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@inproceedings{1354080,
author = {Češka, Milan and Pilař, Petr and Paoletti, Nikola and Brim, Luboš and Kwiatkowska, Marta},
address = {Berlin},
booktitle = {22nd International Conference, TACAS 2016},
doi = {http://dx.doi.org/10.1007/978-3-662-49674-9_21},
edition = {LNCS 9636},
editor = {Marsha Chechik, Jean-François Raskin},
keywords = {GPU; stochastic systems; model checking; parameter synthesis},
howpublished = {tištěná verze "print"},
language = {eng},
location = {Berlin},
isbn = {978-3-662-49673-2},
pages = {367-384},
publisher = {Springer International Publishing},
title = {PRISM-PSY: Precise GPU-Accelerated Parameter Synthesis for Stochastic Systems},
year = {2016}
}
TY - JOUR
ID - 1354080
AU - Češka, Milan - Pilař, Petr - Paoletti, Nikola - Brim, Luboš - Kwiatkowska, Marta
PY - 2016
TI - PRISM-PSY: Precise GPU-Accelerated Parameter Synthesis for Stochastic Systems
PB - Springer International Publishing
CY - Berlin
SN - 9783662496732
KW - GPU
KW - stochastic systems
KW - model checking
KW - parameter synthesis
N2 - 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.
ER -
ČEŠKA, Milan, Petr PILAŘ, Nikola PAOLETTI, Luboš BRIM and Marta KWIATKOWSKA. PRISM-PSY: Precise GPU-Accelerated Parameter Synthesis for Stochastic Systems. In Marsha Chechik, Jean-Fran\c cois Raskin. \textit{22nd International Conference, TACAS 2016}. LNCS 9636. Berlin: Springer International Publishing, 2016, p.~367-384. ISBN~978-3-662-49673-2. Available from: https://dx.doi.org/10.1007/978-3-662-49674-9\_{}21.
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