liu.seSearch for publications in DiVA
Endre søk
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Pruning strategies in adaptive off-line tuning for optimized composition of components on heterogeneous systems
Linköpings universitet, Institutionen för datavetenskap, Programvara och system. Linköpings universitet, Tekniska fakulteten.
Linköpings universitet, Institutionen för datavetenskap, Programvara och system. Linköpings universitet, Tekniska fakulteten.
Linköpings universitet, Institutionen för datavetenskap, Programvara och system. Linköpings universitet, Tekniska fakulteten.ORCID-id: 0000-0001-5241-0026
2016 (engelsk)Inngår i: Parallel Computing, ISSN 0167-8191, E-ISSN 1872-7336, Vol. 51, s. 37-45Artikkel i tidsskrift (Fagfellevurdert) Published
Resurstyp
Text
Abstract [en]

Adaptive program optimizations, such as automatic selection of the expected fastest implementation variant for a computation component depending on hardware architecture and runtime context, are important especially for heterogeneous computing systems but require good performance models. Empirical performance models which require no or little human efforts show more practical feasibility if the sampling and training cost can be reduced to a reasonable level. In previous work we proposed an early version of adaptive sampling for efficient exploration and selection of training samples, which yields a decision-tree based method for representing, predicting and selecting the fastest implementation variants for given run-time call contexts property values. For adaptive pruning we use a heuristic convexity assumption. In this paper we consolidate and improve the method by new pruning techniques to better support the convexity assumption and control the trade-off between sampling time, prediction accuracy and runtime prediction overhead. Our results show that the training time can be reduced by up to 39 times without noticeable prediction accuracy decrease. (C) 2015 Elsevier B.V. All rights reserved.

sted, utgiver, år, opplag, sider
ELSEVIER SCIENCE BV , 2016. Vol. 51, s. 37-45
Emneord [en]
Smart sampling; Heterogeneous systems; Component selection
HSV kategori
Identifikatorer
URN: urn:nbn:se:liu:diva-125830DOI: 10.1016/j.parco.2015.09.003ISI: 000370093800004OAI: oai:DiVA.org:liu-125830DiVA, id: diva2:910226
Merknad

Funding Agencies|EU; SeRC project OpCoReS

Tilgjengelig fra: 2016-03-08 Laget: 2016-03-04 Sist oppdatert: 2018-01-10

Open Access i DiVA

Fulltekst mangler i DiVA

Andre lenker

Forlagets fulltekst

Personposter BETA

Li, LuDastgeer, UsmanKessler, Christoph

Søk i DiVA

Av forfatter/redaktør
Li, LuDastgeer, UsmanKessler, Christoph
Av organisasjonen
I samme tidsskrift
Parallel Computing

Søk utenfor DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric

doi
urn-nbn
Totalt: 487 treff
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf