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Nitro: A Framework for Adaptive Code Variant 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/10.1007/11557654_91openalex-snapshot
Fast Sparse Matrix-Vector Multiplication by Exploiting Variable Block Structure
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Efficient Management of Parallelism in Object-Oriented Numerical Software Libraries
inferredcites →https://doi.org/10.1007/978-3-540-88682-2_24openalex-snapshot
Hamming Embedding and Weak Geometric Consistency for Large Scale Image Search
inferredcites →https://doi.org/10.1007/978-3-642-01970-8_45openalex-snapshot
Towards Low-Cost, High-Accuracy Classifiers for Linear Solver Selection
inferredcites →https://doi.org/10.1016/s0065-2458(08)60520-3openalex-snapshot
The Algorithm Selection Problem
inferredcites →https://doi.org/10.1080/00401706.1999.10485951openalex-snapshot
Statistical Learning Theory
inferredcites →https://doi.org/10.1088/1742-6596/16/1/071openalex-snapshot
OSKI: A library of automatically tuned sparse matrix kernels
inferredcites →https://doi.org/10.1109/cgo.2005.24openalex-snapshot
Optimizing Sorting with Genetic Algorithms
inferredcites →https://doi.org/10.1109/cvpr.2009.5206627openalex-snapshot
Multi-class active learning for image classification
inferredcites →https://doi.org/10.1109/icpp.2005.77openalex-snapshot
Tuning High Performance Kernels through Empirical Compilation
inferredcites →https://doi.org/10.1109/ipdps.2007.370637openalex-snapshot
POET: Parameterized Optimizations for Empirical Tuning
inferredcites →https://doi.org/10.1109/ipdps.2009.5161004openalex-snapshot
Annotation-based empirical performance tuning using Orio
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.2010.5470421openalex-snapshot
An auto-tuning framework for parallel multicore stencil computations
inferredcites →https://doi.org/10.1109/ipdps.2010.5470479openalex-snapshot
Offline library adaptation using automatically generated heuristics
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/jproc.2004.840306openalex-snapshot
SPIRAL: Code Generation for DSP Transforms
inferredcites →https://doi.org/10.1109/sc.2002.10062openalex-snapshot
Active Harmony: Towards Automated Performance Tuning
inferredcites →https://doi.org/10.1109/sc.2006.55openalex-snapshot
Sequoia: Programming the Memory Hierarchy
inferredcites →https://doi.org/10.1109/sc.2008.5222004openalex-snapshot
Stencil computation optimization and auto-tuning on state-of-the-art multicore architectures
inferredcites →https://doi.org/10.1137/1.9780898718133openalex-snapshot
Parallel Processing for Scientific Computing
inferredcites →https://doi.org/10.1145/1065944.1065981openalex-snapshot
A framework for adaptive algorithm selection in STAPL
inferredcites →https://doi.org/10.1145/1654059.1654078openalex-snapshot
Implementing sparse matrix-vector multiplication on throughput-oriented processors
inferredcites →https://doi.org/10.1145/2001576.2001805openalex-snapshot
An efficient evolutionary algorithm for solving incrementally structured problems
inferredcites →https://doi.org/10.1145/2049662.2049663openalex-snapshot
The university of Florida sparse matrix collection
inferredcites →https://doi.org/10.1145/209936.209946openalex-snapshot
High-level optimization via automated statistical modeling
inferredcites →https://doi.org/10.1145/2591635.2667174openalex-snapshot
Optimizing matrix multiply using PHiPAC
inferredcites →https://doi.org/10.1145/263580.263662openalex-snapshot
Optimizing matrix multiply using PHiPAC
inferredcites →https://doi.org/10.1177/1094342004041293openalex-snapshot
Statistical Models for Empirical Search-Based Performance Tuning
inferredcites →https://doi.org/10.1177/1094342004041296openalex-snapshot
Sparsity: Optimization Framework for Sparse Matrix Kernels
inferredcites →https://doi.org/10.1177/1094342010374847openalex-snapshot
Optimizing Sparse Data Structures for Matrix-vector Multiply
inferredcites →https://doi.org/10.1609/socs.v2i1.18184openalex-snapshot
A Preliminary Evaluation of Machine Learning in Algorithm Selection for Search Problems
inferredcites →https://doi.org/10.21236/ada479065openalex-snapshot
The Fastest Fourier Transform in the West
inferredcites →https://doi.org/10.48550/arxiv.1407.4075openalex-snapshot
Finding representative sets of optimizations for adaptive multiversioning applications
inferredcites →https://doi.org/10.5555/1413370.1413375openalex-snapshot
Stencil computation optimization and auto-tuning on state-of-the-art multicore architectures
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Autotuning PolyBench benchmarks with LLVM Clang/Polly loop optimization pragmas using Bayesian optimization
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ytopt: Autotuning Scientific Applications for Energy Efficiency at Large Scales
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A Prediction Framework for Fast Sparse Triangular Solves
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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
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Writing a performance-portable matrix multiplication
inferred← citeshttps://doi.org/10.1109/candarw51189.2020.00043openalex-snapshot
Improving the Accuracy in SpMV Implementation Selection with Machine Learning
inferred← citeshttps://doi.org/10.1109/cgo57630.2024.10444812openalex-snapshot
Seer: Predictive Runtime Kernel Selection for Irregular Problems
inferred← citeshttps://doi.org/10.1109/icmla.2016.0143openalex-snapshot
Learning the Domain of Sparse Matrices
inferred← citeshttps://doi.org/10.1109/icppw.2015.21openalex-snapshot
HetroCV: Auto-tuning Framework and Runtime for Image Processing and Computer Vision Applications on Heterogeneous Platform
inferred← citeshttps://doi.org/10.1109/iiswc47752.2019.9042139openalex-snapshot
One Size Doesn't Fit All: Quantifying Performance Portability of Graph Applications on GPUs
inferred← citeshttps://doi.org/10.1109/ipdps.2016.106openalex-snapshot
Synchronization Trade-Offs in GPU Implementations of Graph Algorithms
inferred← citeshttps://doi.org/10.1109/ipdps.2016.98openalex-snapshot
Compiler-Assisted Workload Consolidation for Efficient Dynamic Parallelism on GPU
inferred← citeshttps://doi.org/10.1109/ipdps.2017.102openalex-snapshot
Autotuning Stencil Computations with Structural Ordinal Regression Learning
inferred← citeshttps://doi.org/10.1109/ipdps.2017.38openalex-snapshot
Apollo: Reusable Models for Fast, Dynamic Tuning of Input-Dependent Code
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.2016.85openalex-snapshot
Exploiting Performance Portability in Search Algorithms for Autotuning
inferred← citeshttps://doi.org/10.1109/ipdpsw.2017.108openalex-snapshot
Use of Synthetic Benchmarks for Machine-Learning-Based Performance Auto-Tuning
inferred← citeshttps://doi.org/10.1109/ipdpsw.2017.28openalex-snapshot
Online-Autotuning in the Presence of Algorithmic Choice
inferred← citeshttps://doi.org/10.1109/ipdpsw.2017.46openalex-snapshot
A Sampling Based Strategy to Automatic Performance Tuning of GPU Programs
inferred← citeshttps://doi.org/10.1109/jproc.2018.2841200openalex-snapshot
Autotuning in High-Performance Computing Applications
inferred← citeshttps://doi.org/10.1109/mcsoc.2016.46openalex-snapshot
A Code Selection Mechanism Using Deep Learning
inferred← citeshttps://doi.org/10.1109/padsw.2018.8644966openalex-snapshot
REOH: Using Probabilistic Network for Runtime Energy Optimization of Heterogeneous Systems
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/sami63904.2025.10883288openalex-snapshot
Exploring GPT-Generated Variations in C Programming Assignments
inferred← citeshttps://doi.org/10.1109/tc.2025.3558606openalex-snapshot
MLCD: Machine Learning-Based Code Version and Device Selection for Heterogeneous Systems
inferred← citeshttps://doi.org/10.1109/tpds.2020.3040150openalex-snapshot
Adaptive SpMV/SpMSpV on GPUs for Input Vectors of Varied Sparsity
inferred← citeshttps://doi.org/10.1145/2737924.2737969openalex-snapshot
Autotuning algorithmic choice for input sensitivity
inferred← citeshttps://doi.org/10.1145/2813885.2737969openalex-snapshot
Autotuning algorithmic choice for input sensitivity
inferred← citeshttps://doi.org/10.1145/2832087.2832093openalex-snapshot
Guided profiling for auto-tuning array layouts on GPUs
inferred← citeshttps://doi.org/10.1145/2851500openalex-snapshot
Autotuning Runtime Specialization for Sparse Matrix-Vector Multiplication
inferred← citeshttps://doi.org/10.1145/2872362.2872411openalex-snapshot
Architecture-Adaptive Code Variant Tuning
inferred← citeshttps://doi.org/10.1145/2916026.2916031openalex-snapshot
Adaptive GPU Array Layout Auto-Tuning
inferred← citeshttps://doi.org/10.1145/2954679.2872411openalex-snapshot
Architecture-Adaptive Code Variant Tuning
inferred← citeshttps://doi.org/10.1145/2954680.2872411openalex-snapshot
Architecture-Adaptive Code Variant Tuning
inferred← citeshttps://doi.org/10.1145/2980024.2872411openalex-snapshot
Architecture-Adaptive Code Variant Tuning
inferred← citeshttps://doi.org/10.1145/3009837.3009846openalex-snapshot
Rigorous floating-point mixed-precision tuning
inferred← citeshttps://doi.org/10.1145/3012011openalex-snapshot
Designing a Tunable Nested Data-Parallel Programming System
inferred← citeshttps://doi.org/10.1145/3078659.3078664openalex-snapshot
Stencil Autotuning with Ordinal Regression
inferred← citeshttps://doi.org/10.1145/3093333.3009846openalex-snapshot
Rigorous floating-point mixed-precision tuning
inferred← citeshttps://doi.org/10.1145/3126908.3126939openalex-snapshot
Input-aware auto-tuning of compute-bound HPC kernels
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Performance modeling under resource constraints using deep transfer learning
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Bootstrapping Parameter Space Exploration for Fast Tuning
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Efficient Auto-Tuning of Parallel Programs with Interdependent Tuning Parameters via Auto-Tuning Framework (ATF)
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On the Anatomy of Predictive Models for Accelerating GPU Convolution Kernels and Beyond
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Managing code transformations for better performance portability
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Sparse matrix partitioning for optimizing SpMV on CPU-GPU heterogeneous platforms
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Integrating ytopt and libEnsemble to autotune OpenMC
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On the Autotuning Potential of Time-stepping methods from Scientific Computing
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A Machine Learning-Based Approach for Selecting SpMV Kernels and Matrix Storage Formats
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Inter-workgroup barrier synchronisation on graphics processing units
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ACCTuner: OpenACC Auto-Tuner For Accelerated Scientific Applications
inferred← citeshttps://doi.org/10.3389/fdata.2021.657218openalex-snapshot
Autotuning of Exascale Applications With Anomalies Detection
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Adaptive Hybrid Storage Format for Sparse Matrix–Vector Multiplication on Multi-Core SIMD CPUs
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A model-driven approach for a new generation of adaptive libraries
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Autotuning PolyBench Benchmarks with LLVM Clang/Polly Loop Optimization Pragmas Using Bayesian Optimization
inferred← citeshttps://doi.org/10.48550/arxiv.2104.13242openalex-snapshot
Autotuning PolyBench Benchmarks with LLVM Clang/Polly Loop Optimization Pragmas Using Bayesian Optimization (extended version)
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Contribution to the emergence of new intelligent parallel and distributed methods using a multi-level programming paradigm for extreme computing
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