NCP-AII Question 2
Select 3You are managing an AI infrastructure utilizing NVIDIA GPUs for training deep learning models. A user reports that their training job is running significantly slower than expected on a multi-GPU system. Upon investigation, you find that GPU utilization is uneven across the GPUs, with some GPUs underutilized. What steps should you take to troubleshoot and optimize the training performance?
- A
Check if the training script is optimized for multi-GPU usage, such as using frameworks like PyTorch DistributedDataParallel or TensorFlow MirroredStrategy.
- B
Verify that the GPUs are correctly configured in the NVIDIA driver and CUDA toolkit installation.
- C
Reduce the batch size to ensure better memory usage across GPUs.
- D
Inspect the data pipeline for bottlenecks, such as slow data loading or preprocessing, which could lead to GPU starvation.
- E
Enable GPU overclocking to force all GPUs to operate at higher performance levels.
Show answer and explanation
Correct answers: A, B, D
Explanation
Uneven GPU utilization in multi-GPU systems often arises from suboptimal workload distribution, misconfigurations in software/hardware, or data pipeline bottlenecks. By ensuring the training script is optimized for multi-GPU usage, verifying correct configurations, and addressing data pipeline inefficiencies, you can resolve the issue. Overclocking and batch size reduction are not appropriate solutions in this scenario.
- A. Correct.
Ensuring that the training script is properly configured for multi-GPU usage is critical. Frameworks like PyTorch DistributedDataParallel or TensorFlow MirroredStrategy optimize workload distribution, preventing uneven GPU utilization.
- B. Correct.
Incorrect driver or CUDA toolkit configurations can lead to improper GPU communication or recognition, causing performance issues.
- C. Incorrect.
Reducing the batch size may lead to underutilization of GPU memory, worsening the problem rather than solving it.
- D. Correct.
Bottlenecks in the data pipeline, such as slow data loading or preprocessing, can prevent GPUs from being fully utilized. Addressing these issues helps balance the workload and optimize performance.
- E. Incorrect.
Enabling GPU overclocking is not a recommended solution for performance optimization in this case and could introduce stability issues.