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Matching Application Signatures for Performance Predictions Using a Single Execution
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Predicting parallel application performance via machine learning approaches
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Performance Modeling: Understanding the Past and Predicting the Future
inferredcites →https://doi.org/10.1016/0010-4655(94)00169-3openalex-snapshot
Skeleton PIC codes for parallel computers
inferredcites →https://doi.org/10.1109/ams.2001.993715openalex-snapshot
Prophesy: automating the modeling process
inferredcites →https://doi.org/10.1109/icpp.2011.35openalex-snapshot
Unveiling Internal Evolution of Parallel Application Computation Phases
inferredcites →https://doi.org/10.1109/icpp.2013.44openalex-snapshot
Sampling-Based Phase Classification and Prediction for Multi-threaded Program Execution on Multi-core Architectures
inferredcites →https://doi.org/10.1109/ipdps.2006.1639570openalex-snapshot
Program phase detection and exploitation
inferredcites →https://doi.org/10.1109/ipdps.2007.370593openalex-snapshot
Low-Overhead LogGP Parameter Assessment for Modern Interconnection Networks
inferredcites →https://doi.org/10.1109/ipdps.2009.5161057openalex-snapshot
Performance projection of HPC applications using SPEC CFP2006 benchmarks
inferredcites →https://doi.org/10.1109/ipdps.2011.21openalex-snapshot
Automatic Recognition of Performance Idioms in Scientific Applications
inferredcites →https://doi.org/10.1137/s1064827598339141openalex-snapshot
Semicoarsening Multigrid on Distributed Memory Machines
inferredcites →https://doi.org/10.1145/1188455.1188677openalex-snapshot
S12---The HPC Challenge (HPCC) benchmark suite
inferredcites →https://doi.org/10.1145/1229428.1229479openalex-snapshot
Methods of inference and learning for performance modeling of parallel applications
inferredcites →https://doi.org/10.1145/1375527.1375580openalex-snapshot
A regression-based approach to scalability prediction
inferredcites →https://doi.org/10.1145/2503210.2503267openalex-snapshot
On the usefulness of object tracking techniques in performance analysis
inferredcites →https://doi.org/10.1145/2503210.2503277openalex-snapshot
Using automated performance modeling to find scalability bugs in complex codes
inferredcites →https://doi.org/10.1177/109434200001400304openalex-snapshot
The Development of Parkbench and Performance Prediction
inferredcites →https://doi.org/10.1177/1094342006064482openalex-snapshot
The Tau Parallel Performance System
inferredcites →https://doi.org/10.1177/109434209100500306openalex-snapshot
The NAS Parallel Benchmarks
inferredcites →https://doi.org/10.2172/1004197openalex-snapshot
TORCH Computational Reference Kernels - A Testbed for Computer Science Research
inferredcites →https://doi.org/10.5555/1898699.1898796openalex-snapshot
Program phase detection and exploitation
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Performance and Energy Consumption Prediction of Randomly Selected Nodes in Heterogeneous Cluster
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Tuning Parallel Applications
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ExtraPeak: Advanced Automatic Performance Modeling for HPC Applications
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Automatic Performance Modeling of HPC Applications
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Using hardware counter-based performance model to diagnose scaling issues of HPC applications
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Performance prediction of parallel applications: a systematic literature review
inferred← citeshttps://doi.org/10.1007/s11227-022-04588-zopenalex-snapshot
Scalable performance analysis method for SPMD applications
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APMT: an automatic hardware counter-based performance modeling tool for HPC applications
inferred← citeshttps://doi.org/10.1016/j.future.2017.07.003openalex-snapshot
Using machine learning to optimize parallelism in big data applications
inferred← citeshttps://doi.org/10.1016/j.sysarc.2020.101959openalex-snapshot
Low learning-cost offline strategies for EDP optimization of parallel applications
inferred← citeshttps://doi.org/10.1109/cluster.2016.57openalex-snapshot
Fast Multi-parameter Performance Modeling
inferred← citeshttps://doi.org/10.1109/hipc.2016.049openalex-snapshot
Performance Prediction of Parallel Applications Based on Small-Scale Executions
inferred← citeshttps://doi.org/10.1109/hpcc/smartcity/dss.2018.00111openalex-snapshot
Performance Prediction of Parallel CPU and GPU Applications Using Fractals
inferred← citeshttps://doi.org/10.1109/hustprotools51951.2020.00015openalex-snapshot
Empirical Modeling of Spatially Diverging Performance
inferred← citeshttps://doi.org/10.1109/ipdps47924.2020.00095openalex-snapshot
Learning Cost-Effective Sampling Strategies for Empirical Performance Modeling
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Decreasing the Learning Cost of Offline Parallel Application Optimization Strategies
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A Performance Model of In-Situ Techniques
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Cost-Effective Empirical Performance Modeling
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PerfSuite: An Automatic Performance Suite for HPC Applications
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Selecting Efficient Cloud Resources for HPC Workloads
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Extracting clean performance models from tainted programs
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Combining Dynamic Concurrency Throttling with Voltage and Frequency Scaling on Task-based Programming Models
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On Similarity of Computational Kernels in our Codes and Proxies
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Laptop Performance Prediction
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Automatic Empirical Performance Modeling of Parallel Programs
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Extra-P—Empirical performance modeling made easy
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Além do Básico: Otimizando Aplicações Paralelas em Arquiteturas Modernas
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