Interest in applying Artificial Intelligence (AI) techniques to compiler optimizations is increasing rapidly, but compiler research has a high entry barrier. Unlike in other domains, compiler and AI researchers do not have access to the datasets and frameworks that enable fast iteration and development of ideas, and getting started requires a significant engineering investment. What is needed is an easy, reusable experimental infrastructure for real world compiler optimization tasks that can serve as a common benchmark for comparing techniques, and as a platform to accelerate progress in the field. We introduce CompilerGym, a set of environments for real world compiler optimization tasks, and a toolkit for exposing new optimization tasks to compiler researchers. CompilerGym enables anyone to experiment on production compiler optimization problems through an easy-to-use package, regardless of their experience with compilers. We build upon the popular OpenAI Gym interface enabling researchers to interact with compilers using Python and a familiar API. We describe the CompilerGym architecture and implementation, characterize the optimization spaces and computational efficiencies of three included compiler environments, and provide extensive empirical evaluations. Compared to prior works, CompilerGym offers larger datasets and optimization spaces, is 27x more computationally efficient, is fault-tolerant, and capable of detecting reproducibility bugs in the underlying compilers. In making it easy for anyone to experiment with compilers - irrespective of their background - we aim to accelerate progress in the AI and compiler research domains.

中文翻译：

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CompilerGym：用于 AI 研究的稳健、高性能的编译器优化环境

将人工智能 (AI) 技术应用于编译器优化的兴趣正在迅速增加，但编译器研究具有很高的进入门槛。与其他领域不同，编译器和 AI 研究人员无法访问支持快速迭代和想法开发的数据集和框架，并且入门需要大量的工程投资。我们需要的是一个简单、可重用的实验基础设施，用于现实世界的编译器优化任务，可以作为比较技术的通用基准，并作为加速该领域进展的平台。我们介绍了 CompilerGym，一组用于现实世界编译器优化任务的环境，以及一个用于向编译器研究人员展示新优化任务的工具包。CompilerGym 使任何人都可以通过一个易于使用的包来试验生产编译器优化问题，无论他们是否有编译器的经验。我们以流行的 OpenAI Gym 界面为基础，使研究人员能够使用 Python 和熟悉的 API 与编译器进行交互。我们描述了 CompilerGym 架构和实现，描述了三个包含的编译器环境的优化空间和计算效率，并提供了广泛的经验评估。与之前的工作相比，CompilerGym 提供了更大的数据集和优化空间，计算效率提高了 27 倍，具有容错能力，并且能够检测底层编译器中的可重现性错误。