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Title:Machine Learning Techniques for Code Generation and Optimization
Author(s):Li, Xiaoming
Doctoral Committee Chair(s):David Padua
Department / Program:Computer Science
Discipline:Computer Science
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):Computer Science
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.
Issue Date:2006
Description:99 p.
Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2006.
Other Identifier(s):(MiAaPQ)AAI3242918
Date Available in IDEALS:2015-09-25
Date Deposited:2006

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