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DEPO: A dynamic energy‐performance optimizer tool for automatic power capping for energy efficient high‐performance computing
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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/cpe.3184openalex-snapshot
PMaC's green queue: a framework for selecting energy optimal DVFS configurations in large scale MPI applications
inferredcites →https://doi.org/10.1002/cpe.4485openalex-snapshot
Investigating power capping toward energy‐efficient scientific applications
inferredcites →https://doi.org/10.1002/cpe.6205openalex-snapshot
On power capping and performance optimization of multithreaded applications
inferredcites →https://doi.org/10.1002/spe.2599openalex-snapshot
Optimizing MapReduce for energy efficiency
inferredcites →https://doi.org/10.1002/spe.952openalex-snapshot
A survey of the research on power management techniques for high‐performance systems
inferredcites →https://doi.org/10.1002/spe.995openalex-snapshot
CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms
inferredcites →https://doi.org/10.1007/978-1-4757-6217-4_15openalex-snapshot
Et2: A Metric for Time and Energy Efficiency of Computation
inferredcites →https://doi.org/10.1007/978-3-030-13325-2openalex-snapshot
High Performance Computing in Science and Engineering ' 18
inferredcites →https://doi.org/10.1007/978-3-030-43222-5_11openalex-snapshot
Performance/Energy Aware Optimization of Parallel Applications on GPUs Under Power Capping
inferredcites →https://doi.org/10.1007/978-3-030-43222-5_15openalex-snapshot
Energy-Efficiency Tuning of a Lattice Boltzmann Simulation Using MERIC
inferredcites →https://doi.org/10.1007/978-3-031-08751-6_48openalex-snapshot
GPU Power Capping for Energy-Performance Trade-Offs in Training of Deep Convolutional Neural Networks for Image Recognition
inferredcites →https://doi.org/10.1007/978-3-642-45249-9_5openalex-snapshot
Optimization of Execution Time under Power Consumption Constraints in a Heterogeneous Parallel System with GPUs and CPUs
inferredcites →https://doi.org/10.1007/s10586-012-0226-7openalex-snapshot
Hierarchical genetic-based grid scheduling with energy optimization
inferredcites →https://doi.org/10.1007/s40012-013-0012-6openalex-snapshot
An intelligent energy optimization approach for MPI based applications in HPC systems
inferredcites →https://doi.org/10.1016/j.future.2013.07.010openalex-snapshot
Exploiting performance counters to predict and improve energy performance of HPC systems
inferredcites →https://doi.org/10.1016/j.simpat.2017.05.009openalex-snapshot
MERPSYS: An environment for simulation of parallel application execution on large scale HPC systems
inferredcites →https://doi.org/10.1016/j.sysarc.2020.101959openalex-snapshot
Low learning-cost offline strategies for EDP optimization of parallel applications
inferredcites →https://doi.org/10.1109/4.535411openalex-snapshot
Energy dissipation in general purpose microprocessors
inferredcites →https://doi.org/10.1109/ccgrid.2014.47openalex-snapshot
Power Consumption Evaluation of an MHD Simulation with CPU Power Capping
inferredcites →https://doi.org/10.1109/cgc.2012.62openalex-snapshot
Green Queue: Customized Large-Scale Clock Frequency Scaling
inferredcites →https://doi.org/10.1109/e2sc.2014.8openalex-snapshot
Simple Power-Aware Scheduler to Limit Power Consumption by HPC System within a Budget
inferredcites →https://doi.org/10.1109/hipc.2017.00025openalex-snapshot
A Novel Approach for Job Scheduling Optimizations Under Power Cap for ARM and Intel HPC Systems
inferredcites →https://doi.org/10.1109/hpca.2010.5416627openalex-snapshot
Architecting for power management: The IBM® POWER7™ approach
inferredcites →https://doi.org/10.1109/hpcs48598.2019.9188149openalex-snapshot
Extended investigation of performance-energy trade-offs under power capping in HPC environments
inferredcites →https://doi.org/10.1109/icac.2019.00015openalex-snapshot
CoPPer: Soft Real-Time Application Performance Using Hardware Power Capping
inferredcites →https://doi.org/10.1109/iccd.2013.6657064openalex-snapshot
Power capping of CPU-GPU heterogeneous systems through coordinating DVFS and task mapping
inferredcites →https://doi.org/10.1109/icpp.2013.98openalex-snapshot
Effects of Dynamic Voltage and Frequency Scaling on a K20 GPU
inferredcites →https://doi.org/10.1109/ipdps.2009.5160980openalex-snapshot
On the energy efficiency of graphics processing units for scientific computing
inferredcites →https://doi.org/10.1109/ipdps.2017.124openalex-snapshot
Co-Run Scheduling with Power Cap on Integrated CPU-GPU Systems
inferredcites →https://doi.org/10.1109/ipdpsw.2012.116openalex-snapshot
Beyond DVFS: A First Look at Performance under a Hardware-Enforced Power Bound
inferredcites →https://doi.org/10.1109/ipdpsw.2015.146openalex-snapshot
Predicting Optimal Power Allocation for CPU and DRAM Domains
inferredcites →https://doi.org/10.1109/ipdpsw.2015.70openalex-snapshot
An Energy Efficiency Feature Survey of the Intel Haswell Processor
inferredcites →https://doi.org/10.1109/mc.2016.311openalex-snapshot
Using Performance-Power Modeling to Improve Energy Efficiency of HPC Applications
inferredcites →https://doi.org/10.1109/pdp.2015.18openalex-snapshot
Application-Agnostic Framework for Improving the Energy Efficiency of Multiple HPC Subsystems
inferredcites →https://doi.org/10.1109/rtas.2009.34openalex-snapshot
Thermal-Aware Global Real-Time Scheduling on Multicore Systems
inferredcites →https://doi.org/10.1109/tcad.2015.2501286openalex-snapshot
Thermal-Aware Task Scheduling for Energy Minimization in Heterogeneous Real-Time MPSoC Systems
inferredcites →https://doi.org/10.1109/tpds.2020.2996314openalex-snapshot
Correlation of Performance Optimizations and Energy Consumption for Stencil-Based Application on Intel Xeon Scalable Processors
inferredcites →https://doi.org/10.1109/tpds.2021.3090334openalex-snapshot
Energy hardware and workload aware job scheduling towards interconnected HPC environments
inferredcites →https://doi.org/10.1109/tpds.2021.3092270openalex-snapshot
Online Power Management for Multi-Cores: A Reinforcement Learning Based Approach
inferredcites →https://doi.org/10.1145/2425248.2425252openalex-snapshot
Measuring energy consumption for short code paths using RAPL
inferredcites →https://doi.org/10.1145/2597917.2597925openalex-snapshot
Symbiotic scheduling of concurrent GPU kernels for performance and energy optimizations
inferredcites →https://doi.org/10.1145/2712386.2712396openalex-snapshot
Energy-efficient computing for HPC workloads on heterogeneous manycore chips
inferredcites →https://doi.org/10.1145/2786763.2694373openalex-snapshot
A Probabilistic Graphical Model-based Approach for Minimizing Energy Under Performance Constraints
inferredcites →https://doi.org/10.1145/2954679.2872375openalex-snapshot
Maximizing Performance Under a Power Cap
inferredcites →https://doi.org/10.1145/2989081.2989088openalex-snapshot
A Validation of DRAM RAPL Power Measurements
inferredcites →https://doi.org/10.1145/3004054openalex-snapshot
A Reconfiguration Algorithm for Power-Aware Parallel Applications
inferredcites →https://doi.org/10.1145/3126908.3126945openalex-snapshot
Towards fine-grained dynamic tuning of HPC applications on modern multi-core architectures
inferredcites →https://doi.org/10.1155/2019/8348791openalex-snapshot
Energy-Aware High-Performance Computing: Survey of State-of-the-Art Tools, Techniques, and Environments
inferredcites →https://doi.org/10.1177/109434209100500306openalex-snapshot
The NAS Parallel Benchmarks
inferredcites →https://doi.org/10.1515/pomr-2015-0062openalex-snapshot
Tryton Supercomputer Capabilities for Analysis of Massive Data Streams
inferredcites →https://doi.org/10.15439/2016f308openalex-snapshot
Modeling energy consumption of parallel applications
inferredcites →https://doi.org/10.15439/2018f177openalex-snapshot
Analyzing energy/performance trade-offs with power capping for parallel applications on modern multi and many core processors
inferredcites →https://doi.org/10.2307/2032161openalex-snapshot
Sequential Minimax Search for a Maximum
inferredcites →https://doi.org/10.3390/en12112204openalex-snapshot
A Comparative Study of Methods for Measurement of Energy of Computing
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/978-3-031-48803-0_1openalex-snapshot
Performance and Energy Aware Training of a Deep Neural Network in a Multi-GPU Environment with Power Capping
inferred← citeshttps://doi.org/10.1007/978-3-031-71115-2_23openalex-snapshot
Investigation of Performance and Energy Consumption of Tokenization Algorithms on Multi-core CPUs Under Power Capping
inferred← citeshttps://doi.org/10.1007/978-3-032-02406-0_23openalex-snapshot
Performance and Energy Comparison of Selected Web Request Processing Models for Reactive and Imperative Spring Based Implementations
inferred← citeshttps://doi.org/10.1007/s11227-025-07295-7openalex-snapshot
Optimization of resource-aware parallel and distributed computing: a review
inferred← citeshttps://doi.org/10.1016/j.future.2023.03.041openalex-snapshot
Dynamic GPU power capping with online performance tracing for energy efficient GPU computing using DEPO tool
inferred← citeshttps://doi.org/10.1016/j.rser.2023.114019openalex-snapshot
A review on the decarbonization of high-performance computing centers
inferred← citeshttps://doi.org/10.1109/access.2026.3738617openalex-snapshot
GNN-Based Energy Modeling of CNN Training Under Power Capping
inferred← citeshttps://doi.org/10.1109/clusterworkshops61563.2024.00012openalex-snapshot
PowerSched - Managing Power Consumption in Overprovisioned Systems
inferred← citeshttps://doi.org/10.1109/iciscois62701.2026.11447834openalex-snapshot
Machine Learning-Based Parkinson's Disease Detection Using Facial Features
inferred← citeshttps://doi.org/10.1109/icpads67057.2025.11323155openalex-snapshot
Dynamic Energy-Performance Optimization Tool Extension with Periodic Power Cap Tuning
inferred← citeshttps://doi.org/10.1109/tsusc.2023.3314916openalex-snapshot
Model-Free GPU Online Energy Optimization
inferred← citeshttps://doi.org/10.1134/s199508022460612xopenalex-snapshot
Scheduling Supercomputer Jobs under Existing Power Consumption Constraints
inferred← citeshttps://doi.org/10.1145/3687997.3695638openalex-snapshot
Trading Runtime for Energy Efficiency: Leveraging Power Caps to Save Energy across Programming Languages
inferred← citeshttps://doi.org/10.3390/en16020890openalex-snapshot
Energy-Aware Scheduling for High-Performance Computing Systems: A Survey
inferred← citeshttps://doi.org/10.54097/pkgmn637openalex-snapshot
Neural Methods for Adaptive Voltage and Frequency Scaling in Energy Aware Processors
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