CPU vs GPU: Understanding the Differences and Performance

CPU (Central Processing Unit) and GPU (Graphics Processing Unit) play crucial roles in computing systems, but initially, they are designed for different purposes. If you constantly mix up these terms or haven’t heard of them before, this blog post is dedicated to you. 

After reading this article, you should have a clear understanding of the difference between CPU and GPU, their functions, and the performance advantages each brings to the table. 

CPU meaning and its main functions

CPU (Central Processing Unit) is the primary processing unit of a computer. It’s often considered the “brain” of the computer because the CPU handles a variety of tasks required to run programs and execute operations. It’s responsible for interpreting and processing instructions from software, as well as performing necessary calculations for the device to work. 

The Central Processing Unit works in a sequential manner, executing one or a few instructions at a time. This approach allows the CPU to handle general-purpose computing tasks effectively but can become a bottleneck when there’s a demand for massive parallel processing.

GPU meaning and its advantages

The GPU meaning is a bit different compared to the CPU. It was originally designed to handle graphical rendering tasks, but now GPU is considered more than that. It specializes in repetitive and highly parallel tasks, making it specifically useful for applications requiring massive computational power. Initially, GPU was built for rendering images and videos but now, it has evolved to play a significant role in AI, machine learning, and scientific computing fields. One of the GPU advantages is also high efficiency in rendering graphics, especially in 3D rendering and gaming.

Key differences between CPUs and GPUs

While both CPUs and GPUs are essential for computing, they differ significantly in terms of architecture and functionality. We listed the key differences between CPU and GPU in this table:

	CPU (Central Processing Unit)	GPU (Graphics Processing Unit)
Core architecture	– Fewer cores (typically between 2 to 64) – Each core is powerful and capable of executing complex tasks	– Contains thousands of smaller cores that are specialized for executing many simpler tasks simultaneously
Processing power	– Designed to handle a wide range of tasks – Built to handle one or a few tasks at a time efficiently	– Specialized for tasks that require processing large amounts of data at the same time – Excels at repetitive tasks
Task specialization	– Handles jobs that require quick decision-making and a wide variety of operations, such as running operating systems and executing applications	– Best for tasks like 3D rendering, video editing, and simulations, where many similar calculations need to be done simultaneously
Latency vs throughput	– Low latency. Ideal for tasks that need immediate execution and interaction, like user commands or system management	– High throughput. Ideal for processing massive amounts of data in parallel, such as rendering high-resolution graphics or training AI models

Why are GPUs faster than CPUs?

The main reason GPUs are faster than CPUs for specific tasks lies in their ability to process many jobs at the same time. While a CPU can process a few highly complex tasks simultaneously, a GPU processes thousands of simpler tasks at once. It’s especially important for:

• Graphics rendering. When rendering an image, especially 3D models, the GPU can calculate many elements (shapes, textures, lighting) in parallel, making it much faster than a CPU, which would process each calculation sequentially.
• Machine learning. Training machine learning models involves manipulating vast amounts of data. GPUs can perform these tasks faster because, as mentioned above, they are optimized for parallel workloads. 

These are just a few examples of where GPU advantages can be used. However, it doesn’t mean the GPU is better than the CPU. While a CPU vs GPU debate may seem to favor the GPU’s ability to handle vast amounts of jobs simultaneously, the CPU is usually praised for general-purpose computing and more challenging tasks. 

CPU vs GPU performance: a comparison

When thinking of GPU vs CPU performance comparison, the best way is to take a few popular use cases and analyze them: 

1. General computing tasks. When performing tasks like browsing the web or running office software, the CPU shines. It can execute instructions quickly and is capable of handling multiple tasks in sequence. The GPU isn’t needed here, as these tasks don’t require the massive parallel power that GPUs provide.
2. Gaming. Graphics are the primary focus in the gaming industry. While the CPU controls game logic, the GPU is responsible for rendering the complex graphics. High-end GPUs deliver a smoother, faster gaming experience, handling the graphical load that would overwhelm a CPU.
3. AI and Machine Learning. A CPU will take a long time to process large datasets for applications like deep learning, whereas a GPU handles these jobs more efficiently. Modern AI frameworks are designed to utilize GPUs, making them the preferred option for training models.

CPUs or GPUs: which one to choose?

Even if the performance and advantages can be compared between the two, it’s not a matter of choosing one over the other. GPUs and CPUs are designed for different purposes, and they usually complement each other. For example, every server or cloud instance requires a CPU as its primary processor. In addition, some servers are equipped with GPUs as additional co-processors. As previously mentioned, certain workloads, such as AI and machine learning tasks or graphics rendering benefit significantly from the capabilities of GPUs. 

Conclusion

In summary, both CPUs and GPUs are vital components of modern computing systems, but their roles are different. CPUs excel at handling general-purpose jobs and managing complex, sequential instructions, while the GPU is optimized for parallel processing, making it faster and perfect for handling a vast amount of repetitive tasks.

Every server or cloud instance relies on a CPU as its primary processor. However, if you’re choosing a server, you may want to check whether it includes GPUs as additional co-processors. For tasks such as machine learning, graphics rendering, or similar computationally intensive operations, powerful GPUs can significantly simplify your work.

Frequently asked questions

What is the meaning of CPU?

CPU stands for Central Processing Unit. It’s the primary processor in a computer or server responsible for executing instructions and managing tasks.

What does GPU stand for?

GPU, or Graphics Processing Unit, is a processor created to handle vast amounts of repetitive tasks, including rendering graphics, performing complex calculations, or accelerating data processing workloads.

Can a GPU replace a CPU in a computer?

No, GPUs cannot replace CPUs in computers. The CPU handles general-purpose tasks and manages the system, while the GPU specializes in parallel processing and specific workloads. They both work together to optimize the device’s performance.