20 Feb 2026·Source: The Hindu

4 min

Science & TechnologyEXPLAINED

Graphics Processing Units (GPUs): How They Work and Their Applications

Explainer on GPUs: their function, rendering pipelines, neural network applications, and market dominance.

Photo by Umberto

Background Context

GPUs excel at parallel processing, which means they can perform many simple calculations simultaneously. This is in contrast to Central Processing Units (CPUs), which are designed to handle a smaller number of complicated tasks quickly and switch between tasks efficiently. To render a scene on a computer screen, the computer must determine the color of millions of pixels multiple times per second. For example, a 1920x1080 screen has over 2 million pixels per frame, and at 60 frames per second, over 120 million pixels are updated each second. GPUs are designed to handle this type of repetitive task more efficiently than CPUs. A GPU consists of hundreds or thousands of cores, each capable of performing calculations. While each core may not be as powerful as a CPU core, the sheer number of cores allows the GPU to complete large, repetitive workloads faster.

Why It Matters Now

GPUs are essential for training neural networks, which are mathematical models used for machine learning. Neural networks require running many tasks in parallel and moving large amounts of data, making GPUs the preferred choice over CPUs.

The math of neural networks involves matrix and tensor operations, which are calculations on multi-dimensional grids of numbers. GPUs are well-suited for these operations because they can apply the same set of mathematical rules to different numbers simultaneously across their many cores.

Furthermore, neural networks can have millions or billions of parameters, requiring the ability to move data quickly. GPUs have high memory bandwidth and specialized tensor cores designed to multiply matrices rapidly, making them ideal for training and running neural networks.

Key Takeaways

•GPUs are designed for parallel processing, making them suitable for tasks like rendering graphics and training neural networks.
•The rendering pipeline involves vertex processing, rasterization, pixel shading, and writing to the frame buffer.
•GPUs have their own dedicated memory called VRAM, which has high bandwidth for moving data in and out quickly.
•Neural networks use GPUs because they can run many tasks in parallel and move a lot of data.
•GPUs consist of hundreds or thousands of cores, allowing them to complete large, repetitive workloads faster than CPUs.
•Matrix and tensor operations are fundamental to neural networks, and GPUs are optimized for these calculations.
•Nvidia is a dominant player in the GPU market, particularly for AI computing platforms.

Related Concepts

Central Processing Unit (CPU)Parallel Processing Rendering Pipeline Neural Networks Machine Learning VRAM (Video RAM)

This article explains how Graphics Processing Units (GPUs) work, comparing them to Central Processing Units (CPUs). GPUs excel at parallel processing, making them suitable for tasks like rendering graphics and training neural networks. The rendering pipeline involves vertex processing, rasterization, pixel shading, and writing to the frame buffer.

GPUs have their own dedicated memory called VRAM. Neural networks use GPUs because they can run many tasks in parallel and move a lot of data. Nvidia holds a dominant position in the GPU market, leading to investigations by European regulators regarding potential monopolistic practices.

The article also discusses the energy consumption of GPUs and their role in AI computing platforms.

Key Facts

A Graphics Processing Unit (GPU) is a computer processor built to perform many simple calculations at the same time.

The rendering pipeline consists of vertex processing, rasterization, pixel shading, and writing to the frame buffer.

GPUs have their own dedicated memory called VRAM, designed to have a high bandwidth.

Neural networks use GPUs because they can run many tasks in parallel and move a lot of data.

UPSC Exam Angles

GS3 - Science and Technology, developments and their applications and effects in everyday life

GS3 - Awareness in the fields of IT, Space, Computers, robotics, nano-technology, bio-technology and issues relating to intellectual property rights.

Potential questions on the role of GPUs in AI, regulatory challenges, and future trends

In Simple Words

GPUs are like super-fast number crunchers in your computer. They're really good at doing many simple math tasks at the same time, which makes your games look great and helps AI learn faster. Think of them as the engine that powers both realistic graphics and smart technology.

India Angle

In India, GPUs are becoming more important as the tech industry grows. From improving the graphics in mobile games played by millions to powering AI systems used in healthcare and agriculture, GPUs are playing a bigger role.

For Instance

Consider how GPUs help doctors analyze medical images like X-rays faster. This allows for quicker diagnoses and better patient care, impacting everyday Indians directly.

GPUs are not just for gamers; they're essential for advancements in AI, healthcare, and many other fields that affect our daily lives.

GPUs: Powering the visuals and intelligence of tomorrow.

Visual Insights

Key Statistics on GPUs

Highlights the dominance of Nvidia in the GPU market and the increasing regulatory scrutiny.

Nvidia's Market Position: Dominant

Frequently Asked Questions

1. What is a Graphics Processing Unit (GPU) and why is it important for UPSC prelims?

A GPU is a specialized computer processor designed to perform many calculations simultaneously. This parallel processing capability makes it crucial for tasks like rendering graphics and training neural networks, which are important concepts in computer science and AI, often featured in the UPSC syllabus.

2. How does a GPU's rendering pipeline work, and what are its key stages?

The rendering pipeline is the process by which a GPU converts 3D data into a 2D image for display. The key stages include vertex processing, rasterization, pixel shading, and writing to the frame buffer. Understanding this process helps in comprehending how GPUs handle graphical tasks.

•Vertex Processing: Modifies the vertices of 3D models.
•Rasterization: Converts vector graphics into pixels.
•Pixel Shading: Calculates the color and other attributes of each pixel.
•Frame Buffer: Stores the final image before display.

3. What is VRAM, and why is it important for GPU performance?

VRAM (Video Random Access Memory) is dedicated memory for GPUs, designed with high bandwidth. It is crucial because it allows the GPU to quickly access and process the large amounts of data needed for graphics rendering and other computationally intensive tasks like AI model training. Insufficient VRAM can lead to performance bottlenecks.

4. Why are GPUs used in neural networks, and how does this relate to current trends in AI?

GPUs are used in neural networks because they can run many tasks in parallel and move a lot of data efficiently. This is essential for training large and complex AI models. Recent advancements in AI, such as large language models, are heavily reliant on GPU processing power.

5. Nvidia's market dominance in the GPU sector is under scrutiny. What are the potential implications of this for consumers and technological innovation?

Nvidia's dominant position raises concerns about potential monopolistic practices, which could stifle competition and innovation. Reduced competition might lead to higher prices for consumers and slower development of alternative GPU technologies. European regulators are investigating these concerns.

6. Why is the energy consumption of GPUs a growing concern, and what are potential solutions?

The high energy consumption of GPUs is a growing concern due to environmental impact and operational costs, especially with the increasing use of GPUs in AI computing platforms. Potential solutions include developing more energy-efficient GPU architectures, optimizing software for energy efficiency, and exploring alternative cooling methods.

Practice Questions (MCQs)

1. Which of the following statements best describes the primary advantage of using Graphics Processing Units (GPUs) over Central Processing Units (CPUs) for training neural networks?

A.GPUs have a higher clock speed than CPUs, allowing for faster individual calculations.
B.GPUs are designed for general-purpose computing, making them more versatile than CPUs.
C.GPUs excel at parallel processing, enabling them to perform many tasks simultaneously.
D.GPUs have a larger instruction set than CPUs, providing more flexibility in programming.

Show Answer

Answer: C

GPUs are designed with a massively parallel architecture, allowing them to perform many calculations simultaneously. This is particularly beneficial for training neural networks, which involves a large number of matrix operations that can be efficiently parallelized. While CPUs are more versatile for general-purpose tasks, GPUs are optimized for the specific type of computations required in neural network training. The article explicitly mentions that neural networks use GPUs because they can run many tasks in parallel.

2. Consider the following statements regarding the rendering pipeline in Graphics Processing Units (GPUs): 1. Vertex processing transforms the shape of objects in the scene. 2. Rasterization converts vector graphics into pixel data. 3. Pixel shading determines the final color of each pixel. Which of the statements given above is/are correct?

A.1 and 2 only
B.2 and 3 only
C.1 and 3 only
D.1, 2 and 3

Show Answer

Answer: D

All three statements accurately describe the stages of the rendering pipeline in GPUs. Vertex processing manipulates the vertices of 3D models. Rasterization converts these vector-based models into a grid of pixels. Pixel shading then calculates the final color of each pixel, taking into account lighting, textures, and other effects. The article mentions all these stages as part of the rendering pipeline.

3. Which of the following statements is NOT correct regarding Graphics Processing Units (GPUs)?

A.GPUs have their own dedicated memory called VRAM.
B.GPUs are primarily designed for serial processing tasks.
C.GPUs are used in AI computing platforms.
D.Nvidia is a major player in the GPU market.

Show Answer

Answer: B

GPUs are designed for parallel processing, not serial processing. They excel at performing many calculations simultaneously, which is why they are well-suited for tasks like rendering graphics and training neural networks. The other statements are correct: GPUs have dedicated VRAM, are used in AI computing, and Nvidia is a dominant player in the GPU market.