Improving the O-GEHL branch prediction accuracy using analytical results

Autores
Tiamkaew, Ekkasit; Kongmunvattana, Angkul
Año de publicación
2007
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The O-GEHL branch predictor has outperformed other prediction schemes using the same set of benchmarks in an international branch prediction contest, CBP-1. In this paper, we present the analysis results on each of the OGEHL branch predictor tables and also on the optimal number of predictor tables. Two methods are subsequently proposed to help increase the O-GEHL prediction accuracy. The first one aims to increase the space utilization of the first predictor table by dynamically adjusting the lengths of branch history regarding to the type of a benchmark currently in execution. The second one adds an extra table into the O-GEHL predictor using the space saved from the sharing of hysteresis bits. Experimental results have confirmed that both schemes improve the accuracy of two different predictor configurations, leading to two promising research directions for future explorations.
Facultad de Informática
Materia
Ciencias Informáticas
Neural nets
branch predictor
perceptron
predictor analysis
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc/3.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/9550
Acceder
id SEDICI_9045099bcb16ba7eedc7541219534d4b
oai_identifier_str oai:sedici.unlp.edu.ar:10915/9550
network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling Improving the O-GEHL branch prediction accuracy using analytical resultsTiamkaew, EkkasitKongmunvattana, AngkulCiencias InformáticasNeural netsbranch predictorperceptronpredictor analysisThe O-GEHL branch predictor has outperformed other prediction schemes using the same set of benchmarks in an international branch prediction contest, CBP-1. In this paper, we present the analysis results on each of the OGEHL branch predictor tables and also on the optimal number of predictor tables. Two methods are subsequently proposed to help increase the O-GEHL prediction accuracy. The first one aims to increase the space utilization of the first predictor table by dynamically adjusting the lengths of branch history regarding to the type of a benchmark currently in execution. The second one adds an extra table into the O-GEHL predictor using the space saved from the sharing of hysteresis bits. Experimental results have confirmed that both schemes improve the accuracy of two different predictor configurations, leading to two promising research directions for future explorations.Facultad de Informática2007-04info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdf171-176http://sedici.unlp.edu.ar/handle/10915/9550enginfo:eu-repo/semantics/altIdentifier/url/http://journal.info.unlp.edu.ar/wp-content/uploads/JCST-Apr07-7.pdfinfo:eu-repo/semantics/altIdentifier/issn/1666-6038info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc/3.0/Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T10:50:44Zoai:sedici.unlp.edu.ar:10915/9550Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 10:50:44.359SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Improving the O-GEHL branch prediction accuracy using analytical results
title Improving the O-GEHL branch prediction accuracy using analytical results
spellingShingle Improving the O-GEHL branch prediction accuracy using analytical results
Tiamkaew, Ekkasit
Ciencias Informáticas
Neural nets
branch predictor
perceptron
predictor analysis
title_short Improving the O-GEHL branch prediction accuracy using analytical results
title_full Improving the O-GEHL branch prediction accuracy using analytical results
title_fullStr Improving the O-GEHL branch prediction accuracy using analytical results
title_full_unstemmed Improving the O-GEHL branch prediction accuracy using analytical results
title_sort Improving the O-GEHL branch prediction accuracy using analytical results
dc.creator.none.fl_str_mv Tiamkaew, Ekkasit
Kongmunvattana, Angkul
author Tiamkaew, Ekkasit
author_facet Tiamkaew, Ekkasit
Kongmunvattana, Angkul
author_role author
author2 Kongmunvattana, Angkul
author2_role author
dc.subject.none.fl_str_mv Ciencias Informáticas
Neural nets
branch predictor
perceptron
predictor analysis
topic Ciencias Informáticas
Neural nets
branch predictor
perceptron
predictor analysis
dc.description.none.fl_txt_mv The O-GEHL branch predictor has outperformed other prediction schemes using the same set of benchmarks in an international branch prediction contest, CBP-1. In this paper, we present the analysis results on each of the OGEHL branch predictor tables and also on the optimal number of predictor tables. Two methods are subsequently proposed to help increase the O-GEHL prediction accuracy. The first one aims to increase the space utilization of the first predictor table by dynamically adjusting the lengths of branch history regarding to the type of a benchmark currently in execution. The second one adds an extra table into the O-GEHL predictor using the space saved from the sharing of hysteresis bits. Experimental results have confirmed that both schemes improve the accuracy of two different predictor configurations, leading to two promising research directions for future explorations.
Facultad de Informática
description The O-GEHL branch predictor has outperformed other prediction schemes using the same set of benchmarks in an international branch prediction contest, CBP-1. In this paper, we present the analysis results on each of the OGEHL branch predictor tables and also on the optimal number of predictor tables. Two methods are subsequently proposed to help increase the O-GEHL prediction accuracy. The first one aims to increase the space utilization of the first predictor table by dynamically adjusting the lengths of branch history regarding to the type of a benchmark currently in execution. The second one adds an extra table into the O-GEHL predictor using the space saved from the sharing of hysteresis bits. Experimental results have confirmed that both schemes improve the accuracy of two different predictor configurations, leading to two promising research directions for future explorations.
publishDate 2007
dc.date.none.fl_str_mv 2007-04
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Articulo
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/9550
url http://sedici.unlp.edu.ar/handle/10915/9550
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://journal.info.unlp.edu.ar/wp-content/uploads/JCST-Apr07-7.pdf
info:eu-repo/semantics/altIdentifier/issn/1666-6038
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc/3.0/
Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc/3.0/
Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)
dc.format.none.fl_str_mv application/pdf
171-176
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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score 13.070432

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