Automatic Run-Time Selection of Power Policies for Operating Systems

Pettis, N.; Ridenour, J.; Lu, Yung-Hsiang

doi:10.1109/date.2006.243860

Cited by 9 publications

(8 citation statements)

References 13 publications

(12 reference statements)

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“…Concerns of power consumption were left for compiler writers, operating system designers, and hardware engineers. Indeed, researchers have made advances in reducing power consumption in these areas by designing computer architectures that are more energy efficient [1,2,3,4,5,6], developing compiler optimizations focused on power usage [7,8,9,10,11,12,13,14], and developing operating systems that help manage power usage [15,16,17,18].…”

Section: Introductionmentioning

confidence: 99%

Towards power reduction through improved software design

Sahin

Cayci

Clause

et al. 2012

2012 IEEE Energytech

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As the use of computers has grown, so too has concern about the amount of power that they consume. Data centers, for example, are limited in scalability as they struggle with soaring energy costs as many large companies rely on fast, reliable, and round-the-clock computing services. On large-scale computing clusters, like data centers, even a small drop in power consumption can have large effects. Across computing contexts, reducing power consumed by computers has become a major focus. In this paper, we present a new approach and tool for mapping software design to power consumption and describe how such mappings can provide software designers and developers useful information about the power behavior of the software they are developing. The goal is for software engineers to use this information in designing and developing more energy efficient solutions.

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Section: Introductionmentioning

confidence: 99%

Towards power reduction through improved software design

Sahin

Cayci

Clause

et al. 2012

2012 IEEE Energytech

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“…At the system software level, studies range from energy-oriented operating systems [5,15,18], over compilers [11,21] to runtime systems [10,20]. Interactions between OpenMP and energy consumption have also been investigated.…”

Section: Related Workmentioning

confidence: 99%

Application-Level Energy Awareness for OpenMP

Alessi

Thoman

Georgakoudis

et al. 2015

Lecture Notes in Computer Science

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Abstract. Power, and consequently energy, has recently attained firstclass system resource status, on par with conventional metrics such as CPU time. To reduce energy consumption, many hardware-and OSlevel solutions have been investigated. However, application-level information -which can provide the system with valuable insights unattainable otherwise -was only considered in a handful of cases. We introduce OpenMPE, an extension to OpenMP designed for power management. OpenMP is the de-facto standard for programming parallel shared memory systems, but does not yet provide any support for power control. Our extension exposes (i) per-region multi-objective optimization hints and (ii) application-level adaptation parameters, in order to create energysaving opportunities for the whole system stack. We have implemented OpenMPE support in a compiler and runtime system, and empirically evaluated its performance on two architectures, mobile and desktop. Our results demonstrate the effectiveness of OpenMPE with geometric mean energy savings across 9 use cases of 15% while maintaining full quality of service.

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“…The estimator iterates over all device accesses, determining the time between each access. If the idleness t idle exceeds the timeout length t o (lines [8][9][10][11][12], the device shuts down and consumes P on t o energy in the idle state. The remaining ðt idle À t o Þ time is spent in the off state.…”

Section: Policy Selectionmentioning

confidence: 99%

“…In our previous work [12], we present a technique called automatic policy selection. Instead of choosing one policy in advance, a group of policies can be eligible at runtime, and one policy is selected in response to the changing request patterns.…”

Section: Introductionmentioning

confidence: 99%

A Homogeneous Architecture for Power Policy Integration in Operating Systems

Pettis

2009

IEEE Trans. Comput.

Self Cite

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Abstract-A significant volume of research has concentrated on operating system (OS)-directed power management. The primary focus of previous research has been the development of better policies. In this paper, we provide evidence that one policy may outperform another under different conditions. Hence, it is difficult, or even impossible, to design the "best" policy for all computers. We explain how to select the best policies at runtime without user or administrator intervention by using a software framework called the Homogeneous Architecture for Power Policy Integration (HAPPI). This architecture is portable across different platforms running Linux. HAPPI specifies common requirements for policies and provides an interface to simplify the implementation of policies in a commodity OS. Our approach allows these policies to be compared simultaneously to select the best policy among a set of distinct policies at runtime. Experimental results indicate that HAPPI achieves energy savings within 4 percent of the best individual policy for each device in several computing systems without a priori knowledge of workloads.

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Automatic Run-Time Selection of Power Policies for Operating Systems

Abstract: Abstract

Cited by 9 publications

References 13 publications

Towards power reduction through improved software design

Towards power reduction through improved software design

Application-Level Energy Awareness for OpenMP

A Homogeneous Architecture for Power Policy Integration in Operating Systems

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