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https://issues.apache.org/jira/browse/SPARK-26104?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16690725#comment-16690725
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Apache Spark commented on SPARK-26104:
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User 'chenqin' has created a pull request for this issue:
https://github.com/apache/spark/pull/23073
> make pci devices visible to task scheduler
> ------------------------------------------
>
> Key: SPARK-26104
> URL: https://issues.apache.org/jira/browse/SPARK-26104
> Project: Spark
> Issue Type: Improvement
> Components: Spark Core
> Affects Versions: 2.4.0
> Reporter: Chen Qin
> Priority: Major
> Labels: Hydrogen
>
> Spark Task scheduling has long time consider CPU only, depending on how many
> vcores each executor has at given moment, the task were scheduled as long as
> enough vcores become available.
> Moving to deep learning use cases, The fundamental computation and processing
> unit switched from CPU to GPU/FPGA + CPU which moves data in and out of GPU
> memory.
> Deep learning framework build on top of GPU fleets requires fixture of task
> to number of GPUs spark haven't support yet. E.g a horord task requires 2
> GPUs running uninterrupted before it finish regardless how CPU availability
> in executor. In Uber peloton executor scheduler, the number of cores
> available could be more than what user asked due to the fact it might get
> over provisioned.
> Without definitive occupy of pci device(/gpu1, /gpu2), such workload may run
> into unexpected states.
>
> related jiras allocating executor containers with gpu resources, serve as
> bootstrap phase usage
> SPARK-19320 Mesos SPARK-24491 K8s SPARK-20327 YARN
> Existing SPIP: Accelerator Aware Task Scheduling For Spark SPARK-24615,
> compatible with design, approach is a bit different as it tacks utilization
> of pci devices where customized taskscheduler could either fallback to "best
> to have" approach or implement "must have" approach stated above.
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