Burger, Michael ; Nguyen, Giang Nam ; Bischof, Christian (2017)
Extending Perfect Spatial Hashing to Index Tuple-based Graphs Representing Super Carbon Nanotubes.
ICCS 2017. Zürich (12.06.2017)
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
In this paper, we demonstrate how to extend perfect spatial hashing (PSH) to the problem domain of indexing nodes in a graph that represents of 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 and the representation of SCNTs. 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.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2017 |
| Autor(en): | Burger, Michael ; Nguyen, Giang Nam ; Bischof, Christian |
| Art des Eintrags: | Bibliographie |
| Titel: | Extending Perfect Spatial Hashing to Index Tuple-based Graphs Representing Super Carbon Nanotubes |
| Sprache: | Englisch |
| Publikationsjahr: | 12 Juni 2017 |
| Buchtitel: | Proceedings of the International Conference on Computational Science |
| Veranstaltungstitel: | ICCS 2017 |
| Veranstaltungsort: | Zürich |
| Veranstaltungsdatum: | 12.06.2017 |
| URL / URN: | http://www.iccs-meeting.org/iccs2017/ |
| Kurzbeschreibung (Abstract): | In this paper, we demonstrate how to extend perfect spatial hashing (PSH) to the problem domain of indexing nodes in a graph that represents of 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 and the representation of SCNTs. 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. |
| Freie Schlagworte: | perfect spatial hashing, data indexing, super carbon nanotubes, simulation |
| 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: | 18 Mai 2017 07:13 |
| Letzte Änderung: | 07 Jan 2021 10:10 |
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