GPU-Accelerated Quantification Filters for Analytical Queries in Multidimensional Databases
- In online analytical processing (OLAP), filtering elements of a given dimensional attribute according to the value of a measure attribute is an essential operation, for example in top-k evaluation. Such filters can involve extremely large amounts of data to be processed, in particular when the filter condition includes “quantification” such as ANY or ALL, where large slices of an OLAP cube have toIn online analytical processing (OLAP), filtering elements of a given dimensional attribute according to the value of a measure attribute is an essential operation, for example in top-k evaluation. Such filters can involve extremely large amounts of data to be processed, in particular when the filter condition includes “quantification” such as ANY or ALL, where large slices of an OLAP cube have to be computed and inspected. Due to the sparsity of OLAP cubes, the slices serving as input to the filter are usually sparse as well, presenting a challenge for GPU approaches which need to work with a limited amount of memory for holding intermediate results. Our CUDA solution involves a hashing scheme specifically designed for frequent and parallel updates, including several optimizations exploiting architectural features of Nvidia’s Fermi and Kepler GPUs.…
Document Type: | Conference Proceeding |
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Conference Type: | Konferenzartikel |
Zitierlink: | https://opus.hs-offenburg.de/1598 | Bibliografische Angaben |
Title (English): | GPU-Accelerated Quantification Filters for Analytical Queries in Multidimensional Databases |
Conference: | East European Conference on Advances in Databases and Information Systems and Associated Satellite Events (18. : September 7-10, 2014 : Ohrid, Macedonia) |
Author: | Peter Tim Strohm, Steffen Wittwer, Alexander Haberstroh, Tobias LauerStaff MemberGND |
Edition: | 1. |
Year of Publication: | 2015 |
Place of publication: | Cham |
Publisher: | Springer |
First Page: | 229 |
Last Page: | 242 |
Parent Title (English): | New Trends in Database and Information Systems II |
Editor: | Nick Bassiliades, Mirjana Ivanović, Margita Kon-Popovska, Yannis Manolopoulos, Themis Palpanas, Goce Trajcevski, Athena Vakali |
Volume: | 2 |
ISBN: | 978-3-319-10518-5 |
ISBN: | 978-3-319-10517-8 |
Language: | English | Inhaltliche Informationen |
Institutes: | Fakultät Elektrotechnik und Informationstechnik (E+I) (bis 03/2019) |
Institutes: | Bibliografie |
GND Keyword: | Datenbank; Informationssystem |
Tag: | Analytical Query; Filter Dimension; Hash Function; Hash Table; Target Path | Formale Angaben |
Open Access: | Closed Access |
Licence (German): | Urheberrechtlich geschützt |
Opac ID: | Link zum Online-Katalog |
Comment: | Parts of the research described in this paper were presented by the authors at Nvidia’s GPU Technology Conference in San Jose, CA (USA) in March 2014. |