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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Design Space Exploration of Compiler Passes: A Co-Exploration Approach for the Embedded Domain,duced hardware complexity. However, they impose higher compiler complexity since the instructions are executed in parallel based on the static compiler schedule. Therefore, finding a promising set of compiler transformations and defining their effects have a significant impact on the overall system

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Selecting the Best Compiler Optimizations: A Bayesian Network Approach,best 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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The Phase-Ordering Problem: An Intermediate Speedup Prediction Approach,p 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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The Phase-Ordering Problem: A Complete Sequence Prediction Approach,.. 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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