NCP-AII Question 5
Select 3You are managing an AI workload using NVIDIA GPUs on a cluster, and the model training is taking significantly longer than expected. Upon investigation, you notice that GPU utilization is consistently below 50%. Which of the following actions should you take to troubleshoot and optimize the workload?
- A
Check if the data loading process is a bottleneck by monitoring CPU utilization and disk I/O.
- B
Reduce the batch size to lower the load on the GPU and improve performance.
- C
Ensure that mixed precision training is enabled to leverage Tensor Cores on NVIDIA GPUs.
- D
Verify that the correct GPU-enabled deep learning framework versions are installed and being used.
- E
Increase the GPU clock speed using NVIDIA nvidia-smi utility to directly improve utilization.
Show answer and explanation
Correct answers: A, C, D
Explanation
Low GPU utilization during AI workloads is often caused by bottlenecks in data loading or suboptimal configuration of the training process. Checking for data bottlenecks, enabling mixed precision training to utilize Tensor Cores, and verifying framework compatibility are effective steps to troubleshoot and optimize performance. Reducing batch size or increasing GPU clock speeds without addressing the root cause is unlikely to resolve the issue and could even introduce new problems.
- A. Correct.
Data loading bottlenecks can cause low GPU utilization as the GPU waits for data to be fed. Monitoring CPU utilization and disk I/O can help identify if this is the issue.
- B. Incorrect.
Reducing the batch size could lead to underutilization of the GPU resources, further decreasing performance rather than optimizing it.
- C. Correct.
Mixed precision training can improve performance by utilizing Tensor Cores on NVIDIA GPUs, which are designed for such operations. This can help increase GPU utilization.
- D. Correct.
Using incorrect or non-optimized versions of frameworks can lead to inefficient GPU use. Ensuring compatibility and the latest optimizations is critical for performance.
- E. Incorrect.
Manually increasing GPU clock speeds can lead to overheating and instability, and it is not a recommended first step for optimization when utilization is low.