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

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.-14.06.2017)
doi: 10.26083/tuprints-00019090
Conference or Workshop Item, Secondary publication, Publisher's Version

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Item Type: Conference or Workshop Item
Type of entry: Secondary publication
Title: Extending Perfect Spatial Hashing to Index Tuple-based Graphs Representing Super Carbon Nanotubes
Language: English
Date: 2021
Place of Publication: Darmstadt
Year of primary publication: 2017
Publisher: Elsevier
Event Title: International Conference on Computational Science (ICCS 2017)
Event Location: Zurich, Switzerland
Event Dates: 12.-14.06.2017
DOI: 10.26083/tuprints-00019090
Corresponding Links:
Origin: Secondary publication service

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: Publisher's Version
URN: urn:nbn:de:tuda-tuprints-190909
Additional Information:

Erscheint auch in: Procedia Computer Science, Volume 108, pages 435-444, ISSN: 1877-0509

Classification DDC: 000 Generalities, computers, information > 004 Computer science
Divisions: 20 Department of Computer Science > Scientific Computing
Exzellenzinitiative > Graduate Schools > Graduate School of Computational Engineering (CE)
Zentrale Einrichtungen > University IT-Service and Computing Centre (HRZ) > Hochleistungsrechner
Date Deposited: 27 Aug 2021 08:16
Last Modified: 07 Nov 2022 10:20
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/19090
PPN: 48616800X
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