# Energy Concerns with HPC Systems and Applications

arXiv: https://arxiv.org/abs/2309.08615 · HTML: https://mlsanity.com/paper/2309.08615/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 28 of its 156 references that are in the map.
- First posted on arXiv 31 Aug 2023.

## Builds on (most cited first, 28 of 28)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Knowledge Distillation: A Survey](https://mlsanity.com/paper/2006.05525/) (2020)
- [Energy and Policy Considerations for Deep Learning in NLP](https://mlsanity.com/paper/1906.02243/) (2019)
- [A Survey of Quantization Methods for Efficient Neural Network Inference](https://mlsanity.com/paper/2103.13630/) (2021)
- [Carbon Emissions and Large Neural Network Training](https://mlsanity.com/paper/2104.10350/) (2021)
- [Quantifying the Carbon Emissions of Machine Learning](https://mlsanity.com/paper/1910.09700/) (2019)
- [Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model](https://mlsanity.com/paper/2211.02001/) (2022)
- [Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning](https://mlsanity.com/paper/2002.05651/) (2020)
- [Sustainable AI: Environmental Implications, Challenges and Opportunities](https://mlsanity.com/paper/2111.00364/) (2021)
- [The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink](https://mlsanity.com/paper/2204.05149/) (2022)
- [Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models](https://mlsanity.com/paper/2007.03051/) (2020)
- [Designing Energy-Efficient Convolutional Neural Networks using Energy-Aware Pruning](https://mlsanity.com/paper/1611.05128/) (2016)
- [Zeus: Understanding and Optimizing GPU Energy Consumption of DNN Training](https://mlsanity.com/paper/2208.06102/) (2022)
- [Eco2AI: carbon emissions tracking of machine learning models as the first step towards sustainable AI](https://mlsanity.com/paper/2208.00406/) (2022)
- [Splitting Steepest Descent for Growing Neural Architectures](https://mlsanity.com/paper/1910.02366/) (2019)
- [Can Federated Learning Save The Planet?](https://mlsanity.com/paper/2010.06537/) (2020)
- [A Survey of Methods for Low-Power Deep Learning and Computer Vision](https://mlsanity.com/paper/2003.11066/) (2020)
- [Energy-Aware Neural Architecture Optimization with Fast Splitting Steepest Descent](https://mlsanity.com/paper/1910.03103/) (2019)
- [Precise Energy Consumption Measurements of Heterogeneous Artificial Intelligence Workloads](https://mlsanity.com/paper/2212.01698/) (2022)
- [A Green(er) World for A.I](https://mlsanity.com/paper/2301.11581/) (2023)
- [iDataCool: HPC with Hot-Water Cooling and Energy Reuse](https://mlsanity.com/paper/1309.4887/) (2013)
- [Green Data Centers: A Survey, Perspectives, and Future Directions](https://mlsanity.com/paper/1608.00687/) (2016)
- [DiG: Enabling Out-of-Band Scalable High-Resolution Monitoring for Data-Center Analytics, Automation and Control (Extended)](https://mlsanity.com/paper/1806.02698/) (2018)
- [Performance and energy consumption of HPC workloads on a cluster based on Arm ThunderX2 CPU](https://mlsanity.com/paper/2007.04868/) (2020)
- [Towards Management of Energy Consumption in HPC Systems with Fault Tolerance](https://mlsanity.com/paper/2012.11396/) (2020)
- [Energy Efficiency Aspects of the AMD Zen 2 Architecture](https://mlsanity.com/paper/2108.00808/) (2021)
- [Understanding the Energy Consumption of HPC Scale Artificial Intelligence](https://mlsanity.com/paper/2212.00582/) (2022)

## 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.