Title: GPU. Lesson 1. Introduction to GPU Computing

Title: GPU. Lesson 1. Introduction to GPU Computing

1. GPUs, or Graphics Processing Units, accelerate parallel processing tasks effectively.

2. GPUs perform calculations across multiple cores simultaneously, unlike CPUs.

3. In deep learning, GPUs process large data sets faster than CPUs.

4. A core is an individual processing unit in a GPU chip.

5. CUDA, Compute Unified Device Architecture, enables parallel computing on NVIDIA GPUs.

6. GPUs handle image rendering, video processing, and AI computations.

7. Memory bandwidth measures data transferred between GPU cores and memory.

8. Tensor Cores, specific to NVIDIA, speed up AI model training.

9. FP32, or 32-bit Floating Point, impacts computation precision in GPUs.

10. FP16, or 16-bit precision, is efficient for training large AI models.

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Technical Examples:

1. CUDA Cores: CUDA Cores enable parallel operations by breaking down complex tasks into smaller pieces.

2. Tensor Cores: Tensor Cores increase speed in matrix calculations essential for AI and ML tasks.

3. FP32 vs. FP16 Precision: Using FP16 precision for AI tasks reduces memory consumption and boosts processing speed.