CPU vs. GPU: Top 11 Key Comparisons

Photo of author
Written By Bharat Kumar

Lorem ipsum dolor sit amet consectetur pulvinar ligula augue . 

A CPU is the central processing unit of a computer that executes logic and arithmetic operations with minimal latency.

A GPU is an embedded or external graphics processing unit that can perform floating-point arithmetic to render the polygonal coordinates of high-resolution images. 

Let's find out how a central processing Unit (CPU), differs from a graphics processor unit (GPU). This article will discuss the differences between a GPU and a CPU.

What is a CPU?

The central component of a computer device is its CPU. It can only function with additional components, such as silicon chips. The silicon chip is placed in a specific socket on the primary circuit board of the device (the motherboard, mainboard).

It is different from memory, which stores temporary information. It is also distinct from the graphics card or graphics chips, which create the 3D images and video on the screen. They are made using graphics processing unit technology (GPU).

Nearly all consumer devices (smartwatches and computers, as well as thermostats) have a CPU.

What is the Working Principle of a CPU?

A control unit is located in the central processing unit. It coordinates the functions of the computer. It collects the commands from the main memory and identifies them. The system interprets these commands so that other components of the system can be activated at the correct time to fulfill their respective functions.

All input information is transferred to the main memory and into the Arithmetic-Logic Units (ALUs). This allows for processing. These include four basic operations: addition, subtraction, and multiplication. These operations include data comparison, problem-solving, and a viable alternative that is based on predetermined decision criteria.

These are the main features of a CPU:

Multicore processors can be embedded in the same chip: Each core functions in the same way. Each core has its cache but can interact with other cores if necessary. In certain areas of mathematical operations, the GPU core of AMD's Accelerated Processing Units (APU) technology performs better than the traditional core. As we'll see, the functioning of the GPU and CPU is different.

The ability to communicate with other components, and control resource allocation: The CPUs are part of the larger platform. To interact with the motherboard's chipsets, it uses data buses (data-carrying circuits). This is known as bandwidth.

Small amounts of memory: The CPU core uses a small amount of cache memory to store its memory. Level 1 cache (L1) is faster but less extensive than Level 2. A third level is found on some CPUs and is shared by several CPU cores. This memory is different from random access memory (RAM).

Speed in hertz: The most common measurement of processor performance is in MHz (megahertz), or GHz (gigahertz). This number is the frequency of the CPU's internal time clock in cycles per second. The clock of a 2.5GHz processor ticks 2.5 billion times per second.

Efficiency in IPC is as important as speed: IPC is an abbreviation that can be used to measure CPU performance. It stands for instructions per cycle. It is important to understand that IPC does not remain static. It varies depending on which running application is being used.

A CPU, or central processing unit, is a general-purpose processor capable of performing many tasks.

The GPU (or graphics processing unit) is a specialized processor that has greater mathematical computing capabilities. It is ideal for machine learning and computer graphics.

Let's take a closer look at this concept.

What is a GPU?

A graphics processing unit (GPU), is an electrical circuit that displays graphics on an electronic device. these are two types of GPUs as follows:

Integrated graphics processor units: These integrated GPUs are integrated into the motherboard of a computer, making them compact, light, and efficient. These units are ideal for basic video editing and gameplay, as well as photo editing.

Discrete graphic processing units: A discrete GPU refers to a component that is inserted into a computer. These are more powerful and energy-dependent than the integrated GPUs, but they are great for video and image editing, graphic design, and gaming. They are used extensively in enterprise operations and scientific research.

GeForce 256 by NVIDIA was NVIDIA's first widely-used GPU. This CPU was specifically designed for real-time graphic applications that require extensive arithmetic operations, high memory bandwidth, and high memory bandwidth. The evolution of real-time graphics made GPUs programmable.

Modern supercomputers are powered by the most powerful graphics processing units (GPUs). It provides intelligence for autonomous vehicles, robotics, and smart cameras.

What is the Working Principle of a GPU?

GPUs were designed for mathematical and geometric calculations. The graphics used in movies and video games consist of polygonal coordinates that are converted into bitmaps, and then into signals to be displayed on a screen.

A GPU with a large computing power is required to perform this transformation. These are the key characteristics of a GPU

A Large number of arithmetic units (ALUs): A GPU is capable of managing large amounts of data across multiple streams. Their large number of ALUs is what enables them to do this. They can therefore perform a lot of difficult mathematical tasks. A GPU can also have hundreds of cores and be able to manage multiple processing threads at once.

Connectivity via ports: Multiple ports can be used to connect a separate GPU to the display. The port must be accessible on both the GPU device and the monitor. VGA, HDMI, and DVI are some of the most popular connection ports.

Ability to perform floating-point arithmetic: GPUs are capable of performing vector and floating-point calculations. This means that they can approximate real numbers by performing mathematical operations. Modern graphics cards with integrated GPUs can handle double-precision floating point values.

Suitability to Parallel Computing: GPUs were designed for tasks that can be significantly parallelized.

Visual rendering was done by the Central Processing Unit (CPU) before the introduction of GPUs in the 1990s. A GPU can be used in conjunction with a CPU to improve computer speed. It can perform computationally intensive tasks such as rendering that the CPU was not previously capable of.

The GPU can perform multiple computations simultaneously, which increases program processing speed. This shift was also responsible for creating software that is more complex and requires more resources.

11 Key Comparisons Between CPU and GPU

Both the CPU and GPU processors are skilled at different tasks in a computer system. While CPUs can be used to perform a single task, GPUs are better suited for processing large data sets at once. These are just a few of the many ways that CPUs and GPUs differ.

1. Intentional Function in Computing

The central processing unit is called the CPU. The CPU is the central processor of all modern computing systems. It executes all commands and processes necessary for the computer and the operating system to work effectively.

The CPU consists of an arithmetic unit (ALU), a control unit (CU), and memory. The control unit manages data flow, while the ALU performs logic and arithmetic operations with the memory-provided information. The CPU controls the speed at which programs run.

A graphics processing unit (also known as a video or graphics card) is also called a GPU. A GPU is a highly optimized processor that processes graphical data. This allows you to convert data, such as images, from one format to another. It can also render images using 2D and 3D images. This is commonly used in 3D printing workflow.

2. Operational Focus

Low latency is the main goal of a CPU. Low-latency computers are optimized to handle large volumes of instructions and data transfers with minimum delays.

Latency is the delay in time between a request being made by a device and its fulfillment by the CPU. This delay can be measured in clock cycles.

Cache misses or misalignments can cause a CPU's latency to increase.

High latency is often associated with slower webpage loads and more application failures.

The GPU, on the other hand, focuses on high throughput. Throughput is the maximum number of similar instructions per clock cycle that can be executed if the operands for each instruction are independent of the previous.

Low throughput could be due to memory bandwidth limitations, algorithm branch divergence, and memory access latency.

3. Operational Functions

The four major functions of a CPU are fetch, decode and execute.

Fetch refers to an operation where the CPU receives instructions in program memory.

Decode refers to the conversion of instructions by the instruction encoder to determine which parts of the CPU are required to continue.

Follow the instructions

Writeback refers to a caching technique that copies data into higher-level caches and memory.
The primary function of a GPU is to improve video and graphics performance.

It includes features like texture mapping, hardware overlay, and decoding Moving Picture Experts Group files (MPEG).

These GPUs are optimized to produce more graphics and reduce the amount of work required. The GPU can also do calculations for floating-point and 3D operations.

4. Cores

Modern CPUs are equipped with between two to 18 cores. Each core can do a different job at the same time. Simultaneous multithreading allows a core to be divided into virtual cores, known as threads. A CPU can have four cores, but it can be divided to create eight threads.

As a processor can run many programs at once and performs a wider range of tasks, its efficiency increases. Therefore, a CPU core is optimized for serial computing and running a database management system (DBMS).

The GPU cores are slower than the CPUs for serial computing, but they are much faster for parallel computing because they have thousands upon thousands of weaker cores that can be used for parallel workloads. GPU cores are processors that specialize in handling graphics manipulations.

5. Processing of Parallel and Serial Instruction

Parallel processing is where multiple tasks are simultaneously performed. Serial processing only handles one task at a given time.

Serial processing is where every task is completed at the same time. Instructions are executed using the first in, last out (FIFO), technique. Because they can execute multiple tasks at once, CPUs are better suited for serial instruction processing. The program counter controls the order in which the instructions are executed.

To reduce the time it takes to execute a program, tasks can be split between multiple processors. Parallel instruction processing is easier with GPUs. GPUs can perform multiple calculations simultaneously across different data streams due to their architecture. This increases the computer's speed. Parallel processing is intended to increase a computer's computational speed as well as its throughput.

6. Interaction With Other Components and Versatility

The CPU has more flexibility than the GPU. The CPU can perform many tasks and has a wider range of instructions. The CPU interacts with many computer components when executing instructions. These include RAM, ROM, and the basic output/output system.

The GPU, on the other hand, can only accept a small number of instructions and only execute graphics-related tasks. When executing instructions, the GPU only interacts with a limited number of computer components. The GPU interacts with a few components when it executes instructions.

7. Execution

Despite their slow speeds, CPUs are capable of handling most consumer-grade tasks. Graphic manipulation tasks can be handled by CPUs with much lower efficiency. Due to the complexity of 3D rendering tasks, CPUs still outperform GPUs. Due to their higher memory capacity, CPUs can be expanded quickly up to 64GB without affecting performance.

GPUs are primarily used to enhance images and render graphics much faster than CPUs. Combining GPUs and high-end components of a computer can render graphics up to 100 times faster than CPUs. GPUs can perform simple tasks and complex tasks despite their speed. The GPU's memory limit of 12GB is limited and cannot be expanded without causing performance drops and bottlenecks.

8. Hardware Limitations

Hardware limitations pose a major obstacle for CPU manufacturers. Moore's Law, which was based on historical trends and projected projections, was established in 1965.

It set the stage for the modern digital revolution. Moore's Law states that silicon chips have a doubled number of transistors, and the price of computers has halved. His observations were probably correct 57 years later.

There is a limit on the number of transistors you can add to a silicon piece. Manufacturers have attempted to overcome hardware limitations with distributed computing and quantum computers.

GPU manufacturers have not yet been subject to hardware limitations. Huang's law states that GPUs are developing at a faster rate than CPUs. It also says that GPUs perform twice as well every two years.

9. API Restrictions

An API works to enable programs to communicate with one another. There are no API restrictions on CPU manufacturers.

Data APIs integrate seamlessly with the CPU and do not limit functionality. The GPUs are limited in graphics APIs, making them difficult to debug and further restricting their application.

OpenCL and Compute Unified Device Architecture are two of the most used graphics rendering APIs for GPUs. OpenCL, an open-source API, works well with AMD GPU hardware.

However, it is slow on Nvidia hardware. CUDA, a proprietary API that Nvidia owns and which is optimized for Nvidia GPUs, is an open-source API. It is difficult to alter the CUDA user's ecosystems because of the way they use it.

10. Context Switching Latency

Context switch latency is the time taken by a processor to complete a process. A dependency chain is initiated when a request with instructions has been made.

Each process is dependent on the other until the request is fulfilled. Because registers store information, a CPU switches slower between threads. GPU tasks, on the other hand, are executed simultaneously.

This eliminates inter-warp context switching. Registers must be saved to memory and restored.

11. The Caching Approach

To save time and energy, the CPU uses a cache to retrieve data from memory quickly. A cache stores data copies from the main memory locations that are frequently used.

It is smaller and more efficient than the CPU's memory and is often embedded within it. There are multiple levels to the CPU cache. Sometimes, it can reach level 3.

Based on the frequency with which memory is accessed, each level determines whether it should be deleted or kept. Modern CPUs perform cache management automatically.

The structure of the GPU's local memory is very similar to the CPU. The GPU memory does not have a uniform memory access architecture. This allows programmers to choose which memory they want to keep and which ones to delete. This allows for better memory optimization.


Modern computing is impossible without the central processing unit (CPU), and graphics processing unit(GPU).

These two building blocks are the foundation of all advancements in this area, from artificial intelligence and supercomputers to predictive analytics. Understanding the differences between GPU and CPU allows individuals and IT decision-makers to better utilize their infrastructure and environments for better outcomes.

Did this article answer your questions about the differences between CPUs & GPUs? You can tell us more in the comment section below, our team will reach out to you and consider your suggestion. We would love to hear from you!

Leave a Comment