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Serial Diskbased Analysis of Large Stochastic Models
 In Proc. Dagstuhl Research Seminar
, 2004
"... The paper presents a survey of outofcore methods available for the analysis of large Markov chains on single workstations. First, we discuss the main sparse matrix storage schemes and review iterative methods for the solution of systems of linear equations typically used in diskbased methods. Nex ..."
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The paper presents a survey of outofcore methods available for the analysis of large Markov chains on single workstations. First, we discuss the main sparse matrix storage schemes and review iterative methods for the solution of systems of linear equations typically used in diskbased methods. Next, various outofcore approaches for the steady state solution of CTMCs are described. In this context, serial outofcore algorithms are outlined and analysed with the help of their implementations. A comparison of time...
Partiallyshared zerosuppressed multiterminal bdds: concept, algorithms and applications
 Formal Methods in System Design
"... Abstract MultiTerminal Binary Decision Diagrams (MTBDDs) are a well accepted technique for the state graph (SG) based quantitative analysis of large and complex systems specified by means of highlevel model description techniques. However, this type of Decision Diagram (DD) is not always the best ..."
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Abstract MultiTerminal Binary Decision Diagrams (MTBDDs) are a well accepted technique for the state graph (SG) based quantitative analysis of large and complex systems specified by means of highlevel model description techniques. However, this type of Decision Diagram (DD) is not always the best choice, since finite functions with small satisfaction sets, and where the fulfilling assignments possess many 0assigned positions, may yield relatively large MTBDD based representations. Therefore, this article introduces zerosuppressed MTBDDs and proves that they are canonical representations of multivalued functions on finite (input) sets. For manipulating DDs of this new type, possibly defined over different sets of function variables, the concept of partiallyshared zerosuppressed MTBDDs and respective algorithms are developed. The efficiency of this new approach is demonstrated by comparing it to the wellknown standard type of MTBDDs, where both types of DDs have been implemented by us within the C++based DDpackage Jinc. The benchmarking takes place in the context of Markovian analysis and probabilistic model checking of systems. In total, the presented work extends existing approaches, since it not only allows one to directly employ (multiterminal) zerosuppressed DDs in the field of quantitative verification, but also clearly demonstrates their efficiency.
Advanced features in SMART: the Stochastic Model checking Analyzer for Reliability and Timing ∗
"... We describe some of the advanced features of the software tool SmArT, the Stochastic Model checking Analyzer for Reliability and Timing. Initially conceived as a software package for numerical solution and discreteevent simulation of stochastic models, SmArT now also provides powerful modelchecking ..."
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We describe some of the advanced features of the software tool SmArT, the Stochastic Model checking Analyzer for Reliability and Timing. Initially conceived as a software package for numerical solution and discreteevent simulation of stochastic models, SmArT now also provides powerful modelchecking capabilities, thanks to its extensive use of various forms of decision diagrams, which in turn also greatly increase the efficiency of its stochastic analysis algorithms. These aspects make it an excellent choice when tackling systems with extremely large state spaces. 1.
Data Representation and Efficient Solution: A Decision Diagram Approach
, 2007
"... Decision diagrams are a family of data structures that can compactly encode many functions on discrete structured domains, that is, domains that are the crossproduct of finite sets. We present some important classes of decision diagrams and show how they can be effectively employed to derive “symb ..."
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Decision diagrams are a family of data structures that can compactly encode many functions on discrete structured domains, that is, domains that are the crossproduct of finite sets. We present some important classes of decision diagrams and show how they can be effectively employed to derive “symbolic” algorithms for the analysis of large discretestate models. In particular, we discuss both explicit and symbolic algorithms for statespace generation, CTL modelchecking, and continuoustime Markov chain solution. We conclude with some suggestions for future research directions.
Implementing the Moebius StateLevel Abstract Functional Interface for ZDDs
"... matrixlayoutindependent numerical solvers be efcient? ..."
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Generalized Stochastic Petri Nets (GSPNs) [1] and Stochastic Wellformed Nets
"... Abstract. Decision diagrams (DDs) have made their way into Petri net (PN) tools either in the form of new tools (usually designed from scratch to use DDs) or as enhancements to existing tools. This paper describes how an existing and established tool (GreatSPN) has been enhanced through the use of D ..."
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Abstract. Decision diagrams (DDs) have made their way into Petri net (PN) tools either in the form of new tools (usually designed from scratch to use DDs) or as enhancements to existing tools. This paper describes how an existing and established tool (GreatSPN) has been enhanced through the use of DDs provided by an existing opensource library (Meddly). We benchmark the enhanced tool and discuss lessons learned while integrating DDs into GreatSPN.