Streaming Sparse Matrix Compression/Decompression

2005 ◽  
pp. 116-129 ◽  
Author(s):  
David Moloney ◽  
Dermot Geraghty ◽  
Colm McSweeney ◽  
Ciaran McElroy
Keyword(s):  
Sparse Matrix ◽  
Matrix Compression

Machine Learning to Design an Auto-tuning System for the Best Compressed Format Detection for Parallel Sparse Computations

2021 ◽  
Author(s):  
Olfa Hamdi-Larbi ◽  
Ichrak Mehrez ◽  
Thomas Dufaud
Keyword(s):  
Machine Learning ◽  
Numerical Method ◽  
High Performance ◽  
Programming Model ◽  
Learning Algorithm ◽  
Sparse Matrix ◽  
Sparse Matrices ◽  
Matrix Compression ◽  
Target Architecture ◽  
Parallel Programming Model

Many applications in scientific computing process very large sparse matrices on parallel architectures. The presented work in this paper is a part of a project where our general aim is to develop an auto-tuner system for the selection of the best matrix compression format in the context of high-performance computing. The target smart system can automatically select the best compression format for a given sparse matrix, a numerical method processing this matrix, a parallel programming model and a target architecture. Hence, this paper describes the design and implementation of the proposed concept. We consider a case study consisting of a numerical method reduced to the sparse matrix vector product (SpMV), some compression formats, the data parallel as a programming model and, a distributed multi-core platform as a target architecture. This study allows extracting a set of important novel metrics and parameters which are relative to the considered programming model. Our metrics are used as input to a machine-learning algorithm to predict the best matrix compression format. An experimental study targeting a distributed multi-core platform and processing random and real-world matrices shows that our system can improve in average up to 7% the accuracy of the machine learning.

FEM-Based Modeling and Deformation of Soft Tissue Accelerated by CUSPARSE and CUBLAS

2013 ◽  
Vol 671-674 ◽  
pp. 3200-3203 ◽  
Author(s):  
Bing Yang ◽  
Xi Chen ◽  
Xiang Yun Liao ◽  
Mian Lun Zheng ◽  
Zhi Yong Yuan
Keyword(s):  
Soft Tissue ◽  
Sparse Matrix ◽  
Research Field ◽  
Virtual Surgery ◽  
Compute Unified Device Architecture ◽  
Device Architecture ◽  
Matrix Compression ◽  
Compressed Sparse Row ◽  
Surgery Simulator ◽  
Conjugate Gradient Solver

Realistic modeling and deformation of soft tissue is one of the key technologies of virtual surgery simulation which is a challenging research field that stimulates the development of new clinical applications such as the virtual surgery simulator. In this paper we adopt the linear FEM (Finite Element Method) and sparse matrix compression stored in CSR (Compressed Sparse Row) format that enables fast modeling and deformation of soft tissue on GPU hardware with NVIDIA’s CUSPARSE (Compute Unified Device Architecture Sparse Matrix) and CUBLAS (Compute Unified Device Architecture Basic Linear Algebra Subroutines) library. We focus on the CGS (Conjugate Gradient Solver) which is the mainly time-consuming part of FEM, and transplant it onto GPU with the two libraries mentioned above. The experimental results show that the accelerating method in this paper can achieve realistic and fast modeling and deformation simulation of soft tissue.

Accelerate DNN Performance with Sparse Matrix Compression in Halide

2019 ◽  
Author(s):  
Chao-Lin Lee ◽  
Chen-Ting Chao ◽  
Jenq-Kuen Lee ◽  
Ming-Yu Hung ◽  
Chung-Wen Huang
Keyword(s):  
Sparse Matrix ◽  
Matrix Compression

Sparse-Matrix Compression Primitives with OpenCL Framework to Support Halide

2019 ◽  
Author(s):  
Chao-Lin Lee ◽  
Chen-Ting Chao ◽  
Jenq-Kuen Lee ◽  
Chung-Wen Huang ◽  
Ming-Yu Hung
Keyword(s):  
Sparse Matrix ◽  
Matrix Compression

An improvement to Ziegler's sparse matrix compression algorithm

1996 ◽  
Vol 35 (1) ◽  
pp. 67-71 ◽  
Author(s):  
C.C. Chang ◽  
D.J. Buehrer ◽  
H.C. Kowng
Keyword(s):  
Sparse Matrix ◽  
Compression Algorithm ◽  
Matrix Compression

The Impact of Missing and Error-Prone Auxiliary Information on Sparse-Matrix Sub-Population Parameter Estimates

Methodology ◽  
2015 ◽  
Vol 11 (3) ◽  
pp. 89-99 ◽  
Author(s):  
Leslie Rutkowski ◽  
Yan Zhou
Keyword(s):  
Sparse Matrix ◽  
Small Body ◽  
Auxiliary Information ◽  
Poor Quality ◽  
Quality Data ◽  
Estimation Methods ◽  
Parameter Estimates ◽  
Population Parameter ◽  
Conditioning Model ◽  
The Impact

Abstract. Given a consistent interest in comparing achievement across sub-populations in international assessments such as TIMSS, PIRLS, and PISA, it is critical that sub-population achievement is estimated reliably and with sufficient precision. As such, we systematically examine the limitations to current estimation methods used by these programs. Using a simulation study along with empirical results from the 2007 cycle of TIMSS, we show that a combination of missing and misclassified data in the conditioning model induces biases in sub-population achievement estimates, the magnitude and degree to which can be readily explained by data quality. Importantly, estimated biases in sub-population achievement are limited to the conditioning variable with poor-quality data while other sub-population achievement estimates are unaffected. Findings are generally in line with theory on missing and error-prone covariates. The current research adds to a small body of literature that has noted some of the limitations to sub-population estimation.

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