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Reduced-Bandwidth Multithreaded Algorithms 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.

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Cilk: An Efficient Multithreaded Runtime System
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On improving the performance of sparse matrix-vector multiplication
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Performance models for evaluation and automatic tuning of symmetric sparse matrix-vector multiply
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Performance models for evaluation and automatic tuning of symmetric sparse matrix-vector multiply
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On the representation and multiplication of hypersparse matrices
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Characterizing the behavior of sparse algorithms on caches
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On BLAS Operations with Recursively Stored Sparse Matrices
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The Design of OpenMP Tasks
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Cache-Oblivious Sparse Matrix–Vector Multiplication by Using Sparse Matrix Partitioning Methods
inferredcites →https://doi.org/10.1137/s0036144502409019openalex-snapshot
A Two-Dimensional Data Distribution Method for Parallel Sparse Matrix-Vector Multiplication
inferredcites →https://doi.org/10.1142/9781848160170_0036openalex-snapshot
TOWARDS A FAST PARALLEL SPARSE MATRIX-VECTOR MULTIPLICATION
inferredcites →https://doi.org/10.1145/1183401.1183444openalex-snapshot
Accelerating sparse matrix computations via data compression
inferredcites →https://doi.org/10.1145/1248377.1248391openalex-snapshot
Optimal sparse matrix dense vector multiplication in the I/O-model
inferredcites →https://doi.org/10.1145/1366230.1366244openalex-snapshot
Optimizing sparse matrix-vector multiplication using index and value compression
inferredcites →https://doi.org/10.1145/1498765.1498785openalex-snapshot
Roofline
inferredcites →https://doi.org/10.1145/1542275.1542294openalex-snapshot
Pattern-based sparse matrix representation for memory-efficient SMVM kernels
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/10.1145/1654059.1654108openalex-snapshot
Memory-efficient optimization of Gyrokinetic particle-to-grid interpolation for multicore processors
inferredcites →https://doi.org/10.1145/2049662.2049663openalex-snapshot
The university of Florida sparse matrix collection
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Scheduling multithreaded computations by work stealing
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Improving performance of sparse matrix-vector multiplication
inferredcites →https://doi.org/10.1145/800195.805928openalex-snapshot
Reducing the bandwidth of sparse symmetric matrices
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Improving the memory-system performance of sparse-matrix vector multiplication
inferredcites →https://doi.org/10.1177/1094342004041294openalex-snapshot
Sparse Tiling for Stationary Iterative Methods
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Sparsity: Optimization Framework for Sparse Matrix Kernels
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Intel threading building blocks
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Characterizing the behavior of sparse algorithms on caches
inferredcites →https://doi.org/10.7551/mitpress/9486.003.0016openalex-snapshot
Intel Threading Building Blocks

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Performance Modeling of the HPCG Benchmark
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Performance Analysis and Optimization of Sparse Matrix-Vector Multiplication on Modern Multi- and Many-Core Processors
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Dynamic Heterogeneous scheduling of GPU-CPU in Distributed Environment
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Improving the Performance of Dynamical Simulations Via Multiple Right-Hand Sides
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Improving the Performance of the Symmetric Sparse Matrix-Vector Multiplication in Multicore
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An Efficient GPU General Sparse Matrix-Matrix Multiplication for Irregular Data
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TileSpMV: A Tiled Algorithm for Sparse Matrix-Vector Multiplication on GPUs
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Bit-GraphBLAS: Bit-Level Optimizations of Matrix-Centric Graph Processing on GPU
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Performance Analysis and Optimization of Sparse Matrix-Vector Multiplication on Intel Xeon Phi
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Programmable Acceleration for Sparse Matrices in a Data-Movement Limited World
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HETEROGENEOUS ARCHITECTURE FOR SPARSE DATA PROCESSING
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An Extended Compression Format for the Optimization of Sparse Matrix-Vector Multiplication
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High-Level Strategies for Parallel Shared-Memory Sparse Matrix-Vector Multiplication
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Adaptive SpMV/SpMSpV on GPUs for Input Vectors of Varied Sparsity
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HYBRID-PARALLEL SPARSE MATRIX-VECTOR MULTIPLICATION WITH EXPLICIT COMMUNICATION OVERLAP ON CURRENT MULTICORE-BASED SYSTEMS
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Sparse matrix-vector multiply on the HICAMP architecture
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clSpMV
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Ligra
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Efficient sparse matrix-vector multiplication on x86-based many-core processors
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SMAT
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SMAT
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yaSpMV
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Collective memory transfers for multi-core chips
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Acceleration of derivative calculations with application to radial basis function
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Optimization by runtime specialization for sparse matrix-vector multiplication
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yaSpMV
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CSR5
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Optimization by runtime specialization for sparse matrix-vector multiplication
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Autotuning Runtime Specialization for Sparse Matrix-Vector Multiplication
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Parallel Transposition of Sparse Data Structures
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A Cross-Platform SpMV Framework on Many-Core Architectures
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Shared-Memory Parallelism Can Be Simple, Fast, and Scalable
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Preface
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Introduction
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Preliminaries and Notation
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Internally Deterministic Parallelism: Techniques and Algorithms
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Deterministic Parallelism in Sequential Iterative Algorithms
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A Deterministic Phase-Concurrent Parallel Hash Table
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Priority Updates: A Contention-Reducing Primitive for Deterministic Programming
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Ligra: A Lightweight Graph Processing Framework for Shared Memory
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Ligra+: Adding Compression to Ligra
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Linear-Work Parallel Graph Connectivity
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Parallel and Cache-Oblivious Triangle Computations
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Parallel Cartesian Tree and Suffix Tree Construction
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Parallel Computation of Longest Common Prefixes
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Parallel Lempel-Ziv Factorization
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Parallel Wavelet Tree Construction
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Conclusion and Future Work
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References
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On tuning the symmetric sparse matrix vector multiplication with CSR and TJDS
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Novel techniques to improve the performance and the energy of vector architectures
inferred← citeshttps://doi.org/10.6035/14028.2016.34889openalex-snapshot
Consumo energético de métodos iterativos para sistemas dispersos en procesadores gráficos
inferred← citeshttps://doi.org/10.7717/peerj-cs.151openalex-snapshot
Computing the sparse matrix vector product using block-based kernels without zero padding on processors with AVX-512 instructions
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