From heterogeneous task scheduling to heterogeneous mixed parallel scheduling

Frédéric Suter; Frédéric Desprez; Henri Casanova

doi:10.1007/978-3-540-27866-5_30

From heterogeneous task scheduling to heterogeneous mixed parallel scheduling

Frédéric Suter, Frédéric Desprez, Henri Casanova

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

37 Scopus citations

Abstract

Mixed-parallelism, the combination of data- and taskparallelism, is a powerful way of increasing the scalability of entire classes of parallel applications on platforms comprising multiple compute clusters. While multi-cluster platforms are predominantly heterogeneous, previous work on mixed-parallel application scheduling targets only homogeneous platforms. In this paper we develop a method for extending existing scheduling algorithms for task-parallel applications on heterogeneous platforms to the mixed-parallel case.

Original language	English
Title of host publication	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Editors	Marco Danelutto, Marco Vanneschi, Domenico Laforenza
Publisher	Springer Verlag
Pages	230-237
Number of pages	8
ISBN (Print)	3540229248
DOIs	https://doi.org/10.1007/978-3-540-27866-5_30
State	Published - 2004
Externally published	Yes

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3149
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

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10.1007/978-3-540-27866-5_30

Cite this

Suter, F., Desprez, F., & Casanova, H. (2004). From heterogeneous task scheduling to heterogeneous mixed parallel scheduling. In M. Danelutto, M. Vanneschi, & D. Laforenza (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 230-237). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3149). Springer Verlag. https://doi.org/10.1007/978-3-540-27866-5_30

Suter, Frédéric ; Desprez, Frédéric ; Casanova, Henri. / From heterogeneous task scheduling to heterogeneous mixed parallel scheduling. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). editor / Marco Danelutto ; Marco Vanneschi ; Domenico Laforenza. Springer Verlag, 2004. pp. 230-237 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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author = "Fr{\'e}d{\'e}ric Suter and Fr{\'e}d{\'e}ric Desprez and Henri Casanova",

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Suter, F, Desprez, F & Casanova, H 2004, From heterogeneous task scheduling to heterogeneous mixed parallel scheduling. in M Danelutto, M Vanneschi & D Laforenza (eds), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3149, Springer Verlag, pp. 230-237. https://doi.org/10.1007/978-3-540-27866-5_30

From heterogeneous task scheduling to heterogeneous mixed parallel scheduling. / Suter, Frédéric; Desprez, Frédéric; Casanova, Henri.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). ed. / Marco Danelutto; Marco Vanneschi; Domenico Laforenza. Springer Verlag, 2004. p. 230-237 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3149).

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

TY - CHAP

T1 - From heterogeneous task scheduling to heterogeneous mixed parallel scheduling

AU - Suter, Frédéric

AU - Desprez, Frédéric

AU - Casanova, Henri

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AB - Mixed-parallelism, the combination of data- and taskparallelism, is a powerful way of increasing the scalability of entire classes of parallel applications on platforms comprising multiple compute clusters. While multi-cluster platforms are predominantly heterogeneous, previous work on mixed-parallel application scheduling targets only homogeneous platforms. In this paper we develop a method for extending existing scheduling algorithms for task-parallel applications on heterogeneous platforms to the mixed-parallel case.

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DO - 10.1007/978-3-540-27866-5_30

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SN - 3540229248

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 230

EP - 237

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

A2 - Danelutto, Marco

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PB - Springer Verlag

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Suter F, Desprez F, Casanova H. From heterogeneous task scheduling to heterogeneous mixed parallel scheduling. In Danelutto M, Vanneschi M, Laforenza D, editors, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag. 2004. p. 230-237. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-27866-5_30

From heterogeneous task scheduling to heterogeneous mixed parallel scheduling

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