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## Distributed and structured analysis approaches to study large and complex systems.

Ciardo, Gianfranco
In:
Brinksma, E.; Hermanns, H.; Katoen, J.P.: *LNCS 2090: Lectures on Formal Methods and Performance Analysis*, pages 344-374.
Springer-Verlag,
2001.

Abstract:
Both the logic and the stochastic analysis of discrete-state systems are
hindered by the combinatorial growth of the state space underlying a
high-level model. In this work, we consider two orthogonal approaches to
cope with this ``state-space explosion''. Distributed algorithms that make
use of the processors and memory overall available on a network of N
workstations can manage models with state spaces approximately N times
larger than what is possible on a single workstation. A second approach,
constituting a fundamental paradigm shift, is instead based on decision
diagrams and related implicit data structures that efficiently encode the
state space or the transition rate matrix of a model, provided that it has
some structure to guide its decomposition; with these implicit methods,
enormous sets can be managed efficiently, but the numerical solution of
the stochastic model, if desired, is still a bottleneck, as it requires
vectors of the size of the state space.

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