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Sparse Matrix-vector Multiplication on GPGPU Clusters: A New Storage Format and a Scalable Implementation
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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.1658openalex-snapshot
A new approach for sparse matrix vector product on NVIDIA GPUs
inferredcites →https://doi.org/10.1007/978-3-642-11515-8_10openalex-snapshot
Automatically Tuning Sparse Matrix-Vector Multiplication for GPU Architectures
inferredcites →https://doi.org/10.1007/978-3-642-13872-0_2openalex-snapshot
Performance Limitations for Sparse Matrix-Vector Multiplications on Current Multi-Core Environments
inferredcites →https://doi.org/10.1007/978-3-642-24025-6_1openalex-snapshot
HICFD: Highly Efficient Implementation of CFD Codes for HPC Many-Core Architectures
inferredcites →https://doi.org/10.1016/j.compfluid.2012.02.013openalex-snapshot
Performance engineering for the lattice Boltzmann method on GPGPUs: Architectural requirements and performance results
inferredcites →https://doi.org/10.1142/s0129626411000254openalex-snapshot
HYBRID-PARALLEL SPARSE MATRIX-VECTOR MULTIPLICATION WITH EXPLICIT COMMUNICATION OVERLAP ON CURRENT MULTICORE-BASED SYSTEMS
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/10.2528/pier11031607openalex-snapshot
A MEMORY EFFICIENT AND FAST SPARSE MATRIX VECTOR PRODUCT ON A GPU
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Performance analysis of distributed symmetric sparse matrix vector multiplication algorithm for multi‐core architectures
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Performance optimization of Sparse Matrix‐Vector Multiplication for multi‐component PDE‐based applications using GPUs
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AXC: A new format to perform the SpMV oriented to Intel Xeon Phi architecture in OpenCL
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An efficient sparse matrix‐vector multiplication on CUDA‐enabled graphic processing units for finite element method simulations
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Reconfigurable and Heterogeneous Computing
inferred← citeshttps://doi.org/10.1007/978-3-319-32152-3_14openalex-snapshot
Semiautomatic Acceleration of Sparse Matrix-Vector Product Using OpenACC
inferred← citeshttps://doi.org/10.1007/978-3-319-92040-5_17openalex-snapshot
Chebyshev Filter Diagonalization on Modern Manycore Processors and GPGPUs
inferred← citeshttps://doi.org/10.1007/978-3-662-48096-0_46openalex-snapshot
High Performance Multi-GPU SpMV for Multi-component PDE-Based Applications
inferred← citeshttps://doi.org/10.1007/s00466-018-1623-4openalex-snapshot
The spectral cell method for wave propagation in heterogeneous materials simulated on multiple GPUs and CPUs
inferred← citeshttps://doi.org/10.1007/s00500-023-08125-9openalex-snapshot
GPU implementation of an incomplete Cholesky conjugate gradient solver for a FEM-generated system using full kernel consolidation
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DIESEL: A novel deep learning-based tool for SpMV computations and solving sparse linear equation systems
inferred← citeshttps://doi.org/10.1016/j.cl.2017.06.003openalex-snapshot
Effective Implementation of Matrix–Vector Multiplication on Intel's AVX multicore Processor
inferred← citeshttps://doi.org/10.1016/j.ifacol.2015.10.112openalex-snapshot
The EIT Forward Problem Parallelized Using a Colored pJDS Matrix Format
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An Efficient Storage Format for Storing Configuration Interaction Sparse Matrices on CPU/GPU
inferred← citeshttps://doi.org/10.1109/fpl.2015.7293988openalex-snapshot
Greedy approach based heuristics for partitioning SpMxV on FPGAs
inferred← citeshttps://doi.org/10.1109/hpcsim.2014.6903665openalex-snapshot
Sparse matrix computations on clusters with GPGPUs
inferred← citeshttps://doi.org/10.1109/icssit46314.2019.8987886openalex-snapshot
Dynamic Heterogeneous scheduling of GPU-CPU in Distributed Environment
inferred← citeshttps://doi.org/10.1109/iscas48785.2022.9937748openalex-snapshot
Structured and tiled-based pruning of Deep Learning models targeting FPGA implementations
inferred← citeshttps://doi.org/10.1109/map.2014.6837065openalex-snapshot
GPU accelerated finite-element computation for electromagnetic analysis
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AlphaSparse: Generating High Performance SpMV Codes Directly from Sparse Matrices
inferred← citeshttps://doi.org/10.1109/tc.2014.2366731openalex-snapshot
Performance Optimization Using Partitioned SpMV on GPUs and Multicore CPUs
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Dynamic Probabilistic Pruning: A General Framework for Hardware-Constrained Pruning at Different Granularities
inferred← citeshttps://doi.org/10.1109/tpds.2015.2401575openalex-snapshot
Evaluation Criteria for Sparse Matrix Storage Formats
inferred← citeshttps://doi.org/10.1109/tpds.2022.3170501openalex-snapshot
TaiChi: A Hybrid Compression Format for Binary Sparse Matrix-Vector Multiplication on GPU
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VCSR: An Efficient GPU Memory-Aware Sparse Format
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GPU-Accelerated Sparse Matrix Vector Product based on Element-by-Element Method for Unstructured FEM using OpenACC
inferred← citeshttps://doi.org/10.1137/130930352openalex-snapshot
A Unified Sparse Matrix Data Format for Efficient General Sparse Matrix-Vector Multiplication on Modern Processors with Wide SIMD Units
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On the GPU performance of cell-centered finite volume method over unstructured tetrahedral meshes
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Acceleration of derivative calculations with application to radial basis function
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Sparse Matrix-Vector Multiplication on GPGPUs
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Vectorized Parallel Sparse Matrix-Vector Multiplication in PETSc Using AVX-512
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SURAA: A Novel Method and Tool for Loadbalanced and Coalesced SpMV Computations on GPUs
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Auto-tuning pour la détection automatique du meilleur format de compression pour matrice creuse
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