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Implementation and analysis of parallel algorithm for matrix multiplication with PTHREAD

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Bibliographic Details
Main Author: Nik Nor Raqbiyatul Huda, Abdull Rahim
Other Authors: Dr. Zahereel Ishwar Abdul Khalib
Format: Learning Object
Language:English
Published: Universiti Malaysia Perlis (UniMAP) 2016
Subjects:
Multi-core processors
Processors
Matrix multiplication
Algorithm
Parallel directive
Online Access:http://dspace.unimap.edu.my:80/xmlui/handle/123456789/41883
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spelling my.unimap-418832016-06-07T04:43:32Z Implementation and analysis of parallel algorithm for matrix multiplication with PTHREAD Nik Nor Raqbiyatul Huda, Abdull Rahim Dr. Zahereel Ishwar Abdul Khalib Multi-core processors Processors Matrix multiplication Algorithm Parallel directive Access is limited to UniMAP community. Multi-core processors are becoming common and they have built-in parallel computational power and which can only be fully utilized only if the program in execution is written accordingly. Most software today is grossly inefficient, because it is not written with sufficient parallelism in mind. Breaking up an application into a few tasks is not a long-term solution. In order to make most of multi-core processors either lots and lots of parallelism are actually needed for efficient execution of a program on larger number of cores, or secondly, concurrent execution of multiple programs on multiple cores. Matrix Multiplication is used as building block in many of applications covering nearly all subject areas. Matrix calculation generally classical analytical concept such as derivatives of functions or exponentials to matrices. Serial and parallel matrix multiplication is always be a challenging task for the programmers because of its extensive computation and memory requirement, standard test set and broad use in all types of scientific and desktop applications. With the advent of multi-core processors, it has become more challenging. Now all the processors have built-in parallel computational capacity in form of cores and existing serial and parallel matrix multiplication techniques have to be revisited to fully utilize the available cores and to get the maximum efficiency and the minimum executing time. 2016-06-07T04:43:32Z 2016-06-07T04:43:32Z 2015-06 Learning Object http://dspace.unimap.edu.my:80/xmlui/handle/123456789/41883 en Universiti Malaysia Perlis (UniMAP) School of Computer and Communication Engineering
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Multi-core processors
Processors
Matrix multiplication
Algorithm
Parallel directive
spellingShingle Multi-core processors
Processors
Matrix multiplication
Algorithm
Parallel directive
Nik Nor Raqbiyatul Huda, Abdull Rahim
Implementation and analysis of parallel algorithm for matrix multiplication with PTHREAD
description Access is limited to UniMAP community.
author2 Dr. Zahereel Ishwar Abdul Khalib
author_facet Dr. Zahereel Ishwar Abdul Khalib
Nik Nor Raqbiyatul Huda, Abdull Rahim
format Learning Object
author Nik Nor Raqbiyatul Huda, Abdull Rahim
author_sort Nik Nor Raqbiyatul Huda, Abdull Rahim
title Implementation and analysis of parallel algorithm for matrix multiplication with PTHREAD
title_short Implementation and analysis of parallel algorithm for matrix multiplication with PTHREAD
title_full Implementation and analysis of parallel algorithm for matrix multiplication with PTHREAD
title_fullStr Implementation and analysis of parallel algorithm for matrix multiplication with PTHREAD
title_full_unstemmed Implementation and analysis of parallel algorithm for matrix multiplication with PTHREAD
title_sort implementation and analysis of parallel algorithm for matrix multiplication with pthread
publisher Universiti Malaysia Perlis (UniMAP)
publishDate 2016
url http://dspace.unimap.edu.my:80/xmlui/handle/123456789/41883
_version_ 1643799841007992832
score 13.214268

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