Alternativer Identifier:
(KITopen-DOI) 10.5445/IR/1000095237
Verwandter Identifier:
-
Ersteller/in:
Looz, Moritz von [Looz, Moritz von]
Beitragende:
-
Titel:
Digital Artefacts and Appendix for the Dissertation of Moritz v. Looz
Weitere Titel:
-
Beschreibung:
(Abstract) 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.
(Technical Remarks) There are two ways to reproduce the graph generation experiments: 1. Unpack the archive hyperbolic-scripts.zip, run the python script download-install.py, followed by the script experiment-plot.py. Install any missing dependencies, rerun if necessary. 2. Use "docker load hyperbolic-image.tar" to load the image and "docker run hyperbolic-reproducibility" to run it. After the experiments are completed, use docker save -o filesystem.tar. to save the filesystem of the docker image to a tarball, inspect the compiled plots in /app. Running all the experiments might take ~100 hours, 256 GiB of memory and 16 cores. 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.
Schlagworte:
graph generation
hyperbolic geometry
random hyperbolic graphs
k-means
graph partitioning
probabilistic query
distributed computing
load balancing
Zugehörige Informationen:
-
Sprache:
-
Erstellungsjahr:
Fachgebiet:
Computer Science
Objekttyp:
Dataset
Datenquelle:
-
Verwendete Software:
-
Datenverarbeitung:
-
Erscheinungsjahr:
Rechteinhaber/in:
Looz, Moritz von
Förderung:
-
Name Speichervolumen Metadaten Upload Aktion

Zugriffe der letzten sechs Monate

Aufrufe der Datenpaket-Seite

56


Downloads des Datenpakets

0


Gesamtstatistik

Zeitraum Aufrufe der Datenpaket-Seite Datenpaket heruntergeladen
Apr. 2024 9 0
März 2024 9 0
Feb. 2024 11 0
Jan. 2024 10 0
Dez. 2023 5 0
Nov. 2023 12 0
Vorher 33 0
Gesamt 89 0
Status:
Publiziert
Eingestellt von:
kitopen
Erstellt am:
Archivierungsdatum:
2023-06-21
Archivgröße:
5,0 GB
Archiversteller:
kitopen
Archiv-Prüfsumme:
8f52017e574b5569a43b10c3389964d1 (MD5)
Ende des Embargo-Zeitraums:
-