What is the most likely reason for slow training in a multi-GPU setup where some GPUs appear to be idle?

In a multi-GPU setup for training, the effectiveness of utilizing all available GPUs hinges significantly on how well they are synchronized during the training process. If some GPUs appear to be idle while others are actively processing, it may indicate that the workload isn't being evenly distributed among them. Proper synchronization is crucial because it ensures that all GPUs are working in tandem, sharing their computational responsibilities effectively.

When GPUs are not synchronized properly, one GPU might finish its training iterations before the others, leading to idle time as the faster GPUs wait for the slower ones to catch up. This mismatch can cause a bottleneck, where the overall training speed is limited by the least efficient GPU. Synchronization issues can stem from various factors, such as improper batch distribution or communication delays between GPUs.

In contrast, issues such as the data being too large for the CPU, a model architecture that is too simple, or insufficient memory in the GPUs might impact training performance but wouldn't specifically result in some GPUs being idle while others are working. Therefore, ensuring that GPUs are properly synchronized is the key to optimizing training time in a multi-GPU setup.