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From "ASF GitHub Bot (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (FLINK-2909) Gelly Graph Generators
Date Fri, 08 Apr 2016 18:23:25 GMT

    [ https://issues.apache.org/jira/browse/FLINK-2909?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15232653#comment-15232653
] 

ASF GitHub Bot commented on FLINK-2909:
---------------------------------------

Github user greghogan commented on the pull request:

    https://github.com/apache/flink/pull/1807#issuecomment-207546079
  
    @vasia thank you for the recommendations, the improvements are almost ready to push. Running
the `Graph500` example on an AWS c4.8xlarge (with 36 'virtual cores') generated a billion
edges (scale 26, edge factor 16) in 25.8s wall time (23.4s execution time). When simplifying
the graph with 'clip-and-flip' the runtime was 2m33s wall time (2m31s execution time) and
when performing a full flip (thus doubling the number of edges) the runtime was 5m00s wall
time (4m58s execution time).
    
    Many algorithms require edges to be sorted so that cost is already accounted for. Edge
generation should scale beautifully.


> Gelly Graph Generators
> ----------------------
>
>                 Key: FLINK-2909
>                 URL: https://issues.apache.org/jira/browse/FLINK-2909
>             Project: Flink
>          Issue Type: New Feature
>          Components: Gelly
>    Affects Versions: 1.0.0
>            Reporter: Greg Hogan
>            Assignee: Greg Hogan
>
> Include a selection of graph generators in Gelly. Generated graphs will be useful for
performing scalability, stress, and regression testing as well as benchmarking and comparing
algorithms, for both Flink users and developers. Generated data is infinitely scalable yet
described by a few simple parameters and can often substitute for user data or sharing large
files when reporting issues.
> There are at multiple categories of graphs as documented by [NetworkX|https://networkx.github.io/documentation/latest/reference/generators.html]
and elsewhere.
> Graphs may be a well-defined, i.e. the [Chvátal graph|https://en.wikipedia.org/wiki/Chv%C3%A1tal_graph].
These may be sufficiently small to populate locally.
> Graphs may be scalable, i.e. complete and star graphs. These should use Flink's distributed
parallelism.
> Graphs may be stochastic, i.e. [RMat graphs|http://snap.stanford.edu/class/cs224w-readings/chakrabarti04rmat.pdf]
. A key consideration is that the graphs should source randomness from a seedable PRNG and
generate the same Graph regardless of parallelism.



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