二维SIMD结构的低功耗调度

针对二维SIMD结构,提出一种可以动态关闭空转部件且结合编译器、指令集和体系结构支持的低功耗调度算法,其中包括编译器优化二维SIMD指令,功耗指令发出部件开关信号,系统接收信号并执行。采用对不同功能单元分别调度的方式和部件局部化的方法。在模拟器上的实验结果表明该方法可以节省整个系统约15％的能量消耗。     

一种可执行功耗管理的无线传感器网络节点电源

为了使无线传感器网络(WSNS)节点能够执行动态电压调整(DVS)和动态电源管理(DPM),采用了独特的低功耗电源能量感知电路,通过调节电源电压、管理外围功能模块的电源,以降低节点功耗.对照实验表明,节点能够预测自己的剩余能量,可以在系统软件的配合下进行有效的DPM和DVS,关闭部分无效工作电路,并将电源电压由3.3 V降低到2 V.采用所设计的电源电路显著延长了节点的使用寿命,保证了传感器节点的工作电压不随电池电压降低而改变,从而使传感器节点在测量的长期一致性和稳定性上优于电池直接供电;使系统各个部分运行在节能模式下,较好地优化了节点能耗,提高了节点能量利用率,从而有效延长了网络的生存寿命.     

软件流水的低功耗编译技术研究

对具有可动态独立调整运行频率/电压的多功能部件配置结构M,基于全局调度的循环依赖关系,使用ILP形式化框架,研究了对给定循环L进行动态频率/电压调整的低功耗软件流水调度的编译优化技术.提出了一种合理而有效的低功耗最优化软件流水调度方法,使其在运行时保持性能不变而消耗的功耗/能量最小.     

一种基于程序段的动态电压缩放算法

动态电压缩放技术是一种能有效优化处理器能耗的方法,它允许处理器在运行时动态地改变其时钟频率和供电电压.针对处理器提出了一种基于程序段的动态电压缩放算法PBVSA,该算法使用建立在指令工作集签名基础上的程序段监测状态机来判断程序段是否发生变化,并作出CPU电压和频率调整决定,在程序段内,通过计算该段的频率缩放因子β(片外工作时间与片上工作时间的比例关系)来设定CPU的电压和频率,在sim-panalyzer模拟器上完成了算法的实现,通过对Mibench测试程序集的测试表明:该算法平均降低了处理器29%的能耗,而性能损失平均为5.3%.     

一种新的基于动态电压调整的单任务调度方法

合理运用动态电压调整技术可以有效降低实时任务运行所需的能耗.提出了一种新的单任务DVS调度方法,针对程序的平均执行信息,并结合参数化动态预测策略,合理设置电压/频率调整点.实验结果表明,该方法能够充分利用动态松弛时间,有效控制调整开销,实现较高的能耗优化率.     

DVS技术中切换时间间隔分配的方法

动态电压调节(DVS)被认为是低功耗设计中最有效的一项技术.但在应用动态电压调节技术的过程中,需要考虑许多细节问题,才能使模型真正接近实际系统,从而在分析中得到较优的结果.本文正是在DVS中考虑到了电压切换产生的时延和能耗,以一种新的模型来分析功耗、改进算法,最终达到降低功耗的目的.文中提出了分配切换时间间隔的一种依据.     

抢占阈值调度的功耗优化

DVS(Dynamic Voltage Scaling)技术的应用使得任务执行时间延长进而使得处理器的静态功耗(由CMOS电路的泄露电流引起)迅速增加.延迟调度(Procrastination Scheduling)算法是近年提出用于减少静态功耗的有效方法,它通过推迟任务的正常执行来尽可能长时间地让处理器处于睡眠或关闭状态,从而避免过多的静态功耗泄露.文中针对可变电压处理器上运用抢占阈值调度策略的周期性任务集合,将节能调度和延迟调度结合起来,提出一种两阶段节能调度算法,先使用离线算法来计算每个任务的最优处理器执行速度,而后使用在线模拟调度算法来计算每个任务的延迟时间,从而动态判定处理器开启/关闭时刻.实例研究和仿真实验表明,作者的方法能够进一步降低抢占阈值任务调度算法的功耗.     

基于TMS320VC5509A的手持设备的功耗优化

为了延长电池供电的便携式电子产品的寿命并满足性能需求,迫切需要降低功耗.以军用手持计算器设计为例,研究了手持设备低功耗设计的主要技术和关键设计环节.从器件选择、动态电压调整(DVS)、供电管理和存储器管理等方面详细阐述了各部件的软硬件协同低功耗实现.测试结果表明,该计算器达到低功耗应用要求,设计方法对于其它的手持设备具有参考价值.     

动态功率管理技术研究

随着工艺技术的缩减,功耗问题日益严重,低功耗优化技术成了当前研究的一大重点.对处理器的功耗优化可以从设计过程、运行过程和空闲状态来考虑.本文重点研究了处理器在运行时的功率管理技术,即动态功率管理技术.它主要包括动态电压缩减DVS (Dynamic Voltage Scaling)和动态阈值电压缩减DVTS (Dynamic VTH Scaling)的方法,其中DVTS又是通过对衬底偏压的调整来实现阈值电压的调制的.本文重点研究了这两种技术的原理和实现结构,并分析了它们目前的研究和应用.     

优化能耗的可变电压禁忌任务调度算法

能耗是影响异构式并行和分布式系统性能的一个重要因素,动态电压缩放(DVS)技术通过将处理器降低到不同频率来达到有效地节约能耗的目标。通常DVS技术包含任务调度及空闲时间片分配两阶段。当前绝大部分研究均针对时间片分配阶段,而在此考虑的是任务分配与空闲时间片间的关系。为了降低异构分布式系统的能耗,提出了一个利用禁忌(Tabu)策略进行调度的DVS算法。此算法首先调度用有向无环图(DAG)表示的任务集到处理器上,再应用禁忌策略来改进它,通过禁止任务再调度到特定处理器,从而增加时间片,分配阶段可用的空闲时间片达到进一步减少能耗的目标。仿真结果表明,本算法能有效地减少计算机系统的能耗。     

Comparing system level power management policies

Reducing power consumption is a challenge to system designers. Portable systems, such as laptop computers and personal digital assistants (PDAs), draw power from batteries, so reducing power consumption extends their operating times. For desktop computers or servers, high power consumption raises temperature and deteriorates performance and reliability. Soaring energy prices and rising concern about the environmental impact of electronics systems further highlight the importance of low power consumption. Power reduction techniques can be classified as static and dynamic. Static techniques, such as synthesis and compilation for low power, are applied at design time. In contrast, dynamic techniques use runtime behavior to reduce power when systems are serving light workloads or are idle. These techniques are known as dynamic power management (DPM). DPM can be achieved in different ways; for example, dynamic voltage scaling (DVS) changes supply voltage at runtime as a method of power management. Here, we use DPM specifically for shutting down unused I/O devices. We built an experimental environment on a laptop computer running Microsoft Windows. We implemented existing power management policies and quantitatively compared their effects on power saving and performance degradation     

Energy- and reliability-aware task scheduling onto heterogeneous MPSoC architectures

Scheduling periodic tasks onto a multiprocessor architecture under several constraints such as performance, cost, energy, and reliability is a major challenge in embedded systems. In this paper, we present an Integer Linear Programming (ILP) based framework that maps a given task set onto an Heterogeneous Multiprocessor System-on-Chip (HMPSoC) architecture. Our framework can be used with several objective functions; minimizing energy consumption, minimizing cost (i.e., the number of heterogeneous processors), and maximizing reliability of the system under performance constraints. We use Dynamic Voltage Scaling (DVS) for reducing energy consumption while we employ task duplication to maximize reliability. We illustrate the effectiveness of our approach through several experiments, each with a different number of tasks to be scheduled. We also propose two heuristics based on Earliest Deadline First (EDF) algorithm for minimizing energy under performance and cost constraints. Our experiments on generated task sets show that ILP-based method reduces the energy consumption up to 62% percent against a method that does not apply DVS. Heuristic methods obtain promising results when compared to optimal results generated by our ILP-based method.     

无线传感器网络中的动态电压调节算法

无线传感器节点一般由电池供电,且电池不易更换,所以传感器网络最关注的问题是如何高效地利用有限的能量.动态电压调节技术允许软件在运行时动态的改变处理器的频率和电压,以减少它的能量消耗.分析了现有的一些动态电压调节算法,根据无线传感器网络多跳路由和拓扑易变化的特点,提出了一种针对中继节点的动态电压调节算法,并在NS2平台上对算法进行了仿真.通过对仿真结果的分析,表明改进的算法能够很好地减少系统能量消耗,延长无线传感器网络的使用寿命.     

Energy-aware scheduling and simulation methodologies for parallel security processors with multiple voltage domains

Dynamic voltage scaling (DVS) and power gating (PG) have become mainstream technologies for low-power optimization in recent years. One issue that remains to be solved is integrating these techniques in correlated domains operating with multiple voltages. This article addresses the problem of power-aware task scheduling on a scalable cryptographic processor that is designed as a heterogeneous and distributed system-on-a-chip, with the aim of effectively integrating DVS, PG, and the scheduling of resources in multiple voltage domains (MVD) to achieve low energy consumption. Our approach uses an analytic model as the basis for estimating the performance and energy requirements between different domains and addressing the scheduling issues for correlated resources in systems. We also present the results of performance and energy simulations from transaction-level models of our security processors in a variety of system configurations. The prototype experiments show that our proposed methods yield significant energy reductions. The proposed techniques will be useful for implementing DVS and PG in domains with multiple correlated resources.     

Performance evaluation and energy consumption of a real-time heterogeneous grid system using DVS and DPM

Energy consumption of large scale systems has been severely studied due to economic and ecological reasons. This paper studies energy gains that come from the application of two popular energy saving techniques, Dynamic Voltage Scaling (DVS) and Dynamic Power Management (DPM), in a real-time 2-level heterogeneous grid system. While these techniques generally work in a competitive way, we show that under certain circumstances they can work together and achieve greater savings when they are both applied at the processor level. A simulation model is used to evaluate the performance of the system. Experimental results show encouraging energy savings up to 46% and minimum performance degradation when both energy saving techniques are applied.     

Feedback EDF Scheduling of Real-Time Tasks Exploiting Dynamic Voltage Scaling

Many embedded systems are constrained by limits on power consumption, which are reflected in the design and implementation for conserving their energy utilization. Dynamic voltage scaling (DVS) has become a promising method for embedded systems to exploit multiple voltage and frequency levels and to prolong their battery life. However, pure DVS techniques do not perform well for systems with dynamic workloads where the job execution times vary significantly. In this paper, we present a novel approach combining feedback control with DVS schemes targeting hard real-time systems with dynamic workloads. Our method relies strictly on operating system support by integrating a DVS scheduler and a feedback controller within the earliest-deadline-first (EDF) scheduling algorithm. Each task is divided into two portions. The objective within the first portion is to exploit frequency scaling for the average execution time. Static and dynamic slack is accumulated for each task with slack-passing and preemption handling schemes. The objective within the second portion is to meet the hard real-time deadline requirements up to the worst-case execution time following a last-chance approach. Feedback control techniques make the system capable of selecting the right frequency and voltage settings for the first portion, as well as guaranteeing hard real-time requirements for the overall task. A feedback control model is given to describe our feedback DVS scheduler, which is used to analyze the system's stability. Simulation experiments demonstrate the ability of our algorithm to save up to 29% more energy than previous work for task sets with different dynamic workload characteristics. This work was supported in part by NSF grants CCR-0208581, CCR-0310860 and CCR-0312695. Preliminary versions of parts of this work appeared in the ACM SIGPLAN Joint Conference Languages, Compilers, and Tools for Embedded Systems (LCTES'02) and Software and Compilers for Embedded Systems (SCOPES'02) (Dudani et al., 2002), in the Workshop on Compilers and Operating Systems for Low Power 2002 (Zhu and Mueller, 2002) and in the IEEE Real-Time Embedded Technology and Applications Symposium 2004 (Zhu and Mueller, 2004a).     

System level fixed priority energy management algorithm for embedded real time application

Dynamic power management (DPM) and dynamic voltage scaling (DVS) are crucial techniques to reduce the energy consumption in embedded real-time systems. Many previous studies have focused on the energy consumption of the processor or I/O devices. In this paper, we focus on the problem of energy management integrating DVS and DPM techniques for periodic embedded real-time applications with rate monotonic (RM) policy and present a system level fixed priority energy-efficient scheduling (SLFPEES) algorithm. The SLFPEES algorithm consists of I/O device scheduling and job scheduling. I/O device scheduling is based on the dynamic power management with rate monotonic (DPM-RM) policy which puts devices into the sleep state when the idle interval is larger than devices break even time. Job scheduling is based on the RM policy and uses stack resource protocol (SRP) to guarantee exclusive access to the shared resources. For energy efficiency, the SLFPEES algorithm schedules the task with a lower speed and a higher speed. The experimental result shows that the SLFPEES algorithm can yield significantly energy savings with respect to the existing techniques.     

基于电池剩余电量的动态电压调节算法的改进

低功耗的设计已经成为嵌入式系统设计中一个非常重要的方面,而动态电压调度算法DVS又被认为是降低功耗的一种有效手段。分析了已有的基于电池剩余电量的DVS调度算法局限性,通过实验对非周期性电流负载对应的电池单元行为进行建模,设计了一种新的低能耗调度算法。实验证明该算法能很好满足复杂的具有非周期性任务特点的嵌入式实时系统的性能与低功耗要求。     

EPTS:一种实时动态电压调整的抢占阈值调度器

低功耗目前已成为嵌入式实时系统设计中非常重要的性能需求。动态电压调度DVS机制通过动态调整处理器电压进而有效降低系统功耗,正在逐渐得到广泛应用。抢占阈值调度策略实现双优先级系统,每个任务具有两个优先级,任务优先级被用于任务之间竞争处理器,而抢占阈值作为任务开始运行后实际使用的优先级,从而减少现场切换次数,降低系统功耗,同时也提高整个任务集合的可调度性。本文提出一种在线节能调度算法EPTS,拓展抢占阈值调度模型,在任务执行过程中动态调节处理器电压,力求在保证任务集合可调度性的前提下尽可能减少系统功耗,提高系统性能。而后在AMDAthlon4处理器和RT-Linux平台上实现了EPTS调度器,实验证明对于实际任务集合能够有效节能,提高了处理器的利用率,改善了RT-Linux的实时性能。     

A parallel bi-objective hybrid metaheuristic for energy-aware scheduling for cloud computing systems

In this paper, we investigate the problem of scheduling precedence-constrained parallel applications on heterogeneous computing systems (HCSs) like cloud computing infrastructures. This kind of application was studied and used in many research works. Most of these works propose algorithms to minimize the completion time (makespan) without paying much attention to energy consumption.We propose a new parallel bi-objective hybrid genetic algorithm that takes into account, not only makespan, but also energy consumption. We particularly focus on the island parallel model and the multi-start parallel model. Our new method is based on dynamic voltage scaling (DVS) to minimize energy consumption.In terms of energy consumption, the obtained results show that our approach outperforms previous scheduling methods by a significant margin. In terms of completion time, the obtained schedules are also shorter than those of other algorithms. Furthermore, our study demonstrates the potential of DVS.