Since the mid-1990s, researchers have been trying to use machine-learning-based approaches to solve a number of different compiler optimization problems. These …
B Roziere, MA Lachaux… - Advances in neural …, 2020 - proceedings.neurips.cc
A transcompiler, also known as source-to-source translator, is a system that converts source code from a high-level programming language (such as C++ or Python) to another …
Abstract Machine learning (ML) is increasingly seen as a viable approach for building compiler optimization heuristics, but many ML methods cannot replicate even the simplest of …
The explosion in workload complexity and the recent slow-down in Moore's law scaling call for new approaches towards efficient computing. Researchers are now beginning to use …
Z Wang, M O'Boyle - Proceedings of the IEEE, 2018 - ieeexplore.ieee.org
In the last decade, machine-learning-based compilation has moved from an obscure research niche to a mainstream activity. In this paper, we describe the relationship between …
Accurate automatic optimization heuristics are necessary for dealing with thecomplexity and diversity of modern hardware and software. Machine learning is aproven technique for …
N Wu, Y Xie - ACM Computing Surveys (CSUR), 2022 - dl.acm.org
It has been a long time that computer architecture and systems are optimized for efficient execution of machine learning (ML) models. Now, it is time to reconsider the relationship …
Interest in applying Artificial Intelligence (AI) techniques to compiler optimizations is increasing rapidly, but compiler research has a high entry barrier. Unlike in other domains …
One of the key challenges arising when compilers vectorize loops for today's SIMD- compatible architectures is to decide if vectorization or interleaving is beneficial. Then, the …