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https://issues.apache.org/jira/browse/SPARK-8520?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14621780#comment-14621780
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Feynman Liang commented on SPARK-8520:
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To clarify, is this [vector free LBGFS |
http://papers.nips.cc/paper/5333-large-scale-l-bfgs-using-mapreduce.pdf] the
solution proposed in 2?
> 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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