Github user MLnick commented on the issue:
https://github.com/apache/spark/pull/17000
Just to be clear - this is essentially just splitting an array up into
smaller chunks so that overall communication is more efficient? It would be
good to look at why Spark is not doing a good job with one big array. Is the
bottleneck really the executor communication (shuffle part)? Or is it
collecting the big array back at the end of tree aggregation (ie this patch
sort of allows more concurrency in the `collect` operation)?
cc @dbtsai @sethah @yanboliang who were looking at linear model
scalability recently.
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