With the rapid development of science and technology today, society has entered the intelligent stage of the information age. Applications such as facial recognition, intelligent customer service, and personalized recommendations have penetrated into every aspect of daily life. Behind these visible applications, invisible computing power silently supports them. In the process of meeting these high computing power requirements, CPU and GPU, as the core components of the computer, play an important load-bearing role. So, what exactly are CPUs and GPUs? What's the difference between them? 

As data becomes one of the five major production factors, the role of computing power becomes increasingly important. Computing power refers to the ability of servers in data centers to process data and output results. 

The concept of computing power originated from the early days of the invention of computers. The earliest computers used mechanical devices to complete computing tasks, and computing power refers to the ability of these mechanical devices. Subsequently, the emergence and development of semiconductor technology and the advent of integrated circuits ushered in the chip era. With the support of chips, computers became more and more powerful and smaller in size. Eventually, the personal computer was born, becoming the most important computing tool for mankind. 

The rapid development of artificial intelligence and big data technology has also driven the rapid growth of computing power demand. Whether training complex neural network models or performing large-scale data analysis and processing, computing power plays a crucial role. Today, chips have become the main carrier of computing power. When we talk about computing power, we are actually talking about the computing power of chips like CPUs and GPUs. In the field of computer science, CPU and GPU are the core components of computers. Although their names are only "one word different", their structures and functions are quite different. 

CPU vs GPU: Concepts

CPU, Central Processing Unit, is the "heart" of many electronic products such as computers and mobile phones. In many scenes of our daily life, such as watching videos, playing games, chatting and interacting, etc., the CPU plays a key role in unified command and scheduling. It is mainly responsible for executing program instructions, performing arithmetic and logical operations, and controlling and coordinating various computer components. 

In order to meet the powerful general performance of processing various different data, the internal structure design of the CPU is very complex. The CPU is composed of multiple cores, each of which contains components such as arithmetic logic units, control units, and caches, and can perform tasks independently. So far, all CPUs follow the basic workflow of the Von Neumann architecture: fetch instructions, decode instructions, execute instructions, write data back, then fetch the next instruction, decode, execute, write back, repeat. Proceed until the program ends. Through this workflow, the CPU can efficiently execute programs and control the operation of the entire system. 

CPU Workflow

However, with the popularization of computers, people's requirements for graphics display continue to increase. For example, when performing complex 3D modeling, a large amount of graphics data needs to be processed to present realistic effects; when playing MMOG, the system is required to be able to handle high resolution graphics and complex game scenarios. Although various methods are used inside the CPU to accelerate instruction execution, as the number of tasks increases, the performance of the CPU becomes somewhat inadequate. 

Faced with this problem, can we consider adding more CPUs to solve it? This is certainly an idea, but it involves a fairly high cost, and the number of stacks cannot guarantee that the problem of insufficient computing power will be completely solved. At this time, smart developers came up with a more ingenious solution: Since the computing power of the CPU is limited, why not stack the computing units on the same chip and let them handle a large number of computing tasks? Thus, the GPU was born. 

GPU, Graphics Processing Unit. As the name suggests, it is a processor specially used to process graphics and image calculations. GPUs were originally designed for graphics rendering and display to accelerate the processing of images in computers, such as in video games, movie rendering, graphic design, and more. It has only a small number of control units and cache units, and most of the space is used to stack computing units, which are mainly responsible for completing many computing-intensive tasks. 

CPU vs GPU: What’s the Difference? 

By introducing GPU, computer systems can take full advantage of its powerful parallel computing capabilities to accelerate graphics and image-related computing tasks. The CPU is responsible for logical tasks, while the GPU handles a large number of simple and repetitive computing tasks. This mode of assigning different types of tasks to different types of processors greatly improves system performance. So, what is the difference between CPU and GPU? 

Architecture

There are significant differences between CPUs and GPUs because they are each designed for different goals and needs: 

• CPU needs to have strong versatility to process various types of data, and it also needs to make logical judgments, including a large number of branch jumps and interrupt processing, which results in an extremely complex internal structure. 

• GPU is mainly oriented to large-scale data with highly uniform types and no dependencies on each other, and is executed in a pure computing environment, so there is no need to process complex logical operations. 

This results in very different architectures for CPU and GPU: 

CPU Architecture

GPU Architecture

The CPU has a larger cache and a complex logic control unit. In comparison, the computing power is only a small part of the CPU. The GPU has a large number of computing units and a long pipeline, but only very simple logic control and smaller cache. 

Aim of Design

The design goals of CPU and GPU are also completely different. First of all, the CPU is designed based on low latency:

• Powerful ALUs(Arithmetic Logic Unit): The CPU has a smaller number of computing units with stronger single computing performance, which can reduce operation latency and respond faster. 

• Large Cache: Store part of the data in the cache, converting high-latency memory access into low-latency cache access. 

• Complex Control Unit: Branch Prediction mechanism can reduce branch latency; Data Forwarding mechanism can reduce data latency. 

The GPU is designed based on high throughput:

• Energy Efficient ALUs: GPU has a large number of computing units. Although the performance of a single unit is not as good as that of the CPU, it can support a very large number of threads to achieve very large throughput. 

• Small Cache: Unlike the CPU, the purpose of the GPU cache is not to store data that needs to be accessed later, but to provide services for threads. If there are many threads that need to access the same data, the cache will merge these accesses and then to access memory. 

• Simple Control Unit: The GPU's control unit does not have branch prediction and data forwarding mechanisms. 

In general, a CPU has a relatively small number (generally no more than double digits) but more powerful cores that can process individual tasks faster, making it particularly suitable for processing types of work such as serial tasks and logic control. In contrast, GPUs have hundreds or thousands of cores. Although each core has low computing power, it can decompose complex tasks into a large number of subtasks and process them in parallel. 

Serial Processing

Parallel Processing

Applicable Scene

The design purposes of CPU and GPU are different, so their applicable scenarios are also completely different. CPUs are better at handling one task at a time, while GPUs can handle multiple tasks simultaneously. 

Use a metaphor to explain. The CPU is like a high-speed airplane, and the GPU is like a fleet of container ships. Their mission is to move large quantities of goods from location A to location B quickly. Although the CPU (plane) is very fast, it can only carry a small amount of cargo at a time, requiring multiple round trips to complete the task. In contrast, although the single execution speed of GPU (container ship) is relatively slow, by working together, each ship can carry part of the cargo at the same time, and ultimately complete the transportation task efficiently with collective strength. 

Comparison of CPU and GPU processing tasks

So while CPUs are faster at executing a single task, the GPU advantage becomes more significant when large amounts of repetitive workloads are required. When encountering computing scenarios where the previous and subsequent calculation steps are closely related, and the first step must be calculated first, and then the second step. Or tasks that require a large amount of logical judgment and complex calculations, such as running an operating system, handling daily office tasks, and performing single-threaded programming. At this time, the efficiency of using the GPU is not as high as that of the CPU. 

To sum up, the CPU is a master of various computing capabilities. Its advantages include strong scheduling, management, and coordination capabilities, and it can perform complex logical operations. However, due to the small number of computing units and cores, it is only suitable for a relatively small number of operations. The GPU cannot work alone. It is equivalent to a large group of pipeline workers who are scheduled by the CPU and is suitable for doing a large number of simple operations. CPU and GPU have their own functional strengths and complement each other. They can be used in conjunction with each other to achieve the best computing performance. 

CPU vs GPU: Evolution and Development

The continuous updating of technology promotes the rapid development of the computer field, and in this continuous evolution process, CPUs and GPUs also continue to develop. They have achieved significant breakthroughs in their respective fields over the past few decades, providing higher performance and efficiency for computer applications. This not only promotes the development of information technology in the digital era, but also brings new possibilities to various fields. 

CPU: Steady Progress 

The CPU has powerful instruction processing and computing capabilities (powerful computing power here means that the CPU can handle more complex computing tasks) and is usually used to handle the core work of the computer, including interpreting computer instructions and processing data in computer software. For example, when we use a personal computer, users and various software will continuously generate a stream of instructions. An important job for the CPU to complete is to process these instructions and ensure that they are executed in the prescribed order. In addition, the CPU is responsible for processing the diverse and logically complex data generated by computer software. 

More than fifty years have passed since the birth of the first CPU 4004. Although CPUs have made great progress in physical form, design and manufacturing, and execution of specific tasks compared with the early days, their basic operating principles have not been understood. Change. In other words, the functions and usage scenarios of the CPU have not changed significantly. It is nothing more than continuous optimization in terms of energy consumption, performance and reliability. 

GPU Acceleration Applications are Blooming Everywhere 

From GPU to GPGPU

The concept of GPU was first proposed in 1999 when NVIDIA released GeForce 256. In the early days, GPU was mainly used for 3D graphics rendering. But unlike the CPU, as the functions and computing power of the GPU become more and more powerful, developers began to explore what else the GPU can do besides graphics processing? 

In 2003, when NVIDIA launched the new product GeForce 8800 GTX, it first proposed the concept of GPGPU (General-purpose computing on Graphics Processing Unit), which means to use the computing power of GPU to perform tasks in non-graphics processing fields. More general and broader computation. 

In 2007, NVIDIA further proposed a new technology called CUDA (Compute Unified Device Architecture), which can be used to perform computing tasks other than image processing through NVIDIA's GPU. It unveiled the GPU computing revolution. prelude. Subsequently, Apple also launched a similar technology: OpenCL (Open Computing Language), and initially improved it in cooperation with AMD, IBM, Intel and NVIDIA technical teams. OpenCL is an open, cross-platform parallel computing framework that allows developers to develop on different hardware platforms (CPU, GPU, FPAG, etc.). 

Since then, GPUs no longer have the sole purpose of 3D acceleration of graphics, but can be widely used in general computing acceleration in various fields, especially in scientific computing, big data analysis, artificial intelligence and other fields. 

LLMs and Vector Database 

The computing power required in today’s internet-connected world is far greater than anyone imagined, and will only increase with the rise of artificial intelligence. Represented by the popular generative AI ChatGPT, GPT-3 with a size of 175B used 1024 A100 GPUs to train for about 34 days. As the number of parameters increases, the computing resources required by AI in the future will be astronomical.  

Large models also triggered a revolution in the field of databases. The previously tepid vector database caught on to this trend and became the new favorite in the AI ​​era. At the 2023 NVIDIA GTC AI Developer Conference, NVIDIA CEO Jensen Huang mentioned vector databases for the first time and emphasized that vector databases are crucial for organizations building proprietary large models. 

For AI, vectors are a universal data form for understanding the world. Whether it is pictures, videos, or audios, they can be converted into vectors for AI to recognize. As a tool specifically for retrieving and storing vectors, vector databases can be used for large numbers of people. The application of the model solves problems such as "Long-term Memory Loss", "Hallucination", and "Untimely Update of Knowledge". 

However, the performance of the vector database itself is also a problem. In the final analysis, the combination with large models is only part of its application. It is essentially a tool for data search and analysis. If the performance cannot meet user needs, it will also be eliminated. Ever since, developers thought of GPU again. 

In a vector database, the core function is vector similarity search, which is to find the vector most similar to a certain vector in a massive amount of data. The calculation amount is very large, and this is exactly what the GPU is good at. Using the GPU The parallel computing power can accelerate vector similarity search, significantly improve query speed and reduce latency. 

Cloud-native vector database PieCloudVector supports GPU acceleration 

One of the three major data computing engines of OpenPie's large model data computing system πDataCS: the vector database PieCloudVector is an analytical database dimensionality increase in the era of large models. The goal is to facilitate multi-modal large model AI applications and further realize the storage and processing of massive vector data. Efficient query. PieCloudVector supports and cooperates with the Embeddings of large models, helping the rapid adaptation and secondary development of basic models in scene AI. It is an essential application for large models. 

PieCloudVector takes full advantage of modern hardware capabilities and supports GPU acceleration capabilities. This feature allows PieCloudVector to use the parallel computing power of the GPU to accelerate vector calculations and data processing tasks, significantly shortening task execution time, improving computing efficiency, and helping users perform data calculations more efficiently. Especially when processing large data sets or performing complex numerical operations, GPU acceleration can significantly improve computing performance.