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What are the key differences between data parallelism and model parallelism in distributed training?
Asked on Nov 26, 2025
Answer
Data parallelism and model parallelism are two strategies used in distributed training to handle large models and datasets. Data parallelism involves distributing data across multiple processors, while model parallelism involves distributing the model itself.
Example Concept: In data parallelism, the same model is replicated across multiple processors, each handling a different subset of the data. After computing the gradients, these are averaged and used to update the model parameters. In model parallelism, the model is split across multiple processors, with each processor handling a different part of the model. This approach is useful when the model is too large to fit into a single processor's memory.
Additional Comment:
- Data parallelism is generally easier to implement and scales well with the number of processors.
- Model parallelism is more complex and requires careful management of data flow between processors.
- Data parallelism is often used when the dataset is large, while model parallelism is used for very large models.
- Combining both strategies can optimize resource utilization in distributed training environments.
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