An Introductory Guide to GPU

Graphics Processing Unit (GPU)
 

A GPU stands for graphics processing unit, defined as a specialized processor designed to accelerate graphics rendering efficiently. Besides a lot of other applications, GPUs power traditional graphics applications.

 

GPUs are highly beneficial in the field of machine learning, video editing, and gaming applications. It goes without saying that GPUs have the capacity to process a large number of pieces of data at the same time. GPUs are not only being integrated into the CPU of a computer but are also offered as a discrete hardware component.

 

 

Roles and Functions of a GPU 

 

A GPU is increasingly being used both for fulfilling the requirements of personal as well as business computing. 

 

The GPU has been designed for parallel processing in the world of the latest innovations in computing technology. The graphics processing unit is well-suited for being utilized in a broad range of applications right from graphics to video rendering.

 

Earlier, GPUs were popular for their capabilities in the domain of gaming, but now GPUs are being increasingly leveraged for use in creative production and artificial intelligence (AI).

 

Initially, GPUs were designed for accelerating the rendering of 3D graphics. But owing to the flexibility and programmability of this computing technology, the capabilities of GPUs have enhanced over time.

 

Graphics programmers have been able to develop interesting and beautiful visual effects and surreal scenes, accompanied by advanced lighting and shadowing solutions. Nowadays, it has become a trend for graphic developers to utilize GPUs for accelerating additional workloads in high-performance computing (HPC), deep learning, and much more.

 

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Difference between GPU and Graphics Card

 

1. People often use GPU and graphics card/ video cards interchangeably, but it is not entirely correct to do so. A graphics card is basically an add-in board that incorporates the GPU just like a motherboard consists of a CPU.

 

2. All the components required for the efficient and effective functioning of the GPU are included in this board only. This also helps in the effortless connection of the GPU with the rest of the system. (From)

 

 

Types of Graphics Processing Unit (GPU) 

 

There are two types of GPUs that are available in the market, that are detailed below: 

 

1. Integrated Graphics Processing Unit or Integrated GPU

 

Integrated GPU is embedded alongside the CPU and does not come on its own separate card at all.  The most common type of GPU available in the market is integrated GPUs. 

 

As integrated GPU comes completely incorporated into the motherboard of a CPU, this enhances the prospects of thinner and lighter systems, reduced power consumption, and lower system costs. Users can experience immersive graphics in systems that run cooler and deliver long battery life.

 

2. Discrete Graphics Processing Unit or Discrete (GPU)

 

Though many computing technologies are compatible with integrated GPUs, a discrete GPU is well-suited for more resource-intensive applications with extensive performance demands.

 

A discrete GPU is also known as a dedicated graphics card. These GPUs just take up additional energy and heat creation but add tremendous processing power.  To ensure maximum performance, discrete GPUs need dedicated cooling.

 

Latest innovations and breakthrough technologies have become more advanced than ever before so that an unbelievably wide range of applications and solutions can be harnessed leading to a futuristic experience beyond traditional graphics rendering.

 

 

Applications of Graphics Processing Unit (GPU) 

 

Until two decades ago, GPUs were primarily used to accelerate real-time 3D graphics applications, such as games. However, as the advancements in the 21st century began, computer scientists realized that GPUs had the amazing potential to solve some of the world’s most difficult computing problems and challenges. 

 

This realization gave birth to the general-purpose GPU era. Nowadays, graphics technology is being applied more extensively to an increasingly broad range of problems. 

 

Today’s GPUs have become much more programmable than ever before, providing them the flexibility and unprecedented capacity to accelerate a wide range of applications that go well beyond traditional graphics rendering. (source)

 

1. GPUs for Gaming

 

Video games have become really computationally intensive in today's fast-paced world with hyper-realistic graphics and huge, complicated in-game worlds. 

 

With advanced display technologies like 4K screens and high refresh rates, accompanied by the unprecedented rise of virtual reality gaming, demands for graphics processing are growing fast. 

 

GPUs are capable of rendering graphics in both 2D and 3D. With better graphics performance, games can be played at higher resolution, at faster frame rates, or both.

 

2. GPUs for Video Editing and Content Creation

 

For years, video editors, graphic designers, and other creative professionals have struggled with the time-consuming process of rendering that tied up computing resources and stifled creative flow. 

 

Now, the parallel processing offered by GPUs makes it unbelievably faster and easier to render video and graphics in higher-definition formats.

 

3. GPU for Machine Learning

 

Some of the most promising and significant applications for GPU technology involve the combination of AI and machine learning. 

 

Because GPUs contain an extraordinary amount of computational capability, they can deliver incredible acceleration in workloads that take advantage of the highly parallel nature of GPUs, such as image recognition. Many of today’s deep learning techniques and solutions rely on GPUs working in integration with CPUs.

 

4. GPUs for Business applications

 

The smooth and effective running of certain business applications is completely reliant on the processing power coming from the GPU. 

 

For instance, a system that has a low-end GPU will certainly struggle to render 3D models through AutoCAD software, leading to incredibly slow performance or even system crashes in severe cases. Perhaps the newest, and most innovative application of GPUs is the creation of neural networks and processing machine learning functions.

 

 

What is Cloud GPUs?

 

Cloud GPUs have the ability to provide enhanced performance for machine learning, scientific computing, and 3D visualization. It can boost machine learning and HPC while optimizing your workload. It provides optimum balance between the processor, memory, high-performance disk, simultaneously efficiently running the workload. 

 

Besides, there are some companies that have made their mark in the world of Graphics Processing Units either as direct sellers or aftermarket GPU vendors. 

 

• Nvidia 

• AMD or Advanced Micro Devices

• EVGA

• Gigabyte

• MSI

• ASUS

 

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Ending Notes

 

This blog ends here. While ending the blog, we can clearly see that the technology of graphics processing is constantly delivering tremendous and unique advantages in the computing world. 

 

GPUs are consistently unlocking the latest possibilities in the world of gaming, content creation, machine learning, and much more. GPUs have proved their efficiency in the handling of computer graphics and visually attractive icons and designs. 

 

They are more beneficial for algorithms where the processing of large blocks of data is done in parallel, as compared to a general-purpose CPU.