Inside GPU Architecture: How It Works

Today’s graphics cards are amazing devices capable of performing trillions of calculations per second. It is this power which makes the truly breath-taking graphics that define today’s video games such as the Cyberpunk 2077. Games now are vastly different from the first games, for example, Mario 64 needed 100 million calculations per second, while the newest games need up to 36 trillion per second.

Understanding Graphics Card Architecture

Graphics card design is complex. At the heart is a chip, like the GA102, with billions of tiny switches, called transistors, in it. Here, there are different kinds of cores in these chips:

• CUDA Cores: These are ordinary processors for a GPU through which simple operations like addition and multiplication are performed.
• Tensor Cores: Handle various advanced matrix calculations which are mostly use case in AI and applications.
• Ray Tracing Cores: These are coprocessors made to work on ray tracing calculations with exceptionally great speed.

This kind of classification of functions makes it possible for the graphics card to be able to process large volumes of data in real time making no compromise to delay.

GPU vs. CPU: What's the Difference?

Essentially, there is a vast difference in the operational procedures of GPUs and CPUs. The GPU has hundreds and, more often, thousands of cores, which means it can perform several operations at once. This makes them perfect for graphic rendering or mining in cryptocurrency. A CPU, however, has fewer cores but they are built to work faster. Such explains why CPU speed is better than quality, when it comes to tasks such as running an application or an operating system. For better understanding let’s imagine it like this:

• GPU: Imagine a cargo vessel where a lot of goods are transported although slowly.
• CPU: Now a jet where the goods transported are fewer yet fast.

Exploring the GA102 GPU Architecture

The GA102 is a stunning engineered device contains 28.3 billion transistors. It comprises;

• 10,752 CUDA cores for basic arithmetic computation.
• 336 Tensor cores for the purposes of artificial Intelligence and neural computations.
• 84 cores for Ray Tracing for realistic images rendering.

All these cores collaborate to meet the ever increasing demands of today's games and applications that use AI.

How CUDA Cores Work

CUDA cores are processing elements located within the GPU like many small calculating devices. These basic functions involve performing processes such as multiplication or addition, and to the extent even more addition of these cores, the device, which is a GPU is capable of carrying out billions and even trillions of calculations in seconds. For instance of square root or division, which are more difficult computation, GPUs have less but high level processing units.

Components of a Graphics Card

Graphics cards comprise of so many crucial components:

• Display Port: Using this port you can connect your monitor.
• Power Connector: Ensure supply of power in order to operate the card.
• Heat Sink and Fans: These are used to maintain a cool temperature in the card when it’s under loads of heavy work.
• Memory Chips: Hold data where they are easily retrievable by the GPU.

Together these components provide high desirable performance in gaming and in other executions.

The Role of High Bandwidth Memory

High bandwidth memory or HBM is unique kind of memory it assists the GPU to manage data in a much efficient manner. HBM memory designed by companies such as Micron uses ‘thinner’ layers of chips and stacks them on top of one another to occupy lesser space and impart a higher speed. This type of memory is beneficial for example in AI work, where lots of datasets are present and need to be processed at high rates. HBM is also energy-saving which enable the reduction of energy usage.

Parallel Processing and SIMD

GPUs are capable of concurrent tasks within the same time frame. They use a technique known as Single Instruction Multiple Data (SIMD). one instruction is performed on a large number of data sets. For example in the video games, the GPUs use SIMD operation and perform operations on thousands of 3D points all simultaneously converting them into what you observe on the screen.

Bitcoin Mining with GPUs

GPUs were once the best choice for mining Bitcoin because they could perform thousands of calculations at the same time. They used an algorithm known as SHA-256 that involves solving of some type of puzzles. But now there are special machines known as ASICs (Application Specific Integrated Circuits) that are many times faster at the task and require far less electricity to operate.

Applications of Tensor Cores

Tensor cores are special units of the graphic processor designed to perform matrix operations. They are very important in AI activities such as training of neurons. These cores contain a lot of performance to handle big amounts of data at a very short time, therefore, are suitable for technologies such as self-driving vehicles and voice assistants.

Frequently Asked Questions

• What is the use of a graphics card?
  A graphics card works to compute information to display pictures, motion pictures and other animations on the screen. It is very suitable for gaming and video and 3D rendering.
• How is a GPU different from a CPU?
  GPUs have thousands of cores for parallel work such as rendering graphics, while CPUs have few cores suited to intensive work in order to run the software.
• What are CUDA cores?
  CUDA cores are ordinary processors for a GPU through which simple operations like addition and multiplication are performed. They allow the GPU to make several billions calculations per second.
• Why do GPUs need so much memory?
  GPUs use memory to store critical data when it is processing images and animation. Quick access defines sharp and clear image quality in typical games and other software applications.
• Is it possible to use GPUs for operations apart from games?
  Indeed, GPUs are also used for cryptocurrency mining, artificial intelligence, and video editing, as well as for scientific research due to the great parallel computational capability.