The Peps Software Tool

Benoît, Anne; Brenner, Léonardo; Fernandes, Paulo; Plateau, Brigitte; Stewart, William J.

doi:10.1007/978-3-540-45232-4_7

Cited by 25 publications

(41 citation statements)

References 8 publications

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“…All numerical experiments were performed using the software package, PEPS ( [12,31]), into which we implemented the new algorithms. All execution times were measured to within a tenth of a second on a 531 MHz Pentium III PC with 128 MB of memory.…”

Section: Numerical Resultsmentioning

confidence: 99%

“…2 We have not yet implemented dynamic reallocation in the package PEPS [31]. This happens very rarely in our experience and is the reason why we have not had need of dynamic reallocation for the moment.…”

Section: Data Structures Usedmentioning

confidence: 99%

“…A series of tests comparing theses shuffle algorithms is presented in Section 5. These algorithms were incorporated into the software package PEPS [12,31] and tested by means of this package.…”

Section: Introductionmentioning

confidence: 99%

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Memory-efficient Kronecker algorithms with applications to the modelling of parallel systems

Benoît¹,

Plateau²,

Stewart

2006

Future Generation Computer Systems

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We present a new algorithm for computing the solution of large Markov chain models whose generators can be represented in the form of a generalized tensor algebra, such as networks of stochastic automata.The tensor structure inherently involves a product state space but inside this product state space, the actual reachable state space can be much smaller.For such cases, we propose an improvement of the standard numerical algorithm, the so-called ``shuffle algorithm'', which necessitates only vectors of the size of the actual state space.With this contribution, numerical algorithms based on tensor products can now handle larger models. Memory-Efficient Kronecker Algorithms with Applications to the Modelling of Parallel SystemsAnne AbstractWe present a new algorithm for computing the solution of large Markov chain models whose generators can be represented in the form of a generalized tensor algebra, such as networks of stochastic automata. The tensor structure inherently involves a product state space but inside this product state space, the actual reachable state space can be much smaller. For such cases, we propose an improvement of the standard numerical algorithm, the so-called "shuffle algorithm", which necessitates only vectors of the size of the actual state space. With this contribution, numerical algorithms based on tensor products can now handle larger models.

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Section: Numerical Resultsmentioning

confidence: 99%

Section: Data Structures Usedmentioning

confidence: 99%

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Memory-efficient Kronecker algorithms with applications to the modelling of parallel systems

Benoît¹,

Plateau²,

Stewart

2006

Future Generation Computer Systems

Self Cite

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“…Hence, the most important point in this descriptor-vector multiplication is to know how one can multiply normal factors. In spite of the Markovian Descriptor optimization, the PEPS application still suffers from a performance decline at the same time that the complexity 3 of the model grows (more details can be found in [6]). …”

Section: Peps Implementation Analysismentioning

confidence: 99%

“…Each subsystem is described as a stochastic automaton, i.e., an automaton in which the transitions are labelled with probabilistic and timing information. The analysis of the theoretical SAN models is performed by a software package called Performance Evaluation for Parallel Systems (PEPS) [6]. Although PEPS has proven its usability during the past years, it presents an important drawback: the execution time to analyze SAN models with too many states is very often impracticable.…”

Section: Introductionmentioning

confidence: 99%

Parallel PEPS Tool Performance Analysis Using Stochastic Automata Networks

Baldo

Fernandes

Roisenberg³

et al. 2004

Lecture Notes in Computer Science

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Abstract. This paper presents a theoretical performance analysis of a parallel implementation of a tool called Performance Evaluation for Parallel Systems (PEPS). This software tool is used to analyze Stochastic Automata Networks (SAN) models. In its sequential version, the execution time becomes impracticable when analyzing large SAN models. A parallel version of PEPS using distributed memory is proposed and modelled with SAN formalism. After, the sequential PEPS itself is applied to predict the performance of this model.

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A Framework to Decompose GSPN Models

Brenner

Fernandes

Sales

et al. 2005

Applications and Theory of Petri Nets 2005

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Abstract. This paper presents a framework to decompose a single GSPN model into a set of small interacting models. This decomposition technique can be applied to any GSPN model with a finite set of tangible markings and a generalized tensor algebra (Kronecker) representation can be produced automatically. The numerical impact of all the possible decompositions obtained by our technique is discussed. To do so we draw the comparison of the results for some practical examples. Finally, we present all the computational gains achieved by our technique, as well as the future extensions of this concept for other structured formalisms.

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The Peps Software Tool

Cited by 25 publications

References 8 publications

Memory-efficient Kronecker algorithms with applications to the modelling of parallel systems

Memory-efficient Kronecker algorithms with applications to the modelling of parallel systems

Parallel PEPS Tool Performance Analysis Using Stochastic Automata Networks

A Framework to Decompose GSPN Models

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