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https://issues.apache.org/jira/browse/SPARK-16697?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Sean Owen resolved SPARK-16697.
-------------------------------
Resolution: Fixed
Fix Version/s: 2.1.0
Issue resolved by pull request 14335
[https://github.com/apache/spark/pull/14335]
> redundant RDD computation in LDAOptimizer
> -----------------------------------------
>
> Key: SPARK-16697
> URL: https://issues.apache.org/jira/browse/SPARK-16697
> Project: Spark
> Issue Type: Improvement
> Components: ML, MLlib
> Affects Versions: 2.0.1
> Reporter: Weichen Xu
> Fix For: 2.1.0
>
> Original Estimate: 24h
> Remaining Estimate: 24h
>
> In mllib.clustering.LDAOptimizer
> the submitMiniBatch method,
> the stats: RDD do not persist but the following code will use it twice.
> so it cause redundant computation on it.
> and there is another problem,
> the expElogbetaBc broadcast variable is unpersist too early,
> and the next statement
> `
> val gammat: BDM[Double] = breeze.linalg.DenseMatrix.vertcat(val gammat:
> BDM[Double] = breeze.linalg.DenseMatrix.vertcat(
> stats.map(_._2).flatMap(list => list).collect().map(_.toDenseMatrix):
> _*)
> `
> will re-compute the stats RDD, it will use expElogbetaBc broadcast variable
> again,
> so the expElogbetaBc broadcast variable will be broadcast again.
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