Burger, Michael ; Nguyen, Giang Nam ; Bischof, Christian (2021)
Extending Perfect Spatial Hashing to Index Tuple-based Graphs Representing Super Carbon Nanotubes.
International Conference on Computational Science (ICCS 2017). Zurich, Switzerland (12.06.2017-14.06.2017)
doi: 10.26083/tuprints-00019090
Konferenzveröffentlichung, Zweitveröffentlichung, Verlagsversion
 | Es ist eine neuere Version dieses Eintrags verfügbar. |
Kurzbeschreibung (Abstract)
In this paper, we demonstrate how to extend perfect spatial hashing (PSH) in order to hash multidimensional scientific data. As a use case we employ the problem domain of indexing nodes in a graph that represents Super Carbon Nanotubes (SCNTs). The goal of PSH is to hash multidimensional data without collisions. Since PSH results from the research on computer graphics, its principles and methods have only been tested on 2- and 3-dimensional problems. In our case, we need to hash up to 28 dimensions. In contrast to the original applications of PSH, we do not focus on GPUs as target hardware but on an efficient CPU implementation. Thus, this paper highlights the extensions to the original algorithm to make it suitable for higher dimensions. Comparing the compression and performance results of the new PSH based graphs and a structure-tailored custom data structure in our parallelized SCNT simulation software, we find that PSH in some cases achieves better compression by a factor of 1.7 while only increasing the total runtime by several percent. In particular, after our extension, PSH can also be employed to index sparse multidimensional scientific data from other domains where PSH can avoid additional index-structures like KD- or R-trees.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2021 |
| Autor(en): | Burger, Michael ; Nguyen, Giang Nam ; Bischof, Christian |
| Art des Eintrags: | Zweitveröffentlichung |
| Titel: | Extending Perfect Spatial Hashing to Index Tuple-based Graphs Representing Super Carbon Nanotubes |
| Sprache: | Englisch |
| Publikationsjahr: | 2021 |
| Ort: | Darmstadt |
| Publikationsdatum der Erstveröffentlichung: | 2017 |
| Verlag: | Elsevier |
| Veranstaltungstitel: | International Conference on Computational Science (ICCS 2017) |
| Veranstaltungsort: | Zurich, Switzerland |
| Veranstaltungsdatum: | 12.06.2017-14.06.2017 |
| DOI: | 10.26083/tuprints-00019090 |
| URL / URN: | https://tuprints.ulb.tu-darmstadt.de/19090 |
| Zugehörige Links: | |
| Herkunft: | Zweitveröffentlichungsservice |
| Kurzbeschreibung (Abstract): | In this paper, we demonstrate how to extend perfect spatial hashing (PSH) in order to hash multidimensional scientific data. As a use case we employ the problem domain of indexing nodes in a graph that represents Super Carbon Nanotubes (SCNTs). The goal of PSH is to hash multidimensional data without collisions. Since PSH results from the research on computer graphics, its principles and methods have only been tested on 2- and 3-dimensional problems. In our case, we need to hash up to 28 dimensions. In contrast to the original applications of PSH, we do not focus on GPUs as target hardware but on an efficient CPU implementation. Thus, this paper highlights the extensions to the original algorithm to make it suitable for higher dimensions. Comparing the compression and performance results of the new PSH based graphs and a structure-tailored custom data structure in our parallelized SCNT simulation software, we find that PSH in some cases achieves better compression by a factor of 1.7 while only increasing the total runtime by several percent. In particular, after our extension, PSH can also be employed to index sparse multidimensional scientific data from other domains where PSH can avoid additional index-structures like KD- or R-trees. |
| Status: | Verlagsversion |
| URN: | urn:nbn:de:tuda-tuprints-190909 |
| Zusätzliche Informationen: | Erscheint auch in: Procedia Computer Science, Volume 108, pages 435-444, ISSN: 1877-0509 |
| Sachgruppe der Dewey Dezimalklassifikatin (DDC): | 000 Allgemeines, Informatik, Informationswissenschaft > 004 Informatik |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Scientific Computing Exzellenzinitiative Exzellenzinitiative > Graduiertenschulen Exzellenzinitiative > Graduiertenschulen > Graduate School of Computational Engineering (CE) Zentrale Einrichtungen Zentrale Einrichtungen > Hochschulrechenzentrum (HRZ) Zentrale Einrichtungen > Hochschulrechenzentrum (HRZ) > Hochleistungsrechner |
| Hinterlegungsdatum: | 27 Aug 2021 08:16 |
| Letzte Änderung: | 30 Aug 2021 06:51 |
| PPN: | |
| Export: | |
| Suche nach Titel in: | TUfind oder in Google |
Verfügbare Versionen dieses Eintrags
- Extending Perfect Spatial Hashing to Index Tuple-based Graphs Representing Super Carbon Nanotubes. (deposited 27 Aug 2021 08:16) [Gegenwärtig angezeigt]
 |
Frage zum Eintrag |
Optionen (nur für Redakteure)
 |
Redaktionelle Details anzeigen |
Drucken
Impressum