Amir H. Ashouri

TL;DR: A recent survey as discussed by the authors summarizes and classifies the recent advances in using machine learning for the compiler optimization field, particularly on the two major problems of (1) selecting the best optimizations, and (2) phase-ordering of optimizations.

TL;DR: The proposed framework is based on the application characterization done dynamically by using independent microarchitecture features and Bayesian networks and demonstrates 4 × and 3 × speedup, respectively, on cBench and Polybench in terms of exploration efficiency given the same quality of the solutions generated by the random iterative compilation model.

TL;DR: This survey summarizes and classifies the recent advances in using machine learning for the compiler optimization field, particularly on the two major problems of selecting the best optimizations, and the phase-ordering of optimizations.

TL;DR: This article proposes an automatic optimization framework called MiCOMP, which Mi tigates the Com piler P hase-ordering problem, and performs phase ordering of the optimizations in LLVM’s highest optimization level using optimization sub-sequences and machine learning.

TL;DR: A machine-learning approach for reducing the cost of the compiler auto-tuning phase and to speedup the application performance in embedded architectures based on an application characterization done dynamically with microarchitecture independent features and based on the usage of Bayesian Networks.