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https://issues.apache.org/jira/browse/SPARK-5842?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Xiangrui Meng updated SPARK-5842:
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Target Version/s: (was: 1.4.0)
> Allow creating broadcast variables on workers
> ---------------------------------------------
>
> Key: SPARK-5842
> URL: https://issues.apache.org/jira/browse/SPARK-5842
> Project: Spark
> Issue Type: New Feature
> Components: MLlib, Spark Core
> Reporter: Xiangrui Meng
>
> Now broadcast variables must be created by the driver. Many algorithms in
> MLlib uses the driver to collect gradient and broadcast the new weights,
> which makes driver a bottleneck. It would be nice if we can create broadcast
> variables on workers and return their handlers to the driver. An ML iteration
> will look like the following after this change:
> (training data + broadcasted weights) ->reduceByKey -> single partition RDD
> with aggregated gradient -> update weights and broadcast it -> driver
> receives the broadcast variable
> where the driver is only doing the scheduling work.
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