Detail publikace

Branch Predictor On-line Evolutionary System

Originální název

Branch Predictor On-line Evolutionary System

Anglický název

Branch Predictor On-line Evolutionary System

Jazyk

en

Originální abstrakt

In this work a branch prediction system which utilizes evolutionary techniques is introduced. It allows the predictor to adapt to the executed code and thus to improve its performance on the fly. Experiments with the predictor system were performed and the results display how various parameters can impact its performance on various executed code. It is evident that a one-level predictor can be evolved whose performance is better than comparable predictors of the same class. The dynamic prediction system predicts with a relative high accuracy and outperforms any static predictor of the same class.

Anglický abstrakt

In this work a branch prediction system which utilizes evolutionary techniques is introduced. It allows the predictor to adapt to the executed code and thus to improve its performance on the fly. Experiments with the predictor system were performed and the results display how various parameters can impact its performance on various executed code. It is evident that a one-level predictor can be evolved whose performance is better than comparable predictors of the same class. The dynamic prediction system predicts with a relative high accuracy and outperforms any static predictor of the same class.

BibTex


@inproceedings{BUT32061,
  author="Karel {Slaný}",
  title="Branch Predictor On-line Evolutionary System",
  annote="In this work a branch prediction system which utilizes evolutionary techniques is
introduced. It allows the predictor to adapt to the executed code and thus to
improve its performance on the fly. Experiments with the predictor system were
performed and the results display how various parameters can impact its
performance on various executed code. It is evident that a one-level predictor
can be evolved whose performance is better than comparable predictors of the same
class. The dynamic prediction system predicts with a relative high accuracy and
outperforms any static predictor of the same class.",
  address="Association for Computing Machinery",
  booktitle="2008 Genetic and Evolutionary Computation Conference GECCO",
  chapter="32061",
  edition="NEUVEDEN",
  howpublished="print",
  institution="Association for Computing Machinery",
  year="2008",
  month="july",
  pages="1643--1648",
  publisher="Association for Computing Machinery",
  type="conference paper"
}