What Are GPU Cores? (Explained Simply)

by John Ruth
GPU Cores

GPU cores, also known as Graphics Processing Unit cores, are specialized electronic circuits designed to rapidly manipulate and alter memory to accelerate the creation of images in a frame buffer intended for output to a display device.

What exactly does this mean? Here’s a simplified breakdown:

GPU vs. CPU Cores – Explained Simply

Imagine cores as like workers in a kitchen. Each core is a worker, and having more cores means more helpers in the kitchen, which means things get done faster, allowing you to carry out larger, more intensive jobs.

  1. CPU Cores are Like Skilled Chefs: It’s really good at doing complex tasks that require a lot of thinking and decision-making. For example, a chef might need to figure out the right order to cook things in, how to adjust the recipe if something goes wrong, and how to manage different parts of the meal that require different skills. CPU cores are great at handling a variety of general tasks that need a lot of brainpower.
  2. GPU Cores are Like a Team of Helpers: On the other hand, a GPU (Graphics Processing Unit) core is like having a team of helpers in the kitchen. Each helper isn’t as skilled as the chef, but they can do simple tasks very quickly and all at the same time. For example, if you need to chop a huge pile of vegetables, it’s much faster to have a team of helpers chopping at the same time than just one chef.
  3. Efficiency in Tasks: Now, if you tried to make the chef do all the chopping, it would take a long time, and the chef wouldn’t have time to do the more complex tasks they’re really good at. Similarly, if you made the helpers try to do the chef’s job, they might get confused because it’s too complicated for them.

In a kitchen, the best results come from letting the chef handle the complex stuff and the helpers do the simple, repetitive tasks. In computers, it’s the same: you use the CPU for complex, varied tasks and the GPU for simple, repetitive tasks (like drawing millions of pixels on your screen).

Note: You might here the term “CUDA core” which is basically a fancy term coined by NVIDIA.

CPU Tasks

The CPU is like the brain of the computer, handling a wide range of general computing tasks, such as:

  1. Operating System Operations: It manages the basic functions of the operating system, like opening, running, and closing programs.
  2. Web Browsing: When you browse the internet, the CPU processes the data from web pages, including text, images, and executing scripts that make websites interactive.
  3. General Computing Tasks: Activities like word processing, spreadsheet calculations, and other office-related software primarily rely on the CPU.
  4. Video Editing (Partly): While GPUs have become more involved in video editing, CPUs are still responsible for certain aspects, such as encoding and some effects processing.
  5. Running Applications: It runs most applications, especially those that don’t require heavy graphics processing. This includes software for productivity, basic photo editing, and media playback.
  6. Data Processing: For tasks that involve large amounts of data but not necessarily graphics, like compiling code or running databases, the CPU plays a major role.

GPU Tasks

GPUs are specialized for handling graphics, ray tracing, and parallel processing tasks. Here are some examples:

  1. Gaming: They render images, animations, and video for games. This includes processing complex 3D graphics, textures, and shading.
  2. Advanced Video Editing: For tasks in video editing that require rendering effects, color grading, and processing high-resolution videos, GPUs are heavily used.
  3. Graphic Design and Rendering: Tasks in software like Adobe Photoshop, Illustrator, and 3D rendering software often rely on GPU for smoother and faster processing.
  4. Machine Learning and AI: GPUs are used for training machine learning models due to their ability to handle parallel tasks, which is essential for processing large datasets.
  5. Scientific Computations: In fields like physics simulations or computational biology, GPUs accelerate calculations involving large datasets.
  6. Cryptocurrency Mining: GPUs are effective in cryptocurrency mining, which involves solving complex mathematical problems.

Certain GPUs such as the RTX 4080 also come with dedicated cores for certain tasks. For example, it has 76 dedicated ray tracing cores, providing it with very good ray tracing performance.

In summary, the CPU handles a wide range of general-purpose tasks and is essential for the overall functioning of the computer, including running the operating system and most applications.

The GPU, on the other hand, is specialized for tasks that require handling multiple operations simultaneously, particularly in graphics rendering and parallel processing tasks. This specialization makes GPUs highly effective for specific applications like gaming, graphic design, video editing, and certain types of scientific and AI computations.

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