GPU and CPU are two essential pieces of hardware in a computer. The GPU, a graphics card, calculates and displays graphical data on monitors. In contrast, the CPU is a computer processor that controls operations and executes all apps installed in a computer.
GPU (Graphics Processing Unit) and CPU (Central Processing Unit) are processors with different processing power. This article discusses why GPU computing is faster than CPU. In addition to this, we will also discuss other aspects of GPU and CPU in the same context. Let’s start our topic now.
Why is GPU Computing Faster Than CPU?
GPU computing is faster than a CPU because a GPU has thousands of cores, while a CPU has a comparatively way less number of cores. The cores are the processing unit in a graphics card or a CPU. Hence, hardware with more cores has more processing power (throughput) than one with fewer cores.
For example, the fastest CPU present today has a maximum of 64 cores, while the fastest GPU has 5120 CUDA Cores and 640 Tensor Cores. More cores allow a graphics card to process more data within a second. GPUs have more processing power, performance, and efficiency than computer processors.
However, it does not mean that CPUs are not good pieces of hardware and should be replaced with graphics cards. The fact is, a computer system cannot execute apps and games without a processor. Instead, a computer will never turn on. Even the GPU depends on the CPU for most cooperative computing.
There are several reasons behind the faster GPU computing compared to a CPU’s power. Let’s discuss the reasons behind the greater processing power of a graphics card (GPU) one by one in great detail to clarify our concept about it:
- More Number of Cores
A GPU has way more cores than a CPU. Each core in a graphics processing unit serves as a small processing unit. Since a graphics card has hundreds of thousands of cores, its processing power is higher than a computer processor. The only limitation of the GPUs is that they can process graphics data.
The latest GPUs have two kinds of cores for processing data during gaming, rendering, video editing, exporting, etc. The first one is known as CUDA cores. These cores are responsible for general-purpose processing, implementing parallel computing concepts for increased throughput and speed.
The second type of core is called Tensor cores in some GPUs, which are responsible for executing AI (Artificial Intelligence) and Deep Learning based algorithms and apps. These cores are essential for the latest games and apps that use AI. Such apps can learn from a user and behave intelligently.
- Great Parallel Processing
GPU computing is faster because of its ability to perform Parallel Processing. Scientists and engineers have designed and manufactured the latest GPUs for a great parallel processing ability compared to the previous GPUs. Installing the official drivers for your GPU is the method to enable it.
Parallel Processing allows a GPU to execute multiple computing processes simultaneously. Hence, it saves time and increases the throughput and performance of a graphics card. In simple words, GPU computing is faster than CPU. But it does not mean that a CPU does not use it.
In parallel processing, the main process is usually broken down into smaller problems and handed over to the cores designed to solve them, as there are multiple cores in a GPU. In the end, the solutions are combined logically for the final output. The graphics cards also support parallel computing.
- High Clock Frequency
GPU computing is faster because graphics cards operate on a higher clock frequency accompanied by thousands of cores. The fastest CPU can operate at 4.3 GHz (gigahertz) when overclocked. In contrast, the fastest GPU can operate on about 1700 MHz. The GPUs have the advantage of cores with speed.
The clock frequency of a GPU is not as high as the operating speed of a CPU, but it helps in faster computing because of the increased number of cores. Each core in a GPU needs the clock frequency to work properly. The GPU computing speed exceeds when thousands of cores use the same frequency.
Clock Frequency defines the speed limits of computer hardware. Nearly all of the hardware connected to a computer uses a clock speed of a certain limit. The GPUs and CPUs also need certain clock frequency ranges to operate on to perform the specific computing tasks they are responsible for.
- A Special and Fast Memory
The reason behind the GPU computing faster than the CPU is the special and fast memory installed. This memory, also known as VRAM (Video Random Access Memory), is specially designed and manufactured for storing the graphics data (images, videos, 3D meshes, etc.) before and after processing.
The maximum memory speed installed in a great and latest graphics card is 2000 MHz. In other words, its speed is about 24GBps (gigabytes per second). It is an extremely high speed for a computer memory that hardware can have, especially the one with a unit for processing graphical data.
Because of the high-speed memory, GPU computing becomes faster. It allows a professional user to connect multiple gaming monitors to their graphics card simultaneously. Only a high-speed graphics card can support the display monitors with higher operating frequencies, such as 144 Hz, 320 Hz, etc.
- Dedicated GPU Memory
The memory installed in a GPU is dedicated, so only a GPU can use it for computing the graphics-based processes during image or video editing and exporting, 3D animation and rendering, graphics-intensive gaming, etc. It can also store the AI and deep learning program instructions for processing.
No other app or game can use the dedicated memory installed in a GPU as it is exclusive to the apps and games that a GPU executes. That is why other apps and games utilize the main memory (RAM) installed on your computer system’s motherboard. The CPU also uses these RAMs on the motherboard.
Implementing dedicated memory in a GPU saves computing resources and isolates the processes for better performance and efficiency of a computer system. It provides more FPS when playing online or offline games. Additionally, most RAM is available for the CPU.
- Great Multitasking
GPU computing is faster than CPU, providing great multitasking by allowing a computer user to connect multiple display monitors to a single graphics card. In this way, a user can keep track of multiple tasks by displaying the apps or games or their certain user interfaces on more than one screen.
For example, a game developer can display the game development part of their gaming engine on one monitor, the gameplay UI on the second screen, the programming IDE on the third monitor, and so on. On the other hand, a CPU cannot do this without disturbing its processing as it is unique to GPUs.
- Increased Productivity
GPU computing is faster than CPU computing as it increases overall productivity. The computer processor is limited to some tasks, and processing graphics-based apps and games needs to be better for it. On the other hand, graphics cards are created to perform tasks such as rendering, gaming, etc.
People who gather data or perform mining use multiple GPUs simultaneously. It allows them to collect data or perform more mining to increase their productivity and profit. On the other hand, the CPUs are neither suitable for such tasks nor can they provide you with that much throughput.
Why is GPU Computing Faster Than CPU Processing for Deep Learning?
The GPU is faster than the CPU for Deep Learning and AI (artificial intelligence) related algorithms and software because it has hundreds of cores dedicated to executing them. These cores are known as the tensor cores in the GPUs of certain companies. GPUs can do such tasks three times faster.
The other reason behind the GPU’s faster computing than a CPU’s faster processing is that the graphics cards are highly optimized for executing the algorithms and software that implement deep learning and AI. Some GPUs alone are equal to more than 30 computer processors in terms of performance.
The GPUs are not limited to executing the algorithms that lie in the category of Deep Learning, but they also help develop such algorithms and software. The popular frameworks used for deep learning depend on the graphics card instead of the CPU. TensorFlow, PyTorch, and JAX, to name a few.
These frameworks use GPU-accelerated libraries, which contain code that only a graphics card can execute efficiently. The high operating frequency and hundreds of cores in a GPU dedicated to deep learning optimization and execution help a lot during development and execution.
How Much Faster Is GPU Computing Than CPU Processing?
GPU computing is three times faster than CPU processing. Some graphics cards have a performance of more or less 32 computer processors. There are several interesting reasons behind this. The first reason is their total number of cores, as the GPUs have thousands of cores installed.
The graphics cards perform multitasking and multiprocessing. Multitasking allows them to execute multiple processes simultaneously. On the other hand, multiprocessing allows them to divide a difficult task into several easy tasks before execution. The final result of tasks is combined in the end.
Each core in a GPU is an independent processing unit. It can execute the part of a process alone. The graphics card has a special and dedicated memory that operates very frequently. A GPU’s overall performance and efficiency increase when this speed helps the cores execute a process.
The size of a VRAM installed in a graphics card also makes GPU computing faster than CPU. The graphics card uses its dedicated memory for graphics-based and graphics-intensive tasks. It leaves the main memory (RAM) for the CPU, and the GPU also becomes independent.
What is The Advantage of GPU over CPU?
GPU computing is faster than CPU because it surpasses the computer processor in many ways. First, the CPU has to control and execute multiple tasks, while a graphics card performs only the graphics-based and graphics-intensive tasks. The GPUs have thousands of processing cores as compared to CPUs.
The cores in a graphics card operate simultaneously and solve a problem by dividing it into smaller problems. This phenomenon is also known as parallel processing. GPU is also better in computing when executing apps and games that use AI and deep learning algorithms.
The GPU has the advantage of a special dedicated memory installed on it, also called VRAM. This memory contains only the apps and games your graphics cards are responsible for execution. It allows the RAM on the motherboard to be used for other apps that a CPU is responsible for running.
GPU computing is faster than CPU because a graphics card has thousands of cores compared to computer processors. Each core is a processing unit that can execute a specific task individually. The throughput of a GPU surpasses the CPU when thousands of cores work together on a divided process.
The companies which have manufactured GPUs have implemented the concept of parallel computing and parallel processing. The first method allows a graphics card to execute multiple graphics-based software simultaneously. The second method uses the divide-and-conquer technique for fast execution.
The GPUs are faster because they have high clock frequencies and thousands of cores. Similarly, their memory is special and dedicated to graphics-based and graphics-intensive tasks such as photo or video editing and exporting, animation and rendering, high-end gaming, etc.
The graphics cards increase multitasking as each GPU can handle multiple display monitors. The operating systems also help the users to display different user interfaces or apps on several computer monitors. GPUs also increase productivity as some are more than 30 times faster than a computer CPU.
The GPUs are best known for developing and executing AI (Artificial Intelligence) and Deep Learning-based apps and algorithms. In simple words, the graphics cards are highly optimized for such tasks and have special kinds of hundreds of cores dedicated to this. They also support such frameworks.
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