Multi-Core Unit Propagation in Functional Languages

Answer Set Programming is a declarative modeling paradigm enabling specialists in diverse disciplines to describe and solve complicated problems. Growth in high performance computing is driving ever smarter and more scalable parallel answer set solvers. To improve on today\u27s cutting-edge, researchers need to develop increasingly intelligent methods for analysis of a solver\u27s runtime information. Reflecting on the solver\u27s search state typically pauses its progress until the analysis is complete. This work introduces methods from the domain of parallel functional programming and immutable type theory to construct a representation of the search state that is both amenable to introspection and efficiently scalable across multiple processor cores

Towards Next Generation Sequential and Parallel SAT Solvers

This thesis focuses on improving the SAT solving technology. The improvements focus on two major subjects: sequential SAT solving and parallel SAT solving. To better understand sequential SAT algorithms, the abstract reduction system Generic CDCL is introduced. With Generic CDCL, the soundness of solving techniques can be modeled. Next, the conflict driven clause learning algorithm is extended with the three techniques local look-ahead, local probing and all UIP learning that allow more global reasoning during search. These techniques improve the performance of the sequential SAT solver Riss. Then, the formula simplification techniques bounded variable addition, covered literal elimination and an advanced cardinality constraint extraction are introduced. By using these techniques, the reasoning of the overall SAT solving tool chain becomes stronger than plain resolution. When using these three techniques in the formula simplification tool Coprocessor before using Riss to solve a formula, the performance can be improved further. Due to the increasing number of cores in CPUs, the scalable parallel SAT solving approach iterative partitioning has been implemented in Pcasso for the multi-core architecture. Related work on parallel SAT solving has been studied to extract main ideas that can improve Pcasso. Besides parallel formula simplification with bounded variable elimination, the major extension is the extended clause sharing level based clause tagging, which builds the basis for conflict driven node killing. The latter allows to better identify unsatisfiable search space partitions. Another improvement is to combine scattering and look-ahead as a superior search space partitioning function. In combination with Coprocessor, the introduced extensions increase the performance of the parallel solver Pcasso. The implemented system turns out to be scalable for the multi-core architecture. Hence iterative partitioning is interesting for future parallel SAT solvers. The implemented solvers participated in international SAT competitions. In 2013 and 2014 Pcasso showed a good performance. Riss in combination with Copro- cessor won several first, second and third prices, including two Kurt-Gödel-Medals. Hence, the introduced algorithms improved modern SAT solving technology

The Configurable SAT Solver Challenge (CSSC)

It is well known that different solution strategies work well for different types of instances of hard combinatorial problems. As a consequence, most solvers for the propositional satisfiability problem (SAT) expose parameters that allow them to be customized to a particular family of instances. In the international SAT competition series, these parameters are ignored: solvers are run using a single default parameter setting (supplied by the authors) for all benchmark instances in a given track. While this competition format rewards solvers with robust default settings, it does not reflect the situation faced by a practitioner who only cares about performance on one particular application and can invest some time into tuning solver parameters for this application. The new Configurable SAT Solver Competition (CSSC) compares solvers in this latter setting, scoring each solver by the performance it achieved after a fully automated configuration step. This article describes the CSSC in more detail, and reports the results obtained in its two instantiations so far, CSSC 2013 and 2014

Automatic construction of parallel portfolios via algorithm configuration

FWN – Publicaties zonder aanstelling Universiteit Leide

Proceedings of SAT Competition 2021 : Solver and Benchmark Descriptions

Non peer reviewe

Thoughts about using Constraint Solvers in Action

SMT solvers power many automated security analysis tools today. Nevertheless, a smooth integration of SMT solvers into programs is still a challenge that lead to different approaches for doing it the right way. In this paper, we review the state of the art for interacting with constraint solvers. Based on the different ideas found in literature we deduce requirements for a constraint solving service simplifying the integration challenge. We identify that for some of those ideas, it is required to run large scale experiments for evaluating some of the ideas behind the requirements empirically. We show that the platform is capable of running such an experiment for the case of measuring the impacts of seeds on the solver runtime

Design, implementation and evaluation of a distributed CDCL framework

The primary subject of this dissertation is practically solving instances of the Boolean satisfiability problem (SAT) that arise from industrial applications. The invention of the conflict-driven clause-learning (CDCL) algorithm led to enormous progress in this field. CDCL has been augmented with effective pre- and inprocessing techniques that boost its effectiveness. While a considerable amount of work has been done on applying shared-memory parallelism to enhance the performance of CDCL, solving SAT on distributed architectures is studied less thoroughly. In this work, we develop a distributed, CDCL-based framework for SAT solving. This framework consists of three main components: 1. An implementation of the CDCL algorithm that we have written from scratch, 2. a novel, parallel SAT algorithm that builds upon this CDCL implementation and 3. a collection of parallel simplification techniques for SAT instances. We call our resulting framework satUZK; our parallel solving algorithm is called the distributed divide-and-conquer (DDC) algorithm. The DDC algorithm employs a parallel lookahead procedure to dynamically partition the search space. Load balancing is used to ensure that all computational resources are utilized during lookahead. This procedure results in a divide-and-conquer tree that is distributed over all processors. Individual threads are routed through this tree until they arrive at unsolved leaf vertices. Upon arrival, the lookahead procedure is invoked again or the leaf vertex is solved via CDCL. Several extensions to the DDC algorithm are proposed. These include clause sharing and a scheme to locally adjust the LBD score relative to the current search tree vertex. LBD is a measure for the usefulness of clauses that participate in a CDCL search. We evaluate our DDC algorithm empirically and benchmark it against the best distributed SAT algorithms. In this experiment, our DDC algorithm is faster than other distributed, state-of-the-art solvers and solves at least as many instances. In addition to running a parallel algorithm for SAT solving we also consider parallel simplifcation. Here, we first develop a theoretical foundation that allows us to prove the correctness of parallel simplification techniques. Using this as a basis, we examine established simplification algorithms for their parallelizability. It turns out that several well-known simplification techniques can be parallelized efficiently. We provide parallel implementation of the techniques and test their effectiveness in empirical experiments. This evaluation finds several combinations of simplification techniques that can solve instances which could not be solved by the DDC algorithm alone