Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/12106#discussion_r58335017
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala ---
@@ -451,10 +451,10 @@ final class OnlineLDAOptimizer extends LDAOptimizer {
}
Iterator((stat, gammaPart))
}
- val statsSum: BDM[Double] = stats.map(_._1).reduce(_ += _)
+ val statsSum: BDM[Double] = stats.map(_._1).treeReduce(_ += _)
expElogbetaBc.unpersist()
val gammat: BDM[Double] = breeze.linalg.DenseMatrix.vertcat(
- stats.map(_._2).reduce(_ ++ _).map(_.toDenseMatrix): _*)
+ stats.map(_._2).treeReduce(_ ++ _).map(_.toDenseMatrix): _*)
--- End diff --
aggregate would send stats.numPartitions elements back to the driver,
whereas treeAggregate would send fewer (for large enough numPartitions), right?
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