PaMiraXT: Parallel SAT Solving with Threads and Message Passing

Schubert, Tobias; Lewis, Matthew; Becker, Bernd

doi:10.3233/sat190068

Cited by 43 publications

(22 citation statements)

References 30 publications

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“…PaMiraXT (Schubert et al, 2009) applies a domain decomposition using the MPI library for parallel computing. Other 3-SAT solvers are conceived to be executed on volunteer grid platforms (Schulz and Blochinger, 2010;Posypkin et al, 2012), where the computing nodes are volatile and they are heterogeneous, both in hardware and software.…”

Section: Parallel 3-sat Solvers In Grid and Cloudmentioning

confidence: 99%

Hybrid algorithms for 3-SAT optimisation using MapReduce on clouds

Barreto

Nesmachnow

Tchernykh

2018

IJICA

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This article presents the advances on applying a MapReduce approach for solving optimisation problems using Hadoop on cloud computing systems. The main advantages and limitations of the proposed strategy are presented and commented. A concrete case study is reported, analysing several algorithmic approaches to solve the 3-SAT, a well-known version of the Boolean satisfiability problem. Several variants of the MapReduce 3-SAT solver are designed and evaluated to demonstrate that the collaborative approach is a promising option for solving optimisation problems in the cloud.

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Section: Parallel 3-sat Solvers In Grid and Cloudmentioning

confidence: 99%

Hybrid algorithms for 3-SAT optimisation using MapReduce on clouds

Barreto

Nesmachnow

Tchernykh

2018

IJICA

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“…Both QBF and SAT solvers feature similar decision heuristics, BCP procedures, and conflict (solution) analysis procedures that allow the solvers to learn as they search for the solution. Parallel SAT has also been well researched and many parallel SAT solver exist [8,12,13,16,26,29,41,42]. However, as explained earlier, QBF solvers are more complicated as they must include the ability to deal with many more possibilities.…”

Section: Comparison To Satmentioning

confidence: 99%

Parallel QBF Solving with Advanced Knowledge Sharing

Lewis

Schubert

Becker

et al. 2011

Fundamenta Informaticae

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In this paper we present the parallel QBF Solver PaQuBE. This new solver leverages the additional computational power that can be exploited from modern computer architectures, from pervasive multicore boxes to clusters and grids, to solve more relevant instances faster than previous generation solvers. Furthermore, PaQuBE's progressive MPI based parallel framework is the first to support advanced knowledge sharing in which solution cubes as well as conflict clauses can be exchanged between solvers. Knowledge sharing plays a critical role in the performance of PaQuBE. However, due to the overhead associated with sending and receiving MPI messages, and the restricted communication/network bandwidth available between solvers, it is essential to optimize not only what information is shared, but the way in which it is shared. In this context, we compare multiple conflict clause and solution cube sharing strategies, and finally show that an adaptive method provides the best overall results. While many QBF solvers are still based on the DPLL algorithm [14], they have advanced considerably in recent years. For instance, some QBF algorithm specific advances include conflict and solution analysis with non-chronological backtracking [10,18,23,46], and preprocessing [20,40]. Modern QBF solvers must combine all these new ideas into an efficient implementation to be competitive. Furthermore, single processor performance has also played a large role in the ability of modern QBF solvers to handle relevant problems. For many years, clock frequencies and single-core performance increased rapidly. However, current improvements in clock frequency and single core processor performance are slowing. To compensate for this, we have seen the introduction of multi-core and/or multi-threaded processors which have resulted in some of the largest jumps in performance potential in recent times. Companies such as INTEL, AMD, SUN, and IBM, now produce CPUs that contain four or more cores. Future QBF solvers must harness this untapped potential if they wish to provide leading edge performance. These new processors, the introduction of cheap clusters in labs, and the availability of parallel programming support in software are the main motivation for the development of PaQuBE.In the domain of parallel solvers, most research has focused on two topics: subproblem generation and knowledge sharing. Here, we discuss both with respect to QBF. Previous research on subproblem generation in parallel QBF was lacking, and modern SAT approaches could not be directly used. With respect to knowledge sharing, using static criteria (which most previous parallel solvers use) for selecting what information should be shared results in many messages being sent, but actually very little good information being shared. This paper highlights this problem, and provides new ideas to improve knowledge sharing within an MPI based system.The paper is structured as follows: the next section will start with a description of the QBF problem (Section 2), and how sequential and p...

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“…Now that we have demonstrated the significant speed-ups that we can obtain using our multithreaded variants of Tarmo we will discuss approaches which distribute runs of Tarmo over several multithreaded workstations. A distributed SAT solver for a similar environment is presented in [17]. The workstations in our department's computing cluster that were already mentioned in Subsection 2.5 are all connected by 1 gigabit Ethernet connections through a cluster switch.…”

Section: Bmc For Worktation Clustersmentioning

confidence: 99%

Tarmo: A Framework for Parallelized Bounded Model Checking

Wieringa¹,

Niemenmaa²,

Heljanko³

2009

Electron. Proc. Theor. Comput. Sci.

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This paper investigates approaches to parallelizing Bounded Model Checking (BMC) for shared memory environments as well as for clusters of workstations. We present a generic framework for parallelized BMC named Tarmo. Our framework can be used with any incremental SAT encoding for BMC but for the results in this paper we use only the current state-of-the-art encoding for full PLTL [4]. Using this encoding allows us to check both safety and liveness properties, contrary to an earlier work on distributing BMC that is limited to safety properties only.Despite our focus on BMC after it has been translated to SAT, existing distributed SAT solvers are not well suited for our application. This is because solving a BMC problem is not solving a set of independent SAT instances but rather involves solving multiple related SAT instances, encoded incrementally, where the satisfiability of each instance corresponds to the existence of a counterexample of a specific length. Our framework includes a generic architecture for a shared clause database that allows easy clause sharing between SAT solver threads solving various such instances.We present extensive experimental results obtained with multiple variants of our Tarmo implementation. Our shared memory variants have a significantly better performance than conventional single threaded approaches, which is a result that many users can benefit from as multi-core and multi-processor technology is widely available. Furthermore we demonstrate that our framework can be deployed in a typical cluster of workstations, where several multi-core machines are connected by a network.

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PaMiraXT: Parallel SAT Solving with Threads and Message Passing

Cited by 43 publications

References 30 publications

Hybrid algorithms for 3-SAT optimisation using MapReduce on clouds

Hybrid algorithms for 3-SAT optimisation using MapReduce on clouds

Parallel QBF Solving with Advanced Knowledge Sharing

Tarmo: A Framework for Parallelized Bounded Model Checking

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