Title: Use of Code Structural Features for Machine Learning to Predict Effective Optimizations
Abstract: Since it is difficult to explicitly express the underlying relationship between a code and appropriate optimizations for it, this paper discusses a possibility of using machine learning to predict appropriate optimizations for a given code. In this paper, selection of appropriate compiler options is taken as an example of the prediction, because it can be seen as selection of code optimizations; use of different compiler options results in enabling different compiler optimizations. One severe problem is that it is difficult to collect a sufficient number of data for a machine learning model to well understand the underlying relationships among codes and their appropriate optimizations. Therefore, in addition to conventional features of a code, such as profiling data and parameterized code features, we directly use a code structure itself to retrieve more information from a limited number of codes. The evaluation results suggest that use of code structural features can potentially improve the prediction accuracy if the training dataset contains data of similar code structures.
Publication Year: 2018
Publication Date: 2018-05-01
Language: en
Type: article
Indexed In: ['crossref']
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Cited By Count: 1
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