Power Modeling

Mair, Jason; Huang, Zhiyi; Eyers, David; Cupertino, Leandro Fontoura; Costa, Georges Da; Pierson, Jean‐Marc; Hlavacs, Helmut

doi:10.1002/9781118981122.ch5

Cited by 4 publications

(2 citation statements)

References 26 publications

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“…The granularity of measurement is crucial for both, power measuring and modeling, and depends on the type of power meter used: external or internal. External meters are placed between the electric outlet and system's power supply unit, while internal meters are located inside the system [24]. Several studies rely on the precision of the power and system monitoring infrastructure to correlate both but without details on the exact methodology.…”

Section: Power Monitoring Devicesmentioning

confidence: 99%

Mastering system and power measures for servers in datacenter

Costa

Pierson

Cupertino

2017

Sustainable Computing: Informatics and Systems

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Using power meters and performance counters to get insight on system's behavior in terms of power consumption is common nowadays. The values coming from these external or internal meters are usually used directly by the research community, for instance to derive higher-level power models with learning techniques or to use them in decision tools such as schedulers in HPC and Cloud Computing. While it is reasonable when one wants only to have a broad view on the power consumption, they can not be used directly in most cases: We prove in this article that the problems of distributed measure and hardware limits are way more complex and create bias, and we give the keys to understand and chose the proper methodology to handle these bias to obtain relevant values for enhanced usage. A generic methodology is analyzed and its main lessons extracted for a direct usage by the research community to master system and power measures for servers in datacenter.

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Section: Power Monitoring Devicesmentioning

confidence: 99%

Mastering system and power measures for servers in datacenter

Costa

Pierson

Cupertino

2017

Sustainable Computing: Informatics and Systems

Self Cite

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“…The system is stressed into different conditions, while a set of predefined Key Performance Indicators (KPIs) along with its power consumption are profiled [10]. To avoid modelling noise, it is important to decouple the static from the dynamic power consumption.…”

Section: Power Estimationmentioning

confidence: 99%

Towards a generic power estimator

Cupertino¹,

Costa²,

Pierson³

2014

Comput Sci Res Dev

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OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. This is an author-deposited version published in : http://oatao.univ-toulouse.fr/ Eprints ID : 13199 Abstract Data centers play an important role on worldwide electrical energy consumption. Understanding their power dissipation is a key aspect to achieve energy efficiency. Some application specific models were proposed, while other generic ones lack accuracy. The contributions of this paper are threefold. First we expose the importance of modelling alternating to direct current conversion losses. Second, a weakness of CPU proportional models is evidenced. Finally, a methodology to estimate the power consumed by applications with machine learning techniques is proposed. Since the results of such techniques are deeply data dependent, a study on devices' power profiles was executed to generate a small set of synthetic benchmarks able to emulate generic applications' behaviour. Our approach is then compared with two other models, showing that the percentage error of energy estimation of an application can be less than 1 %.

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DVFS RK: Performance and Energy Modeling of Frequency-Scaled Multithreaded Runge-Kutta Methods

Rauber

Rünger

2019

2019 27th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP)

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Power Modeling

Cited by 4 publications

References 26 publications

Mastering system and power measures for servers in datacenter

Mastering system and power measures for servers in datacenter

Towards a generic power estimator

DVFS RK: Performance and Energy Modeling of Frequency-Scaled Multithreaded Runge-Kutta Methods

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