An Adaptive Parallel SAT Solver

Audemard, Gilles; Lagniez, Jean-Marie; Szczepanski, Nicolas; Tabary, Sébastien

doi:10.1007/978-3-319-44953-1_3

Cited by 15 publications

(25 citation statements)

References 28 publications

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“…Results are in Figure 1. Due to different computers used, our results are not directly comparable to those in [1]. As noted by [10], solvers like mtsat can use substantial memory on very large instances, limiting the number of processes that can execute in a given amount of memory.…”

Section: Satisfiabilitymentioning

confidence: 60%

“…The idea is that short clauses cut the search tree more than longer clauses; an early version of plingeling also shared only units [11]. We avoided more sophisticated approaches to sharing clauses [3,1], using conflicts to prune the job list L and similar ideas for simplicity.…”

Section: Mtsat: Parallelizing Minisat With Mtsmentioning

confidence: 99%

“…mtsat includes additional options. For example, while the parallel solvers most similar to our approach [1,11] budget using conflicts -we added the option to budget using decisions. Conflict budgets correspond to hitting a leaf in the search tree, while decision budgets correspond to nodes in the search space (omitting propagated variables since those are forced).…”

Section: Mtsat: Parallelizing Minisat With Mtsmentioning

confidence: 99%

“…We use a standard set of hard instances from applications, and count the number of problems that each solver can solve within a given time. We re-use the setup of [1], i.e. the 100 instances in the parallel track of SAT Race 2015 [9] and a timeout of 20 minutes.…”

Section: Satisfiabilitymentioning

confidence: 99%

See 3 more Smart Citations

mts: a light framework for parallelizing tree search codes

Avis

Jordan

2019

Optimization Methods and Software

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We describe mts, a generic framework for parallelizing certain types of tree search programs including reverse search, backtracking, branch and bound and satisfiability testing. It abstracts and generalizes the ideas used in parallelizing lrs, a reverse search code for vertex enumeration. mts supports sharing information between processes which is important for applications such as satisfiability testing and branch-and-bound. No parallelization is implemented in the legacy single processor programs minimizing the changes needed and simplying debugging. mts is written in C, uses MPI for parallelization and can be used on a network of computers. We give four examples of reverse search codes parallelized by using mts: topological sorts, spanning trees, triangulations and Galton-Watson trees. We also give a parallelization of two codes for satisfiability testing. We give experimental results comparing the parallel codes with other codes for the same problems.

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Section: Satisfiabilitymentioning

confidence: 60%

Section: Mtsat: Parallelizing Minisat With Mtsmentioning

confidence: 99%

Section: Mtsat: Parallelizing Minisat With Mtsmentioning

confidence: 99%

Section: Satisfiabilitymentioning

confidence: 99%

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mts: a light framework for parallelizing tree search codes

Avis

Jordan

2019

Optimization Methods and Software

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“…Unlike other SMC techniques, strong induction reduces model checking to pure SAT that does not require any additional features such as solving with assumptions [12], interpolation [24], resolution proofs [17], Maximal Unsatisfiable Subsets (MUS) [2], etc. It easily integrates with existing SAT-solvers and immediately benefits from any improvements in heuristics [22,23], preand in-processing [18], and parallel solving [1]. The simplicity of applying kinduction made it the go-to technique for SMT-based infinite-state model checking [9,11,19].…”

Section: Introductionmentioning

confidence: 99%

Interpolating Strong Induction

Krishnan

Vizel

Gurfinkel

2019

Computer Aided Verification

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The principle of strong induction, also known as k-induction is one of the first techniques for unbounded SAT-based Model Checking (SMC). While elegant and simple to apply, properties as such are rarely k-inductive and when they can be strengthened, there is no effective strategy to guess the depth of induction. It has been mostly displaced by techniques that compute inductive strengthenings based on interpolation and property directed reachability (Pdr). In this paper, we present kAvy, an SMC algorithm that effectively uses k-induction to guide interpolation and Pdr-style inductive generalization. Unlike pure k-induction, kAvy uses Pdr-style generalization to compute and strengthen an inductive trace. Unlike pure Pdr, kAvy uses relative k-induction to construct an inductive invariant. The depth of induction is adjusted dynamically by minimizing a proof of unsatisfiability. We have implemented kAvy within the Avy Model Checker and evaluated it on HWMCC instances. Our results show that kAvy is more effective than both Avy and Pdr, and that using k-induction leads to faster running time and solving more instances. Further, on a class of benchmarks, called shift, kAvy is orders of magnitude faster than Avy, Pdr and k-induction.

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Modular and Efficient Divide-and-Conquer SAT Solver on Top of the Painless Framework

Frioux

Baarir

Sopena

et al. 2019

Lecture Notes in Computer Science

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Over the last decade, parallel SATisfiability solving has been widely studied from both theoretical and practical aspects. There are two main approaches. First, divide-and-conquer (D&C) splits the search space, each solver being in charge of a particular subspace. The second one, portfolio launches multiple solvers in parallel, and the first to find a solution ends the computation. However although D&C based approaches seem to be the natural way to work in parallel, portfolio ones experimentally provide better performances. An explanation resides on the difficulties to use the native formulation of the SAT problem (i.e., the CNF form) to compute an a priori good search space partitioning (i.e., all parallel solvers process their subspaces in comparable computational time). To avoid this, dynamic load balancing of the search subspaces is implemented. Unfortunately, this is difficult to compare load balancing strategies since state-of-the-art SAT solvers appropriately dealing with these aspects are hardly adaptable to various strategies than the ones they have been designed for. This paper aims at providing a way to overcome this problem by proposing an implementation and evaluation of different types of divideand-conquer inspired from the literature. These are relying on the Painless framework, which provides concurrent facilities to elaborate such parallel SAT solvers. Comparison of the various strategies are then discussed.

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An Adaptive Parallel SAT Solver

Cited by 15 publications

References 28 publications

mts: a light framework for parallelizing tree search codes

mts: a light framework for parallelizing tree search codes

Interpolating Strong Induction

Modular and Efficient Divide-and-Conquer SAT Solver on Top of the Painless Framework

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