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

https://doi.org/10.1002/sam.11339
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/9780470057339.vaa008openalex-snapshot
Multivariate Adaptive Regression Splines
inferredcites →https://doi.org/10.1007/978-1-4471-4492-2openalex-snapshot
Energy-Efficient High Performance Computing: Measurement and Tuning
inferredcites →https://doi.org/10.1007/978-3-662-43779-7_6openalex-snapshot
Reducing Energy Costs for IBM Blue Gene/P via Power-Aware Job Scheduling
inferredcites →https://doi.org/10.1016/j.adhoc.2014.11.012openalex-snapshot
Matching renewable energy supply and demand in green datacenters
inferredcites →https://doi.org/10.1017/cbo9780511841040openalex-snapshot
Wavelet Methods for Time SeriesAnalysis
inferredcites →https://doi.org/10.1023/a:1010933404324openalex-snapshot
Random Forests
inferredcites →https://doi.org/10.1093/bioinformatics/btl355openalex-snapshot
Improved peak detection in mass spectrum by incorporating continuous wavelet transform-based pattern matching
inferredcites →https://doi.org/10.1109/cgc.2012.62openalex-snapshot
Green Queue: Customized Large-Scale Clock Frequency Scaling
inferredcites →https://doi.org/10.1109/icpp.2007.29openalex-snapshot
CPU MISER: A Performance-Directed, Run-Time System for Power-Aware Clusters
inferredcites →https://doi.org/10.1109/ipdpsw.2013.269openalex-snapshot
Measuring Power Consumption on IBM Blue Gene/Q
inferredcites →https://doi.org/10.1109/mm.2014.6openalex-snapshot
Designing and Managing Data centers Powered by Renewable Energy
inferredcites →https://doi.org/10.1109/sc.2006.11openalex-snapshot
Adaptive, Transparent Frequency and Voltage Scaling of Communication Phases in MPI Programs
inferredcites →https://doi.org/10.1145/2503210.2503264openalex-snapshot
Integrating dynamic pricing of electricity into energy aware scheduling for HPC systems
inferredcites →https://doi.org/10.1214/aos/1176347963openalex-snapshot
Multivariate Adaptive Regression Splines
inferredcites →https://doi.org/10.2172/1235236openalex-snapshot
Analysis of Application Power and Schedule Composition in a High Performance Computing Environment
inferredcites →https://doi.org/10.2172/1372902openalex-snapshot
United States Data Center Energy Usage Report
inferredcites →https://doi.org/10.2172/1762048openalex-snapshot
High Performance Computing - Power Application Programming Interface Specification. Version 1.1 [DRAFT]
inferredcites →https://doi.org/10.32614/r.manualsopenalex-snapshot
R: A Language and Environment for Statistical Computing

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1007/978-3-030-50743-5_4openalex-snapshot
Predicting Job Power Consumption Based on RJMS Submission Data in HPC Systems
inferred← citeshttps://doi.org/10.1007/978-3-031-69577-3_11openalex-snapshot
Light-Weight Prediction for Improving Energy Consumption in HPC Platforms
inferred← citeshttps://doi.org/10.1016/j.apenergy.2018.03.138openalex-snapshot
A simplified power consumption model of information technology (IT) equipment in data centers for energy system real-time dynamic simulation
inferred← citeshttps://doi.org/10.1016/j.future.2025.108064openalex-snapshot
An online algorithm for power consumption prediction of HPC workload
inferred← citeshttps://doi.org/10.1016/j.ijhydene.2026.156806openalex-snapshot
Cost-decarbonisation trade-offs in solar-powered data centres: A comparative analysis of battery and hydrogen storage
inferred← citeshttps://doi.org/10.1109/icad65464.2025.11114059openalex-snapshot
Energy Forecasting in High Performance Computing Datacenters Using Machine Learning
inferred← citeshttps://doi.org/10.1109/tpds.2025.3586723openalex-snapshot
Scheduling With Lightweight Predictions in Power-Constrained HPC Platforms
inferred← citeshttps://doi.org/10.1145/3332186.3333041openalex-snapshot
Improving HPC System Performance by Predicting Job Resources via Supervised Machine Learning
inferred← citeshttps://doi.org/10.1145/3606043.3606068openalex-snapshot
An Online Incremental Learning Framework for HPC Job Power Consumption Prediction
inferred← citeshttps://doi.org/10.1145/3624062.3624264openalex-snapshot
Augmenting ML-based Predictive Modelling with NLP to Forecast a Job's Power Consumption
inferred← citeshttps://doi.org/10.1145/3708035.3736084openalex-snapshot
Classification of HPC Job Power Consumption Using a Rich Feature Set
inferred← citeshttps://doi.org/10.1145/3731599.3767563openalex-snapshot
Energy-Aware HPC Scheduling with LLM-Based Power Prediction
inferred← citeshttps://doi.org/10.21203/rs.3.rs-2025341/v1openalex-snapshot
Energy Efficient Resource Allocation in Heterogeneous Computing Environments
inferred← citeshttps://doi.org/10.2172/1597242openalex-snapshot
Clustering Analysis of Commercial Vehicles Using Automatically Extracted Features from Time Series Data
inferred← citeshttps://doi.org/10.2172/1783088openalex-snapshot
Artificial Intelligence for Data Center Operations (AI Ops)
inferred← citeshttps://doi.org/10.23919/isc.2025.11017729openalex-snapshot
UoPC: A User-Based Online Framework to Predict Job Power Consumption in HPC Systems
inferred← citeshttps://doi.org/10.3390/fi17050203openalex-snapshot
C6EnPLS: A High-Performance Computing Job Dataset for the Analysis of Linear Solvers’ Power Consumption
inferred← citeshttps://doi.org/10.48550/arxiv.1806.01116openalex-snapshot
Machine Learning for Predictive Analytics of Compute Cluster Jobs
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