SAT Solving

This paper provides a broad overview of the situation in the area of Parallel Search with a specific focus on Parallel SAT Solving. A set of challenges to researchers is presented which, we

Combinatorial Search: From Algorithms to Systems

  • Y. Hamadi
  • Computer Science
    Springer Berlin Heidelberg
  • 2013
This book focuses on knowledge sharing in combinatorial search, the capacity to generate and exploit meaningful information, such as redundant constraints, heuristic hints, and performance measures, during search, which can dramatically improve the performance of a constraint solver.

Beautiful Interpolants

A compositional approach to Craig interpolation based on the heuristic that simpler proofs of special cases are more likely to generalize makes it possible to use interpolation to discover inductive invariants for numerical programs that are challenging for existing techniques.

SAT Solving with Reference Points

This paper presents a suitable data structure for the DMRP approach to overcome the problem above and shows how it can be combined with CDCL solving to be competitive to state-of-the-art solvers and to even improve on some families of industrial instances.

Partitioning SAT Instances for Distributed Solving

New methods for constructing partitions which combine clause learning and lookahead are presented and their performance is demonstrated with an extensive comparison against the best sequential solvers in the SAT competition 2009 as well as against two efficient parallel solvers.

PMiniSat - A parallelization of MiniSat 2.0

The features of PMiniSat, a parallel SAT solver entering SAT Race 2008, are described, which features an extended learnt clause sharing heuristic, global restarts, and a series of data structure changes that increases the speed of the core propagation engine by around 80%.

GrADSAT: A Parallel SAT Solver for the Grid

GrADSAT is compared against the best sequential solver using a wide variety of problem instances and shows that GrADSAT delivers speed-up on most instances and is capable of solving problem instance which were never solved before.

Symbolic Reachability Analysis Based on SAT-Solvers

This paper shows how to adapt standard algorithms for symbolic reachability analysis to work with SAT-solvers and shows that even with relatively simple techniques it is possible to verify systems that are known to be hard for BDD-based model checkers.

Distributed BMC: A Depth-First Approach to Explore Clause Symmetry

Avoiding the unrolling of conflict clauses has a more pronounced effect, because due to the symmetric nature of the formula, a conflict clause for one instant in the execution can be applied to time multiple instants.

ManySat: solver description

ManySat is a DPLL-engine which includes all the classical features like twowatched-literal, unit propagation, activity-based decision heuristics, lemma deletion strategies, and clause learning and incorporates a new technique which extends the classical implication graph used during conflict-analysis to exploit the satisfied clauses of a formula.

Efficient distributed SAT and SAT-based distributed Bounded Model Checking

A method for distributed SAT over a network of workstations using a Master/Client model where each Client workstation has an exclusive partition of the SAT problem and uses knowledge of partition topology to communicate with other Clients to overcome memory limitation of a single server.

Solving SAT and SAT Modulo Theories: From an abstract Davis--Putnam--Logemann--Loveland procedure to DPLL(T)

Extensive experimental evidence shows that DPLL(T) systems can significantly outperform the other state-of-the-art tools, frequently even in orders of magnitude, and have better scaling properties.

Propositional Interpolation and Abstract Interpretation

It is shown that existing interpolation algorithms are abstractions of a more general, parametrised algorithm, and reside in the coarsest abstraction that admits correct interpolationgorithms.