8.3.2. Data Parallelism and Model Parallelism

Try these first

1. 1.

   What is data parallelism?

   Hint

   Consider how data can be handled across multiple devices at once.

2. 2.

   Provide an example of model parallelism.

   Hint

   Think of how complex models require extensive processing.

3. 3.

   What does data parallelism allow for?

   • Processing multiple datasets sequentially
   • Simultaneous processing of data batches
   • Processing data only on one device

   Hint

   Think about how multiple cores help speed up processing.

4. 4.

   True or False: Model parallelism is used to divide data into smaller chunks.

   • True
   • False

   Hint

   Reflect on the focus of model parallelism.

5. 5.

   Given a dataset of 20 million records and a model that requires significant resource allocation, outline how you would implement both data and model parallelism to optimize processing.

   Hint

   Review the limitations and requirements of both strategies.

6. 6.

   Analyze the impact of improper implementation of data parallelism on the performance of an AI model.

   Hint

   Consider the importance of balanced distribution in parallel processing.