Modeling and analyzing the energy consumption of fork‐join‐based task parallel programs

Rauber, Thomas; Rünger, Gudula

doi:10.1002/cpe.3219

Cited by 22 publications

(16 citation statements)

References 50 publications

(71 reference statements)

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“…In contrast to execution time modelling, articles reporting on energy modelling are rare: [45,19] present energy models for generalised workload that take the number of cores of the target machine and the frequency of these cores into account. The model in [45] is verified with the extrapolation method for solving initialvalue problems of ordinary differential equations.…”

Section: Execution Time and Energy Modellingmentioning

confidence: 97%

“…The model in [45] is verified with the extrapolation method for solving initialvalue problems of ordinary differential equations. The model in [19] is verified with the embarrassingly parallel benchmark set.…”

Section: Execution Time and Energy Modellingmentioning

confidence: 99%

“…• MSR_PP0_ENERGY_STATUS for the energy consumption of processor cores including their caches [45], except the last-level cache [20],…”

Section: Cpumentioning

confidence: 99%

See 2 more Smart Citations

An execution time and energy model for an energy-aware execution of a conjugate gradient method with CPU/GPU collaboration

Lang

Rünger

2014

Journal of Parallel and Distributed Computing

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Section: Execution Time and Energy Modellingmentioning

confidence: 97%

Section: Execution Time and Energy Modellingmentioning

confidence: 99%

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An execution time and energy model for an energy-aware execution of a conjugate gradient method with CPU/GPU collaboration

Lang

Rünger

2014

Journal of Parallel and Distributed Computing

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“…Power models for DVFS processors often distinguish the dynamic power consumption P dyn , which is related to the computational activity of the processor, and the static power consumption P stat , which is intended to capture the leakage power consumption occurring also during idle times . P dyn depends on the frequency f and, thus, the scaling factor s , and P stat is often treated to be independent from s .…”

Section: Power and Energy Behavior Of Application Programsmentioning

confidence: 99%

A scheduling selection process for energy‐efficient task execution on DVFS processors

Rauber

Rünger

2018

Concurrency and Computation

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Summary The efficient execution of parallel programs with respect to execution time and energy consumption is a major concern, and often, scheduling methods are used to achieve a good performance. In this article, we consider the problem of scheduling a set of independent tasks on a parallel system with homogeneous execution units providing frequency scaling. The set of tasks has the property that the tasks exhibit a task‐specific inhomogeneous and non‐linear behavior of their specific time‐energy relation. In addition, it is assumed that execution time and energy consumption behave in a non‐linear manner with respect to frequency scaling. For the assignment of these tasks to execution units, we propose a scheduling selection process combining scheduling algorithms, which determine a task assignment, with a subsequent selection of frequency scaling, which we call schedule execution modes. This process builds a rich set of alternative schedule execution modes from which efficient (or Pareto‐optimal) schedule execution modes can be selected. Experiments are done for the SPEC CPU benchmarks. Experimental results illustrate that the enriched scheduling process leads to task assignments resulting in an efficient execution on DVFS processors.

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“…The use of these counters is an efficient and low overhead alternative to measure the power of a system using specialized power meters [45]. Energy modeling approaches and a comparison with measured energy values are discussed in [88].…”

Section: Energy Measurement Techniquesmentioning

confidence: 99%

Energy-efficient Algorithms for Ultrascale Systems

Carretero

Distefano

Petcu

et al. 2015

JSFI

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The chances to reach Exascale or Ultrascale Computing are strongly connected with the problem of the energy consumption for processing applications. For physical and economical reasons, the energy consumption has to be reduced significantly to make Ultrascale Computing possible. The research efforts towards energy-saving mechanisms of the hardware have already made energy-aware hardware systems available. However, to achieve a strong energy reduction, hardware mechanisms must be complemented with new energy-efficient software that can exploit them so that the foreseen energy savings actually result. In the software area, there also exist a multitude of research approaches towards energy saving, often concentrating either on the system software level or the application organization level, reflecting the expertise of the corresponding research group. The challenge of reducing the energy consumption dramatically to make Ultrascale Computing possible is so ambitious that a concerted action combining research efforts through all the software levels seems reasonable. In this article, we discuss the current research efforts and results related to energy efficiency in the diverse areas of software. We conclude with open problems and questions concerning energy-related techniques with an emphasis on the application or algorithmic side.

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Modeling and analyzing the energy consumption of fork‐join‐based task parallel programs

Cited by 22 publications

References 50 publications

An execution time and energy model for an energy-aware execution of a conjugate gradient method with CPU/GPU collaboration

An execution time and energy model for an energy-aware execution of a conjugate gradient method with CPU/GPU collaboration

A scheduling selection process for energy‐efficient task execution on DVFS processors

Energy-efficient Algorithms for Ultrascale Systems

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