Github user shivaram commented on the pull request:
https://github.com/apache/spark/pull/2907#issuecomment-61138697
Agree that using aggregate vs. treeAggregate depends on the computation,
reduction function -- but I don't think its specific to MLLib per se. Any Spark
application that has CPU intensive code can benefit from treeAggregate. My view
is that we shouldn't replace `aggregate` with this -- we should just allow
users to choose the right aggregation strategy based on what they need
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