DReAM
DReAM
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DOI：
10.1145/2939370
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发表时间：
2016-11
期刊：
影响因子：
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通讯作者：
Qixiao Liu;Miquel Moretó;J. Abella;F. Cazorla;M. Valero
中科院分区：
文献类型：
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作者：
Qixiao Liu;Miquel Moretó;J. Abella;F. Cazorla;M. Valero
多核系统中的准确的每任务能量估计将允许在数据中心中执行每任务能量感知任务调度和能量感知计费以及其他应用。共享资源中任务之间的交互影响了任务的能耗，这对任务的能耗估计提出了挑战。最近已经设计出一些精确的机制来估计多核中的片上每个任务消耗的能量，但是对于DRAM存储器缺乏这样的机制。本文介绍了在多核中精确的每任务DRAM能耗计量，这为能源/性能优化开辟了新的途径。特别是，这篇文章的贡献是（i）一个理想的每个任务的DRAM存储器的能量计量模型;（ii）DReAM，一个准确但低成本的理想模型的实现（小于5%的精度误差时，16个任务共享内存）;和（iii）与标准方法（均匀分布和访问计数为基础）的比较证明DReAM比这些其他方法更准确。
Accurate per-task energy estimation in multicore systems would allow performing per-task energy-aware task scheduling and energy-aware billing in data centers, among other applications. Per-task energy estimation is challenged by the interaction between tasks in shared resources, which impacts tasks’ energy consumption in uncontrolled ways. Some accurate mechanisms have been devised recently to estimate per-task energy consumed on-chip in multicores, but there is a lack of such mechanisms for DRAM memories. This article makes the case for accurate per-task DRAM energy metering in multicores, which opens new paths to energy/performance optimizations. In particular, the contributions of this article are (i) an ideal per-task energy metering model for DRAM memories; (ii) DReAM, an accurate yet low cost implementation of the ideal model (less than 5% accuracy error when 16 tasks share memory); and (iii) a comparison with standard methods (even distribution and access-count based) proving that DReAM is much more accurate than these other methods.