Thread motion

Rangan, K.K.; Wei, Gu-Yeon; Brooks, David

doi:10.1145/1555815.1555793

Cited by 53 publications

(2 citation statements)

References 18 publications

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“…Aware Resource Management. Peak temperature control through power management [ 141 ] was one of the first attempts to enable temperature control in a system through available tools. However, even if power management techniques can have an influence on the thermal hot spots across the chip, these techniques are nowadays insufficient to deal with hot spots.…”

Section: Power and Thermalmentioning

confidence: 99%

Predictive Reliability and Fault Management in Exascale Systems

et al. 2020

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Performance and power constraints come together with Complementary Metal Oxide Semiconductor technology scaling in future Exascale systems. Technology scaling makes each individual transistor more prone to faults and, due to the exponential increase in the number of devices per chip, to higher system fault rates. Consequently, High-performance Computing (HPC) systems need to integrate prediction, detection, and recovery mechanisms to cope with faults efficiently. This article reviews fault detection, fault prediction, and recovery techniques in HPC systems, from electronics to system level. We analyze their strengths and limitations. Finally, we identify the promising paths to meet the reliability levels of Exascale systems.

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Section: Power and Thermalmentioning

confidence: 99%

Predictive Reliability and Fault Management in Exascale Systems

et al. 2020

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“…operational efficiency, such as microprocessor energy efficiency and power usage effectiveness (PUE), optimization from industry [41,45,80,82] and academia [74,96,134]. Further operational energy efficiency improvement is increasingly more challenging [13,32,53,65,69,73,102,116,117,118,127,132].…”

Section: Introductionmentioning

confidence: 99%

A Critical Debate on the Project of the (Post)Modern University: University of Ritual or University of Excellence?

Kim¹

2016

Crit Theory Soc Korea

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To improve the environmental implications of the growing demand of computing, future applications need to improve the carbon-efficiency of computing infrastructures. State-of-theart approaches, however, do not consider the intermittent nature of renewable energy. The time and location-based carbon intensity of energy fueling computing has been ignored when determining how computation is carried out. This poses a new challenge -deciding when and where to run applications across consumer devices at the edge and servers in the cloud. Such scheduling decisions become more complicated with the stochastic runtime variance and the amortization of the rising embodied emissions. This work proposes GreenScale, a framework to understand the design and optimization space of carbon-aware scheduling for green applications across the edge-cloud infrastructure. Based on the quantified carbon output of the infrastructure components, we demonstrate that optimizing for carbon, compared to performance and energy efficiency, yields unique scheduling solutions. Our evaluation with three representative categories of applications (i.e., AI, Game, and AR/VR) demonstrate that the carbon emissions of the applications can be reduced by up to 29.1% with the GreenScale -with the scale of edge-cloud application users (in the order of millions), the yearly reduced carbon emission is 232.7tCO 2 , on average, which is on par the average yearly carbon emissions of 55 vehicles. The analysis in this work further provides a detailed road map for edge-cloud application developers to build green applications.

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Energy efficiency on the product roadmap: An empirical study across releases of a software product

Jagroep

Procaccianti

Werf

et al. 2017

J Software Evolu Process

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SUMMARYIn the quest for energy efficient ICT, research has mostly focused on the role of hardware. However, the impact of software on energy consumption has been acknowledged as significant by researchers in software engineering. In spite of that, due to cost and time constraints, many software producing organizations are unable to effectively measure software energy consumption preventing them to include energy efficiency in the product roadmap. In this paper, we apply a software energy profiling method to reliably compare the energy consumed by a commercial software product across two consecutive releases. We demonstrate how the method can be applied and provide an in-depth analysis of energy consumption of software components. Additionally, we investigate the added value of these measurement for multiple stakeholders in a software producing organization, by means of semi-structured interviews. Our results show how the introduction of an encryption module caused a noticeable increase in the energy consumption of the product. Such results were deemed valuable by the stakeholders and provided insights on how specific software changes might affect energy consumption. In addition, our interviews show that such a quantification of software energy consumption helps to create awareness and eventually consider energy efficiency aspects when planning software releases.

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Thread motion

Cited by 53 publications

References 18 publications

Predictive Reliability and Fault Management in Exascale Systems

Predictive Reliability and Fault Management in Exascale Systems

A Critical Debate on the Project of the (Post)Modern University: University of Ritual or University of Excellence?

Energy efficiency on the product roadmap: An empirical study across releases of a software product

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