Program optimization is the process of modifying software to execute more
efficiently. Superoptimizers attempt to find the optimal program by employing
significantly more expensive search and constraint solving techniques.
Generally, these methods do not scale well to programs in real development
scenarios, and as a result, superoptimization has largely been confined to
small-scale, domain-specific, and/or synthetic program benchmarks. In this
paper, we propose a framework to learn to superoptimize real-world programs by
using neural sequence-to-sequence models. We created a dataset consisting of
over 25K real-world x86-64 assembly functions mined from open-source projects
and propose an approach, Self Imitation Learning for Optimization (SILO) that
is easy to implement and outperforms a standard policy gradient learning
approach on our dataset. Our method, SILO, superoptimizes 5.9% of our test set
when compared with the gcc version 10.3 compiler's aggressive optimization
level -O3. We also report that SILO's rate of superoptimization on our test set
is over five times that of a standard policy gradient approach and a model
pre-trained on compiler optimization demonstration.

本文提出了一种使用神经序列到序列模型来学习超级优化现实世界程序的框架，并介绍了一种易于实现的方法（SILO）来增强优化，该方法在测试集上优化了 5.9％，超过了标准策略梯度方法和模型预训练的优化演示。