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https://issues.apache.org/jira/browse/SPARK-2174?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Reynold Xin resolved SPARK-2174.
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Resolution: Fixed
Fix Version/s: 1.1.0
> Implement treeReduce and treeAggregate
> --------------------------------------
>
> Key: SPARK-2174
> URL: https://issues.apache.org/jira/browse/SPARK-2174
> Project: Spark
> Issue Type: New Feature
> Components: MLlib, Spark Core
> Reporter: Xiangrui Meng
> Assignee: Xiangrui Meng
> Fix For: 1.1.0
>
>
> In `reduce` and `aggregate`, the driver node spends linear time on the number
> of partitions. It becomes a bottleneck when there are many partitions and the
> data from each partition is big.
> SPARK-1485 tracks the progress of implementing AllReduce on Spark. I did
> several implementations including butterfly, reduce + broadcast, and
> treeReduce + broadcast. treeReduce + BT broadcast seems to be right way to go
> for Spark. Using binary tree may introduce some overhead in communication,
> because the driver still need to coordinate on data shuffling. In my
> experiments, n -> sqrt(n) -> 1 gives the best performance in general. But it
> certainly needs more testing.
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