Multi Objective Optimization of HPC Kernels for Performance, Power, and Energy

Balaprakash, Prasanna; Tiwari, Ananta; Wild, Stefan M.

doi:10.1007/978-3-319-10214-6_12

Cited by 36 publications

(31 citation statements)

References 31 publications

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“…For example, in Figure 8, we populate the runtime-accuracy plane with some topCalssifiers for waist position and depict the Pareto front. The shaded area represents the region in

f_{1} \times f_{2}

space that is dominated by the point x which is non-dominated and hence belong to the Pareto front [38]. All points in this region are inferior to x in both objectives.…”

Section: Resultsmentioning

confidence: 99%

A Comprehensive Analysis on Wearable Acceleration Sensors in Human Activity Recognition

Janidarmian

Fekr

Radecka

et al. 2017

Sensors

169

137

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Sensor-based motion recognition integrates the emerging area of wearable sensors with novel machine learning techniques to make sense of low-level sensor data and provide rich contextual information in a real-life application. Although Human Activity Recognition (HAR) problem has been drawing the attention of researchers, it is still a subject of much debate due to the diverse nature of human activities and their tracking methods. Finding the best predictive model in this problem while considering different sources of heterogeneities can be very difficult to analyze theoretically, which stresses the need of an experimental study. Therefore, in this paper, we first create the most complete dataset, focusing on accelerometer sensors, with various sources of heterogeneities. We then conduct an extensive analysis on feature representations and classification techniques (the most comprehensive comparison yet with 293 classifiers) for activity recognition. Principal component analysis is applied to reduce the feature vector dimension while keeping essential information. The average classification accuracy of eight sensor positions is reported to be 96.44% ± 1.62% with 10-fold evaluation, whereas accuracy of 79.92% ± 9.68% is reached in the subject-independent evaluation. This study presents significant evidence that we can build predictive models for HAR problem under more realistic conditions, and still achieve highly accurate results.

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“…For example, in Figure 8, we populate the runtime-accuracy plane with some topCalssifiers for waist position and depict the Pareto front. The shaded area represents the region in

f_{1} \times f_{2}

space that is dominated by the point x which is non-dominated and hence belong to the Pareto front [38]. All points in this region are inferior to x in both objectives.…”

Section: Resultsmentioning

confidence: 99%

A Comprehensive Analysis on Wearable Acceleration Sensors in Human Activity Recognition

Janidarmian

Fekr

Radecka

et al. 2017

Sensors

169

137

View full text Add to dashboard Cite

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“…Balaprakash et al use this approach to investigate the trade-offs between runtime and energy consumption for common kernels in scientific computing [2]. A similar technique has also been used to determine optimal checkpoint intervals for energy efficient fault tolerance [1].…”

Section: Related Workmentioning

confidence: 99%

Metrics for Energy-Aware Software Optimisation

Roberts

Wright

Fahmy

et al. 2017

Lecture Notes in Computer Science

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A note on versions:The version presented here may differ from the published version or, version of record, if you wish to cite this item you are advised to consult the publisher's version. Please see the 'permanent WRAP url' above for details on accessing the published version and note that access may require a subscription.For more information, please contact the WRAP Team at: wrap@warwick.ac.uk Metrics for Energy-Aware Software OptimisationStephen I. Roberts, Steven A. Wright, Suhaib A. Fahmy, and Stephen A. Jarvis University of Warwick, U.K. S.I.Roberts@warwick.ac.uk Abstract. Energy consumption is rapidly becoming a limiting factor in scientific computing. As a result, hardware manufacturers increasingly prioritise energy efficiency in their processor designs. Performance engineers are also beginning to explore software optimisation and hardware/software co-design as a means to reduce energy consumption. Energy efficiency metrics developed by the hardware community are often re-purposed to guide these software optimisation efforts. In this paper we argue that established metrics, and in particular those in the Energy Delay Product (Et n ) family, are unsuitable for energy-aware software optimisation. A good metric should provide meaningful values for a single experiment, allow fair comparison between experiments, and drive optimisation in a sensible direction. We show that Et n metrics are unable to fulfil these basic requirements and present suitable alternatives for guiding energy-aware software optimisation. We finish with a practical demonstration of the utility of our proposed metrics.

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“…Most of these works fail to capture the trade-off between these objectives and reduce them to a single one. Only a few works focus on computing and analyzing the trade-off between several conflicting objectives [40], [12], [41].…”

Section: Related Workmentioning

confidence: 99%

A Region-Aware Multi-Objective Auto-Tuner for Parallel Programs

Kofler

Durillo

Gschwandtner

et al. 2017

2017 46th International Conference on Parallel Processing Workshops (ICPPW)

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Abstract-Auto-tuning has become increasingly popular for optimizing non-functional parameters of parallel programs. The typically large search space requires sophisticated techniques to find well performing parameter values in a reasonable amount of time. Different parts of a program often perform best with different parameter values. We therefore subdivide programs into several regions, and try to optimize the parameter values for each of those regions separately as opposed to setting the parameter values globally for the entire program. As this enlarges the search space even further, we have to extend existing autotuning techniques in order to obtain good results. In this paper we introduce a novel enhancement to the RS-GDE3 algorithm which is used to explore the search space for auto-tuning programs with multiple regions regarding several objectives. We have implemented our auto-tuner using the Insieme compiler and runtime system. In comparison to a non-optimized parallel version of the tested programs, our novel approach achieves up to 7.6, 10.5, and 61.6 fold improvements for three tuned objectives wall time, energy consumption, and resource usage, respectively.

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Multi Objective Optimization of HPC Kernels for Performance, Power, and Energy

Cited by 36 publications

References 31 publications

A Comprehensive Analysis on Wearable Acceleration Sensors in Human Activity Recognition

A Comprehensive Analysis on Wearable Acceleration Sensors in Human Activity Recognition

Metrics for Energy-Aware Software Optimisation

A Region-Aware Multi-Objective Auto-Tuner for Parallel Programs

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