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PRISM-PSY: Precise GPU-Accelerated Parameter Synthesis for Stochastic Systems

  • Milan Češka
  • Petr Pilař
  • Nicola Paoletti
  • Luboš Brim
  • Marta Kwiatkowska
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9636)

Abstract

In this paper we present PRISM-PSY, a novel tool that performs precise GPU-accelerated parameter synthesis for continuous-time Markov chains and time-bounded temporal logic specifications. We redesign, in terms of matrix-vector operations, the recently formulated algorithms for precise parameter synthesis in order to enable effective data-parallel processing, which results in significant acceleration on many-core architectures. High hardware utilisation, essential for performance and scalability, is achieved by state space and parameter space parallelisation: the former leverages a compact sparse-matrix representation, and the latter is based on an iterative decomposition of the parameter space. Our experiments on several biological and engineering case studies demonstrate an overall speedup of up to 31-fold on a single GPU compared to the sequential implementation.

Keywords

Synthesis Problem Synthesis Algorithm Probability Bound Satisfaction Probability Space Parallelisation 
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 2016

Authors and Affiliations

  • Milan Češka
    • 1
  • Petr Pilař
    • 2
  • Nicola Paoletti
    • 1
  • Luboš Brim
    • 2
  • Marta Kwiatkowska
    • 1
  1. 1.Department of Computer ScienceUniversity of OxfordOxfordUK
  2. 2.Faculty of InformaticsMasaryk UniversityBrnoCzech Republic

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