A new scheme for memory-efficient probabilistic verification
IFIP TC6/ 6.1 international conference on formal description techniques IX/protocol specification, testing and verification XVI on Formal description techniques IX : theory, application and tools: theory, application and tools
Model checking
Summary cache: a scalable wide-area web cache sharing protocol
IEEE/ACM Transactions on Networking (TON)
Space/time trade-offs in hash coding with allowable errors
Communications of the ACM
An analysis of bistate hashing
Proceedings of the Fifteenth IFIP WG6.1 International Symposium on Protocol Specification, Testing and Verification XV
Reliable Hashing without Collosion Detection
CAV '93 Proceedings of the 5th International Conference on Computer Aided Verification
Exact and approximate membership testers
STOC '78 Proceedings of the tenth annual ACM symposium on Theory of computing
An optimal Bloom filter replacement
SODA '05 Proceedings of the sixteenth annual ACM-SIAM symposium on Discrete algorithms
Linear probing with constant independence
Proceedings of the thirty-ninth annual ACM symposium on Theory of computing
Compact Hash Tables Using Bidirectional Linear Probing
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The Design of a Multicore Extension of the SPIN Model Checker
IEEE Transactions on Software Engineering
Spin model checker, the: primer and reference manual
Spin model checker, the: primer and reference manual
Improving spin's partial-order reduction for breadth-first search
SPIN'05 Proceedings of the 12th international conference on Model Checking Software
Enhanced probabilistic verification with 3spin and 3murphi
SPIN'05 Proceedings of the 12th international conference on Model Checking Software
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Existing techniques for approximate storage of visited states in a model checker are too special-purpose and too DRAM-intensive. Bitstate hashing, based on Bloom filters, is good for exploring most of very large state spaces, and hash compaction is good for high-assurance verification of more tractable problems. We describe a scheme that is good at both, because it adapts at run time to the number of states visited. It does this within a fixed memory space and with remarkable speed and accuracy. In many cases, it is faster than existing techniques, because it only ever requires one random access to main memory per operation; existing techniques require several to have good accuracy. Adapting to accommodate more states happens in place using streaming access to memory; traditional rehashing would require extra space, random memory accesses, and hash computation. The structure can also incorporate search stack matching for partial-order reductions, saving the need for extra resources dedicated to an additional structure. Our scheme is wellsuited for a future in which random accesses to memory are more of a limiting factor than the size of memory.