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An Improved GPU-Based SAT Model Counter

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Principles and Practice of Constraint Programming (CP 2019)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 11802))

Abstract

In this paper, we present and evaluate a new parallel propositional model counter, called gpusat2, which is based on dynamic programming (DP) on tree decompositions using log-counters. gpusat2 extends its predecessor by a novel architecture for DP that includes using customized tree decompositions, storing solutions to parts of the input instance during the computation variably in arrays or binary search trees, and compressing solution parts. In addition, we avoid data transfer between the RAM and the VRAM whenever possible and employ extended preprocessing by means of state-of-the-art preprocessors for propositional model counting. Our novel architecture allows gpusat2 to be competitive with modern model counters when we also take preprocessing into consideration. As a side result, we observe that state-of-the-art preprocessors allow to produce tree decompositions of significantly smaller width.

Our system gpusat2 is available under GPL3 license at github.com/daajoe/GPUSAT. A preliminary version has been presented at the workshop Pragmatics of SAT 2019. The work has been supported by the Austrian Science Fund (FWF), Grants Y698 and P26696, and the German Science Fund (DFG), Grant HO 1294/11-1.

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Notes

  1. 1.

    The sum-of-product problem is often also referred to as weighted counting, partition function, or probability of evidence.

  2. 2.

    Extending an assignment can be done by recursively considering previously computed assignments at the children that correspond to an assignment at the node.

  3. 3.

    See: tinyurl.com/countingbenchmarks.

  4. 4.

    See: reasoning.cs.ucla.edu/c2d.

  5. 5.

    See: cs.rochester.edu/u/kautz/Cachet.

  6. 6.

    See: tools.computational-logic.org.

  7. 7.

    Details on spectre and meltdown: spectreattack.com.

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Authors and Affiliations

  1. TU Dresden, Dresden, Germany

    Johannes K. Fichte

  2. TU Wien, Vienna, Austria

    Markus Hecher & Markus Zisser

  3. University of Potsdam, Potsdam, Germany

    Markus Hecher

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  1. Johannes K. Fichte
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  2. Markus Hecher
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Correspondence to Johannes K. Fichte .

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  1. INRA, Castanet Tolosan, France

    Thomas Schiex

  2. INRA, Castanet Tolosan, France

    Simon de Givry

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Fichte, J.K., Hecher, M., Zisser, M. (2019). An Improved GPU-Based SAT Model Counter. In: Schiex, T., de Givry, S. (eds) Principles and Practice of Constraint Programming. CP 2019. Lecture Notes in Computer Science(), vol 11802. Springer, Cham. https://doi.org/10.1007/978-3-030-30048-7_29

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