GPU – Definition and meaning

Definition: What is a GPU?

GPU stands for "Graphics Processing Unit". This term refers to a specialised microprocessor that is designed to carry out extensive graphical calculations particularly efficiently. While CPUs (Central Processing Units) were developed as all-rounders for a wide range of tasks, the GPU primarily performs parallelisable calculations in the field of image processing and graphics display. Originally created to accelerate 2D and later 3D graphics, the GPU now significantly characterises the user experience of modern computer systems.

Their field of application is no longer limited to graphics calculations. GPUs are also used for demanding computing operations, for example in machine learning, scientific simulation or in the field of blockchain technologies. The reason for this is the highly parallel structure of their computing cores - known as shaders - which process tasks in a short time that would take conventional CPUs significantly longer.

Structure and functional principle of a GPU

The architecture of a GPU differs significantly from that of a CPU. A CPU usually consists of a few, very powerful cores, whereas a GPU relies on a large number - sometimes several thousand - of simply constructed computing units. These are capable of performing identical or similar operations simultaneously. Accordingly, large quantities of pixels or extensive matrices can be processed in parallel, for example.

Typical components of a GPU are

• Shader units: These processing units are designed for the fast execution of graphics and image processing tasks.
• Memory controllers: They control access to the VRAM, the fast working memory that is usually installed directly on the graphics card.
• Texture units and rasterisers: Specialised circuits that are responsible for mapping textures and the final image output.
• Powerful bus systems: Interfaces that are used to transfer data quickly between the GPU, CPU and memory.

GPUs are available either as dedicated plug-in cards, which are installed in the computer via PCI Express, or - in many systems - as integrated solutions (iGPUs) directly in the processor. While dedicated graphics cards are primarily used in performance-intensive devices such as gaming PCs or workstations, iGPUs are preferably used in notebooks and compact desktops, where energy requirements and size play a greater role.

Role of the GPU in modern operating systems

Modern operating systems no longer use GPUs solely to display screen content. Instead, these processors now perform a variety of specialised tasks that can speed up the system as a whole and reduce energy requirements at the same time. Key areas of application are

• Hardware-accelerated user interfaces: Operating systems such as Windows, macOS or the large Linux desktops utilise the GPU to display animations, transparency effects and complex graphical elements. Graphical transitions and window movements are executed smoothly and responsively - for example with Aero in Windows.
• Video playback: The decoding and playback of high-resolution video formats - such as 4K or HDR streams - often takes place directly on the GPU. This leaves the CPU free for other tasks, which is particularly advantageous for mobile devices.
• 3D applications and games: The GPU utilises its full potential in this discipline. Real-time rendering, shader calculations and modern ray tracing create realistic visualisations with high frame rates.
• Compute capabilities: Many software platforms and programming interfaces such as DirectX 12, Vulkan or OpenCL now incorporate the parallel computing capacities of GPUs for a wide variety of tasks in order to utilise system resources more efficiently.

The importance of this becomes clear when scrolling through extensive and animated websites, for example, where the GPU ensures smooth movements and rapid changes to the display. Multi-monitor set-ups also benefit from the high bandwidth and performance of modern graphics hardware.

Real application examples and usage scenarios

GPUs are an integral part of numerous everyday and professional applications. Specific examples demonstrate their versatility:

• Gaming PCs: a powerful graphics card is essential for current computer games, for example if titles such as "Cyberpunk 2077" or "Flight Simulator" are to be played with high details and ray tracing.
• Video and image editing: Programmes such as Adobe Photoshop, DaVinci Resolve or Blender use the high computing power of the GPU to accelerate filters and effects or process video material in real time. This enables faster workflows, especially for larger projects.
• Artificial intelligence and deep learning: In research and industry, users rely on GPU clusters to train neural networks or large amounts of data in a short time, for example. Frameworks such as TensorFlow or PyTorch rely on special interfaces such as CUDA or OpenCL.
• Cloud computing: Data centres offer GPU-based server capacities that companies can rent on a project basis for demanding tasks such as simulations or visual renderings.
• Technical simulations: Complex computing models, for example in medical image processing, weather research or molecular analyses, benefit from the high parallelism and speed of GPUs - compared to CPUs, simulations can be significantly accelerated.

An everyday example is the processing of high-resolution 4K videos on a laptop with a separate GPU: not only does the preview run smoothly, but the export of complex projects is often reduced from several hours to a fraction of the time.

Advantages and challenges of modern GPUs

The use of modern GPUs opens up a wide range of advantages, but also brings with it some challenges.

Advantages:

• High performance for parallel workloads, especially when identical or similar calculations are required.
• Reduces the load on the CPU, making the overall system more efficient and responsive.
• Flexible use for a wide range of requirements, from graphics to science to artificial intelligence.
• Significant performance gains for professional applications and complex software environments.

Challenges and disadvantages:

• Increased energy requirements under full load, especially with powerful models for gaming or science.
• Investment costs can be considerable depending on requirements and future-proofing.
• Requires up-to-date and stable drivers to remain compatible with new operating systems and applications.
• Enormous space, noise and cooling requirements, which must be taken into account, especially in compact systems or with multiple graphics cards.

Particularly in server-based infrastructures, such as when using cloud graphics cards, energy efficiency and customised cooling concepts must be included in the planning.

Specific recommendations for selection and integration

When selecting a GPU, it is primarily the intended applications that determine the optimal choice. In everyday devices such as multimedia notebooks, an integrated GPU is often sufficient. It works efficiently and covers standard tasks such as office applications, Internet use or video streaming without any problems. However, if you regularly work with complex graphics software, design applications or modern games, you should opt for a dedicated solution. A comparison of current graphics card models from providers such as NVIDIA or AMD is recommended, as is checking whether the mainboard, power supply unit and housing are suitable for the selected card.

Specialised workstation GPUs or GPU clusters, which can be tailored precisely to the respective requirements, are ideal for professional applications, such as in research or companies. Cloud services are an attractive option when special tasks need to be solved on a temporary or project-related basis and no local investment is required.

Important selection criteria at a glance:

• Reliable compatibility with the software used and the relevant operating systems
• Sufficiently large VRAM memory, especially for large 3D data volumes or deep learning applications
• Adequate power consumption and well thought-out cooling design - a decisive factor, especially in server operation
• Regular driver updates and long-term support from the manufacturer

Before upgrading hardware, it is advisable to take a close look at the system architecture: while desktop PCs usually allow the graphics card to be replaced or expanded without any problems, mobile computers such as notebooks are much less flexible in this respect. For many modern workflows with a focus on artificial intelligence, 3D visualisation and simulations, a powerful GPU is now a key technological component.