Machine Learning for Generic Energy Models of High Performance Computing Resources
- 13 November 2021
- book chapter
- conference paper
- Published by Springer Science and Business Media LLC
Abstract
No abstract availableKeywords
This publication has 13 references indexed in Scilit:
- The Energy and Carbon Footprint of the Global ICT and E&M Sectors 2010–2015Sustainability, 2018
- Energy-efficient Application Resource Scheduling using Machine Learning ClassifiersPublished by Association for Computing Machinery (ACM) ,2018
- Machine Learning-Based Approaches for Energy-Efficiency Prediction and Scheduling in Composite Cores ArchitecturesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2017
- Optimizing energy consumption for data centersRenewable and Sustainable Energy Reviews, 2016
- Data Center Energy Consumption Modeling: A SurveyIEEE Communications Surveys & Tutorials, 2015
- Holistic multiobjective planning of datacenters powered by renewable energyCluster Computing, 2015
- A Case Study of Energy Aware Scheduling on SuperMUCLecture Notes in Computer Science, 2014
- Measuring energy consumption for short code paths using RAPLACM SIGMETRICS Performance Evaluation Review, 2012
- Collecting Performance Data with PAPI-CPublished by Springer Science and Business Media LLC ,2010
- A Survey on Transfer LearningIEEE Transactions on Knowledge and Data Engineering, 2009