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Using Performance-Power Modeling to Improve Energy Efficiency of HPC Applications

https://doi.org/10.1109/mc.2016.311
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-319-08144-1_5openalex-snapshot
MuMMI: Multiple Metrics Modeling Infrastructure
inferredcites →https://doi.org/10.1007/978-3-642-36949-0_50openalex-snapshot
Performance Patterns and Hardware Metrics on Modern Multicore Processors: Best Practices for Performance Engineering
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/10.1109/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/ipdps.2010.5470463openalex-snapshot
Hybrid MPI/OpenMP power-aware computing
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/10.1145/1077603.1077657openalex-snapshot
Power prediction for intel XScale® processors using performance monitoring unit events
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.2012.2227034openalex-snapshot
Design for low power and power management in IBM Blue Gene/Q
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

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.6516openalex-snapshot
Utilizing ensemble learning for performance and power modeling and improvement of parallel cancer deep learning CANDLE benchmarks
inferred← citeshttps://doi.org/10.1002/cpe.7254openalex-snapshot
Performance and power modeling and prediction using MuMMI and 10 machine learning methods
inferred← citeshttps://doi.org/10.1002/cpe.8322openalex-snapshot
ytopt: Autotuning Scientific Applications for Energy Efficiency at Large Scales
inferred← citeshttps://doi.org/10.1002/spe.3139openalex-snapshot
DEPO: A dynamic energy‐performance optimizer tool for automatic power capping for energy efficient high‐performance computing
inferred← citeshttps://doi.org/10.1007/978-3-030-41050-6_5openalex-snapshot
SMCis: Scientific Applications Monitoring and Prediction for HPC Environments
inferred← citeshttps://doi.org/10.1007/978-981-95-8405-5_24openalex-snapshot
Machine Learning and Data Statistics Based Scalability Bottleneck Detection
inferred← citeshttps://doi.org/10.1007/s10586-023-04151-2openalex-snapshot
Energy efficient power cap configurations through Pareto front analysis and machine learning categorization
inferred← citeshttps://doi.org/10.1007/s11227-021-04273-7openalex-snapshot
Characterizing energy and performance of soft-core-based homogeneous multiprocessor systems
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/10.1016/j.jpdc.2021.01.007openalex-snapshot
Improving the accuracy of energy predictive models for multicore CPUs by combining utilization and performance events model variables
inferred← citeshttps://doi.org/10.1109/access.2020.3013812openalex-snapshot
A Comparative Study of Techniques for Energy Predictive Modeling Using Performance Monitoring Counters on Modern Multicore CPUs
inferred← citeshttps://doi.org/10.1109/access.2021.3075139openalex-snapshot
Energy Predictive Models of Computing: Theory, Practical Implications and Experimental Analysis on Multicore Processors
inferred← citeshttps://doi.org/10.1109/bdcloud.2018.00091openalex-snapshot
Contract-Based Demand Response Model for High Performance Computing Systems
inferred← citeshttps://doi.org/10.1109/cluster49012.2020.00068openalex-snapshot
Toward an End-to-End Auto-tuning Framework in HPC PowerStack
inferred← citeshttps://doi.org/10.1109/ipdps47924.2020.00087openalex-snapshot
What does Power Consumption Behavior of HPC Jobs Reveal? : Demystifying, Quantifying, and Predicting Power Consumption Characteristics
inferred← citeshttps://doi.org/10.1109/mascots.2017.25openalex-snapshot
An Energy Efficient Demand-Response Model for High Performance Computing Systems
inferred← citeshttps://doi.org/10.1109/mchpc54807.2021.00009openalex-snapshot
Performance and Energy Improvement of ECP Proxy App SW4lite under Various Workloads
inferred← citeshttps://doi.org/10.1109/pmbs54543.2021.00011openalex-snapshot
Understanding power variation and its implications on performance optimization on the Cori supercomputer
inferred← citeshttps://doi.org/10.1109/sbac-pad55451.2022.00043openalex-snapshot
STEER: Asymmetry-aware Energy Efficient Task Scheduler for Cluster-based Multicore Architectures
inferred← citeshttps://doi.org/10.1109/tpds.2020.3045983openalex-snapshot
Fine-grained Powercap Allocation for Power-constrained Systems based on Multi-objective Machine Learning
inferred← citeshttps://doi.org/10.1145/3149412.3149416openalex-snapshot
Performance and Power Characteristics and Optimizations of Hybrid MPI/OpenMP LULESH Miniapps under Various Workloads
inferred← citeshttps://doi.org/10.1145/3225058.3225088openalex-snapshot
Energy-efficient Application Resource Scheduling using Machine Learning Classifiers
inferred← citeshttps://doi.org/10.1145/3321551openalex-snapshot
The Power-optimised Software Envelope
inferred← citeshttps://doi.org/10.1145/3337821.3337905openalex-snapshot
Performance, Energy, and Scalability Analysis and Improvement of Parallel Cancer Deep Learning CANDLE Benchmarks
inferred← citeshttps://doi.org/10.1145/3380868.3398199openalex-snapshot
Power modeling for Phytium FT-2000+/64 multi-core architecture
inferred← citeshttps://doi.org/10.1145/3605573.3605586openalex-snapshot
JOSS: Joint Exploration of CPU-Memory DVFS and Task Scheduling for Energy Efficiency
inferred← citeshttps://doi.org/10.1145/3624062.3624200openalex-snapshot
Power Analysis of NERSC Production Workloads
inferred← citeshttps://doi.org/10.1155/2019/8348791openalex-snapshot
Energy-Aware High-Performance Computing: Survey of State-of-the-Art Tools, Techniques, and Environments
inferred← citeshttps://doi.org/10.25148/etd.fidc006527openalex-snapshot
Energy Demand Response for High-Performance Computing Systems
inferred← citeshttps://doi.org/10.3390/app15158570openalex-snapshot
Investigating Supercomputer Performance with Sustainability in the Era of Artificial Intelligence
inferred← citeshttps://doi.org/10.48550/arxiv.2011.06655openalex-snapshot
Performance and Power Modeling and Prediction Using MuMMI and Ten Machine Learning Methods
inferred← citeshttps://doi.org/10.5753/bresci.2020.11185openalex-snapshot
Avaliação do Consumo de Energia e o Impacto da emissão de CO2 para algoritmos de Inteligência Artificial
inferred← citeshttps://doi.org/10.5753/wscad.2020.14068openalex-snapshot
Em Busca de uma Inteligência Artificial Ecologicamente Viável: Um estudo de caso do Consumo Energético de Algoritmos de íArvore de Decisão
inferred← citeshttps://doi.org/10.5753/wscad.2021.18513openalex-snapshot
Predicting Runtime in HPC Environments for an Efficient Use of Computational Resources
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