Regression Strategies for Parameter Space Exploration: A Case Study in Semicoarsening Multigrid and R

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Increasing system and algorithmic complexity, combined with a growing number of tunable 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 ... continued below

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PDF-file: 25 pages; size: 0.7 Mbytes

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Lee, B C; Schulz, M & de Supinski, B R September 28, 2006.

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Increasing system and algorithmic complexity, combined with a growing number of tunable 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.

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PDF-file: 25 pages; size: 0.7 Mbytes

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  • Report No.: UCRL-TR-224851
  • Grant Number: W-7405-ENG-48
  • DOI: 10.2172/900144 | External Link
  • Office of Scientific & Technical Information Report Number: 900144
  • Archival Resource Key: ark:/67531/metadc889183

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  • September 28, 2006

Added to The UNT Digital Library

  • Sept. 22, 2016, 2:13 a.m.

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  • Dec. 7, 2016, 8:20 p.m.

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Lee, B C; Schulz, M & de Supinski, B R. Regression Strategies for Parameter Space Exploration: A Case Study in Semicoarsening Multigrid and R, report, September 28, 2006; Livermore, California. (digital.library.unt.edu/ark:/67531/metadc889183/: accessed September 20, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.