Prediction and characterization of application power use in a high‐performance computing environment-Reference-Cited by-同舟云学术

Prediction and characterization of application power use in a high‐performance computing environment

Published:2017-02-27 Issue:3 Volume:10 Page:155-165
ISSN:1932-1864
Container-title:Statistical Analysis and Data Mining: The ASA Data Science Journal
language:en
Short-container-title:Statistical Analysis and Data Mining: The ASA Data Science Journal

Author:

Bugbee Bruce¹,Phillips Caleb¹,Egan Hilary²,Elmore Ryan³,Gruchalla Kenny¹,Purkayastha Avi¹

Affiliation:

1. Computational Sciences Center National Renewable Energy Laboratory Golden Colorado

2. Department of Astrophysical and Planetary Sciences University of Colorado Boulder Colorado

3. Department of Business Information and Analytics University of Denver Denver Colorado

Funder

Laboratory Directed Research and Development

National Renewable Energy Laboratory

Office of Energy Efficiency and Renewable Energy

U.S. Department of Energy

Publisher

Wiley

Subject

Computer Science Applications,Information Systems,Analysis

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/sam.11339

Reference32 articles.

1. NRDC. America's data centers are wasting huge amounts of energy.2014.http://www.nrdc.org/energy/data‐center‐efficiency‐assessment.asp Accessed April 17 2015.

2. Research and Markets. Global data center construction market 2015–2019.2015.http://www.researchandmarkets.com/reports/3145253/global‐data‐center‐construction‐market‐2015‐2019. Accessed April 17 2015.

3. P.Koegge editor. Exascale computing study: technology challenges in achieving exascale systems. DARPA IPTO Technical Report US Department of Defense 2008.

4. R.StevensandA.White editors. Scientific grand challenges: architectures and technologies for extreme scale computing. DOE/ASCSC Technical Report US Department of Energy 2009.

5. R.Stevens T.Zacharia andH.Simon editors. Modeling and simulation at the exascale for energy and the environment. US DOE Office of Advance Scientific Computing Research Town Hall Meetings Report 2008.

Cited by 7 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Light-Weight Prediction for Improving Energy Consumption in HPC Platforms;Lecture Notes in Computer Science;2024

2. Augmenting ML-based Predictive Modelling with NLP to Forecast a Job's Power Consumption;Proceedings of the SC '23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis;2023-11-12

3. An Online Incremental Learning Framework for HPC Job Power Consumption Prediction;Proceedings of the 2023 7th International Conference on High Performance Compilation, Computing and Communications;2023-06-17

4. Using Data Sciences in Digital Marketing: Framework, methods, and performance metrics;Journal of Innovation & Knowledge;2021-04

5. Predicting Job Power Consumption Based on RJMS Submission Data in HPC Systems;Lecture Notes in Computer Science;2020