Abstract
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.
This work has been supported by the Czech Science Foundation grant GJ20-02328Y and the FIT BUT internal project FIT-S-20-6427.
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Notes
- 1.
Short version of https://www.fit.vut.cz/study/thesis-file/24076/24076.pdf.
- 2.
The extension to Probabilistic Computational Tree Logic is straightforward [4].
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- 4.
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Andriushchenko, R., Češka, M., Marcin, V., Vojnar, T. (2022). GPU-Accelerated Synthesis of Probabilistic Programs. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds) Computer Aided Systems Theory – EUROCAST 2022. EUROCAST 2022. Lecture Notes in Computer Science, vol 13789. Springer, Cham. https://doi.org/10.1007/978-3-031-25312-6_30
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