首页> 外文学位 >Managing resources for high performance and low energy in general-purpose processors.
【24h】

Managing resources for high performance and low energy in general-purpose processors.

机译:管理通用处理器中的高性能和低能耗资源。

获取原文
获取原文并翻译 | 示例

摘要

Microarchitectural techniques, such as superscalar instruction issue, Out-Of-Order instruction execution (OOO), Simultaneous Multi-Threading (SMT) and Chip Multi-Processing (CMP), improve processor performance dramatically. However, as processor design becomes more and more complicated, how to manage the abundant processor resources to achieve optimal performance and power consumption of processors becomes increasingly more sophisticated. This dissertation investigates resource usage controlling techniques for general-purpose microprocessors (supporting both single hardware context and multiple hardware contexts) targeting both energy and performance.;We address the power-inefficient resource usage issue in single-context processors and propose a Compiler-based Adaptive Fetch Throttling (CAFT) technique which combines the benefits of a hardware-based runtime throttling technique and a software-based static throttling technique providing good energy savings with a low performance loss. Our simulation results show that the proposed technique doubles the energy-delay product (EDP) savings compared to the fixed threshold throttling.;We introduce the resource competing problem for SMT processors, which allow multiple threads to simultaneously share processor resources and improve the energy-efficiency indirectly by resource sharing. We present a novel Adaptive Resource Partitioning Algorithm (ARPA) to control the usage and sharing of processor resources in SMT processors. ARPA analyzes the resource usage efficiency of each thread in a time period and assigns more resources to threads which can use them in a more efficient way. Simulation results on a large set of 42 multiprogrammed workloads show that ARPA outperforms the currently best dynamic resource allocation technique, Hill-climbing, by 5.7% with regard to the overall instruction throughput. Considering fairness accorded to each thread, ARPA attains 9.2% improvements over Hill-climbing, using a commonly used fairness metric.;We also propose resource adaptation approaches to adaptively control the number of powered-on ROB entries and partition shared resources among threads for both shared-ROB and divided-ROB structures, targeting both high performance and low energy. Our resource adaptation algorithms approaches consider not only the relative resource usage efficiency of each thread like ARPA, but also take into account the real resource usage of threads to identify cases of inefficient resource usage behavior and save energy. Our experimental results show that for an SMT processor with a shared-ROB structure, our resource adaptation approach achieves 16.7% energy savings over ARPA, while the performance loss is negligible across 42 sample workloads. For an SMT processor with a divided-ROB structure, our resource adaptation approach outperforms ARPA by 4.2% in addition to achieving 12.4% energy savings.
机译:微体系结构技术,例如超标量指令发布,乱序指令执行(OOO),同时多线程(SMT)和芯片多处理(CMP),可显着提高处理器性能。然而,随着处理器设计变得越来越复杂,如何管理丰富的处理器资源以实现处理器的最佳性能和功耗变得越来越复杂。本文研究了针对能源和性能的通用微处理器(同时支持单个硬件上下文和多个硬件上下文)的资源使用控制技术。;我们解决了单上下文处理器中的低功耗资源使用问题,并提出了一种基于编译器的自适应提取节流(CAFT)技术结合了基于硬件的运行时节流技术和基于软件的静态节流技术的优点,可提供良好的节能效果,并且性能损失较低。我们的仿真结果表明,与固定阈值节流相比,所提出的技术使能量延迟积(EDP)节省了两倍。我们引入了SMT处理器的资源竞争问题,该问题使多个线程可以同时共享处理器资源并改善能耗。资源共享间接提高效率。我们提出一种新颖的自适应资源分配算法(ARPA),以控制SMT处理器中处理器资源的使用和共享。 ARPA分析一个时间段内每个线程的资源使用效率,并将更多资源分配给可以更有效地使用它们的线程。在42个多程序工作负载上的仿真结果表明,ARPA在整体指令吞吐量方面比当前最佳的动态资源分配技术Hill-climbing好5.7%。考虑到每个线程的公平性,ARPA使用常用的公平性指标,比Hill-climbing提升了9.2%.;我们还提出了资源自适应方法来自适应控制通电的ROB条目的数量并在两个线程之间分配共享资源针对高性能和低能耗的共享ROB和分割ROB结构。我们的资源自适应算法不仅考虑了像ARPA这样的每个线程的相对资源使用效率,还考虑了线程的实际资源使用情况,以识别低效的资源使用行为并节省能源。我们的实验结果表明,对于具有共享ROB结构的SMT处理器,我们的资源自适应方法比ARPA节省了16.7%的能源,而42个示例工作负载的性能损失可以忽略不计。对于具有ROB分割结构的SMT处理器,我们的资源自适应方法除可节省12.4%的能耗外,还比ARPA高出4.2%。

著录项

  • 作者

    Wang, Huaping.;

  • 作者单位

    University of Massachusetts Amherst.;

  • 授予单位 University of Massachusetts Amherst.;
  • 学科 Engineering Computer.
  • 学位 Ph.D.
  • 年度 2010
  • 页码 109 p.
  • 总页数 109
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

  • 入库时间 2022-08-17 11:36:53

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号