NCP-AII Question 32
Select 3You are deploying an AI workload on an NVIDIA DGX system in your data center. The workload requires high-speed communication between nodes for distributed training. Which network and system configurations should you prioritize to optimize performance?
- A
Ensure the nodes are connected using NVIDIA Mellanox InfiniBand for low latency and high throughput.
- B
Enable RoCE (RDMA over Converged Ethernet) on the Ethernet switches used for the nodes.
- C
Configure the system to use standard TCP/IP over a 1Gbps Ethernet network for simplicity.
- D
Verify that the NVIDIA NCCL library is configured to take advantage of GPU Direct RDMA.
- E
Disable any Quality of Service (QoS) settings to avoid network overhead.
Show answer and explanation
Correct answers: A, B, D
Explanation
Distributed AI workloads, such as deep learning training, require high-speed communication between nodes to ensure efficient data exchange. Leveraging NVIDIA Mellanox InfiniBand or RoCE for low latency and high throughput, and configuring the NVIDIA NCCL library for GPU Direct RDMA, are key to optimizing performance. Standard TCP/IP over a 1Gbps network is insufficient due to its bandwidth and latency limitations. Additionally, QoS settings should not be disabled, as they help manage network traffic and prevent congestion.
- A. Correct.
Using NVIDIA Mellanox InfiniBand ensures low latency and high throughput, which are critical for distributed AI workloads.
- B. Correct.
Enabling RoCE (RDMA over Converged Ethernet) allows direct memory access from one node to another without involving the CPU, reducing latency.
- C. Incorrect.
Standard TCP/IP over a 1Gbps Ethernet network is not sufficient for distributed AI workloads due to its limited bandwidth and higher latency.
- D. Correct.
The NVIDIA NCCL library enables GPU Direct RDMA, which is essential for maximizing communication efficiency between NVIDIA GPUs in distributed training.
- E. Incorrect.
Disabling Quality of Service (QoS) can lead to network congestion, which negatively impacts distributed AI workloads.