GPUs are becoming more and more important for Artificial Intelligence (AI) and machine learning applications. Yet, they remain largely misunderstood. What exactly is a GPU? How does it differ from CPUs? And why should you care?

The graphic processing unit or GPU has become the heart of artificial intelligence and machine learning applications. While CPUs are great at crunching numbers, GPUs excel at visual tasks such as image recognition and rendering 3D graphics.

GPUs are becoming more and more powerful. They allow machines to perform massive calculations in parallel, enabling them to solve complex problems much faster than conventional computers. In addition, they offer better performance per watt, allowing companies to build smaller, cheaper devices.

While GPUs have been around for years, their use has increased exponentially – especially since 2012. That’s when NVIDIA released its first consumer-grade GPU, the GeForce GTX 680. The company also introduced its CUDA deep learning framework that allowed developers to take advantage of the computing power offered by GPUs. Since then, demand for GPUs has skyrocketed.

In this article we will look at these questions:

  • What exactly is a GPU?
  • How does it work?
  • Why should you care?

And we will try to answer these questions.

What is GPU?

A GPU is an abbreviation for Graphical Processing Unit. It was developed to help speed up computer graphics on personal computers. Graphics cards were designed to be used with PCs, but they could not handle all the mathematical operations needed to create realistic images.

Graphical Processing Units were developed to do just that. They can compute millions of arithmetic operations at once. This allows them to render high quality 3D graphics very quickly.

Graphical Processing Units are made up of many small processors called cores. Each core handles one task. For example, if a game needs to calculate how fast a car is going, each core would calculate how fast the wheels are spinning.

Each core operates independently of every other core. So, while one core might be calculating, another core may be rendering. If a core finish working too soon, it can hand off the job to another core.

This means that multiple cores can operate simultaneously. This makes GPUs so efficient that they can generate hundreds of frames per second on a single processor.

How Does GPU Work?

To understand the process behind GPU, let us start with something simple like adding two numbers together.

When we add two numbers together, we simply add the digits of each number together.

For example, the numbers 123 + 456 679.

However, if we want to add two large numbers together, say 10 million and 20 million, we need a different approach. We cannot write down the entire sum. Instead, we must find a way to break the problem into manageable chunks.

We can divide the larger numbers into groups of ten thousand (10K), five hundred thousand (500K) and one million (1M). Then we can add together the sums from each group.

The result is the sum of each group multiplied by ten thousand (10K), five million (5M) and twenty million (20M).

We can apply this same principle to any problem where we need to multiply large numbers.

GPUs are built using similar principles. However, instead of dividing numbers into groups, they divide data into “blocks” or “warps.” A warp is a section of data that contains the results of a calculation.

So, rather than having to multiply two large numbers together, we can multiply two warps together.

If we have 16 warps, we can multiply four blocks of two warps together. This gives us 32 calculations in total.

That’s why GPU is so powerful. In theory, a GPU can perform more calculations in less time than a CPU.

On a GPU, however, everything happens on one single processor. So, it doesn’t matter which core you choose to store your data in, it will all be stored together.

When a program needs to access data, it sends the request to the GPU. The GPU then processes the request and returns the result back to the CPU.

For example, when you open up Photoshop, you see the interface with the various tools. These tools are actually running inside the GPU.

The GPU is responsible for rendering these images onto the screen.

You could think of the GPU like an army of tiny soldiers who each carry out a different job.

Each soldier carries out his or her own task, but together, they form one big army.

GPUs Are Used To Render Images

Most of the time, when you hear about GPUs, you’re hearing about image rendering.

Image rendering refers to taking a series of mathematical formulas and turning them into pixels on a screen. The reason this is important is because it allows computers to display things like photos and videos.

So, if you want to render a photo, you would send a request to the GPU asking it to perform certain calculations.

These calculations include things like lighting, shading and blending. Once the calculations are complete, the GPU returns the result to the CPU.

There are two types of rendering: 2D and 3D.

2D Rendering

2D rendering is usually used for simple photos. In order to render something 2D, all you need to do is ask the GPU to calculate the position of every pixel on the screen.

This means that you can make a lot of changes to a photo without having to redraw it completely.

3D Rendering

3D rendering is more complicated than 2D rendering.

It involves calculating the position of objects as well as their color and texture.

To understand why this is so, let’s go over some basic math.

In order to draw a 3D object, you first need to figure out where its center should be.

Next, you need to determine how far apart its edges are. This distance is called the radius.

After you’ve calculated the radius, you multiply it by pi (π), which is approximately equal to 3.1415926.

Then, you add your x, y and z coordinates together. This gives you the location of the object’s center.

Finally, you divide the length of the object’s radius by the number of sides you have to give it. For instance, a cube has 6 sides. Therefore, you’d divide the length of the radius by 6.

Then, you use this value as the X coordinate of the object’s top-left corner.

Now, you repeat this process for Y and Z. That’s pretty much the gist of what 3D rendering entails. However, there are many other factors that come into play.

One such factor is lighting. You’ll learn more about this later.

Another thing you should know is that 3D rendering isn’t just limited to games. It also comes into play in CAD programs.

Why Should You Care About GPU?

If you are developing games, the best thing you can do for your audience is make sure they play your game as fast as possible.

A GPU will allow you to achieve this goal. Because GPUs are able to work much faster than CPUs, they can produce huge amounts of graphical detail.

In addition, because GPUs use parallel processing, they can run multiple tasks at once.

This means that they can finish their jobs before the CPU has even finished its first task.

As a result, you can get higher frame rates without sacrificing performance.

What Is The Difference Between CPU And GPU?

CPU stands for Central Processing Unit. It is the main part of a computer, responsible for performing all the calculations.

GPU stands for Graphics Processing Unit. It is a separate chip designed specifically for graphics.

GPUs were originally used for gaming purposes. They were designed to take advantage of the fact that most people don’t need high levels of computing power when playing video games.

But since then, GPU chips have become increasingly popular. Today, many companies use them to create complex 3D models.

Because of this, GPU technology has moved away from being just for gamers. Nowadays, it’s also widely used in CAD programs, scientific simulations and other applications.

By Anurag Rathod

Anurag Rathod, as a blogger he used to spread all about app-based business, startup solution, on-demand business tips and ideas and so on.