On the energy-aware partitioning of real-time tasks on homogeneous multi-processor systems

Bambagini, Mario; Lelli, Juri; Buttazzo, Giorgio; Lipari, Giuseppe

doi:10.1109/iceac.2013.6737640

Cited by 4 publications

(3 citation statements)

References 19 publications

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“…Beyond these deployment issues, we also aim at extending this open testbed to consider other power-consuming components, such as GPU [54] and disk, in order to incrementally learn their power model and thus provide wider cartography of the power consumption of a software system. In the future, we believe that PowerAPI can be a cornerstone to new energy-aware scheduling [4,[15][16][17]19], to energyproportional computing [20][21][22][23], to new kind of optimizations [24], and to a better understanding of the power consumption drawn by software [25][26][27].…”

Section: Resultsmentioning

confidence: 99%

“…Beyond the CPU power models and experiments we report in this article, we believe that our contribution offers an open testbed to foster the research on green computing. Our open-source solution can be used to automatically infer the power models and to support the design of energy-aware scheduling heuristics in homogeneous systems [4,[15][16][17][18], as well as in heterogeneous data centers [19], to serve the energy-proportional computing [20][21][22][23] and to evaluate the effectiveness of optimizations applied on binaries [24]. It also targets system administrators and software developers alike in monitoring and better understanding the power consumption of their software assets [25][26][27].…”

Section: Introductionmentioning

confidence: 99%

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The next 700 CPU power models

Colmant

Rouvoy

Kurpicz

et al. 2018

Journal of Systems and Software

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Software power estimation of CPUs is a central concern for energy efficiency and resource management in data centers. Over the last few years, a dozen of ad hoc power models have been proposed to cope with the wide diversity and the growing complexity of modern CPU architectures. However, most of these CPU power models rely on a thorough expertise of the targeted architectures, thus leading to the design of hardware-specific solutions that can hardly be ported beyond the initial settings. In this article, we rather propose a novel toolkit that uses a configurable/interchangeable learning technique to automatically learn the power model of a CPU, independently of the features and the complexity it exhibits. In particular, our learning approach automatically explores the space of hardware performance counters made available by a given CPU to isolate the ones that are best correlated to the power consumption of the host, and then infers a power model from the selected counters. Based on a middleware toolkit devoted to the implementation of software-defined power meters, we implement the proposed approach to generate CPU power models for a wide diversity of CPU architectures (including Intel, ARM, and AMD processors), and using a large variety of both CPU and memoryintensive workloads. We show that the CPU power models generated by our middleware toolkit estimate the power consumption of the whole CPU or individual processes with an accuracy of 98.5% on average, thus competing with the state-of-the-art power models.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The next 700 CPU power models

Colmant

Rouvoy

Kurpicz

et al. 2018

Journal of Systems and Software

View full text Add to dashboard Cite

show abstract

“…As a result, research efforts have focused on the development of suitable heuristic algorithms [7], mostly bin packing variations [8,9], which are be able to find a near optimal partitioning 115 in a reasonable execution time. Different criteria are considered as the optimality of a partitioning problem, such as: minimizing the required number of processors [10], improving load balancing to increase parallelism [11], minimizing the inter-tasks communication time [12,13] and minimizing energy consumption [14].…”

mentioning

confidence: 99%

A resource efficient framework to run automotive embedded software on multi-core ECUs

Faragardi

Lisper

Sandström

et al. 2018

Journal of Systems and Software

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The increasing functionality and complexity of automotive applications requires not only the use of more powerful hardware, e.g., multi-core processors, but also efficient methods and tools to support design decisions. Component-based software engineering proved to be a promising solution for managing software complexity and allowing for reuse. However, there are several challenges inherent in the intersection of resource efficiency and predictability of multi-core processors when it comes to running component-based embedded software. In this paper, we present a software design framework addressing these challenges. The framework includes both mapping of software components onto executable tasks, and the partitioning of the generated task set onto the cores of a multi-core processor. This paper aims at enhancing resource efficiency by optimizing the software design with respect to: 1) the inter-softwarecomponents communication cost, 2) the cost of synchronization among dependent transactions of software components, and 3) the interaction of software components with the basic software services. An engine management system, one of the most complex automotive sub-systems, is considered as a use case, and the experimental results show a reduction of up to 11.2% total CPU usage on a quad-core processor, in comparison with the common framework in the literature.

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A Review for Submodular Optimization on Machine Scheduling Problems

Liu

2020

Complexity and Approximation

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On the energy-aware partitioning of real-time tasks on homogeneous multi-processor systems

Cited by 4 publications

References 19 publications

The next 700 CPU power models

The next 700 CPU power models

A resource efficient framework to run automotive embedded software on multi-core ECUs

A Review for Submodular Optimization on Machine Scheduling Problems

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