提高现代处理器能效的pmu -事件驱动DVFS技术

IF 0.7 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS ACM Transactions on Modeling and Performance Evaluation of Computing Systems Pub Date : 2022-05-25 DOI:10.1145/3538645
Ranjan Hebbar, A. Milenković
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引用次数: 2

摘要

本文描述了我们在运行SPEC CPU2017基准套件的英特尔酷睿i7处理器上进行的基于测量的研究结果,该研究评估了动态电压频率缩放(DVFS)对性能(P),能效(EE)及其产品(PxEE)的影响。结果表明,默认的基于dvfs的电源管理技术严重倾向于性能,导致较差的能源效率。为了解决这个问题,我们引入、实施并评估了四种基于dvfs的电源管理技术,这些技术由以下指标驱动,这些指标来自处理器的性能监控单元:(i)总管道槽失速比(FS-PS), (ii)总周期失速比(FS-TS), (iii)与内存相关的总周期失速比(FS-MS),以及(iv)每千克指令的最后一级缓存丢失次数(FS-LLCM)。所提出的技术将这些指标线性映射到可用的处理器时钟频率上。实验评估结果表明,与现有方法相比,所提出的技术显著提高了EE和PxEE指标。具体来说,当综合考虑所有基准测试时,EE从44%提高到92%,而PxEE从31%提高到48%。此外,我们发现所提出的技术对于一类内存密集型基准测试特别有效——它们将EE从121%提高到183%,将PxEE从100%提高到141%。最后,我们阐明了每种提出的技术的优点和缺点,并提供了使用它们的建议。
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PMU-Events-Driven DVFS Techniques for Improving Energy Efficiency of Modern Processors
This paper describes the results of our measurement-based study, conducted on an Intel Core i7 processor running the SPEC CPU2017 benchmark suites, that evaluates the impact of dynamic voltage frequency scaling (DVFS) on performance (P), energy efficiency (EE), and their product (PxEE). The results indicate that the default DVFS-based power management techniques heavily favor performance, resulting in poor energy efficiency. To remedy this problem, we introduce, implement, and evaluate four DVFS-based power management techniques driven by the following metrics derived from the processor's performance monitoring unit: (i) the total pipeline slot stall ratio (FS-PS), (ii) the total cycle stall ratio (FS-TS), (iii) the total memory-related cycle stall ratio (FS-MS), and (iv) the number of last level cache misses per kilo instructions (FS-LLCM). The proposed techniques linearly map these metrics onto the available processor clock frequencies. The experimental evaluation results show that the proposed techniques significantly improve EE and PxEE metrics compared to the existing approaches. Specifically, EE improves from 44% to 92%, and PxEE improves from 31% to 48% when all the benchmarks are considered together. Furthermore, we find that the proposed techniques are particularly effective for a class of memory-intensive benchmarks – they improve EE from 121% to 183% and PxEE from 100% to 141%. Finally, we elucidate the advantages and disadvantages of each of the proposed techniques and offer recommendations on using them.
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