Communication in Massively-Parallel SAT Solving

Ehlers, Thorsten; Nowotka, Dirk; Sieweck, Philipp

doi:10.1109/ictai.2014.111

Cited by 9 publications

(17 citation statements)

References 13 publications

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“…When receiving unit clauses either from other processes via MPI or from solver threads via the shared memory communication, it checks these clauses against the clauses it has seen already, and only forwards unit clauses which have not yet been seen. In other solvers like TopoSAT [26] and HordeSAT [10], the communication threads hash clauses received either from solver threads within the same process or via MPI. They use Bloom filters for preventing clauses from being shared multiple times.…”

Section: Communicationmentioning

confidence: 99%

“…As mentioned above, the first case was tackled in [10,26] by hashing received clauses and filtering duplicates. Empirically, this does not filter many clauses, and does not help e.g.…”

Section: Lazy Clause Exchangementioning

confidence: 99%

“…The increasing availability of parallel hardware has raised interest in the parallelization of SAT solvers [31,26]. However, there are principle considerations on the problem of SAT solving which have provoked some skepticism about parallel SAT solvers.…”

Section: Introductionmentioning

confidence: 99%

“…This is sound as every learned clause was derived by resolution, and thus is logically implied by the original formula. This clause exchange is crucial in improving the performance of parallel SAT solvers, especially on unsatisfiable formulas [26,10].…”

Section: Introductionmentioning

confidence: 99%

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Tuning Parallel SAT Solvers

Ehlers¹,

Nowotka²

2018

EasyChair Preprints

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In this paper we present new implementation details and benchmarking results for our parallel portfolio solver TopoSAT2. In particular, we discuss ideas and implementation details for the exchange of learned clauses in a massively-parallel SAT solver which is designed to run more that 1, 000 solver threads in parallel. Furthermore, we go back to the roots of portfolio SAT solving, and discuss the impact of diversifying the solver by using different restart-, branching-and clause database management heuristics. We show that these techniques can be used to tune the solver towards different problems. However, in a case study on formulas derived from Bounded Model Checking problems we see the best performance when using a rather simple clause exchange strategy. We show details of these tests and discuss possible explanations for this phenomenon.As computing times on massively-parallel clusters are expensive, we consider it especially interesting to share these kind of experimental results.

show abstract

Section: Communicationmentioning

confidence: 99%

“…As mentioned above, the first case was tackled in [10,26] by hashing received clauses and filtering duplicates. Empirically, this does not filter many clauses, and does not help e.g.…”

Section: Lazy Clause Exchangementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Tuning Parallel SAT Solvers

Ehlers¹,

Nowotka²

2018

EasyChair Preprints

Self Cite

View full text Add to dashboard Cite

show abstract

Section: Introductionmentioning

confidence: 99%

Tuning Parallel SAT Solvers

Ehlers¹,

Nowotka²

EPiC Series in Computing

Self Cite

View full text Add to dashboard Cite

In this paper we present new implementation details and benchmarking results for our parallel portfolio solver TopoSAT2. In particular, we discuss ideas and implementation details for the exchange of learned clauses in a massively-parallel SAT solver which is designed to run more that 1, 000 solver threads in parallel. Furthermore, we go back to the roots of portfolio SAT solving, and discuss the impact of diversifying the solver by using different restart- , branching- and clause database management heuristics. We show that these techniques can be used to tune the solver towards different problems. However, in a case study on formulas derived from Bounded Model Checking problems we see the best performance when using a rather simple clause exchange strategy. We show details of these tests and discuss possible explanations for this phenomenon.As computing times on massively-parallel clusters are expensive, we consider it especially interesting to share these kind of experimental results.

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Scalable SAT Solving in the Cloud

Schreiber

Sanders

2021

Lecture Notes in Computer Science

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Previous efforts on making Satisfiability (SAT) solving fit for high performance computing (HPC) have led to super-linear speedups on particular formulae, but for most inputs cannot make efficient use of a large number of processors. Moreover, long latencies (minutes to days) of job scheduling make large-scale SAT solving on demand impractical for most applications. We address both issues with Mallob, a framework for job scheduling in the context of SAT solving which exploits malleability, i.e., the ability to add or remove processing power from a job during its computation. Mallob includes a massively parallel, distributed, and malleable SAT solving engine based on HordeSat with a more succinct and communication-efficient approach to clause sharing and numerous further improvements over its precursor. Experiments with up to 2560 cores show that Mallob outperforms an improved version of HordeSat and scales significantly better. Moreover, Mallob can solve many formulae in parallel while dynamically adapting the assigned resources, and jobs arriving in the system are usually initiated within a fraction of a second.

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Communication in Massively-Parallel SAT Solving

Cited by 9 publications

References 13 publications

Tuning Parallel SAT Solvers

Tuning Parallel SAT Solvers

Tuning Parallel SAT Solvers

Scalable SAT Solving in the Cloud

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