English

BSP vs MapReduce

Distributed, Parallel, and Cluster Computing 2012-06-19 v2
Authors: Matthew Felice Pace
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Abstract

The MapReduce framework has been generating a lot of interest in a wide range of areas. It has been widely adopted in industry and has been used to solve a number of non-trivial problems in academia. Putting MapReduce on strong theoretical foundations is crucial in understanding its capabilities. This work links MapReduce to the BSP model of computation, underlining the relevance of BSP to modern parallel algorithm design and defining a subclass of BSP algorithms that can be efficiently implemented in MapReduce.

Keywords

distributed systems matrix factorization machine learning

Cite

@article{arxiv.1203.2081,
  title  = {BSP vs MapReduce},
  author = {Matthew Felice Pace},
  journal= {arXiv preprint arXiv:1203.2081},
  year   = {2012}
}

Comments

13 pages, appeared at ICCS 2012

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