On benchmarking task scheduling algorithms for heterogeneous computing systems
Abstract The task scheduling problem on heterogeneous computing systems has been broadly studied, and many heuristic algorithms are proposed to solve this problem. It is interesting to go for significant performance assessment and comparison among these heuristic algorithms. In this work, we first c...
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Gespeichert in:
| Autor*in: |
Maurya, Ashish Kumar [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2018 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Science+Business Media, LLC, part of Springer Nature 2018 |
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| Übergeordnetes Werk: |
Enthalten in: The journal of supercomputing - Springer US, 1987, 74(2018), 7 vom: 07. Apr., Seite 3039-3070 |
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| Übergeordnetes Werk: |
volume:74 ; year:2018 ; number:7 ; day:07 ; month:04 ; pages:3039-3070 |
| Links: |
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| DOI / URN: |
10.1007/s11227-018-2355-0 |
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OLC2033955168 |
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| 520 | |a Abstract The task scheduling problem on heterogeneous computing systems has been broadly studied, and many heuristic algorithms are proposed to solve this problem. It is interesting to go for significant performance assessment and comparison among these heuristic algorithms. In this work, we first carry out performance evaluation and comparison of task scheduling algorithms for heterogeneous computing systems for randomly generated graphs and the graphs generated from real-world applications such as Fast Fourier Transform, Gaussian Elimination, Montage and Epigenomics workflow. Further, we explores possibility of a framework for benchmarking of task scheduling algorithms for heterogeneous computing systems. This proposed approach provides for generation of graphs through a Directed Acyclic Graph generator, then produces schedules through a scheduler which makes use of scheduling algorithms and finally analyses the results obtained by using various performance metrics. The proposed framework is general in nature. | ||
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Abstract The task scheduling problem on heterogeneous computing systems has been broadly studied, and many heuristic algorithms are proposed to solve this problem. It is interesting to go for significant performance assessment and comparison among these heuristic algorithms. In this work, we first carry out performance evaluation and comparison of task scheduling algorithms for heterogeneous computing systems for randomly generated graphs and the graphs generated from real-world applications such as Fast Fourier Transform, Gaussian Elimination, Montage and Epigenomics workflow. Further, we explores possibility of a framework for benchmarking of task scheduling algorithms for heterogeneous computing systems. This proposed approach provides for generation of graphs through a Directed Acyclic Graph generator, then produces schedules through a scheduler which makes use of scheduling algorithms and finally analyses the results obtained by using various performance metrics. The proposed framework is general in nature. © Springer Science+Business Media, LLC, part of Springer Nature 2018 |
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Abstract The task scheduling problem on heterogeneous computing systems has been broadly studied, and many heuristic algorithms are proposed to solve this problem. It is interesting to go for significant performance assessment and comparison among these heuristic algorithms. In this work, we first carry out performance evaluation and comparison of task scheduling algorithms for heterogeneous computing systems for randomly generated graphs and the graphs generated from real-world applications such as Fast Fourier Transform, Gaussian Elimination, Montage and Epigenomics workflow. Further, we explores possibility of a framework for benchmarking of task scheduling algorithms for heterogeneous computing systems. This proposed approach provides for generation of graphs through a Directed Acyclic Graph generator, then produces schedules through a scheduler which makes use of scheduling algorithms and finally analyses the results obtained by using various performance metrics. The proposed framework is general in nature. © Springer Science+Business Media, LLC, part of Springer Nature 2018 |
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Abstract The task scheduling problem on heterogeneous computing systems has been broadly studied, and many heuristic algorithms are proposed to solve this problem. It is interesting to go for significant performance assessment and comparison among these heuristic algorithms. In this work, we first carry out performance evaluation and comparison of task scheduling algorithms for heterogeneous computing systems for randomly generated graphs and the graphs generated from real-world applications such as Fast Fourier Transform, Gaussian Elimination, Montage and Epigenomics workflow. Further, we explores possibility of a framework for benchmarking of task scheduling algorithms for heterogeneous computing systems. This proposed approach provides for generation of graphs through a Directed Acyclic Graph generator, then produces schedules through a scheduler which makes use of scheduling algorithms and finally analyses the results obtained by using various performance metrics. The proposed framework is general in nature. © Springer Science+Business Media, LLC, part of Springer Nature 2018 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC2033955168</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230504054018.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200819s2018 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11227-018-2355-0</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2033955168</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s11227-018-2355-0-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="a">620</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Maurya, Ashish Kumar</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0001-9679-9045</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">On benchmarking task scheduling algorithms for heterogeneous computing systems</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2018</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer Science+Business Media, LLC, part of Springer Nature 2018</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract The task scheduling problem on heterogeneous computing systems has been broadly studied, and many heuristic algorithms are proposed to solve this problem. It is interesting to go for significant performance assessment and comparison among these heuristic algorithms. In this work, we first carry out performance evaluation and comparison of task scheduling algorithms for heterogeneous computing systems for randomly generated graphs and the graphs generated from real-world applications such as Fast Fourier Transform, Gaussian Elimination, Montage and Epigenomics workflow. Further, we explores possibility of a framework for benchmarking of task scheduling algorithms for heterogeneous computing systems. This proposed approach provides for generation of graphs through a Directed Acyclic Graph generator, then produces schedules through a scheduler which makes use of scheduling algorithms and finally analyses the results obtained by using various performance metrics. 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