scholarly journals Fast Sparse Matrix and Sparse Vector Multiplication Algorithm on the GPU

2015 ◽  
Author(s):  
Carl Yang ◽  
Yangzihao Wang ◽  
John D. Owens
Keyword(s):  
Sparse Matrix ◽  
Multiplication Algorithm ◽  
Sparse Vector

scholarly journals On the Implementation of Lattice-Based Identification Schemes

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Information Security ◽  
Sparse Matrix ◽  
Low Complexity ◽  
Experimental Results ◽  
Quantum Computers ◽  
Vector Product ◽  
Identification Schemes ◽  
Sparse Vector ◽  
Post Quantum Cryptography ◽  
Matrix Vector

Identification is one of the important concerns of information security, that is widely used in our daily e-systems to approve authorised users. With the advent of quantum computers, development of quantum secure identification schemes is essential. In this paper, we give the implementation details of quantum secure Kawachi’s and Cayrel’s identification schemes performed in JavaScript. The hardness of these schemes is based on lattice-based problem SIS in post-quantum cryptography, which requires matrix-vector product operations for its execution. It’s important that for efficient implementation choosing an algorithm with low complexity needs more careful. Therefore, in identification schemes chosen for this study, we use algorithms specific to those schemes’ parameter properties. Then, we carry out matrix by sparse vector and sparse matrix by vector product operations.We provide experimental results of both standard and property-specific algorithms’ execution with their comparison. According to the experimental results, we receive improvements in the specific implementations.

Simple sparse matrix multiplication algorithm

1995 ◽  
Vol 85 (2) ◽  
pp. 213-216 ◽  
Author(s):  
Daniel Král ◽  
Pavel Neogrády ◽  
Vladimir Kellö
Keyword(s):  
Sparse Matrix ◽  
Matrix Multiplication ◽  
Matrix Multiplication Algorithm ◽  
Multiplication Algorithm

scholarly journals Performance analysis of distributed symmetric sparse matrix vector multiplication algorithm for multi-core architectures

2015 ◽  
Vol 27 (17) ◽  
pp. 5019-5036 ◽  
Author(s):  
Dossay Oryspayev ◽  
Hasan Metin Aktulga ◽  
Masha Sosonkina ◽  
Pieter Maris ◽  
James P. Vary
Keyword(s):  
Performance Analysis ◽  
Sparse Matrix ◽  
Multiplication Algorithm ◽  
Matrix Vector Multiplication ◽  
Matrix Vector

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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