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https://issues.apache.org/jira/browse/SPARK-18946?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15874628#comment-15874628
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Apache Spark commented on SPARK-18946:
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User 'ZunwenYou' has created a pull request for this issue:
https://github.com/apache/spark/pull/17000
> treeAggregate will be low effficiency when aggregate high dimension vectors
> in ML algorithm
> -------------------------------------------------------------------------------------------
>
> Key: SPARK-18946
> URL: https://issues.apache.org/jira/browse/SPARK-18946
> Project: Spark
> Issue Type: Improvement
> Components: ML, MLlib
> Reporter: zunwen you
> Labels: features
>
> In many machine learning algorithms, we have to treeAggregate large
> vectors/arrays due to the large number of features. Unfortunately, the
> treeAggregate operation of RDD will be low efficiency when the dimension of
> vectors/arrays is bigger than million. Because high dimension of vector/array
> always occupy more than 100MB Memory, transferring a 100MB element among
> executors is pretty low efficiency in Spark.
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