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From "Chen Qingcha (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (YARN-7481) Gpu locality support for Better AI scheduling
Date Tue, 10 Jul 2018 12:38:00 GMT

     [ https://issues.apache.org/jira/browse/YARN-7481?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel

Chen Qingcha updated YARN-7481:
    Attachment: hadoop-2.7.2.gpu-port-20180710_old.patch

> Gpu locality support for Better AI scheduling
> ---------------------------------------------
>                 Key: YARN-7481
>                 URL: https://issues.apache.org/jira/browse/YARN-7481
>             Project: Hadoop YARN
>          Issue Type: New Feature
>          Components: api, RM, yarn
>    Affects Versions: 2.7.2
>            Reporter: Chen Qingcha
>            Priority: Major
>             Fix For: 2.7.2
>         Attachments: GPU locality support for Job scheduling.pdf, hadoop-2.7.2.gpu-port-20180710.patch,
hadoop-2.7.2.gpu-port-20180710_old.patch, hadoop-2.7.2.gpu-port.patch, hadoop-2.9.0.gpu-port.patch,
>   Original Estimate: 1,344h
>  Remaining Estimate: 1,344h
> We enhance Hadoop with GPU support for better AI job scheduling. 
> Currently, YARN-3926 also supports GPU scheduling, which treats GPU as countable resource.

> However, GPU placement is also very important to deep learning job for better efficiency.
>  For example, a 2-GPU job runs on gpu {0,1} could be faster than run on gpu {0, 7}, if
GPU 0 and 1 are under the same PCI-E switch while 0 and 7 are not.
>  We add the support to Hadoop 2.7.2 to enable GPU locality scheduling, which support
fine-grained GPU placement. 
> A 64-bits bitmap is added to yarn Resource, which indicates both GPU usage and locality
information in a node (up to 64 GPUs per node). '1' means available and '0' otherwise in
the corresponding position of the bit.   

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