[jira] [Assigned] (SPARK-21305) The BKM (best known methods) of using native BLAS to improvement ML/MLLIB performance
[ https://issues.apache.org/jira/browse/SPARK-21305?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Sean Owen reassigned SPARK-21305: - Assignee: Peng Meng Flags: (was: Important) Affects Version/s: (was: 2.3.0) 2.2.0 Priority: Minor (was: Critical) Component/s: (was: MLlib) Documentation Issue Type: Improvement (was: Umbrella) > 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: Improvement > Components: Documentation, ML >Affects Versions: 2.2.0 >Reporter: Peng Meng >Assignee: Peng Meng >Priority: Minor > Fix For: 2.3.0 > > 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. -- This message was sent by Atlassian JIRA (v6.4.14#64029) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Assigned] (SPARK-21305) The BKM (best known methods) of using native BLAS to improvement ML/MLLIB performance
[ https://issues.apache.org/jira/browse/SPARK-21305?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Apache Spark reassigned SPARK-21305: Assignee: (was: 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 >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. -- This message was sent by Atlassian JIRA (v6.4.14#64029) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Assigned] (SPARK-21305) The BKM (best known methods) of using native BLAS to improvement ML/MLLIB performance
[ https://issues.apache.org/jira/browse/SPARK-21305?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Apache Spark reassigned SPARK-21305: 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. -- This message was sent by Atlassian JIRA (v6.4.14#64029) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
