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Online Adaptive Code Generation and Tuning
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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.1126openalex-snapshot
MATE: Monitoring, Analysis and Tuning Environment for parallel/distributed applications
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/s10766-006-0012-3openalex-snapshot
Semi-Automatic Composition of Loop Transformations for Deep Parallelism and Memory Hierarchies
inferredcites →https://doi.org/10.1016/j.physa.2005.09.034openalex-snapshot
Multi-block Lattice Boltzmann method: Extension to 3D and validation in turbulence
inferredcites →https://doi.org/10.1109/hpdc.1996.546178openalex-snapshot
Scheduling from the perspective of the application
inferredcites →https://doi.org/10.1109/hpdc.1998.709970openalex-snapshot
Autopilot: adaptive control of distributed applications
inferredcites →https://doi.org/10.1109/icec.1994.349995openalex-snapshot
On the use of non-stationary penalty functions to solve nonlinear constrained optimization problems with GA's
inferredcites →https://doi.org/10.1109/icpp-w.2008.21openalex-snapshot
Performance Analysis and Optimization of Parallel Scientific Applications on CMP Cluster Systems
inferredcites →https://doi.org/10.1109/ipdps.2005.346openalex-snapshot
Power and Energy Profiling of Scientific Applications on Distributed Systems
inferredcites →https://doi.org/10.1109/ipdps.2009.5161054openalex-snapshot
A scalable auto-tuning framework for compiler optimization
inferredcites →https://doi.org/10.1109/pact.2005.32openalex-snapshot
Multiple page size modeling and optimization
inferredcites →https://doi.org/10.1109/sc.2000.10015openalex-snapshot
Tiling Optimizations for 3D Scientific Computations
inferredcites →https://doi.org/10.1109/sc.2002.10062openalex-snapshot
Active Harmony: Towards Automated Performance Tuning
inferredcites →https://doi.org/10.1109/sc.2005.52openalex-snapshot
Parallel Parameter Tuning for Applications with Performance Variability
inferredcites →https://doi.org/10.1109/secon.2010.5453824openalex-snapshot
PowerMon: Fine-grained and integrated power monitoring for commodity computer systems
inferredcites →https://doi.org/10.1145/1183401.1183437openalex-snapshot
Profitable loop fusion and tiling using model-driven empirical search
inferredcites →https://doi.org/10.1145/1375581.1375594openalex-snapshot
Iterative optimization in the polyhedral model
inferredcites →https://doi.org/10.1145/1375581.1375595openalex-snapshot
A practical automatic polyhedral parallelizer and locality optimizer
inferredcites →https://doi.org/10.1145/268998.266708openalex-snapshot
Agile application-aware adaptation for mobility
inferredcites →https://doi.org/10.1145/269005.266708openalex-snapshot
Agile application-aware adaptation for mobility
inferredcites →https://doi.org/10.1145/379539.379583openalex-snapshot
High-level adaptive program optimization with ADAPT
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1002/cpe.4340openalex-snapshot
Evaluating a formal methodology for dynamic tuning of large‐scale parallel applications
inferred← citeshttps://doi.org/10.1002/cpe.6683openalex-snapshot
Autotuning PolyBench benchmarks with LLVM Clang/Polly loop optimization pragmas using Bayesian optimization
inferred← citeshttps://doi.org/10.1002/cpe.8322openalex-snapshot
ytopt: Autotuning Scientific Applications for Energy Efficiency at Large Scales
inferred← citeshttps://doi.org/10.1007/978-3-030-50743-5_19openalex-snapshot
Offsite Autotuning Approach
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/10.1007/978-3-319-16012-2_5openalex-snapshot
Tuning Plugin Development for the Periscope Tuning Framework
inferred← citeshttps://doi.org/10.1007/978-3-319-47474-8_2openalex-snapshot
Self-aware Computing Systems: Related Concepts and Research Areas
inferred← citeshttps://doi.org/10.1007/978-3-319-92040-5_9openalex-snapshot
Applicability of the ECM Performance Model to Explicit ODE Methods on Current Multi-core Processors
inferred← citeshttps://doi.org/10.1007/s00607-016-0532-7openalex-snapshot
The READEX formalism for automatic tuning for energy efficiency
inferred← citeshttps://doi.org/10.1007/s00607-018-0614-9openalex-snapshot
Using meta-heuristics and machine learning for software optimization of parallel computing systems: a systematic literature review
inferred← citeshttps://doi.org/10.1007/s10489-016-0797-8openalex-snapshot
MASE-BDI: agent-based simulator for environmental land change with efficient and parallel auto-tuning
inferred← citeshttps://doi.org/10.1007/s11219-017-9370-xopenalex-snapshot
A multi-aspect online tuning framework for HPC applications
inferred← citeshttps://doi.org/10.1007/s11227-020-03402-yopenalex-snapshot
A performance- and energy-oriented extended tuning process for time-step-based scientific applications
inferred← citeshttps://doi.org/10.1016/j.future.2020.02.069openalex-snapshot
A benchmark set of highly-efficient CUDA and OpenCL kernels and its dynamic autotuning with Kernel Tuning Toolkit
inferred← citeshttps://doi.org/10.1016/j.jocs.2020.101296openalex-snapshot
AutoPas in ls1 mardyn: Massively parallel particle simulations with node-level auto-tuning
inferred← citeshttps://doi.org/10.1016/j.jpdc.2014.03.006openalex-snapshot
Online auto-tuning for the time-step-based parallel solution of ODEs on shared-memory systems
inferred← citeshttps://doi.org/10.1016/j.parco.2026.103185openalex-snapshot
Towards analysis and refinement of auto-tuning spaces
inferred← citeshttps://doi.org/10.1109/access.2020.3015045openalex-snapshot
Auto-Tuning of Dynamic Scheduling Applied to 3D Reverse Time Migration on Multicore Systems
inferred← citeshttps://doi.org/10.1109/access.2022.3147846openalex-snapshot
A Survey of Performance Tuning Techniques and Tools for Parallel Applications
inferred← citeshttps://doi.org/10.1109/cahpc.2018.8645887openalex-snapshot
Exploring Self-Adaptivity Towards Performance and Energy for Time-Stepping Methods
inferred← citeshttps://doi.org/10.1109/cgo51591.2021.9370316openalex-snapshot
YaskSite: Stencil Optimization Techniques Applied to Explicit ODE Methods on Modern Architectures
inferred← citeshttps://doi.org/10.1109/dac18072.2020.9218734openalex-snapshot
DDOT: Data Driven Online Tuning for energy efficient acceleration
inferred← citeshttps://doi.org/10.1109/hicss.2016.711openalex-snapshot
Tuning OpenCL Applications with the Periscope Tuning Framework
inferred← citeshttps://doi.org/10.1109/iccceee.2018.8515778openalex-snapshot
Phase-Based Performance Optimization Utilizing the Dynamic Behavioral Changes in Applications
inferred← citeshttps://doi.org/10.1109/ipdps.2018.00086openalex-snapshot
Taming the "Monster": Overcoming Program Optimization Challenges on SW26010 Through Precise Performance Modeling
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.2015.11openalex-snapshot
Directive-Based Auto-Tuning for the Finite Difference Method on the Xeon Phi
inferred← citeshttps://doi.org/10.1109/ipdpsw.2016.123openalex-snapshot
Utilization and Expansion of ppOpen-AT for OpenACC
inferred← citeshttps://doi.org/10.1109/ipdpsw.2016.49openalex-snapshot
Auto-Tuning of Hybrid MPI/OpenMP Execution with Code Selection by ppOpen-AT
inferred← citeshttps://doi.org/10.1109/ipdpsw.2019.00125openalex-snapshot
AutoPas: Auto-Tuning for Particle Simulations
inferred← citeshttps://doi.org/10.1109/ispdc51135.2020.00023openalex-snapshot
On-the-fly Optimization of Parallel Computation of Symbolic Symplectic Invariants
inferred← citeshttps://doi.org/10.1109/ispdc69862.2026.00022openalex-snapshot
ONACADI: An Online Autotuner for Compiled Applications Based on Debugger Interfaces
inferred← citeshttps://doi.org/10.1109/jproc.2018.2817118openalex-snapshot
Machine Learning in Compiler Optimization
inferred← citeshttps://doi.org/10.1109/jproc.2018.2841200openalex-snapshot
Autotuning in High-Performance Computing Applications
inferred← citeshttps://doi.org/10.1109/mcsoc.2013.15openalex-snapshot
Early Experiences for Adaptation of Auto-tuning by ppOpen-AT to an Explicit Method
inferred← citeshttps://doi.org/10.1109/mcsoc.2014.22openalex-snapshot
Auto-tuning of Computation Kernels from an FDM Code with ppOpen-AT
inferred← citeshttps://doi.org/10.1109/mcsoc.2016.11openalex-snapshot
Pushing the Limits of Online Auto-Tuning: Machine Code Optimization in Short-Running Kernels
inferred← citeshttps://doi.org/10.1109/pmbs51919.2020.00012openalex-snapshot
Autotuning PolyBench Benchmarks with LLVM Clang/Polly Loop Optimization Pragmas Using Bayesian Optimization
inferred← citeshttps://doi.org/10.1109/samos.2015.7363673openalex-snapshot
Application autotuning to support runtime adaptivity in multicore architectures
inferred← citeshttps://doi.org/10.1109/sc.2012.7openalex-snapshot
A multi-objective auto-tuning framework for parallel codes
inferred← citeshttps://doi.org/10.1109/sds49854.2020.9143911openalex-snapshot
A Parameter Selection Process by Data Analysis for Tuning Multi-threaded Time-Stepping Algorithms
inferred← citeshttps://doi.org/10.1111/cgf.13482openalex-snapshot
Runtime Shader Simplification via Instant Search in Reduced Optimization Space
inferred← citeshttps://doi.org/10.1145/2503210.2503278openalex-snapshot
Taming parallel I/O complexity with auto-tuning
inferred← citeshttps://doi.org/10.1145/2536430.2536437openalex-snapshot
OpenMP and MPI application energy measurement variation
inferred← citeshttps://doi.org/10.1145/2555243.2555249openalex-snapshot
Designing and auto-tuning parallel 3-D FFT for computation-communication overlap
inferred← citeshttps://doi.org/10.1145/2692916.2555249openalex-snapshot
Designing and auto-tuning parallel 3-D FFT for computation-communication overlap
inferred← citeshttps://doi.org/10.1145/3152821.3152877openalex-snapshot
Autotuning of OpenCL Kernels with Global Optimizations
inferred← citeshttps://doi.org/10.1145/3205289.3205321openalex-snapshot
Bootstrapping Parameter Space Exploration for Fast Tuning
inferred← citeshttps://doi.org/10.1145/3297663.3310306openalex-snapshot
Performance Prediction of Explicit ODE Methods on Multi-Core Cluster Systems
inferred← citeshttps://doi.org/10.1145/3309205openalex-snapshot
Optimizing I/O Performance of HPC Applications with Autotuning
inferred← citeshttps://doi.org/10.1145/3330345.3330377openalex-snapshot
Efficient hierarchical online-autotuning
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/10.1145/3577193.3593712openalex-snapshot
Transfer-learning-based Autotuning using Gaussian Copula
inferred← citeshttps://doi.org/10.1155/2014/310879openalex-snapshot
Implementation of D-Spline-Based Incremental Performance Parameter Estimation Method with ppOpen-AT
inferred← citeshttps://doi.org/10.1177/1094342013493124openalex-snapshot
Tools for machine-learning-based empirical autotuning and specialization
inferred← citeshttps://doi.org/10.1177/1094342013493198openalex-snapshot
Towards fully automatic auto-tuning
inferred← citeshttps://doi.org/10.1177/1094342013493644openalex-snapshot
Towards making autotuning mainstream
inferred← citeshttps://doi.org/10.1177/1094342013493939openalex-snapshot
Adaptive parallel tiled code generation and accelerated auto-tuning
inferred← citeshttps://doi.org/10.1177/10943420241286476openalex-snapshot
Integrating ytopt and libEnsemble to autotune OpenMC
inferred← citeshttps://doi.org/10.15439/2018f169openalex-snapshot
On the Autotuning Potential of Time-stepping methods from Scientific Computing
inferred← citeshttps://doi.org/10.48550/arxiv.1304.1835openalex-snapshot
Locality Optimization for Data Parallel Programs
inferred← citeshttps://doi.org/10.48550/arxiv.1612.03413openalex-snapshot
Efficient Methods and Parallel Execution for Algorithm Sensitivity Analysis with Parameter Tuning on Microscopy Imaging Datasets
inferred← citeshttps://doi.org/10.48550/arxiv.1805.03441openalex-snapshot
Machine Learning in Compiler Optimisation
inferred← citeshttps://doi.org/10.48550/arxiv.2004.03695openalex-snapshot
Offsite Autotuning Approach -- Performance Model Driven Autotuning Applied to Parallel Explicit ODE Methods
inferred← citeshttps://doi.org/10.48550/arxiv.2010.08040openalex-snapshot
Autotuning PolyBench Benchmarks with LLVM Clang/Polly Loop Optimization Pragmas Using Bayesian Optimization
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