What is GPU computing and how does it work?

GPU computing uses the pro­cess­ing power of graphics proces­sors to perform many cal­cu­la­tions in parallel. Working together with the CPU, it enables the fast pro­cess­ing of large amounts of data and forms the basis for ap­pli­ca­tions such as ar­ti­fi­cial in­tel­li­gence, media pro­cess­ing, sci­en­tif­ic sim­u­la­tions and GPU cloud computing solutions.

What is GPU computing?

GPU stands for “Graphics Pro­cess­ing Unit.” The term does not refer to an entire graphics card but specif­i­cal­ly to the pro­cess­ing chip on the card that performs the actual com­pu­ta­tions. GPU computing uses this pro­cess­ing power de­lib­er­ate­ly to handle complex tasks more quickly than is possible with tra­di­tion­al proces­sors alone. The common technical term for this approach is “GPGPU” (General-Purpose Computing on Graphics Pro­cess­ing Units).

While GPUs were orig­i­nal­ly developed solely for pro­cess­ing images, videos, and 3D graphics, their par­tic­u­lar strengths are now also used for general computing tasks. These strengths lie in the ability to perform a very large number of similar cal­cu­la­tions si­mul­ta­ne­ous­ly. This principle is essential for many modern ap­pli­ca­tions.

How does GPU computing work exactly?

GPU computing doesn’t work in isolation; it operates in col­lab­o­ra­tion with the CPU. The two proces­sors handle different tasks and com­ple­ment each other. The CPU acts as the central control unit since it launches programs, organizes processes, prepares data, and de­ter­mines which cal­cu­la­tions should be offloaded to the GPU. The GPU then takes over the mass cal­cu­la­tions and processes them in parallel. Without the CPU’s control, the GPU wouldn’t be able to function in­de­pen­dent­ly.

Tech­ni­cal­ly, a GPU consists of hundreds to thousands of pro­cess­ing cores, each designed to perform simple cal­cu­la­tions on large datasets si­mul­ta­ne­ous­ly. To make GPU computing efficient, complex com­pu­ta­tion­al problems are divided into many smaller, similar tasks. These sub-tasks are processed in parallel by one or more GPU cores.

To access the GPU, spe­cial­ized pro­gram­ming in­ter­faces and frame­works like CUDA or OpenCL are used. These allow de­vel­op­ers to specify which parts of a program should run on the GPU and which should run on the CPU. For users, this process typically happens in the back­ground.

Key dif­fer­ences between CPUs and GPUs

To really un­der­stand GPU computing, it is important to know the fun­da­men­tal dif­fer­ence between a CPU and a GPU. Both are proces­sors, but they have been optimized for com­plete­ly different tasks.

CPUs at a glance

A CPU is flexible, versatile and designed to process different tasks one after the other. It usually has only a few, but very powerful pro­cess­ing cores that can make complex decisions, control programs, and execute logical op­er­a­tions.

Typical tasks of a CPU include:

• Running operating systems
• Pro­cess­ing user input
• Con­trol­ling programs
• Cal­cu­lat­ing complex, in­ter­de­pen­dent com­pu­ta­tion­al steps

GPUs at a glance

A GPU is spe­cial­ized for par­al­lelism and takes a different approach to a CPU. It has hundreds or thousands of pro­cess­ing cores, each of which is simpler in design than CPU cores. In return, they can execute a very large number of com­pu­ta­tion­al op­er­a­tions at the same time. This is the core of GPGPU, where GPUs are used for a wide range of tasks beyond graphics rendering.

GPUs are ideal when:

• the same cal­cu­la­tion is applied to large amounts of data
• the com­pu­ta­tion­al steps are clearly struc­tured
• the tasks are in­de­pen­dent of one another

Example: GPUs vs CPUs in image editing

When an image is edited, such as when adjusting its bright­ness,, the process involves many identical com­pu­ta­tion­al steps. A digital image consists of millions of in­di­vid­ual pixels, and the same cal­cu­la­tion must be applied to each pixel to adjust its bright­ness or color.

A CPU typically cal­cu­lates the new value of each pixel se­quen­tial­ly. In contrast, with GPU computing, the same operation is dis­trib­uted across a large number of cores. While a typical CPU has around 8 to 16 high-per­for­mance cores, modern GPUs feature several thousand simpler cores that can process pixels in parallel. Simply put, in the time it takes a CPU to process a small number of pixels, the GPU can handle thousands of them si­mul­ta­ne­ous­ly.

What are the ad­van­tages of GPU computing?

Due to their ability to execute numerous similar op­er­a­tions at once, GPUs provide sig­nif­i­cant ad­van­tages over tra­di­tion­al proces­sors. GPU computing is es­pe­cial­ly effective for compute-intensive and data-heavy tasks.

• High computing per­for­mance through parallel pro­cess­ing: GPUs are sig­nif­i­cant­ly faster than CPUs for certain tasks.
• Ac­cel­er­a­tion of modern tech­nolo­gies: GPGPU computing is a core foun­da­tion for ar­ti­fi­cial in­tel­li­gence, machine learning, sim­u­la­tions, and real-time analytics.
• Good scal­a­bil­i­ty: Computing power can be easily increased by adding more GPUs, such as in data centers or GPU cloud computing en­vi­ron­ments.
• High energy ef­fi­cien­cy per com­pu­ta­tion: For many parallel workloads, GPUs deliver more per­for­mance per watt than tra­di­tion­al proces­sors.
• Relief for the CPU: Compute-intensive tasks can be offloaded, allowing the CPU to focus on control and logic.

The most important GPU use cases

GPU computing is in­creas­ing­ly being adopted across various fields, as many modern ap­pli­ca­tions rely on pro­cess­ing large amounts of data and per­form­ing complex cal­cu­la­tions. The ability to process similar com­pu­ta­tion­al tasks in parallel makes this approach highly suitable for a wide range of use cases.

Ar­ti­fi­cial in­tel­li­gence and machine learning

One of the most important ap­pli­ca­tion areas for GPU computing is ar­ti­fi­cial in­tel­li­gence. When training machine learning models, vast amounts of data need to be processed, and math­e­mat­i­cal op­er­a­tions must be repeated millions of times. GPUs can perform these cal­cu­la­tions in parallel, sig­nif­i­cant­ly reducing training times. Without GPU computing, many of today’s AI ap­pli­ca­tions, such as language models, image recog­ni­tion, and rec­om­men­da­tion systems, would be nearly im­pos­si­ble to achieve.

Image, video, and 3D pro­cess­ing

In recent years, the computing power required for image, video, and 3D pro­cess­ing has grown sig­nif­i­cant­ly. Modern media content demands higher res­o­lu­tions, more complex effects, and more realistic visuals. Tasks such as color cor­rec­tion, light and shadow cal­cu­la­tions, effects, or rendering 3D scenes involve per­form­ing countless identical cal­cu­la­tions across millions of pixels or objects.

As editing becomes more demanding, the need for GPU per­for­mance increases. High-res­o­lu­tion videos, complex effects, or real-time previews are nearly im­pos­si­ble to handle ef­fi­cient­ly without GPU computing. Ad­di­tion­al­ly, many creative ap­pli­ca­tions now in­cor­po­rate ar­ti­fi­cial in­tel­li­gence, such as automatic image en­hance­ment, object or person recog­ni­tion, noise reduction, or content upscaling. These AI-powered features also rely on parallel cal­cu­la­tions, further driving the need for powerful GPUs.

Sci­en­tif­ic sim­u­la­tions and research

In sci­en­tif­ic research, GPUs are mainly used to simulate complex processes. This includes ap­pli­ca­tions like climate and weather models, physics sim­u­la­tions, and chemical cal­cu­la­tions. These tasks involve per­form­ing numerous similar com­pu­ta­tions on large datasets.

Data analytics

Modern busi­ness­es handle in­creas­ing­ly large volumes of data. GPU computing enables efficient analysis of these vast data sets, helping to spot patterns and make pre­dic­tions. The parallel pro­cess­ing power of GPUs is par­tic­u­lar­ly important for time-critical analyses, such as those in the financial sector or real-time analytics.

Cloud computing and data centers

With the growth of cloud platforms, GPU cloud computing has become more ac­ces­si­ble to many companies. Rather than main­tain­ing their own hardware, they can rent GPUs as a cloud resource on demand. Providers offer GPU power through their data centers as a service, making compute-intensive ap­pli­ca­tions scalable and cost-effective, even for smaller busi­ness­es or research teams.