Modernise Compiler Technology with Deep Learning

今天的计算系统吸收了地球上大量的资源。数据中心消耗了全球4%的能源，并被要求执行日益昂贵的计算任务。让在它们上面运行的程序变得更有效率是至关重要的。只负责优化这些程序的编译器在过去几十年中几乎没有变化。它们的中间端由许多遍和手工构建的启发式算法组成，每一遍都很复杂，总的来说，太复杂了，任何编译器工程师都无法成功推理。开发优化编译器是一个高技能和艰巨的过程，而且每当设计新的处理器时，不可避免地会有软件延迟。通常需要几代编译器才能开始有效地开发处理器的潜力，这时会出现一个新的处理器，并且该过程再次开始。这种没完没了的追赶游戏意味着我们很少充分利用已发货的处理器，这不可避免地会推迟上市时间。在这个项目中，我们将研究使用机器学习(特别是深度学习)来自动化设计编译器启发式的过程。该项目旨在通过深度学习提高基于编译器的程序优化技术。它将调查使用机器学习来推理复杂的程序优化空间。它将研究自动化编译器启发式设计过程的新方法，并展示所提出的技术在实际应用程序中在性能和能源效率方面的好处。如果成功，我们的工作将导致编译器可以在任何硬件体系结构上提供良好的性能，并可以自动赶上硬件的发展。程序的运行速度将比目前更快，并节省更多的能源。

Today's computing systems adsorb a huge amount of the planet's resources. Datacentres consume 4% of global energy and are asked to perform increasingly computationally expensive tasks. Making the programs that run on them more efficient is of paramount importance. Compilers, which are solely responsible for optimising these programs, have changed little in the last several decades. Their middle ends are comprised of many passes with hand-built heuristics, each of which is complex and, in the aggregate, are too complex for any compiler engineer to successfully reason about.Developing an optimising compiler is a highly skilled and arduous process, and there is inevitably a software delay whenever a new processor is designed. It often takes several generations of a compiler to start to effectively exploit the processors' potential, by which time a new processor appears, and the process starts again. This never-ending game of catch-up means that we rarely fully exploit a shipped processor, and it inevitably delays time to market. In this project, we will investigate the use of machine learning (deep learning in particular) to automate the process of designing compiler heuristics. This project aims to improve compiler-based program optimisation techniques through deep learning. It will investigate the use of machine learning to reason about the complex program optimisation space. It will research new ways to automate the process of compiler heuristic design and demonstrate the benefit of the proposed techniques on real-world applications for performance and energy efficiency. If successful, our work will lead to compilers that can deliver good performance on any hardware architecture and can automatically catch up with the hardware evolution. Programs will be run faster and save more energy than is currently possible.