[SPARK-41188][CORE][ML] Set executorEnv OMP_NUM_THREADS to be spark.task.cpus by default for spark executor JVM processes #38699
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HyukjinKwon
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Nov 18, 2022
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If we are setting it in |
I think we can remove code in PythonRunner.compute |
WeichenXu123
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Nov 19, 2022
…ask.cpus by default for spark executor JVM processes Signed-off-by: Weichen Xu <weichen.xudatabricks.com> ### What changes were proposed in this pull request? Set executorEnv OMP_NUM_THREADS to be spark.task.cpus by default for spark executor JVM processes. ### Why are the changes needed? This is for limiting the thread number for OpenBLAS routine to the number of cores assigned to this executor because some spark ML algorithms calls OpenBlAS via netlib-java, e.g.: Spark ALS estimator training calls LAPACK API `dppsv` (internally it will call BLAS lib), if it calls OpenBLAS lib, by default OpenBLAS will try to use all CPU cores. But spark will launch multiple spark tasks on a spark worker, and each spark task might call `dppsv` API at the same time, and each call internally it will create multiple threads (threads number equals to CPU cores), this causes CPU oversubscription. ### Does this PR introduce _any_ user-facing change? No. ### How was this patch tested? Manually. Closes #38699 from WeichenXu123/SPARK-41188. Authored-by: Weichen Xu <weichen.xu@databricks.com> Signed-off-by: Weichen Xu <weichen.xu@databricks.com> (cherry picked from commit 82a41d8) Signed-off-by: Weichen Xu <weichen.xu@databricks.com>
WeichenXu123
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Nov 19, 2022
…ask.cpus by default for spark executor JVM processes Signed-off-by: Weichen Xu <weichen.xudatabricks.com> ### What changes were proposed in this pull request? Set executorEnv OMP_NUM_THREADS to be spark.task.cpus by default for spark executor JVM processes. ### Why are the changes needed? This is for limiting the thread number for OpenBLAS routine to the number of cores assigned to this executor because some spark ML algorithms calls OpenBlAS via netlib-java, e.g.: Spark ALS estimator training calls LAPACK API `dppsv` (internally it will call BLAS lib), if it calls OpenBLAS lib, by default OpenBLAS will try to use all CPU cores. But spark will launch multiple spark tasks on a spark worker, and each spark task might call `dppsv` API at the same time, and each call internally it will create multiple threads (threads number equals to CPU cores), this causes CPU oversubscription. ### Does this PR introduce _any_ user-facing change? No. ### How was this patch tested? Manually. Closes #38699 from WeichenXu123/SPARK-41188. Authored-by: Weichen Xu <weichen.xu@databricks.com> Signed-off-by: Weichen Xu <weichen.xu@databricks.com> (cherry picked from commit 82a41d8) Signed-off-by: Weichen Xu <weichen.xu@databricks.com>
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Merged to master / branch-3.3 / branch-3.2 |
SandishKumarHN
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Dec 12, 2022
…ask.cpus by default for spark executor JVM processes Signed-off-by: Weichen Xu <weichen.xudatabricks.com> ### What changes were proposed in this pull request? Set executorEnv OMP_NUM_THREADS to be spark.task.cpus by default for spark executor JVM processes. ### Why are the changes needed? This is for limiting the thread number for OpenBLAS routine to the number of cores assigned to this executor because some spark ML algorithms calls OpenBlAS via netlib-java, e.g.: Spark ALS estimator training calls LAPACK API `dppsv` (internally it will call BLAS lib), if it calls OpenBLAS lib, by default OpenBLAS will try to use all CPU cores. But spark will launch multiple spark tasks on a spark worker, and each spark task might call `dppsv` API at the same time, and each call internally it will create multiple threads (threads number equals to CPU cores), this causes CPU oversubscription. ### Does this PR introduce _any_ user-facing change? No. ### How was this patch tested? Manually. Closes apache#38699 from WeichenXu123/SPARK-41188. Authored-by: Weichen Xu <weichen.xu@databricks.com> Signed-off-by: Weichen Xu <weichen.xu@databricks.com>
beliefer
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Dec 15, 2022
…ask.cpus by default for spark executor JVM processes Signed-off-by: Weichen Xu <weichen.xudatabricks.com> ### What changes were proposed in this pull request? Set executorEnv OMP_NUM_THREADS to be spark.task.cpus by default for spark executor JVM processes. ### Why are the changes needed? This is for limiting the thread number for OpenBLAS routine to the number of cores assigned to this executor because some spark ML algorithms calls OpenBlAS via netlib-java, e.g.: Spark ALS estimator training calls LAPACK API `dppsv` (internally it will call BLAS lib), if it calls OpenBLAS lib, by default OpenBLAS will try to use all CPU cores. But spark will launch multiple spark tasks on a spark worker, and each spark task might call `dppsv` API at the same time, and each call internally it will create multiple threads (threads number equals to CPU cores), this causes CPU oversubscription. ### Does this PR introduce _any_ user-facing change? No. ### How was this patch tested? Manually. Closes apache#38699 from WeichenXu123/SPARK-41188. Authored-by: Weichen Xu <weichen.xu@databricks.com> Signed-off-by: Weichen Xu <weichen.xu@databricks.com>
beliefer
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Dec 18, 2022
…ask.cpus by default for spark executor JVM processes Signed-off-by: Weichen Xu <weichen.xudatabricks.com> ### What changes were proposed in this pull request? Set executorEnv OMP_NUM_THREADS to be spark.task.cpus by default for spark executor JVM processes. ### Why are the changes needed? This is for limiting the thread number for OpenBLAS routine to the number of cores assigned to this executor because some spark ML algorithms calls OpenBlAS via netlib-java, e.g.: Spark ALS estimator training calls LAPACK API `dppsv` (internally it will call BLAS lib), if it calls OpenBLAS lib, by default OpenBLAS will try to use all CPU cores. But spark will launch multiple spark tasks on a spark worker, and each spark task might call `dppsv` API at the same time, and each call internally it will create multiple threads (threads number equals to CPU cores), this causes CPU oversubscription. ### Does this PR introduce _any_ user-facing change? No. ### How was this patch tested? Manually. Closes apache#38699 from WeichenXu123/SPARK-41188. Authored-by: Weichen Xu <weichen.xu@databricks.com> Signed-off-by: Weichen Xu <weichen.xu@databricks.com>
Found an issue in YARN (SPARK-42596). Could you double check? |
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Thanks for pointing it out and making a PR. I left a comment in your PR. |
sunchao
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Jun 2, 2023
…ask.cpus by default for spark executor JVM processes Signed-off-by: Weichen Xu <weichen.xudatabricks.com> ### What changes were proposed in this pull request? Set executorEnv OMP_NUM_THREADS to be spark.task.cpus by default for spark executor JVM processes. ### Why are the changes needed? This is for limiting the thread number for OpenBLAS routine to the number of cores assigned to this executor because some spark ML algorithms calls OpenBlAS via netlib-java, e.g.: Spark ALS estimator training calls LAPACK API `dppsv` (internally it will call BLAS lib), if it calls OpenBLAS lib, by default OpenBLAS will try to use all CPU cores. But spark will launch multiple spark tasks on a spark worker, and each spark task might call `dppsv` API at the same time, and each call internally it will create multiple threads (threads number equals to CPU cores), this causes CPU oversubscription. ### Does this PR introduce _any_ user-facing change? No. ### How was this patch tested? Manually. Closes apache#38699 from WeichenXu123/SPARK-41188. Authored-by: Weichen Xu <weichen.xu@databricks.com> Signed-off-by: Weichen Xu <weichen.xu@databricks.com> (cherry picked from commit 82a41d8) Signed-off-by: Weichen Xu <weichen.xu@databricks.com>
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Signed-off-by: Weichen Xu weichen.xu@databricks.com
What changes were proposed in this pull request?
Set executorEnv OMP_NUM_THREADS to be spark.task.cpus by default for spark executor JVM processes.
Why are the changes needed?
This is for limiting the thread number for OpenBLAS routine to the number of cores assigned to this executor because some spark ML algorithms calls OpenBlAS via netlib-java,
e.g.:
Spark ALS estimator training calls LAPACK API
dppsv(internally it will call BLAS lib), if it calls OpenBLAS lib, by default OpenBLAS will try to use all CPU cores. But spark will launch multiple spark tasks on a spark worker, and each spark task might calldppsvAPI at the same time, and each call internally it will create multiple threads (threads number equals to CPU cores), this causes CPU oversubscription.Does this PR introduce any user-facing change?
No.
How was this patch tested?
Manually.