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https://issues.apache.org/jira/browse/SPARK-24615?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Xiangrui Meng updated SPARK-24615:
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Summary: SPIP: Accelerator-aware task scheduling for Spark (was:
Accelerator-aware task scheduling for Spark)
> SPIP: Accelerator-aware task scheduling for Spark
> -------------------------------------------------
>
> Key: SPARK-24615
> URL: https://issues.apache.org/jira/browse/SPARK-24615
> Project: Spark
> Issue Type: Epic
> Components: Spark Core
> Affects Versions: 2.4.0
> Reporter: Saisai Shao
> Assignee: Xingbo Jiang
> Priority: Major
> Labels: Hydrogen, SPIP
> Attachments: Accelerator-aware scheduling in Apache Spark 3.0.pdf,
> SPIP_ Accelerator-aware scheduling.pdf
>
>
> In the machine learning area, accelerator card (GPU, FPGA, TPU) is
> predominant compared to CPUs. To make the current Spark architecture to work
> with accelerator cards, Spark itself should understand the existence of
> accelerators and know how to schedule task onto the executors where
> accelerators are equipped.
> Current Spark’s scheduler schedules tasks based on the locality of the data
> plus the available of CPUs. This will introduce some problems when scheduling
> tasks with accelerators required.
> # CPU cores are usually more than accelerators on one node, using CPU cores
> to schedule accelerator required tasks will introduce the mismatch.
> # In one cluster, we always assume that CPU is equipped in each node, but
> this is not true of accelerator cards.
> # The existence of heterogeneous tasks (accelerator required or not)
> requires scheduler to schedule tasks with a smart way.
> So here propose to improve the current scheduler to support heterogeneous
> tasks (accelerator requires or not). This can be part of the work of Project
> hydrogen.
> Details is attached in google doc. It doesn't cover all the implementation
> details, just highlight the parts should be changed.
>
> CC [~yanboliang] [~merlintang]
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