5.3.2. Parallelism and Distributed Computing

Try these first

1. 1.

   What is parallelism in computing?

   Hint

   Think about how tasks can be divided.

2. 2.

   Define data parallelism.

   Hint

   Consider how data can be distributed.

3. 3.

   What is data parallelism?

   • A method for splitting large models
   • Processing data in batches simultaneously
   • Using multiple GPUs for different tasks

   Hint

   Consider how data is handled during training.

4. 4.

   True or False: Model parallelism is used when a single model can fit into a single device's memory.

   • True
   • False

   Hint

   Think about the limitations of device memory.

5. 5.

   Consider a scenario where an AI model cannot fit into the memory of a single device. Explain how model parallelism would address this and provide an example.

   Hint

   Think about distributing parts of the work to fit within memory constraints.

6. 6.

   Analyze a case where data parallelism could significantly reduce training time for a deep learning model. What factors would contribute to this improvement?

   Hint

   Consider how speed increases as tasks are divided and executed simultaneously.