Modernise Compiler Technology with Deep Learning

通过深度学习实现编译器技术现代化

• 批准号: 2596456
• 金额: --
• 依托单位: University of Leeds
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2596456
• 关键词: Modernise; Compiler; Technology; Deep; Learning

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.

今天的计算系统吸收了地球上大量的资源。数据中心消耗全球能源的4%，并被要求执行越来越昂贵的计算任务。使在它们上运行的程序更有效是至关重要的。在过去的几十年里，专门负责优化这些程序的开发人员几乎没有改变。它们的中间端由许多手工编译的编译器组成，每一个编译器都很复杂，总的来说，对于任何编译器工程师来说都太复杂了。开发一个优化的编译器是一个高度熟练和艰巨的过程，每当设计一个新的处理器时，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利用处理器的潜力，到那时，一个新的处理器出现了，这个过程又开始了。这种无休止的追赶游戏意味着我们很少充分利用已交付的处理器，这不可避免地推迟了上市时间。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的过程。该项目旨在通过深度学习改进基于编译器的程序优化技术。它将研究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中对性能和能源效率的好处。如果成功，我们的工作将导致编译器可以在任何硬件架构上提供良好的性能，并可以自动赶上硬件的发展。程序将运行得更快，并节省更多的能源比目前可能的。