Practice Data Parallelism And Model Parallelism (8.3.2) - Optimization of AI Circuits

Practice: Data Parallelism and Model Parallelism

Lesson 8.3.2Optimization of AI Circuits

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9 questions

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2 cards from this section. Quick, good the night before a test.
Exercise
6 questions · about 4 min · pass mark 70%. Hints available.
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Quiz
3 questions · about 2 min · pass mark 70%. Hints available.
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6 sample questions

Question 1.
What is data parallelism?
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Consider how data can be handled across multiple devices at once.
Question 2.
Provide an example of model parallelism.
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Think of how complex models require extensive processing.
3Question 3.
What does data parallelism allow for?
- Processing multiple datasets sequentially
- Simultaneous processing of data batches
- Processing data only on one device
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Think about how multiple cores help speed up processing.
4Question 4.
True or False: Model parallelism is used to divide data into smaller chunks.
- True
- False
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Reflect on the focus of model parallelism.
Question 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.
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Review the limitations and requirements of both strategies.
Question 6.
Analyze the impact of improper implementation of data parallelism on the performance of an AI model.
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Consider the importance of balanced distribution in parallel processing.

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