A Survey on Compiler Autotuning using Machine Learning

Since the mid-1990s, researchers have been trying to use machine-learning based approaches to solve a number of different compiler optimization problems. These techniques primarily enhance the quality of the obtained results and, more importantly, make it feasible to tackle two main compiler optimization problems: optimization selection (choosing which optimizations to apply) and phase-ordering (choosing the order of applying optimizations). The compiler optimization space continues to grow due to the advancement of applications, increasing number of compiler optimizations, and new target architectures. Generic optimization passes in compilers cannot fully leverage newly introduced optimizations and, therefore, cannot keep up with the pace of increasing options. This survey 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) the phase-ordering of optimizations. The survey highlights the approaches taken so far, the obtained results, the fine-grain classification among different approaches and finally, the influential papers of the field.Comment: version 5.0 (updated on September 2018)- Preprint Version For our Accepted Journal @ ACM CSUR 2018 (42 pages) - This survey will be updated quarterly here (Send me your new published papers to be added in the subsequent version) History: Received November 2016; Revised August 2017; Revised February 2018; Accepted March 2018

Index to NASA Tech Briefs, 1975

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Performance Improvement in Kernels by Guiding Compiler Auto-Vectorization Heuristics

Vectorization support in hardware continues to expand and grow as well we still continue on superscalar architectures. Unfortunately, compilers are not always able to generate optimal code for the hardware;detecting and generating vectorized code is extremely complex. Programmers can use a number of tools to aid in development and tuning, but most of these tools require expert or domain-specific knowledge to use. In this work we aim to provide techniques for determining the best way to optimize certain codes, with an end goal of guiding the compiler into generating optimized code without requiring expert knowledge from the developer. Initally, we study how to combine vectorization reports with iterative comilation and code generation and summarize our insights and patterns on how the compiler vectorizes code. Our utilities for iterative compiliation and code generation can be further used by non-experts in the generation and analysis of programs. Finally, we leverage the obtained knowledge to design a Support Vector Machine classifier to predict the speedup of a program given a sequence of optimization underprediction, with 82% of these accurate within 15 % both ways

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