Multiple Frequency Selection in DVFS‐Enabled Processors to Minimize Energy Consumption

Rizvandi, Nikzad Babaii; Zomaya, Albert Y.; Lee, Young Choon; Boloori, Ali Javadzadeh; Taheri, Javid

doi:10.1002/9781118342015.ch17

Cited by 21 publications

(17 citation statements)

References 21 publications

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“…Babaii [12] has studied the relationship between execution frequency and energy consumption and concluded that when 1 If g(x) is a convex function, n(≥ 2) ∈ I + , x i , t i ∈ + and x i < x k if i < k, then we have:…”

Section: Problem Formulationmentioning

confidence: 99%

“…Take the Dynamic Voltage and Frequency Scaling (DVFS) as an example. DVFS is one of the widely used techniques for power management [12]. However, recent work [14], [16] has shown that transient fault rate increases dramatically when supply voltage for an IC chip is scaled down.…”

Section: Introductionmentioning

confidence: 99%

“…S ps = ((f l+1 , t l+1 ), (f l , t l )) 16: if R(S cp , S ps , S rc ) ≥ R g and t l + t l+1 ≤ D − k i=1 len(i) then end for 20: end if 21: return S ps , S rc ; Line 1 is to initialize the variables, Line 2 -7 is to calculate the desired uniform frequency f opt rde , if f opt rde is not available (line 9), assign the neighboring frequencies (f l and f l+1 ) to the task segments (line [10][11][12][13][14][15][16][17][18][19].…”

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confidence: 99%

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Energy minimization for checkpointing-based approach to guaranteeing real-time systems reliability

Wang

Ren

et al. 2013

16th IEEE International Symposium on Object/Component/Service-Oriented Real-Time Distributed Computing (ISORC 2013)

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In this paper, we study the energy minimization problem for a frame-based real time system with guaranteed reliability using the checkpointing technique. We formally prove that executing a real time task set with a uniform frequency, or neighboring frequencies if the desired frequency is not available, not only optimizes its energy consumption but also achieves maximal reliability. Based on the theoretic conclusion, we further develop a Dynamic Voltage Frequency Scaling (DVFS) and checkpoint allocation strategy for a task set to guarantee both reliability and deadline constraints but with minimal energy consumption. The proposed strategy has very small frequency switching overhead as no more than one frequency change is needed for the entire task set execution and thus is particularly effective for processors with large frequency switching overhead. We further empirically compare our approach with recent work published in the literature. The experimental results show that the proposed approach can reduce as much as 15% energy consumption.

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Section: Problem Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Energy minimization for checkpointing-based approach to guaranteeing real-time systems reliability

Wang

Ren

et al. 2013

16th IEEE International Symposium on Object/Component/Service-Oriented Real-Time Distributed Computing (ISORC 2013)

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“…Furthermore, computer systems not only consuming vast amount of power also emit excessive heat; this often results in system unreliability and performance degradation. It has been reported in previous studies (e.g., [3][4][5]) that system overheating causes system freeze and frequent system failures. According to the authors in [4] the highest energy cost of data centers are used to maintain the running servers.…”

Section: Introductionmentioning

confidence: 99%

A Performance Optimization Model of Task Scheduling Towards Green Cloud Computing

Hussin¹,

Mahmood²

2016

IJNCAA

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Cloud computing becomes a powerful trend in the development of ICT services. It allows dynamic resource scaling from infinite resource pool for supporting Cloud users. Such scenario leads to necessity of larger size of computing infrastructure and increases processing power. Demand on the cloud computing is continually growth that makes it changes to scope of green cloud computing. It aims to reduce energy consumption in Cloud computing while maintaining a better performance. However, there is lack of performance metric that analyzing trade-off between energy consumption and performance. Considering high volume of mixed users' requirements and diversity of services offered; an appropriate performance model for achieving better balance between Cloud performance and energy consumption is needed. In this work, we focus on green Cloud Computing through scheduling optimization model. Specifically, we investigate a relationship between performance metrics that chosen in scheduling approaches with energy consumption for energy efficiency. Through such relationship, we develop an energy-based performance model that provides a clear picture on parameter selection in scheduling for effective energy management. We believed that better understanding on how to model the scheduling performance will lead to green Cloud computing.

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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.…”

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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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Multiple Frequency Selection in DVFS‐Enabled Processors to Minimize Energy Consumption

Cited by 21 publications

References 21 publications

Energy minimization for checkpointing-based approach to guaranteeing real-time systems reliability

Energy minimization for checkpointing-based approach to guaranteeing real-time systems reliability

A Performance Optimization Model of Task Scheduling Towards Green Cloud Computing

Energy Efficiency of Parallel Multicore Programs

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