comaniac commented on a change in pull request #14:
URL: https://github.com/apache/tvm-rfcs/pull/14#discussion_r683728888



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File path: rfcs/0012-pipeline-executor.md
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+- Feature Name: (fill me in with a unique identifier, `my_awesome_feature`)
+- Start Date: (fill me in with today's date, YYYY-MM-DD)
+- RFC PR: [apache/tvm-rfcs#0014](https://github.com/apache/tvm-rfcs/pull/0014)
+- GitHub Issue: [apache/tvm#8596](https://github.com/apache/tvm/issues/8596)
+
+## 1. Summary
+
+
+This proposal introduces Pipeline Executor: A runtime executor that by 
scheduling
+splitted subgraph of relay graph in pipeline to implement task level parallism 
to
+reduce compute latency.

Review comment:
       I actually don't think it's a typical case only in ML. This concept is 
also widely used in HPC. The "latency" is based on the time of executing one 
task; while the "throughput" is the number of finished task in a time unit. 
Pipelining definitely cannot improve latency (of executing one task) but 
throughput, because you can finish more tasks in the same time period.
   
   General speaking, I think this is just the terminology issue, but it would 
be good to use consistent terminology for efficient communications. The 
confusion of "not being processed together" falls into the similar reason. Jorn 
and probably most people may feel confuse about why two stages of the same task 
can be processed together, but you actually meant stage 2 of the _first task_ 
and the stage 1 of the _second task_ can be processed in parallel. This is the 
point you should make it clear in the RFC.




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