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Apollo: Reusable Models for Fast, Dynamic Tuning of Input-Dependent Code

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Large-eddy and unsteady RANS simulations of a shock-accelerated heavy gas cylinder
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A survey of several finite difference methods for systems of nonlinear hyperbolic conservation laws
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A two-dimensional unstructured cell-centered multi-material ALE scheme using VOF interface reconstruction
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Kokkos: Enabling manycore performance portability through polymorphic memory access patterns
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A study of ALE simulations of Rayleigh–Taylor instability
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Simplifying decision trees
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Automated empirical optimizations of software and the ATLAS project
inferredcites →https://doi.org/10.1023/a:1022699900025openalex-snapshot
Incremental Induction of Decision Trees
inferredcites →https://doi.org/10.1088/1742-6596/16/1/071openalex-snapshot
OSKI: A library of automatically tuned sparse matrix kernels
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Using Machine Learning to Focus Iterative Optimization
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Predictive modeling in a polyhedral optimization space
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Identifying energy-efficient concurrency levels using machine learning
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GPGPU performance and power estimation using machine learning
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Prediction and adaptation in Active Harmony
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A Case Study Using Automatic Performance Tuning for Large-Scale Scientific Programs
inferredcites →https://doi.org/10.1109/icpp.2015.15openalex-snapshot
Resident Block-Structured Adaptive Mesh Refinement on Thousands of Graphics Processing Units
inferredcites →https://doi.org/10.1109/iiswc.2012.6402921openalex-snapshot
Model-based, memory-centric performance and power optimization on NUMA multiprocessors
inferredcites →https://doi.org/10.1109/inpar.2012.6339587openalex-snapshot
Machine learning for predictive auto-tuning with boosted regression trees
inferredcites →https://doi.org/10.1109/ipdps.2009.5161004openalex-snapshot
Annotation-based empirical performance tuning using Orio
inferredcites →https://doi.org/10.1109/ipdps.2013.115openalex-snapshot
Exploring Traditional and Emerging Parallel Programming Models Using a Proxy Application
inferredcites →https://doi.org/10.1109/ipdps.2013.73openalex-snapshot
A Simplified and Accurate Model of Power-Performance Efficiency on Emergent GPU Architectures
inferredcites →https://doi.org/10.1109/ipdps.2014.59openalex-snapshot
Nitro: A Framework for Adaptive Code Variant Tuning
inferredcites →https://doi.org/10.1109/jproc.2004.840301openalex-snapshot
The Design and Implementation of FFTW3
inferredcites →https://doi.org/10.1109/sc.2002.10062openalex-snapshot
Active Harmony: Towards Automated Performance Tuning
inferredcites →https://doi.org/10.1109/sc.2004.65openalex-snapshot
Using Information from Prior Runs to Improve Automated Tuning Systems
inferredcites →https://doi.org/10.1145/2063348.2063356openalex-snapshot
Performance modeling for systematic performance tuning
inferredcites →https://doi.org/10.1145/2503210.2503263openalex-snapshot
Predicting application performance using supervised learning on communication features
inferredcites →https://doi.org/10.1145/2503210.2503277openalex-snapshot
Using automated performance modeling to find scalability bugs in complex codes
inferredcites →https://doi.org/10.1145/2628071.2628092openalex-snapshot
OpenTuner
inferredcites →https://doi.org/10.1145/2628071.2628100openalex-snapshot
PEMOGEN
inferredcites →https://doi.org/10.1145/2737924.2737969openalex-snapshot
Autotuning algorithmic choice for input sensitivity
inferredcites →https://doi.org/10.1177/1094342004041291openalex-snapshot
Spiral: A Generator for Platform-Adapted Libraries of Signal Processing Alogorithms
inferredcites →https://doi.org/10.48550/arxiv.1201.0490openalex-snapshot
Scikit-learn: Machine Learning in Python
inferredcites →https://doi.org/10.5555/2190025.2190059openalex-snapshot
Predictive modeling in a polyhedral optimization space

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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ATF: A generic directive‐based auto‐tuning framework
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Artemis: Automatic Runtime Tuning of Parallel Execution Parameters Using Machine Learning
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Guiding Code Optimizations with Deep Learning-Based Code Matching
inferred← citeshttps://doi.org/10.1007/978-3-030-97759-7_3openalex-snapshot
Extending OpenMP for Machine Learning-Driven Adaptation
inferred← citeshttps://doi.org/10.1007/978-3-031-07312-0_15openalex-snapshot
A Motivating Case Study on Code Variant Selection by Reinforcement Learning
inferred← citeshttps://doi.org/10.1007/978-981-99-4366-1_5openalex-snapshot
Graph Analysis for Scalability Analysis
inferred← citeshttps://doi.org/10.1109/ccgrid.2019.00026openalex-snapshot
Autotuning Under Tight Budget Constraints: A Transparent Design of Experiments Approach
inferred← citeshttps://doi.org/10.1109/cluster.2017.43openalex-snapshot
Mira: A Framework for Static Performance Analysis
inferred← citeshttps://doi.org/10.1109/hpcc-smartcity-dss.2017.9openalex-snapshot
ATF: A Generic Auto-Tuning Framework
inferred← citeshttps://doi.org/10.1109/icmla58977.2023.00198openalex-snapshot
Novel Representation Learning Technique Using Graphs for Performance Analytics
inferred← citeshttps://doi.org/10.1109/ipdps.2018.00088openalex-snapshot
PADDLE: Performance Analysis Using a Data-Driven Learning Environment
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/ipdpsw.2018.00172openalex-snapshot
AutoTuneTMP: Auto-Tuning in C++ With Runtime Template Metaprogramming
inferred← citeshttps://doi.org/10.1109/p3hpc49587.2019.00012openalex-snapshot
RAJA: Portable Performance for Large-Scale Scientific Applications
inferred← citeshttps://doi.org/10.1109/sc41405.2020.00032openalex-snapshot
SCALANA: Automating Scaling Loss Detection with Graph Analysis
inferred← citeshttps://doi.org/10.1145/3205289.3205321openalex-snapshot
Bootstrapping Parameter Space Exploration for Fast Tuning
inferred← citeshttps://doi.org/10.1145/3427093openalex-snapshot
Efficient Auto-Tuning of Parallel Programs with Interdependent Tuning Parameters via Auto-Tuning Framework (ATF)
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/10.1145/3581784.3607098openalex-snapshot
Scalable Tuning of (OpenMP) GPU Applications via Kernel Record and Replay
inferred← citeshttps://doi.org/10.1145/3605573.3605578openalex-snapshot
CoTuner: A Hierarchical Learning Framework for Coordinately Optimizing Resource Partitioning and Parameter Tuning
inferred← citeshttps://doi.org/10.1145/3649169openalex-snapshot
Proceedings of the 15th International Workshop on Programming Models and Applications for Multicores and Manycores
inferred← citeshttps://doi.org/10.1145/3649169.3649246openalex-snapshot
MUPPET
inferred← citeshttps://doi.org/10.18255/1818-1015-2021-4-338-355openalex-snapshot
Autotuning Parallel Programs by Model Checking
inferred← citeshttps://doi.org/10.3103/s0146411622070045openalex-snapshot
Autotuning Parallel Programs by Model Checking
inferred← citeshttps://doi.org/10.3929/ethz-b-000462522openalex-snapshot
SCALANA: Automating Scaling Loss Detection with Graph Analysis
inferred← citeshttps://doi.org/10.48550/arxiv.1705.07575openalex-snapshot
Mira: A Framework for Static Performance Analysis
inferred← citeshttps://doi.org/10.48550/arxiv.2006.09473openalex-snapshot
Guiding Optimizations with Meliora: A Deep Walk down Memory Lane
inferred← citeshttps://doi.org/10.70675/cd9bbcb1zcac6z43aezb392z14c267ac9796openalex-snapshot
Toward transparent and parsimonious methods for automatic performance tuning
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