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Identifying the Culprits Behind Network Congestion
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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.1637openalex-snapshot
Optimizing communication for Charm++ applications by reducing network contention
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Congestion Control in the Wormhole-Routed Torus With Clustering and Delayed Deflection
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The Elements of Statistical Learning
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The Elements of Statistical Learning: Data Mining, Inference, and Prediction
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Stochastic gradient boosting
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An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization
inferredcites →https://doi.org/10.1103/physrevd.61.111502openalex-snapshot
Scaling tests of the improved Kogut-Susskind quark action
inferredcites →https://doi.org/10.1109/icpp.2011.80openalex-snapshot
Combining Congested-Flow Isolation and Injection Throttling in HPC Interconnection Networks
inferredcites →https://doi.org/10.1109/mcse.2014.79openalex-snapshot
pF3D Simulations of Laser-Plasma Interactions in National Ignition Facility Experiments
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Mapping applications with collectives over sub-communicators on torus networks
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Book Review: Pattern Recognition and Machine Learning
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QUANTIFYING NETWORK CONTENTION ON LARGE PARALLEL MACHINES
inferredcites →https://doi.org/10.1145/2503210.2503263openalex-snapshot
Predicting application performance using supervised learning on communication features
inferredcites →https://doi.org/10.1145/2688500.2688516openalex-snapshot
Diagnosing the causes and severity of one-sided message contention
inferredcites →https://doi.org/10.1198/tech.2007.s518openalex-snapshot
Pattern Recognition and Machine Learning
inferredcites →https://doi.org/10.14511/pcc.2014.030401openalex-snapshot
A Learning-based Selection for Portfolio Scheduling of Scientific Applications on Heterogeneous Computing Systems
inferredcites →https://doi.org/10.3389/fnbot.2013.00021openalex-snapshot
Gradient boosting machines, a tutorial
inferredcites →https://doi.org/10.48550/arxiv.1201.0490openalex-snapshot
Scikit-learn: Machine Learning in Python
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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BCN: Enhanced Backpressure Flow Control with Rapid Notification in Datacenter Networks
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Study of interconnect errors, network congestion, and applications characteristics for throttle prediction on a large scale HPC system
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Security and Privacy for Low Power IoT Devices on 5G and Beyond Networks: Challenges and Future Directions
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Automatic Communication Optimization of Parallel Applications in Public Clouds
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LIBNVCD: An Extendable and User-friendly Multi-GPU Performance Measurement Tool
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StressBench: A Configurable Full System Network and I/O Benchmark Framework
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Finding Your Niche: An Evolutionary Approach to HPC Topologies
inferred← citeshttps://doi.org/10.1109/hpec62836.2024.10938439openalex-snapshot
HPC Network Simulation Tuning via Automatic Extraction of Hardware Parameters
inferred← citeshttps://doi.org/10.1109/ic3s57698.2023.10169316openalex-snapshot
Image Restoration Using Deep Convolutional Neural Networks for Network Congestion Control
inferred← citeshttps://doi.org/10.1109/icdmw.2016.0112openalex-snapshot
Robust Local Scaling Using Conditional Quantiles of Graph Similarities
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Constrained In-network Computing with Low Congestion in Datacenter Networks
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BCN: A Fast Notified Backpressure Congestion Management
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PADDLE: Performance Analysis Using a Data-Driven Learning Environment
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Detecting Network State in the Presence of Varying Levels of Congestion
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Data-Driven Performance Modeling of Linear Solvers for Sparse Matrices
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A Machine Learning Framework for Performance Coverage Analysis of Proxy Applications
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Mitigating Inter-Job Interference Using Adaptive Flow-Aware Routing
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Unraveling Network-Induced Memory Contention: Deeper Insights with Machine Learning
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Performance optimality or reproducibility
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Delay sensitivity-driven congestion mitigation for HPC systems
inferred← citeshttps://doi.org/10.1155/2020/8890721openalex-snapshot
Communication Optimization Technology Based on Network Dynamic Performance Model
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MACHINE LEARNING-BASED MODELING FOR PERFORMANCE IMPROVEMENT IN AN EXASCALE SYSTEMS
inferred← citeshttps://doi.org/10.3233/idt-230158openalex-snapshot
Design and development of prime herder optimization based BiLSTM congestion predictor model in live video streaming
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Network Congestion Control Using Deep Convolutional Neural Networks
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Is Big Data Performance Reproducible in Modern Cloud Networks?
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Mitigating inter-job interference using adaptive flow-aware routing
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