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Detail publikačního výsledku
ANDRIUSHCHENKO, R.; ČEŠKA, M.; MARCIN, V.; VOJNAR, T.
Originální název
GPU-Accelerated Synthesis of Probabilistic Programs
Anglický název
Druh
Stať ve sborníku v databázi WoS či Scopus
Originální abstrakt
We consider automated synthesis methods for finite-state probabilistic programs satisfying a given temporal specification. Our goal is to accelerate the synthesis process using massively parallel graphical processing units (GPUs). The involved analysis of families of candidate programs is the main computational bottleneck of the process. We thus propose a state-level GPU-parallelisation of the model-checking algorithms for Markov chains and Markov decision processes that leverages the related but distinct topology of the candidate programs. For structurally complex families, we achieve a speedup of the analysis over one order of magnitude. This already leads to a considerable acceleration of the overall synthesis process and paves the way for further improvements.
Anglický abstrakt
Klíčová slova
Markov models, probabilistic programs, graphical processing units
Klíčová slova v angličtině
Autoři
Rok RIV
2025
Vydáno
30.06.2022
Nakladatel
Springer Nature Switzerland AG
Místo
Cham
ISBN
978-3-031-25312-6
Kniha
International Conference on Computer Aided Systems Theory (EUROCAST'22)
Edice
Lecture Notes in Computer Science
Strany od
256
Strany do
266
Strany počet
11
BibTex
@inproceedings{BUT178306, author="Roman {Andriushchenko} and Milan {Češka} and Vladimír {Marcin} and Tomáš {Vojnar}", title="GPU-Accelerated Synthesis of Probabilistic Programs", booktitle="International Conference on Computer Aided Systems Theory (EUROCAST'22)", year="2022", series="Lecture Notes in Computer Science", pages="256--266", publisher="Springer Nature Switzerland AG", address="Cham", isbn="978-3-031-25312-6" }