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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D. Barceló,R. M. Darbra,B. Bilitewskibest compiler passes. It leverages machine learning and an application characterization to find the most promising optimization passes given an application. This chapter proposes .: Compiler autotuning framework using Bayesian Networks. An autotuning methodology based on machine learning to speed up

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Lauran van Oers,Ester van der Voetp prediction approach followed by a full-sequence prediction approach in the next chapter and we show pros and cons of each approach in detail. Today’s compilers offer a vast number of transformation options to choose among, and this choice can significantly impact on the performance of the code bei

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Satellite Imagery Interpretation,.. Here, we present our full-sequence speedup prediction method called MiCOMP.MiCOMP: .tigating the .piler .hase-ordering problem using optimization sub-sequences and machine learning, is an autotuning framework to mitigate the compiler phase-ordering problem based on machine-learning techniques eff

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D. Barceló,R. M. Darbra,B. Bilitewskis 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