What is the most likely cause of GPU utilization imbalance in a multi-GPU setup?

The most likely cause of GPU utilization imbalance in a multi-GPU setup is due to the data loading process not being evenly distributed across the GPUs. In a multi-GPU environment, each GPU ideally should handle a similar amount of workload to ensure efficient utilization. If one GPU is assigned significantly more data to process than another, it will naturally show higher utilization while the others remain underutilized. This leads to inefficiencies and can slow down the overall computation time, defeating the purpose of having multiple GPUs for parallel processing.

Distributing data evenly is crucial because it enables each GPU to work on its share of the processing tasks concurrently. Techniques such as using data parallelism, where each GPU processes a different batch of data concurrently, or careful data partitioning can help achieve this balance.

In contrast, the other options can contribute to challenges in a multi-GPU setup, but they are less direct causes of utilization imbalance. Improper installation might lead to hardware issues but does not inherently cause uneven workloads. Using different GPU models may lead to performance discrepancies, but if properly configured, they can still be utilized effectively. Lastly, optimization of model code for specific GPUs could potentially lead to some GPUs being less efficient, but this does not directly address how data is loaded and