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论文阅读 - Optimizing Graph Algorithms on Pregel-like

wdfnst
 wdfnst
发布于 2016/05/15 08:55
字数 245
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  • Highlight, page 1
    Standard graph algorithms in this setting can incur unnecessary in- efficiencies such as slow convergence or high communication or computation cost

  • Highlight, page 1
    Standard graph algorithms in this setting can incur unnecessary in- efficiencies such as slow convergence or high communication or computation cost

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    large diameters or skew in component sizes.

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    performing some serial computation on a tiny fraction of the in- put graph

  • Underline, page 1
    complementing

  • Highlight, page 1
    our open-source Pregel implementation

  • Highlight, page 2
    FCS monitors the size of the “active” graph on which the computation is executing. If the active graph becomes small enough, FCS sends it to the master, which performs the end of the computation serially inside master.compute(), then sends the results back to the workers.

  • Highlight, page 2
    sets of vertices (called subvertices) are merged to form supervertices.

  • Highlight, page 6
    FCS monitors the size the active- subgraph. Once the size of the active-subgraph is below a threshold (5M edges by default), it sends the active-subgraph to the mas- ter, which performs the rest of the computation serially, and sends the results back to the workers.

  • Highlight, page 8
    Our SEAS optimiza- tion instead stores the edges of a supervertex s in a distributed fash- ion among all of its subvertices.

  • Highlight, page 8
    In our implementation of SEAS, subvertices store a pointer to their latest supervertices.

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