  Code super-optimization is the task of transforming any given program to a
more efficient version while preserving its input-output behaviour. In some
sense, it is similar to the paraphrase problem from natural language processing
where the intention is to change the syntax of an utterance without changing
its semantics. Code-optimization has been the subject of years of research that
has resulted in the development of rule-based transformation strategies that
are used by compilers. More recently, however, a class of stochastic search
based methods have been shown to outperform these strategies. This approach
involves repeated sampling of modifications to the program from a proposal
distribution, which are accepted or rejected based on whether they preserve
correctness, and the improvement they achieve. These methods, however, neither
learn from past behaviour nor do they try to leverage the semantics of the
program under consideration. Motivated by this observation, we present a novel
learning based approach for code super-optimization. Intuitively, our method
works by learning the proposal distribution using unbiased estimators of the
gradient of the expected improvement. Experiments on benchmarks comprising of
automatically generated as well as existing ("Hacker's Delight") programs show
that the proposed method is able to significantly outperform state of the art
approaches for code super-optimization.
