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Book Publication

Causal Analysis for Performance Modeling of Computer Programs

Authors: J. Lemeire, E. Dirkx and F. Verbist

Publisher: IOS Press

Publication Date: Dec. 2007


Abstract:

Causal modeling and the accompanying learning algorithms provide useful extensions for in-depth statistical investigation and automation of performance modeling. We enlarged the scope of existing causal structure learning algorithms by using the form-free information-theoretic concept of mutual information and by introducing the complexity criterion for selecting direct relations among equivalent relations. The underlying probability distribution of experimental data is estimated by kernel density estimation. We then reported on the benefits of a dependency analysis and the decompositional capacities of causal models. Useful qualitative models, providing insight into the role of every performance factor, were inferred from experimental data. This paper reports on the results for a LU decomposition algorithm and on the study of the parameter sensitivity of the Kakadu implementation of the JPEG񮖠 standard. Next, the analysis was used to search for generic performance characteristics of the applications.

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Prof. Jan Lemeire

+32 (0)02 629 167

jlemeire@etrovub.be

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