Corpus ID: 46885893

Semi-MapReduce Meets Congested Clique

@article{Behnezhad2018SemiMapReduceMC,
  title={Semi-MapReduce Meets Congested Clique},
  author={Soheil Behnezhad and M. Derakhshan and M. Hajiaghayi},
  journal={arXiv: Distributed, Parallel, and Cluster Computing},
  year={2018}
}
  • Soheil Behnezhad, M. Derakhshan, M. Hajiaghayi
  • Published 2018
  • Computer Science
  • arXiv: Distributed, Parallel, and Cluster Computing
  • Graph problems are troublesome when it comes to MapReduce. Typically, to be able to design algorithms that make use of the advantages of MapReduce, assumptions beyond what the model imposes, such as the density of the input graph, are required. In a recent shift, a simple and robust model of MapReduce for graph problems, where the space per machine is set to be O(|V|), has attracted considerable attention. We term this model semi-MapReduce, or in short, semiMPC, and focus on its computational… CONTINUE READING
    6 Citations
    Round Compression for Parallel Graph Algorithms in Strongly Sublinear Space
    • 22
    • PDF
    Parallel Algorithms for Small Subgraph Counting
    • 1
    • PDF
    A Massively Parallel Algorithm for Minimum Weight Vertex Cover
    • 2
    • PDF
    Equivalence Classes and Conditional Hardness in Massively Parallel Computations
    • 4
    • PDF
    Distributed Network Design
    • 1
    • PDF

    References

    SHOWING 1-10 OF 16 REFERENCES
    A model of computation for MapReduce
    • 497
    • PDF
    On graph problems in a semi-streaming model
    • 254
    • PDF
    Lessons from the Congested Clique Applied to MapReduce
    • 23
    Affinity Clustering: Hierarchical Clustering at Scale
    • 35
    • PDF
    On the power of the congested clique model
    • 147
    • PDF
    MST in O(1) Rounds of Congested Clique
    • 69
    • PDF