- Titel
- ERIS live
- Untertitel
- A NUMA-aware in-memory storage engine for tera-scale multiprocessor systems
- Zitierfähige Url:
- https://nbn-resolving.org/urn:nbn:de:bsz:14-qucosa2-803930
- Konferenz
- SIGMOD/PODS'14: International Conference on Management of Data. Snowbird, 22. - 27. Juni 2014
- Quellenangabe
- SIGMOD '14: Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data
Herausgeber: Curtis Dyreson
Herausgeber: Feifei Li
Herausgeber: M. Tamer Özsu
Erscheinungsort: New York
Verlag: ACM
Erscheinungsjahr: 2014
Seiten: 689-692
ISBN: 978-1-4503-2376-5 - Erstveröffentlichung
- 2014
- Abstract (EN)
- The ever-growing demand for more computing power forces hardware vendors to put an increasing number of multiprocessors into a single server system, which usually exhibits a non-uniform memory access (NUMA). In-memory database systems running on NUMA platforms face several issues such as the increased latency and the decreased bandwidth when accessing remote main memory. To cope with these NUMA-related issues, a DBMS has to allow flexible data partitioning and data placement at runtime. In this demonstration, we present ERIS, our NUMA-aware in-memory storage engine. ERIS uses an adaptive partitioning approach that exploits the topology of the underlying NUMA platform and significantly reduces NUMA-related issues. We demonstrate throughput numbers and hardware performance counter evaluations of ERIS and a NUMA-unaware index for different workloads and configurations. All experiments are conducted on a standard server system as well as on a system consisting of 64 multiprocessors, 512 cores, and 8 TBs main memory.
- Andere Ausgabe
- Link zum Artikel, der zuerst in der ACM Digital Library erschienen ist.
DOI: 10.1145/2588555.2594524 - Freie Schlagwörter (DE)
- ERIS, NUMA, In-Memory, Speichermaschine, Multiprozessor, Skalierbarkeit
- Freie Schlagwörter (EN)
- ERIS, NUMA, In-Memory, Storage Engine, Multiprocessor, Scalability
- Klassifikation (DDC)
- 004
- Verlag
- ACM, New York
- Förder- / Projektangaben
- Deutsche Forschungsgemeinschaft (DFG)
Sonderforschungsbereich
Highly Adaptive Energy-Efficient Computing
(HAEC)
ID: 164481002 - Bundesministerium für Bildung und Forschung (BMBF)
CoolSilicon
ID: 16N10186 - Version / Begutachtungsstatus
- angenommene Version / Postprint / Autorenversion
- URN Qucosa
- urn:nbn:de:bsz:14-qucosa2-803930
- Veröffentlichungsdatum Qucosa
- 12.08.2022
- Dokumenttyp
- Konferenzbeitrag
- Sprache des Dokumentes
- Englisch
- Lizenz / Rechtehinweis
