[jira] [Commented] (SPARK-1882) Support dynamic memory sharing in Mesos

["Stavros Kontopoulos (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-1882?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15322364#comment-15322364 ] 

Stavros Kontopoulos commented on SPARK-1882:
--------------------------------------------

Does dynamic allocation help with the fragmentation problem in heterogeneous machines in some way? 


> Support dynamic memory sharing in Mesos
> ---------------------------------------
>
>                 Key: SPARK-1882
>                 URL: https://issues.apache.org/jira/browse/SPARK-1882
>             Project: Spark
>          Issue Type: Improvement
>          Components: Mesos
>    Affects Versions: 1.0.0
>            Reporter: Andrew Ash
>
> Fine grained mode Mesos currently supports sharing CPUs very well, but requires that memory be pre-partitioned according to the executor memory parameter.  Mesos supports dynamic memory allocation in addition to dynamic CPU allocation, so we should utilize this feature in Spark.
> See below where when the Mesos backend accepts a resource offer it only checks that there's enough memory to cover sc.executorMemory, and doesn't ever take a fraction of the memory available.  The memory offer is accepted all or nothing from a pre-defined parameter.
> Coarse mode:
> https://github.com/apache/spark/blob/3ce526b168050c572a1feee8e0121e1426f7d9ee/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala#L208
> Fine mode:
> https://github.com/apache/spark/blob/a5150d199ca97ab2992bc2bb221a3ebf3d3450ba/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala#L114



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