Energy-Aware High Performance Computing: A Taxonomy Study

Cai, Chang; Wang, Lizhe; Khan, Samee U.; Tao, Jie

doi:10.1109/icpads.2011.59

Cited by 25 publications

(18 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Physical security can be achieved with the help of security guards, video surveillance, security lighting, sensors, and alarms. The researchers have done a massive amount of work [20][21][22][23][24][25][26][27][28] to provide an energy-aware high performance computing environment. However, there is also a need for energy-efficient security frameworks for mobile devices to provide security and privacy services in an MCC environment [29].…”

Section: Security Threats and Counter Measuresmentioning

confidence: 99%

Towards secure mobile cloud computing: A survey

Khan

Kiah

Khan

et al. 2013

Future Generation Computer Systems

Self Cite

311

125

View full text Add to dashboard Cite

Section: Security Threats and Counter Measuresmentioning

confidence: 99%

Towards secure mobile cloud computing: A survey

Khan

Kiah

Khan

et al. 2013

Future Generation Computer Systems

Self Cite

311

125

View full text Add to dashboard Cite

“…In recent years, we have witnessed a growing interest in reducing energy consumption of large computational systems using various energy conservation techniques [1][2][3][4][5][6][7]. This interest is driven by the fact that energy is the biggest expense in operating large clusters, and energy cost over the life span of such a system may exceed the system's initial capital investment [8].…”

Section: Introductionmentioning

confidence: 99%

Toward building green computing environment for reservoir simulation

Al-Saadoon

Al-Shaikh

Baddourah

et al. 2013

2013 4th Annual International Conference on Energy Aware Computing Systems and Applications (ICEAC)

View full text Add to dashboard Cite

In this paper, we analyze the level of power consumption for running large scale hydrocarbonreservoir simulation on high performance clusters (HPC), which may impact the data center design and the overall environment for running massive simulation models. We derive energy consumption from simulation run time and the power needed to keep associated processors running. We then propose a metric that ties reservoir simulation to Power Usage Effectiveness (PUE) factor based on energy consumption per-node. This factor works as the efficiency target that can be adjusted to balance execution time and power consumption.

show abstract

“…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 distributed architecture: individually on each node or hardware component-level, at the middleware level, at the networking level, and at the application level [6,7,8,9].Suboptimal use of systems resources (e.g. over-provisioning) is the primary source of energy inefficiency at middleware layer [6,7,10].…”

mentioning

confidence: 99%

“…Energy can be reduced at several levels of the distributed architecture: individually on each node or hardware component-level, at the middleware level, at the networking level, and at the application level [6,7,8,9].…”

mentioning

confidence: 99%

See 1 more Smart Citation

Energy Efficiency of Parallel Multicore Programs

Davidović¹,

Depolli²,

Lipić³

et al. 2016

SCPE

View full text Add to dashboard Cite

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...

show abstract

Energy-Aware High Performance Computing: A Taxonomy Study

Cited by 25 publications

References 48 publications

Towards secure mobile cloud computing: A survey

Towards secure mobile cloud computing: A survey

Toward building green computing environment for reservoir simulation

Energy Efficiency of Parallel Multicore Programs

Contact Info

Product

Resources

About