Add some docs

README.md

@@ -282,6 +282,10 @@ For two micro-batch overlapping, you can refer to the following figure. With our
 
 ## Notices
 
+#### Easier potential overall design
+
+Current DeepEP implementation uses queues for communication buffers which saves memory but introduces complexity and potential deadlocks. If you're implementing your own version based on DeepEP, consider using fixed-size buffers allocated to maximum capacity for simplicity and better performance. For a detailed discussion of this alternative approach, see https://github.com/deepseek-ai/DeepEP/issues/39.
+
 #### Undefined-behavior PTX usage
 
 - For extreme performance, we discover and use an undefined-behavior PTX usage: using read-only PTX `ld.global.nc.L1::no_allocate.L2::256B` to **read volatile data**. The PTX modifier `.nc` indicates that a non-coherent cache is used. But the correctness is tested to be guaranteed with `.L1::no_allocate` on Hopper architectures, and performance will be much better. The reason we guess may be: the non-coherent cache is unified with L1, and the L1 modifier is not just a hint but a strong option, so that the correctness can be guaranteed by no dirty data in L1.

deep_ep/buffer.py

@@ -44,7 +44,6 @@ class Buffer:
                 to the number of local experts.
         """
 
-        # TODO: argument docs
         # Initialize the CPP runtime
         self.rank = group.rank()
         self.group_size = group.size()