While monitoring your model training's GPU utilization, you discover that you have a native synchronous implementation. The training data is split into multiple files. You want to reduce the…

Increasing CPU load generally does not solve input pipeline bottlenecks; often, the goal is to reduce CPU load from inefficient processing or use it more effectively, not just increase it.

Adding caching to the pipeline helps performance on subsequent epochs but does not reduce the initial execution time of the first epoch or address the underlying synchronous nature of data loading.

Increasing network bandwidth might help if the data source is remote and network-bound, but for a synchronous pipeline reading from multiple files, the primary bottleneck is often sequential processing, which parallelization addresses more directly.

Adding parallel `interleave` to the pipeline allows it to read and process data from multiple files concurrently, which is crucial for speeding up data loading when data is split across many files and the current implementation is synchronous and likely bottlenecked by sequential I/O.