Algorithm Analysis of Sparse Matrix Multiplication

Matrix is widely used in telecommunication, cryptography, computer science and other field. Especially in wireless sensor network data processing, it is important and necessary to keep data transmitting reliable and resilient. In channel coding and secure communication, matrix is used to realize the...

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Bibliographic Details
Published inIEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C) (Online) pp. 912 - 917
Main Authors Ren, Hui, Ma, Hongwei, Kang, Jian, Liu, Yang, Wang, Lu, Zheng, Xiaogang
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2021
Subjects
Benchmark testing
Bit error rate
Communications technology
Computer network reliability
matrix
multiplication
parallel
Software reliability
sparse
Wireless communication
Wireless sensor networks
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Summary:Matrix is widely used in telecommunication, cryptography, computer science and other field. Especially in wireless sensor network data processing, it is important and necessary to keep data transmitting reliable and resilient. In channel coding and secure communication, matrix is used to realize the coding of transmission information and source information in the channel, which not only reduces the bit error rate of wireless communication, but also realizes the confidentiality of communication. The development of effective algorithms for matrix calculation has been an interesting subject for several centuries and an expanding research field. For some widely used and special matrices, such as sparse matrix and quasi diagonal matrix, there are specific fast algorithms. This paper briefly describes and explains our design of serial algorithms which implementing the sparse matrix multiplication by parallel programming, and also to provide benchmark results to justify the correctness and performance of our design.
ISSN:2693-9371
DOI:10.1109/QRS-C55045.2021.00138