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Utilizing Hardware Performance Counters to Model and Optimize the Energy and Performance of Large Scale Scientific Applications on Power-Aware Supercomputers
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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/s00450-011-0190-0openalex-snapshot
Power-aware predictive models of hybrid (MPI/OpenMP) scientific applications on multicore systems
inferredcites →https://doi.org/10.1007/s00450-013-0239-3openalex-snapshot
E-AMOM: an energy-aware modeling and optimization methodology for scientific applications
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/10.1109/clustr.2006.311876openalex-snapshot
Performance Analysis, Modeling and Prediction of a Parallel Multiblock Lattice Boltzmann Application Using Prophesy System
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/igcc.2013.6604485openalex-snapshot
PowerInsight - A commodity power measurement capability
inferredcites →https://doi.org/10.1109/ipdps.2010.5470463openalex-snapshot
Hybrid MPI/OpenMP power-aware computing
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/micro.2003.1253186openalex-snapshot
Runtime power monitoring in high-end processors: methodology and empirical data
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.2005.39openalex-snapshot
Just In Time Dynamic Voltage Scaling: Exploiting Inter-Node Slack to Save Energy in MPI Programs
inferredcites →https://doi.org/10.1109/snpd.2013.73openalex-snapshot
MuMMI: Multiple Metrics Modeling Infrastructure
inferredcites →https://doi.org/10.1109/tc.2011.47openalex-snapshot
Complete System Power Estimation Using Processor Performance Events
inferredcites →https://doi.org/10.1109/tcsii.2013.2285966openalex-snapshot
A Study on the Use of Performance Counters to Estimate Power in Microprocessors
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/10.1145/1077603.1077657openalex-snapshot
Power prediction for intel XScale® processors using performance monitoring unit events
inferredcites →https://doi.org/10.1145/1122971.1123006openalex-snapshot
Minimizing execution time in MPI programs on an energy-constrained, power-scalable cluster
inferredcites →https://doi.org/10.1145/1183401.1183426openalex-snapshot
Online power-performance adaptation of multithreaded programs using hardware event-based prediction
inferredcites →https://doi.org/10.1145/1454115.1454151openalex-snapshot
Prediction models for multi-dimensional power-performance optimization on many cores
inferredcites →https://doi.org/10.1145/1577129.1577137openalex-snapshot
Real time power estimation and thread scheduling via performance counters
inferredcites →https://doi.org/10.1147/jrd.2012.2227580openalex-snapshot
Application-level power and performance characterization and optimization on IBM Blue Gene/Q systems
inferredcites →https://doi.org/10.1177/1094342011414749openalex-snapshot
Energy and performance characteristics of different parallel implementations of scientific applications on multicore systems
inferredcites →https://doi.org/10.1260/1748-3018.5.2.313openalex-snapshot
Parallel Simulations of Dynamic Earthquake Rupture along Geometrically Complex Faults on CMP Systems
inferredcites →https://doi.org/10.5555/956417.956567openalex-snapshot
Runtime power monitoring in high-end processors: methodology and empirical data
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.5504openalex-snapshot
PAMPAR: A new parallel benchmark for performance and energy consumption evaluation
inferred← citeshttps://doi.org/10.1002/cpe.6516openalex-snapshot
Utilizing ensemble learning for performance and power modeling and improvement of parallel cancer deep learning CANDLE benchmarks
inferred← citeshttps://doi.org/10.1007/978-3-031-22941-1_40openalex-snapshot
System for Collecting Statistics on Power Consumption of Supercomputer Applications
inferred← citeshttps://doi.org/10.1007/978-3-032-02138-0_17openalex-snapshot
A Comparative Analysis of Monolith vs Microservices Energy Consumption
inferred← citeshttps://doi.org/10.1109/access.2020.2990418openalex-snapshot
When Parallel Speedups Hit the Memory Wall
inferred← citeshttps://doi.org/10.1109/ecce47101.2021.9595258openalex-snapshot
Load power estimation using a recurrent neural network for the purpose of computer power energy efficiency improvement
inferred← citeshttps://doi.org/10.1109/etfa.2019.8869545openalex-snapshot
A Framework to Design and Implement Real-time Multicore Schedulers using Machine Learning
inferred← citeshttps://doi.org/10.1109/hpcc64274.2024.00084openalex-snapshot
CPU Power Modeling Through Training Data Selection
inferred← citeshttps://doi.org/10.1109/icac.2019.00018openalex-snapshot
Online Power Consumption Estimation for Functions in Cloud Applications
inferred← citeshttps://doi.org/10.1109/sbesc49506.2019.9046093openalex-snapshot
Anomaly Detection in Multicore Embedded Systems
inferred← citeshttps://doi.org/10.1109/sbesc51047.2020.9277864openalex-snapshot
A Fault Injection Framework for Real-time Multicore Embedded Systems
inferred← citeshttps://doi.org/10.1109/tc.2021.3056070openalex-snapshot
Online Machine Learning for Energy-Aware Multicore Real-Time Embedded Systems
inferred← citeshttps://doi.org/10.1134/s1995080220120161openalex-snapshot
The Energy Efficiency Evaluating Method Determining Energy Consumption of the Parallel Program According to Its Profile
inferred← citeshttps://doi.org/10.1145/3656402openalex-snapshot
VESTA: Power Modeling with Language Runtime Events
inferred← citeshttps://doi.org/10.1145/3727112openalex-snapshot
Diffusion-Based Generative System Surrogates for Scalable Learning-Driven Optimization in Virtual Playgrounds
inferred← citeshttps://doi.org/10.1145/3744916.3764567openalex-snapshot
Portable Power Modeling with Transfer Learning on JVM-Based Applications
inferred← citeshttps://doi.org/10.37099/mtu.dc.etdr/942openalex-snapshot
Contextual Bandit Modeling for Dynamic Runtime Control in Computer Systems
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