[jira] [Assigned] (SPARK-21389) ALS recommendForAll optimization uses Native BLAS

["Apache Spark (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-21389?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Apache Spark reassigned SPARK-21389:
------------------------------------

    Assignee:     (was: Apache Spark)

> ALS recommendForAll optimization uses Native BLAS
> -------------------------------------------------
>
>                 Key: SPARK-21389
>                 URL: https://issues.apache.org/jira/browse/SPARK-21389
>             Project: Spark
>          Issue Type: Improvement
>          Components: ML, MLlib
>    Affects Versions: 2.3.0
>            Reporter: Peng Meng
>   Original Estimate: 168h
>  Remaining Estimate: 168h
>
> In Spark 2.2, we have optimized ALS recommendForAll, which uses a handwriting matrix multiplication, and get the topK items for each matrix. The method effectively reduce the GC problem. However, Native BLAS GEMM, like Intel MKL, and OpenBLAS, the performance of matrix multiplication is about 10X comparing with handwriting method. 
> I have rewritten the code of recommendForAll with GEMM, and got about 20%-30% improvement comparing with the master recommendForAll method. 
> Will clean the code and submit for discussion.



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