What is the most likely cause of low GPU utilization and high CPU utilization in a multi-GPU training setup?

In a multi-GPU training setup, low GPU utilization coupled with high CPU utilization is typically indicative of a bottleneck occurring during the data preprocessing stage rather than an issue with the GPUs themselves. When the CPU is tasked with preparing and feeding data to the GPUs, it needs to ensure that data is processed in a timely manner. If the CPU cannot keep pace with the demand for data, the GPUs will remain underutilized because they have to wait for the CPU to provide them with the necessary input before they can carry out computations.

In this scenario, the GPUs are ready and capable of performing their tasks but are not receiving the data quickly enough from the CPU. This situation results in a wasted opportunity for the GPUs to work efficiently, leading to low utilization rates. Ensuring that data preprocessing is optimized and that there is sufficient computational throughput on the CPU can alleviate this issue, allowing the GPUs to operate at their full potential.

While the other choices also refer to potential issues within a multi-GPU setup, they do not directly align with the symptoms described in the question regarding CPU and GPU utilization. Therefore, recognizing the relationship between preprocessing workloads and GPU readiness highlights why this particular choice is relevant to the scenario.