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https://issues.apache.org/jira/browse/SPARK-8881?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Andrew Or closed SPARK-8881.
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Resolution: Fixed
> Standalone mode scheduling fails because cores assignment is not atomic
> -----------------------------------------------------------------------
>
> Key: SPARK-8881
> URL: https://issues.apache.org/jira/browse/SPARK-8881
> Project: Spark
> Issue Type: Bug
> Components: Deploy
> Affects Versions: 1.4.0, 1.5.0
> Reporter: Nishkam Ravi
> Assignee: Nishkam Ravi
> Priority: Critical
> Fix For: 1.4.2, 1.5.0
>
>
> Current scheduling algorithm (in Master.scala) has two issues:
> 1. cores are allocated one at a time instead of spark.executor.cores at a time
> 2. when spark.cores.max/spark.executor.cores < num_workers, executors are not
> launched and the app hangs (due to 1)
> === Edit by Andrew ===
> Here's an example from the PR. Let's say we have 4 workers with 16 cores
> each. We set `spark.cores.max` to 48 and `spark.executor.cores` to 16.
> Because in spread out mode, the existing code allocates 1 core at a time, we
> end up allocating 12 cores on each worker, and no executors can be launched
> because each one wants at least 16 cores. Instead, we should allocate 16
> cores at a time.
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