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Selecting the right compiler optimisations has a severe impact on programs'performance. Still, the available optimisations keep increasing, and theireffect depends on the specific program, making the task human intractable.Researchers proposed several techniques to search in the space of compileroptimisations. Some approaches focus on finding better search algorithms, whileothers try to speed up the search by leveraging previously collected knowledge.The possibility to effectively reuse previous compilation results inspired ustoward the investigation of techniques derived from the Recommender Systemsfield. The proposed approach exploits previously collected knowledge andimproves its characterisation over time. Differently from currentstate-of-the-art solutions, our approach is not based on performance countersbut relies on Reaction Matching, an algorithm able to characterise programslooking at how they react to different optimisation sets. The proposed approachhas been validated using two widely used benchmark suites, cBench andPolyBench, including 54 different programs. Our solution, on average, extracted90% of the available performance improvement 10 iterations before currentstate-of-the-art solutions, which corresponds to 40% fewer compilations andperformance tests to perform.

A Collaborative Filtering Approach for the Automatic Tuning of Compiler Optimisations