[jira] [Commented] (SPARK-1580) [MLlib] ALS: Estimate communication and computation costs given a partitioner

["Apache Spark (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-1580?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14083305#comment-14083305 ] 

Apache Spark commented on SPARK-1580:
-------------------------------------

User 'mengxr' has created a pull request for this issue:
https://github.com/apache/spark/pull/1731

> [MLlib] ALS: Estimate communication and computation costs given a partitioner
> -----------------------------------------------------------------------------
>
>                 Key: SPARK-1580
>                 URL: https://issues.apache.org/jira/browse/SPARK-1580
>             Project: Spark
>          Issue Type: Improvement
>          Components: MLlib
>            Reporter: Tor Myklebust
>            Priority: Minor
>
> It would be nice to be able to estimate the amount of work needed to solve an ALS problem.  The chief components of this "work" are computation time---time spent forming and solving the least squares problems---and communication cost---the number of bytes sent across the network.  Communication cost depends heavily on how the users and products are partitioned.
> We currently do not try to cluster users or products so that fewer feature vectors need to be communicated.  This is intended as a first step toward that end---we ought to be able to tell whether one partitioning is better than another.



--
This message was sent by Atlassian JIRA
(v6.2#6252)