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https://issues.apache.org/jira/browse/YARN-8821?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16754592#comment-16754592
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Zhankun Tang commented on YARN-8821:
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[~leftnoteasy] , [~cheersyang] , [~sunilg] . Please help to review this
topology scheduling algorithm.
The general steps of the algorithm are listed in the Jira description. The keys
are the cost table and the topology policy. We haven't taken NUMA or CPU
affinity into consideration which might need more high-level changes of YARN
resource isolation. So this topology algorithm can only be coss-grained.
> GPU hierarchy/topology scheduling support
> -----------------------------------------
>
> Key: YARN-8821
> URL: https://issues.apache.org/jira/browse/YARN-8821
> Project: Hadoop YARN
> Issue Type: Sub-task
> Reporter: Zhankun Tang
> Assignee: Zhankun Tang
> Priority: Major
> Attachments: YARN-8821-trunk.001.patch
>
>
> GPU topology affects performance dramatically. There's been a discussion in
> YARN-7481. But we'd like to move related discussions here.
> Please note that YARN-8851 will provide a pluggable device framework which
> can support plugin custom scheduler. And based on the framework, GPU plugin
> could have own topology scheduler. The proposed patch has a topology
> algorithm implemented as below:
> *Step 1*. When plugin inits, parse the output of "nvidia-smi topo -m" to
> build a hash map whose key is all pairs of GPUs and the value is the
> communication cost between the two. The map is like \{"0 - 1"=> 2, "0 -
> 2"=>4, ...} which means the minimum cost of GPU 0 to 1 is 2. The cost is set
> based on the connection type. *Haven't considered CPU affinity or NUMA node
> yet.*
> *Step 2*. And then it constructs a _+cost table+_ which caches all
> combinations of GPUs and corresponding cost between them. The cost table is a
> map whose structure is like
>
> {code:java}
> { 2=>{[0,1]=>2,..},
> 3=>{[0,1,2]=>10,..},
> 4=>{[0,1,2,3]=>18}}.
> {code}
> The key of the map is the count of GPUs, the value of it is a map whose key
> is the combination of GPUs and the value is the calculated communication cost
> of the numbers of GPUs. The cost calculation algorithm is to sum all
> non-duplicate pairs of GPU's cost. For instance, the total cost of [0,1,2]
> GPUs are the sum of cost "0 - 1", "0 - 2" and "1 - 2". And each cost can get
> from the map built in step 1.
> *Step 3*. After the cost table is built, when allocating GPUs based on
> topology, we provide two policy which container can set through an
> environment variable "NVIDIA_TOPO_POLICY". The value can be either "PACK" or
> "SPREAD". The "PACK" means it prefers faster GPU-GPU communication. The
> "SPREAD" means it prefers faster CPU-GPU communication( since GPUs are not
> using the same bus to CPU). And the key difference of the two policy is the
> sort order of the inner map in the cost table. For instance, let's assume 2
> GPUs is wanted. The costTable.get(2) would return a map containing all
> combinations of two GPUs and their cost. If the policy is "PACK", we'll sort
> the map by cost in ascending order. The first entry will be the GPUs has
> minimum GPU-GPU cost. If the policy is "SPREAD", we sort it in descending
> order and get the first one which is the highest GPU-GPU cost which means
> lowest CPU-GPU costs.
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