With gaming and multimedia content drawing in more audiences by the minute, the GPU or the graphics processing unit has become more significant than ever.
The GPU hasn’t been around that long — the earliest ones came in the 1990s — but the difference to resource-intensive tasks like visualization and machine learning is incredible.
Generally speaking, a GPU is an electronic circuit that performs mathematical operations. It is fundamentally much like the CPU, the central processing unit.
However, while the CPU performs general-purpose tasks and system management, a GPU caters to intensive jobs like video editing. It operates on many datasets in parallel, boosting operational efficiency.
Origin of the GPU: How & why was it designed?
The original purpose of the graphics processing unit was to control the display, a step up from the dot matrix screens and vector displays of the 1940s and 50s. At this time, a component called ‘graphics controller’ used the CPU to handle the display.
Work on the GPU originated to match steps with gaming and virtual reality applications, striving to generate an image with multiple pixels. Finally, in the 1990s, NVIDIA launched the GeForce 256, the first-ever GPU.
It was a programmable chip that combined engines for rendition, transformation, clipping, and lighting with the graphics controller. Effectively, GPUs began as Application-Specific Integrated Circuits or ASICs.
By 2007, NVIDIA released CUDA, a software that made parallel processing achievable. The launch of CUDA made GPU programming more widespread, permitting developers to fine-tune these units for intensive tasks like blockchain and machine learning — tasks that demand tremendous computing power.
Modern GPUs have advanced functions like ray tracing, mesh shading, FSAA or full-scene anti-aliasing to smoothen 3D objects, and anisotropic filtering to produce crisper images.
A graphics processing unit contains many multiprocessors with shared memory blocks, processors, and registers. It has fixed and device memory, with the architecture optimized for faster data access.
How Things Work in a GPU
The basic functionality entails receiving information from the CPU, performing the necessary operations, and outputting to the display.
Graphics cards have components like video memory (VRAM) to store pixel information as the image gets generated. They also have connections to the motherboard and the monitor.
GPUs can be of various types depending on the manufacturer and the intended role:
1. Discrete or Dedicated
The GPU is on a graphics card that fits into a slot (usually PCI Express) in the motherboard. The computer will need dedicated cooling since these GPUs generate excessive heat and consume considerable power.
2. Integrated or iGPU
In 2010, iGPUs became available, combining a CPU and a GPU onto one chip. Intel Celeron was one of the first in this group, while Intel Iris X (with Intel UHD) is immensely popular in many portable and affordable laptops. Integrated GPUs mean lower heat generation and longer battery life.
Another example is SoC, or a system on a chip, as seen in smartphones. An SoC also combines components like memory and networking.
3. Virtual
These GPUs are software-based representations to let you work without necessary hardware. For instance, if you use Amazon Web Services, you can choose a GPU from the EC2 platform (Elastic Compute Cloud) that matches your computing requirements.
Beyond Gaming: Real-Life Applications of a GPU
Graphics processing units are perhaps best known for their role in gaming, rendering your favorite titles with realistic effects and blazing speeds. The advancement of GPUs has helped game developers add true-to-life effects to their content, like shadowing. It also lets players enjoy higher resolutions and frame rates.
However, GPUs have many more applications:
- Deep learning and high-performance computing (HPC) roles
- Professional and medical drawing through CAD software and other visualization needs
- Blockchains for proof of work and cryptocurrency-based requirements
- Simulation for scientific applications like molecular and fluid dynamics
- Weather forecasting
- Automotive applications like vehicle design
GPU vs Graphics Card
It is typical to see people use the terms GPU and graphics card interchangeably. However, a GPU is the prime component of a graphics card.
The latter is an AIB, or an add-in board (AIB) that fits into the motherboard and is interchangeable.
What the Future Holds for GPUs
Technology is swiftly advancing in GPUs, introducing newer features like graphics acceleration and inbuilt machine learning.
NVIDIA’s latest lineup is the RTX 4090, which complements powerful processors in high-end gaming and content creation laptops.
Besides NVIDIA, you can explore GPUs from series like the Intel Data Center GPU Flex, Max, and Intel Arc A. Graphics processing units can also include NPUs or neural processing units for AI acceleration.
While purchasing a new computer, you must decide whether to emphasize a more powerful CPU or GPU.
I recommend seriously considering a graphics processing unit if your computing needs are intensive or specialized, like professional gaming, machine learning, 3D rendition, modeling, and digital content creation.