Graph-Based Algorithms for Boolean Function Manipulation
IEEE Transactions on Computers
Who are the variables in your neighborhood
ICCAD '95 Proceedings of the 1995 IEEE/ACM international conference on Computer-aided design
Improving the Variable Ordering of OBDDs Is NP-Complete
IEEE Transactions on Computers
RuleBase: an industry-oriented formal verification tool
DAC '96 Proceedings of the 33rd annual Design Automation Conference
Interleaving based variable ordering methods for ordered binary decision diagrams
ICCAD '93 Proceedings of the 1993 IEEE/ACM international conference on Computer-aided design
Dynamic variable ordering for ordered binary decision diagrams
ICCAD '93 Proceedings of the 1993 IEEE/ACM international conference on Computer-aided design
Symbolic Model Checking
BDDNOW: A Parallel BDD Package
FMCAD '98 Proceedings of the Second International Conference on Formal Methods in Computer-Aided Design
Achieving Scalability in Parallel Reachability Analysis of Very Large Circuits
CAV '00 Proceedings of the 12th International Conference on Computer Aided Verification
Speeding up variable reordering of OBDDs
ICCD '97 Proceedings of the 1997 International Conference on Computer Design (ICCD '97)
On variable ordering of binary decision diagrams for the application of multi-level logic synthesis
EURO-DAC '91 Proceedings of the conference on European design automation
Lower bounds for dynamic BDD reordering
Proceedings of the 2005 Asia and South Pacific Design Automation Conference
Using lower bounds during dynamic BDD minimization
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Parallel disk-based computation for large, monolithic binary decision diagrams
Proceedings of the 4th International Workshop on Parallel and Symbolic Computation
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Dynamic BDD reordering is usually a computationally-demanding process, and may slow down BDD-based applications. We propose a novel algorithm for distributing this process over a number of computers, improving both reordering time and application time. Our algorithm is based on Rudell's popular sifting algorithm, and takes advantage of a few empirical observations we make regarding Rudell's algorithm. Experimental results show the efficiency and scalability of our approach, when applied within an industrial model checker.