Machine learning to ease understanding of data driven compiler optimizations

Abstract

Optimizing compilers use-often hand-crafted-heuristics to control optimizations such as inlining or loop unrolling. These heuristics are based on data such as size and structure of the parts to be optimized. A compilation, however, produces much more (platform specific) data that one could use as input. We thus propose the use of machine learning (ML) to derive better optimization decisions from this wealth of data and to tackle the shortcomings of hand-crafted heuristics. Ultimately, we want to shed light on the quality and performance of optimizations by using empirical data with automated feedback and updates in a production compiler.