The CPU and GPU are integral components of a computer system and are similar to each other in some ways. Both of these components include billions of transistors and can process thousands of operations per second.
With that said, both of these units have entirely different roles in a computer system. In this article, we will discuss the key differences between CPUs and GPUs and their roles in a computing environment.
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What Is a CPU?
The Central Processing Unit or CPU is a microprocessor that performs most of the basic execution of programs (collection of instructions) given by the computer software and other peripheral devices.
Most of these instructions relate to operations, such as arithmetic, logic, algorithms, control, and managing input and output commands.
Since the CPU is responsible for performing all the basic functions of a computer, it is often dubbed as the brain of the machine. CPUs can execute a variety of different computing operations and calculations.
Programmers and software developers rely on the CPU to write, process, and execute the functionality programmed in software.
Unless these programs require extremely high processing power, the CPU is sufficient to execute the majority of commands and instructions. Generally, the standard speed of a CPU is between 1 to 4 GHz.
In most cases, CPUs have more than one processing core, which functions as separate processing units. However, they can be broken down further to even smaller processing units with the help of multi-threading.
What Is a GPU?
The graphics processing unit or GPU is a unique microprocessor that specializes in executing highly complex processing tasks.
As the name suggests, the device is mostly used for processing extremely high-resolution graphics, but programmers also use it to process other tasks that require computation from massive amounts of data.
From rendering high-definition videos to performing complex mathematical operations over and over again, GPUs can perform a wide variety of tasks.
GPUs normally don’t offer the same clock speed in cores in that CPUs offer. Therefore, each individual core in a GPU is slower than a core in a CPU.
However, GPUs are able to perform tasks that need high computation by increasing the number of cores in the processing unit.
A single GPU can contain thousands of cores that break down multi-dimensional mathematical tasks needed for graphic rendering and execute them efficiently.
For instance, a single GPU such as the NVIDIA GTX 1080 or RTX model has as much as 2560 shader cores. With the help of these cores, the processing unit can execute 2560 operations simultaneously during one clock cycle.
Besides rendering graphics, a GPU is essential for executing highly complex machine learning algorithms known as Neural Nets.
These algorithms are usually slow, but GPUs have enabled programmers to train self-learning AI-models by boosting the computing ability of machines.
GPUs can also be useful in processing high computation tasks, such as cracking passwords and mining cryptocurrencies.
Key Differences between CPUs and GPUs
The main distinction between GPU and CPU architecture is that a CPU is principally designed to handle various tasks fast (as indicated by CPU clock speed). However, it is limited in the number of processes that may be done concurrently.
A GPU on the other hand is made to render high-resolution graphics and video in real-time and process large amount of data in parallel.
GPUs are often employed for non-graphical applications such as machine learning and scientific calculation since they can execute parallel operations on several data sets. They can even be used for mining cryptocurrencies which require high performance.
GPUs provide tremendous parallelism by allowing thousands of processor cores to run at the same time. Each core is dedicated to doing efficient operations.
While GPUs can process much more information quicker than CPUs due to remarkable parallelism, GPUs are not as adaptable as CPUs.
CPUs have extensive and comprehensive instruction sets that manage all of a computer’s input and output, which a GPU cannot do.
A powerful server can be equipped with a total of 32 to 64 high-speed CPU cores (two sockets per server).
On the other hand, a GPU card can have 700 to 4000 cores on each GPU. This shows the massive parallel operations that can be performed with a GPU.
However, individual CPU cores are faster and more intelligent than individual GPU cores (as determined by CPU clock speed) as measured by available sets of instructions.
The CPU is made up of only a few cores with a lot of cache memory; thus, it can only manage a few software threads at a time.
On the other hand, a GPU is made up of hundreds of cores that can manage thousands of threads at once.
|CPU is the abbreviation of “Central Processing Unit”||GPU is the abbreviation of “Graphics Processing Unit”|
|The CPU has a broader instruction set and is more versatile and capable of performing numerous tasks.||The GPU has a limited set of instructions and is mainly capable of doing graphics-related activities.|
|CPU is referred to as a general-purpose processor.||GPU is referred to as a special-purpose processor.|
|It can generally handle any task, including graphics, but not in a very efficient manner.||The primary purpose of the GPU is to process pictures and 3D graphics significantly faster than the CPU.|
|The CPU may contain a few powerful cores to share the workload.||The GPU is made up of hundreds of weaker cores to execute the basic operating process.|
|It has a high processing speed and runs at a high clock speed of around 3 to 5 GHz, although it has fewer processing cores.||It features hundreds to thousands of processing cores and runs at a lower lock speeds of about 800-1000 MHz.|
|CPU requires additional memory RAM compared to GPU.||It requires relatively less RAM compared to the CPU. This is the reason why integrated GPUs shared the RAM with the CPU.|
|The CPU is primarily concerned with achieving low latency (the time interval between instructions and data transfer).||The GPU concentrates on achieving the highest possible throughput (parallelism, sum of instruction implementation in a time interval).|
|The CPU is designed to run consecutive instructions.||The GPU is designed to run parallel instructions.|
|The CPU focuses on the calculation of any data that is received.||On the other hand, the GPU receives information from the CPU and generates visual images.|
|To carry out instructions, it interacts with various computer components, including RAM, ROM, and I/O ports.||To show the pixel on the monitor, the GPU solely interacts with the memory and display component.|
Vendors of CPU and GPU Processors
Previously, Intel and AMD existed in two different lanes. However, both of these vendors have now started to penetrate each other’s markets, especially in middle-grade computer systems.
Intel specializes in making a processor that has higher clock speeds, whereas AMD focuses more on increasing the number of cores and providing enhanced multi-threading.
Nevertheless, Intel has the edge over AMD in making hardware for basic computing. With that said, Intel is not able to keep up with AMD when it comes to GPUs.
In contrast, AMD has to contest with NVIDIA for supremacy in the GPU market. NVIDIA has always been the market leader in producing processors that render 3D graphics.
However, in recent times, AMD has been able to capture the attention of high-end graphics users and produce GPU processors that can match the performance of NVIDIA GPUs.
CPUs and GPUs may seem alike in many ways, but both have been optimized for completely different roles. With that said, neither can perform productively without the other and an efficient computer needs both of these units to run properly.
The CPU and GPU are two separate processing units that are equally important in a computer system.
The code generated by each of the devices is incompatible with the code created from the other, and neither device can be replaced by the other; instead, each component compliments the existing infrastructure.
The CPU is by far the best for quickly completing a wide range of tasks and instructions and has the best performance per core.
The GPU is ideal for basic instructions that must be repeated frequently, such as image production, 3D rendering, and animation. However, due to hundreds of cores, the GPU can process massive data concurrently.
Although they have certain similarities, you cannot substitute one with the other because they are both distinct and significant in their own right.