Machine Learning Techniques for Code Generation and Optimization
Li, Xiaoming
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Permalink
https://hdl.handle.net/2142/81735
Description
Title
Machine Learning Techniques for Code Generation and Optimization
Author(s)
Li, Xiaoming
Issue Date
2006
Doctoral Committee Chair(s)
David Padua
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Computer Science
Language
eng
Abstract
We follow a similar approach and use a classifier learning system to generate high performance libraries for matrix-matrix multiplication. Our library generator produces matrix multiplication routines that use recursive layouts and several levels of tiling. Our approach is to use a classifier learning system to search in the space of the different ways to partition the input matrices the one that performs the best. As a result, our system will determine the number of levels of tiling and tile size for each level depending on the target platform and the dimensions of the input matrices.
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