Energy measurement, modeling, and prediction for processors with frequency scaling

Rauber, Thomas; Rünger, Gudula; Schwind, Michael; Xu, Haibin; Melzner, Simon

doi:10.1007/s11227-014-1236-4

Cited by 33 publications

(17 citation statements)

References 30 publications

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“…The programs from the SPEC benchmarks have been chosen, since they reflect, by design, the performance, energy, and execution time behavior of real‐world programs. For the energy measurements, the likwid tool‐set has been used; see the work of Rauber et al for a detailed description of the measurement technique and a validation of the energy consumption data obtained. Based on these measurements, P stat = 5.5 W has been determined using the model from the work of Rauber and Rünger with linear regression.…”

Section: Experimental Evaluationmentioning

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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Section: Experimental Evaluationmentioning

confidence: 99%

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

Rauber

Rünger

2018

Concurrency and Computation

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“…Typically, the power consumption varies during the execution time of the application, depending on the specific execution situation of the application and the resulting usage of the different execution resources. The variations of the power consumption during the execution time can be measured in detail with specialized power meters and power acquisition systems [90] (see Section 2.2), but hardware counters can be used as well (e.g. Intel RAPL interface).…”

Section: Energy Characteristics Of Algorithmsmentioning

confidence: 99%

“…However, the specific interaction of computation and power consumption is Figure 1. SPEC CPU2006 floating-point benchmarks on an Intel Core i7 Haswell processor: energy consumption (left), and power consumption (right) for varying frequencies [90] complex and it is challenging to predict which algorithmic properties lead to which amount of power consumption at a specific point in the execution time.…”

Section: Energy Characteristics Of Algorithmsmentioning

confidence: 99%

“…Fig. 1 shows the dependence of the energy and the power consumption on the frequency for the SPEC CPU2006 floating-point benchmarks, which consist of real (sequential) programs from different application areas, (see [48] and [90] for more details). It can be observed that for most of the programs a frequency between 2.0 and 2.5 GHz leads to the smallest energy consumption.…”

Section: Energy Characteristics Of Algorithmsmentioning

confidence: 99%

“…For parallel applications, the speedup obtained plays a role and Figure 2. PARSEC benchmarks executed with eight threads on an Intel Core i7 Haswell processor: energy consumption (left), and power consumption (right) for varying frequencies [89] it can be observed that applications with a larger speedup tend to have a larger power consumption than applications with a smaller speedup [90]. This can be explained by the fact that applications with a smaller speedup typically include more idle times during which some parts of the processing cores can be powered down, thus reducing the average power consumption.…”

Section: Energy Characteristics Of Algorithmsmentioning

confidence: 99%

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Energy-efficient Algorithms for Ultrascale Systems

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Distefano

Petcu

et al. 2015

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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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On power capping and performance optimization of multithreaded applications

Conoci

Sanzo

Pellegrini

et al. 2021

Concurrency and Computation

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Summary Multithreaded applications facilitate the exploitation of the computing power of multicore architectures. On the other hand, these applications can become extremely energy‐intensive, in contrast with the need for limiting the energy usage of computing systems. In this article, we explore the design of techniques enabling multithreaded applications to maximize their performance under a power cap. We consider two control parameters: the number of cores used by the application, and the core power state. We target the design of an autotuning power‐capping technique with minimal intrusiveness and high portability, which is agnostic about the workload profile of the application. We investigate two different approaches for building the strategy for selecting the best configuration of the parameters under control, namely a heuristic approach and a model‐based approach. Through an extensive experimental study, we evaluate the effectiveness of the proposed technique considering two different selection strategies, and we compare them with existing solutions.

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Energy measurement, modeling, and prediction for processors with frequency scaling

Cited by 33 publications

References 30 publications

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

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

Energy-efficient Algorithms for Ultrascale Systems

On power capping and performance optimization of multithreaded applications

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