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Machine Learning Techniques for Code Generation and Optimization

Xiaoming Li

出版	University of Illinois at Urbana-Champaign, 2006
URL	http://books.google.com.hk/books?id=8mfStAEACAAJ&hl=&source=gbs_api

註釋

Built the first study that selects a "pure" sorting algorithm at the outset of the computation as a function of the input characteristics, we develop algorithms and a classifier system to build hierarchically-organized hybrid sorting algorithms capable of adapting to the input data. Our results show that such algorithms generated using the approach presented in this thesis are quite effective at taking into account the complex interactions between architectural and input data characteristics and that the resulting code performs significantly better than conventional sorting implementations and the code generated by our earlier study. In particular, the routines generated using our approach perform better than all the commercial libraries that we tried including IBM ESSL, INTEL MKL and the C++ STL