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Graphical Processing Unit – Computational Functions and Its Architecture

Graphical Processing Unit – Computational Functions and Its Architecture (GPU)

Graphical Processing Unit  (GPU)
Graphical Processing Unit  (GPU)

We have a processor unit in computing devices that processes data. The central processing unit is the name for this device. This unit's key responsibilities include data encoding and decoding, data collection, data preparation and compilation, data execution, and so on. The processor or operating speed of the computer is determined by the frequency of the CPU. Working with a vast volume of data necessitates more memory storage. We now have high resolution images, transparent graphics, and other benefits thanks to advances in image processing techniques.The mathematical operations needed for these techniques are extremely large, necessitating the use of a more powerful processing unit. The Graphical Processing Unit (GPU) was created to address this problem.

What is a Graphical Processing Unit?

In a computer, processing units are used to do calculations. Concepts such as 3D imagery, High Definition video distribution, Graphics, and others have been developed as a result of technological advancements. Wide and complex mathematical operations must be done at a faster rate to apply these principles on a hardware computer.

Despite its high frequency, the central processing unit is incapable of successfully processing calculations on such a large scale. As a result, a dedicated processing unit was created for performing larger calculations at a high frequency. A Graphical Processing Unit was the name given to this processing unit. A graphics processing unit (GPU) is a sophisticated electronic system that is mostly used for calculations including computer graphics and image processing. These are available as stand-alone chips with dedicated memory units or integrated on the SoC along with the microprocessor or main processor.

Computational Functions

GPU makes use of the transistors in its architecture to perform calculations specific to 3D computer graphics. Geometric operations such as rotation and translation of vertices into various coordinate structures, texture mapping, and polygon rendering are all part of the 3D graphics computations. Many recent GPU functions provide CPU functionality, as well as aliasing reduction strategies such as oversampling and interpolation.

With the advancement in deep learning and machine learning technology, there has been a significant rise in GPU use. A greater number of dynamic calculations must be performed to train a deep learning algorithm. The use of a graphics processing unit (GPU) has made the challenge of teaching machine learning models even simpler.

Graphic processing units have been discovered to be 250 times faster than the CPU. The GPU executes parts of the video encoding process as well as video post-processing in GPU accelerated video decoding. DxVA, VDPAU, VAAPI, XvMC, and XvBA are some of the most widely used APIs for this purpose. DxVA is for Windows-based operating systems, while the others are for Linux-based and Unix-like systems. Only MPEG-1 and MPEG-2 images can be decoded by XvMC.

The following are some of the video encoding processes that a GPU can handle:
  • Compensation for Motion
  • Of the opposite direction Transform of a Discrete Cosine
  • Discrete cosine transform with inverse modification.
  • Filter for deblocking in the loop
  • Prediction within a frame
  • Quantification in reverse
  • Decoding of Variable Duration
  • Deinterlacing of space and time
  • Source interlace identification is done automatically.
  • Production of bitstreams
  • Perfect pixel placement

Graphical Processing Unit Architecture

The GPU is usually used in conjunction with the CPU as a co-processor. The Processor will now do general-purpose science and engineering programming at a higher frequency as a result of this. The time-consuming and computation-intensive portion of the code is transferred to the GPU in this case, while the rest of the code remains on the CPU. The GPU performs sequential processing of the code, increasing the system's performance. Hybrid computing is the term for this style of computing.

Unlike a CPU, which has two to eight cores, a GPU has hundreds of smaller cores. In parallel computing, both of these cores work together. NVIDIA application developers created a parallel programming paradigm named CUDA to efficiently exploit the features of the GPU's parallel computing architecture.

The design of a GPU varies depending on the model. Many Processing Clusters make up the GPU's overall architecture. Multiple Streaming multiprocessors are present in these clusters. Each of the streaming multiprocessors has a layer of layer-1 instruction cache, as well as the cores that go with it.

GPU Forms

Different types of GPUs are present in the industry, depending on their functionality and processing methods. In personal computers, there are two types of GPUs: dedicated graphics cards and integrated graphics. Discrete GPU is another name for a dedicated graphics card. Unified memory architecture and collaborative graphics systems are other terms for integrated graphics.

The majority of GPUs are built with specific applications in mind, such as 3D graphics processing, gaming, and so on. Nvidia Titan is designed for cloud computing, Nvidia Quadro is designed for workstation and 3D animations, Nvidia Tesla is designed for cloud workstation and artificial intelligence testing, and Nvidia Drive PX is designed for the autonomous vehicle, among other things.

Dedicated Graphics Card

DIS Systems are systems that have a dedicated GPU. The term "dedicated" here applies to the fact that these GPU chips have dedicated RAM that is only used by the card. These are normally connected to the motherboard via PCI Express or Accelerated Graphics Port expansion slots. These chips are simple to swap out or update. The dedicated GPU on portable computers is interfaced from a non-standard slot due to size and weight restrictions.

Integrated Graphics Processing Unit

There is no dedicated RAM unit on this model of GPU. Instead, it operates on a part of the computer's memory. This GPU can be designed either as part of the motherboard's chipset or on the same die as the CPU. These have less power than dedicated graphics cards, but they are less expensive to use. This GPU includes the Intel HD Graphics and AMD Accelerated Processing Unit.

Hybrid Graphics Processing

This are often referred to as GPGPUs. The modified stream processor is usually used to perform computer kernels as a general-purpose graphics processing unit. The vast processing power of a modern graphics accelerator's shader is used as general-purpose computing power in this definition. This approach outperforms a basic CPU when performing large vector operations.

External GPU

This graphic processing unit is located on the exterior of the computer unit, similar to a huge external hard drive. These are also attached to notebook computers from the outside. Laptops normally come with plenty of RAM and a strong processor. Instead of a powerful graphics processor, laptops have an onboard graphics chip that is less powerful but more energy consuming. These are insufficiently efficient to render game graphics and do not accommodate higher-resolution titles. As a result, for better performance, this External GPU is used for laptops.

The need for more efficient GPUs is growing as the market for strong graphics and decent image resolutions grows. With the availability of efficient GPUs, even more in the area of high-processing technology like machine learning and deep learning can be accomplished. GPUs have also aided a massive expansion of the game industry. Many high-resolution games have been released that make good use of the GPU's capabilities. Which GPUs can be connected to laptops externally?


1). Is a GPU a Graphic card?

A graphic card present on the computing device is a whole hardware part. Whereas a GPU is a chip present on the graphic card.

2). Which is a faster CPU or GPU?

Today GPU is available with larger memory units, greater processing power, and larger memory bandwidth compared to the traditional CPU. So, GPU is found to be about 50 to 100 times faster than the CPU.

3). How many cores does a GPU have?

GPU does parallel computing. It has hundreds of smaller cores working together. This massive parallel computing gives the GPU its superior computing power.

4). Is RTX or GTX better?

When compared to GTX 1080 Ti, RTX 2080 has newer technology and offers better, faster performance. RTX is lower in cost compared to GTX.

5). Can a GPU replace a CPU?

 GPU is faster than the CPU. They perform the task very fast by performing many tasks at a time. But it can perform only certain higher frequency operation and all other executions like manging of interrupts, data storage are done by CPU. No, GPU cannot replace a CPU.