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Extending Perfect Spatial Hashing to Index Tuple-based Graphs Representing Super Carbon Nanotubes

Burger, Michael ; Nguyen, Giang Nam ; Bischof, Christian :
Extending Perfect Spatial Hashing to Index Tuple-based Graphs Representing Super Carbon Nanotubes.
[Online-Edition: http://www.iccs-meeting.org/iccs2017/]
In: ICCS 2017, 12.06.2017, Zürich. Proceedings of the International Conference on Computational Science
[Konferenz- oder Workshop-Beitrag], (2017) (Im Druck)

Offizielle URL: 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.

Typ des Eintrags: Konferenz- oder Workshop-Beitrag (Keine Angabe)
Erschienen: 2017
Autor(en): Burger, Michael ; Nguyen, Giang Nam ; Bischof, Christian
Titel: Extending Perfect Spatial Hashing to Index Tuple-based Graphs Representing Super Carbon Nanotubes
Sprache: Englisch
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.

Buchtitel: Proceedings of the International Conference on Computational Science
Freie Schlagworte: perfect spatial hashing, data indexing, super carbon nanotubes, simulation
Fachbereich(e)/-gebiet(e): 20 Fachbereich Informatik > Scientific Computing
Exzellenzinitiative > Graduiertenschulen > Graduate School of Computational Engineering (CE)
Zentrale Einrichtungen > Hochschulrechenzentrum (HRZ) > Hochleistungsrechner
Veranstaltungstitel: ICCS 2017
Veranstaltungsort: Zürich
Veranstaltungsdatum: 12.06.2017
Hinterlegungsdatum: 18 Mai 2017 07:13
Offizielle URL: http://www.iccs-meeting.org/iccs2017/
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