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Posted to issues@spark.apache.org by "Xiangrui Meng (JIRA)" <ji...@apache.org> on 2015/02/16 21:01:11 UTC

[jira] [Created] (SPARK-5842) Allow creating broadcast variables on workers

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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