A survey of the research on power management techniques for high‐performance systems

Liu, Yongpeng; Zhu, Hong

doi:10.1002/spe.952

Cited by 50 publications

(38 citation statements)

References 66 publications

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“…These graphs indicate that considering the processor along with disk and network interconnect improves energy performance up to 24% with the same performance degradation as with processor's only optimization. Roughly speaking the disk and network allow to save an additional 5%, which is an interesting result given that the energy consumed by both the network and the disk represents 8% of the energy consumed by a typical supercomputer [1].…”

Section: B Results Analyses and Discussionmentioning

confidence: 96%

“…Although nearly all HPC subsystems -including processor, network, 1 http://www.green500.org/. memory, and IO -are provided with power saving capabilities, efforts for reducing the power consumption of HPC systems are directed toward the processor to the best of our knowledge.…”

Section: Introductionmentioning

confidence: 99%

“…Processors dominate system power consumption in high performance computing; however, network interconnects, memory and disk altogether consume up to 24% of system power consumption [1]. With the processor added, the amount of power consumed by these subsystems exceeds 55% of system power consumption [1].…”

Section: Introductionmentioning

confidence: 99%

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Beyond CPU Frequency Scaling for a Fine-grained Energy Control of HPC Systems

Chetsa

Lefèvre

Pierson

et al. 2012

2012 IEEE 24th International Symposium on Computer Architecture and High Performance Computing

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Abstract-Modern high performance computing subsystems (HPC) -including processor, network, memory, and IOare provided with power management mechanisms. These include dynamic speed scaling and dynamic resource sleeping. Understanding the behavioral patterns of high performance computing systems at runtime can lead to a multitude of optimization opportunities including controlling and limiting their energy usage.In this paper, we present a general purpose methodology for optimizing energy performance of HPC systems considering processor, disk and network. We rely on the concept of execution vector along with a partial phase recognition technique for on-the-fly dynamic management without any a priori knowledge of the workload. We demonstrate the effectiveness of our management policy under two real-life workloads. Experimental results show that our management policy in comparison with baseline unmanaged execution saves up to 24% of energy with less than 4% performance overhead for our real-life workloads.

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Section: B Results Analyses and Discussionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Beyond CPU Frequency Scaling for a Fine-grained Energy Control of HPC Systems

Chetsa

Lefèvre

Pierson

et al. 2012

2012 IEEE 24th International Symposium on Computer Architecture and High Performance Computing

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“…Other DPM solutions are based on system reconfiguration steps that include powering off components while idle, and switching them to low-power operating modes when underutilised (see for example, [31,32,33] and [34] for a recent survey). Recent surveys [34,3,35] indicate that most of the DPM techniques developed so far are targeted at individual components (like processors or disk drives) or at specific systems (like clusters or data centers), see for example [36, Trade-off between energy consumption and performance has been widely investigated in hardware design and network communities, as well as in controlling battery-powered devices since energy savings in these kind of devices have been an intrinsic concern since their creation. However, the interest in trade-offs between performance and energy in hosting centers is much more recent.…”

Section: Related Workmentioning

confidence: 99%

Dynamic power management for QoS-aware applications

Marzolla

Mirandola

2013

Sustainable Computing: Informatics and Systems

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“…Davidović, M. Depolli, T. Lipić, K. Skala and R. Trobec A processor is a component that dominates the energy consumption of each node. Thus, most of techniques are designed to reduce energy consumption of the processor [14,15,16]. Most of the current processors are implemented with dynamic voltage and frequency scaling regulators to optimally adjust the voltage/frequency at run-time according to the workload behavior.…”

mentioning

confidence: 99%

Energy Efficiency of Parallel Multicore Programs

Davidović¹,

Depolli²,

Lipić³

et al. 2016

SCPE

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Abstract. The increasing energy consumption of large-scale high performance resources raises technical and economical concerns. A reduction of consumed energy in multicore systems is possible to some extent with an optimized usage of computing and memory resources that is tailored to specific HPC applications. The essential step towards more sustainable consumption of energy is its reliable measurements for each component of the system and selection of optimally configured resources for specific applications. This paper briefly surveys the current approaches for measuring and profiling power consumption in large scale systems. Then, a practical case study of a real-time power measurement of multicore computing system is presented on two real HPC applications: maximum clique algorithm and numerical weather prediction model. We assume that the computing resources are allocated in a HPC cloud on a pay-per-use basis. The measurements demonstrate that the minimal energy is consumed when all available cores (up to the scaling limit for a particular application) are used on their maximal frequencies and with threads binded to the cores.Key words: Energy efficiency, high-performance, maximum clique, numerical weather prediction AMS subject classifications. 65Y05, 68U201. Introduction. Nowadays, a large number of hard problems is being solved efficiently on large-scale computing systems such as clusters, grids and clouds. The main building blocks of such systems are highperformance computing (HPC) clusters, since they are furthermore embedded both into grid computing systems as well as cloud computing systems [1]. A great advantage of Graphical Processor Unit (GPU) accelerators over multicore platforms was demonstrated in terms of energy efficiency [2] with an overall energy consumption for an order of magnitude smaller for the GPUs. Inside the clusters, the many-core architectures, such as GPUs [3], as well as even more specialized data-flow computers [4] are getting an increasingly large supporting role but the main processing is still performed by the multi-core main processors because of simple programming model.In this research, we analyze the energy consumption profile of the processors on a two processor multi-core computer, while varying its workload.With the increasing number of components in large-scale computing systems the energy consumption is steeply increasing [5]. In computer systems, energy (measured in Joules) is represented as the electricity resource that can power the hardware components to do computation. On the other hand, power is the amount of energy consumed per unit time (measured in Joules per second or Watts). The ever-increasing energy consumption of large-scale computing systems causes higher operation costs (e.g. electricity bills) and has negative environmental impacts (e.g. carbon dioxide emissions). Therefore, when designing large-scale computing systems, the focus is often shifting from performance improvements to energy efficiency.Energy can be reduced at several levels of the distrib...

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A survey of the research on power management techniques for high‐performance systems

Cited by 50 publications

References 66 publications

Beyond CPU Frequency Scaling for a Fine-grained Energy Control of HPC Systems

Beyond CPU Frequency Scaling for a Fine-grained Energy Control of HPC Systems

Dynamic power management for QoS-aware applications

Energy Efficiency of Parallel Multicore Programs

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