In analyzing performance degradation on a multi-GPU server, which approach would be the most effective?

In a multi-GPU server, performance degradation can often be tied to how GPUs are being utilized, particularly regarding memory usage. Analyzing GPU memory usage with tools like nvidia-smi provides direct insights into how much memory each GPU is using and whether any of the GPUs are reaching their memory limit. If a GPU runs out of memory, it can lead to significant performance issues, including slow-downs or even crashing processes. This tool allows you to monitor not only memory usage but also other important metrics like GPU load and temperature, all of which can contribute to performance degradation in training tasks.

The other options, while they may provide some relevant information, won't directly address the core issue of GPU performance. Looking at CPU utilization can be important, but if the bottleneck is primarily related to GPU resources (like memory), then insights gained from monitoring the CPU would not effectively resolve the performance issues. Examining the training data for inconsistencies is also essential for ensuring model accuracy and effectiveness, but it does not specifically target performance problems stemming from GPU operations. Similarly, monitoring power supply levels can indicate whether the hardware is adequately powered, but performance degradation in a multi-GPU setup is more often associated with how GPUs are loaded and how memory is being utilized.