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

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

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.

Item Type: Conference or Workshop Item
Erschienen: 2017
Creators: Burger, Michael and Nguyen, Giang Nam and Bischof, Christian
Title: Extending Perfect Spatial Hashing to Index Tuple-based Graphs Representing Super Carbon Nanotubes
Language: English
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.

Title of Book: Proceedings of the International Conference on Computational Science
Uncontrolled Keywords: perfect spatial hashing, data indexing, super carbon nanotubes, simulation
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
Exzellenzinitiative > Graduate Schools
Zentrale Einrichtungen > University IT-Service and Computing Centre (HRZ)
20 Department of Computer Science
Zentrale Einrichtungen
Exzellenzinitiative
Event Title: ICCS 2017
Event Location: Zürich
Event Dates: 12.06.2017
Date Deposited: 18 May 2017 07:13
Official URL: http://www.iccs-meeting.org/iccs2017/
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