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From "Nick Pentreath (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-20446) Optimize the process of MLLIB ALS recommendForAll
Date Mon, 24 Apr 2017 13:02:04 GMT

    [ https://issues.apache.org/jira/browse/SPARK-20446?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15981129#comment-15981129
] 

Nick Pentreath commented on SPARK-20446:
----------------------------------------

Anyway I'd like to compare the approaches and see which is most efficient. 

> 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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