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Dataset results
6 results for “SAT solving”
Experimental data for "sampling Configurations From Software Product Lines Via Probability-aware Diversification and SAT Solving"
<p>Experimental data for "sampling Configurations From Software Product Lines Via Probability-aware Diversification and SAT Solving" submitted to ASE. </p> <p>1. rSATJ4, PaD+rSATJ4, ProbSAT and PaD+ProbSAT are data for RQs1-2;</p> <p>2. NSbS and PaD+NSbS are data for RQ3;</p> <p>3. SATVaEA, PaD+SATVaEA, ProbSATVaEA, PaD+ProbSATVaEA are data for RQ4.</p> <p> </p> <p> </p>
Large SAT Benchmark Suite for Certified SAT Solving with GPU Accelerated Inprocessing
<p>This submission includes the SAT benchmark dataset for "Certified SAT Solving with GPU Accelerated Inprocessing" article. The dataset is intended to evaluate the performance of ParaFROST GPU solver and to compare with the state of the art.</p>
ParaFROST Proofs for Certified SAT Solving with GPU Accelerated Inprocessing
<p>This submission includes all the proofs of ParaFROST GPU SAT solver for the "Certified SAT Solving with GPU Accelerated Inprocessing" article.</p>
Scalability for SAT-based combinatorial problem solving - Results
<p>The experimental results for the referenced thesis.</p>
Benchmark Collection for A Time Leap Challenge for SAT-Solving
<p>Benchmarks used for the paper</p> <p>Johannes K. Fichte, Markus Hecher, Stefan Szeider: A Time Leap Challenge for SAT-Solving, Proceedings of the 26th International Conference on Principles and Practice of Constraint Programming (CP'2020).</p> <p>For details, we refer to the original sources of the benchmarks (<a href="https://www.cs.uni-potsdam.de/wv/sets/">https://www.cs.uni-potsdam.de/wv/sets/</a> and https://www.cs.uni-potsdam.de/wv/projects/sets/set-industrial-09-12.tar.xz), which was authored by Holger H. Hoos, B. Kaufmann, T. Schaub, and M. Schneider in the publication Robust Benchmark Set Selection for Boolean Constraint Solvers, LION 7, DOI: 10.1007/978-3-642-44973-4_16, 2013.</p>
Scalable SAT Solving and its Application | Experimental Data
<p>Experimental data accompanying the doctoral thesis of Dominik P. Schreiber entitled "Scalable SAT Solving and its Application", Karlsruhe Institute of Technology, 2023.</p><p>Upstream URL: <a href="https://github.com/domschrei/sssaia">https://github.com/domschrei/sssaia</a></p><p>--</p><p>Funding notices:</p><p>This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No. 882500).</p><p>Some of this work was performed on the supercomputer ForHLR funded by the Ministry of Science, Research and the Arts Baden-W ̈urttemberg and by the Federal Ministry of Education and Research.</p><p>Some of this work was performed on the HoreKa supercomputer funded by the Ministry of Science, Research and the Arts Baden-Württemberg and by the Federal Ministry of Education and Research.</p><p>The author gratefully acknowledges the Gauss Centre for Supercomputing e.V. (www.gauss-centre.eu) for funding this project by providing computing time on the GCS Supercomputer SuperMUC-NG at Leibniz Supercomputing Centre (www.lrz.de).</p>
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