Add NCCL2 dist train design doc (#11885)

* add_nccl2_dist_design

* update

* update by comments

doc/fluid/design/dist_train/dist_train_nccl2.md

+# Distributed Training with NCCL2
+
+We design a pattern that can enable training with `ParallelExecutor` and
+using [NCCL2](https://developer.nvidia.com/nccl) as it's collective
+communication library.
+
+In `ParallelExecutor` we can use `AllReduce` or `Reduce` and `Broadcast`
+to do multi GPU training. And if we initialize NCCL2 communicators as
+ranks in a distributed environment, we can simply run the `ParallelExecutor`
+as a distributed program! The only thing that may be different than in
+the single node version is that we need to broadcast the NCCL unique ID
+to all the nodes, and initialize communicators using that ID, so NCCL2
+will know each other as ranks.
+
+To achieve this feature, we introduce a new operator: `gen_nccl_id` op,
+so we are ***not*** "bind to" running NCCL2 with MPI, we can run it in
+what ever platform you like.
+
+It have two running modes:
+
+1. Generate and broadcast mode, which should be used on trainer 0;
+1. Listen and fetch mode, which should be used on trainers other than 0.
+
+In both two modes, this op can save the NCCL ID into current scope as a
+persistable variable, Then we can insert this op at the end of
+"startup program" of fluid, so that all workers can get the same ID to
+initialize NCCL communicator objects.
+
+<img src="src/ncc2_design.png">
+
+The above figure indicates the general process when training with NCCL2
+distributed. Each trainer have the number of communicators equal to the
+number of GPUs, but the ranks should match the global ranks number: here
+we have total 8 GPUs, so `nranks==8`, for each trainer, the ranks should
+be from 0 ~ 3 on trainer 0 and 4 ~ 7 on trainer 1.