To share or not to share vector registers?

Publikation: Beitrag in FachzeitschriftForschungsartikelBeigetragenBegutachtung

Beitragende

Abstract

Query execution techniques in database systems constantly adapt to novel hardware features to achieve high query performance, in particular for analytical queries. In recent years, vectorization based on the Single Instruction Multiple Data parallel paradigm has been established as a state-of-the-art approach to increase single-query performance. However, since concurrent analytical queries running in parallel often access the same columns and perform a same set of vectorized operations, data accesses and computations among different queries may be executed redundantly. Various techniques have already been proposed to avoid such redundancy, ranging from concurrent scans via the construction of materialized views to applying multiple query optimization techniques. Continuing this line of research, we investigate the opportunity of sharing vector registers for concurrently running queries in analytical scenarios in this paper. In particular, our novel sharing approach relies on processing data elements of different queries together within a single vector register. As we are going to show, sharing vector registers to optimize the execution of concurrent analytical queries can be very beneficial in single-threaded as well as multi-thread environments. Therefore, we demonstrate the feasibility and applicability of such a novel work sharing strategy and thus open up a wide spectrum of future research opportunities.

Details

OriginalspracheEnglisch
Aufsatznummer6
Seiten (von - bis)1215-1236
Seitenumfang22
FachzeitschriftThe VLDB journal
Jahrgang31
Ausgabenummer6
PublikationsstatusVeröffentlicht - 28 Apr. 2022
Peer-Review-StatusJa

Externe IDs

Scopus 85128926947
dblp journals/vldb/PietrzykKHL22
Mendeley c208f2b2-22bd-3a9b-b5b4-03cd2292a6c2

Schlagworte

Schlagwörter

  • Database systems, Query execution, SIMD, Vectorization, Work sharing