Performance analysis of parallel programming models for compute-intensive problems in multi-core environment

Parallel programming models have become commonplace, and these models allow developers and programmers to deal with data in many ways. There are many parallel programming models available to date, however, the current study has chosen the seven most recognized parallel programming paradigms to be co...

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Autori principali: Gardan, Gamil, Abdul Hamid, Nor Asilah Wati, Al-Khaffaf, Mustafa Saleh Mahdi
Natura: Articolo
Pubblicazione: World Academy of Research in Science and Engineering 2019
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author Gardan, Gamil
Abdul Hamid, Nor Asilah Wati
Al-Khaffaf, Mustafa Saleh Mahdi
author_facet Gardan, Gamil
Abdul Hamid, Nor Asilah Wati
Al-Khaffaf, Mustafa Saleh Mahdi
author_sort Gardan, Gamil
collection UPM
description Parallel programming models have become commonplace, and these models allow developers and programmers to deal with data in many ways. There are many parallel programming models available to date, however, the current study has chosen the seven most recognized parallel programming paradigms to be compared and benchmarked, namely MPI (point-to-point and collective), OpenMP, PThreads, TBB and hybrid (MPI/OpenMP and MPI/PThreads). Besides that, the benchmark used in this study is matrix multiplication, and they are evaluated based on different matrix sizes. The execution time, speedup, and efficiency of the models are used to analyse the behaviours of these models with different number of processors and matrix sizes. The results have demonstrated that, in most cases, OpenMP and MPI (Point-to-Point) are ideal for compute-intensive problems, and they both benefit from many-core architecture. In addition, the findings have also exhibited that TBB provides good performance with low programming complexity and code changes, especially with small sized computation problems.
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spelling upm.eprints-816662023-09-29T01:33:48Z http://psasir.upm.edu.my/id/eprint/81666/ Performance analysis of parallel programming models for compute-intensive problems in multi-core environment Gardan, Gamil Abdul Hamid, Nor Asilah Wati Al-Khaffaf, Mustafa Saleh Mahdi Parallel programming models have become commonplace, and these models allow developers and programmers to deal with data in many ways. There are many parallel programming models available to date, however, the current study has chosen the seven most recognized parallel programming paradigms to be compared and benchmarked, namely MPI (point-to-point and collective), OpenMP, PThreads, TBB and hybrid (MPI/OpenMP and MPI/PThreads). Besides that, the benchmark used in this study is matrix multiplication, and they are evaluated based on different matrix sizes. The execution time, speedup, and efficiency of the models are used to analyse the behaviours of these models with different number of processors and matrix sizes. The results have demonstrated that, in most cases, OpenMP and MPI (Point-to-Point) are ideal for compute-intensive problems, and they both benefit from many-core architecture. In addition, the findings have also exhibited that TBB provides good performance with low programming complexity and code changes, especially with small sized computation problems. World Academy of Research in Science and Engineering 2019 Article PeerReviewed Gardan, Gamil and Abdul Hamid, Nor Asilah Wati and Al-Khaffaf, Mustafa Saleh Mahdi (2019) Performance analysis of parallel programming models for compute-intensive problems in multi-core environment. International Journal of Advanced Trends in Computer Science and Engineering, 8 (1.4). pp. 272-280. ISSN 2278-3091 http://www.warse.org/IJATCSE/ 10.30534/ijatcse/2019/4281.42019
spellingShingle Gardan, Gamil
Abdul Hamid, Nor Asilah Wati
Al-Khaffaf, Mustafa Saleh Mahdi
Performance analysis of parallel programming models for compute-intensive problems in multi-core environment
title Performance analysis of parallel programming models for compute-intensive problems in multi-core environment
title_full Performance analysis of parallel programming models for compute-intensive problems in multi-core environment
title_fullStr Performance analysis of parallel programming models for compute-intensive problems in multi-core environment
title_full_unstemmed Performance analysis of parallel programming models for compute-intensive problems in multi-core environment
title_short Performance analysis of parallel programming models for compute-intensive problems in multi-core environment
title_sort performance analysis of parallel programming models for compute intensive problems in multi core environment
work_keys_str_mv AT gardangamil performanceanalysisofparallelprogrammingmodelsforcomputeintensiveproblemsinmulticoreenvironment
AT abdulhamidnorasilahwati performanceanalysisofparallelprogrammingmodelsforcomputeintensiveproblemsinmulticoreenvironment
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