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https://issues.apache.org/jira/browse/SPARK-11968?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15983048#comment-15983048
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Nick Pentreath commented on SPARK-11968:
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[[email protected]] would you mind posting your comments here about the
solution from SPARK-20446 as well as the experiment timings? You can rename
your PR to include this JIRA (SPARK-11968) in the title instead, in order to
link it.
Also please include the timings here of the {{ml DataFrame}} version for
comparison. Your approach should also be much faster than the current {{ml}}
SparkSQL approach, I think.
I just did some quick tests using MovieLens {{latest}} data (~24 million
ratings, ~260,000 users, ~39,000 items) and found the following (note these are
very rough timings):
Using default block sizes:
Current {{ml}} master - 262 sec
My approach: 58 sec
Your PR: 35 sec
You're correct that there is still +/- 20-25% GC time overhead using the BLAS 3
+ sorting approach. Potentially it could be slightly improved through some form
of pre-allocation, but even then it does look like any benefit of BLAS 3 is
smaller than the GC cost.
> ALS recommend all methods spend most of time in GC
> --------------------------------------------------
>
> Key: SPARK-11968
> URL: https://issues.apache.org/jira/browse/SPARK-11968
> Project: Spark
> Issue Type: Improvement
> Components: ML, MLlib
> Affects Versions: 1.5.2, 1.6.0
> Reporter: Joseph K. Bradley
> Assignee: Nick Pentreath
>
> After adding recommendUsersForProducts and recommendProductsForUsers to ALS
> in spark-perf, I noticed that it takes much longer than ALS itself. Looking
> at the monitoring page, I can see it is spending about 8min doing GC for each
> 10min task. That sounds fixable. Looking at the implementation, there is
> clearly an opportunity to avoid extra allocations:
> [https://github.com/apache/spark/blob/e6dd237463d2de8c506f0735dfdb3f43e8122513/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala#L283]
> CC: [~mengxr]
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