Practice Parallelism And Distributed Computing (5.3.2) - Techniques for Optimizing Efficiency and Performance in AI Circuits

Practice: Parallelism and Distributed Computing

Lesson 5.3.2Techniques for Optimizing Efficiency and Performance in AI Circuits

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9 questions

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6 sample questions

Question 1.
What is parallelism in computing?
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Think about how tasks can be divided.
Question 2.
Define data parallelism.
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Consider how data can be distributed.
3Question 3.
What is data parallelism?
- A method for splitting large models
- Processing data in batches simultaneously
- Using multiple GPUs for different tasks
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Consider how data is handled during training.
4Question 4.
True or False: Model parallelism is used when a single model can fit into a single device's memory.
- True
- False
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Think about the limitations of device memory.
Question 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.
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Think about distributing parts of the work to fit within memory constraints.
Question 6.
Analyze a case where data parallelism could significantly reduce training time for a deep learning model. What factors would contribute to this improvement?
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Consider how speed increases as tasks are divided and executed simultaneously.

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