Parallel Data Processing

Hasso Plattner ()
Additional contact information
Hasso Plattner: Hasso Plattner Institute

Chapter Chapter 17 in A Course in In-Memory Data Management, 2013, pp 113-120 from Springer

Abstract: Abstract In the following, we discuss how to achieve parallelism in in-memory and traditional database management systems. Pipelined parallelism and data parallelism are two approaches to speed up query processing.

Keywords: Traditional Database Management Systems; Pipeline Parallelism; Single Instruction Multiple Data (SIMD); Shared Memory Segment; Streaming SIMD Extensions (search for similar items in EconPapers)
Date: 2013
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-642-36524-9_17

Ordering information: This item can be ordered from
http://www.springer.com/9783642365249

DOI: 10.1007/978-3-642-36524-9_17

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().