[jira] [Commented] (SPARK-21688) performance improvement in mllib SVM with native BLAS

["Sean Owen (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-21688?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16121163#comment-16121163 ] 

Sean Owen commented on SPARK-21688:
-----------------------------------

Of course native BLAS is typically faster where it is used so you should enable it. What are you specifically saying beyond that?

> performance improvement in mllib SVM with native BLAS 
> ------------------------------------------------------
>
>                 Key: SPARK-21688
>                 URL: https://issues.apache.org/jira/browse/SPARK-21688
>             Project: Spark
>          Issue Type: Improvement
>          Components: MLlib
>    Affects Versions: 2.2.0
>         Environment: 4 nodes: 1 master node, 3 worker nodes
> model name      : Intel(R) Xeon(R) CPU E5-2697 v2 @ 2.70GHz
> Memory : 180G
> num of core per node: 10
>            Reporter: Vincent
>         Attachments: mllib svm training.png, svm1.png, svm2.png, svm-mkl-1.png, svm-mkl-2.png
>
>
> in current mllib SVM implementation, we found that the CPU is not fully utilized, one reason is that f2j blas is set to be used in the HingeGradient computation. As we found out earlier (https://issues.apache.org/jira/browse/SPARK-21305) that with proper settings, native blas is generally better than f2j on the uni-test level, here we make the blas operations in SVM go with MKL blas and get an end to end performance report showing that in most cases native blas outperformance f2j blas up to 50%.
> So, we suggest removing those f2j-fixed calling and going for native blas if available. If this proposal is acceptable, we will move on to benchmark other algorithms impacted. 



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