Digital Artefacts and Appendix for the Dissertation of Moritz v. Looz
Abstract (englisch):
The corresponding dissertation contains several types of experiments. The graph generation experiments were conducted on a shared-memory machine with 16 cores. To reproduce these experiments, this package contains a docker image. For the experiments regarding large-scale graph partitioning, we used phase 1 of the SuperMUC cluster, including tens of thousands of cores. For this set of experiments, we do not include a script to reproduce it, as the original computing environment is no longer available and the experimental pipeline depends on details of the system setup, making an automated reproduction difficult. Instead, we offer the output logs and summarized data of our experiments.
| Zugehörige Institution(en) am KIT | Institut für Theoretische Informatik (ITI) |
| Publikationstyp | Forschungsdaten |
| Publikationsjahr | 2019 |
| Erstellungsdatum | 18.06.2019 |
| Identifikator | DOI: 10.5445/IR/1000095237 KITopen-ID: 1000095237 |
| Lizenz | Creative Commons Namensnennung – Nicht kommerziell – Keine Bearbeitungen 4.0 International |
| Projektinformation | WAVE-Sim - TP ITI (BMFTR, 01IH15004B) SPP 1736 (DFG, DFG KOORD, ME 3619/3-2) |
| Schlagwörter | graph generation, hyperbolic geometry, random hyperbolic graphs, k-means, graph partitioning, probabilistic query, distributed computing, load balancing |
| Liesmich | There are two ways to reproduce the graph generation experiments:
The same methods apply to reproduce the experiments to partition protein graphs, with the script archive protein-scripts.zip and the docker image protein-image.tar. These experiments take about 3 hours and 4 GiB of memory. The experimental logs of the partitioning with balanced k-means are found in the archive Geographer-comparison-log-files.tar.gz. |
| Art der Forschungsdaten | Dataset |
| Relationen in KITopen |