[jira] [Commented] (SPARK-6567) Large linear model parallelism via a join and reduceByKey

["Ben McCann (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-6567?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15371816#comment-15371816 ] 

Ben McCann commented on SPARK-6567:
-----------------------------------

[~hucheng] can you share your code for this?

> Large linear model parallelism via a join and reduceByKey
> ---------------------------------------------------------
>
>                 Key: SPARK-6567
>                 URL: https://issues.apache.org/jira/browse/SPARK-6567
>             Project: Spark
>          Issue Type: Improvement
>          Components: ML, MLlib
>            Reporter: Reza Zadeh
>         Attachments: model-parallelism.pptx
>
>
> To train a linear model, each training point in the training set needs its dot product computed against the model, per iteration. If the model is large (too large to fit in memory on a single machine) then SPARK-4590 proposes using parameter server.
> There is an easier way to achieve this without parameter servers. In particular, if the data is held as a BlockMatrix and the model as an RDD, then each block can be joined with the relevant part of the model, followed by a reduceByKey to compute the dot products.
> This obviates the need for a parameter server, at least for linear models. However, it's unclear how it compares performance-wise to parameter servers.



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