Modernise Compiler Technology With Deep Learning

内存是我们计算堆栈的重要组成部分。编译器将高级源代码转换为低级机器指令以在底层硬件上运行。它负责确保软件高效运行，使我们的计算机能够提供更多的实时信息，更快的服务和更好的用户体验，并且对环境的影响更小。虽然作为一个重要的软件基础设施，今天的编译器仍然依赖于几十年前开发的技术。它们受到许多次优选择的限制，这些次优选择用于绕过30年前设计的计算机的限制。因此，今天的编译器基础设施太旧，无法利用先进的算法，而且太复杂，任何编译器开发人员都无法成功地进行推理。更糟糕的是，现有的编译器都过时了，无法利用现代硬件设计，导致巨大的性能损失和能源效率低下。这种编译器与硬件的不匹配反过来会导致糟糕的用户体验，并阻碍科学发现和商业创新。一场危机正在逼近--如果没有解决方案，要么硬件创新将因软件无法适应而停滞，要么计算性能和能源效率将受到影响。这样的危机要求我们从根本上重新思考如何设计和实现编译器。该项目旨在将编译器技术带入21世纪，使编译器能够利用机器学习（ML）和人工智能（AI）技术以及现代计算硬件。我们的目标是大规模减少开发编译器优化的人工参与，以便编译器可以快速赶上不断变化的硬件，在当前和未来的计算硬件上提供可扩展的性能。我们相信机器学习完全能够从简单的规则中构建高效的编译器优化算法，无需人工指导。这种完全依赖机器学习来学习代码分析和优化策略的想法是高度投机的，以前没有经过测试。然而，最近ML在游戏、自然语言处理、药物发现、芯片设计和自主系统等领域的突破性效果让我们相信，这在编译器中是可能的。如果人工智能可以学会驾驶汽车，它必须能够推理程序来执行优化，比如调度机器指令。这个雄心勃勃的项目如果成功，将对我们设计编译器的方式产生变革性的影响。我们的软件原型将是开源的，并与关键的编译器基础设施集成。它开辟了一种自动化整个编译器开发过程的新方法，允许编译器充分利用新的计算机硬件架构。它将有助于保护当今软硬件生态系统中4000亿美元的巨额投资，并为未来实现更高性能提供途径。如果软件不能利用硬件，目前对专用计算机处理器的推动将不会有效。通过显著减少专家参与编译器开发，该项目为软件提供了一种可持续的方式来管理硬件复杂性，从而实现计算硬件的创新和持续增长。鉴于硬件技术的加速和中断变化以及软件和硬件之间的大规模不匹配，该项目的成功将对提供硬件IP和软件开发工具的公司感兴趣，这两个领域是英国世界领先的。尽管由于摩尔定律的终结，计算机系统发生了根本性的变化，但这也将有助于确保最终用户的性能持续改善。我们相信，我们拥有实现这一宏伟目标的技能、专业知识、合作伙伴和工作计划。我们在基于ML的代码优化方面处于世界领先地位，率先将深度学习用于编译器优化，并与该领域的主要行业利益相关者建立了合作关系。

Compilers are a crucial component of our computing stack. A compiler translates the high-level source code to low-level machine instructions to run on the underlying hardware. It is responsible for ensuring software runs efficiently so that our computers can provide more real-time information, faster services, and better user experience, and has a less environmental impact. While being a vital software infrastructure, today's compilers still rely on techniques developed several decades ago. They are limited by many sub-optimal choices used to work around the constraints of computers designed 30 years ago. As a result, today's compiler infrastructure is too old to utilise advanced algorithms and is too complex for any compiler developer to reason about successfully. Worse, existing compilers are all out-of-date and fail to capitalise on modern hardware design, causing huge performance loss and energy inefficiency. This compiler-hardware mismatch, in turn, leads to poor user experience and hinders scientific discovery and business innovation. A crisis is looming - without a solution, either hardware innovation will stall as software cannot fit, or computing performance and energy efficiency will suffer. Such a crisis requires us to rethink how we design and implement compilers fundamentally. This project aims to bring compiler technology to the 21st century to allow compilers to take advantage of machine learning (ML) and artificial intelligence (AI) techniques and modern computing hardware. Our goal is to massively reduce the human involvement in developing compiler optimisations so that compilers can quickly catch up with the ever-changing hardware to deliver scalable performance on the current and future computing hardware. We believe that ML is entirely capable of constructing efficient compiler optimisation heuristics from simple rules with zero human guidance. This idea of fully relying on ML to learn code analysis and optimisation strategies is highly speculative and has not been tested before. However, the recent breakthrough effectiveness of ML in domains like game playing, natural language processing, drug discovery, chip design, and autonomous systems gives us the confidence that this is now possible in compilers. If AI can learn to drive a car, it must be able to reason about programs to perform optimisations like scheduling machine instructions. This ambitious project, if successful, will have a transformative impact on how we design compilers. Our software prototype will be open-sourced and integrated with a key compiler infrastructure. It opens up a new way to automate the entire compiler development process, allowing compilers to get the most out of new computer hardware architecture. It will help to safeguard the massive $400B investment in today's software-hardware ecosystem and provide a pathway to greater performance in the future. The current push for specialised computer processors will not be effective if the software cannot utilise the hardware. By significantly reducing expert involvement in compiler development, this project offers a sustainable way for software to manage the hardware complexity, enabling innovation and continued growth in computing hardware. Given the accelerated and disrupted changes in hardware technology and the massive mismatch between software and hardware, success in this project will be of interest to companies that provide hardware IP and software development tools, two areas in which the UK is world-leading. It will also help ensure continued performance improvement for end-users, despite the radical changes in computer systems due to the end of Moore's Law.We believe that we have the skills, expertise, partners and work plan to achieve the ambitious goal. We are world-leading in ML-based code optimisation, have pioneered in employing deep learning for compiler optimisation and have collaborative links with key industry stakeholders in the areas.