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Design and Implementation of Adaptive SpMV Library for Multicore and Many-Core Architecture
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Outgoing claims: the papers this work cites, builds on, or argues with.
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Automatically Tuning Sparse Matrix-Vector Multiplication for GPU Architectures
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CRSD: Application Specific Auto-tuning of SpMV for Diagonal Sparse Matrices
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A framework for general sparse matrix–matrix multiplication on GPUs and heterogeneous processors
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Optimization techniques for sparse matrix–vector multiplication on GPUs
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Optimization of sparse matrix-vector multiplication on emerging multicore platforms
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Speculative segmented sum for sparse matrix-vector multiplication on heterogeneous processors
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Adaptive Multi-level Blocking Optimization for Sparse Matrix Vector Multiplication on GPU
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OSKI: A library of automatically tuned sparse matrix kernels
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Model-Driven Sparse CP Decomposition for Higher-Order Tensors
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2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS)
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Predicting an Optimal Sparse Matrix Format for SpMV Computation on GPU
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The Design and Implementation of FFTW3
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An introduction to algebraic multigrid
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Proceedings 2001 IEEE International Conference on Cluster Computing
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A Unified Sparse Matrix Data Format for Efficient General Sparse Matrix-Vector Multiplication on Modern Processors with Wide SIMD Units
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Model-driven autotuning of sparse matrix-vector multiply on GPUs
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The university of Florida sparse matrix collection
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Efficient sparse matrix-vector multiplication on x86-based many-core processors
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Automatic Selection of Sparse Matrix Representation on GPUs
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An input-adaptive and in-place approach to dense tensor-times-matrix multiply
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Sparse Matrix-Vector Multiplication on GPGPUs
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CVR: efficient vectorization of SpMV on x86 processors
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Register-based implementation of the sparse general matrix-matrix multiplication on GPUs
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Proceedings of the 1997 ACM/IEEE conference on Supercomputing (CDROM) - Supercomputing '97
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What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Observe Locally, Classify Globally: Using GNNs to Identify Sparse Matrix Structure
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Register-Aware Optimizations for Parallel Sparse Matrix–Matrix Multiplication
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DIESEL: A novel deep learning-based tool for SpMV computations and solving sparse linear equation systems
inferred← citeshttps://doi.org/10.1007/s11227-021-04123-6openalex-snapshot
An effective SPMV based on block strategy and hybrid compression on GPU
inferred← citeshttps://doi.org/10.1016/j.future.2020.10.036openalex-snapshot
ALBUS: A method for efficiently processing SpMV using SIMD and Load balancing
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Optimizing sparse tensor times matrix on GPUs
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Morpheus: A library for efficient runtime switching of sparse matrix storage formats
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An adaptive approach for compression format based on bagging algorithm
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HASpMV: Heterogeneity-Aware Sparse Matrix-Vector Multiplication on Modern Asymmetric Multicore Processors
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hpSpMV: A Heterogeneous Parallel Computing Scheme for SpMV on the Sunway TaihuLight Supercomputer
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Efficiently Running SpMV on Multi-Core DSPs for Block Sparse Matrix
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TileSpMV: A Tiled Algorithm for Sparse Matrix-Vector Multiplication on GPUs
inferred← citeshttps://doi.org/10.1109/ipdps54959.2023.00072openalex-snapshot
Feature-based SpMV Performance Analysis on Contemporary Devices
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Optimizing Sparse Linear Algebra Through Automatic Format Selection and Machine Learning
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Towards Dynamic Autotuning of SpMV in CUSP Library
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Exploiting dynamic sparse matrices for performance portable linear algebra operations
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Sampling-based Sparse Format Selection on GPUs
inferred← citeshttps://doi.org/10.1109/sbac-pad55451.2022.00014openalex-snapshot
Mixed and Multi-Precision SpMV for GPUs with Row-wise Precision Selection
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AlphaSparse: Generating High Performance SpMV Codes Directly from Sparse Matrices
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A Parallel Jacobi-Embedded Gauss-Seidel Method
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A Pattern-Based SpGEMM Library for Multi-Core and Many-Core Architectures
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Leveraging One-Sided Communication for Sparse Triangular Solvers
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A Message-Driven, Multi-GPU Parallel Sparse Triangular Solver
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An Autotuning Protocol to Rapidly Build Autotuners
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Explaining the Performance of Supervised and Semi-Supervised Methods for Automated Sparse Matrix Format Selection
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DASP: Specific Dense Matrix Multiply-Accumulate Units Accelerated General Sparse Matrix-Vector Multiplication
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A Heterogeneous Parallel Computing Approach Optimizing SpTTM on CPU-GPU via GCN
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A Survey of Accelerating Parallel Sparse Linear Algebra
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MatXtract: Sparsity-Aware Matrix Transformation via Cascaded Compute Density EXtraction for SpMV
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Demystifying SpMV Performance: Bottleneck Characterization and Open Challenges
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Accelerating irregular applications via efficient synchronization and data access techniques
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Enabling Energy-Efficient and Low-Latency of Sparse Matrix-Vector Multiplication on GPUs
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A Review of Parallel Optimization Techniques of Solving Ultra-Large-Scale Sparse Linear Equations
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