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Mitigating Processor Variation through Dynamic Load Balancing
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1109/icppw.2012.33openalex-snapshot
Efficient 'Cool Down' of Parallel Applications
inferredcites →https://doi.org/10.1109/iiswc.2009.5306782openalex-snapshot
Evaluation of the Intel® Core™ i7 Turbo Boost feature
inferredcites →https://doi.org/10.1109/ipdps.2015.37openalex-snapshot
Minimizing Thermal Variation Across System Components
inferredcites →https://doi.org/10.1145/1283780.1283826openalex-snapshot
Thermal-aware task scheduling at the system software level
inferredcites →https://doi.org/10.1145/2712386.2712396openalex-snapshot
Energy-efficient computing for HPC workloads on heterogeneous manycore chips
inferredcites →https://doi.org/10.1145/2742351openalex-snapshot
Noise-Tolerant Explicit Stencil Computations for Nonuniform Process Execution Rates
inferredcites →https://doi.org/10.1145/2807591.2807638openalex-snapshot
Analyzing and mitigating the impact of manufacturing variability in power-constrained supercomputing
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1002/ett.4829openalex-snapshot
Energy‐efficient load balancing for divisible tasks on heterogeneous clusters
inferred← citeshttps://doi.org/10.1007/978-3-030-17872-7_13openalex-snapshot
Visualizing, Measuring, and Tuning Adaptive MPI Parameters
inferred← citeshttps://doi.org/10.1007/978-3-030-39181-2_11openalex-snapshot
Bad Nodes Considered Harmful: How to Find and Fix the Problem
inferred← citeshttps://doi.org/10.1109/cluster.2016.38openalex-snapshot
A Case for Criticality Models in Exascale Systems
inferred← citeshttps://doi.org/10.1109/cluster52292.2023.00012openalex-snapshot
SciLance: Mitigate Load Imbalance for Parallel Scientific Applications in Cloud Environments
inferred← citeshttps://doi.org/10.1109/eurosp68448.2026.00073openalex-snapshot
The Unbearable Randomness of Fuzzing
inferred← citeshttps://doi.org/10.1109/icdcs57875.2023.00072openalex-snapshot
Distributed Online Min-Max Load Balancing with Risk-Averse Assistance
inferred← citeshttps://doi.org/10.1109/icdcsw63273.2025.00036openalex-snapshot
Towards Efficient Straggler Management in Distributed Deep Learning Training
inferred← citeshttps://doi.org/10.1109/ipdpsw63119.2024.00204openalex-snapshot
Proactive, Accuracy-aware Straggler Mitigation in Machine Learning Clusters
inferred← citeshttps://doi.org/10.1109/sc41404.2022.00070openalex-snapshot
Not All GPUs Are Created Equal: Characterizing Variability in Large-Scale, Accelerator-Rich Systems
inferred← citeshttps://doi.org/10.1109/sc41406.2024.00032openalex-snapshot
PAL: A Variability-Aware Policy for Scheduling ML Workloads in GPU Clusters
inferred← citeshttps://doi.org/10.1109/sips.2018.8598317openalex-snapshot
Energy Modeling of Cluster System
inferred← citeshttps://doi.org/10.1109/tcc.2021.3102593openalex-snapshot
Accelerating Distributed Learning in Non-Dedicated Environments
inferred← citeshttps://doi.org/10.1109/tpds.2017.2732951openalex-snapshot
Machine Learning-Based Temperature Prediction for Runtime Thermal Management Across System Components
inferred← citeshttps://doi.org/10.1145/3095770.3095778openalex-snapshot
The Effect of Asymmetric Performance on Asynchronous Task Based Runtimes
inferred← citeshttps://doi.org/10.48550/arxiv.1806.02508openalex-snapshot
Semi-Dynamic Load Balancing: Efficient Distributed Learning in Non-Dedicated Environments
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