Graphical Processing Unit (GPU)

What Is a Graphical Processing Unit?

What Do GPUs Do?

The graphical processing unit (GPU) is a computer component and has been developed for parallel processing but can be used in a variety of applications such as graphics and video rendering. While recognized for their aptitude in gaming, GPUs are gaining traction in creative production and artificial intelligence (AI). 

Originally intended to expedite 3D graphics rendering, GPUs have evolved to offer increased flexibility and programmability, augmenting their capabilities. This has enabled graphics programmers to generate more intriguing visual effects and more realistic scenes, integrating advanced lighting and shadowing techniques. Additionally, other developers have harnessed the power of GPUs to significantly expedite additional workloads in areas such as High Performance Computing (HPC) and deep learning. 

What Are GPUs Used For?

The GPU assumes responsibility for managing the computational demands of graphics-intensive functions on a computer. In demanding scenarios, GPUs are leveraged to enable the delivery of crisp and fluid visuals when engaging in activities such as video gaming. In the business world, GPUs are employed to run specialized programs like 3D and 4D rendering software as well as video editing software like Adobe Premiere Pro. 

• Gaming - Video games now feature hyperrealistic graphics, intricate in-game settings, and higher processing demands. The need for graphics processing is increasing quickly due to the use of cutting-edge display technologies like 4K panels and high refresh rates, as well as the popularity of virtual reality games. Both 2D and 3D graphics can be rendered using GPUs. Games can be played at higher resolutions, quicker frame rates, or both with an improved visual performance. 

• Video Editing and Content Creation - Long rendering periods that clogged up computing resources and hampered creative flow have long been a problem for video editors, graphic designers, and other creative professionals. Currently, rendering video and graphics in higher-definition formats is quicker and simpler, thanks to the parallel processing provided by GPUs.

• Machine Learning -  Because GPUs have a staggering amount of computational power, they can significantly speed up applications like image recognition that benefit from their highly parallel architecture. Many deep learning techniques today rely on GPUs and CPUs working together.

GPU vs. CPU

GPU architecture and central processing unit (CPU) architectures are remarkably similar. Yet, CPUs are responsible for executing and processing the fundamental commands that power a computer, whereas GPUs are made to produce high-resolution images and videos quickly. 

A GPU’s primary purpose is to speed up data parallelism by quickly applying the same instruction to multiple data items (SIMD). On the other hand, a CPU is designed to support task parallelism by carrying out separate operations. 

What Is High Performance Computing?

High-Performance Computing (HPC) is not confined to one specific definition as it can be applied to a diverse range of scenarios. These may include a graduate student who uses a high-powered cloud instance to accelerate their AI research, small or mid-sized HPC clusters that cater to researchers and engineers at university campuses or private businesses, or even larger-scale clusters such as the ones listed on the TOP500. The primary objective is to employ dedicated resources to enhance a specific computational workload beyond the capacity of a typical personal system. 

Why Are GPUs Used for HPC?

GPUs are often considered to be the best choice for HPC due to their highly parallel architecture, specialized processing capabilities, and high memory bandwidth. GPUs are designed to perform many calculations simultaneously, which allows them to perform computations much faster than traditional CPUs. This is especially important for applications that require significant computing power such as scientific simulations, machine learning, and data analytics. Additionally, GPUs are more energy-efficient than CPUs when it comes to performing complex calculations, making them more cost-effective and environmentally friendly. As a result, the use of GPUs for HPC has helped to accelerate computations and enable faster analysis of large datasets, making it a popular choice for scientific research and artificial intelligence.