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Optimization Method to Reduce Matrices Multiplications in the Context of CUDA

by Arezoo Khatibi, Omid Khatibi
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 182 - Number 15
Year of Publication: 2018
Authors: Arezoo Khatibi, Omid Khatibi
10.5120/ijca2018917780

Arezoo Khatibi, Omid Khatibi . Optimization Method to Reduce Matrices Multiplications in the Context of CUDA. International Journal of Computer Applications. 182, 15 ( Sep 2018), 5-7. DOI=10.5120/ijca2018917780

@article{ 10.5120/ijca2018917780,
author = { Arezoo Khatibi, Omid Khatibi },
title = { Optimization Method to Reduce Matrices Multiplications in the Context of CUDA },
journal = { International Journal of Computer Applications },
issue_date = { Sep 2018 },
volume = { 182 },
number = { 15 },
month = { Sep },
year = { 2018 },
issn = { 0975-8887 },
pages = { 5-7 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume182/number15/29936-2018917780/ },
doi = { 10.5120/ijca2018917780 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:15:01.743158+05:30
%A Arezoo Khatibi
%A Omid Khatibi
%T Optimization Method to Reduce Matrices Multiplications in the Context of CUDA
%J International Journal of Computer Applications
%@ 0975-8887
%V 182
%N 15
%P 5-7
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Parallel programming is an effective way to increase the speed of processing applications. It is carried out simultaneously by multiple processors rather than by a single processor. We compare the number of necessary calculations for multiplying the chain matrix in normal mode with the parallel mode. Since we used the famous parallel language named CUDA in our program, we will first present a brief description of the language and secondly, we explain essential mathematical notions and compare the performance of both programs.

References
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Index Terms

Computer Science
Information Sciences

Keywords

CUDA GPU Parallel programming

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