Spark MLlib: network intensive algorithms

[mshiryae <mi...@intel.com>]

Hi,

I have a question wrt ML algorithms.
What are the most network intensive algorithms in Spark MLlib?

I have already looked at ALS (as pointed here:
https://databricks.com/blog/2014/07/23/scalable-collaborative-filtering-with-spark-mllib.html
ALS is pretty communication and computation intensive but in the latest
Spark ALS is pretty optimized in terms of communications).

As far as I understand ML algorithm do many computations on node and
periodically perform small exchanges between nodes.

May be you know such examples when algorithm is network-bounded or when
network exchanges consume noticeable time in training time?



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