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Autotuning Runtime Specialization for Sparse Matrix-Vector Multiplication
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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.1732openalex-snapshot
Generating optimal CUDA sparse matrix–vector product implementations for evolving GPU hardware
inferredcites →https://doi.org/10.1007/11428831_13openalex-snapshot
Vectorized Sparse Matrix Multiply for Compressed Row Storage Format
inferredcites →https://doi.org/10.1007/978-3-319-06486-4_7openalex-snapshot
Intel Math Kernel Library
inferredcites →https://doi.org/10.1007/978-3-642-38750-0_12openalex-snapshot
Auto-tuning of Sparse Matrix-Vector Multiplication on Graphics Processors
inferredcites →https://doi.org/10.1007/978-3-662-44199-2_102openalex-snapshot
Generating Optimized Sparse Matrix Vector Product over Finite Fields
inferredcites →https://doi.org/10.1007/s10766-010-0145-2openalex-snapshot
A Library for Pattern-based Sparse Matrix Vector Multiply
inferredcites →https://doi.org/10.1016/b978-044482851-4.50030-xopenalex-snapshot
Towards Realistic Performance Bounds for Implicit CFD Codes
inferredcites →https://doi.org/10.1016/j.parco.2008.12.006openalex-snapshot
Optimization of sparse matrix-vector multiplication on emerging multicore platforms
inferredcites →https://doi.org/10.1016/j.procs.2010.04.016openalex-snapshot
Runtime sparse matrix format selection
inferredcites →https://doi.org/10.1016/j.scico.2004.03.011openalex-snapshot
A tour of Tempo: a program specializer for the C language
inferredcites →https://doi.org/10.1016/j.scico.2008.09.008openalex-snapshot
Multi-stage programming with functors and monads: Eliminating abstraction overhead from generic code
inferredcites →https://doi.org/10.1016/s0167-8191(00)00087-9openalex-snapshot
Automated empirical optimizations of software and the ATLAS project
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/10.1108/ir.1999.04926fae.001openalex-snapshot
Lecture Notes in Computer Science 1205
inferredcites →https://doi.org/10.1109/clustr.2008.4663802openalex-snapshot
Reinforcement learning for automated performance tuning: Initial evaluation for sparse matrix format selection
inferredcites →https://doi.org/10.1109/icpp.2011.53openalex-snapshot
Optimizing SpMV for Diagonal Sparse Matrices on GPU
inferredcites →https://doi.org/10.1109/ipdps.2011.73openalex-snapshot
Reduced-Bandwidth Multithreaded Algorithms for Sparse Matrix-Vector Multiplication
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.2014.160openalex-snapshot
Predicting an Optimal Sparse Matrix Format for SpMV Computation on GPU
inferredcites →https://doi.org/10.1109/jproc.2004.840306openalex-snapshot
SPIRAL: Code Generation for DSP Transforms
inferredcites →https://doi.org/10.1109/pdp.2008.41openalex-snapshot
Understanding the Performance of Sparse Matrix-Vector Multiplication
inferredcites →https://doi.org/10.1109/tpds.2013.123openalex-snapshot
A Performance Modeling and Optimization Analysis Tool for Sparse Matrix-Vector Multiplication on GPUs
inferredcites →https://doi.org/10.1137/1.9780898718003openalex-snapshot
Iterative Methods for Sparse Linear Systems
inferredcites →https://doi.org/10.1145/1183401.1183444openalex-snapshot
Accelerating sparse matrix computations via data compression
inferredcites →https://doi.org/10.1145/1583991.1584053openalex-snapshot
Parallel sparse matrix-vector and matrix-transpose-vector multiplication using compressed sparse blocks
inferredcites →https://doi.org/10.1145/1654059.1654078openalex-snapshot
Implementing sparse matrix-vector multiplication on throughput-oriented processors
inferredcites →https://doi.org/10.1145/1693453.1693471openalex-snapshot
Model-driven autotuning of sparse matrix-vector multiply on GPUs
inferredcites →https://doi.org/10.1145/1837853.1693471openalex-snapshot
Model-driven autotuning of sparse matrix-vector multiply on GPUs
inferredcites →https://doi.org/10.1145/1880037.1880041openalex-snapshot
Exploiting compression opportunities to improve SpMxV performance on shared memory systems
inferredcites →https://doi.org/10.1145/1964179.1964196openalex-snapshot
Automatically generating and tuning GPU code for sparse matrix-vector multiplication from a high-level representation
inferredcites →https://doi.org/10.1145/2049662.2049663openalex-snapshot
The university of Florida sparse matrix collection
inferredcites →https://doi.org/10.1145/231379.231407openalex-snapshot
Optimizing ML with run-time code generation
inferredcites →https://doi.org/10.1145/2426890.2426917openalex-snapshot
Shonan challenge for generative programming
inferredcites →https://doi.org/10.1145/2464996.2465013openalex-snapshot
Efficient sparse matrix-vector multiplication on x86-based many-core processors
inferredcites →https://doi.org/10.1145/2480359.2429128openalex-snapshot
Optimizing data structures in high-level programs
inferredcites →https://doi.org/10.1145/2658761.2658773openalex-snapshot
Optimization by runtime specialization for sparse matrix-vector multiplication
inferredcites →https://doi.org/10.1145/2737924.2737969openalex-snapshot
Autotuning algorithmic choice for input sensitivity
inferredcites →https://doi.org/10.1145/2737924.2738003openalex-snapshot
Loop and data transformations for sparse matrix code
inferredcites →https://doi.org/10.1145/2775053.2658773openalex-snapshot
Optimization by runtime specialization for sparse matrix-vector multiplication
inferredcites →https://doi.org/10.1145/2813885.2737969openalex-snapshot
Autotuning algorithmic choice for input sensitivity
inferredcites →https://doi.org/10.1145/2813885.2738003openalex-snapshot
Loop and data transformations for sparse matrix code
inferredcites →https://doi.org/10.1145/301618.301661openalex-snapshot
A fast Fourier transform compiler
inferredcites →https://doi.org/10.1145/321556.321565openalex-snapshot
Symbolic Generation of an Optimal Crout Algorithm for Sparse Systems of Linear Equations
inferredcites →https://doi.org/10.1145/335231.335240openalex-snapshot
Next-generation generic programming and its application to sparse matrix computations
inferredcites →https://doi.org/10.1145/76263.76272openalex-snapshot
Supercomputing of circuits simulation
inferredcites →https://doi.org/10.1177/1094342004038951openalex-snapshot
Optimizing Sparse Matrix–Vector Product Computations Using Unroll and Jam
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/10.48550/arxiv.1201.0490openalex-snapshot
Scikit-learn: Machine Learning in Python
inferredcites →https://doi.org/10.5555/1953048.2078195openalex-snapshot
Scikit-learn: Machine Learning in Python
inferredcites →https://doi.org/10.5555/776261.776266openalex-snapshot
Jumbo: run-time code generation for Java and its applications
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.4701openalex-snapshot
A sparse matrix‐vector multiplication method with low preprocessing cost
inferred← citeshttps://doi.org/10.1007/s11036-019-01318-3openalex-snapshot
ZAKI: A Smart Method and Tool for Automatic Performance Optimization of Parallel SpMV Computations on Distributed Memory Machines
inferred← citeshttps://doi.org/10.1109/candarw.2018.00055openalex-snapshot
Preconditioner Auto-Tuning Using Deep Learning for Sparse Iterative Algorithms
inferred← citeshttps://doi.org/10.1109/ipdps.2018.00104openalex-snapshot
Overhead-Conscious Format Selection for SpMV-Based Applications
inferred← citeshttps://doi.org/10.1109/jproc.2018.2857721openalex-snapshot
The Sparse Polyhedral Framework: Composing Compiler-Generated Inspector-Executor Code
inferred← citeshttps://doi.org/10.1109/tpds.2018.2871189openalex-snapshot
Performance-Aware Model for Sparse Matrix-Matrix Multiplication on the Sunway TaihuLight Supercomputer
inferred← citeshttps://doi.org/10.1109/tpds.2019.2932931openalex-snapshot
Enabling Runtime SpMV Format Selection through an Overhead Conscious Method
inferred← citeshttps://doi.org/10.1109/tpds.2024.3488053openalex-snapshot
DyLaClass: Dynamic Labeling Based Classification for Optimal Sparse Matrix Format Selection in Accelerating SpMV
inferred← citeshttps://doi.org/10.12694/scpe.v19i1.1390openalex-snapshot
SCALE-EA: A Scalability Aware Performance Tuning Framework for OpenMP Applications
inferred← citeshttps://doi.org/10.21205/deufmd.2020226501openalex-snapshot
Duff Aygıtı Tabanlı Seyrek Matris-Vektör Çarpımı
inferred← citeshttps://doi.org/10.48550/arxiv.1802.00699openalex-snapshot
Data Dwarfs: A Lens Towards Fully Understanding Big Data and AI Workloads
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