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Scalable discrete algorithms for big data applications

In: High Performance Computing in Science and Engineering '21

Author

Listed:
  • Demian Hespe

    (Institute for Theoretical Informatics: Algorithms II, Karlsruhe Institute of Technology (KIT))

  • Lukas Hübner

    (Institute for Theoretical Informatics: Algorithms II, Karlsruhe Institute of Technology (KIT)
    Computational Molecular Evolution Group, Heidelberg Institute for Theoretical Studies)

  • Lorenz Hübschle-Schneider

    (Institute for Theoretical Informatics: Algorithms II, Karlsruhe Institute of Technology (KIT))

  • Peter Sanders

    (Institute for Theoretical Informatics: Algorithms II, Karlsruhe Institute of Technology (KIT))

  • Dominik Schreiber

    (Institute for Theoretical Informatics: Algorithms II, Karlsruhe Institute of Technology (KIT))

Abstract

In the past year, the project “Scalable Discrete Algorithms for Big Data Applications” dealt with High-Performance SAT Solving, Malleable Job Scheduling and Load Balancing, and Fault-Tolerant Algorithms. We used the massively parallel nature of ForHLR II to obtain novel results in the areas of SAT solving and faulttolerant algorithms.

Suggested Citation

  • Demian Hespe & Lukas Hübner & Lorenz Hübschle-Schneider & Peter Sanders & Dominik Schreiber, 2023. "Scalable discrete algorithms for big data applications," Springer Books, in: Wolfgang E. Nagel & Dietmar H. Kröner & Michael M. Resch (ed.), High Performance Computing in Science and Engineering '21, pages 439-449, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-17937-2_27
    DOI: 10.1007/978-3-031-17937-2_27
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