On the public citation graph
H-EARtH: Heterogeneous Multicore Platform Energy Management
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/11574859_10openalex-snapshot
Effective Dynamic Voltage Scaling Through CPU-Boundedness Detection
inferredcites →https://doi.org/10.1109/ipdps.2009.5160979openalex-snapshot
Modeling and evaluating energy-performance efficiency of parallel processing on multicore based power aware systems
inferredcites →https://doi.org/10.1109/l-ca.2012.32openalex-snapshot
Energy Aware Race to Halt: A Down to EARtH Approach for Platform Energy Management
inferredcites →https://doi.org/10.1109/micro.2006.30openalex-snapshot
Live, Runtime Phase Monitoring and Prediction on Real Systems with Application to Dynamic Power Management
inferredcites →https://doi.org/10.1109/mm.2012.12openalex-snapshot
Power-Management Architecture of the Intel Microarchitecture Code-Named Sandy Bridge
inferredcites →https://doi.org/10.1145/2503210.2503281openalex-snapshot
Supercomputing with commodity CPUs
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1002/cpe.4688openalex-snapshot
Energy‐aware load balancing of parallel evolutionary algorithms with heavy fitness functions in heterogeneous CPU‐GPU architectures
inferred← citeshttps://doi.org/10.1007/978-981-10-8554-3_1openalex-snapshot
Power Management of Modern Processors
inferred← citeshttps://doi.org/10.1007/s11227-019-02908-4openalex-snapshot
Time-energy analysis of multilevel parallelism in heterogeneous clusters: the case of EEG classification in BCI tasks
inferred← citeshttps://doi.org/10.1016/j.future.2025.107753openalex-snapshot
Energy–time modelling of distributed multi-population genetic algorithms with dynamic workload in HPC clusters
inferred← citeshttps://doi.org/10.1089/cmb.2018.0080openalex-snapshot
A Power–Performance Perspective to Multiobjective Electroencephalogram Feature Selection on Heterogeneous Parallel Platforms
inferred← citeshttps://doi.org/10.1109/access.2019.2902353openalex-snapshot
Energy-Efficient Intra-task DVFS Scheduling Using Linear Programming Formulation
inferred← citeshttps://doi.org/10.1109/cloud.2018.00097openalex-snapshot
Improving Energy Efficiency in NFV Clouds with Machine Learning
inferred← citeshttps://doi.org/10.1109/icce46568.2020.9043140openalex-snapshot
Performance-efficient CPU resource management algorithm on Heterogeneous multi-processor
inferred← citeshttps://doi.org/10.1109/iccworkshops49005.2020.9145197openalex-snapshot
Towards Very Low-Power Mobile Terminals through Optimized Computational Offloading
inferred← citeshttps://doi.org/10.1109/isca.2018.00049openalex-snapshot
Yukta: Multilayer Resource Controllers to Maximize Efficiency
inferred← citeshttps://doi.org/10.1109/itherm.2017.7992569openalex-snapshot
An advanced energy efficient rack server design
inferred← citeshttps://doi.org/10.1109/mcs.2019.2961733openalex-snapshot
Control Systems for Computing Systems: Making computers efficient with modular, coordinated, and robust control
inferred← citeshttps://doi.org/10.1109/mm.2022.3164338openalex-snapshot
Intel Alder Lake CPU Architectures
inferred← citeshttps://doi.org/10.11606/d.3.2019.tde-30012019-100044openalex-snapshot
Dynamic CPU frequency scaling using machine learning for NFV applications.
inferred← citeshttps://doi.org/10.21203/rs.3.rs-6343058/v1openalex-snapshot
Introducing Sustainability Metrics in Cryptography: Evaluating RSA Algorithms Through Energy Profiling Tools
inferred← citeshttps://doi.org/10.30827/digibug.47374openalex-snapshot
Consumo de energía y asignaturas de arquitectura y tecnología de computadores
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