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

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

     [ https://issues.apache.org/jira/browse/SPARK-32120?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Enrico Minack updated SPARK-32120:
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    Attachment: screenshot-1.png

> 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-1.png
>
>
> 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}
> {"name": "gpu", "addresses": ["0", "1"]}
> {code}
> Spark local cluster setup through `spark-shell`:
> {code}
> ./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 of this cluster:
> Code run in the Spark shell:
> {code}
> 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:
> 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}
> res0: Array[(String, (String, Array[String]))] = Array((gpu,(gpu,Array(0))), (gpu,(gpu,Array(1))))
> {code}
> Happy to contribute a patch if this is an accepted bug.



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