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https://issues.apache.org/jira/browse/SPARK-21305?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Apache Spark reassigned SPARK-21305:
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Assignee: Apache Spark
> The BKM (best known methods) of using native BLAS to improvement ML/MLLIB
> performance
> -------------------------------------------------------------------------------------
>
> Key: SPARK-21305
> URL: https://issues.apache.org/jira/browse/SPARK-21305
> Project: Spark
> Issue Type: Umbrella
> Components: ML, MLlib
> Affects Versions: 2.3.0
> Reporter: Peng Meng
> Assignee: Apache Spark
> 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.
> https://github.com/xianyi/OpenBLAS/wiki/faq#multi-threaded
> https://software.intel.com/en-us/articles/recommended-settings-for-calling-intel-mkl-routines-from-multi-threaded-applications
> I think we should add some comments in docs/ml-guilde.md for this first.
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