Scalable discrete algorithms for big data applications

Demian Hespe (), Lukas Hübner (), Lorenz Hübschle-Schneider (), Peter Sanders () and Dominik Schreiber ()
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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)
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)

A chapter in High Performance Computing in Science and Engineering '21, 2023, pp 439-449 from Springer

Abstract: 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.

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-17937-2_27

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DOI: 10.1007/978-3-031-17937-2_27

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