Towards Systematic Parallel Programming over MapReduce

  • Yu Liu
  • Zhenjiang Hu
  • Kiminori Matsuzaki
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6853)


MapReduce is a useful and popular programming model for data-intensive distributed parallel computing. But it is still a challenge to develop parallel programs with MapReduce systematically, since it is usually not easy to derive a proper divide-and-conquer algorithm that matches MapReduce. In this paper, we propose a homomorphism-based framework named Screwdriver for systematic parallel programming with MapReduce, making use of the program calculation theory of list homomorphisms. Screwdriver is implemented as a Java library on top of Hadoop. For any problem which can be resolved by two sequential functions that satisfy the requirements of the third homomorphism theorem, Screwdriver can automatically derive a parallel algorithm as a list homomorphism and transform the initial sequential programs to an efficient MapReduce program. Users need neither to care about parallelism nor to have deep knowledge of MapReduce. In addition to the simplicity of the programming model of our framework, such a calculational approach enables us to resolve many problems that it would be nontrivial to resolve directly with MapReduce.


Virtual Machine Parallel Programming Sequential Program Reduce Phase MapReduce Framework 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Yu Liu
    • 1
  • Zhenjiang Hu
    • 2
  • Kiminori Matsuzaki
    • 3
  1. 1.The Graduate University for Advanced StudiesJapan
  2. 2.National Institute of InformaticsJapan
  3. 3.School of InformationKochi University of TechnologyJapan

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