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https://issues.apache.org/jira/browse/SPARK-26632?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Apache Spark reassigned SPARK-26632:
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Assignee: Apache Spark
> Separate Thread Configurations of Driver and Executor
> -----------------------------------------------------
>
> Key: SPARK-26632
> URL: https://issues.apache.org/jira/browse/SPARK-26632
> Project: Spark
> Issue Type: Improvement
> Components: Spark Core
> Affects Versions: 3.0.0
> Reporter: jiafu zhang
> Assignee: Apache Spark
> Priority: Minor
>
> During the benchmark of Spark 2.4.0 on HPC (High Performance Computing), we
> identified an area can be optimized to improve RPC performance on large
> number of HPC nodes with omini-path NIC. It's same thread configurations for
> both driver and executor. From the test, we find driver and executor should
> have different thread configurations because driver has far more RPC messages
> than single executor.
> These configurations are,
> ||Config Key||for Driver||for Executor||
> |spark.rpc.io.serverThreads|spark.driver.rpc.io.serverThreads|spark.executor.rpc.io.serverThreads|
> |spark.rpc.io.clientThreads|spark.driver.rpc.io.clientThreads|spark.executor.rpc.io.clientThreads|
> |spark.rpc.netty.dispatcher.numThreads|spark.driver.rpc.netty.dispatcher.numThreads|spark.executor.rpc.netty.dispatcher.numThreads|
> When Spark reads thread configurations, it tries to read driver's
> configurations or executor's configurations first. Then fall back to the
> common thread configurations.
> After the separation, the performance is improved a lot in 256 nodes and 512
> nodes. see below test result of SimpleMapTask.
> ||
> ||spark.driver.rpc.io.serverThreads||spark.driver.rpc.io.clientThreads||spark.driver.rpc.netty.dispatcher.numThreads||spark.executor.rpc.netty.dispatcher.numThreads||Overall
> Time (s)||Overall Time without Separation (s)||Improvement||
> |128 nodes|15|15|10|30|107|108|0.9%|
> |256 nodes|12|15|10|30|159|196|18.8%|
> |512 nodes|12|15|10|30|283|377|24.9%|
>
> The implementation is almost done. We are working on the code merge.
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