- Titel
- Limitations of Intra-operator Parallelism Using Heterogeneous Computing Resources
- Zitierfähige Url:
- https://nbn-resolving.org/urn:nbn:de:bsz:14-qucosa2-839039
- Konferenz
- Advances in Databases and Information Systems: 20th East European Conference, ADBIS 2016. Prag, 28.-31.08.2016
- Quellenangabe
- Advances in Databases and Information Systems
; 20th East European Conference, ADBIS 2016
Erscheinungsort: Cham
Verlag: Springer
Erscheinungsjahr: 2016
Titel Schriftenreihe: Lecture Notes in Computer Science
Bandnummer Schriftenreihe: 9809
Seiten: 291-305
ISBN: 978-3-319-44038-5 - Erstveröffentlichung
- 2016
- Abstract (EN)
- The hardware landscape is changing from homogeneous multi-core systems towards wildly heterogeneous systems combining different computing units, like CPUs and GPUs. To utilize these heterogeneous environments, database query execution has to adapt to cope with different architectures and computing behaviors. In this paper, we investigate the simple idea of partitioning an operator’s input data and processing all data partitions in parallel, one partition per computing unit. For heterogeneous systems, data has to be partitioned according to the performance of the computing units. We define a way to calculate the partition sizes, analyze the parallel execution exemplarily for two database operators, and present limitations that could hinder significant performance improvements. The findings in this paper can help system developers to assess the possibilities and limitations of intra-operator parallelism in heterogeneous environments, leading to more informed decisions if this approach is beneficial for a given workload and hardware environment.
- Andere Ausgabe
- Link zum Artikel, der zuerst bei Springer Link erschienen ist
DOI: 10.1007/978-3-319-44039-2_20 - Freie Schlagwörter (DE)
- Intra-Operator-Parallelität, Heterogene Systeme, Datenfluss-Parallelität, Datenpartitionierung, GPU
- Freie Schlagwörter (EN)
- Intra-operator parallelism, Heterogeneous systems, Dataflow parallelism, Data partitioning, GPU
- Klassifikation (DDC)
- 004
- Verlag
- Springer, Berlin [u. a.]
- Förder- / Projektangaben
- Deutsche Forschungsgemeinschaft (DFG)
Cluster of Excellence
Zentrum für Perspektiven in der Elektronik Dresden
(EXC 1056)
ID: 194636624 - Version / Begutachtungsstatus
- angenommene Version / Postprint / Autorenversion
- URN Qucosa
- urn:nbn:de:bsz:14-qucosa2-839039
- Veröffentlichungsdatum Qucosa
- 02.03.2023
- Dokumenttyp
- Konferenzbeitrag
- Sprache des Dokumentes
- Englisch
- Lizenz / Rechtehinweis
