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ytopt: Autotuning Scientific Applications for Energy Efficiency at Large Scales
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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.1551openalex-snapshot
Automatic performance analysis with periscope
inferredcites →https://doi.org/10.1002/cpe.4029openalex-snapshot
Machine learning‐based auto‐tuning for enhanced performance portability of OpenCL applications
inferredcites →https://doi.org/10.1002/cpe.6683openalex-snapshot
Autotuning PolyBench benchmarks with LLVM Clang/Polly loop optimization pragmas using Bayesian optimization
inferredcites →https://doi.org/10.1007/978-3-030-28596-8_4openalex-snapshot
A Framework for Enabling OpenMP Autotuning
inferredcites →https://doi.org/10.1007/978-3-319-58667-0_21openalex-snapshot
Global Extensible Open Power Manager: A Vehicle for HPC Community Collaboration on Co-Designed Energy Management Solutions
inferredcites →https://doi.org/10.1007/978-3-319-92040-5_3openalex-snapshot
Bayesian Optimization of HPC Systems for Energy Efficiency
inferredcites →https://doi.org/10.1007/978-3-642-13374-9_21openalex-snapshot
Effective Source-to-Source Outlining to Support Whole Program Empirical Optimization
inferredcites →https://doi.org/10.1007/978-3-642-21487-5_12openalex-snapshot
Performance Analysis and Tuning of Automatically Parallelized OpenMP Applications
inferredcites →https://doi.org/10.1007/s10915-011-9531-1openalex-snapshot
A Fourth Order Accurate Finite Difference Scheme for the Elastic Wave Equation in Second Order Formulation
inferredcites →https://doi.org/10.1016/j.anucene.2012.06.040openalex-snapshot
The OpenMC Monte Carlo particle transport code
inferredcites →https://doi.org/10.1016/j.future.2018.08.004openalex-snapshot
Kernel Tuner: A search-optimizing GPU code auto-tuner
inferredcites →https://doi.org/10.1109/ccgrid.2019.00026openalex-snapshot
Autotuning Under Tight Budget Constraints: A Transparent Design of Experiments Approach
inferredcites →https://doi.org/10.1109/cgo.2005.10openalex-snapshot
Combining Models and Guided Empirical Search to Optimize for Multiple Levels of the Memory Hierarchy
inferredcites →https://doi.org/10.1109/cgo.2017.7863744openalex-snapshot
Minimizing the cost of iterative compilation with active learning
inferredcites →https://doi.org/10.1109/hpcc-smartcity-dss.2017.9openalex-snapshot
ATF: A Generic Auto-Tuning Framework
inferredcites →https://doi.org/10.1109/icpp.2015.106openalex-snapshot
Generating Efficient Tensor Contractions for GPUs
inferredcites →https://doi.org/10.1109/ipdps.2011.86openalex-snapshot
Online Adaptive Code Generation and Tuning
inferredcites →https://doi.org/10.1109/ipdps.2014.59openalex-snapshot
Nitro: A Framework for Adaptive Code Variant Tuning
inferredcites →https://doi.org/10.1109/ipdpsw.2016.85openalex-snapshot
Exploiting Performance Portability in Search Algorithms for Autotuning
inferredcites →https://doi.org/10.1109/ipdpsw.2018.00111openalex-snapshot
Energy and Power Aware Job Scheduling and Resource Management: Global Survey — Initial Analysis
inferredcites →https://doi.org/10.1109/llvmhpchipar51896.2020.00007openalex-snapshot
Autotuning Search Space for Loop Transformations
inferredcites →https://doi.org/10.1109/mc.2016.308openalex-snapshot
Standardizing Power Monitoring and Control at Exascale
inferredcites →https://doi.org/10.1109/mc.2016.311openalex-snapshot
Using Performance-Power Modeling to Improve Energy Efficiency of HPC Applications
inferredcites →https://doi.org/10.1109/mchpc54807.2021.00009openalex-snapshot
Performance and Energy Improvement of ECP Proxy App SW4lite under Various Workloads
inferredcites →https://doi.org/10.1109/mcsoc.2015.10openalex-snapshot
CLTune: A Generic Auto-Tuner for OpenCL Kernels
inferredcites →https://doi.org/10.1109/mcsoc35816.2015openalex-snapshot
2015 IEEE 9th International Symposium on Embedded Multicore/Many-core Systems-on-Chip
inferredcites →https://doi.org/10.1109/pmbs51919.2020.00012openalex-snapshot
Autotuning PolyBench Benchmarks with LLVM Clang/Polly Loop Optimization Pragmas Using Bayesian Optimization
inferredcites →https://doi.org/10.1109/pmbs54543.2021.00015openalex-snapshot
Customized Monte Carlo Tree Search for LLVM/Polly's Composable Loop Optimization Transformations
inferredcites →https://doi.org/10.1109/sc.1998.10004openalex-snapshot
Automatically Tuned Linear Algebra Software
inferredcites →https://doi.org/10.1109/sc.2002.10062openalex-snapshot
Active Harmony: Towards Automated Performance Tuning
inferredcites →https://doi.org/10.1142/s0129626412500107openalex-snapshot
POLLY — PERFORMING POLYHEDRAL OPTIMIZATIONS ON A LOW-LEVEL INTERMEDIATE REPRESENTATION
inferredcites →https://doi.org/10.1145/3149412.3149416openalex-snapshot
Performance and Power Characteristics and Optimizations of Hybrid MPI/OpenMP LULESH Miniapps under Various Workloads
inferredcites →https://doi.org/10.1145/3197978openalex-snapshot
A Survey on Compiler Autotuning using Machine Learning
inferredcites →https://doi.org/10.1145/3205289.3205321openalex-snapshot
Bootstrapping Parameter Space Exploration for Fast Tuning
inferredcites →https://doi.org/10.1145/3306346.3322967openalex-snapshot
Learning to optimize halide with tree search and random programs
inferredcites →https://doi.org/10.1145/3330345.3330377openalex-snapshot
Efficient hierarchical online-autotuning
inferredcites →https://doi.org/10.1145/3427093openalex-snapshot
Efficient Auto-Tuning of Parallel Programs with Interdependent Tuning Parameters via Auto-Tuning Framework (ATF)
inferredcites →https://doi.org/10.1145/3453483.3454109openalex-snapshot
Bliss: auto-tuning complex applications using a pool of diverse lightweight learning models
inferredcites →https://doi.org/10.1145/3577193.3593712openalex-snapshot
Transfer-learning-based Autotuning using Gaussian Copula
inferredcites →https://doi.org/10.1177/10943420241286476openalex-snapshot
Integrating ytopt and libEnsemble to autotune OpenMC
inferredcites →https://doi.org/10.48550/arxiv.1401.4063openalex-snapshot
Towards Autotuning of OpenMP Applications on Multicore Architectures
inferredcites →https://doi.org/10.48550/arxiv.1904.03383openalex-snapshot
On the Representation of Partially Specified Implementations and its Application to the Optimization of Linear Algebra Kernels on GPU
inferredcites →https://doi.org/10.48550/arxiv.1908.06756openalex-snapshot
BOAH: A Tool Suite for Multi-Fidelity Bayesian Optimization & Analysis of Hyperparameters
inferredcites →https://doi.org/10.48550/arxiv.2005.13685openalex-snapshot
ProTuner: Tuning Programs with Monte Carlo Tree Search
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/978-3-031-72567-8_2openalex-snapshot
Performance Porting the ExaStar Multi-Physics App Thornado On Heterogeneous Systems - A Fortran-OpenMP Code-Base Evaluation
inferred← citeshttps://doi.org/10.1007/978-3-032-02436-7_13openalex-snapshot
PEAK: Generating High-Performance Schedules in MLIR
inferred← citeshttps://doi.org/10.1007/s11227-026-08770-5openalex-snapshot
A multi-backend autotuning study of feature selection on GPUs
inferred← citeshttps://doi.org/10.1016/j.micpro.2025.105219openalex-snapshot
Power/accuracy-aware dynamic workload optimization combining application autotuning and runtime resource management on homogeneous architectures
inferred← citeshttps://doi.org/10.1016/j.ridd.2025.105181openalex-snapshot
Application of machine learning to predict employment attainment among individuals with intellectual and developmental disabilities
inferred← citeshttps://doi.org/10.1109/ccgrid64434.2025.00035openalex-snapshot
Evaluating Energy Efficiency of Ai Accelerators Using Two Mlperf Benchmarks
inferred← citeshttps://doi.org/10.1109/cgo57630.2024.10444795openalex-snapshot
Energy-Aware Tile Size Selection for Affine Programs on GPUs
inferred← citeshttps://doi.org/10.1109/hpec67600.2025.11196674openalex-snapshot
Performance-Energy Characterization of ML Inference on Heterogeneous Edge AI Platforms
inferred← citeshttps://doi.org/10.1109/icecet65726.2026.11633097openalex-snapshot
Autotuning Function-Level Compiler Optimizations for Performance Improvement
inferred← citeshttps://doi.org/10.1109/ipdpsw63119.2024.00138openalex-snapshot
An Exploration of Global Optimization Strategies for Autotuning OpenMP-based Codes
inferred← citeshttps://doi.org/10.1109/ipdpsw63119.2024.00143openalex-snapshot
Cost-Effective Methodology for Complex Tuning Searches in HPC: Navigating Interdependencies and Dimensionality
inferred← citeshttps://doi.org/10.1109/ipdpsw71298.2026.00033openalex-snapshot
Constraint-aware Optimization in Auto-Tuning
inferred← citeshttps://doi.org/10.1145/3624062.3626079openalex-snapshot
Autotuning Apache TVM-based Scientific Applications Using Bayesian Optimization
inferred← citeshttps://doi.org/10.1145/3650200.3656626openalex-snapshot
Accelerated Auto-Tuning of GPU Kernels for Tensor Computations
inferred← citeshttps://doi.org/10.1145/3731599.3767401openalex-snapshot
Agentic AI vs ML-based Autotuning: A Comparative Study for Loop Reordering Optimization
inferred← citeshttps://doi.org/10.1177/10943420251362001openalex-snapshot
Parallelizing autotuning for HPC applications: Unveiling the potential of the speculation strategy in Bayesian optimization
inferred← citeshttps://doi.org/10.26089/nummet.v27r327openalex-snapshot
Investigation of the applicability of autotuners in supercomputer co-design problems using MLKAPS
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