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...
Ausführliche Beschreibung
Autor*in: |
Maurya, Ashish Kumar [verfasserIn] |
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Sprache: |
Englisch |
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2018 |
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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 |
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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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10.1007/s11227-018-2355-0 doi (DE-627)OLC2033955168 (DE-He213)s11227-018-2355-0-p DE-627 ger DE-627 rakwb eng 004 620 VZ Maurya, Ashish Kumar verfasserin (orcid)0000-0001-9679-9045 aut On benchmarking task scheduling algorithms for heterogeneous computing systems 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 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. Task scheduling Workflow scheduling Benchmarking Performance evaluation Heterogeneous computing systems Tripathi, Anil Kumar aut Enthalten in The journal of supercomputing Springer US, 1987 74(2018), 7 vom: 07. Apr., Seite 3039-3070 (DE-627)13046466X (DE-600)740510-8 (DE-576)018667775 0920-8542 nnns volume:74 year:2018 number:7 day:07 month:04 pages:3039-3070 https://doi.org/10.1007/s11227-018-2355-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 AR 74 2018 7 07 04 3039-3070 |
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10.1007/s11227-018-2355-0 doi (DE-627)OLC2033955168 (DE-He213)s11227-018-2355-0-p DE-627 ger DE-627 rakwb eng 004 620 VZ Maurya, Ashish Kumar verfasserin (orcid)0000-0001-9679-9045 aut On benchmarking task scheduling algorithms for heterogeneous computing systems 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 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. Task scheduling Workflow scheduling Benchmarking Performance evaluation Heterogeneous computing systems Tripathi, Anil Kumar aut Enthalten in The journal of supercomputing Springer US, 1987 74(2018), 7 vom: 07. Apr., Seite 3039-3070 (DE-627)13046466X (DE-600)740510-8 (DE-576)018667775 0920-8542 nnns volume:74 year:2018 number:7 day:07 month:04 pages:3039-3070 https://doi.org/10.1007/s11227-018-2355-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 AR 74 2018 7 07 04 3039-3070 |
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10.1007/s11227-018-2355-0 doi (DE-627)OLC2033955168 (DE-He213)s11227-018-2355-0-p DE-627 ger DE-627 rakwb eng 004 620 VZ Maurya, Ashish Kumar verfasserin (orcid)0000-0001-9679-9045 aut On benchmarking task scheduling algorithms for heterogeneous computing systems 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 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. Task scheduling Workflow scheduling Benchmarking Performance evaluation Heterogeneous computing systems Tripathi, Anil Kumar aut Enthalten in The journal of supercomputing Springer US, 1987 74(2018), 7 vom: 07. Apr., Seite 3039-3070 (DE-627)13046466X (DE-600)740510-8 (DE-576)018667775 0920-8542 nnns volume:74 year:2018 number:7 day:07 month:04 pages:3039-3070 https://doi.org/10.1007/s11227-018-2355-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 AR 74 2018 7 07 04 3039-3070 |
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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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