Since the mid-1990s, researchers have been trying to use machine-learning-based
approaches to solve a number of different compiler optimization problems. These …

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 …

Language models (LMs) built upon deep neural networks (DNNs) have recently
demonstrated breakthrough effectiveness in software engineering tasks like code …

Writing optimising compilers is difficult. The range of programs that may be presented to the
compiler is huge and the systems on which they run are complex, heterogeneous, non …

High-level program optimizations, such as loop transformations, are critical for high
performance on multi-core targets. However, complex sequences of loop transformations …

Using machine learning has proven effective at choosing the right set of optimizations for a
particular program. For machine learning techniques to be most effective, compiler writers …

Heterogeneous architectures are currently widespread. With the advent of easy-to-program
general purpose GPUs, virtually every recent desktop computer is a heterogeneous system …

System designers typically use well-studied benchmarks to evaluate and improve new
architectures and compilers. We design tomorrow's systems based on yesterday's …

Performance tuning is an important and time consuming task which may have to be
repeated for each new application and platform. Although iterative optimisation can …