By Allie May 
People throw around terms such as GPU (Graphics Processing Unit) and CPU (Central Processing Unit) while talking about computer hardware. But the nuances between these two are very often missing to most people. These two parts form the backbone of any computer, and their specific roles need to be understood before building or upgrading any computer. Similar is the case with gaming, video editing and even machine learning.
In this article, we will dissect every aspect of each type of processor and why a basic understanding of them is crucial. In addition to this, we will tackle the major elements that impact the speed processing of each, along with which one would be better suited for your purposes.
What is a CPU?
Your Computer’s Brain Explains
General purpose activities in the system are looked after by the CPU. It is better labeled as the “brain” of the computer since it single-handedly looks after almost all tasks. The CPU is the component that processes data and provides instructions to the Operating system, software applications and any attached devices along with themselves.
Normally, a CPU is built for serial processing for a single task, though it performs them at lightning speed. Nowadays, CPUs are usually multicore- meaning they have 4 to 16 cores, enabling them to operate several threads or tasks at the same time. But even with the increase in cores, the tasks are usually performed one after the other.
How Does a CPU Work?
A CPU has the job of fetching, decoding and executing the instructions from memory. For carrying out the system functions, it operates with other devices like RAM, storage, and input/output devices.
- Clock Speed: In GHz, the speed of executing instructions by the CPUs is calculated. The more clock speeds a CPU has, the quicker it is able to process information.
- Cores and Threads: Current-day processors have more than one core, and each core is capable of processing multiple threads. For instance, a processor with 8 cores can accommodate 16 threads with the simultaneous multi-threading (SMT) technology.
Primary Use Cases of a CPU
A CPU is used for:
- Operating systems (Mac, Linux, Windows)
- Software applications (Browsers, Office, Video Editors)
- General purpose and multitasking computers
- Data input and output (keyboard, mouse, etc.)
What is a GPU?
A GPU is a Graphic Processing Unit and usually acts as a Parallel Processing Work House.
CPUs are built to handle a variety of computing chores. However, the GPU is tailored to perform specific functions. The GPU excels in processes where heavy data is dealt with simultaneously. Initially, GPUs were created to speed up the rendering of graphics in both gaming and professional design. They have now broadened their scope to include other compute-intensive activities such as AI and machine learning.
Compared to CPUs, GPUs have thousands of smaller cores (or processors), so they have better core counts. Having more cores allows the CPU to complete several tasks simultaneously. Thus, GPUs are perfect for parallel processing. With this method of operation, GPUs are capable of Ultra-speed conversion, rendering high-end graphics, and simulating large-scale calculations for various applications such as machine-learning models.
How Does a GPU Function?
Much like a CPU, each GPU also retrieves instructions and performs them. The main difference is that, instead of managing a few intricate tasks, the GPU tackles a multitude of simple tasks simultaneously. This is the main reason why the GPU is extremely efficient in 3D rendering, video editing, and deep learning of vast amounts of data.
- CUDA Cores: NVIDIA CADs operate with cores known as CUDA. These cores enable concurrent operations to be performed. AMD also has Stream Processors for parallel processes.
- Memory Bandwidth: With high memory bandwidth, access to large amounts of data is possible in an instant. This characteristic of GPUs is mandatory for data-rich activities such as gaming, military modeling, and AI training.
Primary Use Cases of a GPU
Achieving higher parallelism is a necessity in these tasks:
- 3D gaming (graphic rendering)
- Video and photo editing
- Deep Learning and AI
- Scientific modeling
- Cryptocurrency mining (certain algorithms)
Key Differences Between CPU and GPU
1. Processing Power and Task Focus
In terms of differentiating a CPU and a GPU, the obvious difference surfaces when looking at data processing.
1. Specific Purpose and Design Focus
- CPU: It performs single-threaded tasks like executing office systems and running operating systems or software instructions. It is equipped with a few cores designed to execute complex instructions at high speed and with greater efficiency.
- GPU: GPUs are specifically made to perform parallel architecture to multitask with a high level of efficiency. With thousands of smaller and more specialized cores, a GPU performs remarkably well at repetitive, data-heavy tasks like calculations and rendering graphics or within the frameworks of machine learning.
2. Amount of Cores and their Performance
- CPU: A CPU has about four to sixteen powerful cores that, despite their smaller number, are capable of performing huge complex tasks.
- GPU: GPUs have either hundreds or thousands of smaller cores. Although each of these cores is less powerful than a CPU core, the amount compels the GPU to be favorable in processing thousands of smaller tasks at once for greater efficiency during heavy workloads.
3. Electricity Consumption
- CPU: Compared to GPUs, CPUs utilize less energy to complete basic computing tasks. Generally, CPUs are designed to be efficient, consuming energy during periods of no activity or light workload.
- GPU: Larger core processing units, as well as heavy data input-output during graphic rendering or AI model training, use significant amounts of energy. This might seem like overhead, but it is the reason why a high-end GPU workstation needs powerful cooling systems, as well as why gaming PCs tend to fall in that category.
More Definition
CPUs are significantly more powerful than their processing unit counterparts. With the ability to perform system management, run different applications or even multitask efficiently, CPUs take the edge when it comes to versatility.
GPUs, however, are specialized departments within IT, able to multitask but not master-level experts in system operations like operating systems or even common software applications.
What are GPUs Most Effective For?
Selecting between GPUs and CPUs mainly depends on the workload that you prioritize the most.
For everyday tasks, such as using MS Office, managing system resources, or even just working on the internet, a CPU is more efficient since it can handle multiple tasks at once and general problem-solving.
For Gaming or Making Videos: When it’s about video gaming or doing some serious video editing, GPUs are a must. Present-day video games are graphically intensive. Editing videos also needs to work with large video files which GPUs can handle easily compared to CPUs.
For Learning Machines and AI: For people who are working with deep learning, artificial intelligence, and other data-intensive workloads, GPUs are becoming the go-to devices. A GPU’s parallel processing feature makes it perfect for training models with large datasets.
Conclusion
The CPU and GPU are both important when it comes to computing today, but they do very different jobs. The CPU is a multipurpose processor that is able to accomplish many distinct jobs, such as running operating systems and processing complex instructions. The GPU is a computer designed for parallel computing specifically for graphics rendering, AI, and scientific calculations; therefore, it is better suited for these kinds of tasks.
Whether to get a CPU or a GPU entirely depends on what you will be doing. For the majority of computing tasks, a CPU will be more than enough. But for gaming and video rendering, as well as data-heavy tasks carried out in machine learning, a GPU will be a necessity.
Knowing the pros of each processor can come in handy for you when constructing or upgrading your computer system so that you get the correct hardware to get the task done.