DVFS RK: Performance and Energy Modeling of Frequency-Scaled Multithreaded Runge-Kutta Methods

Rauber, Thomas; Rünger, Gudula

doi:10.1109/empdp.2019.8671593

Cited by 4 publications

(1 citation statement)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Dynamic voltage and frequency scaling (DVFS) is a common approach to improve the energy efficiency of software applications. In HPC, methodologies and tools are developed using models with the help of DVFS, reducing the overall energy consumption while also improving performance for scientific applications [11][12][13]. E-AMOM [14] is an example of a framework developed upon these methodologies, using models, DVFS, and dynamic concurrency throttling (DCT) to reach these goals.…”

Section: Related Workmentioning

confidence: 99%

Energy-efficient power cap configurations through Pareto Front Analysis and Machine Learning Categorization

Cabrera

Almeida

Castellanos–Nieves

et al. 2023

Preprint

View full text Add to dashboard Cite

The growing demand for more computational resources has increased the overall energy consumption in computer systems. To support the increasing requirements, power and energy consumption have to be considered as a constraint to execute software. Modern architectures provide tools to manage directly the power constraints of a system. The Intel Power Cap is a relatively recent tool developed to offer fine control of power usage to users at a central processing unit (CPU) level. We propose a methodology to analyze the performance and the energy efficiency trade-offs using this power cap technology for a given algorithm. We extract a Pareto front for the multi-objective performance and energy problem to represent multiple feasible configurations for both objectives. We perform an extensive experimentation to categorize the different algorithms to reduce the total amount of optimal power cap configurations. We propose the use of Machine Learning (ML) clustering techniques to categorize any algorithm in the target architecture.The use of Machine Learning allows to automate the procedure and simplifies the effort required to solve the optimization problem.We present a practical case where we categorize the kernels using ML techniques, with the option to include a new algorithm into an already existing categorization.

show abstract

Section: Related Workmentioning

confidence: 99%

Energy-efficient power cap configurations through Pareto Front Analysis and Machine Learning Categorization

Cabrera

Almeida

Castellanos–Nieves

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

Energy efficient power cap configurations through Pareto front analysis and machine learning categorization

Cabrera,

Almeida,

Castellanos-Nieves

et al. 2023

Cluster Comput

View full text Add to dashboard Cite

The growing demand for more computing resources has increased the overall energy consumption of computer systems. To support this increasing demand, power and energy consumption must be considered as a constraint on software execution. Modern architectures provide tools for managing the power constraints of a system directly. The Intel Power Cap is a relatively new tool developed to give users fine-grained control over power usage at the central processing unit (CPU) level. The complexity of these tools, in addition to the high variety of modern heterogeneous architectures, hinders predictions of the energy consumption and the performance of any target software. The application of power capping technologies usually leads to the bi-objective optimization problem for energy efficiency and execution time but optimal power constraints could also produce exceeding performance losses. Thus, methods and tools are needed to calculate the proper parameters for power capping technologies, and to optimize energy efficiency. We propose a methodology to analyze the performance and the energy efficiency trade-offs using this power cap technology for a given application. A Pareto front is extracted for the multi-objective performance and energy problem, which represents multiple feasible configurations for both objectives. An extensive experimentation is carried out to categorize the different applications to determine the overall optimal power cap configurations. We propose the use of machine learning (ML) clustering techniques to categorize each application in the target architecture. The use of ML allows us to automate the process and simplifies the effort required to solve the optimization problem. A practical case is presented where we categorize the applications using ML techniques, with the possibility of adding a new application into an existing categorization.

show abstract

A frequency-aware and energy-saving strategy based on DVFS for Spark

Wei

Xiong

et al. 2021

J Supercomput

View full text Add to dashboard Cite

DVFS RK: Performance and Energy Modeling of Frequency-Scaled Multithreaded Runge-Kutta Methods

Cited by 4 publications

References 25 publications

Energy-efficient power cap configurations through Pareto Front Analysis and Machine Learning Categorization

Energy-efficient power cap configurations through Pareto Front Analysis and Machine Learning Categorization

Energy efficient power cap configurations through Pareto front analysis and machine learning categorization

A frequency-aware and energy-saving strategy based on DVFS for Spark

Contact Info

Product

Resources

About