Techniques to Measure, Model, and Manage Power

Goel, Bhavishya; McKee, Sally A.; Själander, Magnus

doi:10.1016/b978-0-12-396528-8.00002-x

Cited by 12 publications

(12 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We measure power consumption at the ATX CPU power rails using infrastructure that we built for previous work [10]. This infrastructure uses current transducers to convert measured current to voltage, which a data acquisition unit then logs at high accuracy with high sampling rates.…”

Section: Methodsmentioning

confidence: 99%

“…Goel et al [9] further refine and validate this general approach, demonstrating consistently high model accuracy across several architectures. Goel et al [10] investigate the impact of different power measurement infrastructures on model accuracy.…”

Section: Related Workmentioning

confidence: 99%

“…Given the limited utility of power meters in commodity hardware, many software power estimation models have been proposed. These models strive to provide detailed power estimation at granularities ranging from the entire CPU [5]- [7] to individual cores [8]- [10], individual microarchitectural components [11]- [13], or individual cores plus the uncore [14]. To the best of our knowledge, none of these models makes an explicit distinction between static and dynamic power consumption.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Methodology for Modeling Dynamic and Static Power Consumption for Multicore Processors

Goel

McKee

2016

2016 IEEE International Parallel and Distributed Processing Symposium (IPDPS)

Self Cite

View full text Add to dashboard Cite

System designers and application programmers must consider trade-offs between performance and energy. Making energy-aware decisions when designing an application or runtime system requires quantitative information about power consumed by different processor components. We present a methodology to model static and dynamic power consumption of individual cores and the uncore components, and we validate our power model for both sequential and parallel benchmarks at different voltage-frequency pairs on an Intel R Haswell platform.Our power models yield the following insights about energyefficient scaling. (1) We show that uncore energy accounts for up to 74% of total energy. In particular, uncore static energy can be as high as 61% of total energy, potentially making it a major source of energy inefficiency. (2) We find that the frequency at which an application expends the lowest energy depends on how memory-bound it is.(3) We demonstrate that even though using more cores may improve performance, the energy consumed by stalled cores during serial portions of the program can make using fewer cores more energy-efficient.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Methodology for Modeling Dynamic and Static Power Consumption for Multicore Processors

Goel

McKee

2016

2016 IEEE International Parallel and Distributed Processing Symposium (IPDPS)

Self Cite

View full text Add to dashboard Cite

show abstract

“…The static power depends on leakage current and supply voltage linearly while the leakage current itself depends on the supply voltage and temperature exponentially. In this analysis we approximate these exponential relations linearly for the range in which device temperatures occur [2]. To understand the dependency of static power and temperature we run a number of experiments with no load on the GPU varying the fan rate and frequency at a constant voltage of 0.82V to generate different temperature profiles.…”

Section: Temperature Effectsmentioning

confidence: 99%

“…Taking these points into account, the novelty of this work can be summarized as follows: 1 We perform power modelling on an embedded GPU device with integrated power measurement and voltage/frequency scaling compared with previous work largely focused on desktop GPUs. 2 We limit the number of explanatory variables used in the model to enable the run-time collection of this information using a limited number of hardware registers. 3 We propose a novel unified model that includes temperature, frequency and voltage as global states and compare it against models with coefficients optimized for single temperatures and frequency/voltage pairs.…”

Section: Introductionmentioning

confidence: 99%

Run-Time Power Modelling in Embedded GPUs with Dynamic Voltage and Frequency Scaling

Nunez-Yanez

Nikov

Eder

et al. 2020

Proceedings of the 11th Workshop on Parallel Programming and Run-Time Management Techniques for Many-Core Architectures / 9th W

View full text Add to dashboard Cite

This paper investigates the application of a robust CPU-based power modelling methodology that performs an automatic search of explanatory events derived from performance counters to embedded GPUs. A 64-bit Tegra TX1 SoC is configured with DVFS enabled and multiple CUDA benchmarks are used to train and test models optimized for each frequency and voltage point. These optimized models are then compared with a simpler unified model that uses a single set of model coefficients for all frequency and voltage points of interest. To obtain this unified model, a number of experiments are conducted to extract information on idle, clock and static power to derive power usage from a single reference equation. The results show that the unified model offers competitive accuracy with an average 5% error with four explanatory variables on the test data set and it is capable to correctly predict the impact of voltage, frequency and temperature on power consumption. This model could be used to replace direct power measurements when these are not available due to hardware limitations or worst-case analysis in emulation platforms. CCS CONCEPTS• Hardware → Power estimation and optimization.

show abstract

How to Measure Energy Consumption in Machine Learning Algorithms

García-Martín

Lavesson

Grahn

et al. 2019

ECML PKDD 2018 Workshops

View full text Add to dashboard Cite

Machine learning algorithms are responsible for a significant amount of computations. These computations are increasing with the advancements in different machine learning fields. For example, fields such as deep learning require algorithms to run during weeks consuming vast amounts of energy. While there is a trend in optimizing machine learning algorithms for performance and energy consumption, still there is little knowledge on how to estimate an algorithm's energy consumption. Currently, a straightforward cross-platform approach to estimate energy consumption for different types of algorithms does not exist. For that reason, well-known researchers in computer architecture have published extensive works on approaches to estimate the energy consumption. This study presents a survey of methods to estimate energy consumption, and maps them to specific machine learning scenarios. Finally, we illustrate our mapping suggestions with a case study, where we measure energy consumption in a big data stream mining scenario. Our ultimate goal is to bridge the current gap that exists to estimate energy consumption in machine learning scenarios.

show abstract

Techniques to Measure, Model, and Manage Power

Cited by 12 publications

References 29 publications

A Methodology for Modeling Dynamic and Static Power Consumption for Multicore Processors

A Methodology for Modeling Dynamic and Static Power Consumption for Multicore Processors

Run-Time Power Modelling in Embedded GPUs with Dynamic Voltage and Frequency Scaling

How to Measure Energy Consumption in Machine Learning Algorithms

Contact Info

Product

Resources

About