Titlebook: Automatic Tuning of Compilers Using Machine Learning; Amir H. Ashouri,Gianluca Palermo,Cristina Silvano Book 2018 The Author(s) 2018 Embed

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2191-530X oad readership, including researchers interested in Computer Architecture, Electronic Design Automation and Machine Learning, as well as computer architects and compiler developers..978-3-319-71488-2978-3-319-71489-9Series ISSN 2191-530X Series E-ISSN 2191-5318

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Selecting the Best Compiler Optimizations: A Bayesian Network Approach,s are carried out on an ARM embedded platform and GCC compiler by considering two benchmark suites with 39 applications. The set of compiler configurations selected by the model (less than 7% of the search space), demonstrated an application performance speedup of up?to 4.6. on Polybench (1.85. on a

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The Phase-Ordering Problem: An Intermediate Speedup Prediction Approach,tion permutations and repetitions and (.) to extract the application dynamic features to predict the next-best optimization to be applied to maximize the performance given the current status. Experimental results are done by assessing the proposed methodology with utilizing two different search heur

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The Phase-Ordering Problem: A Complete Sequence Prediction Approach,oding technique to application-based reordering of passes while using a number of predictive models. We performed statistical analysis on the prediction space and compared against (i) standard optimization levels O2 and O3, (ii) random iterative compilation, and (iii) two recent non-iterative approa

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Book 2018n and machine learning techniques. It demonstrates that not all the optimization passes are suitable for use within an optimization sequence and that, in fact, many of the available passes tend to counteract one another. After providing a comprehensive survey of currently available methodologies, in

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