Detailed Information on Publication Record
2016
PRISM-PSY: Precise GPU-Accelerated Parameter Synthesis for Stochastic Systems
ČEŠKA, Milan, Petr PILAŘ, Nikola PAOLETTI, Luboš BRIM, Marta KWIATKOWSKA et. al.Basic information
Original name
PRISM-PSY: Precise GPU-Accelerated Parameter Synthesis for Stochastic Systems
Name in Czech
PRISM-PSY: Precise GPU-Accelerated Parameter Synthesis for Stochastic Systems
Authors
ČEŠKA, Milan (203 Czech Republic), Petr PILAŘ (203 Czech Republic, belonging to the institution), Nikola PAOLETTI (826 United Kingdom of Great Britain and Northern Ireland), Luboš BRIM (203 Czech Republic, guarantor, belonging to the institution) and Marta KWIATKOWSKA (826 United Kingdom of Great Britain and Northern Ireland)
Edition
LNCS 9636. Berlin, 22nd International Conference, TACAS 2016, p. 367-384, 18 pp. 2016
Publisher
Springer International Publishing
Other information
Language
English
Type of outcome
Stať ve sborníku
Field of Study
10201 Computer sciences, information science, bioinformatics
Country of publisher
Germany
Confidentiality degree
není předmětem státního či obchodního tajemství
Publication form
printed version "print"
Impact factor
Impact factor: 0.402 in 2005
RIV identification code
RIV/00216224:14330/16:00088144
Organization unit
Faculty of Informatics
ISBN
978-3-662-49673-2
ISSN
UT WoS
000406428000021
Keywords in English
GPU; stochastic systems; model checking; parameter synthesis
Tags
International impact, Reviewed
Změněno: 16/4/2019 09:41, prof. RNDr. Luboš Brim, CSc.
Abstract
V originále
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.
Links
GA15-11089S, research and development project |
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