Dealing with big data outside of the cloud: GPU accelerated sort

John Vidler; Paul Rayson; Laurence Anthony; Andrew Scott; John Mariani

Dealing with big data outside of the cloud: GPU accelerated sort

John Vidler
, Paul Rayson
, Laurence Anthony
, Andrew Scott
, John Mariani

Faculty of Science and Engineering, Waseda University

Research output: Contribution to conference - Without ISBN/ISSN › Conference paper › peer-review

Abstract

The demands placed on systems to analyse corpus data increase with input size, and the traditional approaches to processing this data are increasingly having impractical run-times. We show that the use of desktop GPUs presents a significant opportunity to accelerate a number of stages in the normal corpus analysis pipeline. This paper contains our exploratory work and findings into applying high-performance computing technology and methods to the problem of sorting large numbers of concordance lines.

Original language	English
Pages	21-24
Number of pages	4
Publication status	Published - 31/05/2014
Event	LREC2014 Workshop on Challenges in the Management of Large Corpora (CMLC-2) - Reykjavik, Iceland Duration: 31/05/2014 → …

Workshop

Workshop	LREC2014 Workshop on Challenges in the Management of Large Corpora (CMLC-2)
Country/Territory	Iceland
City	Reykjavik
Period	31/05/14 → …

User-defined Keywords

Very Large Corpora
CONCURRENCY
GPU Computing
High Performance Computing
Concordances
Sorting

Access to Document

http://www.lrec-conf.org/proceedings/lrec2014/workshops/LREC2014Workshop-CMLC2%20Proceedings-rev2.pdf

UCREL - University Centre for Computer Corpus Research on Language (Organisational unit)
Vidler, J. (Member)
2016 → …
Activity: Membership types › Member of an organisation

Cite this

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title = "Dealing with big data outside of the cloud: GPU accelerated sort",

abstract = "The demands placed on systems to analyse corpus data increase with input size, and the traditional approaches to processing this data are increasingly having impractical run-times. We show that the use of desktop GPUs presents a significant opportunity to accelerate a number of stages in the normal corpus analysis pipeline. This paper contains our exploratory work and findings into applying high-performance computing technology and methods to the problem of sorting large numbers of concordance lines.",

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AU - Scott, Andrew

AU - Mariani, John

PY - 2014/5/31

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N2 - The demands placed on systems to analyse corpus data increase with input size, and the traditional approaches to processing this data are increasingly having impractical run-times. We show that the use of desktop GPUs presents a significant opportunity to accelerate a number of stages in the normal corpus analysis pipeline. This paper contains our exploratory work and findings into applying high-performance computing technology and methods to the problem of sorting large numbers of concordance lines.

AB - The demands placed on systems to analyse corpus data increase with input size, and the traditional approaches to processing this data are increasingly having impractical run-times. We show that the use of desktop GPUs presents a significant opportunity to accelerate a number of stages in the normal corpus analysis pipeline. This paper contains our exploratory work and findings into applying high-performance computing technology and methods to the problem of sorting large numbers of concordance lines.

KW - Very Large Corpora

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Dealing with big data outside of the cloud: GPU accelerated sort

Abstract

Workshop

User-defined Keywords

Access to Document

Activities

UCREL - University Centre for Computer Corpus Research on Language (Organisational unit)

Cite this