research-article

Transformer: A New Paradigm for Building Data-Parallel Programming Models

Authors:

Bibo Tu,

Le WanAuthors Info & Claims

IEEE Micro, Volume 30, Issue 4

Pages 55 - 64

https://doi.org/10.1109/MM.2010.75

Published: 01 July 2010 Publication History

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Abstract

Cloud computing drives the design and development of diverse programming models for massive data processing. The Transformer programming framework aims to facilitate the building of diverse data-parallel programming models. Transformer has two layers: a common runtime system and a model-specific system. Using Transformer, the authors show how to implement three programming models: Dryad-like data flow, MapReduce, and All-Pairs.

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Zhao SYang QXing JZhou QXue GChen W(2020)INRScientific Programming10.1155/2020/36598492020Online publication date: 1-Jan-2020
https://dl.acm.org/doi/10.1155/2020/3659849
(2015)High frequency batch-oriented computations over large sliding time windowsFuture Generation Computer Systems10.1016/j.future.2014.09.00843:C(1-11)Online publication date: 1-Feb-2015
https://dl.acm.org/doi/10.1016/j.future.2014.09.008
Aniello LQuerzoni LBaldoni RShin SMaldonado J(2013)Input data organization for batch processing in time window based computationsProceedings of the 28th Annual ACM Symposium on Applied Computing10.1145/2480362.2480437(363-370)Online publication date: 18-Mar-2013
https://dl.acm.org/doi/10.1145/2480362.2480437

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Published In

IEEE Micro Volume 30, Issue 4

July 2010

86 pages

ISSN:0272-1732

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Publisher

IEEE Computer Society Press

Washington, DC, United States

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Published: 01 July 2010

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Zhao SYang QXing JZhou QXue GChen W(2020)INRScientific Programming10.1155/2020/36598492020Online publication date: 1-Jan-2020
https://dl.acm.org/doi/10.1155/2020/3659849
(2015)High frequency batch-oriented computations over large sliding time windowsFuture Generation Computer Systems10.1016/j.future.2014.09.00843:C(1-11)Online publication date: 1-Feb-2015
https://dl.acm.org/doi/10.1016/j.future.2014.09.008
Aniello LQuerzoni LBaldoni RShin SMaldonado J(2013)Input data organization for batch processing in time window based computationsProceedings of the 28th Annual ACM Symposium on Applied Computing10.1145/2480362.2480437(363-370)Online publication date: 18-Mar-2013
https://dl.acm.org/doi/10.1145/2480362.2480437

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Cited By

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G-Hadoop: MapReduce across distributed data centers for data-intensive computing

Big Data Processing Technology Research and Application Prospects

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