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Parallel Approaches to the Numerical Transient Analysis of Stochastic Reward Nets

  • Susann Allmaier
  • David Kreische
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1639)

Abstract

This paper presents parallel approaches to the complete transient numerical analysis of stochastic reward nets (SRNs) for both shared and distributed-memory machines. Parallelization concepts and implementation issues are discussed for the three main analysis steps that are (1) generation of the underlying continuous-time Markov chain (CTMC), (2) solving the CTMC numerically for the desired time points and (3) converting the results back to the net level by evaluating reward based result measure functions. The distributed-memory approach implements dynamic load balancing mechanisms in step (1) to guarantee an equal distribution of the state space onto the main memories of the clustered machines. The shared-memory algorithms are based on elaborated synchronization mechanisms which allow parallel read and write access to the global irregular data structure of the CTMC. Performance measurements on different architectures and a comparison of the approaches are given. All the algorithms are integrated in PANDA which consequently is a parallel SRN modeling tool suitable for different multiprocessor platforms.

Keywords

Transient Solution Rate Reward Master Process Linear List Markov Reward Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1999

Authors and Affiliations

  • Susann Allmaier
    • 1
  • David Kreische
    • 1
  1. 1.Lehrstuhl für Rechnerstrukturen (IMMD III)Universität Erlangen-NürnbergErlangenGermany

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