The term "GPU" was first introduced by Nvidia in 1999 when they released the GeForce 256. Originally, graphics processors were created for rendering images in computer graphics, but over time they began to be used for machine learning, AI, HCI, and scientific research.
In this article, we will discuss what a GPU is, how graphics processors differ from video cards, and in which industries and for what tasks they are used.

What is a GPU?

A GPU, or graphics processing unit, is a specialized processor that is designed to accelerate the creation and rendering of images and videos. GPUs are often used in gaming, video editing, and other applications that require high-quality graphics.

GPU vs. Video Card

GPUs are often confused with video cards, but they are actually two different things. A video card is a physical device that contains a GPU, as well as other components such as memory and cooling. The GPU is the part of the video card that does the actual processing of graphics.

How GPUs Work

GPUs are able to accelerate graphics processing by performing many calculations in parallel. This is because GPUs have many cores, each of which can perform a calculation independently. This parallel processing power makes GPUs ideal for tasks such as rendering 3D graphics and processing large amounts of data.

Applications of GPUs

GPUs are used in a wide variety of applications, including:

• Gaming: GPUs are essential for gaming, as they are able to render the high-quality graphics that are required for modern games.
• Video editing: GPUs are also used in video editing, as they can speed up the process of rendering and encoding videos.
• 3D modeling: GPUs are used in 3D modeling to create realistic and detailed models.
• Machine learning: GPUs are also being used in machine learning, as they can accelerate the training of machine learning models.

Applications of GPUs

Graphics and Rendering
In the animation industry, GPUs are used to render detailed and realistic effects and 3D graphics. Pixar and DreamWorks use graphics processors to create animated characters and virtual worlds. For example, Pixar's RenderMan is a toolset that uses GPUs to render high-quality images.
Programs such as Adobe Photoshop and Illustrator, AutoCAD, use the capabilities of graphics processors to improve performance. GPU acceleration allows you to quickly process images, perform 3D rendering, and correct images and display the result on the display in real time.
Some features in Adobe Photoshop are GPU accelerated, such as focus selection, montage areas, and blur gallery. But there are functions that cannot work without a GPU. For example, 3D, bird's eye view, rendering (picture frame and tree), smooth brush resize.

The Gaming Industry

With each passing year, games are becoming more and more realistic. Developers are creating exciting virtual universes and immersing gamers in the gameplay experience to the fullest extent possible. In order for characters to be beautifully rendered, objects to be reflected correctly and move according to the laws of physics, and there to be no delays in online gaming, graphics processors are needed.
In games, it is necessary to determine the colors and positions of each pixel on the screen. This requires fast and repeated calculations to maintain a high frame rate and create smooth visual effects. The GPU allows these operations to be performed quickly and simultaneously to display 3D graphics in real time.

In games, graphics processors model physical calculations, realistic movements, interactions, and AI computations that dictate the behavior of non-player characters and objects.
The GPU performs the same operation on multiple data points. This allows the CPU to focus on other game logic, resulting in smoother and more responsive gameplay.

With game streaming, the game is run on a server and the GPU is responsible for rendering the game and encoding the video for its smooth transmission over the Internet. In virtual reality, the requirements for the GPU are even higher.
To create a stereoscopic 3D effect, the graphics processor must simultaneously render two slightly different views of the same scene. This doubles the rendering load, so a high-performance GPU is needed to maintain a high frame rate and prevent motion sickness.

Scientific Research and HCI

In scientific fields such as bioinformatics, astrophysics, and climatology, large amounts of data are generated that need to be processed and analyzed. Graphics processors, with their parallel processing capabilities, are well suited for these tasks. They can perform many calculations simultaneously, allowing scientists to obtain research results faster.

To model and simulate complex physical processes, from particle interactions in a physics experiment to climate models in meteorology, complex mathematical equations need to be solved for thousands or even millions of data points. GPUs can perform calculations for each data point simultaneously, reducing the time required for simulations.

Human-computer interaction (HCI) is a field of study that focuses on the interaction between humans and computers. HCI researchers use GPUs to study how people interact with computers and to develop new ways to improve the user experience.
Artificial Intelligence and Machine Learning
To train neural networks to recognize patterns and make predictions, the network needs to adjust its internal parameters based on input data. This task involves a large number of mathematical operations that GPUs can easily handle.
Effective training of machine learning models often requires large amounts of data and computational resources. Distributed computing involves dividing the training process across multiple graphics processors, allowing the model to process more data in less time. This approach, combined with the parallel processing capabilities of GPUs, can significantly accelerate the training of machine learning models.
GPUs are also used in specific AI and ML applications, such as image processing and natural language processing. In image processing, GPUs can quickly process and analyze visual data, making them useful for tasks like image recognition and classification. In natural language processing, GPUs can assist with tasks like speech recognition and language translation.
Industry and Manufacturing
Graphics processors are used for modeling and optimizing production and logistics chains. This involves creating a digital twin of the production process, which can be used to test scenarios and determine the most efficient and cost-effective approach. For example, NVIDIA's Metropolis for Factories offers a set of AI-based automation workflows.
Rapid processing and analysis of big data helps businesses make timely decisions and optimize business processes to increase efficiency and reduce costs. GPUs are also used for visualizing 3D models and projections, which are crucial during the design and prototyping stages of manufacturing.
Finance and Cryptocurrency
In the financial sector, GPUs are used for analyzing and forecasting financial data using complex models and algorithms. This includes processing large volumes of data to identify trends and patterns that can aid in making informed financial decisions.
Organizations use NVIDIA's AI, including deep learning, machine learning, and natural language processing (NLP), to improve risk management efficiency, enhance data-driven decisions, enhance security, and improve customer service.↳

Graphics processors also help process transactions and perform verification calculations to ensure the security and efficiency of financial operations. In the world of cryptocurrency, GPUs are used for mining, which involves performing complex computational tasks to validate transactions and add them to the blockchain.

Medicine and Biotechnology
Graphics processors are used for processing and analyzing medical images, including CT and MRI scans. This allows doctors to identify anomalies and patterns to diagnose diseases and develop treatment plans. In the field of drug discovery and therapeutic development, GPUs help model and simulate biological systems and reactions.
GPUs assist in comparing DNA sequences, identifying patterns, and making predictions about diseases and their treatments. With the help of graphics processors, researchers can process genomic data faster and with higher accuracy. Accelerating genome analysis in population and oncology genomic research can help identify rare diseases and bring customized therapeutic drugs to market more quickly.

GPUs - The Catalyst of Innovation

The graphics processing unit (GPU), which was originally designed for rendering graphics, has gradually evolved into a powerful tool that has enabled advancements across various industries - from accelerating scientific discoveries and optimizing industrial processes, to enhancing gaming experiences and powering cloud services.
The GPU's ability to perform parallel data processing has now made it a valuable asset for high-performance computing, artificial intelligence, machine learning, and data analysis.
Scientists predict that the demand for high-performance computing will continue to grow, multi-GPU systems will become more widespread, and AI-dedicated cores will be integrated into GPUs. Therefore, the prospects for this technology are vast, as are the range of challenges it helps to solve.