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From "Apache Spark (JIRA)" <>
Subject [jira] [Commented] (SPARK-21305) The BKM (best known methods) of using native BLAS to improvement ML/MLLIB performance
Date Thu, 06 Jul 2017 08:06:00 GMT


Apache Spark commented on SPARK-21305:

User 'mpjlu' has created a pull request for this issue:

> The BKM (best known methods) of using native BLAS to improvement ML/MLLIB performance
> -------------------------------------------------------------------------------------
>                 Key: SPARK-21305
>                 URL:
>             Project: Spark
>          Issue Type: Umbrella
>          Components: ML, MLlib
>    Affects Versions: 2.3.0
>            Reporter: Peng Meng
>            Priority: Critical
>   Original Estimate: 504h
>  Remaining Estimate: 504h
> Many ML/MLLIB algorithms use native BLAS (like Intel MKL, ATLAS, OpenBLAS) to improvement
the performance. 
> The methods to use native BLAS is important for the performance,  sometimes (high opportunity)
native BLAS even causes worse performance.  
> For example, for the ALS recommendForAll method before SPARK 2.2 which uses BLAS gemm
for matrix multiplication. 
> If you only test the matrix multiplication performance of native BLAS gemm (like Intel
MKL, and OpenBLAS) and netlib-java F2j BLAS gemm , the native BLAS is about 10X performance
improvement.  But if you test the Spark Job end-to-end performance, F2j is much faster than
native BLAS, very interesting. 
> I spend much time for this problem, and find we should not use native BLAS (like OpenBLAS
and Intel MKL) which support multi-thread with no any setting. By default, this native BLAS
will enable multi-thread, which will conflict with Spark executor.  You can use multi-thread
native BLAS, but it is better to disable multi-thread first. 
> I think we should add some comments in docs/ for this first. 

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