- Titel
- Make Larger Vector Register Sizes New Challenges?
- Untertitel
- Lessons Learned from the Area of Vectorized Lightweight Compression Algorithms
- Zitierfähige Url:
- https://nbn-resolving.org/urn:nbn:de:bsz:14-qucosa2-806292
- Konferenz
- SIGMOD/PODS '18: International Conference on Management of Data. Houston, 15. Juni 2018
- Quellenangabe
- DBTest'18: Proceedings of the Workshop on Testing Database SystemsHerausgeber: Association for Computing Machinery
Erscheinungsort: New York
Verlag: ACM
Erscheinungsjahr: 2018
ISBN: 978-1-4503-5826-2
Artikelnummer: 8 - Erstveröffentlichung
- 2018
- Abstract (EN)
- The exploitation of data as well as hardware properties is a core aspect for efficient data management. This holds in particular for the field of in-memory data processing. Aside from increasing main memory capacities, in-memory data processing also benefits from novel processing concepts based on lightweight compressed data. To speed up compression as well as decompression, an active research field deals with the specialization of these algorithms to hardware features such as vectorization using SIMD instructions. Most of the vectorized implementations have been proposed for 128 bit vector registers. However, hardware vendors still increase the vector register sizes, whereby a straightforward transformation to these wider vector sizes is possible in most-cases. Thus, we systematically investigated the impact of different SIMD instruction set extensions with wider vector sizes on the behavior of straightforward transformed implementations. In this paper, we will describe our evaluation methodology and present selective results of our exhaustive evaluation. In particular, we will highlight some challenges and present first approaches to tackle them.
- Andere Ausgabe
- Link zum Artikel, der zuerst in der ACM Digital Library erschienen ist.
DOI: 10.1145/3209950.3209957 - Freie Schlagwörter (DE)
- In-Memory, Datenbanksysteme, leichte Datenkompression, Vektorisierung, experimentelle Bewertung
- Freie Schlagwörter (EN)
- in-memory, database systems, lightweight data compression, vectorization, experimental evaluation
- Klassifikation (DDC)
- 004
- Verlag
- ACM, New York
- Förder- / Projektangaben
- Deutsche Forschungsgemeinschaft (DFG)
Sonderforschungsbereiche
HAEC - Highly Adaptive Energy-Efficient Computing
(SFB 912)
ID: 164481002 - Version / Begutachtungsstatus
- angenommene Version / Postprint / Autorenversion
- URN Qucosa
- urn:nbn:de:bsz:14-qucosa2-806292
- Veröffentlichungsdatum Qucosa
- 15.09.2022
- Dokumenttyp
- Konferenzbeitrag
- Sprache des Dokumentes
- Englisch
- Lizenz / Rechtehinweis
