message: Update hugectr_user_guide.md
  Update the GPUDirect expression
diff: @@ -143,7 +143,7 @@ To achieve the best performance on different embeddings, use various embedding l
  ### Multi-Node Training ###
  Multi-node training makes it easy to train an embedding table of arbitrary size. In a multi-node solution, the sparse model, which is referred to as the embedding layer, is distributed across the nodes. Meanwhile, the dense model, such as DNN, is data parallel and contains a copy of the dense model in each GPU (see Fig. 2). With our implementation, HugeCTR leverages NCCL for high speed and scalable inter-node and intra-node communication.
  -To run with multiple nodes, HugeCTR should be built with OpenMPI. GPUDirect support is recommended for high performance. For more information, see our [DCN multi-node training sample](../samples/dcn/dcn_2node_8gpu.py).
  +To run with multiple nodes, HugeCTR should be built with OpenMPI. [GPUDirect RDMA](https://docs.nvidia.com/cuda/gpudirect-rdma/index.html) support is recommended for high performance. For more information, see our [DCN multi-node training sample](../samples/dcn/dcn_2node_8gpu.py).
  ### Mixed Precision Training ###
  Mixed precision training is supported to help improve and reduce the memory throughput footprint. In this mode, TensorCores are used to boost performance for matrix multiplication-based layers, such as `FullyConnectedLayer` and `InteractionLayer`, on Volta, Turing, and Ampere architectures. For the other layers, including embeddings, the data type is changed to FP16 so that both memory bandwidth and capacity are saved. To enable mixed precision mode, specify the mixed_precision option in the configuration file. When [`mixed_precision`](https://arxiv.org/abs/1710.03740) is set, the full FP16 pipeline will be triggered. Loss scaling will be applied to avoid the arithmetic underflow (see Fig. 5). Mixed precision training can be enabled using the configuration file.
  