A1.1.2 Role of a GPU Notes

What is a GPU?

1. GPU stands for Graphics Processing Unit.
2. It is a specialised processor designed to handle graphics rendering and complex parallel tasks.
3. Originally built to improve image and video performance, but now used in areas like AI, scientific computing, and data analysis.

What does the GPU do?

1. Processes visual data, turning code into images you see on a screen.
2. Performs parallel computations, many small tasks at the same time.

Works alongside the CPU to take on specialised work and free up resources.

Understanding GPU Architecture

1. Parallel Processing:
   1. GPUs are designed with thousands of smaller cores, enabling them to perform parallel processing.
   2. This architecture is ideal for tasks that can be broken down into smaller, independent operations.
2. High Throughput:
   1. GPUs are optimized for high throughput, meaning they can process large amounts of data simultaneously.
   2. This is crucial for tasks like graphics rendering and machine learning.
3. Specialized Memory:
   1. GPUs use high-speed memory, such as VRAM (Video RAM), to handle large textures and data sets efficiently.

Real-World Applications of GPUs

1. Graphics Rendering:
   1. GPUs are essential for rendering complex graphics in video games.
   2. This enables high-resolution textures, realistic lighting effects, and smooth frame rates.
2. Machine Learning:
   1. GPUs accelerate the training of neural networks by performing parallel computations on large data sets.
   2. This is vital for applications like image recognition and natural language processing.
3. Scientific Simulations:
   1. In fields like climate modeling and bioinformatics, GPUs speed up simulations by processing large-scale data in parallel.