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
---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at infrastruct...@apache.org or file a JIRA ticket
with INFRA.
---
---------------------------------------------------------------------
To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org
For additional commands, e-mail: reviews-h...@spark.apache.org
