A Method of Ultra-Large-Scale Matrix Inversion Using Block Recursion
Ultra-large-scale matrix inversion has been applied as the fundamental operation of numerous domains, owing to the growth of big data and matrix applications. Using cryptography as an example, the solution of ultra-large-scale linear equations over finite fields is important in many cryptanalysis sc...
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doaj-94850868ff874146ada5c7b7ac46bc8a2020-11-25T04:05:26ZengMDPI AGInformation2078-24892020-11-011152352310.3390/info11110523A Method of Ultra-Large-Scale Matrix Inversion Using Block RecursionHouZhen Wang0Yan Guo1HuanGuo Zhang2Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University, Wuhan 430072, ChinaKey Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University, Wuhan 430072, ChinaKey Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University, Wuhan 430072, ChinaUltra-large-scale matrix inversion has been applied as the fundamental operation of numerous domains, owing to the growth of big data and matrix applications. Using cryptography as an example, the solution of ultra-large-scale linear equations over finite fields is important in many cryptanalysis schemes. However, inverting matrices of extremely high order, such as in millions, is challenging; nonetheless, the need has become increasingly urgent. Hence, we propose a parallel distributed block recursive computing method that can process matrices at a significantly increased scale, based on Strassen’s method; furthermore, we describe the related well-designed algorithm herein. Additionally, the experimental results based on comparison show the efficiency and the superiority of our method. Using our method, up to 140,000 dimensions can be processed in a supercomputing center.https://www.mdpi.com/2078-2489/11/11/523cryptanalysismatrix inversionalgebraic attackdistributed computing |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
HouZhen Wang Yan Guo HuanGuo Zhang |
spellingShingle |
HouZhen Wang Yan Guo HuanGuo Zhang A Method of Ultra-Large-Scale Matrix Inversion Using Block Recursion Information cryptanalysis matrix inversion algebraic attack distributed computing |
author_facet |
HouZhen Wang Yan Guo HuanGuo Zhang |
author_sort |
HouZhen Wang |
title |
A Method of Ultra-Large-Scale Matrix Inversion Using Block Recursion |
title_short |
A Method of Ultra-Large-Scale Matrix Inversion Using Block Recursion |
title_full |
A Method of Ultra-Large-Scale Matrix Inversion Using Block Recursion |
title_fullStr |
A Method of Ultra-Large-Scale Matrix Inversion Using Block Recursion |
title_full_unstemmed |
A Method of Ultra-Large-Scale Matrix Inversion Using Block Recursion |
title_sort |
method of ultra-large-scale matrix inversion using block recursion |
publisher |
MDPI AG |
series |
Information |
issn |
2078-2489 |
publishDate |
2020-11-01 |
description |
Ultra-large-scale matrix inversion has been applied as the fundamental operation of numerous domains, owing to the growth of big data and matrix applications. Using cryptography as an example, the solution of ultra-large-scale linear equations over finite fields is important in many cryptanalysis schemes. However, inverting matrices of extremely high order, such as in millions, is challenging; nonetheless, the need has become increasingly urgent. Hence, we propose a parallel distributed block recursive computing method that can process matrices at a significantly increased scale, based on Strassen’s method; furthermore, we describe the related well-designed algorithm herein. Additionally, the experimental results based on comparison show the efficiency and the superiority of our method. Using our method, up to 140,000 dimensions can be processed in a supercomputing center. |
topic |
cryptanalysis matrix inversion algebraic attack distributed computing |
url |
https://www.mdpi.com/2078-2489/11/11/523 |
work_keys_str_mv |
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