Xiangrui Meng created SPARK-5842:
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Summary: Allow creating broadcast variables on workers
Key: SPARK-5842
URL: https://issues.apache.org/jira/browse/SPARK-5842
Project: Spark
Issue Type: New Feature
Components: MLlib, Spark Core
Reporter: Xiangrui Meng
Now broadcast variables must be created by the driver. Many algorithms in MLlib
uses the driver to collect gradient and broadcast the new weights, which makes
driver a bottleneck. It would be nice if we can create broadcast variables on
workers and return their handlers to the driver. An ML iteration will look like
the following after this change:
(training data + broadcasted weights) ->reduceByKey -> single partition RDD
with aggregated gradient -> update weights and broadcast it -> driver receives
the broadcast variable
where the driver is only doing the scheduling work.
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