ReOPN: Retargetable Statistical Optimisation and Parallelisation for Network Processors

ReOPN：网络处理器的可重定向统计优化和并行化

• 批准号: EP/E037569/1
• 负责人: Björn Franke
• 金额: $ 15.8万
• 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: 2007
• 资助国家: 英国
• 起止时间: 2007 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FE037569%2F1
• 关键词: ReOPN; Retargetable; Statistical; Optimisation; Parallelisation

Network processors are application specific devices optimised to support network protocols at the highest possible speed. Tight constraints on performance, cost, and energy consumption have led to the development of heterogeneous multi-core architectures with domain specific instruction set extensions and application specific processing elements. However, this adaptation has a strong impact on the programmability of these devices and makes software development a time-consuming and expensive task. This project investigates novel statistical approaches to retargetable and adaptive compiler directed optimisation and parallelisation for network processors to conquer a looming software development crisis caused by the rapidly increasing complexity of network protocols and applications. This research will enable the efficient implementation of high-level programming languages and increase programmer productivity in a highly cost and performance sensitive embedded computing domain.

网络处理器是针对特定应用程序的设备，经过优化以尽可能高的速度支持网络协议。对性能、成本和能耗的严格约束导致了具有域特定指令集扩展和应用特定处理元件的异构多核架构的发展。然而，这种适应对这些设备的可编程性有很大影响，并使软件开发成为一项耗时且昂贵的任务。该项目研究了新的统计方法，可重定向和自适应编译器指导的优化和并行化网络处理器，以克服迫在眉睫的软件开发危机所造成的网络协议和应用程序的快速增长的复杂性。这项研究将使高层次的编程语言的有效实施，并提高程序员的生产力在一个高度成本和性能敏感的嵌入式计算领域。

项目成果

Raced Profiles: Efficient Selection of Competing Compiler Optimizations

竞赛配置文件：竞争编译器优化的有效选择

• 发表时间: 2009
• 作者: Bruce Worton

Raced profiles

比赛概况

• DOI: 10.1145/1542452.1542460
• 发表时间: 2009
• 作者: Leather H

MILEPOST GCC: machine learning based research compiler

MILEPOST GCC：基于机器学习的研究编译器

• 发表时间: 2008
• 作者: Ayal Zaks