What is Graphics Processing Unit (GPU?)

Graphics processing is an advanced technology that has evolved specifically for computing. The graphics processing unit (GPU) is an advanced and specialised processor which makes computing possibilities for machine learning, deep learning, gaming, content creation, and many more related technologies.

A graphics processing unit (GPU) is a specialised electrical circuit that accelerates computer graphics and image processing. GPUs are useful for non-graphic computations such as neural networks and cryptocurrency.

GPUs were originally developed to speed up the rendering of 3D visuals. They gradually became more adaptable and programmable and expanded their capabilities. This enabled graphics programmers to generate more fascinating visual effects and realistic scenes using advanced lighting and shadowing methods. In other applications, GPUs are most widely used to significantly work with high-performance computing (HPC), deep learning, and other domains.

Significances of GPUs

GPUs are mostly used to speed up real-time 3D graphics applications like gaming. However, as the twenty-first century began, computer experts recognised that GPUs could address some of the world”s most complex computing problems.

This realisation ushered in the era of general-purpose GPUs. Graphics technology is now being used to solve a broader range of challenges. Today”s GPUs are more programmable than ever before, allowing them to power a wide range of applications beyond standard graphics rendering.

GPUs are significant for the following reasons −

GPUs for Gaming

Video games have become more computationally intensive, with hyperrealistic graphics and vast, complicated in-game worlds. With advanced display technologies, such as 4K screens and high refresh rates, along with the rise of virtual reality gaming, demands on 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.

GPUs for Video Editing and Content Creation

GPUs provide a computational solution for parallel processing, with built-in AI features and sophisticated acceleration for faster execution and easier rendering of video and graphics in higher-defined formats.

GPU for Machine Learning

Artificial intelligence and machine learning are among the most interesting applications for GPU technology. Because GPUs have an enormous amount of computational power, they can provide remarkable acceleration in workloads that make use of GPUs” highly parallel nature, such as image recognition. Many of today”s deep learning solutions rely on GPUs to work alongside CPUs.

How do GPUs work?

A GPU is ideally suited for tasks or programs that require large volumes of data and repetitive computation. GPUs have a similar fetch-decode-execute cycle, it has designed in such a way that they can handle thousands of threads at once, making them well-suited for parallel processing tasks −

• Fetching − The GPU fetches instructions for the execution from its internal memory. (VRAM).
• Decoding − The control units of streaming multiprocessors (SMs) decode instructions. Each SM can process multiple instructions at once and assign them to available cores.
• Executing − The decoded instructions are executed by multiple small cores in the SM. These cores perform operations like matrix multiplication and vector operations for graphics rendering and machine learning.
• Writing back − The findings are either returned to GPU memory or transmitted to the display output.

Overall, GPU working includes −

• Parallel Processing − GPUs, unlike CPUs, have thousands of small cores which are integrated with the system to do parallel processing. This makes it extremely efficient for larger and more complex tasks that can be divided into smaller ones and apply parallel processing to them.
• CUDA cores (NVIDIA) or stream processors (AMD) − These are the basic processing units of a GPU, which can execute the processes on their own. Modern GPUs exist with thousands of these cores.
• Memory − A GPU has its memory which is known as Graphics Memory or VRAM (Video RAM). A VRAM is capable enough to do multiple tasks at once. This allows them to rapidly process and render images.
• Shader − Shaders are specialised processing units within a GPU. They are specifically designed to perform vertex shading, geometry shading, pixel shading, and computation. These units support rendering graphics and computations.
• APIs − To optimise GPUs, software applications use programming interfaces such as CUDA or OpenCL to manage and perform parallel activities. These APIs give support to developers with tools, frameworks and libraries to help them design programmes that make the best use of GPU capabilities.
• Data and task parallelism − GPUs are capable enough to perform data parallelism and task parallelism on different sets of data simultaneously.
• SIMD (Single Instruction, Multiple Data) − This paradigm enables GPUs to perform the same action on multiple data points at once making them useful for image processing and scientific computations.
• Task distribution − Workloads for graphics rendering or scientific simulations are divided into different smaller jobs; each of them is assigned to a separate thread that the GPU then runs concurrently. This strategy considerably reduces total processing time.
• Thread execution − Threads are smaller units of work that allow concurrent processing; this process shows how GPUs function. A GPU can support thousands of threads at a time.

Characteristics of GPU

Some of the key characteristics of GPU are as follows −

• Parallel processing − GPUs contain thousands of specialized to perform multiple tasks simultaneously. GPUs include SIMD, this feature allows the GPU to apply the same operation on multiple data points in parallel.
• Memory − GPUs have their memory. GPUs Video RAM enables rapid access to data and faster data transfer.
• High Throughput − The parallel processing feature of GPUs provides high throughput.
• Shaders − Shaders allow for transformations and lighting, texturing, shading and computational tasks beyond graphics rendering
• Scalability − GPUs can scale up that system from a few cores to thousands.
• Software Ecosystem − The GPU ecosystem supports APIs like as OpenGL, DirectX, and Vulkan, as well as frameworks like CUDA (NVIDIA) and OpenCL, allowing developers to use GPU capability for both graphics and general-purpose computing.

Applications of GPU

Some of the common application areas where GPUs are most widely used are as follows −

• Gaming − Gaming is most widely used for real-time rendering of high-resolution and complex graphics.
• Visual effects − It is used in films and television shows to produce realistic simulations, 3D animations, and intricate special effects,
• 3D Modelling − CAD and 3D rendering applications fully rely on GPUs for 3D models.
• AI and Machine Learning − GPUs can process complex computations due to this; GPUs are used in AI and machine learning for training and inference of neural networks.
• Scientific Computing − GPUs are extremely useful in the domain of physics, chemistry, biology, simulations and data analysis.
• Cryptocurrency Mining − GPUs are used to validate transactions on blockchain networks, solving cryptographic puzzles.
• Scientific Computing − Scientific computing includes simulations and data analysis.

Advantages of GPUs

Some of the key advantages of GPUs are as follows −

• Quicker execution − GPU computing does fast execution and gives quicker results.
• High processing power − GPUs are made with thousands of small processing cores which perform tasks in parallel mode. This parallel processing enables a GPU to do multiple complex processing at a time.
• Scalability − GPUs are highly scalable.
• Cost-effective solutions − Graphics processing units are a cost-effective solution for a wide range of computing tasks, as they eliminate the need for specialised processors or hardware.
• Graphics rendering − High-quality 3D visuals and visual effects in animation, video games and simulations are rendered using GPU computing.

Disadvantages of GPUs

Some of the main disadvantages of GPUs are as follows −

• Complexity − Developing applications that properly utilise GPU capabilities is difficult and requires skills in specialised programming languages and frameworks.
• Limited single-thread performance − A small core of GPUs is less powerful than CPU cores for single-threaded tasks.
• Memory constraints − GPU memory is limited which can hamper tasks that require extensive memory access.
• Compatibility and driver issues − Updating GPU drivers is essential to optimise GPU performance.