Regression Modeling Strategies for Parameter Space Exploration


Benjamin Lee, David Brooks, Bronis Supinski, and Martin Schulz. 9/29/2006. “Regression Modeling Strategies for Parameter Space Exploration”.


Increasing system and algorithmic complexity, combined with a growing number of tuanble application parameters, pose significant challenges for analytical performance modeling. This report outlines a series of robust techniques that enable efficient parameter space exploration based on empirical statistical modeling. In particular, this report applies statistical techniques such as clustering, association, correlation analyses to understand the parameter space better. Results from these statistical techniques guide the construction of piecewise polynomial regression models. Residual and significance tests ensure the resulting model is unbiased and efficient We demonstrate these techniques in R, a statistical computing environment, for predicting the performance of semicoarsening multigrid. 50 and 75 percent of predictions achieve error rates of 5.5 and 10.0 percent or less, respectively.
Last updated on 05/03/2022