Graph-Based Algorithms for Boolean Function Manipulation
IEEE Transactions on Computers
Verification of synchronous sequential machines based on symbolic execution
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Tableau-based model checking in the propositional mu-calculus
Acta Informatica
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TAPSOFT '89 2nd international joint conference on Theory and practice of software development
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Information and Computation - Special issue: Selections from 1990 IEEE symposium on logic in computer science
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Reachability analysis using partitioned-ROBDDs
ICCAD '97 Proceedings of the 1997 IEEE/ACM international conference on Computer-aided design
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POPL '85 Proceedings of the 12th ACM SIGACT-SIGPLAN symposium on Principles of programming languages
Model checking
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A Scalable Parallel Algorithm for Reachability Analysis of Very Large Circuits
Formal Methods in System Design
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CAV '00 Proceedings of the 12th International Conference on Computer Aided Verification
An Improved Algorithm for the Evaluation of Fixpoint Expressions
CAV '94 Proceedings of the 6th International Conference on Computer Aided Verification
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IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
A Multi-Core Solver for Parity Games
Electronic Notes in Theoretical Computer Science (ENTCS)
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Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
On parallel software verification using boolean equation systems
SPIN'12 Proceedings of the 19th international conference on Model Checking Software
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In this paper we propose a distributed symbolic algorithm for model checking of propositional 驴-calculus formulas. 驴-calculus is a powerful formalism and 驴-calculus model checking can solve many problems, including, for example, verification of (fair) CTL and LTL properties. Previous works on distributed symbolic model checking were restricted to reachability analysis and safety properties. This work thus significantly extends the scope of properties that can be verified distributively, enabling us to use them for very large designs.The algorithm distributively evaluates subformulas. It results in sets of states which are evenly distributed among the processes. We show that this algorithm is scalable and therefore can be implemented on huge distributed clusters of computing nodes. The memory modules of the computing nodes collaborate to create a very large memory space, thus enabling the checking of much larger designs. We formally prove the correctness of the parallel algorithm. We complement the distribution of the state sets by showing how to distribute the transition relation.