ACM Transactions on Computer Systems (TOCS)
Design and validation of computer protocols
Design and validation of computer protocols
On the solution of GSPN reward models
Performance Evaluation
A decomposition approach for stochastic reward net models
Performance Evaluation
Hierarchical Markovian models: symmetries and reduction
Performance Evaluation - Special issue: 6th international conference on modelling techniques and tools for computer performance evaluation
Using MPI: portable parallel programming with the message-passing interface
Using MPI: portable parallel programming with the message-passing interface
PVM: Parallel virtual machine: a users' guide and tutorial for networked parallel computing
PVM: Parallel virtual machine: a users' guide and tutorial for networked parallel computing
Open, Closed, and Mixed Networks of Queues with Different Classes of Customers
Journal of the ACM (JACM)
An analysis of bistate hashing
Proceedings of the Fifteenth IFIP WG6.1 International Symposium on Protocol Specification, Testing and Verification XV
Parallel Shared-Memory State-Space Exploration in Stochastic Modeling
IRREGULAR '97 Proceedings of the 4th International Symposium on Solving Irregularly Structured Problems in Parallel
Improved probabilistic verification by hash compaction
CHARME '95 Proceedings of the IFIP WG 10.5 Advanced Research Working Conference on Correct Hardware Design and Verification Methods
Parallel State Space Exploration for GSPN Models
Proceedings of the 16th International Conference on Application and Theory of Petri Nets
Reliable Hashing without Collosion Detection
CAV '93 Proceedings of the 5th International Conference on Computer Aided Verification
Distributed State Space Generation of Discrete-State Stochastic Models
INFORMS Journal on Computing
Numerical analysis of superposed GSPNs
PNPM '95 Proceedings of the Sixth International Workshop on Petri Nets and Performance Models
Performance Evaluation: Origins and Directions
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We present a new dynamic probabilistic state exploration algorithm based on hash compaction. Our method has a low state omission probability and low memory usage that is independent of the length of the state vector. In addition, the algorithm can be easily parallelised. This combination of probability and parallelism enables us to rapidly explore state spaces that are an order of magnitude larger than those obtainable using conventional exhaustive techniques. We implement our technique on a distributed-memory parallel computer and we present results showing good speedups and scalability. Finally, we discuss suitable choices for the three hash functions upon which our algorithm is based.