187 results found
Knottenbelt W, Harrison P, Mestern M, et al., 2000, A Probabilistic Dynamic Technique for the Distributed Generation of Very Large State Spaces, Publisher: Elsevier, Pages: 127-148, ISSN: 0166-5316
Conventional methods for state space exploration are limited to the analysis of small systems because they suffer from excessive memory and computational requirements. We have developed a new dynamic probabilistic state exploration algorithm which addresses this problem for general, structurally unrestricted state spaces.\r\n\r\nOur 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.\r\n\r\nWe derive a performance model of this new algorithm in order to quantify its benefits in terms of distributed run-time, speedup and efficiency. We implement our technique on a distributed-memory parallel computer and demonstrate results which compare favourably with the performance model. Finally, we discuss suitable choices for the three hash functions upon which our algorithm is based.
Harrison PG, Knottenbelt WJ, 2000, Passage Time Distributions in Large Markov Chains, IFIP Working Group 7.3 & University of Central Florida Symposium on Advanced Performance Modeling (SAPM), Orlando, Florida
Harrison PG, Knottenbelt WJ, 1999, Distributed disk-based solution techniques for large Markov models, Proc 3rd Int. Conference on the Numerical Solution of Markov Chains, NSMC 99, Zaragoza
Harrison PG, Knottenbelt WJ, Jmestern M, et al., 1998, Probability, Parallelism and the State Space Exploration, Proc 10th Int. Conf, Tools 98, Computer Performance Evaluation, Palma de Mallorca, Publisher: Springer Verlag, Pages: 165-179
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.
Harrison PG, Knottenbelt WJ, 1998, A Scalable Distributed Algorithm for the Exploration of Very Large State graphs, Proceedings 8th Int. Parallel Computing Workshop, PCW '98, Singapore, Pages: 377-384
McDonald D, Juritz J, Cowling R, et al., 1995, Modelling the biological aspects of local endemism in South African Fynbos, Plant Systematics and Evolution, Vol: 195, Pages: 137-147
The biological attributes, dispersal mode, growth form, and regeneration strategy were determined for the endemic and non-endemic flora of the southern Langeberg, Cape Province, South Africa. Logistic regression analysis was used to assess the simultaneous effects and interactions between these biological attributes on the occurrence of endemism. The model allowed numerical estimation of the probability that a species with a given set of attributes would be endemic. This approach extends a contingency table analysis of the data, which merely indicated the association between individual biological traits and endemism. Furthermore, the logistic model allows scope for the analysis of the influence of biological traits in determining endemism in other floras, and also tentative prediction of the probability of endemism in species with combinations of biological traits not yet observed in the flora of the southern Langeberg.\r\n
MCDONALD DJ, JURITZ JM, COWLING RM, et al., 1995, MODELING THE BIOLOGICAL ASPECTS OF LOCAL ENDEMISM IN SOUTH-AFRICAN FYNBOS, PLANT SYSTEMATICS AND EVOLUTION, Vol: 195, Pages: 137-147, ISSN: 0378-2697
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