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We present SPNP, a powerful GSPN package developed at Duke University. SPNP allows the modeling of complex system behaviors. Advanced constructs are available, such as marking-dependent arc multiplicities, enabling functions, arrays of places or transitions, and subnets; in addition, the full expressive power of the C programming language is available to… (More)

- Gianfranco Ciardo, R. L. Jones, Andrew S. Miner, Radu Siminiceanu
- Computer Performance Evaluation / TOOLS
- 2003

We describe the main features of S m A r T, a software package providing a seamless environment for the logic and probabilistic analysis of complex systems. S m A r T can combine different formalisms in the same modeling study. For the analysis of logical behavior, both explicit and symbolic state-space generation techniques, as well as symbolic CTL… (More)

- Gianfranco Ciardo, Reinhard German, Christoph Lindemann
- IEEE Trans. Software Eng.
- 1993

— Stochastic Petri nets (SPNs) with generally distributed firing times can model a large class of systems, but simulation is the only feasible approach for their solution. We explore a hierarchy of SPN classes where modeling power is reduced in exchange for an increasingly efficient solution. Generalized stochastic Petri nets (GSPNs), deterministic and… (More)

We discuss how to describe the Markov c hain underlying a generalized stochastic Petri net using Kro-necker operators on smaller matrices. We extend previous approaches by allowing both an extensive type of marking-dependent behavior for the transitions and the presence of immediate synchronizations. The derivation of the results is thoroughly formalized,… (More)

| SMART is a new tool designed to allow various high-level stochastic modeling formalisms (such as stochas-tic Petri nets and queueing networks) to be described in a uniform environment and solved using a variety of solution techniques, including numerical methods and simulation. Since SMART is intended as a research tool, it is written in a modular way… (More)

- Gianfranco Ciardo, Kishor S. Trivedi
- Perform. Eval.
- 1993

We present a decomposition approach for the solution of large stochastic reward nets (SRNs) based on the concept of near-independence. The overall model consists of a set of submodels whose interactions are described by an import graph. Each node of the graph corresponds to a parametric SRN submodel and an arc from submodel A to submodel B corresponds to a… (More)

- Peter Buchholz, Gianfranco Ciardo, Susanna Donatelli, Peter Kemper
- INFORMS Journal on Computing
- 2000

We present new algorithms for the solution of large structured Markov models whose infinitesimal generator can be expressed as a Kronecker expression of sparse matrices. We then compare them with the shuffle-based method commonly used in this context and show how our new algorithms can be advantageous in dealing with very sparse matrices and in supporting… (More)

- Gianfranco Ciardo, Gerald Lüttgen, Radu Siminiceanu
- TACAS
- 2001

We present a novel algorithm for generating state spaces of asynchronous systems using Multi–valued Decision Diagrams. In contrast to related work, we encode the next–state function of a system not as a single Boolean function, but as cross–products of integer functions. This permits the application of various iteration strategies to build a system's state… (More)

- Gianfranco Ciardo, Joshua Gluckman, David M. Nicol
- INFORMS Journal on Computing
- 1998

High-level formalisms such as stochastic Petri nets can be used to model complex systems. Analysis of logical and numerical properties of these models often requires the generation and storage of the entire underlying state space. This imposes practical limitations on the types of systems which can be modeled. Because of the vast amount of memory consumed,… (More)