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

基于预测建模的异构多核可移植并行编译方法

• 批准号: EP/H051988/1
• 负责人: Michael O'Boyle
• 金额: $ 62.96万
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2010
• 资助国家: 英国
• 起止时间: 2010 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FH051988%2F1
• 关键词: Predictive; Modelling; based; Approach; Portable

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learning to determine what type of cores to use to give thebest performance and also predicts when software is out-of date.If successful it will be of significant benefit to academics workingin the area, UK industry and in the long term applicationsprogrammers.

现代计算机的核心是由多个处理元件组成的。这些多核处理器能够以更低的能耗提供高性能，但编程具有很高的挑战性。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，内核的数量将会增加，并且在类型和能力上会发生变化.目前，这意味着程序员很快就必须并行思考，并解决如何将程序的不同部分划分为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。由于这一过程的复杂性，随着硬件规模和复杂性的增加，软件将无法发挥其潜力，导致软件停滞。该项目旨在通过推广新技术来自动学习如何利用新的多核平台来防止这种软件停滞。利用人工智能中最先开发的思想和技术，我们将开发一个系统，它可以自动学习如何使软件适应新的平台。它使用机器学习来确定使用什么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，它将对该地区的学者，英国工业和长期应用程序员产生重大影响。

项目成果

Smart, adaptive mapping of parallelism in the presence of external workload

• DOI: 10.1109/cgo.2013.6495010
• 发表时间: 2013-02
• 期刊: Proceedings of the 2013 IEEE/ACM International Symposium on Code Generation and Optimization (CGO)
• 作者: M. Emani;Zheng Wang;M. O’Boyle

Exploiting GPU Hardware Saturation for Fast Compiler Optimization

• DOI: 10.1145/2588768.2576791
• 发表时间: 2014-03
• 期刊: Proceedings of Workshop on General Purpose Processing Using GPUs
• 作者: A. Magni;Christophe Dubach;M. O’Boyle

Automatic optimization of thread-coarsening for graphics processors

• DOI: 10.1145/2628071.2628087
• 发表时间: 2014-08
• 期刊: 2014 23rd International Conference on Parallel Architecture and Compilation (PACT)
• 作者: A. Magni;Christophe Dubach;M. O’Boyle

Languages and Compilers for Parallel Computing - 27th International Workshop, LCPC 2014, Hillsboro, OR, USA, September 15-17, 2014, Revised Selected Papers

并行计算的语言和编译器 - 第 27 届国际研讨会，LCPC 2014，美国俄勒冈州希尔斯伯勒，2014 年 9 月 15-17 日，修订后的精选论文

• DOI: 10.1007/978-3-319-17473-0_14
• 发表时间: 2015
• 作者: Emani M

Compiler Construction

编译器构建

• DOI: 10.1007/978-3-540-71229-9_15
• 发表时间: 2007
• 作者: Falconer H