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Posted to issues@spark.apache.org by "zunwen you (JIRA)" <ji...@apache.org> on 2017/02/20 13:50:44 UTC

[jira] [Reopened] (SPARK-18946) treeAggregate will be low effficiency when aggregate high dimension vectors in ML algorithm

     [ https://issues.apache.org/jira/browse/SPARK-18946?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

zunwen you reopened SPARK-18946:
--------------------------------

I have implement a sliceAggregate for RDD, which proform better than treeAggregate when dimension of vector is large.

> 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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