GitHub user moustaki opened a pull request:
https://github.com/apache/spark/pull/9457
[SPARK-11496][GRAPHX] Parallel implementation of personalized pagerank
This implements a parallel version of personalized pagerank, which runs all
propagations for a list of source vertices in parallel.
I ran a few benchmarks on the full [DBpedia](http://dbpedia.org/) graph.
When running personalized pagerank for only one source node, the existing
implementation is twice as fast as the parallel one (because of the
SparseVector overhead). However for 10 source nodes, the parallel
implementation is four times as fast. When increasing the number of source
nodes, this difference becomes even greater.
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/moustaki/spark parallel-ppr
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/spark/pull/9457.patch
To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:
This closes #9457
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commit f41975efdbe1794fe456babb7f79d0290ecfc70d
Author: Yves Raimond <[email protected]>
Date: 2015-10-30T23:05:20Z
Parallel personalized pagerank implementation
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