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Posted to issues@spark.apache.org by "Xiangrui Meng (JIRA)" <ji...@apache.org> on 2015/06/22 08:31:00 UTC

[jira] [Assigned] (SPARK-8520) Improve GLM's scalability on number of features

     [ https://issues.apache.org/jira/browse/SPARK-8520?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Xiangrui Meng reassigned SPARK-8520:
------------------------------------

    Assignee: Xiangrui Meng

> Improve GLM's scalability on number of features
> -----------------------------------------------
>
>                 Key: SPARK-8520
>                 URL: https://issues.apache.org/jira/browse/SPARK-8520
>             Project: Spark
>          Issue Type: Improvement
>          Components: ML
>    Affects Versions: 1.4.0
>            Reporter: Xiangrui Meng
>            Assignee: Xiangrui Meng
>            Priority: Critical
>              Labels: advanced
>
> MLlib's GLM implementation uses driver to collect gradient updates. When there exist many features (>20 million), the driver becomes the performance bottleneck. In practice, it is common to see a problem with a large feature dimension, resulting from hashing or other feature transformations. So it is important to improve MLlib's scalability on number of features.
> There are couple possible solutions:
> 1. still use driver to collect updates, but reduce the amount of data it collects at each iteration.
> 2. apply 2D partitioning to the training data and store the model coefficients distributively (e.g., vector-free l-bfgs)
> 3. parameter server
> 4. ...



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