NCP-AII Question 34
Select 3An AI research team is deploying a distributed training workload across multiple GPU-enabled servers. During initial testing, they notice significant delays in data transmission between nodes. As an AI infrastructure specialist, what steps would you take to optimize the system and network for this workload?
- A
Enable RDMA (Remote Direct Memory Access) for faster data transfer between nodes.
- B
Upgrade the network to a high-bandwidth, low-latency interconnect such as InfiniBand.
- C
Increase the batch size of the training workload to reduce the number of communication steps.
- D
Disable NCCL (NVIDIA Collective Communications Library) to simplify inter-GPU communication.
- E
Configure NUMA (Non-Uniform Memory Access) settings to ensure data locality for GPU processes.
Show answer and explanation
Correct answers: A, B, E
Explanation
Distributed training workloads are heavily dependent on efficient data transfer and system configurations to minimize bottlenecks. Enabling RDMA, upgrading to a high-performance interconnect, and optimizing NUMA settings are all critical steps to improve performance. These optimizations directly address the communication and system-level requirements of distributed AI workloads, helping to reduce delays and maximize throughput.
- A. Correct.
Enabling RDMA allows for direct memory access between nodes without involving the CPU, significantly reducing communication latency and improving performance in distributed training workloads.
- B. Correct.
Upgrading to a high-bandwidth and low-latency interconnect, such as InfiniBand, ensures faster data transmission between nodes, which is crucial for distributed AI training.
- C. Incorrect.
While increasing the batch size can reduce communication frequency, it does not directly address network latency or system-level optimizations.
- D. Incorrect.
Disabling NCCL would be counterproductive, as NCCL is designed to optimize inter-GPU communication for distributed training workloads.
- E. Correct.
Configuring NUMA settings ensures that GPU processes access memory that is closest to them, enhancing performance by reducing memory access latency.