Abstract. This paper addresses the analysis of concurrent programs with shared memory. Such an analysis is undecidable in the presence of multiple procedures. One approach used in recent work obtains decidability by providing only a partial guarantee of correctness: the approach bounds the number of context switches allowed in the concurrent program, and aims to prove safety, or find bugs, under the given bound. In this paper, we show how to obtain simple and efficient algorithms for the analysis of concurrent programs with a context bound. We give a general reduction from a concurrent program P, and a given context bound K, to a slightly larger sequential program P K s such that the analysis of P K s can be used to prove properties about P. The reduction introduces symbolic constants and assume statements in P K s. Thus, any sequential analysis that can deal with these two additions can be extended to handle concurrent programs as well, under the context bound. We give instances of the reduction for common program models used in model checking, such as Boolean programs, pushdown systems (PDSs), and symbolic PDSs. 1

Abstract. Due to the significant progress in automated verification, there are often several techniques for a particular verification problem. In many circumstances different techniques are complementary — each technique works well for different type of input instances. Unfortunately, it is not clear how to choose an appropriate technique for a specific instance of a problem. In this work we argue that this problem, selection of a technique and tuning its parameter values, should be considered as a standalone problem (a verification meta-search). We propose several classifications of models of asynchronous system and discuss applications of these classifications in the context of explicit finite state model checking. 1

Model checking is a popular technique to systematically and automatically verify system properties. Unfortunately, the well-known state explosion problem often limits the extent to which it can be applied to realistic specifications, due to the huge resulting memory requirements. Distributedmemory model checkers exist, but have thus far only been evaluated on small-scale clusters, with mixed results. We examine one well-known distributed model checker in detail, and show how a number of additional optimizations in its runtime system enable it to efficiently check very demanding problem instances on a large-scale, multi-core compute cluster. We analyze the impact of the distributed algorithms employed, the problem instance characteristics and network overhead. Finally, we show that the model checker can even obtain good performance in a high-bandwidth computational grid environment. 1

Revisiting resistant graph algorithms are those that can tolerate reexploration of edges without yielding incorrect results. Revisiting resistant I/O efficient graph algorithms exhibit considerable speed-up in practice in comparison to non-revisiting resistant algorithms. In the paper we present a new revisiting resistant I/O efficient LTL model checking algorithm. We analyze its theoretical I/O complexity and we experimentally compare its performance to already existing I/O efficient LTL model checking algorithms.

Explicit model checking algorithms explore the full state space of a system. State spaces are usually treated as directed graphs without any specific features. We gather a large collection of state spaces and extensively study their structural properties. Our results show that state spaces have several typical properties, i.e., they are not arbitrary graphs. We also demonstrate that state spaces differ significantly from random graphs and that different classes of models (application domains, academic vs industrial) have different properties. We discuss consequences of these results for model checking experiments and we point out how to exploit typical properties of state spaces in practical model checking algorithms.

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Abstract. Duplicate detection is an expensive operation of disk-based model checkers. It consists of comparing some potentially new states, the candidate states, to previous visited states. We propose a new approach to this technique called dynamic delayed duplicate detection. This one exploits some typical properties of states spaces, and adapts itself to the structure of the state space to dynamically decide when duplicate detection must be conducted. We implemented this method in a new algorithm and found out that it greatly cuts down the cost of duplicate detection. On some classes of models, it performs significantly better than some previously published algorithms. Model checking, or state space analysis, is a method to prove that finite state systems match their specification. Given a model of the system and a property, e.g., a temporal logic formula, it explores all the possible configurations, i.e., the state space, of the system to check the validity of the property. Despite its simplicity, its practical application is limited due to the well-known state

Abstract. Automata over infinite words provide a powerful framework, which we can use to solve various decision problems. However, the automatized reasoning with restricted classes of automata over infinite words is often simpler and more efficient. For instance, weak deterministic Büchi automata, which recognize the ω-regular languages in the Borel class Fσ ∩ Gδ, can be handled algorithmically almost as efficient as deterministic automata over finite words. In this paper, we show how and when we can determinize automata over infinite words by the standard powerset construction for finite words. The presented construction is more efficient than all-purpose constructions for automata that recognize languages in Fσ ∩ Gδ. Further, based on the powerset construction, we present an improved automata construction that handles the quantification in the automata-based approach for FO(R, Z,+,<) much more efficiently. 1

Distributed processing of real-world graphs is challenging duetotheirsizeandtheinherentirregularstructureofgraph computations. We present HipG, a distributed framework that facilitates programming parallel graph algorithms by composing the parallel application automatically from the user-defined pieces of sequential work on graph nodes. To make the user code high-level, the framework provides a unified interface to executing methods on local and non-local graph nodes and an abstraction of exclusive execution. The graph computations are managed by logical objects called synchronizers, which we used, for example, to implement distributed divide-and-conquer decomposition into strongly connected components. The code written in HipG is independent of a particular graph representation, to the point that the graph can be created on-the-fly, i.e. by the algorithm that computes on this graph, which we used to implement a distributed model checker. HipG programs are in general short and elegant; they achieve good portability, memory utilization, and performance. 1.