PROMETHEUS: A Proactive Method for Thermal Management of Heterogeneous MPSoCs
PROMETHEUS: A Proactive Method for Thermal Management of Heterogeneous MPSoCs
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PROMETHEUS：异构 MPSoC 热管理的主动方法
DOI：
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发表时间：
2013
影响因子：
2.9
通讯作者：
Tajana Simunic
中科院分区：
文献类型：
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作者：
Shervin Sharifi;D. Krishnaswamy;Tajana Simunic
在本文中，我们提出了 PROMETHEUS，这是一种在单指令集架构异构多处理器片上系统上对嵌入式工作负载进行主动温度感知调度的框架。它系统地结合了温度感知任务分配、任务迁移以及动态电压和频率缩放。 PROMETHEUS 基于我们新颖的低塔顶温度预测技术 Tempo。与之前的工作相比，Tempo 可以准确估计未来调度决策的潜在热效应，而无需任何运行时调整。它将最大预测误差降低了一个数量级。 PROMETHEUS 框架使用 Tempo 提供两种温度感知调度技术，可主动避免导致未来热紧急情况的电源状态，同时将性能需求与工作负载要求相匹配。第一种技术 TempoMP 将 Tempo 与在线多参数优化方法相集成，以指导任务分配、迁移以及以温度感知方式设置核心功率状态的决策。我们的第二种调度技术 TemPrompt 在启发式算法中使用 Tempo，以较低的开销提供相当的效率。平均而言，与之前的工作相比，这两种技术将任务延迟减少了 2.5 倍，能量延迟积 (ELP) 减少了 5 倍。
In this paper, we propose PROMETHEUS, a framework for proactive temperature aware scheduling of embedded workloads on single instruction set architecture heterogeneous multiprocessor systems-on-chip. It systematically combines temperature aware task assignment, task migration, and dynamic voltage and frequency scaling. PROMETHEUS is based on our novel low overhead temperature prediction technique, Tempo. In contrast to previous work, Tempo allows accurate estimation of potential thermal effects of future scheduling decisions without requiring any runtime adaptation. It reduces the maximum prediction error by up to an order of magnitude. Using Tempo, PROMETHEUS framework provides two temperature aware scheduling techniques that proactively avoid power states leading to future thermal emergencies while matching the performance needs to the workload requirements. The first technique, TempoMP, integrates Tempo with an online multiparametric optimization method to guide decisions on task assignment, migration, and setting core power states in a temperature aware fashion. Our second scheduling technique, TemPrompt uses Tempo in a heuristic algorithm that provides comparable efficiency at lower overhead. On average, these two techniques reduce the lateness of the tasks by 2.5× and energy-lateness product (ELP) by 5× compared to the previous work.