Results 201 to 210 of about 3,401,789 (270)

Hyperspectral Images Denoising via Nonconvex Regularized Low-Rank and Sparse Matrix Decomposition

IEEE Transactions on Image Processing, 2020
Hyperspectral images (HSIs) are often degraded by a mixture of various types of noise during the imaging process, including Gaussian noise, impulse noise, and stripes. Such complex noise could plague the subsequent HSIs processing.
Ting Xie, Shutao Li, Bin Sun
semanticscholar   +3 more sources

Adaptive sparse matrix-matrix multiplication on the GPU

Proceedings of the 24th Symposium on Principles and Practice of Parallel Programming, 2019
In the ongoing efforts targeting the vectorization of linear algebra primitives, sparse matrix-matrix multiplication (SpGEMM) has received considerably less attention than sparse Matrix-Vector multiplication (SpMV). While both are equally important, this disparity can be attributed mainly to the additional formidable challenges raised by SpGEMM.
Winter, M., Mlakar, D., Zayer, R., Seidel, H. ; https://orcid.org/0000-0002-1343-8613, Steinberger, M.   +4 more
openaire   +3 more sources

The university of Florida sparse matrix collection

ACM Transactions on Mathematical Software, 2011
T. Davis, Yifan Hu
semanticscholar   +3 more sources

SpaceA: Sparse Matrix Vector Multiplication on Processing-in-Memory Accelerator

International Symposium on High-Performance Computer Architecture, 2021
Sparse matrix-vector multiplication (SpMV) is an important primitive across a wide range of application domains such as scientific computing and graph analytics.
Xinfeng Xie, Zheng Liang, P. Gu, Abanti Basak, Lei Deng, Ling Liang, Xing Hu, Yuan Xie   +7 more
semanticscholar   +1 more source

TileSpMV: A Tiled Algorithm for Sparse Matrix-Vector Multiplication on GPUs

IEEE International Parallel and Distributed Processing Symposium, 2021
With the extensive use of GPUs in modern supercomputers, accelerating sparse matrix-vector multiplication (SpMV) on GPUs received much attention in the last couple of decades.
Yuyao Niu, Zhengyang Lu, Meichen Dong, Zhou Jin, Weifeng Liu, Guangming Tan   +5 more
semanticscholar   +1 more source

TileSpGEMM: a tiled algorithm for parallel sparse general matrix-matrix multiplication on GPUs

ACM SIGPLAN Symposium on Principles & Practice of Parallel Programming, 2022
Sparse general matrix-matrix multiplication (SpGEMM) is one of the most fundamental building blocks in sparse linear solvers, graph processing frameworks and machine learning applications.
Yuyao Niu, Zhengyang Lu, H. Ji, Shuhui Song, Zhou Jin, Weifeng Liu   +5 more
semanticscholar   +1 more source

MatRaptor: A Sparse-Sparse Matrix Multiplication Accelerator Based on Row-Wise Product

Micro, 2020
Sparse-sparse matrix multiplication (SpGEMM) is a computation kernel widely used in numerous application domains such as data analytics, graph processing, and scientific computing.
Nitish Srivastava, Hanchen Jin, Jie Liu, D. Albonesi, Zhiru Zhang   +4 more
semanticscholar   +1 more source

A Low-Rank and Sparse Matrix Decomposition-Based Mahalanobis Distance Method for Hyperspectral Anomaly Detection

IEEE Transactions on Geoscience and Remote Sensing, 2016
Yuxiang Zhang, Bo Du, Liangpei Zhang, Shugen Wang   +3 more
semanticscholar   +3 more sources

Total Variation Regularized Reweighted Sparse Nonnegative Matrix Factorization for Hyperspectral Unmixing

IEEE Transactions on Geoscience and Remote Sensing, 2017
Wei He, Hongyan Zhang, Liangpei Zhang
semanticscholar   +3 more sources