[jira] [Commented] (SPARK-20587) Improve performance of ML ALS recommendForAll

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

    [ https://issues.apache.org/jira/browse/SPARK-20587?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15995480#comment-15995480 ] 

Apache Spark commented on SPARK-20587:
--------------------------------------

User 'MLnick' has created a pull request for this issue:
https://github.com/apache/spark/pull/17845

> Improve performance of ML ALS recommendForAll
> ---------------------------------------------
>
>                 Key: SPARK-20587
>                 URL: https://issues.apache.org/jira/browse/SPARK-20587
>             Project: Spark
>          Issue Type: Improvement
>          Components: ML
>    Affects Versions: 2.2.0
>            Reporter: Nick Pentreath
>            Assignee: Nick Pentreath
>
> SPARK-11968 relates to excessive GC pressure from using the "blocked BLAS 3" approach for generating top-k recommendations in {{mllib.recommendation.MatrixFactorizationModel}}.
> The solution there is still based on blocking factors, but efficiently computes the top-k elements *per block* first (using {{BoundedPriorityQueue}}) and then computes the global top-k elements.
> This improves performance and GC pressure substantially for {{mllib}}'s ALS model. The same approach is also a lot more efficient than the current "crossJoin and score per-row" used in {{ml}}'s {{DataFrame}}-based method. This adapts the solution in SPARK-11968 for {{DataFrame}}.



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