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Accelerating Sparse Matrix Vector Multiplication in Iterative Methods Using GPU
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Outgoing claims: the papers this work cites, builds on, or argues with.
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Accelerating Large Graph Algorithms on the GPU Using CUDA
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Implementing Blocked Sparse Matrix-Vector Multiplication on NVIDIA GPUs
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Automatically Tuning Sparse Matrix-Vector Multiplication for GPU Architectures
inferredcites →https://doi.org/10.1016/j.parco.2008.12.006openalex-snapshot
Optimization of sparse matrix-vector multiplication on emerging multicore platforms
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Improving the Performance of the Sparse Matrix Vector Product with GPUs
inferredcites →https://doi.org/10.1109/hipc.2009.5433184openalex-snapshot
Acceleration of conjugate gradient method for circuit simulation using CUDA
inferredcites →https://doi.org/10.1109/ipdps.2007.370266openalex-snapshot
On the Design and Analysis of Irregular Algorithms on the Cell Processor: A Case Study of List Ranking
inferredcites →https://doi.org/10.1109/ipdps.2009.5161005openalex-snapshot
Designing efficient sorting algorithms for manycore GPUs
inferredcites →https://doi.org/10.1109/ipdps.2010.5470455openalex-snapshot
Optimization of linked list prefix computations on multithreaded GPUs using CUDA
inferredcites →https://doi.org/10.1109/tpds.2007.70811openalex-snapshot
Efficient Breadth-First Search on the Cell/BE Processor
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Applied Numerical Linear Algebra
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Implementation of a portable nested data-parallel language
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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/10.1145/2049662.2049663openalex-snapshot
The university of Florida sparse matrix collection
inferredcites →https://doi.org/10.14778/1938545.1938548openalex-snapshot
Fast sparse matrix-vector multiplication on GPUs
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Scan Primitives for GPU Computing
inferredcites →https://doi.org/10.48550/arxiv.1103.2405openalex-snapshot
Fast Sparse Matrix-Vector Multiplication on GPUs: Implications for Graph Mining
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Applied numerical linear algebra
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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A segment‐based sparse matrix–vector multiplication on CUDA
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Recursive Hybrid Compression for Sparse Matrix‐Vector Multiplication on GPU
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Performance Portability of SpMV for CSR and BSR Storage Formats Implemented Using OpenACC and SYCL
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Semiautomatic Acceleration of Sparse Matrix-Vector Product Using OpenACC
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Optimization of Sparse Matrix-Vector Multiplication for CRS Format on NVIDIA Kepler Architecture GPUs
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SpMV and BiCG-Stab optimization for a class of hepta-diagonal-sparse matrices on GPU
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Conjugate gradient method with graphics processing unit acceleration: CUDA vs OpenCL
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MPI-CUDA sparse matrix–vector multiplication for the conjugate gradient method with an approximate inverse preconditioner
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MMSparse: 2D partitioning of sparse matrix based on mathematical morphology
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CUDA-enabled Sparse Matrix–Vector Multiplication on GPUs using atomic operations
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Efficient automatic parallelization of a single GPU program for a multiple GPU system
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Optimized Schwarz method without overlap for the gravitational potential equation on cluster of graphics processing unit
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An Efficient Storage Format for Storing Configuration Interaction Sparse Matrices on CPU/GPU
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Iterative Methods for Sparse Linear Systems on Graphics Processing Unit
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Accelerated Solution of Helmholtz Equation with Iterative Krylov Methods on GPU
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Optimizing Sparse Matrix-Vector Multiplication on GPUs via Index Compression
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LSRB-CSR: A Low Overhead Storage Format for SpMV on the GPU Systems
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Adapting Sparse Triangular Solution to GPUs
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Predicting an Optimal Sparse Matrix Format for SpMV Computation on GPU
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A GPU Framework for Sparse Matrix Vector Multiplication
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Research on Performance Optimization for Sparse Matrix-Vector Multiplication in Multi/Many-core Architecture
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Optimizing sparse matrix vector multiplication on emerging multicores
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Analysis of Sparse Matrix-Vector Multiplication Using Iterative Method in CUDA
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Workload Partitioning for Accelerating Applications on Heterogeneous Platforms
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Real-Time Passive Acoustic Mapping Using Sparse Matrix Multiplication
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ParSecureML: An Efficient Parallel Secure Machine Learning Framework on GPUs
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New Sparse Matrix Storage Format to Improve The Performance of Total SPMV Time
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Performance of Portable Sparse Matrix-Vector Product Implemented Using OpenACC
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Fast iterative solvers for large compressed-sparse row linear systems on graphics processing unit
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Fast Iterative Solvers for Large Compressed-Sparse Row Linear Systems on Graphics Processing Unit
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Exploring high performance sparse operations on GPUs
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Heterogeneous parallel algorithms for computational fluid dynamics on unstructured meshes
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