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Active Learning in Performance Analysis
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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/icdm.2013.104openalex-snapshot
Maximizing Expected Model Change for Active Learning in Regression
inferredcites →https://doi.org/10.1109/igarss.2011.6049994openalex-snapshot
Gaussian process regression within an active learning scheme
inferredcites →https://doi.org/10.1109/infcomw.2016.7562042openalex-snapshot
Introducing configuration management capabilities into CloudLab experiments
inferredcites →https://doi.org/10.2172/1131029openalex-snapshot
HPGMG 1.0: A Benchmark for Ranking High Performance Computing Systems
inferredcites →https://doi.org/10.5860/choice.29-3347openalex-snapshot
The art of computer systems performance analysis: techniques for experimental design, measurement, simulation, and modeling
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/978-3-319-64203-1_8openalex-snapshot
Following the Blind Seer – Creating Better Performance Models Using Less Information
inferred← citeshttps://doi.org/10.1007/s10586-020-03219-7openalex-snapshot
DiG: enabling out-of-band scalable high-resolution monitoring for data-center analytics, automation and control (extended)
inferred← citeshttps://doi.org/10.1007/s10922-020-09547-8openalex-snapshot
Optimized Sampling Strategies to Model the Performance of Virtualized Network Functions
inferred← citeshttps://doi.org/10.1016/j.cageo.2017.04.014openalex-snapshot
Performance prediction of finite-difference solvers for different computer architectures
inferred← citeshttps://doi.org/10.1038/s41467-021-25757-3openalex-snapshot
Turn-key constrained parameter space exploration for particle accelerators using Bayesian active learning
inferred← citeshttps://doi.org/10.1109/ccgrid49817.2020.00-56openalex-snapshot
In Datacenter Performance, The Only Constant Is Change
inferred← citeshttps://doi.org/10.1109/icbaie52039.2021.9390062openalex-snapshot
Combined Transfer and Active Learning for High Accuracy Music Genre Classification Method
inferred← citeshttps://doi.org/10.1109/ipdps.2018.00031openalex-snapshot
Evaluating Active Learning with Cost and Memory Awareness
inferred← citeshttps://doi.org/10.1109/ipdps47924.2020.00090openalex-snapshot
Auto-tuning Parameter Choices in HPC Applications using Bayesian Optimization
inferred← citeshttps://doi.org/10.1109/ipdps47924.2020.00095openalex-snapshot
Learning Cost-Effective Sampling Strategies for Empirical Performance Modeling
inferred← citeshttps://doi.org/10.1109/ipdps49936.2021.00012openalex-snapshot
Noise-Resilient Empirical Performance Modeling with Deep Neural Networks
inferred← citeshttps://doi.org/10.1109/tpds.2025.3646119openalex-snapshot
Cost-Effective Empirical Performance Modeling
inferred← citeshttps://doi.org/10.1134/s1995080221070192openalex-snapshot
Influence of Noisy Environments on Behavior of HPC Applications
inferred← citeshttps://doi.org/10.1145/3205289.3205321openalex-snapshot
Bootstrapping Parameter Space Exploration for Fast Tuning
inferred← citeshttps://doi.org/10.1145/3225058.3225124openalex-snapshot
Scalable Behavioral Emulation of Extreme-Scale Systems Using Structural Simulation Toolkit
inferred← citeshttps://doi.org/10.1145/3453483.3454109openalex-snapshot
Bliss: auto-tuning complex applications using a pool of diverse lightweight learning models
inferred← citeshttps://doi.org/10.1145/3458817.3476197openalex-snapshot
Bootstrapping in-situ workflow auto-tuning via combining performance models of component applications
inferred← citeshttps://doi.org/10.1145/3581784.3607069openalex-snapshot
Application Performance Modeling via Tensor Completion
inferred← citeshttps://doi.org/10.1145/3624062.3624204openalex-snapshot
Extra-Deep: Automated Empirical Performance Modeling for Distributed Deep Learning
inferred← citeshttps://doi.org/10.1145/3806645.3816122openalex-snapshot
Accelerating high-accuracy runtime estimation in adaptive sample selection for parallel scalability analysis with reiforcement learning
inferred← citeshttps://doi.org/10.1177/10943420211023549openalex-snapshot
Online data analysis and reduction: An important Co-design motif for extreme-scale computers
inferred← citeshttps://doi.org/10.3390/electronics10212690openalex-snapshot
Intelligent Performance Prediction: The Use Case of a Hadoop Cluster
inferred← citeshttps://doi.org/10.48550/arxiv.1802.03455openalex-snapshot
Don't Repeat Yourself: Seamless Execution and Analysis of Extensive Network Experiments
inferred← citeshttps://doi.org/10.48550/arxiv.2008.06991openalex-snapshot
In-situ Workflow Auto-tuning via Combining Performance Models of Component Applications
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