Energy-Aware Modeling of Scaled Heterogeneous Systems

Marowka, Ami

doi:10.1007/s10766-016-0453-2

Cited by 8 publications

(4 citation statements)

References 18 publications

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“…In [1] the author presents analytical models based on scaled power metrics to analyze the impact of various architectural design choices, including hybrid systems, on scaled performance and power savings. The power consumption implications of different processing schemes and various chip configurations were also analyzed.…”

Section: Methodologies and Tools For Hybrid Hpc Systems With Gpu Techmentioning

confidence: 99%

Guest Editorial for Hybrid Parallelism in New HPC Systems

Laccetti

Foster

Lapegna

et al. 2016

Int J Parallel Prog

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Section: Methodologies and Tools For Hybrid Hpc Systems With Gpu Techmentioning

confidence: 99%

Guest Editorial for Hybrid Parallelism in New HPC Systems

Laccetti

Foster

Lapegna

et al. 2016

Int J Parallel Prog

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“…Models for heterogeneous computing hardware in the literature often rely on analytical expressions [33], [34] or different techniques, such as regressional analysis [28], [35], [36], aiding the selection of hardware-or software-specific parameters. This section presents an energy model based on our early studies [28], [29], which relies on regressional analysis to quantify the computations energy of any configuration of computations c σ i within the bounds (see Definition II.1).…”

Section: B Energy Model For the Computationsmentioning

confidence: 99%

Energy-Aware Planning-Scheduling for Autonomous Aerial Robots

Seewald¹,

Marina²,

Midtiby³

et al. 2022

Preprint

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In this paper, we present an online planningscheduling approach for battery-powered autonomous aerial robots. The approach consists of simultaneously planning a coverage path and scheduling onboard computational tasks. We further derive a novel variable coverage motion robust to airborne constraints and an empirically motivated energy model. The model includes the energy contribution of the schedule based on an automatic computational energy modeling tool. Our experiments show how an initial flight plan is adjusted online as a function of the available battery, accounting for uncertainty. Our approach remedies possible in-flight failure in case of unexpected battery drops, e.g., due to adverse atmospheric conditions, and increases the overall fault tolerance.

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“…Marowka developed a power-metrics based analytical model, to exploit the possibility of considerably increasing energy efficiency by choosing an optimal chip configuration, which we extended to evaluate the impact of different architectural design choices on energy efficiency [19]. Marowka's theoretical contribution shows three processing schemes for heterogeneous computing with a comparison of their respective energy efficiency.…”

Section: Related Workmentioning

confidence: 99%

Coarse-Grained Computation-Oriented Energy Modeling for Heterogeneous Parallel Embedded Systems

Seewald

Schultz

Ebeid

et al. 2019

Int J Parallel Prog

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Limited energy availability is among the most challenging considerations developers face for heterogeneous systems and is critical for battery-powered devices. For complex systems composed of mechanical and computational units, such as drones and mobile robots, more than half of the power consumption can be due to the computational operations. Critically, these systems are often composed of many components, interacting concurrently to achieve specific functionality. As a result, power prediction and estimation can be a challenging task, especially if different computational units, such as CPU and GPU, should be modeled. In this paper, we focus on limited energy availability for mobile heterogeneous devices powered by a battery and present a coarse-grained computation-oriented energy modeling approach. Our approach predicts the energy consumption of a set of software components, in a specific configuration, executed according to a given scheduling policy. The model, determined numerically from several empirical power samples, describes the energy consumed by a software configuration and can be used for energy-aware planning and optimization from a computational point of view. It can potentially support a complex embedded system in maximizing the level of autonomy while minimizing power consumption and preserving the most appropriate amount of battery charge by finding the right rate of quality of service. Our approach is supported and validated by the design and implementation of a profiling tool. The tool abstracts computational energy behavior and describes the current battery drain as a function of all the admissible configurations.

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Energy-Aware Modeling of Scaled Heterogeneous Systems

Cited by 8 publications

References 18 publications

Guest Editorial for Hybrid Parallelism in New HPC Systems

Guest Editorial for Hybrid Parallelism in New HPC Systems

Energy-Aware Planning-Scheduling for Autonomous Aerial Robots

Coarse-Grained Computation-Oriented Energy Modeling for Heterogeneous Parallel Embedded Systems

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