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SIGmA: GPU Accelerated Simplification of SAT Formulas

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Integrated Formal Methods (IFM 2019)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 11918))

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Abstract

We present SIGmA (SAT sImplification on GPU Architectures), a preprocessor to accelerate SAT solving that runs on NVIDIA GPUs. We discuss the tool, focussing on its full functionality and how it can be used in combination with state-of-the-art SAT solvers. SIGmA performs various types of simplification, such as variable elimination, subsumption elimination, blocked clause elimination and hidden redundancy elimination. We study the effectiveness of our tool when applied prior to SAT solving. Overall, for our large benchmark set of problems, SIGmA enables MiniSat and Lingeling to solve many problems in less time compared to applying the SatElite preprocessor.

This work is part of the GEARS project with project number TOP2.16.044, which is (partly) financed by the Netherlands Organisation for Scientific Research (NWO).

We gratefully acknowledge the support of NVIDIA Corporation with the donation of the GeForce Titan Xp’s used for this research.

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Notes

  1. 1.

    https://docs.nvidia.com/cuda/cuda-c-programming-guide.

  2. 2.

    See https://www.cs.ubc.ca/~hoos/SATLIB/benchm.html.

  3. 3.

    See http://www.satcompetition.org.

  4. 4.

    Tables with all the data are available at http://gears.win.tue.nl/software.

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Authors and Affiliations

  1. Eindhoven University of Technology, 5600 MB, Eindhoven, The Netherlands

    Muhammad Osama & Anton Wijs

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  1. Muhammad Osama
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  2. Anton Wijs
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Correspondence to Muhammad Osama or Anton Wijs .

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Editors and Affiliations

  1. Chalmers University of Technology, Gothenburg, Sweden

    Wolfgang Ahrendt

  2. University of Oslo, Oslo, Norway

    Silvia Lizeth Tapia Tarifa

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Osama, M., Wijs, A. (2019). SIGmA: GPU Accelerated Simplification of SAT Formulas. In: Ahrendt, W., Tapia Tarifa, S. (eds) Integrated Formal Methods. IFM 2019. Lecture Notes in Computer Science(), vol 11918. Springer, Cham. https://doi.org/10.1007/978-3-030-34968-4_29

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  • DOI: https://doi.org/10.1007/978-3-030-34968-4_29

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