What is the most likely cause of significant delays in data processing times when GPU utilization is below 80%?

Choosing the reason behind significant delays in data processing times when GPU utilization is low is crucial for troubleshooting performance issues. The most plausible explanation, in this case, is related to inefficient data transfer between nodes in the cluster.

When GPU utilization remains below an optimal threshold of 80%, it often indicates that the GPUs are not the bottleneck of the processing pipeline. This situation indicates that there may be delays in getting the necessary data into the GPU for processing. Inefficient data transfer between nodes suggests that there is a lag in moving data where it needs to go, which can lead to GPUs sitting idly while they wait for data to be fed into them. If nodes are not communicating efficiently, perhaps due to network congestion, inappropriate routing, or bandwidth limitations, this can create significant delays in overall processing times despite the hardware's capability being underutilized.

While high CPU usage could cause preprocessing bottlenecks, this situation typically would show a higher GPU utilization as the GPUs might be waiting on the CPU to feed them data. Overprovisioning GPU resources could lead to idle times, but this would not directly cause delays in processing times unless the GPUs themselves were over-saturated. Insufficient memory bandwidth on the GPUs could also theoretically contribute to performance issues, but