Energy Concerns with HPC Systems and Applications
Not on the map. Why?
Abstract (arXiv)
For various reasons including those related to climate changes, em energy has become a critical concern in all relevant activities and technical designs. For the specific case of computer activities, the problem is exacerbated with the emergence and pervasiveness of the so called em intelligent devices. From the application side, we point out the special topic of em Artificial Intelligence, who clearly needs an efficient computing support in order to succeed in its purpose of being a em ubiquitous assistant. There are mainly two contexts where em energy is one of the top priority concerns: em embedded computing and em supercomputing. For the former, power consumption is critical because the amount of energy that is available for the devices is limited. For the latter, the heat dissipated is a serious source of failure and the financial cost related to energy is likely to be a significant part of the maintenance budget. On a single computer, the problem is commonly considered through the electrical power consumption. This paper, written in the form of a survey, we depict the landscape of energy concerns in computer activities, both from the hardware and the software standpoints.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 28 of its 156 references that are in the map. First posted on arXiv 31 Aug 2023.
Builds on
28 of 156 references.
References · 28
- 2015Deep Residual Learning for Image Recognitioncited by 51,809
- 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,243
- 2020Knowledge Distillation: A Surveycited by 1,578
- 2019Energy and Policy Considerations for Deep Learning in NLPcited by 1,242
- 2021A Survey of Quantization Methods for Efficient Neural Network Inferencecited by 798
- 2021Carbon Emissions and Large Neural Network Trainingcited by 561
- 2019Quantifying the Carbon Emissions of Machine Learningcited by 516
- 2022Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Modelcited by 264
- 2020Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learningcited by 246
- 2021Sustainable AI: Environmental Implications, Challenges and Opportunitiescited by 242
- 2022The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrinkcited by 183
- 2020Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Modelscited by 168
16 more
- 2016Designing Energy-Efficient Convolutional Neural Networks using Energy-Aware Pruningcited by 133
- 2022Zeus: Understanding and Optimizing GPU Energy Consumption of DNN Trainingcited by 78
- 2022Eco2AI: carbon emissions tracking of machine learning models as the first step towards sustainable AIcited by 58
- 2019Splitting Steepest Descent for Growing Neural Architecturescited by 52
- 2020Can Federated Learning Save The Planet?cited by 52
- 2020A Survey of Methods for Low-Power Deep Learning and Computer Visioncited by 37
- 2019Energy-Aware Neural Architecture Optimization with Fast Splitting Steepest Descentcited by 17
- 2022Precise Energy Consumption Measurements of Heterogeneous Artificial Intelligence Workloadscited by 5
- 2023A Green(er) World for A.Icited by 5
- 2013iDataCool: HPC with Hot-Water Cooling and Energy Reusecited by 1
- 2016Green Data Centers: A Survey, Perspectives, and Future Directionscited by 1
- 2018DiG: Enabling Out-of-Band Scalable High-Resolution Monitoring for Data-Center Analytics, Automation and Control (Extended)cited by 1
- 2020Performance and energy consumption of HPC workloads on a cluster based on Arm ThunderX2 CPUcited by 1
- 2020Towards Management of Energy Consumption in HPC Systems with Fault Tolerancecited by 1
- 2021Energy Efficiency Aspects of the AMD Zen 2 Architecturecited by 1
- 2022Understanding the Energy Consumption of HPC Scale Artificial Intelligencecited by 1
Built on by
Not cited yet.