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A Flexible Parallel Runtime for Large Scale Block-Based Matrix Multiplication

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Web Technologies and Applications (APWeb 2012)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 7234))

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Abstract

Block-based matrix multiplication plays an important role in statics computing. It is hard to make large scale matrix multiplication in data statistics and analysis. A flexible parallel runtime for large scale block-based matrix is proposed in this paper. With MapReduce framework, four parallel matrix multiplication methods have been discussed. Three methods use the HDFS to be the storage and one method utilizes the Cloud storage to be the storage. The parallel runtime will determine to use the appropriate block-based matrix multiplication. Experiments have been made to test the proposed flexible parallel runtime with large scale randomly generated data and public matrix collection. The results have shown that the proposed runtime has a good effect to select the best matrix multiplication strategy.

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References

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Author information

Authors and Affiliations

  1. Nokia-Simense-Network CTO Research, JiuXianQiao Road 14, Chaoyang District, Beijing, P.R.C

    Keyan Liu & Yanyu Ma

  2. RenMin University of China, No. 59 Zhong Guan Cun Street, Beijing, P.R.C

    Shaohua Song & Ningnan Zhou

Authors
  1. Keyan Liu
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  2. Shaohua Song
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  3. Ningnan Zhou
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  4. Yanyu Ma
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Editor information

Editors and Affiliations

  1. University of Southern Queensland, Toowoomba, QLD, Australia

    Hua Wang

  2. Peking University, Beijing, China

    Lei Zou  & Dongyan Zhao  & 

  3. Victoria University, Melbourne, Australia

    Guangyan Huang  & Jing He  & 

  4. CSIRO, The Australian E-Health Research Center, Australia

    Chaoyi Pang

  5. NIT, Zhejiang University, 1 Xuefu Road, Ningbo, China

    Hao Lan Zhang

  6. Zhejiang Gongshang University, Hangzhou, China

    Zhuang Yi

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© 2012 Springer-Verlag Berlin Heidelberg

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Liu, K., Song, S., Zhou, N., Ma, Y. (2012). A Flexible Parallel Runtime for Large Scale Block-Based Matrix Multiplication. In: Wang, H., et al. Web Technologies and Applications. APWeb 2012. Lecture Notes in Computer Science, vol 7234. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29426-6_8

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  • DOI: https://doi.org/10.1007/978-3-642-29426-6_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-29425-9

  • Online ISBN: 978-3-642-29426-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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