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https://issues.apache.org/jira/browse/SPARK-20446?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15983018#comment-15983018
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Nick Pentreath commented on SPARK-20446:
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By the way when I say it is a duplicate I mean for the JIRA ticket. I agree
that PR9980 was not the correct solution - JIRA tickets can have multiple PRs
linked to them.
I'd prefer to close this ticket and move the discussion to SPARK-11968 (also
there are watchers on that ticket that may be interested in the outcome).
> Optimize the process of MLLIB ALS recommendForAll
> -------------------------------------------------
>
> Key: SPARK-20446
> URL: https://issues.apache.org/jira/browse/SPARK-20446
> Project: Spark
> Issue Type: Improvement
> Components: ML, MLlib
> Affects Versions: 2.3.0
> Reporter: Peng Meng
>
> The recommendForAll of MLLIB ALS is very slow.
> GC is a key problem of the current method.
> The task use the following code to keep temp result:
> val output = new Array[(Int, (Int, Double))](m*n)
> m = n = 4096 (default value, no method to set)
> so output is about 4k * 4k * (4 + 4 + 8) = 256M. This is a large memory and
> cause serious GC problem, and it is frequently OOM.
> Actually, we don't need to save all the temp result. Suppose we recommend
> topK (topK is about 10, or 20) product for each user, we only need 4k * topK
> * (4 + 4 + 8) memory to save the temp result.
> I have written a solution for this method with the following test result.
> The Test Environment:
> 3 workers: each work 10 core, each work 30G memory, each work 1 executor.
> The Data: User 480,000, and Item 17,000
> BlockSize: 1024 2048 4096 8192
> Old method: 245s 332s 488s OOM
> This solution: 121s 118s 117s 120s
>
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