Keep GPUs Fed with an Efficient Data-Preparation Pipeline

During model training, GPUs often sit idle waiting for the next batch of input data. Describe how you would diagnose and improve the data preparation and loading process.

Constraints & Assumptions

The question concerns CPU, I/O, preprocessing, and batch assembly. It does not assume any particular framework or storage system. You must maintain data semantics and reproducibility while increasing throughput.

Clarifying Questions

Where do the samples reside? What transformations are applied to each sample? Do batch shapes vary? Is the training job distributed across multiple nodes? Are the GPU idle periods due to input starvation or caused by synchronization delays elsewhere?

What a Strong Answer Covers

A solid answer should measure the data pipeline, isolate storage versus CPU bottlenecks, overlap steps where possible to hide latency, and verify sharding, ordering, and restart correctness after modifications.

Follow-up Questions

In what situations can adding more data-loader workers hurt performance? What are the trade-offs of caching, prefetching, pinned memory, and sequence bucketing? How would you avoid duplicate samples being processed by different workers?

Overview: Pinpoint GPU input starvation by examining storage, CPU preprocessing, batching, transfer, and synchronization; then increase overlap where possible while preserving data correctness.

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