On the public citation graph
Online Power Estimation of Graphics Processing Units
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1109/greencomp.2010.5598315openalex-snapshot
Statistical power modeling of GPU kernels using performance counters
inferredcites →https://doi.org/10.1109/hpca.2006.1598119openalex-snapshot
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
inferredcites →https://doi.org/10.1109/icpp.2014.46openalex-snapshot
Adaptive Configuration Selection for Power-Constrained Heterogeneous Systems
inferredcites →https://doi.org/10.1109/igcc.2013.6604488openalex-snapshot
Statistical modeling of power/energy of scientific kernels on a multi-GPU system
inferredcites →https://doi.org/10.1109/igcc.2013.6604520openalex-snapshot
Trends in energy-efficient computing: A perspective from the Green500
inferredcites →https://doi.org/10.1109/iiswc.2009.5306797openalex-snapshot
Rodinia: A benchmark suite for heterogeneous computing
inferredcites →https://doi.org/10.1109/iiswc.2012.6402920openalex-snapshot
CHAOS: Composable Highly Accurate OS-based power models
inferredcites →https://doi.org/10.1109/ipdps.2013.73openalex-snapshot
A Simplified and Accurate Model of Power-Performance Efficiency on Emergent GPU Architectures
inferredcites →https://doi.org/10.1109/ipdps.2014.23openalex-snapshot
Power and Performance Characterization and Modeling of GPU-Accelerated Systems
inferredcites →https://doi.org/10.1109/mc.2007.445openalex-snapshot
The Green500 List: Encouraging Sustainable Supercomputing
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/sc.2005.3openalex-snapshot
A Power-Aware Run-Time System for High-Performance Computing
inferredcites →https://doi.org/10.1109/tc.2011.47openalex-snapshot
Complete System Power Estimation Using Processor Performance Events
inferredcites →https://doi.org/10.1145/1077603.1077657openalex-snapshot
Power prediction for intel XScale® processors using performance monitoring unit events
inferredcites →https://doi.org/10.1145/1577129.1577137openalex-snapshot
Real time power estimation and thread scheduling via performance counters
inferredcites →https://doi.org/10.1145/1735688.1735702openalex-snapshot
The Scalable Heterogeneous Computing (SHOC) benchmark suite
inferredcites →https://doi.org/10.1145/1815961.1815998openalex-snapshot
An integrated GPU power and performance model
inferredcites →https://doi.org/10.1145/1816038.1815998openalex-snapshot
An integrated GPU power and performance model
inferredcites →https://doi.org/10.1145/2464996.2465009openalex-snapshot
Exploring hardware overprovisioning in power-constrained, high performance computing
inferredcites →https://doi.org/10.1145/2576779.2576783openalex-snapshot
Measuring GPU Power with the K20 Built-in Sensor
inferredcites →https://doi.org/10.1145/2588768.2576783openalex-snapshot
Measuring GPU Power with the K20 Built-in Sensor
inferredcites →https://doi.org/10.1145/2749246.2749262openalex-snapshot
Practical Resource Management in Power-Constrained, High Performance Computing
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.5124openalex-snapshot
Rapid and accurate energy models through calibration with IPMI and RAPL
inferred← citeshttps://doi.org/10.1109/access.2019.2951218openalex-snapshot
GPU Static Modeling Using PTX and Deep Structured Learning
inferred← citeshttps://doi.org/10.1109/hpca.2018.00072openalex-snapshot
GPGPU Power Modeling for Multi-domain Voltage-Frequency Scaling
inferred← citeshttps://doi.org/10.1109/inocon60754.2024.10512242openalex-snapshot
A Performance and Power Comparison of Contemporary GPGPU Architectures
inferred← citeshttps://doi.org/10.1109/ispass64960.2025.00019openalex-snapshot
FinGraV: Methodology for Fine-Grain GPU Power Visibility and Insights
inferred← citeshttps://doi.org/10.1109/smc42975.2020.9283092openalex-snapshot
Power and Performance Analysis of Deep Neural Networks for Energy-aware Heterogeneous Systems
inferred← citeshttps://doi.org/10.1109/tpds.2019.2917181openalex-snapshot
Modeling and Decoupling the GPU Power Consumption for Cross-Domain DVFS
inferred← citeshttps://doi.org/10.1109/tpds.2020.3004623openalex-snapshot
GPGPU Performance Estimation With Core and Memory Frequency Scaling
inferred← citeshttps://doi.org/10.1109/tpds.2022.3181096openalex-snapshot
Energy-Aware Non-Preemptive Task Scheduling With Deadline Constraint in DVFS-Enabled Heterogeneous Clusters
inferred← citeshttps://doi.org/10.1109/tpds.2025.3637511openalex-snapshot
Concurrent and Orthogonal Software Power Meters for Accurate Runtime Energy Profiling of Parallel Hybrid Programs on Heterogeneous Hybrid Servers
inferred← citeshttps://doi.org/10.1109/wscad.2018.00034openalex-snapshot
Performance and Energy Efficiency Evaluation for HPC Applications in Heterogeneous Architectures
inferred← citeshttps://doi.org/10.1145/3203217.3203273openalex-snapshot
GPU power prediction via ensemble machine learning for DVFS space exploration
inferred← citeshttps://doi.org/10.1145/3705956.3705962openalex-snapshot
Resource Aware power modelling for compute-bound tasks on GPUs
inferred← citeshttps://doi.org/10.32920/26883748openalex-snapshot
Power and Performance Based Autotuning of Heterogeneous Applications for CPU-GPU Systems
inferred← citeshttps://doi.org/10.32920/26883748.v1openalex-snapshot
Power and Performance Based Autotuning of Heterogeneous Applications for CPU-GPU Systems
inferred← citeshttps://doi.org/10.3390/app14062385openalex-snapshot
Agnostic Energy Consumption Models for Heterogeneous GPUs in Cloud Computing
inferred← citeshttps://doi.org/10.48550/arxiv.2104.00486openalex-snapshot
Energy-aware Task Scheduling with Deadline Constraint in DVFS-enabled Heterogeneous Clusters
inferred← citeshttps://doi.org/10.5220/0006470803380345openalex-snapshot
Modular Modelling of an Embedded Mobile CPU-GPU Chip for Feature Estimation
inferred← citeshttps://doi.org/10.5753/wperformance.2017.3360openalex-snapshot
Analysis of GPU Power Consumption Using Internal Sensors
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