NCP-AII Question 4
Select 3An AI training workload running on an NVIDIA GPU server is experiencing suboptimal performance. Upon investigation, you find that GPU utilization is low, and the system logs show intermittent memory allocation errors. Which actions should you take to troubleshoot and optimize the workload?
- A
Check if the batch size of the training job can be increased to better utilize the GPU memory.
- B
Verify that the GPU driver and CUDA toolkit versions are compatible with the framework being used.
- C
Disable ECC (Error-Correcting Code) on the GPUs to increase performance.
- D
Monitor PCIe bandwidth usage to identify potential data transfer bottlenecks between the CPU and GPU.
- E
Reduce the number of data augmentation processes to decrease CPU overhead.
Show answer and explanation
Correct answers: A, B, D
Explanation
To troubleshoot and optimize low GPU utilization and memory allocation errors, it is essential to ensure that the workload is properly configured for the hardware. Adjusting the batch size helps utilize GPU memory more efficiently. Ensuring compatibility between GPU drivers, CUDA, and the AI framework avoids software-related bottlenecks or errors. Additionally, monitoring PCIe bandwidth provides insights into potential data transfer inefficiencies, which could hinder GPU performance. While other options like disabling ECC or reducing data augmentation may have minor effects, they do not directly address the primary issues in this scenario.
- A. Correct.
Increasing the batch size can help fully utilize the GPU's memory and computational resources, thus improving performance. This is a valid troubleshooting step.
- B. Correct.
Driver and CUDA version mismatches can lead to suboptimal GPU performance or errors. Ensuring compatibility is a critical troubleshooting step.
- C. Incorrect.
Disabling ECC might marginally increase performance, but it is not recommended for AI workloads due to the risk of data corruption, especially in training scenarios.
- D. Correct.
Monitoring PCIe bandwidth can help identify if the CPU-GPU data transfer is a bottleneck, which is crucial when optimizing multi-component AI systems.
- E. Incorrect.
Reducing data augmentation processes may slightly reduce CPU overhead, but it is unlikely to address the core issue of GPU underutilization in this scenario.