Implementing Parallel Google Map-Reduce in Eden

Jost Berthold; Mischa Dieterle; Rita Loogen

doi:10.1007/978-3-642-03869-3

Implementing Parallel Google Map-Reduce in Eden

Jost Berthold, Mischa Dieterle, Rita Loogen

Datalogisk Institut

14 Citationer (Scopus)

Abstract

Recent publications have emphasised map-reduce as a general programming model (labelled Google map-reduce), and described existing high-performance implementations for large data sets. We present two parallel implementations for this Google map-reduce skeleton, one following earlier work, and one optimised version, in the parallel Haskell extension Eden. Eden's specific features, like lazy stream processing, dynamic reply channels, and nondeterministic stream merging, support the efficient implementation of the complex coordination structure of this skeleton. We compare the two implementations of the Google map-reduce skeleton in usage and performance, and deliver runtime analyses for example applications. Although very flexible, the Google map-reduce skeleton is often too general, and typical examples reveal a better runtime behaviour using alternative skeletons.

Originalsprog	Engelsk
Titel	Proceedings of the 15th International Euro-Par Conference on Parallel Processing
Antal sider	13
Forlag	Springer
Publikationsdato	2009
Sider	990-1002
ISBN (Trykt)	978-3-642-03868-6
DOI	https://doi.org/10.1007/978-3-642-03869-3
Status	Udgivet - 2009
Begivenhed	Euro-Par 2009 - Delft, Holland Varighed: 25 aug. 2009 → 28 aug. 2009 Konferencens nummer: 15

Konference

Konference	Euro-Par 2009
Nummer	15
Land/Område	Holland
By	Delft
Periode	25/08/2009 → 28/08/2009

Navn	Lecture notes in computer science
Vol/bind	5704
ISSN	0302-9743

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10.1007/978-3-642-03869-3

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Berthold, J., Dieterle, M., & Loogen, R. (2009). Implementing Parallel Google Map-Reduce in Eden. I Proceedings of the 15th International Euro-Par Conference on Parallel Processing: Recent publications have emphasised map-reduce as a general programming model (labelled Google map-reduce), and described existing high-performance implementations for large data sets. We present two parallel implementations for this Google map-reduce skeleton, one following earlier work, and one optimised version, in the parallel Haskell extension Eden. Eden’s specific features, like lazy stream processing, dynamic reply channels, and nondeterministic stream merging, support the efficient implementation of the complex coordination structure of this skeleton. We compare the two implementations of the Google map-reduce skeleton in usage and performance, and deliver runtime analyses for example applications. Although very flexible, the Google map-reduce skeleton is often too general, and typical examples reveal a better runtime behaviour using alternative skeletons. (s. 990-1002). Springer. Lecture notes in computer science Bind 5704 https://doi.org/10.1007/978-3-642-03869-3

Implementing Parallel Google Map-Reduce in Eden. / Berthold, Jost; Dieterle, Mischa; Loogen, Rita.

Proceedings of the 15th International Euro-Par Conference on Parallel Processing: Recent publications have emphasised map-reduce as a general programming model (labelled Google map-reduce), and described existing high-performance implementations for large data sets. We present two parallel implementations for this Google map-reduce skeleton, one following earlier work, and one optimised version, in the parallel Haskell extension Eden. Eden’s specific features, like lazy stream processing, dynamic reply channels, and nondeterministic stream merging, support the efficient implementation of the complex coordination structure of this skeleton. We compare the two implementations of the Google map-reduce skeleton in usage and performance, and deliver runtime analyses for example applications. Although very flexible, the Google map-reduce skeleton is often too general, and typical examples reveal a better runtime behaviour using alternative skeletons.. Springer, 2009. s. 990-1002 (Lecture notes in computer science, Bind 5704).

Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › peer review

Berthold, J, Dieterle, M & Loogen, R 2009, Implementing Parallel Google Map-Reduce in Eden. i Proceedings of the 15th International Euro-Par Conference on Parallel Processing: Recent publications have emphasised map-reduce as a general programming model (labelled Google map-reduce), and described existing high-performance implementations for large data sets. We present two parallel implementations for this Google map-reduce skeleton, one following earlier work, and one optimised version, in the parallel Haskell extension Eden. Eden’s specific features, like lazy stream processing, dynamic reply channels, and nondeterministic stream merging, support the efficient implementation of the complex coordination structure of this skeleton. We compare the two implementations of the Google map-reduce skeleton in usage and performance, and deliver runtime analyses for example applications. Although very flexible, the Google map-reduce skeleton is often too general, and typical examples reveal a better runtime behaviour using alternative skeletons.. Springer, Lecture notes in computer science, bind 5704, s. 990-1002, Euro-Par 2009, Delft, Holland, 25/08/2009. https://doi.org/10.1007/978-3-642-03869-3

Berthold J, Dieterle M, Loogen R. Implementing Parallel Google Map-Reduce in Eden. I Proceedings of the 15th International Euro-Par Conference on Parallel Processing: Recent publications have emphasised map-reduce as a general programming model (labelled Google map-reduce), and described existing high-performance implementations for large data sets. We present two parallel implementations for this Google map-reduce skeleton, one following earlier work, and one optimised version, in the parallel Haskell extension Eden. Eden’s specific features, like lazy stream processing, dynamic reply channels, and nondeterministic stream merging, support the efficient implementation of the complex coordination structure of this skeleton. We compare the two implementations of the Google map-reduce skeleton in usage and performance, and deliver runtime analyses for example applications. Although very flexible, the Google map-reduce skeleton is often too general, and typical examples reveal a better runtime behaviour using alternative skeletons.. Springer. 2009. s. 990-1002. (Lecture notes in computer science, Bind 5704). doi: 10.1007/978-3-642-03869-3

Berthold, Jost ; Dieterle, Mischa ; Loogen, Rita. / Implementing Parallel Google Map-Reduce in Eden. Proceedings of the 15th International Euro-Par Conference on Parallel Processing: Recent publications have emphasised map-reduce as a general programming model (labelled Google map-reduce), and described existing high-performance implementations for large data sets. We present two parallel implementations for this Google map-reduce skeleton, one following earlier work, and one optimised version, in the parallel Haskell extension Eden. Eden’s specific features, like lazy stream processing, dynamic reply channels, and nondeterministic stream merging, support the efficient implementation of the complex coordination structure of this skeleton. We compare the two implementations of the Google map-reduce skeleton in usage and performance, and deliver runtime analyses for example applications. Although very flexible, the Google map-reduce skeleton is often too general, and typical examples reveal a better runtime behaviour using alternative skeletons.. Springer, 2009. s. 990-1002 (Lecture notes in computer science, Bind 5704).

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Implementing Parallel Google Map-Reduce in Eden

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