Practice Machine Learning On Gpus (10.7.2) - Vector, SIMD, GPUs - Computer Architecture

Practice: Machine Learning on GPUs

Lesson 10.7.2Vector, SIMD, GPUs

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10 questions

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4 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
4 questions · about 3 min · pass mark 70%. Hints available.
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6 sample questions

Question 1.
What does GPU stand for?
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Think of how GPUs are used in gaming.
Question 2.
Why are GPUs better suited for deep learning than CPUs?
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Consider how many operations need to happen at once.
3Question 3.
What is a GPU primarily used for in machine learning?
- Single-threaded processing
- Parallel processing
- Memory storage
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Think about how many operations need to occur at once in ML tasks.
4Question 4.
True or False: GPUs can only be used for graphics rendering.
- True
- False
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Consider the versatility of GPU applications.
Question 5.
Analyze how the performance of a deep learning model like a CNN would change if trained on a GPU instead of a CPU. Provide metrics such as training time, accuracy, and scalability.
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Consider the computational demands of the model and how GPUs manage these demands.
Question 6.
Discuss the potential challenges and limitations that arise when using GPUs for machine learning, particularly in terms of resource management and model complexity.
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Think about both hardware and software aspects in machine learning.

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