GPU

TL;DR

• A specialized processor for the complex calculations needed to render images and video.
• Commonly used in gaming, video editing, and machine learning.
• Performs many calculations in parallel, allowing faster and more efficient processing than a CPU for certain workloads.

Definition

A GPU, or Graphics Processing Unit, is a specialized type of processor that is designed to handle the complex calculations required for rendering images and video. This makes them well-suited for use in applications such as gaming, video editing, and machine learning.

Explanation

In gaming, the GPU is responsible for rendering the images on the screen in real-time. This involves complex calculations to create 3D models, apply lighting and shading effects, and generate the final image for each frame of the game. Using a dedicated GPU allows this process to be performed much more quickly and efficiently than if it were handled by the computer’s CPU.

In machine learning, algorithms often involve complex mathematical operations that require a large amount of computing power. Using a GPU to perform these calculations enables machine learning models to be trained much more quickly and efficiently than on a CPU because GPUs are designed to perform many calculations in parallel, allowing them to handle large amounts of data more quickly.

Examples

Gaming

When you play a video game, the GPU in your computer is responsible for rendering the images on the screen in real-time. This involves complex calculations to create the 3D models, apply lighting and shading effects, and generate the final image for each frame of the game. By using a dedicated GPU, this process can be performed much more quickly and efficiently than if it were handled by the computer’s CPU.

Machine learning

Machine learning algorithms often involve complex mathematical operations that require a large amount of computing power. By using a GPU to perform these calculations, machine learning models can be trained much more quickly and efficiently than if they were run on a CPU. This is because GPUs are designed to perform many calculations in parallel, allowing them to handle large amounts of data much more quickly than a CPU.

Use cases

• Gaming
• Video editing
• Machine learning

Related terms

• Graphics Processing Unit (full form of GPU)
• CPU