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Dynamically Improving Branch Prediction Accuracy Between Contexts

Dynamically Improving Branch Prediction Accuracy Between Contexts

https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2072235712

Dynamically Improving Branch Prediction Accuracy Between Contexts

About this item

Full title

Dynamically Improving Branch Prediction Accuracy Between Contexts

Publisher

Ithaca: Cornell University Library, arXiv.org

Journal title

arXiv.org, 2018-05

Language

English

Formats

Publication information

Publisher

Ithaca: Cornell University Library, arXiv.org

More information

Scope and Contents

Contents

Branch prediction is a standard feature in most processors, significantly improving the run time of programs by allowing a processor to predict the direction of a branch before it has been evaluated. Current branch prediction methods can achieve excellent prediction accuracy through global tables, various hashing methods, and even machine learning...

Alternative Titles

Full title

Dynamically Improving Branch Prediction Accuracy Between Contexts

Authors, Artists and Contributors

Identifiers

Primary Identifiers

Record Identifier

TN_cdi_proquest_journals_2072235712

Permalink

https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2072235712

Other Identifiers

E-ISSN

2331-8422

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