Energy prediction of CUDA application instances using dynamic regression models

Rejitha, R. S.; Benedict, Shajulin; Alex, Suja A.; Infanto, Shany

doi:10.1007/s00607-016-0534-5

Cited by 6 publications

(2 citation statements)

References 33 publications

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“…We want to highlight that random forests can also be used for regression tasks. For instances, it has been used to model energy consumption of OpenMP [76] and CUDA [77] programs.…”

Section: Machine Learning Modelsmentioning

confidence: 99%

Machine Learning in Compiler Optimization

2018

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Abstract-In the last decade, machine learning based compilation has moved from an an obscure research niche to a mainstream activity. In this article, we describe the relationship between machine learning and compiler optimisation and introduce the main concepts of features, models, training and deployment. We then provide a comprehensive survey and provide a road map for the wide variety of different research areas. We conclude with a discussion on open issues in the area and potential research directions. This paper provides both an accessible introduction to the fast moving area of machine learning based compilation and a detailed bibliography of its main achievements.

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“…We want to highlight that random forests can also be used for regression tasks. For instances, it has been used to model energy consumption of OpenMP [76] and CUDA [77] programs.…”

Section: Machine Learning Modelsmentioning

confidence: 99%

Machine Learning in Compiler Optimization

2018

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“…However, these techniques have never focused on energy, nor they targeted ultra-low-power embedded architectures. From the other side, previous research work investigated power and energy modelling of parallel architectures using features extracted from code execution profiling [7], [8].…”

Section: Introductionmentioning

confidence: 99%

Source Code Classification for Energy Efficiency in Parallel Ultra Low-Power Microcontrollers

Parisi

Barchi

Bartolini

et al. 2021

2021 Design, Automation &Amp; Test in Europe Conference &Amp; Exhibition (DATE)

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The analysis of source code through machine learning techniques is an increasingly explored research topic aiming at increasing smartness in the software toolchain to exploit modern architectures in the best possible way. In the case of lowpower, parallel embedded architectures, this means finding the configuration, for instance in terms of the number of cores, leading to minimum energy consumption. Depending on the kernel to be executed, the energy optimal scaling configuration is not trivial. While recent work has focused on general-purpose systems to learn and predict the best execution target in terms of the execution time of a snippet of code or kernel (e.g. offload OpenCL kernel on multicore CPU or GPU), in this work we focus on static compile-time features to assess if they can be successfully used to predict the minimum energy configuration on PULP, an ultra-low-power architecture featuring an on-chip cluster of RISC-V processors. Experiments show that using machine learning models on the source code to select the best energy scaling configuration automatically is viable and has the potential to be used in the context of automatic system configuration for energy minimisation.

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