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