Machine Learning in Compiler Optimisation – Arxiv Vanity

EAs are useful for exploring a large optimisation space where it is infeasible to just enumerate all possible solutions. This is because an EA can often converge to the most promising area in the optimisation space quicker than a general search heuristic. The EA is also shown to be faster than a dynamic programming based search [24] in finding the optimal transformation for the Fast Fourier Transformation (FFT) [102]. When compared to supervised learning, EAs have the advantage of requiring little problem specific knowledge, and hence that they can be applied on a broad range of problems. However, because an EA typically relies on the empirical evidences (e.g.