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SHF: SMALL: LUCID: Low-overhead, Unobtrusive Cache Contention Detection and Repair

SHF: SMALL: LUCID: Low-overhead, Unobtrusive Cache Contention Detection and Repair
SHF:SMALL:LUCID:低开销、不显眼的缓存争用检测和修复
批准号:
1525296
负责人:
Joseph Devietti
金额:
$48.24万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2015
资助国家:
美国
项目状态:
已结题
起止时间:
2015-07-01 至 2020-06-30

项目摘要

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中文摘要
翻译
标题:SHF:Small:Lucid:低开销、低调的高速缓存争用检测和修复过去十年见证了处理器设计向多核体系结构的历史性转变。从服务器到平板电脑、手机和手表,处理器架构师都在使用多核来实现性能和能源目标。不幸的是,编写在这些处理器上运行良好的并行代码远非易事。该项目的智力优势包括开发新的算法,通过集成来自整个计算堆栈(包括硬件、编译器和运行时系统)的支持来查找和修复并行代码中的性能错误。该项目更广泛的意义和重要性是提高并行代码的性能和能效,为所有多核计算设备的用户提供好处。该项目还寻求鼓励代表不足的群体参与下一代计算机科学家。该项目利用硬件性能计数器的最新进展,允许低开销、不引人注目地分析真实系统中的高速缓存争用。该性能计数器信息推动了无需程序员干预即可发现和自动修复高速缓存争用的新技术。这项研究探索了将争用检测和修复与托管语言运行时相结合。研究人员计划公开分发为该项目构建的系统,以方便其他研究人员的审查,并将研究成果整合到教育中。
英文摘要
Title: SHF: SMALL: LUCID: Low-overhead, Unobtrusive Cache Contention Detection and RepairThe last decade has overseen a historic shift in processor design towards multicore architectures. From servers to tablets, phones and watches, processor architects are using multiple cores to achieve performance and energy targets. Unfortunately, writing parallel code that performs well on these processors is far from easy. The intellectual merits of this project include developing new algorithms for finding and fixing performance bugs in parallel code by integrating support from across the computing stack including hardware, compilers and runtime systems. The project's broader significance and importance is to improve the performance and energy efficiency of parallel code, providing benefits for the users of all multicore computing devices. This project also seeks to encourage participation from underrepresented groups in the next generation of computer scientists.This project leverages recent advances in hardware performance counters that allow for low-overhead, unobtrusive profiling of cache contention in real systems. This performance counter information drives new techniques for finding and automatically fixing cache contention without programmer intervention. The research explores integrating contention detection and repair with managed language runtimes. The researchers plan to openly distribute the systems built for this project to facilitate examination by other researchers and to integrate the research results into education.
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