[jira] [Commented] (SPARK-32120) Single GPU is allocated multiple times

["wuyi (Jira)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-32120?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17151550#comment-17151550 ] 

wuyi commented on SPARK-32120:
------------------------------

[~EnricoMi] Thanks for reporting! I am looking into this.

> Single GPU is allocated multiple times
> --------------------------------------
>
>                 Key: SPARK-32120
>                 URL: https://issues.apache.org/jira/browse/SPARK-32120
>             Project: Spark
>          Issue Type: Bug
>          Components: Scheduler
>    Affects Versions: 3.0.0
>            Reporter: Enrico Minack
>            Priority: Major
>         Attachments: screenshot-2.png, screenshot-3.png
>
>
> I am running Spark in a {{local-cluster[2,1,1024]}} with one GPU per worker, task and executor, and two GPUs provided through a GPU discovery script. The same GPU is allocated to both executors.
> Discovery script output:
> {code:java}
> {"name": "gpu", "addresses": ["0", "1"]}
> {code}
> Spark local cluster setup through {{spark-shell}}:
> {code:java}
> ./spark-3.0.0-bin-hadoop2.7/bin/spark-shell --master "local-cluster[2,1,1024]" --conf spark.worker.resource.gpu.discoveryScript=/tmp/gpu.json --conf spark.worker.resource.gpu.amount=1 --conf spark.task.resource.gpu.amount=1 --conf spark.executor.resource.gpu.amount=1
> {code}
> Executor page of this cluster:
>  !screenshot-2.png!
> You can see that both executors have the same GPU allocated: {{[1]}}
> Code run in the Spark shell:
> {code:java}
> scala> import org.apache.spark.TaskContext
> import org.apache.spark.TaskContext
> scala> def fn(it: Iterator[java.lang.Long]): Iterator[(String, (String, Array[String]))] = { TaskContext.get().resources().mapValues(v => (v.name, v.addresses)).iterator }
> fn: (it: Iterator[Long])Iterator[(String, (String, Array[String]))]
> scala> spark.range(0,2,1,2).mapPartitions(fn).collect
> res0: Array[(String, (String, Array[String]))] = Array((gpu,(gpu,Array(1))), (gpu,(gpu,Array(1))))
> {code}
> The result shows that each task got GPU {{1}}. The executor page shows that each task has been run on different executors (see above screenshot).
> The expected behaviour would have been to have GPU {{0}} assigned to one executor and GPU {{1}} to the other executor. Consequently, each partition / task should then see a different GPU.
> With Spark 3.0.0-preview2 the allocation was as expected (identical code and Spark shell setup):
> {code:java}
> res0: Array[(String, (String, Array[String]))] = Array((gpu,(gpu,Array(0))), (gpu,(gpu,Array(1))))
> {code}
> !screenshot-3.png!
> Happy to contribute a patch if this is an accepted bug.



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