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Chain Multiplication of Dense Matrices: Proposing a Shared Memory based Parallel Algorithm

by Tirtharaj Dash, Tanistha Nayak
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 58 - Number 1
Year of Publication: 2012
Authors: Tirtharaj Dash, Tanistha Nayak
10.5120/9245-3408

Tirtharaj Dash, Tanistha Nayak . Chain Multiplication of Dense Matrices: Proposing a Shared Memory based Parallel Algorithm. International Journal of Computer Applications. 58, 1 ( November 2012), 11-16. DOI=10.5120/9245-3408

@article{ 10.5120/9245-3408,
author = { Tirtharaj Dash, Tanistha Nayak },
title = { Chain Multiplication of Dense Matrices: Proposing a Shared Memory based Parallel Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { November 2012 },
volume = { 58 },
number = { 1 },
month = { November },
year = { 2012 },
issn = { 0975-8887 },
pages = { 11-16 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume58/number1/9245-3408/ },
doi = { 10.5120/9245-3408 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:01:24.227382+05:30
%A Tirtharaj Dash
%A Tanistha Nayak
%T Chain Multiplication of Dense Matrices: Proposing a Shared Memory based Parallel Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 58
%N 1
%P 11-16
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Chain multiplication of matrices is widely used for scientific computing. It becomes more challenging when there is large number of floating point dense matrices. Because, floating point operations take more time than integer operations. It would be interesting to lower the time of such chain operations. Now-a-days every multicore processor system has built in parallel computational power. This power can only be utilized when compatible parallel algorithms were used. So, in this work, a shared memory based parallel algorithms has been proposed to compute the multiplication of a long sequence of dense matrices. The algorithms have been tested with long sequence of matrices as input. The approach has been with 2×108 flops. The input matrix sequence length was typically varied from 2 to 30. Maximum number of processors used was eight (Eight core processor). Different parameters like speedup, efficiency etc. were also noted. It was concluded that the parallel algorithms could achieve approximately 90% efficiency at best case. The algorithms also showed improved scalability.

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Index Terms

Computer Science
Information Sciences

Keywords

Chain multiplication computing dense matrix multicore shared memory flops efficiency speedup scalability

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