Energise: Refactorings and Skeletons for Energy-Aware Applications on High-Performance Embedded Systems

包括例如能量在内的非功能属性对于对嵌入式设备进行编程正变得越来越重要并且至关重要。这些设备通常严重依赖电池供电，并受到运行限制(或能源预算)的限制。平板电脑、手机、无人机、医疗设备和物联网等设备在社会上变得越来越常见，这些设备正在推动对在这些设备上执行的应用程序优化能源使用的迫切需求。此外，随着全球气候变化危机变得严重，我们的碳足迹正在以惊人的速度加速，部分原因是现代世界计算设备的能源消耗增加。为了在不降低计算设备整体性能的情况下降低计算设备的整体能耗，需要新的绿色计算技术。尽管如此，处理能源消耗问题通常被视为次要问题，当解决问题时，对普通开发人员来说，这是一种黑艺术，开发人员经常通过随机更改源代码或应用优化来满足能源预算，希望间接实现能源预算。此外，这些嵌入式设备正变得越来越并行，多核硬件现在智能手机和平板电脑等中司空见惯。然而，在语言层面处理能源属性通常需要开发人员拥有高度专业的技能，并使用低级工具和技术。即使是小型设备，如树莓PI和杰森Nano，也以非常实惠的价格提供高性能、低能耗的硬件模型。并行硬件可以为硬件制造商提供一条低能耗的途径，因为多核通常提高了软件的性能，同时也降低了其能源使用。因此，非常清楚和及时地需要为典型的非专业程序员提供必要的软件开发工具和编程抽象，以开发能够符合特定能源要求的应用程序，从而使软件开发人员更精简、更灵活和更高效；并且软件和他们运行的设备更环保。到目前为止，在以下基本和基本问题上的努力很少：-向程序员提供适当的抽象，允许他们以能量为目标进行编程；以及，-确定如何正确地转换代码结构，使其产生最佳的能量结果，或将软件的目标保持在预定义的能量预算内。Engise项目将通过建立和补充当前对程序转换和算法框架的研究，直接解决这些基本问题。不同寻常，也是非常重要的是，项目{}\emph{从软件工程的角度解决了能源危机}，采用了软件开发技术，如重构，以及使用新的能源优化框架的高级编程抽象。这是解决能源危机的关键和基本目标-因此，从总体上解决绿色计算--危机。

Non-functional properties, including, for example, energy, are becoming increasingly and critically important for programming embedded devices. These devices, typically rely heavily on battery power, and have restrictive constraints (or energy budgets) within which to operate. Devices such as tablets, phones, drones, medical devices and the Internet of Things, all of which are becoming ever more commonplace in society, are driving the pressing demand for optimisation of energy usage in applications that execute on them.Furthermore, as the global climate change crisis becomes critical, our carbon footprints are accelerating at an alarming rate, partly due to increased energy consumption from computing devices in the modern world. New green-computing techniques are needed in order to reduce the overall energy consumption of computing devices, without decreasing their overall performance. Despite this, dealing with energy consumption is often treated as a secondary concern, and when addressed, something of a black-art to the average developer, where energy budgets are often met by developers randomly changing source code or applying optimisations in the hope that energy budgets will be achieved indirectly. Furthermore, these embedded devices are becoming increasingly parallel, with multi-core hardware now commonplace in e.g. smart phones and tablets. However, dealing with energy properties at the language level usually requires developers to have highly specialised skills, and use low-level tools and techniques.Even small devices, such as the Raspberry Pi and the Jetson Nano, offer high-performance low-energy models of hardware, at very affordable prices. Parallel hardware can offer hardware manufacturers a route to low-energy consumption, as multi-core typically increases the performance of the software, while also decreasing its energy usage.Consequently, there is a very clear and timely need to provide the typical non-specialist programmer with the necessary software development tools and programming abstractions required to develop applications that are able to conform to specified energy requirements, thereby making software developers more lean, agile and productive; and software, and the devices they run on, more greener. To date, there has been very little effort on the fundamental and essential problems of:- providing suitable abstractions to the programmer, allowing them to program with energy as a target goal; and,- determining how to correctly transform code structure so that it yields optimal energy results, or targets the software to stay within a pre-defined energy budget.The Energise project will directly tackle these fundamental problems by building on and complementing current research into both program transformation and algorithmic skeletons. Unusually, and very importantly, \TheProject{} \emph{tackles the energy crisis from a software-engineering perspective}, employing software-development techniques such as refactoring, and high-level programming abstractions using novel energy-optimised skeletons.This is a critical and fundamental goal to solving the energy ---and, therefore, the green-computing--- crisis in general.