Instruction level analytic prediction of parallel CPU architecture performance

Gloria, Alessandro De; Ancarani, F.; Bellotti, F.; Olivieri, Mauro

doi:10.1109/iis.1997.645379

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“…Kibler et al [4] adopts linear regression estimated the CPU performance. De Gloria et al [2] developed a heuristic model to find out a reasonable prediction of the performance of a pipelined CPU. Srinivasan et al [7] adopted extending the Monte Carlo approach to CPU performance.…”

Section: Introductionmentioning

confidence: 99%

Application of Least-Squares Support Vector Regression with PSO for CPU Performance Forecasting

Lin

2012

AMR

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The success of CPU performance prediction will make many benefits. This study adopts the least-squares support vector regression (LS-SVR) with particle swarm optimization (PSO) algorithm to improver accuracy of CPU performance prediction. LS-SVR with PSO, support vector regression (SVR) with PSO, general regression neural network (GRNN), radial basis neural network (RBNN), and linear regression are employed for CPU performance prediction. Empirical results indicate that the LS-SVR (Linear kernel) with PSO has better performance in terms of forecasting accuracy than the other methods. Therefore, the LS-SVR (Linear kernel) with PSO model can efficiently provide credible CPU performance estimated value.

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Section: Introductionmentioning

confidence: 99%

Application of Least-Squares Support Vector Regression with PSO for CPU Performance Forecasting

Lin

2012

AMR

View full text Add to dashboard Cite

show abstract

Instruction level analytic prediction of parallel CPU architecture performance

Cited by 1 publication

References 3 publications

Application of Least-Squares Support Vector Regression with PSO for CPU Performance Forecasting

Application of Least-Squares Support Vector Regression with PSO for CPU Performance Forecasting

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