An analytical methodology to derive power models based on hardware and software metrics

Dolz, Manuel F.; Kunkel, Julian; Chasapis, Konstantinos; Catalán, Sandra

doi:10.1007/s00450-015-0298-8

Cited by 6 publications

(8 citation statements)

References 11 publications

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“…We employ the dual-socket multicore platform (HCLServer1) and seventeen optimized and unoptimized base applications (Table 4). We select six PMCs common to the state-ofthe-art models [18], [27], [28], [33], [52], [53]. The PMCs ({X 1 , ..., X 6 }) and their additivity errors are shown in the Table 5.…”

Section: Improving the Accuracy Of Platform-level Linear Energy Predictive Models Using The Consistency Testmentioning

confidence: 99%

Energy Predictive Models of Computing: Theory, Practical Implications and Experimental Analysis on Multicore Processors

et al. 2021

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The energy efficiency in ICT is becoming a grand technological challenge and is now a first-class design constraint in all computing settings. Energy predictive modelling based on performance monitoring counters (PMCs) is the leading method for application-level energy optimization. However, a sound theoretical framework to understand the fundamental significance of the PMCs to the energy consumption and the causes of the inaccuracy of the models is lacking. In this work, we propose a small but insightful theory of energy predictive models of computing, which formalizes both the assumptions behind the existing PMC-based energy predictive models and properties, heretofore unconsidered, that are basic implications of the universal energy conservation law. The theory's basic practical implications include selection criteria for model variables, model intercept, and model coefficients. The experiments on two modern Intel multicore servers show that applying the proposed selection criteria improves the prediction accuracy of state-of-the-art linear regression models from 31.2% to 18%. Finally, we demonstrate that employing energy models constructed using the proposed theory for energy optimization can save a significant amount of energy (up to 80% for applications used in experiments) compared to state-of-the-art energy measurement tools.

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Section: Improving the Accuracy Of Platform-level Linear Energy Predictive Models Using The Consistency Testmentioning

confidence: 99%

Energy Predictive Models of Computing: Theory, Practical Implications and Experimental Analysis on Multicore Processors

et al. 2021

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“…The authors in [30] propose an approach to build linear power models for hardware components (CPU, memory, network, disk) by applying a per component analysis. Their technique uses 4 benchmarks during the training phase and collect various metrics gathered from hardware performance counters, OS statistics, and sensors.…”

Section: Cpu Power Modelsmentioning

confidence: 99%

“…As part of this article, we consider both CPUand memory-intensive workloads, thus modeling the power consumption of these components (CPU, memory) and their side-effects on related components (motherboard, fans, etc.). While the state-of-the-art has investigated several regression techniques, from linear [12,30], to polynomial ones [5,28], this article reports on the adoption of the robust ridge regression [46,47], which belongs to the family of multivariate linear regressions. This technique has been chosen to eliminate outliers and to limit the effect of collinearity between variables-i.e., HPC events in our case.…”

Section: ��mentioning

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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“…It is noteworthy that some non-additive PMCs are used as predictor variables in many energy predictive models [5,6,10,20,23]. These are ICache events, L2 Transactions, and L2 Requests.…”

Section: Additivity Of Likwid Pmcsmentioning

confidence: 99%

Additivity: A Selection Criterion for Performance Events for Reliable Energy Predictive Modeling

Shahid

Fahad

Reddy

et al. 2017

JSFI

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Performance events or performance monitoring counters (PMCs) are now the dominant predictor variables for modeling energy consumption. Modern hardware processors provide a large set of PMCs. Determination of the best subset of PMCs for energy predictive modeling is a nontrivial task given the fact that all the PMCs can not be determined using a single application run. Several techniques have been devised to address this challenge. While some techniques are based on a statistical methodology, some use expert advice to pick a subset (that may not necessarily be obtained in one application run) that, in experts' opinion, are significant contributors to energy consumption. However, the existing techniques have not considered a fundamental property of predictor variables that should have been applied in the first place to remove PMCs unfit for modeling energy. We address this oversight in this paper.We propose a novel selection criterion for PMCs called additivity, which can be used to determine the subset of PMCs that can potentially be used for reliable energy predictive modeling. It is based on the experimental observation that the energy consumption of a serial execution of two applications is the sum of energy consumptions observed for the individual execution of each application. A linear predictive energy model is consistent if and only if its predictor variables are additive in the sense that the vector of predictor variables for a serial execution of two applications is the sum of vectors for the individual execution of each application. The criterion, therefore, is based on a simple and intuitive rule that the value of a PMC for a serial execution of two applications is equal to the sum of its values obtained for the individual execution of each application. The PMC is branded as non-additive on a platform if there exists an application for which the calculated value differs significantly from the value observed for the application execution on the platform. The use of non-additive PMCs in a model renders it inconsistent.We study the additivity of PMCs offered by the popular state-of-the-art tools, Likwid and PAPI, by employing a detailed experimental methodology on a modern Intel Haswell multicore server CPU. We show that many PMCs in Likwid and PAPI that are widely used in models as key predictor variables are non-additive. This brings into question the reliability and the reported prediction accuracy of these models.

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An analytical methodology to derive power models based on hardware and software metrics

Cited by 6 publications

References 11 publications

Energy Predictive Models of Computing: Theory, Practical Implications and Experimental Analysis on Multicore Processors

Energy Predictive Models of Computing: Theory, Practical Implications and Experimental Analysis on Multicore Processors

The next 700 CPU power models

Additivity: A Selection Criterion for Performance Events for Reliable Energy Predictive Modeling

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