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

Author(s):  
Carl Yang ◽  
Yangzihao Wang ◽  
John D. Owens

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.


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

2015 ◽  
Vol 27 (17) ◽  
pp. 5019-5036 ◽  
Author(s):  
Dossay Oryspayev ◽  
Hasan Metin Aktulga ◽  
Masha Sosonkina ◽  
Pieter Maris ◽  
James P. Vary

Methodology ◽  
2015 ◽  
Vol 11 (3) ◽  
pp. 89-99 ◽  
Author(s):  
Leslie Rutkowski ◽  
Yan Zhou

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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