Power optimization of variable-voltage core-based systems

Hong, Inki; Kirovski, Darko; Qu, Gang; Potkonjak, Miodrag; Srivastava, Mani

doi:10.1109/43.811318

Cited by 177 publications

(53 citation statements)

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“…The algorithm is also useful for coding schemes that lack frame dependencies, such as JPEG2000 [25], because the need to account for limited display memory remains. To our knowledge, that aspect has not been addressed by prior investigations [12].…”

Section: Discussionmentioning

confidence: 85%

“…We made three assumptions for the extrapolation. First, frequency is inversely proportional to gate delay [12]. Second, the number of cycles per frame remains constant at any processor frequency.…”

Section: Methodsmentioning

confidence: 99%

“…That work, too, assumes pre-emptable tasks and does not include precedence constraints. Heuristics for scheduling non-pre-emptable tasks are proposed by Hong et al [12]. That work, however, also does not respect precedence constraints.…”

Section: Related Workmentioning

confidence: 99%

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Reduced energy decoding of MPEG streams

Mesarina

Turner

2003

Multimedia Systems

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Long battery life and high performance multimedia decoding are competing design goals for portable appliances. For a target level of QoS, the achievable battery life can be increased by dynamically adjusting the supply voltage throughout execution. In this paper, an efficient offline scheduling algorithm is proposed for preprocessing stored MPEG audio and video streams. It computes the order and voltage settings at which the appliance's CPU decodes the frames, reducing energy consumption without violating timing or buffering constraints. Our experimental results elucidate the trade-off between QoS and energy consumption. They demonstrate that the scheduler reduces CPU energy consumption by 19%, without any sacrifice of quality, and by nearly 50%, with only slightly reduced quality. The results also evaluate how the QoS/energy trade-off is affected by buffering and processor speed.

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Section: Discussionmentioning

confidence: 85%

Section: Methodsmentioning

confidence: 99%

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Reduced energy decoding of MPEG streams

Mesarina

Turner

2003

Multimedia Systems

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“…The latest allowable completion times of all the other tasks are calculated in a top-down manner by recursively applying the second term on the righthand side of (18).…”

Section: Calculate Important Parametersmentioning

confidence: 99%

“…A large body of research has been conducted into poweraware scheduling [2,3,18,37,39]. For example, variable voltage scheduling schemes were proposed to achieve minimum energy consumption by adjusting voltage and frequency of processors [19,24].…”

Section: Architecture Of Mobile Cluster and Related Workmentioning

confidence: 99%

Energy efficient scheduling for parallel applications on mobile clusters

et al. 2007

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During the past decade, cluster computing and mobile communication technologies have been extensively deployed and widely applied because of their giant commercial value. The rapid technological advancement makes it feasible to integrate these two technologies and a revolutionary application called mobile cluster computing is arising on the horizon. Mobile cluster computing technology can further enhance the power of our laptops and mobile devices by running parallel applications. However, scheduling parallel applications on mobile clusters is technically challenging due to the significant communication latency and limited battery life of mobile devices. Therefore, shortening schedule length and conserving energy consumption have become two major concerns in designing efficient and energy-aware scheduling algorithms for mobile clusters. In this paper, we propose two novel scheduling strategies aimed at leveraging performance and power consumption for parallel applications running on mobile clusters. Our research focuses on scheduling precedence constrained parallel tasks and thus duplication heuristics are applied to schedule parallel tasks to minimize communication overheads. However, existing duplication algorithms are developed with consideration of schedule lengths, completely ignoring energy consumption of clusters. In this regard, we design two energy-aware duplication scheduling algorithms, called EADUS and TEBUS, to schedule precedence constrained parallel tasks with a complexity of O(n 2 ), where n is the number of tasks in a parallel task set. Unlike the existing duplication-based scheduling algorithms that replicate all the possible predecessors of each task, the proposed algorithms judiciously replicate predecessors of a task if the duplication can help in conserving energy. Our energy-aware scheduling strategies are conducive to balancing scheduling lengths and energy savings of a set of precedence constrained parallel tasks. We conducted extensive experiments using both synthetic benchmarks and real-world applications to compare our algorithms with two existing approaches. Experimental results based on simulated mobile clusters demonstrate the effectiveness and practicality of the proposed duplicationbased scheduling strategies. For example, EADUS and TABUS can reduce energy consumption for the Gaussian Elimination application by averages of 16.08% and 8.1% with merely 5.7% and 2.2% increase in schedule length respectively.

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Power Allocation and Task Scheduling on Multiprocessor Computers with Energy and Time Constraints

2012

Energy‐Efficient Distributed Computing Systems

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Performance-driven computer development has lasted for over six decades. Computers have been developed to achieve higher performance. As of June 2010, three supercomputers have achieved petaflops speed: Cray Jaguar (224,162 processors, 1.759 petaflops), Dawning Nebulae (120,640 processors, 1.271 petaflops), and IBM Roadrunner (122,400 processors, 1.042 petaflops) (1). According to Moore's law of computing hardware, the following quantities increase (decrease) exponentially, doubling (halving) approximately every 2 years: the number of transistors per integrated circuit (cost per transistor), processing speed, memory/storage capacity (cost per unit of information), and network capacity (2).While performance/cost has increased dramatically, power consumption in computer systems has also increased according to Moore's law. To achieve higher computing performance per processor, microprocessor manufacturers have doubled the power density at an exponential speed over decades, which will soon reach that of a nuclear reactor (3). Such increased energy consumption causes severe economic, ecological, and technical problems. • Economic Impact. Computer systems consume tremendous amount of energy and natural resources. It has been reported that desktop computers in the United States account for over 10% of commercial electricity Energy-Efficient Distributed Computing Systems, First Edition. Edited by Albert Y. Zomaya and Young Choon Lee.

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Power optimization of variable-voltage core-based systems

Cited by 177 publications

References 50 publications

Reduced energy decoding of MPEG streams

Reduced energy decoding of MPEG streams

Energy efficient scheduling for parallel applications on mobile clusters

Power Allocation and Task Scheduling on Multiprocessor Computers with Energy and Time Constraints

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