Evaluating Iterative Optimization Across 1000 Data Sets

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 compiler optimizations across data sets. Up to now, most iterative optimization studies find the best optimizations through repeated runs on the same data set. Only a handful of studies have attempted to exercise iterative optimization on a few tens of data sets. In this paper, the authors truly put iterative compilation to the test for the first time by evaluating its effectiveness across a large number of data sets.