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Bliss: auto-tuning complex applications using a pool of diverse lightweight learning models
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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
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hypre: A Library of High Performance Preconditioners
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A Framework for Enabling OpenMP Autotuning
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Multi-Objective Auto-Tuning with Insieme: Optimization and Trade-Off Analysis for Time, Energy and Resource Usage
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Performance Prediction of Multigrid-Solver Configurations
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Performance, Design, and Autotuning of Batched GEMM for GPUs
inferredcites →https://doi.org/10.1007/978-3-540-79561-2_2openalex-snapshot
CLOMP: Accurately Characterizing OpenMP Application Overheads
inferredcites →https://doi.org/10.1023/a:1019034926845openalex-snapshot
Prediction and adaptation in Active Harmony
inferredcites →https://doi.org/10.1038/s42256-019-0048-xopenalex-snapshot
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
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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Software challenges in extreme scale systems
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/cloudcom.2019.00045openalex-snapshot
ParaOpt: Automated Application Parameterization and Optimization for the Cloud
inferredcites →https://doi.org/10.1109/cluster.2013.6702683openalex-snapshot
Active-learning-based surrogate models for empirical performance tuning
inferredcites →https://doi.org/10.1109/cluster.2016.63openalex-snapshot
Active Learning in Performance Analysis
inferredcites →https://doi.org/10.1109/hpca.2002.995701openalex-snapshot
Tuning garbage collection in an embedded Java environment
inferredcites →https://doi.org/10.1109/hpdc.2006.1652135openalex-snapshot
A Case Study Using Automatic Performance Tuning for Large-Scale Scientific Programs
inferredcites →https://doi.org/10.1109/icpp.2016.58openalex-snapshot
Declarative Tuning for Locality in Parallel Programs
inferredcites →https://doi.org/10.1109/ipdps.2009.5161054openalex-snapshot
A scalable auto-tuning framework for compiler optimization
inferredcites →https://doi.org/10.1109/ipdps.2011.70openalex-snapshot
PATUS: A Code Generation and Autotuning Framework for Parallel Iterative Stencil Computations on Modern Microarchitectures
inferredcites →https://doi.org/10.1109/ipdps.2011.86openalex-snapshot
Online Adaptive Code Generation and Tuning
inferredcites →https://doi.org/10.1109/ipdps.2017.38openalex-snapshot
Apollo: Reusable Models for Fast, Dynamic Tuning of Input-Dependent Code
inferredcites →https://doi.org/10.1109/ipdps.2018.00061openalex-snapshot
Auto-tuning Streamed Applications on Intel Xeon Phi
inferredcites →https://doi.org/10.1109/ipdps47924.2020.00090openalex-snapshot
Auto-tuning Parameter Choices in HPC Applications using Bayesian Optimization
inferredcites →https://doi.org/10.1109/ipdpsw.2015.85openalex-snapshot
Machine Learning Based Auto-Tuning for Enhanced OpenCL Performance Portability
inferredcites →https://doi.org/10.1109/ipdpsw.2016.85openalex-snapshot
Exploiting Performance Portability in Search Algorithms for Autotuning
inferredcites →https://doi.org/10.1109/islped.2017.8009208openalex-snapshot
A case for efficient accelerator design space exploration via Bayesian optimization
inferredcites →https://doi.org/10.1109/jproc.2004.840301openalex-snapshot
The Design and Implementation of FFTW3
inferredcites →https://doi.org/10.1109/jproc.2015.2494218openalex-snapshot
Taking the Human Out of the Loop: A Review of Bayesian Optimization
inferredcites →https://doi.org/10.1109/jproc.2018.2841200openalex-snapshot
Autotuning in High-Performance Computing Applications
inferredcites →https://doi.org/10.1109/mm.2019.2961722openalex-snapshot
Connectivity! Connectivity! Connectivity! May You Be More Connected Than Ever!!
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Autotuning PolyBench Benchmarks with LLVM Clang/Polly Loop Optimization Pragmas Using Bayesian Optimization
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/10.1109/sc.2012.7openalex-snapshot
A multi-objective auto-tuning framework for parallel codes
inferredcites →https://doi.org/10.1109/sc41405.2020.00053openalex-snapshot
PLINER: Isolating Lines of Floating-Point Code for Compiler-Induced Variability
inferredcites →https://doi.org/10.1109/seams.2017.11openalex-snapshot
Transfer Learning for Improving Model Predictions in Highly Configurable Software
inferredcites →https://doi.org/10.1109/tcad.2018.2857278openalex-snapshot
Auto-Tuning CNNs for Coarse-Grained Reconfigurable Array-Based Accelerators
inferredcites →https://doi.org/10.1109/tpds.2015.2449299openalex-snapshot
RFHOC: A Random-Forest Approach to Auto-Tuning Hadoop's Configuration
inferredcites →https://doi.org/10.1145/1941553.1941589openalex-snapshot
Auto-tuning of fast fourier transform on graphics processors
inferredcites →https://doi.org/10.1145/2001576.2001805openalex-snapshot
An efficient evolutionary algorithm for solving incrementally structured problems
inferredcites →https://doi.org/10.1145/2737924.2737969openalex-snapshot
Autotuning algorithmic choice for input sensitivity
inferredcites →https://doi.org/10.1145/2737924.2737999openalex-snapshot
Celebrating diversity: a mixture of experts approach for runtime mapping in dynamic environments
inferredcites →https://doi.org/10.1145/2903150.2903470openalex-snapshot
The ANTAREX approach to autotuning and adaptivity for energy efficient HPC systems
inferredcites →https://doi.org/10.1145/2954679.2872375openalex-snapshot
Maximizing Performance Under a Power Cap
inferredcites →https://doi.org/10.1145/3126908.3126969openalex-snapshot
Performance modeling under resource constraints using deep transfer learning
inferredcites →https://doi.org/10.1145/3168805openalex-snapshot
Optimal DNN primitive selection with partitioned boolean quadratic programming
inferredcites →https://doi.org/10.1145/3205289.3205321openalex-snapshot
Bootstrapping Parameter Space Exploration for Fast Tuning
inferredcites →https://doi.org/10.1145/3341301.3359642openalex-snapshot
A generic communication scheduler for distributed DNN training acceleration
inferredcites →https://doi.org/10.1177/1094342004041291openalex-snapshot
Spiral: A Generator for Platform-Adapted Libraries of Signal Processing Alogorithms
inferredcites →https://doi.org/10.1177/1094342011414744openalex-snapshot
Auto-tuning full applications: A case study
inferredcites →https://doi.org/10.1201/b10509-11openalex-snapshot
End-to-End Auto-Tuning with Active Harmony
inferredcites →https://doi.org/10.1609/aaai.v34i04.5971openalex-snapshot
Bayesian Optimization for Categorical and Category-Specific Continuous Inputs
inferredcites →https://doi.org/10.2172/1423458openalex-snapshot
DEGAS: Dynamic Exascale Global Address Space Programming Environments
inferredcites →https://doi.org/10.2172/1438753openalex-snapshot
Quantitative Performance Assessment of Proxy Apps and Parents
inferredcites →https://doi.org/10.2172/1604127openalex-snapshot
Compiler and Runtime Approaches to Enable Large-Scale Irregular Programs. Final report, July 2013 - July 2019
inferredcites →https://doi.org/10.23919/date.2019.8714959openalex-snapshot
Learning to infer: RL-based search for DNN primitive selection on Heterogeneous Embedded Systems
inferredcites →https://doi.org/10.3850/9783981537079_1012openalex-snapshot
AutoTuning and Adaptivity appRoach for Energy Efficient eXascale HPC Systems: The ANTAREX Approach
inferredcites →https://doi.org/10.4230/oasics.dx.2024.27openalex-snapshot
On a Method to Measure Supervised Multiclass Model’s Interpretability: Application to Degradation Diagnosis (Short Paper)
inferredcites →https://doi.org/10.5555/3049832.3049859openalex-snapshot
Minimizing the cost of iterative compilation with active learning
inferredcites →https://doi.org/10.5555/3291168.3291211openalex-snapshot
TVM: An Automated End-to-End Optimizing Compiler for Deep Learning
inferredcites →https://doi.org/10.5555/509058.509096openalex-snapshot
Automatically Tuned Linear Algebra Software
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.2139/ssrn.4169445openalex-extracted
Turbo: A Cost-Efficient Configuration Auto-Tuning Approach for Cluster-Based Big Data Frameworks
inferred← citeshttps://doi.org/10.1002/cpe.8322openalex-snapshot
ytopt: Autotuning Scientific Applications for Energy Efficiency at Large Scales
inferred← citeshttps://doi.org/10.1007/978-3-031-30251-0_3openalex-snapshot
Automated Parallelization of Software for Identifying Parameters of Intraparticle Diffusion and Adsorption in Heterogeneous Nanoporous Media
inferred← citeshttps://doi.org/10.1007/978-981-96-4207-6_40openalex-snapshot
NAAM: Enhancing Automatic Task Mapping Efficiency on NUMA Machines
inferred← citeshttps://doi.org/10.1007/s11227-021-04103-wopenalex-snapshot
Performance benchmarking and auto-tuning for scientific applications on virtual cluster
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/10.1007/s42979-022-01559-2openalex-snapshot
Formal Techniques for Development and Auto-tuning of Parallel Programs
inferred← citeshttps://doi.org/10.1109/ccgrid59990.2024.00019openalex-snapshot
COTuner: Joint Optimization of Resource Configuration and Software Parameters for Recurring Streaming Jobs on the Cloud
inferred← citeshttps://doi.org/10.1109/hipc62374.2024.00011openalex-snapshot
HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach
inferred← citeshttps://doi.org/10.1109/hipc66333.2025.00043openalex-snapshot
Predicting Executability and Performance of CNN Kernels on Tenstorrent Hardware Using Machine Learning
inferred← citeshttps://doi.org/10.1109/hpec58863.2023.10363596openalex-snapshot
An Analysis of Energy Requirement for Computer Vision Algorithms
inferred← citeshttps://doi.org/10.1109/ipdps54959.2023.00060openalex-snapshot
Power Constrained Autotuning using Graph Neural Networks
inferred← citeshttps://doi.org/10.1109/ipdps54959.2023.00069openalex-snapshot
Harnessing the Crowd for Autotuning High-Performance Computing Applications
inferred← citeshttps://doi.org/10.1109/ipdps64566.2025.00054openalex-snapshot
Leveraging Compilation Statistics for Compiler Phase Ordering
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/mcsoc51149.2021.00044openalex-snapshot
Enhancing Autotuning Capability with a History Database
inferred← citeshttps://doi.org/10.1109/tpds.2023.3325630openalex-snapshot
Adaptive Auto-Tuning Framework for Global Exploration of Stencil Optimization on GPUs
inferred← citeshttps://doi.org/10.1109/tpds.2024.3459889openalex-snapshot
DeepCAT+: A Low-Cost and Transferrable Online Configuration Auto-Tuning Approach for Big Data Frameworks
inferred← citeshttps://doi.org/10.1109/tpds.2025.3587673openalex-snapshot
High Performance OpenCL-Based GEMM Kernel Auto-Tuned by Bayesian Optimization
inferred← citeshttps://doi.org/10.1145/3497776.3517769openalex-snapshot
Automating reinforcement learning architecture design for code optimization
inferred← citeshttps://doi.org/10.1145/3546868openalex-snapshot
Phronesis: Efficient Performance Modeling for High-dimensional Configuration Tuning
inferred← citeshttps://doi.org/10.1145/3546918.3546925openalex-snapshot
Analysing and Predicting Energy Consumption of Garbage Collectors in OpenJDK
inferred← citeshttps://doi.org/10.1145/3567955.3567958openalex-snapshot
CAFQA: A Classical Simulation Bootstrap for Variational Quantum Algorithms
inferred← citeshttps://doi.org/10.1145/3577193.3593712openalex-snapshot
Transfer-learning-based Autotuning using Gaussian Copula
inferred← citeshttps://doi.org/10.1145/3581784.3607069openalex-snapshot
Application Performance Modeling via Tensor Completion
inferred← citeshttps://doi.org/10.1145/3581784.3607098openalex-snapshot
Scalable Tuning of (OpenMP) GPU Applications via Kernel Record and Replay
inferred← citeshttps://doi.org/10.1145/3588195.3592984openalex-snapshot
Performance Optimization using Multimodal Modeling and Heterogeneous GNN
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/10.1145/3623278.3624770openalex-snapshot
BaCO: A Fast and Portable Bayesian Compiler Optimization Framework
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/10.1145/3656019.3676895openalex-snapshot
MIREncoder: Multi-modal IR-based Pretrained Embeddings for Performance Optimizations
inferred← citeshttps://doi.org/10.1145/3669940.3707259openalex-snapshot
DarwinGame: Playing Tournaments for Tuning Applications in Noisy Cloud Environments
inferred← citeshttps://doi.org/10.1145/3712031.3712334openalex-snapshot
When HPC Scheduling Meets Active Learning: Maximizing The Performance with Minimal Data
inferred← citeshttps://doi.org/10.1145/3754598.3754665openalex-snapshot
HHOTuner: Efficient Performance Tuning with Harris Hawks Optimization
inferred← citeshttps://doi.org/10.1145/3818685openalex-snapshot
Power Scheduling for Maximizing Throughput and Fairness in Co-running Applications
inferred← citeshttps://doi.org/10.1177/10943420241286476openalex-snapshot
Integrating ytopt and libEnsemble to autotune OpenMC
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/10.31274/td-20240329-302openalex-snapshot
Evaluation of Graph Neural Networks for code pattern detection and autotuning of OpenMP loops
inferred← citeshttps://doi.org/10.3390/electronics11091316openalex-snapshot
Building and Using Multiple Stacks of Models for the Classification of Learners and Custom Recommending of Quizzes
inferred← citeshttps://doi.org/10.3390/s26103028openalex-snapshot
PFTuner: An Efficient and Effective Multi-Objective Configuration Tuning Framework Adaptive to Different Software Systems
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