What approach is most effective in diagnosing slowdowns during data ingestion on NVIDIA GPU clusters?

Profiling the I/O operations on the storage system is the most effective approach when diagnosing slowdowns during data ingestion on NVIDIA GPU clusters. Data ingestion involves moving large volumes of data into the system, and often the bottleneck originates from how quickly the storage system can read data for processing. By profiling the I/O operations, you can identify issues such as slow read/write speeds, improper throughput, or contention for storage resources. This detailed analysis allows you to pinpoint specific inefficiencies in the data pipeline, which can be crucial for optimizing overall ingestion times.

In contrast, optimizing the AI model's inference code primarily focuses on the processing and computational performance once the data has been ingested, not on the data ingestion process itself. Switching to a different data preprocessing framework may have benefits, but it does not directly address the root cause of input/output delays, which are primarily tied to storage performance. Increasing the number of GPUs could potentially assist with processing speed after data ingestion, but if the bottleneck lies in the stored data not being read quickly enough, adding more GPUs would not resolve the underlying I/O issues. Each of these alternative approaches may enhance aspect of the workload, but none directly focus on resolving the specific slowdowns experienced during the ingestion phase like profiling the I