Data Parallelism: One Task, Many Data Chunks
Data parallelism splits a huge dataset across multiple processors, each running the same task on its own chunk. It's how large models are trained on massive datasets, with each GPU handling a different batch of data.
Data parallelism is like having many workers perform the exact same task on different parts of a huge dataset—it splits data, not tasks. This is the standard for training large ML models, where a giant dataset is split across many GPUs, each processing its own batch to calculate gradients in parallel. The main footgun is assuming it's always efficient; the overhead of synchronizing results across all workers after each step can become a major bottleneck, negating the parallelism gains.
Read the original → Wikipedia: Data parallelism
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