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Dagster: Parallel Structured Search with Case Studies

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PRICAI 2022: Trends in Artificial Intelligence (PRICAI 2022)

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Abstract

We describe Dagster, a system that implements a new approach to scheduling interdependent (Boolean) SAT search activities in high-performance computing (HPC) environments. This system allows practitioners to solve challenging problems by efficiently distributing search effort across computing cores in a customizable way. Our solver takes as input a set of disjunctive clauses (i.e., DIMACS CNF) and a labelled directed acyclic graph (DAG) structure describing how the clauses are decomposed into a set of interrelated search problems. Component problems are solved using standard systematic backtracking search, which may optionally be coupled to (stochastic dynamic) local search and/or clause-strengthening processes. We show the performance of Dagster in combinatorial case study examples, particularly the model counting of Costas arrays, and in finding solutions to large Pentomino tiling problems. We also use Dagster to exhibit a novel workflow for Bounded Model Checking of network protocols where we perform independent searches at different problem fidelities, in parallel. Low fidelity solutions trigger further independent searches for refined solutions in higher fidelity models.

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Notes

  1. 1.

    A serial portfolio with ‘pre-solving’ and ‘solving’ stages amenable to parallel/distributed computing.

  2. 2.

    For example, in AI planning, automatic problem decomposition is available using factoring techniques [9], and also using obligation approaches in [8].

  3. 3.

    Sourcecode: Zenodo [11], and GitHub, https://github.com/ANU-HPC/dagster.

  4. 4.

    https://github.com/ThomWillingham/bmc-summer2122.

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Acknowledgements

Contributions to the Dagster project were made by Eamon Barrett—specifically, functionality related to decision diagrams—and Marshall Clifton—specifically, the clause strengthening subsystem—during their undergraduate studies at the Australian National University. This research was undertaken with the assistance of resources from the National Computational Infrastructure (NCI Australia), an NCRIS enabled capability supported by the Australian Government.

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Authors and Affiliations

  1. Australian National University, Canberra, Australia

    Mark Alexander Burgess, Charles Gretton, Josh Milthorpe, Luke Croak, Thomas Willingham & Alwen Tiu

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  1. Mark Alexander Burgess
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Correspondence to Mark Alexander Burgess .

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Editors and Affiliations

  1. CSIRO Australian e-Health Research Centre, Brisbane, QLD, Australia

    Sankalp Khanna

  2. Shanghai Jiao Tong University, Shanghai, China

    Jian Cao

  3. University of Tasmania, Hobart, TAS, Australia

    Quan Bai

  4. University of Technology Sydney, Sydney, NSW, Australia

    Guandong Xu

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Burgess, M.A., Gretton, C., Milthorpe, J., Croak, L., Willingham, T., Tiu, A. (2022). Dagster: Parallel Structured Search with Case Studies. In: Khanna, S., Cao, J., Bai, Q., Xu, G. (eds) PRICAI 2022: Trends in Artificial Intelligence. PRICAI 2022. Lecture Notes in Computer Science, vol 13629. Springer, Cham. https://doi.org/10.1007/978-3-031-20862-1_6

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