How Important Are GPUs and What Should You Know About Them?

In today's data-driven intelligent era, fields such as high-performance computing, machine learning, and graphics processing are rapidly evolving. They rely not only on advanced algorithms and vast amounts of data but also on powerful computing capabilities. When discussing computing power, GPUs are indispensable. What exactly is a GPU? Why is the development and application of machine learning inseparable from GPUs? This article discusses the importance of GPUs.

What Is a GPU?

A GPU, or Graphics Processing Unit, also known as a graphics core, visual processor, or graphics chip, is a chip specifically designed to handle graphics-related computing tasks. Initially invented to meet the demands of computer games and graphic design, GPUs can quickly render complex 3D scenes and animations, enhancing visual effects and user experience.

Unlike the structure of a CPU, a GPU has a large number of parallel processing units, allowing it to execute multiple simple operations simultaneously. In contrast, a CPU is better suited for executing a few complex operations. Therefore, a GPU is more appropriate for tasks that can be broken down into many small, similar, and repetitive calculations.

Over time, GPUs have become more flexible and programmable, making them useful for more applications and workloads, such as machine learning, deep learning, data mining, and scientific computing. In these fields, GPUs can accelerate model training and process large amounts of data, significantly improving computational efficiency and speed.

How Does GPU Work?

The working principle of a GPU is similar to that of a CPU, both complete computing tasks by executing instructions. The difference is that a CPU completes tasks through serial execution of instructions, while a GPU completes tasks through parallel execution. The parallel computing method of a GPU can perform multiple tasks simultaneously, greatly improving computational efficiency and speed.

What Are the Types of GPUs?

GPUs in computers can be divided into two types, each designed to meet specific needs and applications.: integrated GPUs and dedicated GPUs.

Integrated GPUs

Integrated GPUs are smaller in size and are generally built into the computer's motherboard, or even integrated into the CPU. They do not have separate memory for graphics/video and share system memory with the CPU. Because they are built into the processor, integrated GPUs typically consume less power and generate less heat, extending battery life, and are commonly found in laptops.

Dedicated GPUs

Dedicated GPUs are larger and come as separate cards, usually connected in a PCI high-speed slot. Besides the chip, dedicated GPUs include many components necessary for the GPU to operate and connect to the rest of the system. They have their dedicated memory and power source, resulting in higher performance compared to integrated GPUs. However, since they are separate from the processor chip, they consume more power and generate more heat.

What Are the Use Scenarios of GPUs?

In recent years, due to their parallel computing characteristics, GPUs have been applied in fields requiring extensive computation, such as machine learning, deep learning, data mining, and scientific computing. In these areas, GPUs can accelerate model training and handle large data sets, significantly enhancing computational efficiency and speed. Consequently, GPUs have become an essential part of modern computers, widely used across various industries.

• Healthcare: In healthcare, AI technology combined with GPU power can achieve rapid medical image analysis. Through deep learning models, GPUs can process large amounts of CT, MRI, and other medical images in parallel, helping doctors quickly identify diseased areas, improving diagnostic accuracy and efficiency.

• Autonomous Vehicles: GPUs accelerate key algorithms such as real-time image recognition, object detection, and scene understanding, enabling autonomous driving systems to quickly analyse the vast data collected by cameras, radars, and LiDAR to make safe driving decisions.

• Financial Services: In finance, GPUs are used for fraud detection, risk management, high-frequency trading, and credit evaluation, enhancing predictive accuracy and decision-making efficiency by accelerating data analysis and machine learning model training.

• Manufacturing: In smart manufacturing, systems supported by GPUs are used for quality control, predictive maintenance, and production optimisation. By analysing production line data, potential faults can be identified early, processes optimised, costs reduced, and efficiency improved.

• Natural Language Processing (NLP): GPUs speed up the training of large language models like BERT and GPT series, advancing applications such as machine translation, sentiment analysis, and chatbots, enhancing the naturalness and intelligence of human-computer interaction.

GPUs VS. CPUs

GPUs and CPUs are different types of processors with significant differences in structure, function, and application.

• Processor Structure: CPUs typically have a small number of high-performance cores suited for sequential computing and complex control tasks. GPUs, on the other hand, have a large number of lower-performance cores, making them suitable for parallel computing and large-scale data processing.

• Applicable Fields: CPUs are suited for sequential computing, control tasks, and general-purpose computing, applicable across various scenarios. GPUs, however, are mainly used for graphics and image data processing, scientific computing, deep learning, and artificial intelligence, which require large-scale parallel computing.

• Power Consumption and Heat Dissipation: Due to fewer processing cores, CPUs generally have lower power consumption and heat dissipation. In contrast, GPUs, with their numerous cores and high-performance features, typically require higher power consumption and sophisticated heat dissipation designs.

GPUs VS. Graphics Cards

Graphics cards, also known as display cards or video cards, are vital components connecting the display and the motherboard of a computer. The main function of a graphics card is to process instructions and data provided by the CPU into text or images that can be displayed on the monitor, guiding the user on whether to continue running or terminate a programme.

GPUs and graphics cards are closely related. Simply put, the GPU is the core component of the graphics card, essentially its "heart". The performance of a graphics card largely depends on the GPU's performance. A good graphics card must have a high-performance GPU as its support. Additionally, graphics cards include other auxiliary components, such as video memory and cooling systems, which work together with the GPU to render and output graphics.

The Final Thoughts

The future development of GPUs will primarily focus on more powerful computational capabilities, faster memory and larger caches, deeper integration of CPUs and GPUs, advancements in machine learning, virtualisation and cloud computing, scalability, and eco-friendly solutions. These trends will further drive the evolution and application of GPUs, providing more efficient and environmentally friendly computing solutions for fields such as computer graphics, scientific computing, numerical simulation, and machine learning.