A Bayesian Method of Online Automatic Tuning



This chapter discusses mathematical methods for online automatic tuning. After formulating the abstract model of automatic tuning, we review the proposed method, which comprises several novel concepts of automatic tuning, such as online automatic tuning, Bayesian data analysis for quantitative treatments of uncertainties, Bayesian suboptimal sequential experimental design, asymptotic optimality, finite startup, and infinite dilution. Experimental results reveal that the Bayesian sequential experimental design has advantages over random sampling, although random sampling combined with an accurate cost function model can be as good as the Bayesian sequential experimental design.


Objective Function Cost Function Posterior Distribution Prior Distribution Tuning Parameter 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The author sincerely appreciates the members of the Automatic Tuning Research Group for engaging in valuable discussions and collaborations and providing valuable suggestions. The author is also grateful to Prof. Akimichi Takemura, Prof. Tatsuya Kubokawa, Dr. Kazuki Yoshizoe, and Mr. Junya Honda for their invaluable and essential suggestions.

This study is supported in part by a Grant-in-Aid for Scientific Research “Research on Mathematical Core for Robust Auto-Tuning System in Information Explosion Era” from MEXT Japan and the Core Research of Evolutional Science and Technology (CREST) project “ULP-HPC: Ultra Low-Power, High-Performance Computing via Modeling and Optimization of Next Generation HPC Technologies” of the Japan Science and Technology Agency (JST).


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© Springer New York 2011

Authors and Affiliations

  1. 1.Department of Computer Science, Graduate School of Information Science and TechnologyThe University of TokyoTokyoJapan
  2. 2.CREST, JSTTokyoJapan

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