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Automatic feature generation for machine learning-based optimising compilation

Leather, Hugh; Bonilla, Edwin; O'Boyle, Michael


Recent work has shown that machine learning can automate and in some cases outperform handcrafted compiler optimisations. Central to such an approach is that machine learning techniques typically rely upon summaries or features of the program. The quality of these features is critical to the accuracy of the resulting machine learned algorithm; nomachine learning method will work well with poorly chosen features. However, due to the size and complexity of programs, theoretically there are an...[Show more]

CollectionsANU Research Publications
Date published: 2014
Type: Journal article
Source: ACM Transactions on Architecture and Code Optimization
DOI: 10.1145/2536688


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