Results 211 to 220 of about 3,401,789 (270)

Sparse Matrix-Matrix Products Executed Through Coloring

SIAM Journal on Matrix Analysis and Applications, 2015
Summary: Sparse matrix-matrix products appear in multigrid solvers among other applications. Some implementations of these products require the inner product of two sparse vectors. In this paper, we propose a new algorithm for computing sparse matrix-matrix products by exploiting their nonzero structure through the process of graph coloring.
McCourt, Michael, Smith, Barry, Zhang, Hong   +2 more
openaire   +1 more source

Online Learning for Matrix Factorization and Sparse Coding

Journal of machine learning research, 2009
Sparse coding--that is, modelling data vectors as sparse linear combinations of basis elements--is widely used in machine learning, neuroscience, signal processing, and statistics.
J. Mairal, F. Bach, J. Ponce, G. Sapiro
semanticscholar   +1 more source

Performance-Aware Model for Sparse Matrix-Matrix Multiplication on the Sunway TaihuLight Supercomputer

IEEE Transactions on Parallel and Distributed Systems, 2019
General sparse matrix-sparse matrix multiplication (SpGEMM) is one of the fundamental linear operations in a wide variety of scientific applications.
Yuedan Chen, Kenli Li, Wangdong Yang, Guoqing Xiao, Xianghui Xie, Tao Li   +5 more
semanticscholar   +1 more source

Soft detection of 5-day BOD with sparse matrix in city harbor water using deep learning techniques.

Water Research, 2019
To better control and manage harbor water quality is an important mission for coastal cities such as New York City (NYC). To achieve this, managers and governors need keep track of key quality indicators, such as temperature, pH, and dissolved oxygen ...
Jun Ma, Yuexiong Ding, Jack C. P. Cheng, Feifeng Jiang, Zherui Xu   +4 more
semanticscholar   +1 more source

IA-SpGEMM: an input-aware auto-tuning framework for parallel sparse matrix-matrix multiplication

International Conference on Supercomputing, 2019
Sparse matrix-matrix multiplication (SpGEMM) is a sparse kernel that is used in a number of scientific applications. Although several SpGEMM algorithms have been proposed, almost all of them are restricted to the compressed sparse row (CSR) format, and ...
Zhen Xie, Guangming Tan, Weifeng Liu, Ninghui Sun   +3 more
semanticscholar   +1 more source

Design Principles for Sparse Matrix Multiplication on the GPU

European Conference on Parallel Processing, 2018
We implement two novel algorithms for sparse-matrix dense-matrix multiplication (SpMM) on the GPU. Our algorithms expect the sparse input in the popular compressed-sparse-row (CSR) format and thus do not require expensive format conversion.
Carl Yang, A. Buluç, John Douglas Owens
semanticscholar   +1 more source

A User-Friendly Hybrid Sparse Matrix Class in C++

International Congress on Mathematical Software, 2018
When implementing functionality which requires sparse matrices, there are numerous storage formats to choose from, each with advantages and disadvantages. To achieve good performance, several formats may need to be used in one program, requiring explicit
Conrad Sanderson, Ryan R. Curtin
semanticscholar   +1 more source

SPARSE MATRIX–VECTOR MULTIPLICATION

2004
Abstract This chapter introduces irregular algorithms and presents the example of parallel sparse matrix-vector multiplication (SpMV), which is the central operation in iterative linear system solvers. The irregular sparsity pattern of the matrix does not change during the multiplication, which may be repeated many times.
openaire   +1 more source

Spectral-spatial stacked autoencoders based on low-rank and sparse matrix decomposition for hyperspectral anomaly detection

Infrared physics & technology, 2018
Nowadays, some algorithms based on deep learning have drawn increasing attention in hyperspectral image (HSI) analysis. In this paper, we propose spectral-spatial stacked autoencoders based on low-rank and sparse matrix decomposition (LRaSMD-SSSAE) for ...
C. Zhao, Lili Zhang
semanticscholar   +1 more source

Optimizing Sparse Matrix—Matrix Multiplication for the GPU

ACM Transactions on Mathematical Software, 2015
Sparse matrix--matrix multiplication (SpGEMM) is a key operation in numerous areas from information to the physical sciences. Implementing SpGEMM efficiently on throughput-oriented processors, such as the graphics processing unit (GPU), requires the programmer to expose substantial fine-grained parallelism while conserving the limited off-chip memory ...
Dalton, Steven, Bell, Nathan, Olson, Luke   +2 more
openaire   +2 more sources