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Machine learning method for energy reduction by utilizing dynamic mixed precision on GPU‐based 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/10.1002/cpe.3219openalex-snapshot
Modeling and analyzing the energy consumption of fork‐join‐based task parallel programs
inferredcites →https://doi.org/10.1002/cpe.3970openalex-snapshot
Systematic adaptation of stencil‐based 3D MPDATA to GPU architectures
inferredcites →https://doi.org/10.1002/cpe.4016openalex-snapshot
Energy‐aware mechanism for stencil‐based MPDATA algorithm with constraints
inferredcites →https://doi.org/10.1002/fld.1071openalex-snapshot
Multidimensional positive definite advection transport algorithm: an overview
inferredcites →https://doi.org/10.1007/978-3-319-21909-7_43openalex-snapshot
Parallelization of 3D MPDATA Algorithm Using Many Graphics Processors
inferredcites →https://doi.org/10.1007/978-3-642-31500-8_40openalex-snapshot
Parallelization of EULAG Model on Multicore Architectures with GPU Accelerators
inferredcites →https://doi.org/10.1007/978-3-662-43880-0_52openalex-snapshot
Towards Efficient Decomposition and Parallelization of MPDATA on Hybrid CPU-GPU Cluster
inferredcites →https://doi.org/10.1007/s00450-011-0188-7openalex-snapshot
DVFS-control techniques for dense linear algebra operations on multi-core processors
inferredcites →https://doi.org/10.1007/s10586-014-0402-zopenalex-snapshot
Assessing the impact of the CPU power-saving modes on the task-parallel solution of sparse linear systems
inferredcites →https://doi.org/10.1007/s11227-015-1600-zopenalex-snapshot
Adapting concurrency throttling and voltage–frequency scaling for dense eigensolvers
inferredcites →https://doi.org/10.1007/s11227-016-1774-zopenalex-snapshot
Performance modeling of 3D MPDATA simulations on GPU cluster
inferredcites →https://doi.org/10.1007/s11227-017-2020-zopenalex-snapshot
Modeling power consumption of 3D MPDATA and the CG method on ARM and Intel multicore architectures
inferredcites →https://doi.org/10.1016/j.parco.2014.03.005openalex-snapshot
Energy profile of rollback-recovery strategies in high performance computing
inferredcites →https://doi.org/10.1109/cluster.2010.15openalex-snapshot
Energy-Aware Scheduling in Virtualized Datacenters
inferredcites →https://doi.org/10.1109/cluster.2013.6702684openalex-snapshot
Optimizing power allocation to CPU and memory subsystems in overprovisioned HPC systems
inferredcites →https://doi.org/10.1109/greencomp.2010.5598316openalex-snapshot
Characterizing the energy consumption of data transfers and arithmetic operations on x86−64 processors
inferredcites →https://doi.org/10.1109/icppw.2014.30openalex-snapshot
Performance and Energy-Aware Characterization of the Sparse Matrix-Vector Multiplication on Multithreaded Architectures
inferredcites →https://doi.org/10.1109/tc.2016.2582151openalex-snapshot
Beyond the Roofline: Cache-Aware Power and Energy-Efficiency Modeling for Multi-Cores
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/10.1145/1592568.1592584openalex-snapshot
Cutting the electric bill for internet-scale systems
inferredcites →https://doi.org/10.1145/1787275.1787338openalex-snapshot
Interval-based models for run-time DVFS orchestration in superscalar processors
inferredcites →https://doi.org/10.1145/1791314.1791349openalex-snapshot
Towards energy-aware scheduling in data centers using machine learning
inferredcites →https://doi.org/10.1145/2464996.2465018openalex-snapshot
Automatically adapting programs for mixed-precision floating-point computation
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/10.1145/2636342openalex-snapshot
A Survey of Methods for Analyzing and Improving GPU Energy Efficiency
inferredcites →https://doi.org/10.1145/2661642openalex-snapshot
A Model for Context in the Design of Open Production Communities
inferredcites →https://doi.org/10.1145/3148226.3148237openalex-snapshot
Investigating half precision arithmetic to accelerate dense linear system solvers
inferredcites →https://doi.org/10.1155/2014/818579openalex-snapshot
From Single- to Multi-Objective Auto-Tuning of Programs: Advantages and Implications
inferredcites →https://doi.org/10.1201/b10376openalex-snapshot
Scientific Computing with Multicore and Accelerators
inferredcites →https://doi.org/10.3233/978-1-61499-843-3-297openalex-snapshot
Implications of Reduced-Precision Computations in HPC: Performance, Energy and Error
inferredcites →https://doi.org/10.48550/arxiv.1410.5561openalex-snapshot
Towards energy efficiency and maximum computational intensity for stencil algorithms using wavefront diamond temporal blocking
inferredcites →https://doi.org/10.48550/arxiv.1606.00251openalex-snapshot
Profile-Driven Automated Mixed Precision
inferredcites →https://doi.org/10.7763/ijmo.2015.v5.456openalex-snapshot
Adaptation of Multidimensional Positive Definite Advection Transport Algorithm to Modern High-Performance Computing Platforms
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.5130openalex-snapshot
Cerebral micro‐bleeding identification based on a nine‐layer convolutional neural network with stochastic pooling
inferred← citeshttps://doi.org/10.1002/cpe.8326openalex-snapshot
Auto‐Tuning Mixed‐Precision Computation by Specifying Multiple Regions
inferred← citeshttps://doi.org/10.1007/978-3-030-43222-5_11openalex-snapshot
Performance/Energy Aware Optimization of Parallel Applications on GPUs Under Power Capping
inferred← citeshttps://doi.org/10.1007/978-3-031-10542-5_39openalex-snapshot
Impact of Reduced and Mixed-Precision on the Efficiency of a Multi-GPU Platform on CFD Applications
inferred← citeshttps://doi.org/10.1007/978-3-031-36021-3_50openalex-snapshot
Chemical Mixing Simulations with Integrated AI Accelerator
inferred← citeshttps://doi.org/10.1007/s11227-019-03034-xopenalex-snapshot
An study of the effect of process malleability in the energy efficiency on GPU-based clusters
inferred← citeshttps://doi.org/10.1109/bigdata52589.2021.9671377openalex-snapshot
Reducing numerical precision preserves classification accuracy in Mondrian Forests
inferred← citeshttps://doi.org/10.1109/hpec67600.2025.11196214openalex-snapshot
Enhancing the Real-Time Solutions of Parametric Linear Systems on a GPU through Hybrid Coarse-Grained Transprecision Computing
inferred← citeshttps://doi.org/10.1109/tpds.2020.2996314openalex-snapshot
Correlation of Performance Optimizations and Energy Consumption for Stencil-Based Application on Intel Xeon Scalable Processors
inferred← citeshttps://doi.org/10.1145/3381039openalex-snapshot
Tools for Reduced Precision Computation
inferred← citeshttps://doi.org/10.1177/1094342020972461openalex-snapshot
CFD code adaptation to the FPGA architecture
inferred← citeshttps://doi.org/10.70675/d357297fzb90az40aezad7azae2b17ff34ffopenalex-snapshot
Fast and efficient bit-level precision tuning
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