[jira] [Assigned] (SPARK-20587) Improve performance of ML ALS recommendForAll
[
https://issues.apache.org/jira/browse/SPARK-20587?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Apache Spark reassigned SPARK-20587:
Assignee: Nick Pentreath (was: Apache Spark)
> 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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[jira] [Assigned] (SPARK-20587) Improve performance of ML ALS recommendForAll
[
https://issues.apache.org/jira/browse/SPARK-20587?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Apache Spark reassigned SPARK-20587:
Assignee: Apache Spark (was: Nick Pentreath)
> 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: Apache Spark
>
> 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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