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Modeling Power and Energy Usage of HPC Kernels
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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An Approach to Performance Prediction for Parallel Applications
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Modern Applied Statistics with S
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Loop Transformation Recipes for Code Generation and Auto-Tuning
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Reducing Energy Usage with Memory and Computation-Aware Dynamic Frequency Scaling
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Power-aware predictive models of hybrid (MPI/OpenMP) scientific applications on multicore systems
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Artificial neural networks: fundamentals, computing, design, and application
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Artificial neural networks: a tutorial
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Portable, scalable, per-core power estimation for intelligent resource management
inferredcites →https://doi.org/10.1109/hpcmp-ugc.2010.54openalex-snapshot
Fine-Grained Energy Consumption Characterization and Modeling
inferredcites →https://doi.org/10.1109/intera.2003.1192355openalex-snapshot
The effect of compiler optimizations on Pentium 4 power consumption
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A scalable auto-tuning framework for compiler optimization
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Automatic Recognition of Performance Idioms in Scientific Applications
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Complete System Power Estimation: A Trickle-Down Approach Based on Performance Events
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Automatically Tuned Linear Algebra Software
inferredcites →https://doi.org/10.1109/secon.2010.5453824openalex-snapshot
PowerMon: Fine-grained and integrated power monitoring for commodity computer systems
inferredcites →https://doi.org/10.1145/1168857.1168881openalex-snapshot
Accurate and efficient regression modeling for microarchitectural performance and power prediction
inferredcites →https://doi.org/10.1145/1229428.1229479openalex-snapshot
Methods of inference and learning for performance modeling of parallel applications
inferredcites →https://doi.org/10.1145/1542275.1542350openalex-snapshot
Prediction-based power estimation and scheduling for CMPs
inferredcites →https://doi.org/10.1145/1944862.1944880openalex-snapshot
Automated empirical tuning of scientific codes for performance and power consumption
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Feedforward Neural Network Construction Using Cross Validation
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Building Predictive Models in R Using the caret Package
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Automatically Tuned Linear Algebra Software
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Estimating the Execution Time of the Coupled Stage in Multiscale Numerical Simulations
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Modeling the Impact of Reduced Memory Bandwidth on HPC Applications
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Multi Objective Optimization of HPC Kernels for Performance, Power, and Energy
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AutoMOMML: Automatic Multi-objective Modeling with Machine Learning
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Exploring Energy Efficiency for GPU-Accelerated POWER Servers
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Seven Pillars to Achieve Energy Efficiency in High-Performance Computing Data Centers
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Artificial Intelligence: Sustainability Challenges, Opportunities, and the Case of 6G
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System-wide trade-off modeling of performance, power, and resilience on petascale systems
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Performance modeling of the sparse matrix–vector product via convolutional neural networks
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Quantitative modeling of power performance tradeoffs on extreme scale systems
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A survey of power and energy efficient techniques for high performance numerical linear algebra operations
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Program optimizations: The interplay between power, performance, and energy
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Towards Green Multi-frontal Solver for Adaptive Finite Element Method
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Small scale to extreme: Methods for characterizing energy efficiency in supercomputing applications
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Low learning-cost offline strategies for EDP optimization of parallel applications
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Improving the energy efficiency of data-intensive applications running on clusters
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Active-learning-based surrogate models for empirical performance tuning
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Optimizing Explicit Hydrodynamics for Power, Energy, and Performance
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Improving the Energy Efficiency for Parallel Applications Running on Clusters
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Using Early Power and Timing Estimations of Massively Heterogeneous Computation Platforms to Create Optimized HPC Applications
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Support for Power Efficient Proactive Cooling Mechanisms
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Application Modeling for Scalable Simulation of Massively Parallel Systems
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Split Wisely: When Work Partitioning is Energy-Optimal on Heterogeneous Hardware
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FIFO: Fuzzy Cluster Identification and High-dimensional Feature Clustering Optimization Based CPU Power Sampling Optimization
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CPU Power Modeling Through Training Data Selection
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Node level power measurements on a petaflop system
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Application of energy reduction techniques using niched pareto GA of energy analzyer for HPC applications
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DVFS automatic tuning plugin for energy related tuning objectives
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Building Blocks for a System-Wide Power and Thermal Management Framework
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Evaluating energy and power profiling techniques for HPC workloads
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Inferring Large-Scale Computation Behavior via Trace Extrapolation
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Characterizing the Impact of Program Optimizations on Power and Energy for Explicit Hydrodynamics
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Predicting Optimal Power Allocation for CPU and DRAM Domains
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OpenMP and MPI application energy measurement variation
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Performance and Power Characteristics and Optimizations of Hybrid MPI/OpenMP LULESH Miniapps under Various Workloads
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Adaptive and polymorphic VLIW processor to optimize fault tolerance, energy consumption, and performance
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Effectiveness of Neural Networks for Power Modeling for Cloud and HPC
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Power Analysis of NERSC Production Workloads
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Convolutional neural nets for estimating the run time and energy consumption of the sparse matrix-vector product
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A Study on Cross-Architectural Modelling of Power Consumption Using Neural Networks
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Modeling energy consumption of parallel applications
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Empirical performance modeling of GPU kernels using active learning
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Energy-Aware System-Level Design of Cyber-Physical Systems
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