GPU performance and power tuning using regression trees

GPU performance and power tuning is difficult, requiring extensive user expertise and time-consuming trial and error. To accelerate design tuning, statistical design space exploration methods have been proposed. This article presents Starchart, a novel design space partitioning tool that uses regression trees to approach GPU tuning problems. Improving on prior work, Starchart offers more automation in identifying key design trade-offs and models design subspaces with distinctly different behaviors. Starchart achieves good model accuracy using very few random samples: less than 0.3% of a given design space; iterative sampling can more quickly target subspaces of interest.