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A Roofline Model of Energy
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/3-540-52048-1_52openalex-snapshot
Energy-time trade-offs in VLSI computations
inferredcites →https://doi.org/10.1007/978-3-031-01721-6openalex-snapshot
Computer Architecture Techniques for Power-Efficiency
inferredcites →https://doi.org/10.1007/978-3-642-24270-0_32openalex-snapshot
Tradeoff Exploration between Reliability, Power Consumption, and Execution Time
inferredcites →https://doi.org/10.1007/s00450-010-0119-zopenalex-snapshot
A new energy aware performance metric
inferredcites →https://doi.org/10.1007/s00450-011-0190-0openalex-snapshot
Power-aware predictive models of hybrid (MPI/OpenMP) scientific applications on multicore systems
inferredcites →https://doi.org/10.1007/s00450-011-0191-zopenalex-snapshot
Profiling high performance dense linear algebra algorithms on multicore architectures for power and energy efficiency
inferredcites →https://doi.org/10.1007/s00450-012-0224-2openalex-snapshot
Power profiling of Cholesky and QR factorizations on distributed memory systems
inferredcites →https://doi.org/10.1016/0021-9991(87)90140-9openalex-snapshot
A fast algorithm for particle simulations
inferredcites →https://doi.org/10.1016/0167-8191(89)90100-2openalex-snapshot
: A parameter to characterize memory and communication bottlenecks
inferredcites →https://doi.org/10.1016/s0020-0190(00)00214-3openalex-snapshot
Towards an energy complexity of computation
inferredcites →https://doi.org/10.1109/4.535411openalex-snapshot
Energy dissipation in general purpose microprocessors
inferredcites →https://doi.org/10.1109/cgc.2012.113openalex-snapshot
Energy Footprint of Advanced Dense Numerical Linear Algebra Using Tile Algorithms on Multicore Architectures
inferredcites →https://doi.org/10.1109/cluster.2011.37openalex-snapshot
An ISO-Energy-Efficient Approach to Scalable System Power-Performance Optimization
inferredcites →https://doi.org/10.1109/ecrts.2011.17openalex-snapshot
Enhanced Race-To-Halt: A Leakage-Aware Energy Management Approach for Dynamic Priority Systems
inferredcites →https://doi.org/10.1109/greencom-cpscom.2010.138openalex-snapshot
Statistical Power and Performance Modeling for Optimizing the Energy Efficiency of Scientific Computing
inferredcites →https://doi.org/10.1109/greencom-cpscom.2010.143openalex-snapshot
Power and Performance Characterization of Computational Kernels on the GPU
inferredcites →https://doi.org/10.1109/hipc.2012.6507482openalex-snapshot
Energy-aware scheduling under reliability and makespan constraints
inferredcites →https://doi.org/10.1109/hpca.2011.5749745openalex-snapshot
A quantitative performance analysis model for GPU architectures
inferredcites →https://doi.org/10.1109/icpp.2009.74openalex-snapshot
Analysis of Parallel Algorithms for Energy Conservation in Scalable Multicore Architectures
inferredcites →https://doi.org/10.1109/iiswc.2012.6402921openalex-snapshot
Model-based, memory-centric performance and power optimization on NUMA multiprocessors
inferredcites →https://doi.org/10.1109/ipdps.2005.346openalex-snapshot
Power and Energy Profiling of Scientific Applications on Distributed Systems
inferredcites →https://doi.org/10.1109/ipdps.2006.1639600openalex-snapshot
Making a case for a Green500 list
inferredcites →https://doi.org/10.1109/ipdps.2013.32openalex-snapshot
Perfect Strong Scaling Using No Additional Energy
inferredcites →https://doi.org/10.1109/ipdpsw.2012.123openalex-snapshot
The Green Index: A Metric for Evaluating System-Wide Energy Efficiency in HPC Systems
inferredcites →https://doi.org/10.1109/ispa.2008.127openalex-snapshot
Computation with Energy-Time Trade-Offs: Models, Algorithms and Lower-Bounds
inferredcites →https://doi.org/10.1109/l-ca.2012.3openalex-snapshot
MultiAmdahl: How Should I Divide My Heterogenous Chip?
inferredcites →https://doi.org/10.1109/mc.2003.1250885openalex-snapshot
Leakage current: Moore's law meets static power
inferredcites →https://doi.org/10.1109/mc.2008.209openalex-snapshot
Amdahl's Law in the Multicore Era
inferredcites →https://doi.org/10.1109/mc.2008.494openalex-snapshot
Extending Amdahl's Law for Energy-Efficient Computing in the Many-Core Era
inferredcites →https://doi.org/10.1109/mm.2011.78openalex-snapshot
What GPU Computing Means for High-End Systems
inferredcites →https://doi.org/10.1109/mm.2011.89openalex-snapshot
GPUs and the Future of Parallel Computing
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/10.1109/sc.2005.57openalex-snapshot
Performance-constrained Distributed DVS Scheduling for Scientific Applications on Power-aware Clusters
inferredcites →https://doi.org/10.1109/tpds.2007.1026openalex-snapshot
Analyzing the Energy-Time Trade-Off in High-Performance Computing Applications
inferredcites →https://doi.org/10.1109/tpds.2009.76openalex-snapshot
PowerPack: Energy Profiling and Analysis of High-Performance Systems and Applications
inferredcites →https://doi.org/10.1109/wcnc.2005.1424799openalex-snapshot
Towards a model of energy complexity for algorithms [mobile wireless applications]
inferredcites →https://doi.org/10.1145/1555754.1555775openalex-snapshot
An analytical model for a GPU architecture with memory-level and thread-level parallelism awareness
inferredcites →https://doi.org/10.1145/1693453.1693470openalex-snapshot
An adaptive performance modeling tool for GPU architectures
inferredcites →https://doi.org/10.1145/17356.17362openalex-snapshot
Memory requirements for balanced computer architectures
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/10.1145/1837853.1693470openalex-snapshot
An adaptive performance modeling tool for GPU architectures
inferredcites →https://doi.org/10.1145/197912.197923openalex-snapshot
The hidden cost of low bandwidth communication
inferredcites →https://doi.org/10.1145/2209249.2209272openalex-snapshot
Looking back and looking forward
inferredcites →https://doi.org/10.1145/2304576.2304604openalex-snapshot
On the communication complexity of 3D FFTs and its implications for Exascale
inferredcites →https://doi.org/10.5555/1898699.1898827openalex-snapshot
Making a case for a Green500 list
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Chip‐level and multi‐node analysis of energy‐optimized lattice Boltzmann CFD simulations
inferred← citeshttps://doi.org/10.1002/cpe.4143openalex-snapshot
Evaluation of DVFS techniques on modern HPC processors and accelerators for energy‐aware applications
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Design of self‐adaptable data parallel applications on multicore clusters automatically optimized for performance and energy through load distribution
inferred← citeshttps://doi.org/10.1002/cpe.5928openalex-snapshot
A novel data partitioning algorithm for dynamic energy optimization on heterogeneous high‐performance computing platforms
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Evaluating and analyzing the energy efficiency of CNN inference on high‐performance GPU
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FPGA‐based HPC accelerators: An evaluation on performance and energy efficiency
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Performance/Energy Aware Optimization of Parallel Applications on GPUs Under Power Capping
inferred← citeshttps://doi.org/10.1007/978-3-030-66057-4_4openalex-snapshot
A Tool for Runtime Analysis of Performance and Energy Usage in NUMA Systems
inferred← citeshttps://doi.org/10.1007/978-3-030-80126-7_35openalex-snapshot
Hierarchical Roofline Performance Analysis for Deep Learning Applications
inferred← citeshttps://doi.org/10.1007/978-3-319-10214-6_12openalex-snapshot
Multi Objective Optimization of HPC Kernels for Performance, Power, and Energy
inferred← citeshttps://doi.org/10.1007/978-3-319-17248-4_7openalex-snapshot
Roofline Model Toolkit: A Practical Tool for Architectural and Program Analysis
inferred← citeshttps://doi.org/10.1007/978-3-319-32152-3_6openalex-snapshot
Energy Performance Modeling with TIA and EML
inferred← citeshttps://doi.org/10.1007/978-3-319-58667-0_22openalex-snapshot
Metrics for Energy-Aware Software Optimisation
inferred← citeshttps://doi.org/10.1007/978-3-319-78054-2_11openalex-snapshot
Performance and Energy Analysis of Scientific Workloads Executing on LPSoCs
inferred← citeshttps://doi.org/10.1007/978-3-642-55224-3_73openalex-snapshot
Measuring the Sensitivity of Graph Metrics to Missing Data
inferred← citeshttps://doi.org/10.1007/s00450-014-0263-yopenalex-snapshot
Are our dense linear algebra libraries energy-friendly?
inferred← citeshttps://doi.org/10.1007/s00450-014-0269-5openalex-snapshot
Measuring energy consumption using EML (energy measurement library)
inferred← citeshttps://doi.org/10.1007/s00466-023-02427-3openalex-snapshot
Knowledge-based modeling of simulation behavior for Bayesian optimization
inferred← citeshttps://doi.org/10.1007/s10586-016-0686-2openalex-snapshot
Energy aware scheduling model and online heuristics for stencil codes on heterogeneous computing architectures
inferred← citeshttps://doi.org/10.1007/s11227-016-1654-6openalex-snapshot
Time and energy modeling of a high-performance multi-threaded Cholesky factorization
inferred← citeshttps://doi.org/10.1016/j.cpc.2020.107521openalex-snapshot
Efficient update of determinants for many-electron wave function overlaps
inferred← citeshttps://doi.org/10.1016/j.future.2017.03.009openalex-snapshot
Tuning linear algebra for energy efficiency on multicore machines by adapting the ATLAS library
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FFT, FMM, and multigrid on the road to exascale: Performance challenges and opportunities
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High-performance dynamic elastic pipelines
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Time and energy modeling of high–performance Level-3 BLAS on x86 architectures
inferred← citeshttps://doi.org/10.1016/j.suscom.2021.100530openalex-snapshot
PortAuthority: Integrating energy efficiency analysis into cross-platform development cycles via dynamic program analysis
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Uncovering input-sensitive energy bottlenecks in oversubscribed GPU workloads
inferred← citeshttps://doi.org/10.1088/1757-899x/1152/1/012021openalex-snapshot
Performance Optimization on GPGPU & Multicore CPU Using Roofline Model
inferred← citeshttps://doi.org/10.1109/access.2019.2903574openalex-snapshot
A Unified Approach for Compression and Authentication of Smart Meter Reading in AMI
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An Energy-Efficient Edge Computing Paradigm for Convolution-Based Image Upsampling
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A Cross-Layer Survey of Low-Power Machine Learning From Algorithm to Hardware
inferred← citeshttps://doi.org/10.1109/ccgrid49817.2020.00-70openalex-snapshot
Energy efficiency and performance modeling of stencil applications on manycore and GPU computing resources
inferred← citeshttps://doi.org/10.1109/cgo68049.2026.11395211openalex-snapshot
PolyUFC: Polyhedral Compilation Meets Roofline Analysis for Uncore Frequency Capping
inferred← citeshttps://doi.org/10.1109/dls51937.2020.00007openalex-snapshot
Time-Based Roofline for Deep Learning Performance Analysis
inferred← citeshttps://doi.org/10.1109/e2sc.2014.7openalex-snapshot
Measurement and Interpretation of Micro-benchmark and Application Energy Use on the Cray XC30
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Hybrid Bonding Interconnect for Advanced Heterogeneously Integrated Processors
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Parallel Performance-Energy Predictive Modeling of Browsers: Case Study of Servo
inferred← citeshttps://doi.org/10.1109/iccworkshops63917.2026.11586279openalex-snapshot
Latency-Centric RAN Orchestration with World Model for Satellite-Assisted Mobile Edge Computing
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Parallelizing Stream Compression for IoT Applications on Asymmetric Multicores
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SpikeRL: A Scalable and Energy-efficient Framework for Deep Spiking Reinforcement Learning*
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ICE: A General and Validated Energy Complexity Model for Multithreaded Algorithms
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Analytical Energy Models for MPI Communications on a Sandy-Bridge Architecture
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A Pareto Framework for Data Analytics on Heterogeneous Systems: Implications for Green Energy Usage and Performance
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POSE: A mathematical and visual modelling tool to guide energy aware code optimisation
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Understanding the impact of object oriented programming and design patterns on energy efficiency
inferred← citeshttps://doi.org/10.1109/iiswc.2014.6983061openalex-snapshot
Extending the roofline model: Bottleneck analysis with microarchitectural constraints
inferred← citeshttps://doi.org/10.1109/informatics.2015.7377807openalex-snapshot
Impact of GCC optimization levels in energy consumption during C/C++ program execution
inferred← citeshttps://doi.org/10.1109/ipdps.2014.54openalex-snapshot
Algorithmic Time, Energy, and Power on Candidate HPC Compute Building Blocks
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An Approach for Energy Efficient Execution of Hybrid Parallel Programs
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An Early Performance Study of Large-Scale POWER8 SMP Systems
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Reducing Pagerank Communication via Propagation Blocking
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An Energy-Efficient Single-Source Shortest Path Algorithm
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Introducing Application Awareness Into a Unified Power Management Stack
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RLP: Power Management Based on a Latency-Aware Roofline Model
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Power Constrained Autotuning using Graph Neural Networks
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Toward Energy-Efficient HPC: Insights from Power Profiling a Cloud-Resolving Earth System Model
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A Roofline-Based Performance Estimator for Distributed Matrix-Multiply on Intel CnC
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Analyzing the Energy Efficiency of the Fast Multipole Method Using a DVFS-Aware Energy Model
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Power-aware Computing with Optane Persistent Memory Modules
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Eosinophil Counting with Green Learning
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Applying the roofline model
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Exploring GPU performance, power and energy-efficiency bounds with Cache-aware Roofline Modeling
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EnergAIzer: Fast and Accurate GPU Power Estimation Framework for AI Workloads
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Technology Prospects for Data-Intensive Computing
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How to Keep Pushing ML Accelerator Performance? Know Your Rooflines!
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Prometheus: Coherent Exploration of Hardware and Software Optimizations Using Aspen
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Roofline Model Based Performance-Aware Energy Management for Scientific Computing
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Impact of Frequency Scaling on One Sided Remote Memory Accesses
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The Performance and Energy Efficiency Potential of FPGAs in Scientific Computing
inferred← citeshttps://doi.org/10.1109/pmbs56514.2022.00010openalex-snapshot
Going green: optimizing GPUs for energy efficiency through model-steered auto-tuning
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Power models supporting energy-efficient co-design on ultra-low power embedded systems
inferred← citeshttps://doi.org/10.1109/sbac-pad49847.2020.00035openalex-snapshot
Predicting the Energy Consumption of CUDA Kernels using SimGrid
inferred← citeshttps://doi.org/10.1109/sc.2016.56openalex-snapshot
GreenLA: Green Linear Algebra Software for GPU-accelerated Heterogeneous Computing
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Energy Efficiency Modeling of Parallel Applications
inferred← citeshttps://doi.org/10.1109/scw63240.2024.00047openalex-snapshot
Shifting Between Compute and Memory Bounds: A Compression-Enabled Roofline Model
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P-MoVE: Performance Monitoring and Visualization with Encoded Knowledge
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A power modelling approach for many-core architectures
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BA-GUSL: Boundary-Aware Green U-Shaped Learning for Nuclei Segmentation
inferred← citeshttps://doi.org/10.1109/swc62898.2024.00277openalex-snapshot
Optimal partitioning method among heterogeneous edge nodes in CNN models
inferred← citeshttps://doi.org/10.1109/tc.2016.2582151openalex-snapshot
Beyond the Roofline: Cache-Aware Power and Energy-Efficiency Modeling for Multi-Cores
inferred← citeshttps://doi.org/10.1109/tc.2017.2742513openalex-snapshot
Bi-Objective Optimization of Data-Parallel Applications on Homogeneous Multicore Clusters for Performance and Energy
inferred← citeshttps://doi.org/10.1109/tc.2020.3015531openalex-snapshot
Modeling Data Reuse in Deep Neural Networks by Taking Data-Types into Cognizance
inferred← citeshttps://doi.org/10.1109/tcad.2025.3528349openalex-snapshot
Spatz: Clustering Compact RISC-V-Based Vector Units to Maximize Computing Efficiency
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Multicore-Optimized Wavefront Diamond Blocking for Optimizing Stencil Updates
inferred← citeshttps://doi.org/10.1145/2425676.2425691openalex-snapshot
How much (execution) time and energy does my algorithm cost?
inferred← citeshttps://doi.org/10.1145/2576779.2576783openalex-snapshot
Measuring GPU Power with the K20 Built-in Sensor
inferred← citeshttps://doi.org/10.1145/2611758openalex-snapshot
Power Modeling for GPU Architectures Using McPAT
inferred← citeshttps://doi.org/10.1145/2693656openalex-snapshot
On Using the Roofline Model with Lower Bounds on Data Movement
inferred← citeshttps://doi.org/10.1145/2834899.2834903openalex-snapshot
Modeling performance and energy for applications offloaded to Intel Xeon Phi
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Performance Analysis of Applications in the Context of Architectural Rooflines
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A Survey of Power and Energy Predictive Models in HPC Systems and Applications
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Towards fine-grained dynamic tuning of HPC applications on modern multi-core architectures
inferred← citeshttps://doi.org/10.1145/3152041.3152087openalex-snapshot
Verification of the Extended Roofline Model for Asynchronous Many Task Runtimes
inferred← citeshttps://doi.org/10.1145/3155290openalex-snapshot
Multidimensional Intratile Parallelization for Memory-Starved Stencil Computations
inferred← citeshttps://doi.org/10.1145/3293320.3293321openalex-snapshot
An Extended Roofline Model with Communication-Awareness for Distributed-Memory HPC Systems
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The Power-optimised Software Envelope
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System evaluation of the Intel optane byte-addressable NVM
inferred← citeshttps://doi.org/10.1145/3387902.3392613openalex-snapshot
Verified instruction-level energy consumption measurement for NVIDIA GPUs
inferred← citeshttps://doi.org/10.1145/3410463.3414651openalex-snapshot
Intelligent Data Placement on Discrete GPU Nodes with Unified Memory
inferred← citeshttps://doi.org/10.1145/3431731openalex-snapshot
A Simple Model for Portable and Fast Prediction of Execution Time and Power Consumption of GPU Kernels
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Power Analysis of NERSC Production Workloads
inferred← citeshttps://doi.org/10.1145/3712285.3759815openalex-snapshot
Benchmark-driven Models for Energy Analysis and Attribution of GPU-Accelerated Supercomputing
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Quantifying AWS S3 I/O Performance Boundaries Using the Roofline Model
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Modelling Load Imbalance In Shared Memory Multicore Systems
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A survey on software methods to improve the energy efficiency of parallel computing
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Effect of frequency scaling granularity on energy-saving strategies
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Statistical and machine learning models for optimizing energy in parallel applications
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Editorial to special issue on energy efficient architectures for embedded systems
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Compressive sensing based power signal compression in advanced metering infrastructure
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C6EnPLS: A High-Performance Computing Job Dataset for the Analysis of Linear Solvers’ Power Consumption
inferred← citeshttps://doi.org/10.3390/jimaging12070316openalex-snapshot
NS-GUSL: Green U-Shaped Learning for Nuclei Segmentation from Histopathology Images
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A Roofline Visualization Framework
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8 Steps to 3.7 TFLOP/s on NVIDIA V100 GPU: Roofline Analysis and Other Tricks
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Hierarchical Roofline Analysis: How to Collect Data using Performance Tools on Intel CPUs and NVIDIA GPUs
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Stratégies de parallélisation espace-temps pour la simulation numérique des écoulements turbulents
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