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https://issues.apache.org/jira/browse/YARN-6223?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Wangda Tan updated YARN-6223:
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Attachment: YARN-6223.wip.2.patch
Attached ver.2 WIP patch which discussed with [~chris.douglas] / [~vinodkv] /
[[email protected]] / [~sidharta-s] / [~vvasudev].
I want you guys to help look at overall implementation in c side. I made it
better modularized, and the new logics can be turned on/off.
This is related to YARN-5673 but doesn't include refactoring of existing logics
/ dynamic load modules, etc.
TODO items:
- More validation / tests need for C side, now doesn't handle memory leak, etc.
properly.
- Doesn't include logics of GPU allocation recovery on NM restart.
+[~tangzhankun], this may related to FPGA isolation as well.
I will be traveling till early next week so please expect delayed responses :).
> [Umbrella] Natively support GPU configuration/discovery/scheduling/isolation
> on YARN
> ------------------------------------------------------------------------------------
>
> Key: YARN-6223
> URL: https://issues.apache.org/jira/browse/YARN-6223
> Project: Hadoop YARN
> Issue Type: New Feature
> Reporter: Wangda Tan
> Assignee: Wangda Tan
> Attachments: YARN-6223.Natively-support-GPU-on-YARN-v1.pdf,
> YARN-6223.wip.1.patch, YARN-6223.wip.2.patch
>
>
> As varieties of workloads are moving to YARN, including machine learning /
> deep learning which can speed up by leveraging GPU computation power.
> Workloads should be able to request GPU from YARN as simple as CPU and memory.
> *To make a complete GPU story, we should support following pieces:*
> 1) GPU discovery/configuration: Admin can either config GPU resources and
> architectures on each node, or more advanced, NodeManager can automatically
> discover GPU resources and architectures and report to ResourceManager
> 2) GPU scheduling: YARN scheduler should account GPU as a resource type just
> like CPU and memory.
> 3) GPU isolation/monitoring: once launch a task with GPU resources,
> NodeManager should properly isolate and monitor task's resource usage.
> For #2, YARN-3926 can support it natively. For #3, YARN-3611 has introduced
> an extensible framework to support isolation for different resource types and
> different runtimes.
> *Related JIRAs:*
> There're a couple of JIRAs (YARN-4122/YARN-5517) filed with similar goals but
> different solutions:
> For scheduling:
> - YARN-4122/YARN-5517 are all adding a new GPU resource type to Resource
> protocol instead of leveraging YARN-3926.
> For isolation:
> - And YARN-4122 proposed to use CGroups to do isolation which cannot solve
> the problem listed at
> https://github.com/NVIDIA/nvidia-docker/wiki/GPU-isolation#challenges such as
> minor device number mapping; load nvidia_uvm module; mismatch of CUDA/driver
> versions, etc.
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