A parallel portfolio SAT solver with lockless physical clause sharing

Since multi-core architectures have become well-established the enquiry for parallel SAT solvers has drastically increased. Meanwhile, several successful SAT solvers have been presented that can be run in parallel mode. However, there are only a few solvers that use the shared memory architectures for physical clause sharing. In this paper we present a parallel SAT solver that allows for sharing clauses between several threads logically and physically. Yet any thread is still able to keep its own set of clauses. We show how physical clause sharing can be used to propagate one thread's improvements on the clause database to all solving threads. Despite the extensive sharing of data our solver does not require any operating system lock

Experimental Aspects of Synthesis

We discuss the problem of experimentally evaluating linear-time temporal logic (LTL) synthesis tools for reactive systems. We first survey previous such work for the currently publicly available synthesis tools, and then draw conclusions by deriving useful schemes for future such evaluations. In particular, we explain why previous tools have incompatible scopes and semantics and provide a framework that reduces the impact of this problem for future experimental comparisons of such tools. Furthermore, we discuss which difficulties the complex workflows that begin to appear in modern synthesis tools induce on experimental evaluations and give answers to the question how convincing such evaluations can still be performed in such a setting.Comment: In Proceedings iWIGP 2011, arXiv:1102.374

Alternative Ansätze für das Lösen von SAT Instanzen

This thesis explores SAT solving techniques that go beyond small changes to the predominant conflict-driven SAT solving approach with clause learning (CDCL). Whilst this work starts with techniques that are close to state-of-the-art CDCL solving, it goes further, and explores and evaluates some rather uncommon ideas. The general purpose is to widen the range of instances for which SAT solvers may compute a result in reasonable time. We first present some extensions that can be incorporated into CDCL solvers. We introduce a novel improvement of the data structure to represent clauses within a CDCL solver. Moreover, we study the enhancement of simplification techniques for SAT formulae. The underlying techniques are asymmetric branching and hyper-binary resolution. We propose and evaluate an algorithm to improve the quality of both techniques. A crucial part of conflict-driven SAT solving is the so-called Boolean constraint propagation (BCP) of assignments. Any clause that has only one literal left implies an assignment of the corresponding variable. We extend BCP to more general cases where any number of literals may be left. The technique is based on the general concept of hyper-resolution that was introduced by Robinson. Two different implementations are evaluated to study the tradeoff between speed and quality. We further depart from the standard CDCL algorithm by exploring the alternative DMRP solving approach. Decision making with reference points (DMRP) was introduced by Goldberg. Compared to the CDCL algorithm the DMRP approach requires more information for SAT solving. Consequently, more effort has to be spent on maintaining the underlying data structures. We present an efficient implementation for the increased requirements of the DMRP algorithm. Moreover, we suggest a hybrid approach that is competitive to pure CDCL solving. All the techniques and approaches presented in this thesis have been combined within one SAT solver. We present the implementation of our parallel solver, SArTagnan, with a high degree of information sharing among different threads. More complex techniques are justified by the benefits of having several solvers running in parallel.In der Dissertation werden Ansätze zum Lösen von SAT Problemen präsentiert und evaluiert, die über die vorherrschende Technik des sogenannten CDCL Verfahrens (Conflict Driven Solving with Clause Learning) hinaus gehen. Dabei werden zunächst solche Ansätze betrachtet, die den CDCL Algorithmus leicht verändern oder erweitern. Es werden aber auch Techniken analysiert, die sich vom CDCL Verfahren grundlegend unterscheiden. In der Arbeit wird eine Verbesserung der Datenstruktur zum Speichern von Klauseln vorgestellt, die in verschiedene Implementierungen des CDCL Algorithmus integriert werden kann. Darüber hinaus wird die Simplifizierung von SAT Instanzen analysiert, insbesondere die beiden Ansätze „Asymmetric Branching“ und „Hyper-Binary Resolution“. Es wird ein Algorithmus präsentiert und evaluiert, der beide Ansätze sinnvoll kombiniert. Ein wesentlicher Bestandteil von erfolgreichen SAT Solvern ist die sogenannte Boolean Constraint Propagation (BCP): Führt eine partielle Belegung der Variablen dazu, dass eine Klausel nur noch ein freies Literal enthält, so wird die entsprechende Belegung einer weiteren Variable impliziert. In der Dissertation wird eine wesentliche Erweiterung dieses Vorgehens entwickelt, so dass weitere Belegungen von Variablen auch impliziert werden können, wenn eine Klausel mehr als nur ein freies Literal enthält. Dabei werden zwei Ansätze vorgeschlagen und deren Auswirkung auf Laufzeit und Qualität verglichen. Ein alternativer Ansatz zum Lösen von SAT Problemen wurde von Goldberg erarbeitet. Der DMRP Algorithmus (Decision Making with Reference Points) benötigt während des Lösungsprozesses wesentlich mehr Informationen als das CDCL Verfahren. Dies führt zu einem erhöhten Verwaltungsaufwand für die zugrunde liegende Datenstruktur. In der Dissertation wird eine effiziente Datenstruktur und deren Implementierung vorgestellt, um den DMRP Algorithmus zu realisieren. Die praktische Evaluierung motiviert ein hybrides Verfahren, das sich mit gängigen CDCL Solvern messen kann. Alle in der Dissertation vorgestellten Ansätze werden in einem parallelen SAT Solver „SarTagnan“ kombiniert. Daraus ergeben sich spezifische Anforderungen an die Implementierung, um einen möglichst hohen Grad an Parallelität erzielen zu können. Der erhöhte Rechenaufwand für komplexe alternative Ansätze wird dadurch gerechtfertigt und motiviert, dass diese Informationen sämtlichen parallel laufenden Prozessen zu Gute kommen

Visualizing Large and Clustered Networks

The need to visualize large and complex networks has strongly increased in the last decade. Although networks with more than 1000 vertices seem to be prohibitive for a comprehensive layout, real-world networks exhibit a very inhomogenous edge density that can be harnessed to derive an aesthetic and structured layout. Here, we will present a heuristic that finds a spanning tree with a very low average spanner property for the non-tree edges, the so-called "backbone" of a network. This backbone can then be used to apply a modified tree-layout algorithm to draw the whole graph in a way that highlights dense parts of the graph, so-called clusters, and their inter-connections

A Discharge Plasma Source Development Platform for Accelerators: The ADVANCE Lab at DESY

Novel plasma-based accelerators, as well as advanced, high-gradient beam-manipulation techniques’for example passive or active plasma lenses’require reliable and well-characterized plasma sources, each optimized for their individual task. A very efficient and proven way of producing plasmas for these applications is by directly discharging an electrical current through a confined gas volume. To host the development of such discharge-based plasma sources for advanced accelerators, the ATHENA Discharge deVelopment ANd Characterization Experiment (ADVANCE) laboratory has been established at DESY. In this contribution we introduce the laboratory, give a summary of available infrastructure and diagnostics, as well as a brief overview of current and planned scientific goals