A Predictive Modelling based Approach to Portable Parallel Compilation for Heterogeneous Multi-cores

现代计算机的核心是由多个处理元件组成。这些多核处理器能够在降低能耗的情况下提供高性能，但对编程来说非常具有挑战性。由于目前内核的数量相对较少，操作系统可以很好地利用它们。然而，在不久的将来，核心的数量将会增加，类型和能力也会有所不同。目前，这意味着程序员很快将不得不并行思考，并弄清楚如何将程序的不同部分分区，以便在不同类型的内核上运行。每次在新平台上运行此程序或升级当前版本时，都必须重复此任务。由于这个过程的复杂性，随着硬件尺寸和复杂性的增加，软件将无法利用其潜力，导致软件停滞不前。该项目旨在通过研究自动学习如何利用新的多核心平台的新技术来防止这种软件停滞。我们将利用人工智能领域最先发展起来的理念和技术，开发一个能够自动学习如何使软件适应新平台的系统。它使用统计机器学习来确定使用哪种类型的内核来提供最佳性能，并预测软件何时过时。如果成功，它将对在该地区工作的学者、英国工业和长期应用程序程序员有重大好处。

Modern computers at their heart consist of multiple processingelements. These multi-core processors have the capability ofdelivering high performance with reduced energy consumption, but arehighly challenging to program. As the number of cores is relativelysmall in number at present, operating systems can make good use ofthem. In the near future, however, the number of cores will rise andvary in type and capability. Currently, this means that programmerswill soon have to think in parallel and work out how to partitiondifferent parts of their programs to run on different types ofcores. Each time this program is run on a new platfrom or the currentone is upgraded, this task will have to be repeated. Due to the sheercomplexity of this process, as hardware increases in size andcomplexity, software will not be able to utilise its potential, leadingto software stagnation.This project aims to prevent this software stagnation by investigatingnew techniques to automatically learn how to utilise new multi-coreplatforms. Using ideas and techniques first developed in artificialintelligence, we will develop a system that automatically learns howto adapt software to work on new platforms. It uses statisticalmachine learning to determine what type of cores to use to give thebest performance and also predicts when software is out-of date.If successful it will be of significant benefit to academics workingin the area, UK industry and in the long term applicationsprogrammers.