research-article

Twister: a runtime for iterative MapReduce

Authors:

Jaliya Ekanayake,

Bingjing Zhang,

Thilina Gunarathne,

Geoffrey FoxAuthors Info & Claims

HPDC '10: Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing

Pages 810 - 818

https://doi.org/10.1145/1851476.1851593

Published: 21 June 2010 Publication History

Abstract

MapReduce programming model has simplified the implementation of many data parallel applications. The simplicity of the programming model and the quality of services provided by many implementations of MapReduce attract a lot of enthusiasm among distributed computing communities. From the years of experience in applying MapReduce to various scientific applications we identified a set of extensions to the programming model and improvements to its architecture that will expand the applicability of MapReduce to more classes of applications. In this paper, we present the programming model and the architecture of Twister an enhanced MapReduce runtime that supports iterative MapReduce computations efficiently. We also show performance comparisons of Twister with other similar runtimes such as Hadoop and DryadLINQ for large scale data parallel applications.

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Mu XLiu LZhang PLi JLi H(2024)A method of test case set generation in the commutativity test of reduce functionsScience of Computer Programming10.1016/j.scico.2023.103006231:COnline publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1016/j.scico.2023.103006
Leutwyler NLezoche MFranciosi CPanetto HTeste LTorres D(2024)Methods for concept analysis and multi-relational data mining: a systematic literature reviewKnowledge and Information Systems10.1007/s10115-024-02139-x66:9(5113-5150)Online publication date: 30-May-2024
https://doi.org/10.1007/s10115-024-02139-x
Abeykoon VCharles Fox G(2023)Trends in High-Performance Data Engineering for Data AnalyticsNew Trends and Challenges in Open Data 10.5772/intechopen.1001458Online publication date: 3-May-2023
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Index Terms

Twister: a runtime for iterative MapReduce
1. Computing methodologies
  1. Distributed computing methodologies
    1. Distributed programming languages
  2. Parallel computing methodologies
    1. Parallel programming languages
2. Software and its engineering
  1. Software notations and tools
    1. General programming languages
      1. Language types
        Distributed programming languages
        Parallel programming languages

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cover image ACM Conferences

HPDC '10: Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing

June 2010

911 pages

ISBN:9781605589428

DOI:10.1145/1851476

General Chairs:
Salim Hariri
University of Arizona
,
Kate Keahey
University of Chicago

Copyright © 2010 ACM.

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SIGARCH: ACM Special Interest Group on Computer Architecture

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Published: 21 June 2010

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University of Arizona
SIGARCH

HPDC '10: The 19th International Symposium on High Performance Distributed Computing

June 21 - 25, 2010

Illinois, Chicago

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Overall Acceptance Rate 166 of 966 submissions, 17%

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Leutwyler NLezoche MFranciosi CPanetto HTeste LTorres D(2024)Methods for concept analysis and multi-relational data mining: a systematic literature reviewKnowledge and Information Systems10.1007/s10115-024-02139-x66:9(5113-5150)Online publication date: 30-May-2024
https://doi.org/10.1007/s10115-024-02139-x
Abeykoon VCharles Fox G(2023)Trends in High-Performance Data Engineering for Data AnalyticsNew Trends and Challenges in Open Data 10.5772/intechopen.1001458Online publication date: 3-May-2023
https://doi.org/10.5772/intechopen.1001458
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