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

["Xiangrui Meng (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-1580?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Xiangrui Meng resolved SPARK-1580.
----------------------------------

       Resolution: Fixed
    Fix Version/s: 1.1.0

Issue resolved by pull request 1731
[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
>            Assignee: Tor Myklebust
>            Priority: Minor
>             Fix For: 1.1.0
>
>
> 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.



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