A Novel Statistical Learning-Based Methodology for Measuring the Goodness of Energy Profiles of Applications Executing on Multicore Computing Platforms

Fahad, Muhammad; Shahid, Arsalan; Manumachu, Ravi Reddy; Lastovetsky, Alexey

doi:10.3390/en13153944

Cited by 6 publications

(3 citation statements)

References 34 publications

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“…This method is one of the taxonomic methods included in the clustering methods group [29]. It is based on the study of Euclidean distances between the studied objects [108,109]. Taxonomic methods are used to assess the level of differentiation of objects (in this case countries) in terms of the statistical features assigned to them.…”

Section: Methodsmentioning

confidence: 99%

Energy Consumption in Central and Eastern Europe (CEE) Households in the Platform Economics

Poniatowska-Jaksch

2021

Energies

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The ongoing digitization of the economy has led to the creation and functioning of platform model socio-economic systems. It is also reflected in the changes in patterns of energy consumption in households. In the first cross-section, it is an industrial revolution, with environmental benefits. However, platforms are primarily a revolution in the consumption sphere, and here, the effects of digitization are not fully recognized. Our social needs are increasingly met “through accessibility” without us leaving our home. Due to the home's multifunctionality, based on the availability of platform services, household energy consumption should be viewed differently today than before. The article aims to show the changes in Central and Eastern Europe (CEE) household energy consumption between 2008–2018 and their assessment through the prism of the economy’s platformization methods. The study presents the changes in energy consumption in households and determines the correlations between platformization (the author’s index) and changes in energy consumption in households with the use of taxonomic methods. The platformization leaders—Estonia and Lithuania—were subjected to a more detailed analysis. The presented method(s) may be useful in predicting the changes in households’ energy consumption caused by the digitization of other countries in the region (countries under transformation and outsiders-Bulgaria, Romania), in implementing household energy management systems, and in a better adjustment of regulations directed at these consumers.

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Section: Methodsmentioning

confidence: 99%

Energy Consumption in Central and Eastern Europe (CEE) Households in the Platform Economics

Poniatowska-Jaksch

2021

Energies

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“…We follow a detailed energy measurement methodology [32]- [34] to ensure the reliability of experimental results. A Watts Up Pro power meter is used to measure the energy.…”

Section: Experimental Methodology Of Energy Measurementmentioning

confidence: 99%

Towards Optimal Matrix Partitioning for Data Parallel Computing on a Hybrid Heterogeneous Server

Malik

Lastovetsky

2021

IEEE Access

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Optimal partitioning of a square computational domain over several heterogeneous processors, balancing the load of the processors and minimizing the inter-processor communication cost, is crucial for data parallel dense linear algebra and other applications having similar communication pattern on modern hybrid servers. Although a solution has been found for two processors, the cases of three and more processors are still open. The state of-the-art solution for three processors uses an approximation communication cost function which fails to accurately account for the total amount of data moved between processors, leaving thus the question of its global optimality unanswered. In this work, we formulate and solve a mathematical problem of optimal partitioning a real-valued square over three heterogeneous processors using a new cost function, which accurately accounts for the total amount of data communicated between processors. We also develop an original method for accurate experimental evaluation of the communication time of data movement between memories of the compute devices in the hybrid platform during the execution of data parallel applications. We successfully use this method in the experimental validation of our mathematical results. Finally, we propose a communication energy model predicting the dynamic energy consumption of data movement between processors and experimentally validate its accuracy. This model predicts, and the experiments confirm, that the performance-optimal partition is not necessarily energy optimal.

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“…to design experiments [20] or to evaluate the goodness of energy profiles compared to measurements obtained from external power meters [21]. As it has proven to work in countless cases, we are applying ML techniques to manage decision-making automatically, focused in the energy efficiency and performance trade-offs of a given application.…”

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

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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.

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A Novel Statistical Learning-Based Methodology for Measuring the Goodness of Energy Profiles of Applications Executing on Multicore Computing Platforms

Cited by 6 publications

References 34 publications

Energy Consumption in Central and Eastern Europe (CEE) Households in the Platform Economics

Energy Consumption in Central and Eastern Europe (CEE) Households in the Platform Economics

Towards Optimal Matrix Partitioning for Data Parallel Computing on a Hybrid Heterogeneous Server

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

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