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

Tuning compiler optimizations for rapidly evolving hardware makes porting and extending
an optimizing compiler for each new platform extremely challenging. Iterative optimization is …

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 …

While iterative optimization has become a popular compiler optimization approach, it is
based on a premise which has never been truly evaluated: that it is possible to learn the best …

Consider a team of robots equipped with sensors that collaborate with one another to
achieve a common goal. Sensors on robots produce periodic updates that must be …

Iterative search combined with machine learning is a promising approach to design
optimizing compilers harnessing the complexity of modern computing systems. While …

For the past 25 years, we have witnessed an extensive application of Machine Learning to
the Compiler space; the selection and the phase-ordering problem. However, limited works …

An important aspect of system optimization research is the discovery of program traits or
behaviors. In this paper, we present an automated method of program characterization …

Iterative optimization is a simple but powerful approach that searches the best possible
combination of compiler optimizations for a given workload. However, iterative optimization …

Program performance optimisations, feedback-directed iterative compilation and auto-tuning
systems all assume a fixed estimation of execution time given a fixed input data for the …